Baseball Musings
Baseball Musings
December 09, 2008
Comparing Fielding Systems
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Tom Tango compares both UZR models with PMR at The Book Blog.

Posted by StatsGuru at 06:16 PM | Comments (0) | TrackBack (0)
December 08, 2008
UZR/PMR Comparison
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Dan Turkenkopf runs correlations of UZR and PMR and I find them surprisingly low, given the two systems use the same data.

Both PMR and UZR were calculated using the Baseball Info Solutions (BIS) data set this season. I wonder if David or MGL might be able to give some ideas as to where the differences might come from.

I don't know enough about the UZR calculations to speculate. I base my models mostly on visiting players in parks, however. UZR might use all the data. I also don't know if UZR, like +/-, doesn't penalize players for outs made by others. In PMR. If the right fielder catches a ball that the centerfielder might be able to catch, the centerfielder is penalized. In +/-, the centerfielder is not. Given the low correlation with centerfielders, I suspect that's the case.

Posted by StatsGuru at 08:36 AM | Comments (0) | TrackBack (0)
December 07, 2008
Defensive Charts
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The defensive charts for 2008 are now available here. These provide a nice visualization of the probabilistic model of range. Chase Utley's charts, for example, shows how well he does on ground balls toward first base.

Posted by StatsGuru at 11:00 PM | Comments (1) | TrackBack (0)
December 01, 2008
Defense and Runs Allowed
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Dan Turkenkopf takes the data from the PMR's look at defense behind the pitcher to see how many runs defense added to or removed from a pitcher's record per nine innings.

Posted by StatsGuru at 08:50 AM | Comments (0) | TrackBack (0)
November 29, 2008
Finding the Holes
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Billfer tries to explain the poor defense behind Nate Robertson and decides that batters were just very good at hitting balls in holes against the Tigers pitcher.

Posted by StatsGuru at 01:15 PM | Comments (1) | TrackBack (0)
November 23, 2008
Probabilistic Model of Range, 2008, Defense Behind Pitchers
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As we know, a pitcher's ERA can be influenced by the defense behind him. This posts explores which pitchers were helped or hurt by their defenses based on how well fielders turned balls in play into outs based on how difficult they were off the bat.

Team PMR, 2008, Defense Behind Pitchers, Visit Smooth Distance Model, 2008 data only
Pitcher TeamIn PlayActual OutsPredicted OutsDERPredicted DERRatio
Chien-Ming WangNYY306215 200.92 0.703 0.657 107.01
Daisuke MatsuzakaBos449327 306.07 0.728 0.682 106.84
Jesse LitschTor569407 382.14 0.715 0.672 106.51
Tim WakefieldBos539405 382.33 0.751 0.709 105.93
Ryan Rowland-SmithSea366262 249.06 0.716 0.680 105.20
Justin DuchschererOak409308 292.97 0.753 0.716 105.13
CC SabathiaCle334228 217.23 0.683 0.650 104.96
Tim HudsonAtl435317 302.69 0.729 0.696 104.73
Scott KazmirTB378277 264.57 0.733 0.700 104.70
Roy OswaltHou617436 416.75 0.707 0.675 104.62
Jeremy SowersCle409279 266.73 0.682 0.652 104.60
CC SabathiaMil353246 235.49 0.697 0.667 104.46
Armando GalarragaDet525391 375.29 0.745 0.715 104.19
Greg SmithOak578423 406.63 0.732 0.704 104.03
John LackeyLAA469329 316.59 0.701 0.675 103.92
Kyle KendrickPhi560380 365.69 0.679 0.653 103.91
Glen PerkinsMin520357 343.77 0.687 0.661 103.85
Ryan DempsterChC572406 391.16 0.710 0.684 103.80
Shaun MarcumTor427317 305.52 0.742 0.716 103.76
Paul ByrdCle446319 307.48 0.715 0.689 103.75
Brian MoehlerHou509356 343.33 0.699 0.675 103.69
R.A. DickeySea374263 253.88 0.703 0.679 103.59
Joe SaundersLAA623445 429.72 0.714 0.690 103.56
Dustin McGowanTor337228 220.26 0.677 0.654 103.51
Adam WainwrightStL404289 279.63 0.715 0.692 103.35
Josh BeckettBos492333 322.69 0.677 0.656 103.19
Jorge CampilloAtl490347 336.39 0.708 0.687 103.16
Ben SheetsMil589417 404.45 0.708 0.687 103.10
John LannanWas560401 389.21 0.716 0.695 103.03
Zach MinerDet385276 268.08 0.717 0.696 102.95
Kevin SloweyMin480340 330.37 0.708 0.688 102.92
Vicente PadillaTex524356 345.93 0.679 0.660 102.91
Jake PeavySD459331 322.53 0.721 0.703 102.63
Jeremy GuthrieBal587428 417.45 0.729 0.711 102.53
Cole HamelsPhi635464 453.13 0.731 0.714 102.40
David BushMil567422 413.33 0.744 0.729 102.10
Paul MaholmPit621437 428.22 0.704 0.690 102.05
Jeff FrancisCol469321 314.83 0.684 0.671 101.96
John DanksCWS569396 388.39 0.696 0.683 101.96
Scott BakerMin497354 347.21 0.712 0.699 101.96
Roy HalladayTor713501 491.48 0.703 0.689 101.94
Matt GarzaTB560399 391.43 0.712 0.699 101.93
Micah OwingsAri312220 215.84 0.705 0.692 101.93
Johan SantanaNYM668476 467.22 0.713 0.699 101.88
Scott FeldmanTex488344 337.73 0.705 0.692 101.86
Oliver PerezNYM527380 373.11 0.721 0.708 101.85
Derek LoweLAD644453 444.85 0.703 0.691 101.83
Scott OlsenFla640463 454.69 0.723 0.710 101.83
Felix HernandezSea577391 384.12 0.678 0.666 101.79
Doug DavisAri457303 298.15 0.663 0.652 101.63
Edwin JacksonTB582401 394.74 0.689 0.678 101.59
Dan HarenAri610421 414.60 0.690 0.680 101.54
Aaron CookCol725489 481.78 0.674 0.665 101.50
Kyle LohseStL650453 446.33 0.697 0.687 101.49
Jeff SuppanMil589407 401.18 0.691 0.681 101.45
Hiroki KurodaLAD598418 412.03 0.699 0.689 101.45
Dana EvelandOak519351 346.09 0.676 0.667 101.42
Jered WeaverLAA513355 350.05 0.692 0.682 101.41
Todd WellemeyerStL579419 413.37 0.724 0.714 101.36
Carlos ZambranoChC570404 398.66 0.709 0.699 101.34
Jamie MoyerPhi625437 431.36 0.699 0.690 101.31
Jon GarlandLAA684462 456.10 0.675 0.667 101.29
Braden LooperStL653453 447.51 0.694 0.685 101.23
Miguel BatistaSea379257 254.22 0.678 0.671 101.09
Jair JurrjensAtl589401 397.07 0.681 0.674 100.99
Matt CainSF630436 431.93 0.692 0.686 100.94
Kevin CorreiaSF382248 245.75 0.649 0.643 100.91
Gavin FloydCWS625450 446.51 0.720 0.714 100.78
Javier VazquezCWS598405 401.92 0.677 0.672 100.77
Tim LincecumSF562385 382.12 0.685 0.680 100.75
Jason MarquisChC554390 387.25 0.704 0.699 100.71
Aaron HarangCin552379 376.37 0.687 0.682 100.70
Jose ContrerasCWS402280 278.09 0.697 0.692 100.69
Johnny CuetoCin500344 341.79 0.688 0.684 100.65
Joel PineiroStL505342 339.90 0.677 0.673 100.62
Brad PennyLAD311212 211.06 0.682 0.679 100.45
Jon LesterBos632438 436.20 0.693 0.690 100.41
Boof BonserMin382249 248.24 0.652 0.650 100.31
Greg MadduxSD511360 359.16 0.705 0.703 100.23
Aaron LaffeyCle316217 216.67 0.687 0.686 100.15
Manny ParraMil499322 321.73 0.645 0.645 100.08
Gil MecheKC611420 419.79 0.687 0.687 100.05
Mike MussinaNYY613409 409.01 0.667 0.667 100.00
Jarrod WashburnSea512350 350.13 0.684 0.684 99.96
Chad BillingsleyLAD556370 370.36 0.665 0.666 99.90
Cliff LeeCle670462 462.50 0.690 0.690 99.89
Zack GreinkeKC587399 399.62 0.680 0.681 99.84
Ted LillyChC574412 412.67 0.718 0.719 99.84
Tim ReddingWas572397 397.98 0.694 0.696 99.75
Wandy RodriguezHou393266 266.71 0.677 0.679 99.74
Andy SonnanstineTB632432 433.24 0.684 0.686 99.71
Chris SampsonHou383267 267.83 0.697 0.699 99.69
Daniel CabreraBal594409 410.64 0.689 0.691 99.60
Bronson ArroyoCin605408 409.76 0.674 0.677 99.57
Joe BlantonOak440303 304.31 0.689 0.692 99.57
Jason BergmannWas445310 311.45 0.697 0.700 99.53
Brandon WebbAri671458 460.45 0.683 0.686 99.47
Ervin SantanaLAA605422 424.34 0.698 0.701 99.45
Zach DukePit669445 447.62 0.665 0.669 99.42
Kenny RogersDet598400 402.42 0.669 0.673 99.40
Ubaldo JimenezCol572395 397.49 0.691 0.695 99.37
Carlos SilvaSea564365 367.36 0.647 0.651 99.36
Nate RobertsonDet563365 367.59 0.648 0.653 99.30
Jo-Jo ReyesAtl361241 242.79 0.668 0.673 99.26
Clayton KershawLAD306204 205.65 0.667 0.672 99.20
Ricky NolascoFla606432 435.96 0.713 0.719 99.09
James ShieldsTB641448 452.73 0.699 0.706 98.96
Justin VerlanderDet598415 419.47 0.694 0.701 98.93
Mike PelfreyNYM652446 450.98 0.684 0.692 98.89
Randy JohnsonAri531359 363.22 0.676 0.684 98.84
Kyle DaviesKC361248 251.20 0.687 0.696 98.73
Edinson VolquezCin511350 354.63 0.685 0.694 98.69
Nick BlackburnMin658445 451.38 0.676 0.686 98.59
John MaineNYM399286 290.11 0.717 0.727 98.58
Pedro MartinezNYM337225 228.38 0.668 0.678 98.52
A.J. BurnettTor613405 411.37 0.661 0.671 98.45
Jorge de la RosaCol361240 243.99 0.665 0.676 98.37
Mark HendricksonFla439302 307.04 0.688 0.699 98.36
Brian BurresBal460309 314.46 0.672 0.684 98.26
Kevin MillwoodTex569360 366.45 0.633 0.644 98.24
Brian BannisterKC603408 415.51 0.677 0.689 98.19
Luke HochevarKC430291 297.11 0.677 0.691 97.94
Randy WolfSD348237 242.07 0.681 0.696 97.91
Brandon BackeHou512341 348.41 0.666 0.680 97.87
Barry ZitoSF576393 401.59 0.682 0.697 97.86
Cha Seung BaekSD353238 243.40 0.674 0.690 97.78
Brett MyersPhi554379 388.08 0.684 0.701 97.66
Mark BuehrleCWS699466 477.66 0.667 0.683 97.56
Odalis PerezWas507337 345.85 0.665 0.682 97.44
Andrew MillerFla336216 222.12 0.643 0.661 97.24
Tom GorzelannyPit332225 231.65 0.678 0.698 97.13
Jonathan SanchezSF442297 306.08 0.672 0.692 97.03
Livan HernandezMin525339 349.78 0.646 0.666 96.92
Garrett OlsonBal451295 304.47 0.654 0.675 96.89
Carlos VillanuevaMil320220 228.02 0.688 0.713 96.48
Ian SnellPit522335 347.60 0.642 0.666 96.38
Andy PettitteNYY641420 439.26 0.655 0.685 95.62
Darrell RasnerNYY387257 269.56 0.664 0.697 95.34
Adam EatonPhi356236 248.23 0.663 0.697 95.07
Fausto CarmonaCle405273 288.06 0.674 0.711 94.77

Those are pretty impressive numbers for Daisuke Matsuzaka and Tim Wakefield. Not only were balls in play against them easy to field, the Red Sox did a great job of turning them into outs. For Yankees fans who are concerned about New York signing Andy Pettitte again, better defense would improve Andy's runs allowed a great deal. CC Sabathia certainly benefitted from good defense in both Cleveland and Milwaukee, so teams looking to sign him should be prepared to send their best fielders out behind the lefty.

Brian Bannister shows how important defense is to a low strikeout pitcher. His expected DER is low, and with the Royals doing a poor job fielding behind him, his actual DER was even lower. Bannister really needs to play for a team of defensive wizards.

Posted by StatsGuru at 10:38 AM | Comments (10) | TrackBack (0)
November 19, 2008
Probabilistic Model of Range, 2008, Pitchers
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The survey of the positions ends with the pitchers. First the teams:

Team Pitchers PMR, 2008, Visit Smooth Distance Model, 2008 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Blue Jays 4215 181 161.32 0.043 0.038 112.20
Tigers 4536 187 171.99 0.041 0.038 108.72
Padres 4419 217 202.55 0.049 0.046 107.13
Royals 4413 170 161.90 0.039 0.037 105.00
Mariners 4512 161 154.23 0.036 0.034 104.39
Twins 4607 190 183.22 0.041 0.040 103.70
Mets 4335 200 192.92 0.046 0.045 103.67
Nationals 4417 193 186.24 0.044 0.042 103.63
Dodgers 4265 217 209.44 0.051 0.049 103.61
Cubs 4156 179 174.74 0.043 0.042 102.44
Phillies 4396 198 193.35 0.045 0.044 102.40
Rockies 4535 204 201.22 0.045 0.044 101.38
Cardinals 4597 205 203.96 0.045 0.044 100.51
Braves 4383 213 211.93 0.049 0.048 100.51
Indians 4513 169 168.95 0.037 0.037 100.03
Astros 4292 159 160.96 0.037 0.038 98.78
Marlins 4338 162 164.14 0.037 0.038 98.70
Red Sox 4232 154 156.88 0.036 0.037 98.17
Diamondbacks 4224 193 196.62 0.046 0.047 98.16
Pirates 4683 211 215.78 0.045 0.046 97.78
White Sox 4409 190 194.51 0.043 0.044 97.68
Rangers 4667 178 184.54 0.038 0.040 96.46
Angels 4374 148 154.21 0.034 0.035 95.97
Reds 4299 174 181.34 0.040 0.042 95.95
Orioles 4540 152 158.73 0.033 0.035 95.76
Yankees 4349 186 194.41 0.043 0.045 95.67
Athletics 4285 142 149.52 0.033 0.035 94.97
Rays 4264 114 124.63 0.027 0.029 91.47
Giants 4232 142 156.07 0.034 0.037 90.98
Brewers 4354 183 205.85 0.042 0.047 88.90

The Blue Jays not only posted an excellent team ERA, but helped themselves defensively as well. The Brewers staff depended more on the fielders behind them. In looking at the individuals, experience appears to be a key to doing well:

Individual Pitchers PMR, 2008, Visit Smooth Distance Model, 2008 data only (500 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Jesse Litsch 569 39 24.20 0.069 0.043 161.16
Greg Maddux 649 59 39.59 0.091 0.061 149.04
Kenny Rogers 598 56 38.39 0.094 0.064 145.87
Livan Hernandez 674 31 22.40 0.046 0.033 138.39
Javier Vazquez 598 29 21.39 0.048 0.036 135.61
Felix Hernandez 577 32 24.02 0.055 0.042 133.25
Jeremy Guthrie 587 23 17.74 0.039 0.030 129.67
Jon Garland 684 31 24.42 0.045 0.036 126.92
Justin Verlander 598 23 18.21 0.038 0.030 126.33
Gil Meche 611 28 22.50 0.046 0.037 124.43
Kyle Kendrick 560 32 26.17 0.057 0.047 122.28
Zack Greinke 587 26 21.34 0.044 0.036 121.86
Bronson Arroyo 605 32 26.95 0.053 0.045 118.72
Cole Hamels 635 35 29.59 0.055 0.047 118.30
Joel Pineiro 505 28 23.71 0.055 0.047 118.07
Tim Wakefield 539 16 13.55 0.030 0.025 118.07
Tim Redding 572 22 18.86 0.038 0.033 116.64
Jason Marquis 554 35 30.06 0.063 0.054 116.42
Ryan Dempster 572 36 31.12 0.063 0.054 115.67
Joe Saunders 623 31 27.14 0.050 0.044 114.24
Glen Perkins 520 23 20.25 0.044 0.039 113.58
Vicente Padilla 524 20 17.70 0.038 0.034 113.02
Roy Oswalt 617 31 27.46 0.050 0.045 112.90
Brandon Webb 671 54 48.10 0.080 0.072 112.27
Aaron Cook 725 44 40.31 0.061 0.056 109.15
Jeff Suppan 589 28 25.91 0.048 0.044 108.06
Scott Olsen 640 20 18.55 0.031 0.029 107.84
Jair Jurrjens 589 37 34.69 0.063 0.059 106.66
Gavin Floyd 625 26 24.44 0.042 0.039 106.39
Zach Duke 669 37 35.21 0.055 0.053 105.09
Oliver Perez 527 17 16.28 0.032 0.031 104.42
Barry Zito 576 20 19.28 0.035 0.033 103.75
Paul Maholm 621 31 29.90 0.050 0.048 103.69
Matt Cain 630 27 26.14 0.043 0.041 103.30
Andy Sonnanstine 632 20 19.44 0.032 0.031 102.89
Hiroki Kuroda 598 39 37.92 0.065 0.063 102.84
Jon Lester 632 21 20.46 0.033 0.032 102.63
Kevin Millwood 569 25 24.56 0.044 0.043 101.80
Greg Smith 578 16 15.75 0.028 0.027 101.56
Brett Myers 554 23 22.85 0.042 0.041 100.64
Ted Lilly 574 15 15.03 0.026 0.026 99.79
Brian Bannister 603 30 30.11 0.050 0.050 99.63
Jamie Moyer 625 20 20.19 0.032 0.032 99.04
John Danks 569 24 24.25 0.042 0.043 98.97
Jarrod Washburn 512 20 20.39 0.039 0.040 98.07
Ubaldo Jimenez 572 35 35.69 0.061 0.062 98.06
Edinson Volquez 511 24 24.49 0.047 0.048 98.02
Mike Pelfrey 652 29 29.63 0.044 0.045 97.88
Kyle Lohse 650 30 30.67 0.046 0.047 97.81
CC Sabathia 687 27 27.67 0.039 0.040 97.59
Paul Byrd 608 17 17.52 0.028 0.029 97.04
Roy Halladay 713 31 32.28 0.043 0.045 96.03
Derek Lowe 644 39 40.83 0.061 0.063 95.52
Chad Billingsley 556 21 22.07 0.038 0.040 95.14
Joe Blanton 652 25 26.28 0.038 0.040 95.13
Odalis Perez 507 23 24.23 0.045 0.048 94.93
Dana Eveland 519 17 17.94 0.033 0.035 94.76
John Lannan 560 29 30.68 0.052 0.055 94.53
Armando Galarraga 525 13 14.04 0.025 0.027 92.57
Mike Mussina 613 28 30.47 0.046 0.050 91.88
Johan Santana 668 30 32.68 0.045 0.049 91.81
Ian Snell 522 21 23.11 0.040 0.044 90.85
Jered Weaver 513 10 11.02 0.019 0.021 90.71
Brandon Backe 512 16 18.01 0.031 0.035 88.85
Todd Wellemeyer 579 15 16.95 0.026 0.029 88.51
Carlos Zambrano 570 23 26.30 0.040 0.046 87.45
Nick Blackburn 658 24 27.47 0.036 0.042 87.36
Mark Buehrle 699 27 31.03 0.039 0.044 87.02
A.J. Burnett 613 24 27.68 0.039 0.045 86.70
James Shields 641 19 21.96 0.030 0.034 86.52
Carlos Silva 564 14 16.40 0.025 0.029 85.36
Dan Haren 610 24 28.16 0.039 0.046 85.21
Matt Garza 560 15 17.82 0.027 0.032 84.18
Edwin Jackson 582 12 14.35 0.021 0.025 83.61
Johnny Cueto 500 18 21.67 0.036 0.043 83.08
Ervin Santana 605 18 22.09 0.030 0.037 81.49
Ricky Nolasco 606 17 21.08 0.028 0.035 80.65
Nate Robertson 563 20 24.99 0.036 0.044 80.03
Randy Wolf 557 25 31.51 0.045 0.057 79.34
Tim Lincecum 562 17 21.44 0.030 0.038 79.28
Braden Looper 653 20 25.71 0.031 0.039 77.80
Brian Moehler 509 12 15.68 0.024 0.031 76.53
Aaron Harang 552 13 17.37 0.024 0.031 74.84
David Bush 567 18 24.87 0.032 0.044 72.37
Randy Johnson 531 12 16.86 0.023 0.032 71.17
Andy Pettitte 641 22 30.94 0.034 0.048 71.11
Cliff Lee 670 17 28.39 0.025 0.042 59.89
Daniel Cabrera 594 11 18.98 0.019 0.032 57.95
Ben Sheets 589 14 24.34 0.024 0.041 57.52

Jesse Litsch is young, but 2-4 are all veterans, and Maddux seems to come out near the top quite often. Mike Mussina, however, did not appear to deserve his gold glove. Remember to take this ranking with a grain of salt, since pitchers are not in the field that often compared to position players, and get many fewer chances to field balls. Luck is a much bigger factor in this group.

Posted by StatsGuru at 08:49 AM | Comments (0) | TrackBack (0)
November 17, 2008
PMR Runs, Leftfielders
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Dan Turkenkopf calculates runs allowed or saved for leftfielders based on their PMR stats.

Update: Here are the runs for first basemen as well.

Posted by StatsGuru at 12:46 PM | Comments (0) | TrackBack (0)
November 16, 2008
Probabilistic Model of Range, 2008, Catcher
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Our survey of the positions continues with catchers. Catchers don't field very many balls, so take these rankings with a grain of salt. First, the teams:

Team Catchers PMR, 2008, Visit Smooth Distance Model, 2008 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Brewers 4354 71 63.52 0.016 0.015 111.78
Mets 4335 54 48.85 0.012 0.011 110.53
Astros 4292 41 37.24 0.010 0.009 110.11
Pirates 4683 53 48.61 0.011 0.010 109.03
Twins 4607 45 41.93 0.010 0.009 107.32
Yankees 4349 56 52.39 0.013 0.012 106.89
Rangers 4667 49 46.47 0.010 0.010 105.44
Rockies 4535 58 55.02 0.013 0.012 105.42
Orioles 4540 41 39.10 0.009 0.009 104.86
Phillies 4396 58 56.28 0.013 0.013 103.06
Braves 4383 63 61.73 0.014 0.014 102.07
Diamondbacks 4224 50 49.19 0.012 0.012 101.65
Nationals 4417 34 33.64 0.008 0.008 101.08
Padres 4419 64 63.44 0.014 0.014 100.88
Mariners 4512 42 42.22 0.009 0.009 99.47
Dodgers 4265 38 38.45 0.009 0.009 98.82
Blue Jays 4215 57 57.84 0.014 0.014 98.54
Angels 4374 44 44.74 0.010 0.010 98.35
Tigers 4536 58 59.27 0.013 0.013 97.86
White Sox 4409 39 39.88 0.009 0.009 97.80
Giants 4232 48 49.80 0.011 0.012 96.38
Rays 4264 50 51.91 0.012 0.012 96.32
Red Sox 4232 51 53.09 0.012 0.013 96.07
Royals 4413 49 51.24 0.011 0.012 95.62
Marlins 4338 58 60.75 0.013 0.014 95.47
Indians 4513 45 47.90 0.010 0.011 93.94
Cardinals 4597 47 50.66 0.010 0.011 92.78
Athletics 4285 28 31.42 0.007 0.007 89.12
Reds 4299 52 60.30 0.012 0.014 86.23
Cubs 4156 42 51.03 0.010 0.012 82.31

It looks like the Mets trade for Brian Schneider turned out to be a good one from a defensive standpoint. It also looks like rookie of the year Geovany Soto might have some things to learn behind the plate. Let's look at the individuals:

Individual Catchers PMR, 2008, Visit Smooth Distance Model, 2008 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Wil Nieves 1327 14 9.94 0.011 0.007 140.90
J.R. Towles 1252 8 6.01 0.006 0.005 133.04
Kevin Cash 1131 14 10.77 0.012 0.010 129.95
Ryan Doumit 2933 31 26.57 0.011 0.009 116.66
Carlos Ruiz 2517 42 36.54 0.017 0.015 114.95
Guillermo Quiroz 1176 8 7.01 0.007 0.006 114.11
Chris Iannetta 2633 35 30.83 0.013 0.012 113.52
Humberto Quintero 1321 15 13.41 0.011 0.010 111.87
Victor Martinez 1388 16 14.48 0.012 0.010 110.53
Jason Kendall 3988 67 60.93 0.017 0.015 109.97
Jason LaRue 1288 16 14.58 0.012 0.011 109.72
Joe Mauer 3805 41 37.60 0.011 0.010 109.03
Ramon Castro 1041 14 12.97 0.013 0.012 107.97
Gregg Zaun 1807 30 28.04 0.017 0.016 106.97
Dioner Navarro 2911 38 35.59 0.013 0.012 106.77
Ivan Rodriguez 2947 42 39.65 0.014 0.013 105.93
Brian Schneider 2575 30 28.52 0.012 0.011 105.19
Jeff Mathis 2351 33 31.46 0.014 0.013 104.90
Josh Bard 1253 21 20.28 0.017 0.016 103.56
Ramon Hernandez 3272 33 32.09 0.010 0.010 102.83
Miguel Montero 1191 15 14.77 0.013 0.012 101.53
Chris Snyder 2733 33 32.54 0.012 0.012 101.41
Brad Ausmus 1719 18 17.82 0.010 0.010 101.04
Gerald Laird 2419 24 23.76 0.010 0.010 101.00
Jarrod Saltalamacchia 1470 17 16.85 0.012 0.011 100.90
Jose Molina 2152 23 22.80 0.011 0.011 100.89
Brandon Inge 1541 21 21.16 0.014 0.014 99.24
A.J. Pierzynski 3428 31 31.53 0.009 0.009 98.31
Brian McCann 3470 49 50.81 0.014 0.015 96.44
Miguel Olivo 1509 19 19.85 0.013 0.013 95.70
John Buck 2902 30 31.39 0.010 0.011 95.57
Yorvit Torrealba 1819 21 22.19 0.012 0.012 94.65
Kurt Suzuki 3608 25 26.45 0.007 0.007 94.50
Matt Treanor 1561 23 24.36 0.015 0.016 94.43
Nick Hundley 1482 18 19.13 0.012 0.013 94.11
Bengie Molina 3272 36 39.00 0.011 0.012 92.31
Geovany Soto 3302 34 37.47 0.010 0.011 90.75
Rod Barajas 2262 24 26.60 0.011 0.012 90.24
Russell Martin 3655 31 34.63 0.008 0.009 89.52
Jason Varitek 3002 37 42.26 0.012 0.014 87.56
Jesus Flores 2116 13 15.03 0.006 0.007 86.52
David Ross 1238 17 19.69 0.014 0.016 86.35
Kenji Johjima 2617 20 23.53 0.008 0.009 85.01
Yadier Molina 3185 29 34.24 0.009 0.011 84.70
Mike Napoli 1931 10 12.08 0.005 0.006 82.77
Paul Bako 2272 20 24.25 0.009 0.011 82.48
Kelly Shoppach 2774 25 30.66 0.009 0.011 81.54
John Baker 1477 13 15.96 0.009 0.011 81.46
Chris Coste 1853 15 18.62 0.008 0.010 80.57
Shawn Riggans 1041 11 14.08 0.011 0.014 78.12

Here's another reason Joe Mauer gets my vote for AL MVP. He's not only a great offensive catcher, but he fields his position well also. For those teams interested in Jason Varitek, his ranking here is certainly another strike against him.

It's also interesting to note that Jarrod Saltalamacchia wasn't terrible behind the plate. I know there's much more to the position than the ability to field, but in this regard, Saltalamacchia shows a positive behind the plate.

Posted by StatsGuru at 09:58 AM | Comments (1) | TrackBack (0)
November 14, 2008
Probabilistic Model of Range, 2008, First Basemen
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The Probabilistic Model of Range survey continues with first basemen:

Team First Basemen PMR, 2008, Visit Smooth Distance Model, 2008 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Cardinals 4597 363 330.17 0.079 0.072 109.94
Rays 4264 337 309.47 0.079 0.073 108.90
Astros 4292 352 334.44 0.082 0.078 105.25
Angels 4374 348 332.57 0.080 0.076 104.64
Reds 4299 340 326.62 0.079 0.076 104.10
Orioles 4540 330 317.28 0.073 0.070 104.01
Braves 4383 309 301.26 0.070 0.069 102.57
Giants 4232 306 298.46 0.072 0.071 102.53
Mariners 4512 312 305.65 0.069 0.068 102.08
Padres 4419 314 308.13 0.071 0.070 101.90
Athletics 4285 287 282.84 0.067 0.066 101.47
Cubs 4156 339 334.40 0.082 0.080 101.38
Mets 4335 323 319.48 0.075 0.074 101.10
Pirates 4683 293 290.12 0.063 0.062 100.99
White Sox 4409 295 292.85 0.067 0.066 100.73
Red Sox 4232 300 298.07 0.071 0.070 100.65
Blue Jays 4215 346 345.24 0.082 0.082 100.22
Rangers 4667 292 292.72 0.063 0.063 99.75
Dodgers 4265 288 290.00 0.068 0.068 99.31
Rockies 4535 311 318.74 0.069 0.070 97.57
Phillies 4396 335 345.93 0.076 0.079 96.84
Tigers 4536 258 267.90 0.057 0.059 96.31
Brewers 4354 299 311.39 0.069 0.072 96.02
Royals 4413 270 282.47 0.061 0.064 95.58
Nationals 4417 279 292.73 0.063 0.066 95.31
Indians 4513 276 290.25 0.061 0.064 95.09
Yankees 4349 270 286.22 0.062 0.066 94.33
Marlins 4338 296 314.60 0.068 0.073 94.09
Diamondbacks 4224 292 311.45 0.069 0.074 93.76
Twins 4607 262 283.33 0.057 0.061 92.47

As you might expect from the ranking of the top two National League teams, Pujols and Berkman competed with the glove as well as the bat:

Individual First Basemen PMR, 2008, Visit Smooth Distance Model, 2008 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Albert Pujols 3833 310 275.70 0.081 0.072 112.44
Carlos Pena 3428 272 250.39 0.079 0.073 108.63
Rich Aurilia 1398 94 88.21 0.067 0.063 106.57
Lance Berkman 3899 329 309.21 0.084 0.079 106.40
Mark Teixeira 4009 322 302.91 0.080 0.076 106.30
Kevin Millar 3607 264 250.96 0.073 0.070 105.20
Joey Votto 3686 300 285.79 0.081 0.078 104.97
Todd Helton 2272 165 158.79 0.073 0.070 103.91
Casey Kotchman 3659 268 259.14 0.073 0.071 103.42
Paul Konerko 3069 214 207.95 0.070 0.068 102.91
Kevin Youkilis 2835 212 206.26 0.075 0.073 102.78
Adrian Gonzalez 4302 307 300.33 0.071 0.070 102.22
Derrek Lee 3848 322 315.59 0.084 0.082 102.03
Daric Barton 3322 211 207.92 0.064 0.063 101.48
Carlos Delgado 4088 306 305.60 0.075 0.075 100.13
Lyle Overbay 3919 330 329.98 0.084 0.084 100.01
James Loney 4023 267 267.57 0.066 0.067 99.79
Chris Davis 1295 81 81.23 0.063 0.063 99.72
Miguel Cairo 1223 84 84.91 0.069 0.069 98.93
Adam LaRoche 3647 224 226.60 0.061 0.062 98.85
Aaron Boone 1040 61 62.74 0.059 0.060 97.23
Richie Sexson 2103 133 137.24 0.063 0.065 96.91
Ryan Howard 4254 322 336.79 0.076 0.079 95.61
Nick Swisher 1340 81 84.91 0.060 0.063 95.40
Prince Fielder 4133 280 293.92 0.068 0.071 95.26
Chad Tracy 1496 110 115.89 0.074 0.077 94.92
Ross Gload 2727 163 171.75 0.060 0.063 94.91
John Bowker 1607 104 110.60 0.065 0.069 94.03
Miguel Cabrera 3772 220 234.22 0.058 0.062 93.93
Ryan Garko 3323 198 211.16 0.060 0.064 93.77
Conor Jackson 1696 109 117.40 0.064 0.069 92.85
Sean Casey 1042 65 70.15 0.062 0.067 92.65
Justin Morneau 4289 242 261.41 0.056 0.061 92.57
Jason Giambi 2795 164 177.51 0.059 0.064 92.39
Garrett Atkins 1638 101 110.77 0.062 0.068 91.18
Mike Jacobs 2860 175 194.95 0.061 0.068 89.77

Year after year Albert Pujols shows his defensive skill at first base. Lance Berkman is up there, also, making the MVP argument between the two that much closer. Mark Teixeira also offers an excellent glove to go along with his fine offense.

The most surprising ranking to me, however, is Justin Morneau. Justin is still young and shouldn't have lost a step. He's someone worth looking at in more detail. Of course, at the very bottom is Mike Jacobs, giving Royals fans another reason to dislike the trade.

Posted by StatsGuru at 08:51 AM | Comments (5) | TrackBack (0)
November 13, 2008
Rightfield Runs
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Dan Turkenkopf list PMR Runs for rightfielders.

Posted by StatsGuru at 12:12 PM | Comments (0) | TrackBack (0)
November 12, 2008
PMR Runs
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Dan Turkenkopf continues to translate PMR stats into runs, taking on third basemen this time.

Posted by StatsGuru at 10:54 AM | Comments (0) | TrackBack (0)
Probabilistic Model of Range, 2008, Leftfielders
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The survery of range continues with leftfielders. The following table shows how the thirty teams fared at the position:

Team Leftfielders PMR, 2008, Visit Smooth Distance Model, 2008 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Royals 4413 368 353.21 0.083 0.080 104.19
Indians 4513 302 290.89 0.067 0.064 103.82
Rays 4264 344 331.81 0.081 0.078 103.67
Nationals 4417 350 339.72 0.079 0.077 103.03
Mets 4335 308 299.09 0.071 0.069 102.98
Diamondbacks 4224 306 298.79 0.072 0.071 102.41
Braves 4383 279 273.49 0.064 0.062 102.02
Brewers 4354 305 299.07 0.070 0.069 101.98
White Sox 4409 293 287.74 0.066 0.065 101.83
Rangers 4667 323 317.21 0.069 0.068 101.83
Orioles 4540 362 355.59 0.080 0.078 101.80
Athletics 4285 333 328.28 0.078 0.077 101.44
Astros 4292 282 278.71 0.066 0.065 101.18
Padres 4419 310 306.42 0.070 0.069 101.17
Cardinals 4597 312 308.84 0.068 0.067 101.02
Red Sox 4232 292 291.37 0.069 0.069 100.22
Dodgers 4265 286 285.62 0.067 0.067 100.13
Tigers 4536 356 355.77 0.078 0.078 100.06
Giants 4232 308 308.26 0.073 0.073 99.92
Yankees 4349 316 316.76 0.073 0.073 99.76
Angels 4374 285 286.29 0.065 0.065 99.55
Cubs 4156 302 304.23 0.073 0.073 99.27
Blue Jays 4215 270 272.75 0.064 0.065 98.99
Pirates 4683 293 299.34 0.063 0.064 97.88
Reds 4299 280 288.41 0.065 0.067 97.08
Rockies 4535 282 290.95 0.062 0.064 96.92
Marlins 4338 289 299.06 0.067 0.069 96.64
Mariners 4512 324 336.18 0.072 0.075 96.38
Twins 4607 306 327.83 0.066 0.071 93.34
Phillies 4396 260 279.09 0.059 0.063 93.16

As in rightfield, there doesn't seem to be a huge correlation between doing well in left and winning. The Royals displayed the best defense at the position, while the Phillies came out at the bottom of the pack.

The list of individuals in left shows that very few teams employ a regular at the position:

Individual Leftfielder PMR, 2008, Visit Smooth Distance Model, 2008 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Skip Schumaker 1085 85 73.79 0.078 0.068 115.20
David DeJesus 1522 136 121.91 0.089 0.080 111.56
Brandon Boggs 1818 131 118.10 0.072 0.065 110.92
Matt Joyce 1249 94 84.77 0.075 0.068 110.89
Ben Francisco 2021 150 138.34 0.074 0.068 108.43
Juan Pierre 1816 125 116.83 0.069 0.064 107.00
Willie Harris 1685 145 135.55 0.086 0.080 106.97
Carl Crawford 2715 231 217.97 0.085 0.080 105.98
Conor Jackson 1944 146 139.06 0.075 0.072 104.99
Gregor Blanco 1547 86 82.08 0.056 0.053 104.78
Jay Payton 1293 132 126.31 0.102 0.098 104.51
Luke Scott 2668 200 196.08 0.075 0.073 102.00
Johnny Damon 1998 155 152.00 0.078 0.076 101.97
Ryan Braun 3919 275 270.93 0.070 0.069 101.50
Wily Mo Pena 1260 99 97.68 0.079 0.078 101.35
Carlos Lee 2840 187 185.42 0.066 0.065 100.85
David Dellucci 1164 75 74.64 0.064 0.064 100.48
Adam Dunn 2942 210 209.06 0.071 0.071 100.45
Alfonso Soriano 2653 186 185.23 0.070 0.070 100.42
Fred Lewis 2622 178 177.57 0.068 0.068 100.24
Carlos Quentin 3465 228 228.42 0.066 0.066 99.81
Jack Cust 1753 129 129.74 0.074 0.074 99.43
Emil Brown 1229 89 89.70 0.072 0.073 99.21
Chase Headley 2159 156 157.62 0.072 0.073 98.97
Matt Holliday 3850 240 243.20 0.062 0.063 98.68
Manny Ramirez 2894 190 193.40 0.066 0.067 98.24
Adam Lind 1712 113 115.52 0.066 0.067 97.81
Xavier Nady 1212 87 89.04 0.072 0.073 97.71
Chris Duncan 1012 73 74.80 0.072 0.074 97.59
Raul Ibanez 4203 303 312.07 0.072 0.074 97.09
Garret Anderson 2113 144 148.53 0.068 0.070 96.95
Jose Guillen 1098 83 85.62 0.076 0.078 96.94
Luis Gonzalez 1547 105 109.32 0.068 0.071 96.05
David Murphy 1317 86 89.62 0.065 0.068 95.96
Josh Willingham 2551 166 173.80 0.065 0.068 95.51
Eric Byrnes 1209 76 80.12 0.063 0.066 94.86
Jason Bay 4215 254 268.19 0.060 0.064 94.71
Marcus Thames 1537 120 127.42 0.078 0.083 94.18
Delmon Young 4209 282 301.19 0.067 0.072 93.63
Pat Burrell 3646 202 223.39 0.055 0.061 90.42

Ryan Braun is the first player on the list on the field in left for over 3000 balls in play. Some of this was caused by injuries (Soriano, Matsui), but for the most part, managers mix and match at the position. The move to left was clearly the right one for Braun.

The other rankings of note belong to Manny Ramirez and Jason Bay. Manny actually did better than Jason in 2008. I'm going to need to break down the two by team to see how much the parks might have made a difference. Bay certainly looked better than Manny watching him play for the Red Sox.

All those late inning substitutions Charlie Manuel made for Pat Burrell looked proper, also. Pat ranks as the worst leftfielder in baseball in 2008, so it's no wonder Charlie wanted a better glove in left when the Phillies had the lead late.

Posted by StatsGuru at 08:43 AM | Comments (8) | TrackBack (0)
November 10, 2008
Probabilistic Model of Range, 2008, Rightfielders
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The Probabilistic Model of Range reports continue with rightfielders. First, the team data:

Team Rightfielders PMR, 2008, Visit Smooth Distance Model, 2008 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Twins 4607 397 374.04 0.086 0.081 106.14
Blue Jays 4215 303 286.97 0.072 0.068 105.58
Giants 4232 392 372.99 0.093 0.088 105.10
Indians 4513 374 358.85 0.083 0.080 104.22
Padres 4419 339 329.60 0.077 0.075 102.85
Phillies 4396 318 310.41 0.072 0.071 102.45
Red Sox 4232 325 318.64 0.077 0.075 102.00
Braves 4383 313 307.02 0.071 0.070 101.95
Rangers 4667 382 375.27 0.082 0.080 101.79
Nationals 4417 353 346.81 0.080 0.079 101.78
Marlins 4338 345 340.25 0.080 0.078 101.40
Cubs 4156 333 329.14 0.080 0.079 101.17
Cardinals 4597 362 360.58 0.079 0.078 100.39
Diamondbacks 4224 269 268.33 0.064 0.064 100.25
Athletics 4285 377 376.50 0.088 0.088 100.13
Mariners 4512 309 310.73 0.068 0.069 99.44
Dodgers 4265 278 279.64 0.065 0.066 99.41
Brewers 4354 316 318.38 0.073 0.073 99.25
Pirates 4683 386 389.32 0.082 0.083 99.15
Royals 4413 334 336.94 0.076 0.076 99.13
Orioles 4540 338 341.58 0.074 0.075 98.95
Mets 4335 356 360.14 0.082 0.083 98.85
Astros 4292 357 365.06 0.083 0.085 97.79
Rays 4264 345 354.99 0.081 0.083 97.19
Reds 4299 327 338.05 0.076 0.079 96.73
Tigers 4536 301 311.48 0.066 0.069 96.64
White Sox 4409 296 308.31 0.067 0.070 96.01
Angels 4374 308 322.64 0.070 0.074 95.46
Yankees 4349 301 316.77 0.069 0.073 95.02
Rockies 4535 249 273.59 0.055 0.060 91.01

It seems rightfielder defense didn't have that much influence on playoff teams. Five of the eight post-season teams finished in the bottom half of the majors. Here's a look at the individuals:

Individual Rightfielder PMR, 2008, Visit Smooth Distance Model, 2008 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Alex Rios 2373 170 156.05 0.072 0.066 108.94
Denard Span 2099 192 176.27 0.091 0.084 108.92
Franklin Gutierrez 2400 224 207.25 0.093 0.086 108.08
Jayson Werth 1964 143 133.49 0.073 0.068 107.12
Randy Winn 3247 309 291.22 0.095 0.090 106.10
Matt Kemp 1391 97 91.48 0.070 0.066 106.04
Endy Chavez 1176 109 103.84 0.093 0.088 104.97
Austin Kearns 2268 187 179.28 0.082 0.079 104.30
Michael Cuddyer 1640 123 118.33 0.075 0.072 103.95
Justin Upton 2531 175 168.66 0.069 0.067 103.76
Kosuke Fukudome 3164 246 240.12 0.078 0.076 102.45
Jeff Francoeur 4016 284 278.05 0.071 0.069 102.14
David Murphy 1279 107 104.84 0.084 0.082 102.06
Ryan Sweeney 1462 136 133.41 0.093 0.091 101.94
Ichiro Suzuki 2491 176 172.83 0.071 0.069 101.83
Mark Teahen 2292 185 181.68 0.081 0.079 101.82
Brian Giles 3845 276 271.51 0.072 0.071 101.65
Jeremy Hermida 3310 266 263.10 0.080 0.079 101.10
J.D. Drew 2658 184 183.15 0.069 0.069 100.47
Gabe Gross 2225 186 185.51 0.084 0.083 100.26
Nick Markakis 4353 329 328.98 0.076 0.076 100.00
Corey Hart 4134 304 305.57 0.074 0.074 99.49
Brad Wilkerson 1428 95 95.58 0.067 0.067 99.39
Ryan Church 2158 180 181.26 0.083 0.084 99.31
Geoff Jenkins 1974 141 142.41 0.071 0.072 99.01
Elijah Dukes 1840 137 138.55 0.074 0.075 98.88
Shin-Soo Choo 1255 89 90.51 0.071 0.072 98.33
Hunter Pence 4112 341 349.04 0.083 0.085 97.70
Emil Brown 1264 112 114.89 0.089 0.091 97.49
Jay Bruce 1777 143 147.07 0.080 0.083 97.23
Jose Guillen 1673 121 124.68 0.072 0.075 97.05
Andre Ethier 2620 171 176.94 0.065 0.068 96.64
Ryan Ludwick 3037 232 240.07 0.076 0.079 96.64
Vladimir Guerrero 2541 180 186.37 0.071 0.073 96.58
Xavier Nady 2497 199 207.14 0.080 0.083 96.07
Magglio Ordonez 3588 220 229.25 0.061 0.064 95.96
Jermaine Dye 3981 266 277.60 0.067 0.070 95.82
Bobby Abreu 3933 271 284.58 0.069 0.072 95.23
Eric Hinske 1001 88 92.73 0.088 0.093 94.90
Ken Griffey Jr. 2257 157 166.16 0.070 0.074 94.48
Gary Matthews Jr. 1013 77 82.08 0.076 0.081 93.81
Brad Hawpe 3645 188 213.67 0.052 0.059 87.99

Denard Span not only improved the Twins leadoff slot, he also did a great job tracking down balls in rightfield. While I'm not surprised to see older players like Ken Griffey and Bobby Abreu near the bottom of the list, I didn't expect to see Gary Matthews, Jr. there.

Ichiro Suzuki also adds some interest. He came out near the top in center, but in the middle in right. It's a bit of a mystery why he does better in center than he does in right.

Posted by StatsGuru at 08:05 PM | Comments (5) | TrackBack (0)
November 09, 2008
Probabilistic Model of Range, 2008, Third Basemen
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The Blue Jays and Cardinals made a challenge trade at the start of the season, swapping Scott Rolen and Troy Glaus. Defensively, at least, the Blue Jays came out on top. Here are the Probabilistic Model of Range team rankings for third base:

Team Third Basemen PMR, 2008, Visit Smooth Distance Model, 2008 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Blue Jays 4215 415 392.25 0.098 0.093 105.80
Mariners 4512 403 384.91 0.089 0.085 104.70
Red Sox 4232 440 423.21 0.104 0.100 103.97
Dodgers 4265 427 411.45 0.100 0.096 103.78
Angels 4374 391 377.04 0.089 0.086 103.70
Braves 4383 404 390.88 0.092 0.089 103.36
Rays 4264 420 406.66 0.098 0.095 103.28
Tigers 4536 471 458.72 0.104 0.101 102.68
Brewers 4354 396 387.21 0.091 0.089 102.27
White Sox 4409 463 455.46 0.105 0.103 101.66
Padres 4419 382 376.01 0.086 0.085 101.59
Astros 4292 408 402.20 0.095 0.094 101.44
Athletics 4285 385 380.25 0.090 0.089 101.25
Pirates 4683 445 442.19 0.095 0.094 100.64
Rockies 4535 412 410.31 0.091 0.090 100.41
Nationals 4417 416 415.43 0.094 0.094 100.14
Yankees 4349 379 378.81 0.087 0.087 100.05
Cubs 4156 341 342.34 0.082 0.082 99.61
Mets 4335 374 376.09 0.086 0.087 99.44
Indians 4513 419 423.48 0.093 0.094 98.94
Marlins 4338 370 375.82 0.085 0.087 98.45
Royals 4413 375 386.63 0.085 0.088 96.99
Phillies 4396 411 425.13 0.093 0.097 96.68
Rangers 4667 372 386.02 0.080 0.083 96.37
Diamondbacks 4224 350 363.76 0.083 0.086 96.22
Twins 4607 382 397.09 0.083 0.086 96.20
Cardinals 4597 416 432.51 0.090 0.094 96.18
Giants 4232 338 356.28 0.080 0.084 94.87
Reds 4299 337 356.45 0.078 0.083 94.54
Orioles 4540 421 446.86 0.093 0.098 94.21

I'm impressed that the Braves rank so high. Chipper Jones isn't known for his defense at third, but he played well this season.

Individual Third Baseman PMR, 2008, Visit Smooth Distance Model, 2008 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Chone Figgins 2787 249 230.03 0.089 0.083 108.25
Andy Marte 1849 185 173.47 0.100 0.094 106.64
Evan Longoria 3059 304 286.97 0.099 0.094 105.94
Ian Stewart 1651 160 151.25 0.097 0.092 105.78
Adrian Beltre 3804 338 321.08 0.089 0.084 105.27
Carlos Guillen 2396 246 233.91 0.103 0.098 105.17
Jack Hannahan 2882 267 254.14 0.093 0.088 105.06
Mike Lowell 2717 279 266.51 0.103 0.098 104.69
Geoff Blum 1773 179 171.00 0.101 0.096 104.68
Blake DeWitt 2152 233 223.28 0.108 0.104 104.35
Joe Crede 2492 251 242.47 0.101 0.097 103.52
Bill Hall 2709 238 229.92 0.088 0.085 103.51
Chipper Jones 2981 274 265.13 0.092 0.089 103.34
Scott Rolen 2935 274 267.64 0.093 0.091 102.37
Kevin Kouzmanoff 4179 368 361.20 0.088 0.086 101.88
Alex Rodriguez 3377 297 291.79 0.088 0.086 101.79
Jose Bautista 2478 243 240.95 0.098 0.097 100.85
Greg Dobbs 1000 92 91.52 0.092 0.092 100.53
Ryan Zimmerman 2786 275 273.75 0.099 0.098 100.46
David Wright 4234 367 365.59 0.087 0.086 100.39
Willy Aybar 1048 108 107.77 0.103 0.103 100.21
Andy LaRoche 1573 152 151.71 0.097 0.096 100.19
Juan Uribe 1424 156 157.08 0.110 0.110 99.31
Aramis Ramirez 3664 290 294.47 0.079 0.080 98.48
Edwin Encarnacion 3673 288 295.33 0.078 0.080 97.52
Mike Lamb 1508 117 120.22 0.078 0.080 97.32
Brian Buscher 1564 141 145.30 0.090 0.093 97.04
Jose Castillo 2560 214 220.73 0.084 0.086 96.95
Garrett Atkins 2528 221 228.04 0.087 0.090 96.91
Mark Reynolds 3759 304 315.66 0.081 0.084 96.31
Jorge Cantu 3264 271 281.48 0.083 0.086 96.28
Alex Gordon 3583 316 329.28 0.088 0.092 95.97
Pedro Feliz 2972 280 292.73 0.094 0.098 95.65
Casey Blake 3318 288 301.47 0.087 0.091 95.53
Ty Wigginton 2013 175 183.48 0.087 0.091 95.38
Troy Glaus 3908 351 368.31 0.090 0.094 95.30
Melvin Mora 3362 320 342.26 0.095 0.102 93.50
Ramon Vazquez 1712 138 149.87 0.081 0.088 92.08
Rich Aurilia 1271 93 106.18 0.073 0.084 87.59

Adrian Beltre comes through here as an interesting player. With the run up in salaries the last few seasons, his $12 million contract for 2009 is pretty good. It's the same amount Mike Lowell will make next season. The two are also very close in terms of win shares. If a team is looking to upgrade their defense at third base, Beltre is a great pickup. The Mariners may be looking to move his contract. If the surgery he underwent in September helps with his hitting, he could be a very good pickup.

Evan Longoria certainly was a big part of the Rays defensive improvement. Unexpected major leaguers Blake DeWitt and Jack Hannahan also provided much needed defense. In addition, Andy Marte hasn't hit but he did field the position well in limited playing time.

At the other end of the spectrum, Casey Blake appears to be as overrated defensively as offensively. Alex Gordon and Mark Reynolds look like they won't have long careers at the position, given their poor play at a young age.

Posted by StatsGuru at 06:53 PM | Comments (2) | TrackBack (0)
Watching Uggla
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Mike Emeigh took me up on my offer to watch the best and worst plays of a second baseman. I sent Mike the highest probability plays Dan Uggla didn't make, and the lowest probability plays Uggla did turn into outs. Here's his report:

Of Uggla's eight low-probability plays made, four of them were plays on which the Marlins had an infield shift on - three by Ryan Howard, one by Carlos Delgado - and in each case the ball was hit directly to where Uggla was playing; it's very likely that any 2B would have made those plays in that position, and I don't know thow much credit you want to give Uggla for being there. Two more plays - the 8/15 play against Alfonso Soriano and the 7/10 play against James Loney - also appear to be primarily due to positioning. On the former, Uggla was playing Soriano fairly far up the middle and had a good angle on his popup; on the latter, Uggla was pulled over fairly close to 1B. The other two plays were good, far-ranging plays - Uggla going far to his left to throw out Brian Schneider on 5/26, and ranging well to the left side of 2B in shallow center to grab a Mark Reynolds flare on 5/20 - although on the latter positioning also played a role, as Uggla was playing Reynolds up the middle and the CF was playing fairly deep. It was a good play, but one that a good CF or SS might have been able to make, also.

The six high-probability plays that Uggla should have made but didn't:

  • 6/17: routing GB by Ichiro, just booted it
  • 8/6: hard-hit "at 'em" GB by Jimmy Rollins, basically one of those you either catch or don't catch
  • 5/30: GB by Pedro Feliz that took a bad hop as it got to Uggla; he barehanded it across his body, which threw off his timing
  • 4/18: GB by Ryan Zimmerman toward the middle; Uggla made the play but didn't get enough on the throw, which Mike Jacobs should have scooped anyway
  • 9/21: With Jamie Moyer on 1B, Jimmy Rollins hit a slow grounder to the right side. Uggla stopped to avoid a collision with Moyer, which made him have to hurry the play when he did get to the ball. Had he kept coming Moyer would likely have collided with him, which would have been interference on Moyer
  • 7/28: Endy Chavez hit a GB which took a funky hop as it got to Uggla, who booted it

Mike brings up something I've discussed before. Range is probably a poor word for what we're studying here. Range isn't just the ability to move a long distance to field a ball. It also includes the ability to position yourself (or have someone position you) so you don't need to move very far. Uggla (and Utley) put themselves into position to field low probability balls without having too many high probability outs sneak through their vacated normal positions. Someday, we'll measure range directly.

Posted by StatsGuru at 12:06 PM | Comments (4) | TrackBack (0)
November 08, 2008
Probabilistic Model of Range, Centerfielders, 2008
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The third position presented in this year's Probabilistic Model of Range study belongs to centerfielders. First, the overall team numbers:

Team Centerfielders PMR, 2008, Visit Smooth Distance Model, 2008 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Mets 4335 435 422.51 0.100 0.097 102.96
Rays 4264 451 438.07 0.106 0.103 102.95
Phillies 4396 389 378.54 0.088 0.086 102.76
Twins 4607 488 475.27 0.106 0.103 102.68
Athletics 4285 444 433.07 0.104 0.101 102.52
Reds 4299 413 404.02 0.096 0.094 102.22
Angels 4374 429 421.94 0.098 0.096 101.67
Brewers 4354 406 400.20 0.093 0.092 101.45
Astros 4292 427 420.91 0.099 0.098 101.45
Mariners 4512 435 429.86 0.096 0.095 101.19
Rockies 4535 405 400.30 0.089 0.088 101.17
Yankees 4349 407 402.92 0.094 0.093 101.01
Diamondbacks 4224 402 398.46 0.095 0.094 100.89
Dodgers 4265 377 374.62 0.088 0.088 100.64
Red Sox 4232 406 404.43 0.096 0.096 100.39
Marlins 4338 438 436.46 0.101 0.101 100.35
Braves 4383 360 361.20 0.082 0.082 99.67
Pirates 4683 433 434.85 0.092 0.093 99.58
Indians 4513 412 414.22 0.091 0.092 99.46
Orioles 4540 440 442.64 0.097 0.097 99.40
Padres 4419 453 456.46 0.103 0.103 99.24
Rangers 4667 451 456.50 0.097 0.098 98.79
Cubs 4156 391 396.42 0.094 0.095 98.63
Giants 4232 454 460.66 0.107 0.109 98.55
White Sox 4409 362 369.04 0.082 0.084 98.09
Blue Jays 4215 381 389.50 0.090 0.092 97.82
Tigers 4536 450 460.98 0.099 0.102 97.62
Nationals 4417 426 439.80 0.096 0.100 96.86
Royals 4413 417 436.29 0.094 0.099 95.58
Cardinals 4597 388 409.47 0.084 0.089 94.76

It was a good year for the Mets and the Rays. I find it interesting that the Rangers rank so low, as a number of people noted Josh Hamilton's defense this season. The next table will show where he ranks as an individual:

Individual Centerfielders PMR, 2008, Visit Smooth Distance Model, 2008 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Rajai Davis 1483 153 142.55 0.103 0.096 107.33
Ichiro Suzuki 1887 195 186.20 0.103 0.099 104.72
B.J. Upton 3642 378 362.20 0.104 0.099 104.36
Carlos Gonzalez 1585 176 169.00 0.111 0.107 104.14
Carlos Gomez 3988 437 422.56 0.110 0.106 103.42
Carlos Beltran 4171 418 407.47 0.100 0.098 102.58
Marlon Byrd 1372 149 145.28 0.109 0.106 102.56
Gregor Blanco 1495 128 124.87 0.086 0.084 102.51
Brian Anderson 1295 102 99.83 0.079 0.077 102.17
Willy Taveras 3124 282 276.52 0.090 0.089 101.98
Jacoby Ellsbury 1660 171 168.01 0.103 0.101 101.78
Torii Hunter 3587 350 344.29 0.098 0.096 101.66
Chris Young 4091 393 387.93 0.096 0.095 101.31
Andruw Jones 1481 133 131.63 0.090 0.089 101.04
Shane Victorino 3619 314 310.92 0.087 0.086 100.99
Michael Bourn 3051 291 289.00 0.095 0.095 100.69
Melky Cabrera 2919 272 270.56 0.093 0.093 100.53
Corey Patterson 2388 242 241.55 0.101 0.101 100.19
Alfredo Amezaga 1451 146 145.75 0.101 0.100 100.17
Grady Sizemore 4199 382 382.73 0.091 0.091 99.81
Adam Jones 3487 337 337.64 0.097 0.097 99.81
Matt Kemp 2425 209 209.73 0.086 0.086 99.65
Coco Crisp 2534 234 235.17 0.092 0.093 99.50
Jeremy Reed 1439 132 132.92 0.092 0.092 99.31
Cody Ross 2561 254 255.96 0.099 0.100 99.24
Aaron Rowand 3750 411 414.28 0.110 0.110 99.21
Mike Cameron 3174 293 295.47 0.092 0.093 99.16
Jim Edmonds 2420 242 244.43 0.100 0.101 99.00
Alex Rios 1531 156 158.34 0.102 0.103 98.52
Jody Gerut 1816 189 191.84 0.104 0.106 98.52
Mark Kotsay 2145 173 176.54 0.081 0.082 98.00
Reed Johnson 1618 144 147.07 0.089 0.091 97.91
Nate McLouth 4228 380 388.88 0.090 0.092 97.72
Scott Hairston 1152 114 116.69 0.099 0.101 97.69
Josh Hamilton 2977 268 274.44 0.090 0.092 97.65
Vernon Wells 2582 217 222.72 0.084 0.086 97.43
Joey Gathright 2242 197 202.50 0.088 0.090 97.28
Curtis Granderson 3740 366 379.16 0.098 0.101 96.53
Nick Swisher 1650 138 144.00 0.084 0.087 95.84
Lastings Milledge 3632 348 365.21 0.096 0.101 95.29
Rick Ankiel 2433 213 224.60 0.088 0.092 94.83
Skip Schumaker 1760 136 143.45 0.077 0.082 94.81
Ryan Sweeney 1053 95 102.85 0.090 0.098 92.37
David DeJesus 1524 151 163.83 0.099 0.108 92.17

It was a very good year to be a centerfielder named Carlos. B.J. Upton, however, gets the nod as the best everyday DF. Looking at individuals, it becomes apparent why the Cardinals rated so poorly at the position. Skip Shumaker and Rick Ankiel were equally below average.

As for Josh Hamilton, he ranks 35th out of 44 fielders in the study. He's going to be worth exploring in more detail, since I suspect people who watch him give him better marks.

Posted by StatsGuru at 06:35 PM | Comments (9) | TrackBack (0)
Ball Hogs
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Before I get to the centerfielders, a question arises sometimes that I'd like to address. I'm sometimes asked when a fielder does well, especially an outfielder on fly balls, what about ball hogs? So for outfielders, I'd like to take a look at where balls get hogged, and who does the hogging.

The first graph shows the percentage of plays made on each Probabilistic Model of Range vector, each vector representing about five degrees. Data is all outs made by major league outfielders in 2008. Leftfield is represented by low number vectors, rightfield by high number vectors. Straight-away centerfield is vector 36 (click for a larger image).

BallHogPercentage.jpg

There are two things to notice from this graph. The first is that there are very few vectors in which ball hogging might occur. There are only six, in fact. Second, centerfielder hog more balls from leftfielder than they do from rightfielders. This makes some sense, since most hitters are right-handed, meaning centerfielders are going to be shaded toward left most of the time.

The other thing I want to point out is that balls are hogged in places where fewer outs get recorded (click for a larger image):

PlaysMadeOF.jpg

So, it's tough for an outfielder to get a huge boost by ball hogging. They don't stray that far into another's territory, and when they do there are fewer outs to be gathered in anyway.

This also gives us a tool to use to look at individual teams if a question of ball hogging comes up.

Posted by StatsGuru at 02:05 PM | Comments (0) | TrackBack (0)
Shortstop Runs
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Beyond the Boxscore translates PMR for shortstops into runs.

Posted by StatsGuru at 12:38 PM | Comments (0) | TrackBack (0)
November 06, 2008
Probabilistic Model of Range, 2008, Second Basemen
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The following table shows how team second basemen ranked according to the Probabilistic Model of Range:

Team Second Basemen PMR, 2008, Visit Smooth Distance Model, 2008 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Marlins 4338 527 500.98 0.121 0.115 105.19
Phillies 4396 528 504.35 0.120 0.115 104.69
Reds 4299 498 478.19 0.116 0.111 104.14
Diamondbacks 4224 561 539.98 0.133 0.128 103.89
Cubs 4156 500 487.52 0.120 0.117 102.56
Rockies 4535 564 552.06 0.124 0.122 102.16
Tigers 4536 505 495.02 0.111 0.109 102.02
Angels 4374 545 535.72 0.125 0.122 101.73
Indians 4513 554 545.22 0.123 0.121 101.61
Twins 4607 513 505.30 0.111 0.110 101.52
Athletics 4285 518 510.99 0.121 0.119 101.37
Blue Jays 4215 532 525.31 0.126 0.125 101.27
Brewers 4354 508 503.13 0.117 0.116 100.97
White Sox 4409 535 533.38 0.121 0.121 100.30
Orioles 4540 498 498.38 0.110 0.110 99.92
Cardinals 4597 517 517.91 0.112 0.113 99.82
Yankees 4349 556 557.46 0.128 0.128 99.74
Red Sox 4232 505 508.07 0.119 0.120 99.40
Astros 4292 464 467.09 0.108 0.109 99.34
Mariners 4512 602 608.69 0.133 0.135 98.90
Rangers 4667 539 546.88 0.115 0.117 98.56
Royals 4413 547 555.11 0.124 0.126 98.54
Nationals 4417 464 471.01 0.105 0.107 98.51
Braves 4383 526 534.13 0.120 0.122 98.48
Pirates 4683 466 478.32 0.100 0.102 97.43
Mets 4335 476 492.58 0.110 0.114 96.63
Giants 4232 417 432.81 0.099 0.102 96.35
Rays 4264 472 490.56 0.111 0.115 96.22
Padres 4419 475 499.74 0.107 0.113 95.05
Dodgers 4265 484 514.65 0.113 0.121 94.04

The Marlins number one at second base? That certainly flies in the face of Dan Uggla's performance in the All-Star Game. It's not that surprising, however, to see the Dodgers with the aging Jeff Kent coming in last. On to the individual players:

Individual Second Baseman PMR, 2008, Visit Smooth Distance Model, 2008 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Adam Kennedy 2036 247 226.55 0.121 0.111 109.03
Mike Fontenot 1448 175 160.82 0.121 0.111 108.82
Emilio Bonifacio 1008 100 93.17 0.099 0.092 107.33
Chase Utley 4231 513 485.09 0.121 0.115 105.75
Marco Scutaro 1077 144 136.95 0.134 0.127 105.15
Placido Polanco 3806 424 405.94 0.111 0.107 104.45
Dan Uggla 3841 465 445.31 0.121 0.116 104.42
Howie Kendrick 2341 308 295.94 0.132 0.126 104.07
Joe Inglett 1554 205 197.44 0.132 0.127 103.83
Asdrubal Cabrera 2446 316 304.98 0.129 0.125 103.61
Juan Uribe 1112 138 133.57 0.124 0.120 103.32
Brandon Phillips 3704 429 416.27 0.116 0.112 103.06
Clint Barmes 1519 183 177.61 0.120 0.117 103.03
Mark Ellis 3006 373 365.23 0.124 0.122 102.13
Alexi Casilla 2611 288 282.01 0.110 0.108 102.12
Orlando Hudson 2668 346 339.70 0.130 0.127 101.86
Kaz Matsui 2485 267 265.25 0.107 0.107 100.66
Rickie Weeks 3150 355 353.07 0.113 0.112 100.55
Dustin Pedroia 4003 479 477.12 0.120 0.119 100.39
Brian Roberts 4195 471 469.83 0.112 0.112 100.25
Robinson Cano 4152 531 530.64 0.128 0.128 100.07
Sean Rodriguez 1229 149 148.91 0.121 0.121 100.06
Mark Loretta 1110 129 128.96 0.116 0.116 100.03
Jose Lopez 3861 531 533.54 0.138 0.138 99.52
Alexei Ramirez 3081 371 373.04 0.120 0.121 99.45
Luis Castillo 2054 219 220.31 0.107 0.107 99.41
Mark Grudzielanek 2175 280 282.08 0.129 0.130 99.26
Tadahito Iguchi 1962 217 218.94 0.111 0.112 99.12
Jamey Carroll 1800 206 207.94 0.114 0.116 99.07
Ian Kinsler 3462 413 417.34 0.119 0.121 98.96
Kelly Johnson 3631 441 448.84 0.121 0.124 98.25
Mark DeRosa 1930 232 236.45 0.120 0.123 98.12
Freddy Sanchez 3688 368 378.01 0.100 0.102 97.35
Eugenio Velez 1355 128 133.20 0.094 0.098 96.09
Jeff Baker 1174 139 144.85 0.118 0.123 95.96
Felipe Lopez 2435 266 279.15 0.109 0.115 95.29
Aaron Hill 1375 164 172.51 0.119 0.125 95.07
Akinori Iwamura 3916 435 457.88 0.111 0.117 95.00
Aaron Miles 1551 171 182.78 0.110 0.118 93.55
Alberto Callaspo 1128 128 137.62 0.113 0.122 93.01
Ray Durham 2160 212 228.31 0.098 0.106 92.86
Edgar Gonzalez 1701 191 205.90 0.112 0.121 92.76
Brendan Harris 1016 101 109.08 0.099 0.107 92.59
Damion Easley 1607 170 186.57 0.106 0.116 91.12
Jeff Kent 2630 290 318.37 0.110 0.121 91.09

One thing I need to look at more closely is why Dan Uggla does so well. In the previous post on shortstops, a couple of commenters wanted more proof that this system actually works. I was a bit suprised by Akinori Iwamura rating so low, so I thought I would look at his poorest plays to see if they made sense. Of his four worst plays, all with a probablility of .889 or higher of being turned, two were errors hit right at him. One was a grounder to his right when he was playing too far left (poor positioning) and one was just bad judgement on a double play ball.

To compare, I looked at Utley's best play, since he was the best regular at the position. All three of his best plays were balls to the right of first base that got by Howard off the bats of left handers. In each case, Utley ranged into the outfield to field the ball and throw out the batter at first, twice I believe to the pitcher covering. He made those plays because Howard couldn't, but he was positioned so well he was in the right place to cover for Ryan.

The other thing I noticed is that toughest plays Utley made were much tougher than the best plays Iwamura executed. At the other end, easiest balls in play that Iwamura failed to turn into outs were much easier than Utley's worse plays.

If anyone would like to review video on MLB.com for a particular player, I'll be happy to send you the dates and innings of their best and worst plays.

In case you want to check my work, Iwamura's worst plays were on 9/7, 3rd inning, 8/20, 9th inning, 4/25, 9th inning, 7/30, 5th inning. Utley's best plays were on 7/23, 1st inning, 7/1, 1st inning and 8/3, 3rd inning.

Posted by StatsGuru at 08:21 PM | Comments (9) | TrackBack (0)
PMR Runs
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Dan Turkenkopf at Beyond the Boxscore is translating PMR into runs so I don't need to. Thanks, Dan.

Posted by StatsGuru at 11:06 AM | Comments (0) | TrackBack (0)
November 05, 2008
Probabilistic Model of Range, 2008, Shortstops
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The first position to examine is the most important of the fielders working behind the pitcher, the shortstop. As a reference, the first table looks at the position on a team-wide basis:

Team Shortstop PMR, 2008, Visit Smooth Distance Model, 2008 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Brewers 4354 551 526.00 0.127 0.121 104.75
Giants 4232 492 469.81 0.116 0.111 104.72
Marlins 4338 517 501.00 0.119 0.115 103.19
Angels 4374 524 510.64 0.120 0.117 102.62
Cardinals 4597 580 566.37 0.126 0.123 102.41
Red Sox 4232 480 472.55 0.113 0.112 101.58
Phillies 4396 557 551.85 0.127 0.126 100.93
Braves 4383 566 561.40 0.129 0.128 100.82
Diamondbacks 4224 469 465.74 0.111 0.110 100.70
Cubs 4156 498 495.33 0.120 0.119 100.54
Astros 4292 500 497.32 0.116 0.116 100.54
Athletics 4285 477 474.54 0.111 0.111 100.52
Rangers 4667 538 536.31 0.115 0.115 100.31
Dodgers 4265 546 544.65 0.128 0.128 100.25
Indians 4513 542 540.73 0.120 0.120 100.23
White Sox 4409 548 546.97 0.124 0.124 100.19
Royals 4413 508 507.30 0.115 0.115 100.14
Rays 4264 490 490.56 0.115 0.115 99.89
Orioles 4540 537 539.06 0.118 0.119 99.62
Rockies 4535 587 589.61 0.129 0.130 99.56
Pirates 4683 577 580.37 0.123 0.124 99.42
Blue Jays 4215 476 479.71 0.113 0.114 99.23
Twins 4607 578 584.70 0.125 0.127 98.85
Yankees 4349 491 499.00 0.113 0.115 98.40
Nationals 4417 526 538.02 0.119 0.122 97.76
Mariners 4512 480 493.64 0.106 0.109 97.24
Padres 4419 520 536.39 0.118 0.121 96.94
Reds 4299 468 485.83 0.109 0.113 96.33
Tigers 4536 519 544.40 0.114 0.120 95.33
Mets 4335 498 524.64 0.115 0.121 94.92

Notice the Mets are last. While recent articles mention Jeter as the worst fielder in the majors, in 2008 he wasn't the worst shortstop in New York:

Individual Shortstop PMR, 2008, Visit Smooth Distance Model, 2008 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Marco Scutaro 1352 173 157.04 0.128 0.116 110.16
Omar Vizquel 1863 210 193.60 0.113 0.104 108.47
Mike Aviles 2277 271 252.42 0.119 0.111 107.36
Maicer Izturis 1307 151 144.80 0.116 0.111 104.28
Jed Lowrie 1142 123 118.01 0.108 0.103 104.23
J.J. Hardy 3804 477 460.72 0.125 0.121 103.53
Erick Aybar 2437 305 295.72 0.125 0.121 103.14
Alex Cora 1137 140 135.75 0.123 0.119 103.13
Cesar Izturis 3136 408 396.19 0.130 0.126 102.98
Jack Wilson 2231 285 276.88 0.128 0.124 102.93
Bobby Crosby 3740 423 411.71 0.113 0.110 102.74
Jason Bartlett 3208 380 372.71 0.118 0.116 101.96
Hanley Ramirez 3986 472 462.98 0.118 0.116 101.95
Juan Castro 1331 153 150.32 0.115 0.113 101.78
Jimmy Rollins 3537 451 443.43 0.128 0.125 101.71
Luis Rodriguez 1191 143 140.93 0.120 0.118 101.47
Yunel Escobar 3344 440 434.04 0.132 0.130 101.37
Nick Punto 1646 227 224.05 0.138 0.136 101.32
Orlando Cabrera 4218 527 521.06 0.125 0.124 101.14
Adam Everett 1183 156 154.33 0.132 0.130 101.08
Miguel Tejada 4062 472 469.63 0.116 0.116 100.50
Jhonny Peralta 3963 469 467.52 0.118 0.118 100.32
Michael Young 4165 489 487.98 0.117 0.117 100.21
Ryan Theriot 3615 425 424.27 0.118 0.117 100.17
Julio Lugo 1947 216 217.95 0.111 0.112 99.11
Angel Berroa 1730 225 227.09 0.130 0.131 99.08
Derek Jeter 3815 429 433.24 0.112 0.114 99.02
Stephen Drew 3820 422 429.34 0.110 0.112 98.29
Cristian Guzman 3640 441 449.15 0.121 0.123 98.19
John McDonald 1387 150 154.82 0.108 0.112 96.89
Yuniesky Betancourt 4173 446 460.45 0.107 0.110 96.86
Troy Tulowitzki 2730 354 365.56 0.130 0.134 96.84
Edgar Renteria 3696 428 449.40 0.116 0.122 95.24
Jose Reyes 4196 480 504.15 0.114 0.120 95.21
Khalil Greene 2841 327 345.54 0.115 0.122 94.64
Tony F Pena 1808 199 211.52 0.110 0.117 94.08
Brendan Harris 1480 159 170.68 0.107 0.115 93.16
Jeff Keppinger 2636 274 296.50 0.104 0.112 92.41
David Eckstein 1445 149 163.53 0.103 0.113 91.11

Jose Reyes converted 24 fewer balls into outs than expected while Jeter was just down four. It was actually one of Derek's better years.

Omar Vizquel remains impressive at an advanced age. He didn't play a whole season, so he didn't get a chance to wear down, but if any team is looking for a great late inning defensive replacement, Omar is it.

Mike Aviles came up as a great find for the Royals in terms of his batting, but he also performed well with the glove.

Troy Tulowitzki went from first to almost worst in 2008. His injuries seemed to limit his range.

Jimmy Rollins won the Gold Glove today, but J.J. Hardy deserved it based on this data.

Feel free to comment and criticize.

Posted by StatsGuru at 08:06 PM | Comments (18) | TrackBack (0)
November 04, 2008
Probabilistic Model of Range, 2008
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Baseball Info Solutions sent me their fielding data, and that means it's time to start presenting the 2008 Probabilistic Model of Range. If you're new to this, you can find explanations in this archive. Basically, for each fieldable (non inside the park home runs) ball put in play, six parameters are used to determine how difficult it was to field the ball. A probability of turning the ball into an out is calculated, and those probabilities are summed. That gives us expected batted balls turned into outs. We turn that into a predicted DER (defensive efficiency record), compare that to the actual DER and calculate a ranking.

The model is based primarily on visiting player data, smoothed, using distance on fly balls and a hard hit indicator on ground balls. Only 2008 data was used to construct the model.

Note that a team can post a poor DER during the season, but do well in this model if the balls put into play were extremely difficult to field. This helps the Braves rank second.

Probabilistic Model of Range, 2007 Data, Teams, Visit Smooth Distance Model, Ranked by Difference
Team In Play Actual Outs Predicted Outs DER Predicted DER Index
Blue Jays 4215 2961 2896.74 0.702 0.687 102.22
Braves 4383 3033 2977.44 0.692 0.679 101.87
Rays 4264 3023 2979.66 0.709 0.699 101.45
Athletics 4285 2991 2950.73 0.698 0.689 101.36
Red Sox 4232 2953 2913.30 0.698 0.688 101.36
Astros 4292 2990 2952.74 0.697 0.688 101.26
Angels 4374 3022 2985.77 0.691 0.683 101.21
Brewers 4354 3036 3000.17 0.697 0.689 101.19
Cardinals 4597 3190 3163.77 0.694 0.688 100.83
Dodgers 4265 2941 2919.81 0.690 0.685 100.73
Cubs 4156 2925 2906.58 0.704 0.699 100.63
Twins 4607 3161 3144.82 0.686 0.683 100.51
Mariners 4512 3068 3053.72 0.680 0.677 100.47
Indians 4513 3093 3082.17 0.685 0.683 100.35
White Sox 4409 3021 3013.27 0.685 0.683 100.26
Marlins 4338 3002 2994.74 0.692 0.690 100.24
Diamondbacks 4224 2892 2886.85 0.685 0.683 100.18
Giants 4232 2897 2898.76 0.685 0.685 99.94
Tigers 4536 3105 3109.78 0.685 0.686 99.85
Phillies 4396 3054 3062.15 0.695 0.697 99.73
Mets 4335 3024 3033.17 0.698 0.700 99.70
Rangers 4667 3124 3136.62 0.669 0.672 99.60
Padres 4419 3074 3088.40 0.696 0.699 99.53
Pirates 4683 3157 3175.46 0.674 0.678 99.42
Rockies 4535 3072 3090.76 0.677 0.682 99.39
Nationals 4417 3041 3060.09 0.688 0.693 99.38
Orioles 4540 3119 3139.36 0.687 0.691 99.35
Yankees 4349 2962 2984.01 0.681 0.686 99.26
Reds 4299 2889 2921.00 0.672 0.679 98.90
Royals 4413 3038 3076.09 0.688 0.697 98.76

The Rays turned in the best combination of good pitching and good defense. Their .699 predicted DER was second to the Mets. Unlike the Mets, however, the Rays fielded more balls than expected, giving the best DER, but only the third best Index. The Blue Jays turned in a tremendous defensive season, a big reason their pitching staff did so well in ERA in 2008.

The bottom of this chart is very interesting. From the Padres down, teams 23-30 all turned out to be very poor teams with the exception of the Yankees. Defense didn't necessarily help a team win, as the Phillies were pretty middle of the road, but it certainly seemed to indicate a pretty bad team.

Note that last season, the Devil Rays were at the very bottom of the list. They improved both their predicted DER and their ability to turn batted balls into outs. That was enough to lower their runs allowed from 944 to 671 and make them American League champions.

Posted by StatsGuru at 08:08 PM | Comments (2) | TrackBack (0)
February 17, 2008
PMR in The Globe
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Gideon Gil attended the AAAS symposia yesterday and gave both SAFE and PMR a nice writeup.

Posted by StatsGuru at 10:10 AM | Comments (0) | TrackBack (0)
February 15, 2008
Range Presentation
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On Saturday morning I'll make a presentation at the American Association for the Advancement of Science (AAAS) meeting on the Probabilistic Model of Range. The symposia is called New Techniques in the Evaluation and Prediction of Baseball Performance and meets in the Hynes Convention Center, Second Level, Room 202 at 8:30 AM. Alan Schwarz and Shane Jensen will also present.

Posted by StatsGuru at 06:56 AM | Comments (0) | TrackBack (0)
January 10, 2008
2007 Defensive Charts
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The 2007 Defensive Charts are up. These provide a visualization of the Probabilistic Model of Range data based on position and batted ball type. Enjoy!

Posted by StatsGuru at 07:02 PM | Comments (1) | TrackBack (0)
November 26, 2007
Does a Good Offense Improve a Defense?
Permalink

One criticism leveled at the Probabilistic Model of Range this year is that due to building the models based on the visiting team fielders, teams with good offenses get a bonus defensively. The example given is the Yankees, but there are other high offense teams ranked high in terms of PMR.

The idea is that since the Yankees hit well, their opponents DER must by definition be low. A ball put in play by the Yankees must have a lower probability of being turned into an out. This causes the model to underestimate the fielding ability of opponents, and over estimate the fielding ability of the Yankees. If the model contained one parameter, ballpark, then this would be absolutely true. However, there are six parameters, including a vector indicating the direction of the ball. I propose that the Yankees hit better than their opponents not because a random ball in play has a higher probability of falling for a hit, but because the Yankees do a better job of hitting balls where they are tough to field.

The following table shows the number of ground balls hit by the Yankees and their opponents by vector:

Vector Yankees Opponents Predicted DER
254 4 0.000
261214 0.000
273726 0.766
2811857 0.898
29175118 0.706
30193156 0.671
31148119 0.844
3211182 0.934
33164136 0.868
34114105 0.585
3510074 0.535
36117124 0.624
37101108 0.617
38116150 0.838
39119131 0.865
40163139 0.764
41165174 0.550
42110130 0.688
436155 0.847
443540 0.572
457 13 0.010
465 5 0.000

As you can see, the low probability vectors are 29-30, the shortstop hole, 34-37, up the middle, 41-42, the second base hole, and 44, right down the first base line. I'm not looking at the foul vectors where a ball is always a hit. Breaking these down:

Ground ballsYankeesOpponents
In Holes11101029
At Fielders882895

So the Yankees hit more grounders where they are less likely to be fielded, and fewer grounders where they are more likely to be fielded than their opponents. Later I want to look at how the Yankees field home and road. If they field much better at home, then the objection still my have some validity.

Posted by StatsGuru at 08:27 AM | Comments (3) | TrackBack (0)
November 25, 2007
Probabilistic Model of Range, Pitchers, 2007
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To complete the survery of range, here are how pitchers rank. First the teams:

Team Pitchers PMR, 2007, Visit Smooth Distance Model, 2007 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Astros 4530 205 183.71 0.045 0.041 111.59
Padres 4476 243 228.50 0.054 0.051 106.35
Rockies 4599 218 206.59 0.047 0.045 105.52
Indians 4548 181 171.92 0.040 0.038 105.28
Mets 4362 173 164.61 0.040 0.038 105.09
White Sox 4545 196 186.52 0.043 0.041 105.08
Yankees 4511 181 172.92 0.040 0.038 104.67
Tigers 4486 167 159.73 0.037 0.036 104.55
Red Sox 4226 149 142.79 0.035 0.034 104.35
Mariners 4535 174 167.33 0.038 0.037 103.99
Blue Jays 4349 200 194.00 0.046 0.045 103.09
Phillies 4505 193 187.33 0.043 0.042 103.02
Pirates 4608 204 200.82 0.044 0.044 101.58
Cubs 4177 166 163.95 0.040 0.039 101.25
Rangers 4518 197 195.59 0.044 0.043 100.72
Braves 4404 206 204.60 0.047 0.046 100.69
Devil Rays 4378 148 147.07 0.034 0.034 100.63
Twins 4384 150 152.30 0.034 0.035 98.49
Orioles 4403 160 162.54 0.036 0.037 98.44
Nationals 4591 167 170.78 0.036 0.037 97.78
Marlins 4491 178 182.52 0.040 0.041 97.52
Angels 4325 143 146.86 0.033 0.034 97.37
Giants 4467 159 163.87 0.036 0.037 97.03
Diamondbacks 4351 207 213.40 0.048 0.049 97.00
Cardinals 4587 158 166.21 0.034 0.036 95.06
Athletics 4499 165 174.70 0.037 0.039 94.45
Brewers 4392 179 192.64 0.041 0.044 92.92
Dodgers 4310 189 205.96 0.044 0.048 91.76
Reds 4533 162 180.13 0.036 0.040 89.93
Royals 4528 151 179.20 0.033 0.040 84.27

The Padres not only induce the most predicted outs back to the pitcher, they exceed those outs by a great deal. Maddux is one reason:

Individual Pitcher PMR, 2007, Visit Smooth Distance Model, 2007 data only (400 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Chris Sampson 414 24 15.23 0.058 0.037 157.55
Matt Cain 571 26 19.79 0.046 0.035 131.37
Chad Durbin 417 15 11.48 0.036 0.028 130.65
Shaun Marcum 456 27 20.71 0.059 0.045 130.37
Steve Trachsel 549 35 26.89 0.064 0.049 130.17
Mike Mussina 512 27 20.84 0.053 0.041 129.54
Woody Williams 632 36 27.90 0.057 0.044 129.04
Aaron Cook 572 37 28.69 0.065 0.050 128.98
Miguel Batista 615 26 20.17 0.042 0.033 128.92
Jon Garland 705 34 26.84 0.048 0.038 126.66
Kelvim Escobar 572 17 13.58 0.030 0.024 125.21
Wandy Rodriguez 536 21 16.87 0.039 0.031 124.46
Greg Maddux 681 53 42.87 0.078 0.063 123.64
Ervin Santana 457 13 10.59 0.028 0.023 122.72
Jake Peavy 571 30 24.58 0.053 0.043 122.03
Brandon Webb 692 53 43.55 0.077 0.063 121.69
Mike Bacsik 414 15 12.35 0.036 0.030 121.42
Tim Wakefield 600 24 19.83 0.040 0.033 121.05
Carlos Zambrano 610 30 25.00 0.049 0.041 119.98
Javier Vazquez 583 28 23.46 0.048 0.040 119.34
Adam Eaton 525 22 18.49 0.042 0.035 119.00
Nate Robertson 573 27 22.77 0.047 0.040 118.56
John Danks 427 15 12.83 0.035 0.030 116.94
James Shields 615 26 22.26 0.042 0.036 116.80
Justin Verlander 577 17 14.69 0.029 0.025 115.76
Chien-Ming Wang 643 34 29.61 0.053 0.046 114.84
Carlos Silva 699 27 23.54 0.039 0.034 114.70
John Smoltz 586 30 26.16 0.051 0.045 114.69
Dustin McGowan 484 31 27.18 0.064 0.056 114.04
Justin Germano 426 21 18.42 0.049 0.043 114.04
Ted Lilly 586 24 21.09 0.041 0.036 113.81
Dontrelle Willis 667 39 34.32 0.058 0.051 113.64
Kyle Davies 432 16 14.08 0.037 0.033 113.60
Sergio Mitre 522 29 25.58 0.056 0.049 113.35
Daisuke Matsuzaka 555 24 21.21 0.043 0.038 113.18
Joe Blanton 750 28 25.10 0.037 0.033 111.54
Jake Westbrook 481 27 24.51 0.056 0.051 110.17
Andy Sonnanstine 408 14 12.81 0.034 0.031 109.28
Matt Chico 548 16 14.77 0.029 0.027 108.33
Jamie Moyer 633 30 27.83 0.047 0.044 107.81
Johan Santana 555 24 22.27 0.043 0.040 107.75
Tom Glavine 674 27 25.15 0.040 0.037 107.37
C.C. Sabathia 701 24 22.47 0.034 0.032 106.80
Brett Tomko 415 16 14.99 0.039 0.036 106.77
Jarrod Washburn 627 20 18.81 0.032 0.030 106.33
Noah Lowry 502 23 21.69 0.046 0.043 106.02
Jeremy Guthrie 527 21 19.85 0.040 0.038 105.81
Chris Capuano 456 28 26.47 0.061 0.058 105.77
Fausto Carmona 654 36 34.14 0.055 0.052 105.43
Roy Halladay 722 36 34.31 0.050 0.048 104.94
Mark Buehrle 648 33 31.47 0.051 0.049 104.86
Bronson Arroyo 661 27 25.79 0.041 0.039 104.71
David Bush 594 24 23.12 0.040 0.039 103.80
Kyle Kendrick 401 20 19.29 0.050 0.048 103.69
David Wells 545 20 19.32 0.037 0.035 103.52
Erik Bedard 431 17 16.46 0.039 0.038 103.29
Jeff Suppan 708 34 32.99 0.048 0.047 103.05
Barry Zito 608 21 20.40 0.035 0.034 102.94
Jason Marquis 626 25 24.34 0.040 0.039 102.70
Jeff Francis 662 30 29.40 0.045 0.044 102.04
Kameron Loe 464 28 27.74 0.060 0.060 100.93
Livan Hernandez 704 38 37.71 0.054 0.054 100.78
Paul Maholm 583 30 29.99 0.051 0.051 100.02
Matt Morris 693 28 28.14 0.040 0.041 99.50
Kip Wells 522 20 20.10 0.038 0.039 99.48
Ian Snell 606 20 20.14 0.033 0.033 99.33
Odalis Perez 494 18 18.33 0.036 0.037 98.20
John Maine 527 17 17.37 0.032 0.033 97.86
Cole Hamels 495 23 23.78 0.046 0.048 96.70
Chad Gaudin 603 21 21.76 0.035 0.036 96.51
A.J. Burnett 414 15 15.67 0.036 0.038 95.73
Mike Maroth 417 17 17.85 0.041 0.043 95.22
Tim Hudson 722 41 43.25 0.057 0.060 94.81
Felix Hernandez 567 26 27.45 0.046 0.048 94.73
Jered Weaver 514 18 19.04 0.035 0.037 94.51
Brian Bannister 540 20 21.19 0.037 0.039 94.40
Oliver Perez 483 11 11.65 0.023 0.024 94.40
Micah Owings 461 22 23.32 0.048 0.051 94.34
Kyle Lohse 615 22 23.48 0.036 0.038 93.71
Jeff Weaver 511 10 10.72 0.020 0.021 93.32
Chuck James 484 15 16.10 0.031 0.033 93.18
Tom Gorzelanny 642 24 25.76 0.037 0.040 93.18
Roy Oswalt 675 36 38.80 0.053 0.057 92.79
Adam Wainwright 654 28 30.29 0.043 0.046 92.44
Jose Contreras 647 22 23.80 0.034 0.037 92.43
Scott Kazmir 534 16 17.46 0.030 0.033 91.64
Lenny DiNardo 430 15 16.48 0.035 0.038 91.04
Derek Lowe 604 27 29.69 0.045 0.049 90.95
Andy Pettitte 690 26 28.59 0.038 0.041 90.93
Paul Byrd 686 21 23.15 0.031 0.034 90.72
Aaron Harang 642 23 25.44 0.036 0.040 90.42
Doug Davis 597 32 36.17 0.054 0.061 88.47
Scott Olsen 578 21 23.75 0.036 0.041 88.41
Josh Fogg 556 21 23.79 0.038 0.043 88.26
Scott Baker 454 13 14.84 0.029 0.033 87.58
Rich Hill 527 21 23.98 0.040 0.046 87.57
Brad Penny 643 25 28.93 0.039 0.045 86.41
Kevin Millwood 571 16 18.75 0.028 0.033 85.32
John Lackey 668 24 28.71 0.036 0.043 83.60
Braden Looper 581 19 23.06 0.033 0.040 82.41
Chad Billingsley 400 17 20.94 0.043 0.052 81.18
Josh Beckett 566 11 13.68 0.019 0.024 80.41
Vicente Padilla 407 12 15.11 0.029 0.037 79.44
Chris Young 448 11 14.27 0.025 0.032 77.08
Claudio Vargas 419 14 18.20 0.033 0.043 76.91
Edwin Jackson 516 12 15.91 0.023 0.031 75.43
Jeremy Bonderman 533 14 18.78 0.026 0.035 74.53
Boof Bonser 539 14 18.92 0.026 0.035 74.00
Jorge de la Rosa 431 11 15.32 0.026 0.036 71.79
Gil Meche 663 20 28.05 0.030 0.042 71.31
Julian Tavarez 455 11 15.86 0.024 0.035 69.38
Brad Thompson 451 10 14.58 0.022 0.032 68.61
Matt Belisle 570 15 22.66 0.026 0.040 66.18
Dan Haren 661 17 26.01 0.026 0.039 65.36
Daniel Cabrera 608 13 20.82 0.021 0.034 62.44
Ben Sheets 431 11 19.19 0.026 0.045 57.31
Curt Schilling 485 7 12.87 0.014 0.027 54.40

Peavy is also very good, however. Looking at Schilling's low ranking should give his opponents a clue as to his weakness next season. Bunting for hits against Curt might be a very good idea.

Posted by StatsGuru at 07:26 PM | Comments (4) | TrackBack (0)
November 21, 2007
Probabilistic Model of Range, Catchers, 2007
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Fielding by catchers isn't the most important aspect of the job, and the number of outs attributed to the postion are few. But for completeness, here are the tables for the position. First, teams:

Team Catchers PMR, 2007, Visit Smooth Distance Model, 2007 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Cardinals 4587 57 47.59 0.012 0.010 119.76
Braves 4404 64 55.33 0.015 0.013 115.67
Rockies 4599 76 66.39 0.017 0.014 114.48
Yankees 4511 66 59.51 0.015 0.013 110.90
Dodgers 4310 68 62.43 0.016 0.014 108.91
Angels 4325 39 35.96 0.009 0.008 108.47
Marlins 4491 57 53.73 0.013 0.012 106.09
Nationals 4591 60 57.21 0.013 0.012 104.87
Astros 4530 58 55.59 0.013 0.012 104.33
Tigers 4486 50 47.96 0.011 0.011 104.25
White Sox 4545 50 49.10 0.011 0.011 101.82
Giants 4467 58 57.06 0.013 0.013 101.64
Cubs 4177 51 50.42 0.012 0.012 101.15
Reds 4533 74 73.68 0.016 0.016 100.44
Blue Jays 4349 50 49.79 0.011 0.011 100.42
Royals 4528 46 45.90 0.010 0.010 100.22
Rangers 4518 48 48.05 0.011 0.011 99.90
Red Sox 4226 49 49.56 0.012 0.012 98.88
Devil Rays 4378 41 41.89 0.009 0.010 97.88
Indians 4548 36 37.23 0.008 0.008 96.70
Diamondbacks 4351 50 51.94 0.011 0.012 96.26
Padres 4476 59 61.48 0.013 0.014 95.97
Orioles 4403 37 38.96 0.008 0.009 94.97
Mariners 4535 42 44.86 0.009 0.010 93.63
Pirates 4608 51 54.75 0.011 0.012 93.15
Phillies 4505 56 60.25 0.012 0.013 92.95
Twins 4384 30 32.50 0.007 0.007 92.32
Athletics 4499 37 41.10 0.008 0.009 90.03
Mets 4362 50 56.67 0.011 0.013 88.22
Brewers 4392 51 59.91 0.012 0.014 85.13

The Mets are Brewers, who just completed a trade at the position, came out at the bottom team wise. New York might have been better off with Torrealba, at least fielding wise.

Individual Catcher PMR, 2007, Visit Smooth Distance Model, 2007 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Yadier Molina 2719 32 26.82 0.012 0.010 119.33
Brian McCann 3433 52 43.65 0.015 0.013 119.14
Yorvit Torrealba 2863 54 45.73 0.019 0.016 118.07
Miguel Olivo 3131 44 37.90 0.014 0.012 116.11
Jorge Posada 3484 50 43.52 0.014 0.012 114.90
Eric Munson 1012 17 15.02 0.017 0.015 113.21
Jeff Mathis 1421 21 18.96 0.015 0.013 110.78
Jose Molina 1431 16 14.52 0.011 0.010 110.18
Kelly Shoppach 1365 14 12.72 0.010 0.009 110.07
Gerald Laird 3118 37 33.82 0.012 0.011 109.40
Russell Martin 3687 60 55.76 0.016 0.015 107.60
Gregg Zaun 2559 32 29.91 0.013 0.012 106.98
Brad Ausmus 2728 33 31.07 0.012 0.011 106.22
Chris Iannetta 1613 20 18.83 0.012 0.012 106.20
Toby Hall 1002 10 9.45 0.010 0.009 105.82
Gary Bennett 1223 15 14.18 0.012 0.012 105.75
Jesus Flores 1258 21 19.87 0.017 0.016 105.69
Ivan Rodriguez 3216 41 38.98 0.013 0.012 105.19
John Buck 2879 30 28.52 0.010 0.010 105.18
Brian Schneider 3333 39 37.34 0.012 0.011 104.43
Mike Napoli 1814 12 11.53 0.007 0.006 104.09
Miguel Montero 1629 20 19.57 0.012 0.012 102.20
Javier Valentin 1494 20 19.72 0.013 0.013 101.42
Bengie Molina 3389 42 41.51 0.012 0.012 101.17
Mike Rabelo 1270 9 8.99 0.007 0.007 100.16
Dave Ross 2603 46 46.32 0.018 0.018 99.32
A.J. Pierzynski 3270 37 37.40 0.011 0.011 98.92
Ronny Paulino 3423 40 40.81 0.012 0.012 98.02
Michael Barrett 2291 33 33.76 0.014 0.015 97.74
Ramon Hernandez 2617 24 24.82 0.009 0.009 96.71
Josh Bard 2761 38 39.31 0.014 0.014 96.67
Mike Redmond 1461 11 11.42 0.008 0.008 96.30
Kurt Suzuki 1696 14 14.61 0.008 0.009 95.82
Paul Lo Duca 2922 33 34.63 0.011 0.012 95.29
Dioner Navarro 2901 25 26.29 0.009 0.009 95.09
Jason LaRue 1537 16 16.89 0.010 0.011 94.72
Jason Kendall 3448 31 32.85 0.009 0.010 94.37
Carlos Ruiz 2802 44 46.89 0.016 0.017 93.83
Jason Varitek 3061 33 35.49 0.011 0.012 92.99
Chris Snyder 2611 26 28.37 0.010 0.011 91.64
Johnny Estrada 2922 36 39.51 0.012 0.014 91.12
Kenji Johjima 3548 32 35.22 0.009 0.010 90.85
Rob Bowen 1268 11 12.14 0.009 0.010 90.59
Jarrod Saltalamacchia 1201 11 12.25 0.009 0.010 89.78
Victor Martinez 3183 22 24.51 0.007 0.008 89.76
Joe Mauer 2331 16 18.22 0.007 0.008 87.83
Paul Bako 1290 8 9.42 0.006 0.007 84.90
Matt Treanor 1317 13 15.83 0.010 0.012 82.12
Jason Phillips 1025 7 8.88 0.007 0.009 78.82
Damian Miller 1367 13 17.73 0.010 0.013 73.30

Two of the old men, Posada and Ivan Rodriguez, are still cat like behind the plate.

Posted by StatsGuru at 01:21 PM | Comments (4) | TrackBack (0)
November 17, 2007
Probabilistic Model of Range, Firstbasemen, 2007
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Here's a look at the range of first basemen. First, the team table. The Yankees at least did a good job of improving their defense at the position:

Team First Basemen PMR, 2007, Visit Smooth Distance Model, 2007 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Cardinals 4587 366 329.22 0.080 0.072 111.17
Yankees 4511 314 285.78 0.070 0.063 109.87
Giants 4467 325 304.72 0.073 0.068 106.66
Royals 4528 315 296.64 0.070 0.066 106.19
Padres 4476 311 295.59 0.069 0.066 105.21
Cubs 4177 283 269.44 0.068 0.065 105.03
Braves 4404 320 306.86 0.073 0.070 104.28
Angels 4325 308 296.32 0.071 0.069 103.94
Pirates 4608 315 304.66 0.068 0.066 103.39
Rockies 4599 336 326.21 0.073 0.071 103.00
Astros 4530 335 328.42 0.074 0.072 102.00
Red Sox 4226 323 321.65 0.076 0.076 100.42
Brewers 4392 294 293.80 0.067 0.067 100.07
Diamondbacks 4351 292 292.06 0.067 0.067 99.98
Devil Rays 4378 316 317.18 0.072 0.072 99.63
Blue Jays 4349 337 339.17 0.077 0.078 99.36
Orioles 4403 273 277.29 0.062 0.063 98.45
Athletics 4499 303 310.84 0.067 0.069 97.48
Dodgers 4310 285 293.60 0.066 0.068 97.07
Mariners 4535 297 308.37 0.065 0.068 96.31
Mets 4362 285 296.03 0.065 0.068 96.27
White Sox 4545 309 321.66 0.068 0.071 96.06
Indians 4548 295 307.96 0.065 0.068 95.79
Tigers 4486 296 310.16 0.066 0.069 95.44
Rangers 4518 283 297.18 0.063 0.066 95.23
Phillies 4505 302 317.82 0.067 0.071 95.02
Marlins 4491 291 307.52 0.065 0.068 94.63
Twins 4384 311 337.63 0.071 0.077 92.11
Reds 4533 263 290.87 0.058 0.064 90.42
Nationals 4591 270 299.99 0.059 0.065 90.00

It looks like the Nationals missed Nick Johnson's glove at first base. It's even more evident in the individual listing:

Individual First Basemen PMR, 2007, Visit Smooth Distance Model, 2007 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Doug Mientkiewicz 1427 109 92.24 0.076 0.065 118.17
Rich Aurilia 1115 70 59.31 0.063 0.053 118.02
Andy Phillips 1325 93 80.91 0.070 0.061 114.95
Albert Pujols 4220 349 308.82 0.083 0.073 113.01
Ryan Shealy 1336 88 78.66 0.066 0.059 111.87
Derrek Lee 3691 254 239.65 0.069 0.065 105.99
Casey Kotchman 3085 225 214.03 0.073 0.069 105.12
Adrian Gonzalez 4401 307 292.23 0.070 0.066 105.06
Tony Clark 1345 98 93.58 0.073 0.070 104.72
Scott Thorman 1859 126 120.75 0.068 0.065 104.35
Todd Helton 4170 306 293.61 0.073 0.070 104.22
Ryan Klesko 2504 190 183.08 0.076 0.073 103.78
Ben Broussard 1057 72 69.60 0.068 0.066 103.45
James Loney 2355 168 162.45 0.071 0.069 103.42
Ross Gload 2169 153 148.21 0.071 0.068 103.23
Carlos Pena 3708 277 268.41 0.075 0.072 103.20
Adam LaRoche 4141 283 274.52 0.068 0.066 103.09
Nick Swisher 1075 91 88.68 0.085 0.082 102.61
Kevin Youkilis 3208 253 249.42 0.079 0.078 101.44
Matt Stairs 1024 86 85.07 0.084 0.083 101.10
Lance Berkman 3315 229 229.22 0.069 0.069 99.91
Lyle Overbay 2887 221 221.32 0.077 0.077 99.86
Prince Fielder 4073 266 271.21 0.065 0.067 98.08
Conor Jackson 2647 173 176.80 0.065 0.067 97.85
Mark Teixeira 3404 240 246.41 0.071 0.072 97.40
Carlos Delgado 3649 244 251.39 0.067 0.069 97.06
Kevin Millar 2666 171 176.83 0.064 0.066 96.70
Robert Fick 1221 80 82.79 0.066 0.068 96.64
Aubrey Huff 1295 67 69.45 0.052 0.054 96.47
Ryan Howard 3871 263 274.29 0.068 0.071 95.88
Paul Konerko 3864 256 267.48 0.066 0.069 95.71
Richie Sexson 3137 201 210.24 0.064 0.067 95.61
Aaron Boone 1219 85 89.73 0.070 0.074 94.72
Brad Wilkerson 1444 82 86.94 0.057 0.060 94.32
Ryan Garko 3271 209 223.33 0.064 0.068 93.58
Sean Casey 3100 198 211.63 0.064 0.068 93.56
Dan Johnson 2679 166 177.43 0.062 0.066 93.56
Justin Morneau 3872 281 302.07 0.073 0.078 93.02
Mike Jacobs 2821 170 183.72 0.060 0.065 92.53
Jeff Conine 1595 86 94.78 0.054 0.059 90.74
Scott Hatteberg 2457 144 160.66 0.059 0.065 89.63
Nomar Garciaparra 1678 106 118.73 0.063 0.071 89.28
Dmitri Young 2808 162 184.81 0.058 0.066 87.66

Once again, Albert Pujols comes out on top among every day first basemen. If the Yankees had kept Miguel Cairo off first, they might have finished first as a team. Not only did Nomar not hit like a first baseman, he didn't even field well.

Correction: Cairo, not Cabrera.

Posted by StatsGuru at 08:04 PM | Comments (3) | TrackBack (0)
November 16, 2007
Probabilistic Model of Range, Leftfielders, 2007
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Here's something the Orioles excelled at during 2007, fielding by leftfielders:

Team Leftfielders PMR, 2007, Visit Smooth Distance Model, 2007 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Orioles 4403 362 343.61 0.082 0.078 105.35
Indians 4548 339 325.23 0.075 0.072 104.23
Braves 4404 316 306.69 0.072 0.070 103.04
Rangers 4518 337 327.32 0.075 0.072 102.96
Nationals 4591 352 341.94 0.077 0.074 102.94
Yankees 4511 334 324.93 0.074 0.072 102.79
Brewers 4392 322 314.81 0.073 0.072 102.28
Mets 4362 324 317.91 0.074 0.073 101.91
Padres 4476 310 305.07 0.069 0.068 101.62
Royals 4528 373 367.39 0.082 0.081 101.53
Devil Rays 4378 339 334.26 0.077 0.076 101.42
Cubs 4177 341 337.65 0.082 0.081 100.99
Diamondbacks 4351 349 345.83 0.080 0.079 100.92
Blue Jays 4349 294 292.63 0.068 0.067 100.47
Dodgers 4310 288 287.78 0.067 0.067 100.08
Angels 4325 340 341.60 0.079 0.079 99.53
Giants 4467 314 317.61 0.070 0.071 98.86
Tigers 4486 327 331.60 0.073 0.074 98.61
Marlins 4491 274 278.60 0.061 0.062 98.35
Astros 4530 285 290.69 0.063 0.064 98.04
Athletics 4499 337 344.34 0.075 0.077 97.87
White Sox 4545 318 325.73 0.070 0.072 97.63
Pirates 4608 303 310.81 0.066 0.067 97.49
Rockies 4599 317 326.69 0.069 0.071 97.03
Reds 4533 326 336.27 0.072 0.074 96.95
Twins 4384 334 345.60 0.076 0.079 96.64
Phillies 4505 282 295.91 0.063 0.066 95.30
Red Sox 4226 284 299.24 0.067 0.071 94.91
Cardinals 4587 320 346.16 0.070 0.075 92.44
Mariners 4535 288 315.31 0.064 0.070 91.34

Among individuals, Matt Diaz had a career year with the glove as well as the bat.

Individual Leftfielder PMR, 2007, Visit Smooth Distance Model, 2007 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Matt Diaz 2064 155 142.05 0.075 0.069 109.11
Jose Cruz 1099 89 82.33 0.081 0.075 108.10
Joey Gathright 1595 154 142.69 0.097 0.089 107.93
Jay Payton 2776 231 214.85 0.083 0.077 107.52
David Dellucci 1210 97 91.32 0.080 0.075 106.22
Scott Hairston 1689 115 108.48 0.068 0.064 106.01
Wily Mo Pena 1126 68 64.97 0.060 0.058 104.66
Ryan Church 2304 196 188.59 0.085 0.082 103.93
Geoff Jenkins 2985 243 234.55 0.081 0.079 103.60
Carl Crawford 3623 286 276.65 0.079 0.076 103.38
Hideki Matsui 3091 214 207.16 0.069 0.067 103.30
Adam Lind 1969 137 132.73 0.070 0.067 103.22
Jason Michaels 1567 117 113.59 0.075 0.072 103.00
Reggie Willits 1557 151 146.83 0.097 0.094 102.84
Reed Johnson 1518 108 105.47 0.071 0.069 102.40
Emil Brown 1909 155 153.22 0.081 0.080 101.16
Eric Byrnes 2924 239 236.90 0.082 0.081 100.89
Alfonso Soriano 3074 245 243.79 0.080 0.079 100.50
Rob Mackowiak 1468 98 97.52 0.067 0.066 100.49
Kenny Lofton 1189 82 82.28 0.069 0.069 99.66
Willie Harris 1873 138 139.21 0.074 0.074 99.13
Ryan Ludwick 1011 86 86.83 0.085 0.086 99.04
Frank Catalanotto 1540 98 99.82 0.064 0.065 98.18
Jason Bay 3974 266 271.62 0.067 0.068 97.93
Luis Gonzalez 3008 192 196.31 0.064 0.065 97.81
Matt Holliday 4331 296 303.68 0.068 0.070 97.47
Carlos Lee 4244 261 268.68 0.061 0.063 97.14
Moises Alou 2105 138 142.80 0.066 0.068 96.64
Shannon Stewart 3606 277 287.12 0.077 0.080 96.47
Kevin Mench 1139 55 57.41 0.048 0.050 95.81
Craig Monroe 2512 166 174.76 0.066 0.070 94.99
Garret Anderson 2169 143 150.84 0.066 0.070 94.81
Scott Podsednik 1421 108 114.15 0.076 0.080 94.61
Josh Willingham 3653 211 223.26 0.058 0.061 94.51
Adam Dunn 3691 245 259.98 0.066 0.070 94.24
Terrmel Sledge 1192 77 82.16 0.065 0.069 93.72
Barry Bonds 2588 162 173.93 0.063 0.067 93.14
Jason Kubel 2153 159 172.31 0.074 0.080 92.27
Raul Ibanez 3559 224 243.95 0.063 0.069 91.82
Manny Ramirez 2925 182 198.85 0.062 0.068 91.53
Chris Duncan 2437 158 175.74 0.065 0.072 89.90
Pat Burrell 3176 176 198.31 0.055 0.062 88.75

There's no real surprises at the bottom of the list. Bonds, however, fell off quite a bit. He was average in 2006, but well below average in 2007. You can also see that there are few regular leftfielders. Only twelve players on the list were on the field at that position for at least 3000 balls in play.

Posted by StatsGuru at 03:35 PM | Comments (2) | TrackBack (0)
November 15, 2007
Probabilistic Model of Range, Rightfielders, 2007
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The following table presents probabilistic model of range data for team rightfielders:

Team Rightfielders PMR, 2007, Visit Smooth Distance Model, 2007 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Phillies 4505 363 328.75 0.081 0.073 110.42
Rangers 4518 341 317.30 0.075 0.070 107.47
Yankees 4511 341 328.36 0.076 0.073 103.85
Royals 4528 410 397.12 0.091 0.088 103.24
Nationals 4591 392 381.19 0.085 0.083 102.84
Indians 4548 313 304.41 0.069 0.067 102.82
Marlins 4491 379 368.66 0.084 0.082 102.81
Astros 4530 360 354.55 0.079 0.078 101.54
Brewers 4392 393 387.42 0.089 0.088 101.44
Diamondbacks 4351 336 331.82 0.077 0.076 101.26
Athletics 4499 330 327.02 0.073 0.073 100.91
Blue Jays 4349 281 278.50 0.065 0.064 100.90
Cubs 4177 303 301.51 0.073 0.072 100.50
Angels 4325 311 310.00 0.072 0.072 100.32
Padres 4476 331 331.59 0.074 0.074 99.82
Twins 4384 306 307.17 0.070 0.070 99.62
Tigers 4486 318 319.88 0.071 0.071 99.41
Red Sox 4226 287 289.46 0.068 0.068 99.15
Mets 4362 340 343.80 0.078 0.079 98.89
Orioles 4403 314 317.86 0.071 0.072 98.79
Braves 4404 331 336.45 0.075 0.076 98.38
Devil Rays 4378 309 314.27 0.071 0.072 98.32
Reds 4533 377 384.09 0.083 0.085 98.15
Pirates 4608 312 319.06 0.068 0.069 97.79
Cardinals 4587 316 323.36 0.069 0.070 97.72
White Sox 4545 345 354.49 0.076 0.078 97.32
Dodgers 4310 317 326.76 0.074 0.076 97.01
Giants 4467 338 349.14 0.076 0.078 96.81
Mariners 4535 305 323.57 0.067 0.071 94.26
Rockies 4599 296 316.91 0.064 0.069 93.40

As shown below, Jayson Werth and Shane Victorino made quite the dynamic duo in rightfield for the Phillies. My uncle Anthony will not be happy with this list, however. He's a Yankees season ticket holder and he loves to tell me how much Bobby Abreu is afraid of the wall. It looks like he's still getting to lots of balls.

Individual Rightfielder PMR, 2007, Visit Smooth Distance Model, 2007 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Jayson Werth 1389 109 95.35 0.078 0.069 114.32
Shane Victorino 2837 229 210.62 0.081 0.074 108.72
Nick Swisher 1289 109 101.65 0.085 0.079 107.23
Carlos Quentin 1718 138 129.11 0.080 0.075 106.89
Franklin Gutierrez 1757 136 128.55 0.077 0.073 105.79
Nelson Cruz 1922 148 141.26 0.077 0.073 104.77
Luke Scott 2560 198 190.34 0.077 0.074 104.02
Bobby Abreu 4148 313 302.45 0.075 0.073 103.49
Corey Hart 2641 253 246.33 0.096 0.093 102.71
Austin Kearns 4356 375 366.16 0.086 0.084 102.41
Mark Teahen 3663 318 311.33 0.087 0.085 102.14
Alex Rios 3730 243 240.17 0.065 0.064 101.18
Travis Buck 1561 110 109.03 0.070 0.070 100.89
Jeremy Hermida 3035 247 245.88 0.081 0.081 100.46
Randy Winn 2686 209 208.12 0.078 0.077 100.42
Delmon Young 3463 252 251.16 0.073 0.073 100.33
Trot Nixon 2140 129 129.17 0.060 0.060 99.86
Michael Cuddyer 3749 256 256.95 0.068 0.069 99.63
Nick Markakis 4279 303 306.74 0.071 0.072 98.78
Magglio Ordonez 3835 261 264.54 0.068 0.069 98.66
Jeff Francoeur 4356 328 333.45 0.075 0.077 98.37
Jermaine Dye 3682 284 289.80 0.077 0.079 98.00
Shawn Green 2771 203 207.55 0.073 0.075 97.81
Vladimir Guerrero 2819 208 213.12 0.074 0.076 97.60
Matt Kemp 1851 129 132.50 0.070 0.072 97.36
Brian Giles 3199 216 223.54 0.068 0.070 96.63
J.D. Drew 3128 212 219.98 0.068 0.070 96.37
Ken Griffey Jr. 3649 291 302.61 0.080 0.083 96.16
Andre Ethier 2315 177 184.39 0.076 0.080 95.99
Xavier Nady 2390 162 168.97 0.068 0.071 95.88
Jose Guillen 4063 268 284.73 0.066 0.070 94.13
Juan Encarnacion 1983 125 132.90 0.063 0.067 94.06
Jack Cust 1205 79 84.93 0.066 0.070 93.01
Brad Hawpe 3851 247 267.07 0.064 0.069 92.48
Cliff Floyd 1185 69 78.30 0.058 0.066 88.12

Mark Teahen did a much better job of adjusting to rightfield than Ken Griffey, Jr. Of course, Junior is old and slow, and with all the injuries might be better off as a DH in AL at this point.

Posted by StatsGuru at 04:04 PM | Comments (10) | TrackBack (0)
November 13, 2007
Probabilistic Model of Range, Third Basemen, 2007
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The Washington Nationals and Ryan Zimmerman lead the way at third base.

Team Third Basemen PMR, 2007, Visit Smooth Distance Model, 2007 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Nationals 4591 448 403.22 0.098 0.088 111.11
Cubs 4177 433 400.61 0.104 0.096 108.09
Giants 4467 434 404.44 0.097 0.091 107.31
Mets 4362 415 390.52 0.095 0.090 106.27
Red Sox 4226 377 356.44 0.089 0.084 105.77
Tigers 4486 446 426.09 0.099 0.095 104.67
Yankees 4511 370 354.08 0.082 0.078 104.50
Orioles 4403 423 406.97 0.096 0.092 103.94
Mariners 4535 423 407.60 0.093 0.090 103.78
Angels 4325 341 329.04 0.079 0.076 103.64
Cardinals 4587 468 459.66 0.102 0.100 101.81
Rangers 4518 385 378.51 0.085 0.084 101.72
Blue Jays 4349 376 371.58 0.086 0.085 101.19
Dodgers 4310 380 376.79 0.088 0.087 100.85
Braves 4404 364 364.35 0.083 0.083 99.90
Brewers 4392 369 371.77 0.084 0.085 99.26
Athletics 4499 399 405.18 0.089 0.090 98.47
Padres 4476 376 382.21 0.084 0.085 98.37
Astros 4530 401 409.30 0.089 0.090 97.97
Phillies 4505 433 442.07 0.096 0.098 97.95
Reds 4533 398 406.34 0.088 0.090 97.95
Royals 4528 378 387.55 0.083 0.086 97.54
Devil Rays 4378 361 370.39 0.082 0.085 97.46
White Sox 4545 426 438.55 0.094 0.096 97.14
Twins 4384 393 405.89 0.090 0.093 96.82
Pirates 4608 450 470.32 0.098 0.102 95.68
Indians 4548 400 421.84 0.088 0.093 94.82
Diamondbacks 4351 344 363.60 0.079 0.084 94.61
Marlins 4491 359 395.76 0.080 0.088 90.71
Rockies 4599 349 393.86 0.076 0.086 88.61

Given that David Wright scores better than Alex Rodriguez, if the Mets sign A-Rod, they should move him to first.

Individual Third Base PMR, 2007, Visit Smooth Distance Model, 2007 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Ryan Zimmerman 4528 443 398.27 0.098 0.088 111.23
Joe Crede 1184 125 113.00 0.106 0.095 110.62
Pedro Feliz 3718 373 341.20 0.100 0.092 109.32
Aramis Ramirez 3208 329 308.47 0.103 0.096 106.65
David Wright 4260 403 378.91 0.095 0.089 106.36
Mike Lowell 3890 342 321.90 0.088 0.083 106.24
Melvin Mora 3225 318 301.25 0.099 0.093 105.56
Maicer Izturis 1320 101 95.97 0.077 0.073 105.24
Brandon Inge 4062 400 380.28 0.098 0.094 105.18
Adrian Beltre 4036 379 362.12 0.094 0.090 104.66
Alex Rodriguez 4158 342 330.85 0.082 0.080 103.37
Scott Rolen 2983 296 287.61 0.099 0.096 102.92
Troy Glaus 2803 241 234.49 0.086 0.084 102.78
Ramon Vazquez 1671 152 148.03 0.091 0.089 102.68
Chone Figgins 2561 198 192.85 0.077 0.075 102.67
Hank Blalock 1082 83 80.94 0.077 0.075 102.55
Morgan Ensberg 1778 165 162.23 0.093 0.091 101.71
Travis Metcalf 1376 114 113.26 0.083 0.082 100.65
Chipper Jones 3231 278 277.24 0.086 0.086 100.27
Nomar Garciaparra 1103 87 86.81 0.079 0.079 100.21
Eric Chavez 2379 218 218.17 0.092 0.092 99.92
Akinori Iwamura 3174 260 261.21 0.082 0.082 99.54
Abraham Nunez 1792 205 206.06 0.114 0.115 99.49
Alex Gordon 3609 318 321.97 0.088 0.089 98.77
Edwin Encarnacion 3620 308 314.99 0.085 0.087 97.78
Chad Tracy 1152 96 98.41 0.083 0.085 97.55
Wilson Betemit 1235 94 96.37 0.076 0.078 97.54
Nick Punto 2518 239 245.24 0.095 0.097 97.46
Jack Hannahan 1122 93 96.13 0.083 0.086 96.74
Wes Helms 1394 116 120.21 0.083 0.086 96.50
Greg Dobbs 1309 111 115.27 0.085 0.088 96.29
Kevin Kouzmanoff 3415 279 292.44 0.082 0.086 95.40
Ty Wigginton 1995 186 195.65 0.093 0.098 95.07
Mike Lamb 1320 108 113.69 0.082 0.086 95.00
Mark Reynolds 2542 196 208.42 0.077 0.082 94.04
Jose Bautista 3413 317 338.96 0.093 0.099 93.52
Casey Blake 3743 325 351.27 0.087 0.094 92.52
Josh Fields 2234 200 217.44 0.090 0.097 91.98
Miguel Cabrera 4055 330 361.80 0.081 0.089 91.21
Ryan Braun 2886 211 239.01 0.073 0.083 88.28
Garrett Atkins 4136 305 352.46 0.074 0.085 86.53

The bottom three on the list pretty much define all-hit and no-field.

Posted by StatsGuru at 06:24 PM | Comments (2) | TrackBack (0)
November 12, 2007
Probabilistic Model of Range, Second Basemen, 2007
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Here are the PMR numbers for second basemen. First the team stats.

Team Second Basemen PMR, 2007, Visit Smooth Distance Model, 2007 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Reds 4533 517 470.97 0.114 0.104 109.77
Phillies 4505 507 486.30 0.113 0.108 104.26
Yankees 4511 551 528.65 0.122 0.117 104.23
Diamondbacks 4351 536 514.78 0.123 0.118 104.12
Rangers 4518 561 541.66 0.124 0.120 103.57
Twins 4384 508 491.17 0.116 0.112 103.43
Athletics 4499 610 592.38 0.136 0.132 102.97
Blue Jays 4349 589 574.80 0.135 0.132 102.47
Tigers 4486 505 494.43 0.113 0.110 102.14
Royals 4528 453 444.63 0.100 0.098 101.88
Red Sox 4226 524 515.37 0.124 0.122 101.67
Rockies 4599 558 548.82 0.121 0.119 101.67
Angels 4325 505 497.11 0.117 0.115 101.59
Mariners 4535 554 546.62 0.122 0.121 101.35
Nationals 4591 491 485.03 0.107 0.106 101.23
Indians 4548 564 557.26 0.124 0.123 101.21
Orioles 4403 534 528.33 0.121 0.120 101.07
White Sox 4545 467 466.32 0.103 0.103 100.15
Mets 4362 493 494.92 0.113 0.113 99.61
Cubs 4177 471 476.31 0.113 0.114 98.88
Brewers 4392 447 456.43 0.102 0.104 97.93
Braves 4404 521 533.24 0.118 0.121 97.70
Pirates 4608 428 440.07 0.093 0.096 97.26
Devil Rays 4378 493 507.26 0.113 0.116 97.19
Dodgers 4310 480 494.92 0.111 0.115 96.99
Cardinals 4587 509 525.15 0.111 0.114 96.92
Padres 4476 556 575.50 0.124 0.129 96.61
Marlins 4491 463 487.55 0.103 0.109 94.96
Giants 4467 450 478.37 0.101 0.107 94.07
Astros 4530 461 495.20 0.102 0.109 93.09

Looking at the teams at the bottom of the list, old second basemen are a detriment to defense. Not only did Biggio at second not help the Astros offensively, it hurt them defensively as well. Now for the individual players.

Individual Second Base PMR, 2007, Visit Smooth Distance Model, 2007 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Brandon Phillips 4288 488 442.09 0.114 0.103 110.38
Chase Utley 3571 410 386.97 0.115 0.108 105.95
Jose Valentin 1123 154 145.56 0.137 0.130 105.80
Orlando Hudson 3552 435 412.20 0.122 0.116 105.53
Esteban German 1248 117 111.06 0.094 0.089 105.35
Ian Kinsler 3581 459 438.84 0.128 0.123 104.59
Ronnie Belliard 3168 337 322.49 0.106 0.102 104.50
Robinson Cano 4380 532 509.76 0.121 0.116 104.36
Josh Barfield 3237 396 381.63 0.122 0.118 103.76
Mark Ellis 4119 561 540.88 0.136 0.131 103.72
Kaz Matsui 2634 335 323.55 0.127 0.123 103.54
Aaron Hill 4230 576 558.01 0.136 0.132 103.22
B.J. Upton 1305 174 168.87 0.133 0.129 103.04
Placido Polanco 3724 420 409.07 0.113 0.110 102.67
Jose Lopez 3899 486 475.59 0.125 0.122 102.19
Mike Fontenot 1343 152 148.82 0.113 0.111 102.14
Howie Kendrick 2222 276 270.90 0.124 0.122 101.88
Alexi Casilla 1262 144 141.60 0.114 0.112 101.70
Mark Grudzielanek 3021 312 307.78 0.103 0.102 101.37
Luis Castillo 3569 370 365.52 0.104 0.102 101.23
Tadahito Iguchi 3285 359 354.84 0.109 0.108 101.17
Geoff Blum 1481 178 176.84 0.120 0.119 100.65
Brian Roberts 4068 487 487.37 0.120 0.120 99.92
Dustin Pedroia 3365 417 417.32 0.124 0.124 99.92
Kevin Frandsen 1044 111 112.56 0.106 0.108 98.61
Danny Richar 1554 152 154.45 0.098 0.099 98.42
Jamey Carroll 1396 165 168.34 0.118 0.121 98.02
Adam Kennedy 2060 250 256.18 0.121 0.124 97.59
Freddy Sanchez 4064 378 387.80 0.093 0.095 97.47
Kelly Johnson 3474 412 423.58 0.119 0.122 97.27
Felipe Lopez 1208 129 134.76 0.107 0.112 95.72
Jeff Kent 3237 355 372.41 0.110 0.115 95.33
Mark DeRosa 2056 223 234.54 0.108 0.114 95.08
Marcus Giles 2883 364 383.01 0.126 0.133 95.04
Aaron Miles 1834 183 194.00 0.100 0.106 94.33
Dan Uggla 4310 438 466.30 0.102 0.108 93.93
Rickie Weeks 3003 301 320.45 0.100 0.107 93.93
Ray Durham 3183 320 343.81 0.101 0.108 93.08
Craig Biggio 2878 283 308.32 0.098 0.107 91.79
Brendan Harris 1206 110 124.59 0.091 0.103 88.29

Rickie Weeks and Dan Uggla need to be at the top of their offensive games to stay at this important defensive position.

Correction: Fixed caption on first table.

Posted by StatsGuru at 08:59 AM | Comments (14) | TrackBack (0)
November 11, 2007
Probabilistic Model of Range, Centerfielders, 2007
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Here are the team rankings for centerfielders:

Team Centerfielder PMR, 2007, Visit Smooth Distance Model, 2007 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Mariners 4535 452 423.84 0.100 0.093 106.64
Red Sox 4226 481 452.99 0.114 0.107 106.18
Tigers 4486 468 445.78 0.104 0.099 104.98
Cubs 4177 414 400.21 0.099 0.096 103.45
Mets 4362 464 449.88 0.106 0.103 103.14
Braves 4404 431 421.41 0.098 0.096 102.28
Dodgers 4310 379 371.16 0.088 0.086 102.11
Rockies 4599 414 407.81 0.090 0.089 101.52
Padres 4476 409 404.18 0.091 0.090 101.19
Cardinals 4587 417 412.78 0.091 0.090 101.02
Reds 4533 455 451.03 0.100 0.100 100.88
Giants 4467 438 437.08 0.098 0.098 100.21
Nationals 4591 486 485.40 0.106 0.106 100.12
Royals 4528 424 425.45 0.094 0.094 99.66
Yankees 4511 468 470.38 0.104 0.104 99.49
Phillies 4505 418 421.10 0.093 0.093 99.26
Twins 4384 415 418.19 0.095 0.095 99.24
White Sox 4545 415 418.55 0.091 0.092 99.15
Angels 4325 441 445.14 0.102 0.103 99.07
Marlins 4491 453 458.41 0.101 0.102 98.82
Astros 4530 433 439.56 0.096 0.097 98.51
Blue Jays 4349 366 372.05 0.084 0.086 98.37
Pirates 4608 448 456.67 0.097 0.099 98.10
Diamondbacks 4351 406 414.42 0.093 0.095 97.97
Indians 4548 413 422.64 0.091 0.093 97.72
Rangers 4518 388 399.38 0.086 0.088 97.15
Athletics 4499 398 410.38 0.088 0.091 96.98
Orioles 4403 409 423.66 0.093 0.096 96.54
Devil Rays 4378 419 444.79 0.096 0.102 94.20
Brewers 4392 410 437.27 0.093 0.100 93.76

The Mariners come out on top of the Red Sox overall, but Boston has the better individual fielder:

Individual Centerfielder PMR, 2007, Visit Smooth Distance Model, 2007 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Coco Crisp 3560 408 377.29 0.115 0.106 108.14
Ichiro Suzuki 4233 424 394.49 0.100 0.093 107.48
Felix Pie 1169 120 112.75 0.103 0.096 106.43
Curtis Granderson 3995 424 402.22 0.106 0.101 105.42
Jacque Jones 1911 195 187.25 0.102 0.098 104.14
Darin Erstad 1117 105 101.18 0.094 0.091 103.77
Willy Taveras 2274 212 204.80 0.093 0.090 103.52
So Taguchi 1190 118 114.17 0.099 0.096 103.35
Ryan Church 1024 118 114.35 0.115 0.112 103.19
Andruw Jones 4080 396 385.38 0.097 0.094 102.76
Juan Pierre 4215 366 356.47 0.087 0.085 102.67
Josh Hamilton 1702 168 163.71 0.099 0.096 102.62
Carlos Beltran 3733 389 380.89 0.104 0.102 102.13
Johnny Damon 1211 121 118.84 0.100 0.098 101.82
Gary Matthews Jr. 3462 362 356.66 0.105 0.103 101.50
Mike Cameron 4016 365 360.75 0.091 0.090 101.18
Nook Logan 2398 248 245.18 0.103 0.102 101.15
Norris Hopper 1280 133 132.11 0.104 0.103 100.67
Dave Roberts 2334 224 222.68 0.096 0.095 100.59
Torii Hunter 4034 389 389.12 0.096 0.096 99.97
David DeJesus 4256 400 400.98 0.094 0.094 99.76
Alfredo Amezaga 2005 208 208.88 0.104 0.104 99.58
Jim Edmonds 2688 244 245.68 0.091 0.091 99.32
Aaron Rowand 4243 392 394.89 0.092 0.093 99.27
Hunter Pence 2636 260 261.99 0.099 0.099 99.24
Chris Duffy 1693 172 174.17 0.102 0.103 98.75
Melky Cabrera 3297 347 351.54 0.105 0.107 98.71
Rajai Davis 1162 124 125.75 0.107 0.108 98.60
Ryan Freel 1419 136 138.16 0.096 0.097 98.44
Vernon Wells 3813 321 326.31 0.084 0.086 98.37
Grady Sizemore 4383 399 407.44 0.091 0.093 97.93
Jerry Owens 2294 208 212.80 0.091 0.093 97.75
Chris Young 3824 354 364.20 0.093 0.095 97.20
B.J. Upton 2014 204 210.16 0.101 0.104 97.07
Mark Kotsay 1492 141 145.40 0.095 0.097 96.98
Nick Swisher 1515 139 144.94 0.092 0.096 95.90
Marlon Byrd 1541 114 119.68 0.074 0.078 95.25
Nate McLouth 1583 142 150.82 0.090 0.095 94.15
Kenny Lofton 2219 188 199.69 0.085 0.090 94.15
Corey Patterson 3225 281 298.69 0.087 0.093 94.08
Bill Hall 3159 295 314.62 0.093 0.100 93.76
Elijah Dukes 1010 82 92.28 0.081 0.091 88.86

Note to that the shift of Bill Hall to center worked neither offensively nor defensively. Andruw Jones may not be as good as he once was, but he can still go get the ball.

Posted by StatsGuru at 11:55 AM | Comments (11) | TrackBack (0)
November 07, 2007
Probabilistic Model of Range, Shortstops
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A number of people are suggesting new ways to construct the models, but before I try those methods I'd like to present the model used last year for the nine fielding positions, starting with shortstops. I am including something new, however, the full team at the position.

Team Shortstop PMR, 2007, Visit Smooth Distance Model, 2007 data only
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Rockies 4599 657 602.67 0.143 0.131 109.01
Twins 4384 556 523.57 0.127 0.119 106.19
Dodgers 4310 556 526.50 0.129 0.122 105.60
Royals 4528 543 514.33 0.120 0.114 105.57
Blue Jays 4349 567 544.69 0.130 0.125 104.09
Phillies 4505 531 516.45 0.118 0.115 102.82
Indians 4548 571 558.76 0.126 0.123 102.19
Pirates 4608 588 575.51 0.128 0.125 102.17
Red Sox 4226 500 492.12 0.118 0.116 101.60
Giants 4467 592 584.51 0.133 0.131 101.28
Diamondbacks 4351 493 488.99 0.113 0.112 100.82
Brewers 4392 501 497.76 0.114 0.113 100.65
Angels 4325 502 498.77 0.116 0.115 100.65
Marlins 4491 508 506.53 0.113 0.113 100.29
Mariners 4535 515 514.50 0.114 0.113 100.10
Orioles 4403 505 506.89 0.115 0.115 99.63
Astros 4530 561 563.85 0.124 0.124 99.49
Braves 4404 516 520.04 0.117 0.118 99.22
Cardinals 4587 539 544.84 0.118 0.119 98.93
Reds 4533 496 502.70 0.109 0.111 98.67
Athletics 4499 531 538.40 0.118 0.120 98.62
Padres 4476 536 544.49 0.120 0.122 98.44
Mets 4362 506 518.72 0.116 0.119 97.55
Cubs 4177 481 495.42 0.115 0.119 97.09
White Sox 4545 563 580.23 0.124 0.128 97.03
Tigers 4486 517 536.95 0.115 0.120 96.28
Rangers 4518 531 556.38 0.118 0.123 95.44
Devil Rays 4378 441 466.20 0.101 0.106 94.59
Nationals 4591 532 566.26 0.116 0.123 93.95
Yankees 4511 478 516.85 0.106 0.115 92.48

The above table will give you an idea of how the regular shortstop fit in the team context. You might imagine that Troy Tulowitzki was very good and Derek Jeter very bad:

Individual Shortstop PMR, 2007, Visit Smooth Distance Model, 2007 data only (1000 balls in play)
Player In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Troy Tulowitzki 4294 615 564.54 0.143 0.131 108.94
Tony F Pena 4010 480 449.44 0.120 0.112 106.80
Rafael Furcal 3574 473 445.28 0.132 0.125 106.23
John McDonald 2389 311 294.27 0.130 0.123 105.69
Jason Bartlett 3631 466 443.58 0.128 0.122 105.05
Jimmy Rollins 4447 528 511.62 0.119 0.115 103.20
Jack Wilson 3657 470 457.15 0.129 0.125 102.81
Yunel Escobar 1116 135 131.47 0.121 0.118 102.69
Jhonny Peralta 4206 512 502.37 0.122 0.119 101.92
Omar Vizquel 3739 504 497.76 0.135 0.133 101.25
Julio Lugo 3592 431 426.14 0.120 0.119 101.14
Adam Everett 1631 217 214.61 0.133 0.132 101.12
Orlando Cabrera 3997 462 456.91 0.116 0.114 101.11
Alex Gonzalez 2728 306 306.06 0.112 0.112 99.98
J.J. Hardy 3873 442 442.35 0.114 0.114 99.92
Cesar Izturis 1904 216 216.36 0.113 0.114 99.83
Bobby Crosby 2524 313 313.77 0.124 0.124 99.75
Stephen Drew 3877 434 435.25 0.112 0.112 99.71
Hanley Ramirez 4054 460 462.96 0.113 0.114 99.36
Ryan Theriot 2494 301 303.06 0.121 0.122 99.32
Khalil Greene 4206 504 507.64 0.120 0.121 99.28
Mark Loretta 1537 177 178.28 0.115 0.116 99.28
Yuniesky Betancourt 4103 464 467.60 0.113 0.114 99.23
Edgar Renteria 3067 361 365.13 0.118 0.119 98.87
Eric Bruntlett 1075 131 132.81 0.122 0.124 98.63
Royce Clayton 1538 200 202.77 0.130 0.132 98.63
Marco Scutaro 1064 122 124.14 0.115 0.117 98.28
Juan Uribe 4113 513 524.43 0.125 0.128 97.82
Jose Reyes 4295 500 511.97 0.116 0.119 97.66
David Eckstein 3002 349 357.57 0.116 0.119 97.60
Miguel Tejada 3317 363 373.46 0.109 0.113 97.20
Jeff Keppinger 1209 130 135.67 0.108 0.112 95.82
Carlos Guillen 3361 389 408.05 0.116 0.121 95.33
Felipe Lopez 2949 359 377.76 0.122 0.128 95.03
Michael Young 4083 476 504.85 0.117 0.124 94.29
Josh Wilson 1340 141 151.37 0.105 0.113 93.15
Brendan Harris 2336 234 253.12 0.100 0.108 92.45
Derek Jeter 4117 421 461.63 0.102 0.112 91.20
Cristian Guzman 1189 117 130.96 0.098 0.110 89.34

Troy really blew the competition away in terms of PMR, and Tony Pena did his best to make up for his poor hitting. And while New York enjoys two fine offensive shortstops, neither exactly sparkles with the glove. You can also see why the Tigers are moving Carlos Guillen to first. Michael Young may not be far behind him.

Posted by StatsGuru at 10:59 PM | Comments (15) | TrackBack (0)
November 05, 2007
Probabilistic Model of Range, Defense Behind Pitchers
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One thing PMR can measure is the luck of pitchers by looking at the predicted DER and actual DER behind them. The following table rates pitchers with at least 300 balls in play against them:

Probabilistic Model of Range, Defense Behind Pitchers, 2007. Visit Smoothed Distance Model. 2007 Data Only
Pitcher Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Chien-Ming Wang NYY 643 448 414.94 0.697 0.645 107.97
Jeremy Guthrie Bal 527 375 356.60 0.712 0.677 105.16
Dustin McGowan Tor 484 346 330.21 0.715 0.682 104.78
Sean Marshall ChC 330 231 221.12 0.700 0.670 104.47
Roger Clemens NYY 307 215 205.94 0.700 0.671 104.40
Brian Bannister KC 540 393 376.52 0.728 0.697 104.38
Jarrod Washburn Sea 627 440 422.32 0.702 0.674 104.19
Mike Bacsik Was 414 291 279.42 0.703 0.675 104.14
Tom Glavine NYM 674 474 455.80 0.703 0.676 103.99
Jason Hirsh Col 340 252 242.53 0.741 0.713 103.91
Ted Lilly ChC 586 427 411.59 0.729 0.702 103.74
Braden Looper StL 581 416 401.23 0.716 0.691 103.68
Chris Sampson Hou 414 292 281.66 0.705 0.680 103.67
Cole Hamels Phi 495 348 336.00 0.703 0.679 103.57
Brad Penny LAD 643 450 435.62 0.700 0.677 103.30
Dontrelle Willis Fla 667 442 428.96 0.663 0.643 103.04
Yovani Gallardo Mil 318 216 209.67 0.679 0.659 103.02
Jesse Litsch Tor 371 259 251.51 0.698 0.678 102.98
Jason Bergmann Was 332 248 241.02 0.747 0.726 102.90
Anthony Reyes StL 332 236 229.52 0.711 0.691 102.82
Curt Schilling Bos 485 338 328.75 0.697 0.678 102.82
Chuck James Atl 484 352 342.43 0.727 0.707 102.80
Nate Robertson Det 573 389 378.44 0.679 0.660 102.79
Aaron Cook Col 572 401 390.44 0.701 0.683 102.70
Tim Lincecum SF 389 277 269.87 0.712 0.694 102.64
Jon Garland CWS 705 493 480.70 0.699 0.682 102.56
Steve Trachsel Bal 491 351 342.47 0.715 0.697 102.49
Daisuke Matsuzaka Bos 555 384 375.16 0.692 0.676 102.36
Noah Lowry SF 502 349 340.97 0.695 0.679 102.35
Tim Hudson Atl 722 504 492.65 0.698 0.682 102.30
C.C. Sabathia Cle 701 476 465.40 0.679 0.664 102.28
Chad Durbin Det 417 304 297.37 0.729 0.713 102.23
Carlos Zambrano ChC 610 439 429.45 0.720 0.704 102.22
Micah Owings Ari 461 332 324.88 0.720 0.705 102.19
James Shields TB 615 435 425.93 0.707 0.693 102.13
Erik Bedard Bal 431 306 299.70 0.710 0.695 102.10
Jake Westbrook Cle 481 329 322.43 0.684 0.670 102.04
John Lackey LAA 668 459 450.14 0.687 0.674 101.97
Oliver Perez NYM 483 341 334.57 0.706 0.693 101.92
Justin Verlander Det 577 407 399.34 0.705 0.692 101.92
Barry Zito SF 608 441 432.73 0.725 0.712 101.91
Roy Halladay Tor 722 497 488.79 0.688 0.677 101.68
Jason Marquis ChC 626 440 432.86 0.703 0.691 101.65
Zack Greinke KC 350 239 235.18 0.683 0.672 101.63
Buddy Carlyle Atl 335 229 225.46 0.684 0.673 101.57
A.J. Burnett Tor 414 301 296.47 0.727 0.716 101.53
Johan Santana Min 555 394 388.14 0.710 0.699 101.51
Jake Peavy SD 571 409 403.20 0.716 0.706 101.44
Kyle Kendrick Phi 401 284 280.03 0.708 0.698 101.42
Greg Maddux SD 681 466 459.63 0.684 0.675 101.39
Tim Wakefield Bos 600 425 419.24 0.708 0.699 101.37
Fausto Carmona Cle 654 463 456.92 0.708 0.699 101.33
Kelvim Escobar LAA 572 387 382.00 0.677 0.668 101.31
Joe Blanton Oak 750 520 513.28 0.693 0.684 101.31
Rich Hill ChC 527 378 373.19 0.717 0.708 101.29
Odalis Perez KC 494 325 320.93 0.658 0.650 101.27
Matt Morris SF 473 315 311.16 0.666 0.658 101.23
Carlos Silva Min 699 485 479.14 0.694 0.685 101.22
Adam Eaton Phi 525 356 351.83 0.678 0.670 101.19
Felix Hernandez Sea 567 372 367.73 0.656 0.649 101.16
Wandy Rodriguez Hou 536 366 361.86 0.683 0.675 101.14
Vicente Padilla Tex 407 270 266.96 0.663 0.656 101.14
Aaron Harang Cin 642 451 446.11 0.702 0.695 101.10
Livan Hernandez Ari 704 488 482.76 0.693 0.686 101.08
Orlando Hernandez NYM 388 299 295.82 0.771 0.762 101.08
Jamie Moyer Phi 633 432 427.41 0.682 0.675 101.08
Ian Snell Pit 606 413 408.93 0.682 0.675 101.00
Andy Pettitte NYY 690 457 452.68 0.662 0.656 100.96
Tom Gorzelanny Pit 642 439 435.75 0.684 0.679 100.75
Matt Albers Hou 362 247 245.52 0.682 0.678 100.60
Lenny DiNardo Oak 430 302 300.28 0.702 0.698 100.57
John Danks CWS 427 289 287.39 0.677 0.673 100.56
Mark Hendrickson LAD 395 262 260.58 0.663 0.660 100.55
Jorge Sosa NYM 361 256 254.94 0.709 0.706 100.42
Brandon Webb Ari 692 480 478.35 0.694 0.691 100.34
Carlos Villanueva Mil 318 229 228.36 0.720 0.718 100.28
John Maine NYM 527 377 376.07 0.715 0.714 100.25
Justin Germano SD 426 302 301.31 0.709 0.707 100.23
Chad Billingsley LAD 400 279 278.70 0.697 0.697 100.11
Ben Sheets Mil 431 307 306.74 0.712 0.712 100.09
Roy Oswalt Hou 675 456 456.10 0.676 0.676 99.98
Jered Weaver LAA 514 348 348.13 0.677 0.677 99.96
Mike Mussina NYY 512 335 335.31 0.654 0.655 99.91
Josh Beckett Bos 566 385 385.40 0.680 0.681 99.90
Matt Chico Was 548 380 380.44 0.693 0.694 99.88
Matt Belisle Cin 570 378 378.52 0.663 0.664 99.86
Shaun Marcum Tor 456 329 329.69 0.721 0.723 99.79
Jeff Weaver Sea 511 340 340.84 0.665 0.667 99.75
Derek Lowe LAD 604 412 413.67 0.682 0.685 99.60
Kameron Loe Tex 464 305 306.28 0.657 0.660 99.58
Joe Saunders LAA 358 235 236.04 0.656 0.659 99.56
Brad Thompson StL 451 307 308.45 0.681 0.684 99.53
Josh Fogg Col 556 381 383.08 0.685 0.689 99.46
Horacio Ramirez Sea 361 231 232.31 0.640 0.644 99.44
Jeff Francis Col 662 447 449.57 0.675 0.679 99.43
Miguel Batista Sea 615 415 417.51 0.675 0.679 99.40
Paul Byrd Cle 686 465 467.91 0.678 0.682 99.38
Gil Meche KC 663 459 462.21 0.692 0.697 99.31
Claudio Vargas Mil 419 281 283.02 0.671 0.675 99.29
Mark Buehrle CWS 648 455 458.82 0.702 0.708 99.17
Boof Bonser Min 539 359 362.02 0.666 0.672 99.17
Javier Vazquez CWS 583 409 412.68 0.702 0.708 99.11
Edwin Jackson TB 516 333 336.02 0.645 0.651 99.10
Bartolo Colon LAA 328 205 206.87 0.625 0.631 99.09
Tony Armas Jr. Pit 305 208 209.93 0.682 0.688 99.08
Jorge de la Rosa KC 431 285 287.91 0.661 0.668 98.99
Jason Jennings Hou 319 214 216.25 0.671 0.678 98.96
Edgar Gonzalez Ari 324 228 230.41 0.704 0.711 98.96
Chris Young SD 448 336 339.55 0.750 0.758 98.96
Julian Tavarez Bos 455 307 310.39 0.675 0.682 98.91
Woody Williams Hou 632 443 448.01 0.701 0.709 98.88
Daniel Cabrera Bal 608 415 419.74 0.683 0.690 98.87
Bronson Arroyo Cin 661 449 454.60 0.679 0.688 98.77
Kyle Lohse Cin 426 293 296.71 0.688 0.697 98.75
Cliff Lee Cle 317 216 218.74 0.681 0.690 98.75
Paul Maholm Pit 583 391 396.00 0.671 0.679 98.74
Chad Gaudin Oak 603 413 418.34 0.685 0.694 98.72
Ervin Santana LAA 457 302 306.05 0.661 0.670 98.68
Doug Davis Ari 597 400 405.62 0.670 0.679 98.61
Sergio Mitre Fla 522 343 347.92 0.657 0.667 98.59
Adam Wainwright StL 654 441 447.57 0.674 0.684 98.53
Byung-Hyun Kim Fla 316 212 215.40 0.671 0.682 98.42
Ramon Ortiz Min 324 217 220.56 0.670 0.681 98.39
Kevin Correia SF 306 217 220.82 0.709 0.722 98.27
Kevin Millwood Tex 571 364 370.63 0.637 0.649 98.21
Jeremy Bonderman Det 533 354 360.70 0.664 0.677 98.14
Scott Baker Min 454 302 308.06 0.665 0.679 98.03
Dan Haren Oak 661 457 466.27 0.691 0.705 98.01
Randy Wolf LAD 309 205 209.32 0.663 0.677 97.93
Jeff Suppan Mil 708 472 482.96 0.667 0.682 97.73
Josh Towers Tor 347 229 234.38 0.660 0.675 97.71
Matt Cain SF 571 409 419.20 0.716 0.734 97.57
John Smoltz Atl 586 400 410.60 0.683 0.701 97.42
Brandon McCarthy Tex 340 232 238.54 0.682 0.702 97.26
Taylor Buchholz Col 305 207 212.87 0.679 0.698 97.24
Andy Sonnanstine TB 408 272 280.02 0.667 0.686 97.13
Brian Burres Bal 378 249 256.88 0.659 0.680 96.93
Brett Tomko LAD 339 219 226.04 0.646 0.667 96.89
Joe Kennedy Oak 346 242 250.10 0.699 0.723 96.76
Scott Kazmir TB 534 346 358.19 0.648 0.671 96.60
Chris Capuano Mil 456 297 307.78 0.651 0.675 96.50
Robinson Tejeda Tex 302 204 212.16 0.675 0.703 96.16
David Wells SD 416 271 282.44 0.651 0.679 95.95
David Bush Mil 594 395 412.87 0.665 0.695 95.67
Zach Duke Pit 399 246 258.54 0.617 0.648 95.15
Jose Contreras CWS 647 420 441.74 0.649 0.683 95.08
Kip Wells StL 522 342 360.50 0.655 0.691 94.87
Scott Olsen Fla 578 366 387.16 0.633 0.670 94.53

Chien-Ming Wang comes out on top by far, not surprising given the Yankees overall defensive rating. What bothers me about Wang, however, is the low level of his predicted DER. You would think that someone who gets a lot of ground balls would be somewhat higher. The following chart breaks down Wang by ball in play type:

CM Wang by Batted Ball Type, 2007
Batted Ball Type In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Fly 112 101 98.85 0.902 0.883 102.18
Liner 92 29 16.14 0.315 0.175 179.66
Grounder 377 291 269.40 0.772 0.715 108.02
Bunt Grounder 6 4 4.20 0.667 0.700 95.24
Bunt Fly 1 1 1.00 1.000 1.000 100.00
Fliner (Fly) 29 13 14.12 0.448 0.487 92.09
Fliner (Liner) 26 9 11.23 0.346 0.432 80.12

Notice that the defense behind Wang caught a lot more line drives than predicted. Line drives tend to fall for hits, so by adding thirteen extra outs with liners, the Yankees really helped Wang. So Chien-Ming got a bit lucky that way. The grounders, however, is where the defense really shined. They picked up about twenty one more outs than expected on ground balls. How did they do that? The Yankees made a lot of plays on low probability vectors:

Wang Ground Balls by Vector, 2007
Vector In Play Actual Outs Predicted Outs DER Predicted DER Ratio
28 8 6 7.02 0.750 0.877 85.52
29 17 13 12.05 0.765 0.709 107.90
30 29 21 17.57 0.724 0.606 119.49
31 28 27 24.76 0.964 0.884 109.04
32 19 18 18.43 0.947 0.970 97.66
33 32 29 26.75 0.906 0.836 108.40
34 17 12 9.48 0.706 0.558 126.59
35 11 9 7.38 0.818 0.671 121.97
36 23 14 13.01 0.609 0.566 107.58
37 22 12 13.66 0.545 0.621 87.82
38 27 24 23.07 0.889 0.854 104.04
39 31 30 25.58 0.968 0.825 117.26
40 22 19 17.41 0.864 0.792 109.11
41 34 24 17.12 0.706 0.504 140.19
42 27 17 19.83 0.630 0.734 85.73
43 11 9 9.71 0.818 0.883 92.67
44 10 5 4.56 0.500 0.456 109.71

The vectors go from a low of 28 at the third base line to a high of 44 at the first base line. By looking at the Predicted DER column, you can see where the holes are in the infield. Vector 30 represents the hole between third and short, vectors 34-37 the area around second base where ground balls go into centerfield, and vector 41, the hole between first and second. Note that Wang does well in the holes, as if the defense were shifted a bit toward first base. Both the line drive and ground ball data make me wonder if someone was doing a very good job of positioning the Yankees fielders. I don't know who was in charge of that, but in the case of Wang, they did a very good job.

That brings up a point I haven't made in a while. Range is probably a poor word for the ability measured here. Range implies that the fielder can move a long way to get a ball. But sometimes anticipating where the ball gets hit is just as important. So the ability to move and the ability to position are two factors in what the model means by range.

On the other end of the spectrum, Matt Cain not only received no run support, he didn't get much defensive support either. And the defense behind Kazmir was just ridiculous. Here's a pitcher who keeps balls in play to a minimum, and his defense can't turn the few hit to them into outs.

I'll start on individual positions tomorrow.

Posted by StatsGuru at 06:20 PM | Comments (7) | TrackBack (0)
November 04, 2007
Probabilistic Model of Range, 2007, Teams
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Baseball Info Solutions sent me their final stats for 2007 over the weekend. That means it's time to start presenting the 2007 Probabilistic Model or Range. If you're new to this, you can find explanations in this archive. Basically, for each fieldable (non inside the park home runs) ball put in play, six parameters are used to determine how difficult it was to field the ball. A probability of turning the ball into an out is calculated, and those probabilities are summed. That gives us expected batted balls turned into outs. We turn that into a predicted DER (defensive efficiency record), compare that to the actual DER and calculate a ranking.

The model is based primarily on visiting player data, smoothed, distance on fly balls. Only 2007 data was used to construct the model.

Note that a team can post a poor DER during the season, but do well in this model if the balls put into play were extremely difficult to field. In fact, the team ranked first in 2007 is a bit of a surprise for that very reason.

Probabilistic Model of Range, 2007 Data, Teams, Visit Smooth Distance Model, Ranked by Difference
Team In Play Actual Outs Predicted Outs DER Predicted DER Difference
Yankees 4511 3103 3041.46 0.688 0.674 0.01364
Red Sox 4226 2974 2919.61 0.704 0.691 0.01287
Cubs 4177 2943 2895.51 0.705 0.693 0.01137
Blue Jays 4349 3060 3017.22 0.704 0.694 0.00984
Royals 4528 3093 3058.20 0.683 0.675 0.00768
Angels 4325 2930 2900.79 0.677 0.671 0.00675
Phillies 4505 3085 3056.00 0.685 0.678 0.00644
Rockies 4599 3221 3195.95 0.700 0.695 0.00545
Tigers 4486 3094 3072.58 0.690 0.685 0.00477
Braves 4404 3069 3048.96 0.697 0.692 0.00455
Mets 4362 3050 3033.08 0.699 0.695 0.00388
Giants 4467 3108 3096.80 0.696 0.693 0.00251
Orioles 4403 3017 3006.12 0.685 0.683 0.00247
Rangers 4518 3071 3061.36 0.680 0.678 0.00213
Nationals 4591 3198 3191.04 0.697 0.695 0.00152
Indians 4548 3112 3107.26 0.684 0.683 0.00104
Padres 4476 3131 3128.60 0.700 0.699 0.00054
Mariners 4535 3050 3051.99 0.673 0.673 -0.00044
Diamondbacks 4351 3013 3016.84 0.692 0.693 -0.00088
Dodgers 4310 2942 2945.91 0.683 0.684 -0.00091
Cardinals 4587 3150 3154.99 0.687 0.688 -0.00109
Twins 4384 3003 3014.01 0.685 0.688 -0.00251
Astros 4530 3099 3120.86 0.684 0.689 -0.00483
Reds 4533 3068 3096.08 0.677 0.683 -0.00619
Pirates 4608 3099 3132.67 0.673 0.680 -0.00731
Athletics 4499 3110 3144.35 0.691 0.699 -0.00763
Brewers 4392 2966 3011.82 0.675 0.686 -0.01043
White Sox 4545 3089 3141.16 0.680 0.691 -0.01148
Marlins 4491 2962 3039.28 0.660 0.677 -0.01721
Devil Rays 4378 2867 2943.31 0.655 0.672 -0.01743

That's right, the Yankees are number one. Without running the individual numbers, I'm guessing that a full season of Melky Cabrera and keeping Giambi off first really helped. The Red Sox defense turned a higher percentage of their balls in play into outs, but they also were given easier balls to field in general.

I wondered why the Tampa Bay pitching staff did so poorly with the high number of strikeouts they collected, and the reason is clear in these numbers. The Devil Rays defense was horrible. In fact, the state of Florida just can't play defense, with the Marlins ranking 29th in the majors.

For the second year in a row, the Kansas City Royals look a lot better than their posted DER. If they ever get a good set of pitchers on that team, they're going to post a low ERA.

For those of you who prefer a ranking by ratio of DER/Predicted DER, here's the table with that data.

Probabilistic Model of Range, 2007 Data, Teams, Visit Smooth Distance Model, Ranked by Difference
Team In Play Actual Outs Predicted Outs DER Predicted DER Ratio
Yankees 4511 3103 3041.46 0.688 0.674 102.02
Red Sox 4226 2974 2919.61 0.704 0.691 101.86
Cubs 4177 2943 2895.51 0.705 0.693 101.64
Blue Jays 4349 3060 3017.22 0.704 0.694 101.42
Royals 4528 3093 3058.20 0.683 0.675 101.14
Angels 4325 2930 2900.79 0.677 0.671 101.01
Phillies 4505 3085 3056.00 0.685 0.678 100.95
Rockies 4599 3221 3195.95 0.700 0.695 100.78
Tigers 4486 3094 3072.58 0.690 0.685 100.70
Braves 4404 3069 3048.96 0.697 0.692 100.66
Mets 4362 3050 3033.08 0.699 0.695 100.56
Orioles 4403 3017 3006.12 0.685 0.683 100.36
Giants 4467 3108 3096.80 0.696 0.693 100.36
Rangers 4518 3071 3061.36 0.680 0.678 100.31
Nationals 4591 3198 3191.04 0.697 0.695 100.22
Indians 4548 3112 3107.26 0.684 0.683 100.15
Padres 4476 3131 3128.60 0.700 0.699 100.08
Mariners 4535 3050 3051.99 0.673 0.673 99.93
Diamondbacks 4351 3013 3016.84 0.692 0.693 99.87
Dodgers 4310 2942 2945.91 0.683 0.684 99.87
Cardinals 4587 3150 3154.99 0.687 0.688 99.84
Twins 4384 3003 3014.01 0.685 0.688 99.63
Astros 4530 3099 3120.86 0.684 0.689 99.30
Reds 4533 3068 3096.08 0.677 0.683 99.09
Pirates 4608 3099 3132.67 0.673 0.680 98.93
Athletics 4499 3110 3144.35 0.691 0.699 98.91
Brewers 4392 2966 3011.82 0.675 0.686 98.48
White Sox 4545 3089 3141.16 0.680 0.691 98.34
Marlins 4491 2962 3039.28 0.660 0.677 97.46
Devil Rays 4378 2867 2943.31 0.655 0.672 97.41
Posted by StatsGuru at 08:40 PM | Comments (34) | TrackBack (0)
March 25, 2007
Probabilistic Model of GDPs
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A few days ago I introduced the idea of a probabilistic model of Ground into Double Plays (GDP). The probabilistic model of range just measures the ability to turn a ball into an out. For infielders, however, they're often asked to turn a ground ball into more than one out. The idea is to take a very specific situation; ground ball hit, man on first, less than two out and build a model that measures both plays made and GDP turned. With that model, we can ask which fielders perform well in that situation.

In building this model, I left parks out of the parameters. Basically, I thought the sample size would be too small if I left the parks in. This probably hurts the three teams that play of artifical turf.

Let's start by looking at the ability of shortstops to start a double play. The following table looks at three indexes for each fielder. The Plays Made (PM) index measures Plays Made / Predicted Plays Made. This measures the fielder's ability to turn a ball into an out. The GDP index does the same for ground double plays. Does the fielder start the expected number of double plays? And finally, an outs index that looks at the total number of outs accured to the fielder on these balls in play. It could be a fielder is making up for a lack of range by being really good at starting GDPs, or vice versa. Remember, this says nothing about the pivot man or the receiver at first base. In this context, we're only looking at the fielder who starts the play.

Probabilistic Model of GDPs, Ground Balls, Man on First, Less than Two Out, Shortstops Starting GDP (2006 Data Used to Build Model)
Player Ground Balls
In Play
Actual
Plays Made
Predicted
Plays Made
PM
Index
Actual
GDP
Predicted
GDP
GDP
Index
Actual
Outs
Predicted
Outs
Outs
Index
Craig Counsell 183 40 32.14 124.44 32 21.23 150.72 72 53.38 134.89
Khalil Greene 219 53 45.27 117.08 31 28.68 108.07 84 73.95 113.59
Stephen Drew 121 26 23.68 109.82 17 14.29 118.92 43 37.97 113.25
Clint Barmes 283 56 53.75 104.18 42 33.03 127.16 98 86.78 112.92
Juan Uribe 274 58 50.75 114.29 34 31.85 106.75 92 82.60 111.38
Hanley Ramirez 352 71 67.58 105.05 49 43.18 113.48 120 110.76 108.34
Miguel Tejada 342 76 72.84 104.33 52 45.35 114.66 128 118.20 108.30
David Eckstein 298 61 56.74 107.50 38 34.92 108.81 99 91.67 108.00
Jack Wilson 290 64 62.47 102.45 46 39.39 116.77 110 101.86 107.99
Rafael Furcal 396 92 85.54 107.55 58 55.24 104.99 150 140.78 106.55
Bill Hall 228 55 50.83 108.21 33 32.90 100.30 88 83.73 105.10
Bobby Crosby 237 44 42.12 104.47 29 27.37 105.94 73 69.49 105.05
Alex Gonzalez 244 50 51.18 97.69 38 33.41 113.74 88 84.59 104.03
Jimmy Rollins 333 70 69.50 100.72 48 44.05 108.97 118 113.54 103.92
Carlos Guillen 303 65 64.24 101.18 45 41.63 108.10 110 105.87 103.90
Adam Everett 309 66 63.51 103.91 41 40.15 102.11 107 103.67 103.22
Ronny Cedeno 240 49 46.28 105.87 28 29.54 94.79 77 75.82 101.55
Michael Young 410 88 86.89 101.28 54 55.41 97.46 142 142.30 99.79
Jason A Bartlett 214 50 47.40 105.49 28 31.86 87.90 78 79.25 98.42
Jose Reyes 304 67 66.70 100.45 40 43.66 91.62 107 110.36 96.96
Omar Vizquel 297 64 65.48 97.73 42 44.00 95.45 106 109.49 96.82
John McDonald 170 25 26.52 94.26 18 17.91 100.50 43 44.43 96.77
Orlando Cabrera 319 58 60.40 96.03 38 39.02 97.38 96 99.42 96.56
Felipe Lopez 319 67 64.47 103.93 34 40.58 83.78 101 105.05 96.15
Angel Berroa 337 69 72.29 95.44 46 47.91 96.02 115 120.20 95.67
Jhonny Peralta 357 82 84.45 97.10 50 55.63 89.88 132 140.08 94.23
Alex Cora 127 30 32.94 91.07 21 22.16 94.76 51 55.10 92.56
Marco Scutaro 146 34 37.58 90.47 24 25.22 95.15 58 62.81 92.35
Edgar Renteria 347 63 67.67 93.10 39 43.19 90.30 102 110.86 92.01
Yuniesky Betancourt 350 60 66.40 90.36 43 46.02 93.44 103 112.42 91.62
Julio Lugo 182 32 35.29 90.68 21 23.66 88.74 53 58.95 89.90
Ben T Zobrist 131 25 28.09 89.00 17 18.93 89.82 42 47.02 89.33
Juan Castro 146 23 25.18 91.33 13 15.27 85.11 36 40.46 88.98
Derek Jeter 336 63 70.96 88.79 40 45.81 87.32 103 116.77 88.21
Royce Clayton 234 43 47.02 91.44 20 29.67 67.40 63 76.70 82.14
Aaron W Hill 108 16 20.33 78.70 7 12.82 54.62 23 33.14 69.39


Notice how few chances fielders get to turn GDPs. On the best teams, they get a little over two chances a game. Secondly, Arizona does a good job of picking out shortstops, as Counsell and Drew are near the top of the list. And if you don't like Derek Jeter, here's another area where you can pick on him.

The other thing that strikes me about the list is that shortstops who are good at making plays are also the ones good at starting double plays. Ronny Cedeno is unusual in that he's good at getting an out, but didn't do well starting DPs. Could it be that Todd Walker was just a poor pivot man? I hope further research using these models will help answer that question.

Posted by StatsGuru at 11:46 AM | Comments (2) | TrackBack (0)
March 21, 2007
Probabilistic Model of GDPs
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Something that's been on my mind is using ideas from the Proabilistic Model of Range on a very specific issue, double plays. The idea is to look at a particular set of balls in play, ground balls with a man on first and less than two outs, and see what fielders do well. What might make this very interesting, however, is that we can not only look at who starts the double play, but who is the pivot man, and who finishes the job. I'm imagining we can look at shortstop/second baseman combinations and see if the probabilities go up or down with a change in personnel, or with who is fielding vs. who is pivoting.

As always, your thoughts are welcome. Here's a couple of tables to start us off. The first shows how often each of the infield positions starts a double play.

Probability of a Fielding Position Starting a GDP, Groundballs Only, Man on First, Less Than Two Out, 2006
Position GDP Total Pct
1 274 11076 0.025
2 4 11076 0.00036
3 234 11076 0.021
4 1099 11076 0.099
5 802 11076 0.072
6 1484 11076 0.134

Pretty much what you'd expect, although I'm impressed that third basemen start as many as they do. This next chart divides the infield into eighteen pie slices, five degrees wide. Zero represents the third base line, 17 the first base line. The probability given is the probability of a ball being turned into a double play on that vector.

Probability of a GDP on a Ball Hit on the Vector, Groundballs Only, Man on First, Less Than Two Out, 2006
Vector GDP Total Probability
0 17 177 0.096
1 112 334 0.335
2 369 869 0.425
3 243 987 0.246
4 213 803 0.265
5 370 711 0.520
6 533 842 0.633
7 335 626 0.535
8 119 452 0.263
9 264 643 0.411
10 212 464 0.457
11 394 660 0.597
12 331 647 0.512
13 129 636 0.203
14 52 765 0.068
15 109 761 0.143
16 85 414 0.205
17 10 102 0.098

You can see from this that second baseman cheat more toward the bag than shortstops. Vector 8 represents the five degrees to the shortstop side of the bag. Vector 9 represents the five degrees to the second base side of the bag. As you can see, a lot more GDP's are started on the second base side. That makes sense, of course, as there are more right-handed hitters, and against a righty, a shortstop can't cheat as much. And while the lines are great places to hit the ball to avoid a double play, the absolute best place is the hole between second and first. I guess there is something to the idea of hitting behind the runner!

Baseball Musings is holding a pledge drive in March. If research like this interests you, I hope you'll consider a donation.

Posted by StatsGuru at 08:05 AM | Comments (2) | TrackBack (0)
March 13, 2007
Pitcher's Luck
Permalink

Speaking to a reporter this afternoon about the Probabilistic Model of Range (PMR), I realized I never published the chart of how pitchers were helped or hurt by their defense in 2006. So without further ado:

Probabilistic Model of Range, Fielders Behind Pitchers, 2006. Smoothed Visit Model with Distance for Fly Balls
Pitcher Team In Play Actual Outs Predicted Outs DER Predicted DER Difference
Kris Benson Bal 595 425 402.81 0.714 0.677 0.03730
Chris Young SD 468 357 339.63 0.763 0.726 0.03712
Ervin R Santana LAA 603 435 412.64 0.721 0.684 0.03708
Roy Halladay Tor 686 491 466.71 0.716 0.680 0.03540
Kevin Millwood Tex 670 458 434.82 0.684 0.649 0.03460
Joel Pineiro Sea 569 378 361.55 0.664 0.635 0.02891
Kenny Rogers Det 656 472 454.00 0.720 0.692 0.02743
Chris Carpenter StL 638 455 437.61 0.713 0.686 0.02726
David T Bush Mil 621 440 423.21 0.709 0.681 0.02704
Jeff Suppan StL 634 435 420.48 0.686 0.663 0.02290
Carlos Zambrano ChC 563 409 396.36 0.726 0.704 0.02244
Johan Santana Min 603 433 419.91 0.718 0.696 0.02171
Tim Wakefield Bos 440 320 310.49 0.727 0.706 0.02162
Chris Capuano Mil 677 461 446.73 0.681 0.660 0.02108
Woody Williams SD 489 350 339.78 0.716 0.695 0.02089
Kirk Saarloos Oak 421 287 278.99 0.682 0.663 0.01902
Noah Lowry SF 521 370 360.15 0.710 0.691 0.01890
Chien-Ming Wang NYY 758 526 511.78 0.694 0.675 0.01875
John Smoltz Atl 662 457 444.95 0.690 0.672 0.01820
Bronson Arroyo Cin 707 509 496.17 0.720 0.702 0.01815
Mike Mussina NYY 570 395 384.84 0.693 0.675 0.01783
Steve Trachsel NYM 552 389 379.44 0.705 0.687 0.01733
Greg Maddux ChC 454 311 303.18 0.685 0.668 0.01722
Matt Morris SF 687 481 469.65 0.700 0.684 0.01652
Brandon Webb Ari 701 483 472.33 0.689 0.674 0.01522
Nate Robertson Det 640 448 438.33 0.700 0.685 0.01512
Jose Contreras CWS 614 433 424.27 0.705 0.691 0.01421
Carlos Silva Min 664 446 436.62 0.672 0.658 0.01413
Ricky Nolasco Fla 443 298 291.93 0.673 0.659 0.01371
Josh Johnson Fla 440 311 305.16 0.707 0.694 0.01328
Josh Beckett Bos 590 429 421.42 0.727 0.714 0.01284
Roy Oswalt Hou 668 459 450.87 0.687 0.675 0.01217
Randy Johnson NYY 590 416 409.33 0.705 0.694 0.01131
Erik Bedard Bal 583 394 387.98 0.676 0.665 0.01033
Jon Garland CWS 715 486 479.00 0.680 0.670 0.00979
Josh Fogg Col 581 394 388.42 0.678 0.669 0.00960
Freddy Garcia CWS 695 494 487.36 0.711 0.701 0.00955
Gil Meche Sea 539 372 366.94 0.690 0.681 0.00939
Jaret Wright NYY 467 313 308.80 0.670 0.661 0.00899
Felix A Hernandez Sea 551 371 366.17 0.673 0.665 0.00877
Eric Milton Cin 495 354 349.80 0.715 0.707 0.00848
Justin B Verlander Det 564 394 389.49 0.699 0.691 0.00800
Mark Buehrle CWS 688 471 465.66 0.685 0.677 0.00776
Jason Jennings Col 655 456 451.33 0.696 0.689 0.00713
Dan Haren Oak 668 466 461.60 0.698 0.691 0.00658
Jamie Moyer Sea 531 370 366.51 0.697 0.690 0.00656
Jason Schmidt SF 607 432 428.09 0.712 0.705 0.00643
Jarrod Washburn Sea 619 437 433.23 0.706 0.700 0.00609
Jeff W Francis Col 626 445 441.42 0.711 0.705 0.00572
Aaron Cook Col 744 507 503.15 0.681 0.676 0.00517
Clay A Hensley SD 571 402 399.10 0.704 0.699 0.00509
Casey Fossum TB 413 284 281.93 0.688 0.683 0.00502
Tim Hudson Atl 705 482 479.36 0.684 0.680 0.00375
Tom Glavine NYM 621 431 429.08 0.694 0.691 0.00310
Matt Cain SF 528 383 381.46 0.725 0.722 0.00291
Derek Lowe LAD 716 492 490.39 0.687 0.685 0.00225
Curt Schilling Bos 592 399 397.74 0.674 0.672 0.00212
Vicente Padilla Tex 608 415 414.25 0.683 0.681 0.00123
John Lackey LAA 635 433 432.39 0.682 0.681 0.00096
Rodrigo Lopez Bal 616 407 407.54 0.661 0.662 -0.00087
Brad Radke Min 549 371 371.63 0.676 0.677 -0.00114
John V Koronka Tex 424 294 294.76 0.693 0.695 -0.00178
Brad Penny LAD 583 386 387.05 0.662 0.664 -0.00180
Ted Lilly Tor 524 362 363.16 0.691 0.693 -0.00221
Doug Davis Mil 619 421 422.39 0.680 0.682 -0.00225
Jason Marquis StL 648 456 457.72 0.704 0.706 -0.00266
Jake Westbrook Cle 720 479 480.93 0.665 0.668 -0.00268
Jamey Wright SF 507 354 355.81 0.698 0.702 -0.00357
Scott M Olsen Fla 490 348 349.80 0.710 0.714 -0.00367
Andy Pettitte Hou 652 430 433.13 0.660 0.664 -0.00480
Mark Redman KC 573 385 387.79 0.672 0.677 -0.00488
Miguel Batista Ari 692 467 470.53 0.675 0.680 -0.00510
Brian Moehler Fla 436 283 285.38 0.649 0.655 -0.00545
Jon Lieber Phi 557 378 381.60 0.679 0.685 -0.00647
Paul G Maholm Pit 559 371 375.05 0.664 0.671 -0.00724
Paul Byrd Cle 647 426 430.91 0.658 0.666 -0.00760
Tony Armas Jr. Was 500 343 346.80 0.686 0.694 -0.00760
Esteban Loaiza Oak 520 351 355.50 0.675 0.684 -0.00866
Byung-Hyun Kim Col 473 306 310.22 0.647 0.656 -0.00893
Jake Peavy SD 542 372 377.82 0.686 0.697 -0.01074
Wandy E Rodriguez Hou 427 281 285.85 0.658 0.669 -0.01135
Jeremy Bonderman Det 615 413 420.29 0.672 0.683 -0.01186
Cliff Lee Cle 658 455 463.03 0.691 0.704 -0.01220
Chan Ho Park SD 436 302 307.47 0.693 0.705 -0.01254
C.C. Sabathia Cle 562 383 390.88 0.681 0.696 -0.01402
Barry Zito Oak 655 464 473.26 0.708 0.723 -0.01414
Javier Vazquez CWS 594 399 407.80 0.672 0.687 -0.01481
Claudio Vargas Ari 538 365 373.65 0.678 0.695 -0.01607
Zach Duke Pit 726 480 491.99 0.661 0.678 -0.01651
Ian D Snell Pit 539 362 371.02 0.672 0.688 -0.01673
Sean C Marshall ChC 400 280 287.55 0.700 0.719 -0.01888
Kelvim Escobar LAA 570 383 394.00 0.672 0.691 -0.01930
Brett Myers Phi 549 378 388.95 0.689 0.708 -0.01994
Ramon Ortiz Was 654 442 457.19 0.676 0.699 -0.02323
Dontrelle Willis Fla 690 466 482.36 0.675 0.699 -0.02372
Joe M Blanton Oak 668 439 456.28 0.657 0.683 -0.02586
Ryan Madson Phi 441 278 291.21 0.630 0.660 -0.02994
Livan Hernandez Was 496 332 346.94 0.669 0.699 -0.03012
Aaron Harang Cin 684 454 476.58 0.664 0.697 -0.03301

Looking at the Hardball Times, Benson has a much better ERA than FIP, as does Chris Young. Hernandez is negative for Washington, as is Harang for Cincinnati. So it looks like the arrows are pointing in the right direction.

In discussing this, another question arose. Which pitchers give their fielders the easiest balls to field? We can answer that by sorting on Predicted DER. At the top of the list is Young, Zito, Cain, Sean Marshall and Scott Olsen. At the bottom, the worst was Joel Pineiro, followed by Millwood, Moehler, B. Kim and Carlos Silva. Now you need to be careful with those number, since home field has a say in this (more balls turn into outs in PETCO). I'm going to try to work on an adjustment for that. However, not that Young also led this category in 2005 in a completely different home park and league.

Posted by StatsGuru at 08:17 PM | Comments (1) | TrackBack (0)
December 11, 2006
Welcome SoxTalk Readers!
Permalink

I noticed the link posted is to the main page. The defensive statistics mentioned are here. Please take a look around the site as well, and remember to vote for your favorite sports blog!

Posted by StatsGuru at 11:16 PM | Comments (0) | TrackBack (0)
Welcome SoxTalk Readers!
Permalink

I noticed the link posted is to the main page. The defensive statistics mentioned are here. Please take a look around the site as well, and remember to vote for your favorite sports blog!

Posted by StatsGuru at 11:16 PM | Comments (0) | TrackBack (0)
More Defensive Charts
Permalink

Defensive charts for each player by position in 2006 are now available for your viewing pleasure. Click here and you will be able to select a player by position. As always, I welcome your comments and suggestions. The 2004 charts were done differently and are available here. I'm going to try to make each year's charts available in the same format. Just to be clear:

Enjoy!

Update Jan 10, 2008: Added charts for 2007.

Posted by StatsGuru at 10:44 PM | Comments (4) | TrackBack (0)
December 10, 2006
Defensive Charts
Permalink

Blogging was light today as I've been working on creating charts for each player/position/batted ball type to give you a good visual the Probabilistic Model of Range. Here's a sample of a chart (click on the chart for a full size version):

0009110901.jpg

Over the next couple of days I should have these set up where you pick a player's name and position and see all the charts for that position.

Posted by StatsGuru at 11:12 PM | Comments (4) | TrackBack (0)
December 08, 2006
Probabilistic Model of Range, Rightfielders, 2006
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Here's the outs table for rightfielders:

Probabilistic Model of Range, Rightfielders. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player InPlay Actual Outs Predicted Outs Out Difference Out Ratio
Carlos J Quentin 1156 96 87.98 8.02 109.12
Ryan Freel 1122 101 92.89 8.11 108.73
Jay Payton 1173 89 82.55 6.45 107.82
Damon J Hollins 1440 134 125.21 8.79 107.02
Juan Encarnacion 3085 219 206.96 12.04 105.82
Moises Alou 2026 154 147.83 6.17 104.18
Jose Guillen 1774 164 157.88 6.12 103.87
Ichiro Suzuki 3252 250 241.19 8.81 103.65
Reggie Sanders 1942 170 164.27 5.73 103.49
Mark DeRosa 1654 125 120.88 4.12 103.41
Alex I Rios 2862 218 210.94 7.06 103.35
Jacque Jones 3476 275 266.89 8.11 103.04
J.D. Drew 3472 284 276.20 7.80 102.83
Joe Borchard 1060 84 81.92 2.08 102.54
Emil Brown 1349 110 108.41 1.59 101.46
Vladimir Guerrero 3258 253 249.59 3.41 101.37
Randy Winn 1996 184 181.86 2.14 101.18
Austin Kearns 3928 346 342.73 3.27 100.96
Casey Blake 2586 210 208.24 1.76 100.85
Geoff Jenkins 3333 247 245.22 1.78 100.73
Milton Bradley 2518 191 190.14 0.86 100.45
Bobby Abreu 4047 293 292.75 0.25 100.08
Jermaine Dye 3915 305 305.35 -0.35 99.89
Nick Markakis 2843 240 240.60 -0.60 99.75
Brad B Hawpe 3769 280 281.37 -1.37 99.51
Jeff B Francoeur 4434 317 318.59 -1.59 99.50
Trot Nixon 2700 212 214.27 -2.27 98.94
Jason Lane 2049 155 157.68 -2.68 98.30
Russell Branyan 1163 87 88.64 -1.64 98.15
Jeremy R Hermida 2003 157 160.05 -3.05 98.09
Xavier Nady 2560 187 191.96 -4.96 97.42
Michael Cuddyer 3637 245 251.88 -6.88 97.27
Shawn Green 3393 220 226.92 -6.92 96.95
Jay Gibbons 1107 97 100.99 -3.99 96.05
Magglio Ordonez 3893 258 268.74 -10.74 96.00
Kevin Mench 1541 112 119.34 -7.34 93.85
Brian Giles 4169 298 318.55 -20.55 93.55
Bernie Williams 1347 98 104.84 -6.84 93.48
Jeromy Burnitz 1988 120 137.33 -17.33 87.38
Posted by StatsGuru at 05:59 PM | Comments (1) | TrackBack (0)
Probabilistic Model of Range, Centerfielders, 2006
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Here's the outs table for centerfielders:

Probabilistic Model of Range, Centerfielders. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player InPlay Actual Outs Predicted Outs Out Difference Out Ratio
Shane Victorino 1691 161 142.02 18.98 113.37
Ichiro Suzuki 1017 114 105.32 8.68 108.24
Carlos Beltran 3517 357 339.18 17.82 105.25
Brian N Anderson 2996 305 293.22 11.78 104.02
Corey Patterson 3360 345 331.93 13.07 103.94
Johnny Damon 3378 306 294.61 11.39 103.86
Randy Winn 1366 137 131.98 5.02 103.81
Ryan Freel 1211 127 122.76 4.24 103.45
Marlon Byrd 1272 125 120.87 4.13 103.41
Coco Crisp 2814 246 238.19 7.81 103.28
Joey R Gathright 3272 341 330.21 10.79 103.27
Brady Clark 2748 250 243.70 6.30 102.58
Willy Taveras 3304 335 327.09 7.91 102.42
Curtis Granderson 4014 385 376.01 8.99 102.39
Andruw Jones 4109 377 369.40 7.60 102.06
Rocco Baldelli 2368 228 223.60 4.40 101.97
Aaron Rowand 2742 251 247.27 3.73 101.51
Jim Edmonds 2471 223 219.79 3.21 101.46
Mike Cameron 3723 367 362.92 4.08 101.12
Eric Byrnes 3208 270 267.57 2.43 100.91
Juan Pierre 4103 380 377.29 2.71 100.72
Alfredo Amezaga 1580 155 153.92 1.08 100.70
Gary Matthews Jr. 3909 333 332.10 0.90 100.27
Chone Figgins 2455 242 241.43 0.57 100.24
Choo Freeman 1021 101 101.08 -0.08 99.92
Reggie D Abercrombie 1833 172 172.25 -0.25 99.85
Vernon Wells 3918 332 332.89 -0.89 99.73
Torii Hunter 3715 343 344.56 -1.56 99.55
Chris Duffy 2053 166 166.80 -0.80 99.52
Nate McLouth 1072 84 84.56 -0.56 99.34
Grady Sizemore 4455 409 412.99 -3.99 99.03
So Taguchi 1095 90 91.04 -1.04 98.86
Steve Finley 3013 287 290.77 -3.77 98.70
Jay Payton 1196 104 105.67 -1.67 98.42
Mark Kotsay 3261 281 288.40 -7.40 97.43
Ryan M Church 1172 122 127.23 -5.23 95.89
David DeJesus 1561 149 156.42 -7.42 95.26
Jeremy T Reed 1535 129 135.86 -6.86 94.95
Rob Mackowiak 1415 119 126.37 -7.37 94.17
Cory Sullivan 2666 225 239.39 -14.39 93.99
Jose A Bautista 1323 114 121.31 -7.31 93.98
Kenny Lofton 2999 241 257.05 -16.05 93.76
Ken Griffey Jr. 2753 229 250.78 -21.78 91.32
Posted by StatsGuru at 04:46 PM | Comments (3) | TrackBack (0)
Probabilistic Model of Range, Leftfielders
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Here's the outs table for leftfielders:

Probabilistic Model of Range, Leftfielders. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player InPlay Actual Outs Predicted Outs Out Difference Out Ratio
Melky Cabrera 3063 217 198.64 18.36 109.24
Dave Roberts 2887 239 219.66 19.34 108.81
Brandon W Fahey 1164 101 93.56 7.44 107.96
Matt Diaz 1798 163 151.27 11.73 107.75
Reed Johnson 1915 129 120.94 8.06 106.66
Emil Brown 2359 163 152.84 10.16 106.65
Luke B Scott 1188 81 76.76 4.24 105.53
Jason Bay 4269 316 299.59 16.41 105.48
Ryan Langerhans 2240 156 148.87 7.13 104.79
Jason Michaels 3283 214 208.11 5.89 102.83
Andre E Ethier 2779 172 168.24 3.76 102.23
Matt Murton 3026 240 234.98 5.02 102.13
David DeJesus 1736 138 136.42 1.58 101.15
Frank Catalanotto 2308 140 138.53 1.47 101.06
So Taguchi 1141 87 86.31 0.69 100.79
Alfonso Soriano 4405 326 325.21 0.79 100.24
Nick T Swisher 2035 170 169.92 0.08 100.05
Matt T Holliday 4234 277 276.97 0.03 100.01
Cliff Floyd 2280 148 148.21 -0.21 99.86
Carl Crawford 4006 302 302.84 -0.84 99.72
Brad Wilkerson 2106 139 139.59 -0.59 99.58
Garret Anderson 2377 192 193.18 -1.18 99.39
Raul Ibanez 4289 302 304.45 -2.45 99.20
Juan Rivera 1440 126 127.34 -1.34 98.95
Luis Gonzalez 4063 256 259.76 -3.76 98.55
Preston Wilson 2639 156 158.77 -2.77 98.25
Josh D Willingham 3255 206 210.68 -4.68 97.78
Scott Podsednik 3417 245 252.50 -7.50 97.03
Barry Bonds 2708 188 194.71 -6.71 96.56
Pat Burrell 2990 205 212.40 -7.40 96.51
Chris E Duncan 1015 66 68.47 -2.47 96.39
Jay Payton 1442 119 123.80 -4.80 96.12
Kevin Mench 1217 80 83.30 -3.30 96.04
Jeff Conine 1436 88 92.18 -4.18 95.47
Carlos Lee 3883 227 240.61 -13.61 94.34
Craig Monroe 2909 168 178.63 -10.63 94.05
Adam Dunn 4132 279 300.46 -21.46 92.86
Marcus Thames 1193 70 76.87 -6.87 91.07
Manny Ramirez 3151 175 194.87 -19.87 89.80
Bobby Kielty 1030 80 92.02 -12.02 86.94
Posted by StatsGuru at 04:33 PM | Comments (3) | TrackBack (0)
December 07, 2006
Probabilistic Model of Range, Shortstops, 2006
Permalink

Here's the outs table for the shortstops.

Probabilistic Model of Range, Shortstops. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player InPlay Actual Outs Predicted Outs Out Difference Out Ratio
Bill Hall 3311 404 377.82 26.18 106.93
Adam Everett 3801 500 467.66 32.34 106.92
Juan Castro 1743 205 192.64 12.36 106.42
Khalil Greene 3007 352 332.02 19.98 106.02
Julio Lugo 2103 253 242.98 10.02 104.13
Craig Counsell 2274 310 297.95 12.05 104.04
Jason A Bartlett 2570 348 334.94 13.06 103.90
Ben T Zobrist 1395 173 166.54 6.46 103.88
Alex Cora 1338 163 157.05 5.95 103.79
Carlos Guillen 3808 465 449.34 15.66 103.49
Clint Barmes 3411 404 392.62 11.38 102.90
Omar Vizquel 3974 441 429.14 11.86 102.76
Jhonny Peralta 4086 533 522.47 10.53 102.01
Yuniesky Betancourt 4225 501 491.93 9.07 101.84
Alex Gonzalez 2991 350 343.78 6.22 101.81
John McDonald 2024 237 232.99 4.01 101.72
Rafael Furcal 4257 538 529.11 8.89 101.68
Edgar Renteria 3958 446 441.08 4.92 101.12
Juan Uribe 3553 429 424.93 4.07 100.96
David Eckstein 3222 385 381.39 3.61 100.95
Jack Wilson 3485 454 451.20 2.80 100.62
Hanley Ramirez 4016 466 465.15 0.85 100.18
Orlando Cabrera 3903 433 434.02 -1.02 99.77
Michael Young 4307 536 538.40 -2.40 99.55
Ronny Cedeno 3258 398 400.82 -2.82 99.30
Bobby Crosby 2595 307 311.18 -4.18 98.66
Jose Reyes 3887 443 449.24 -6.24 98.61
Geoff Blum 1168 149 151.32 -2.32 98.47
Angel Berroa 3670 412 418.66 -6.66 98.41
Royce Clayton 3338 400 407.20 -7.20 98.23
Miguel Tejada 4027 465 475.33 -10.33 97.83
Jimmy Rollins 4206 499 510.75 -11.75 97.70
Derek Jeter 4009 450 468.35 -18.35 96.08
Stephen Drew 1475 161 168.09 -7.09 95.78
Aaron W Hill 1273 140 148.94 -8.94 94.00
Felipe Lopez 4245 438 472.63 -34.63 92.67
Marco Scutaro 1773 207 224.85 -17.85 92.06
Posted by StatsGuru at 07:27 PM | Comments (11) | TrackBack (0)
Probabilistic Model of Range, Second Basemen, 2006
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Continuing to put up the outs table for all fielders, here's the one for the second basemen.

Probabilistic Model of Range, Second Basemen. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player InPlay Actual Outs Predicted Outs Out Difference Out Ratio
Tony Graffanino 1702 186 165.06 20.94 112.68
Aaron W Hill 2777 358 334.22 23.78 107.12
Jamey Carroll 2806 396 370.20 25.80 106.97
Mark Grudzielanek 3595 367 344.87 22.13 106.42
Orlando Hudson 4128 552 520.38 31.62 106.08
Chase Utley 4151 476 451.34 24.66 105.46
Jose Valentin 2367 316 301.32 14.68 104.87
Mark Ellis 3407 390 376.45 13.55 103.60
Joe S Inglett 1349 157 151.57 5.43 103.58
Jose C Lopez 4045 473 457.26 15.74 103.44
Neifi Perez 1374 166 160.56 5.44 103.39
Robinson Cano 3160 385 373.02 11.98 103.21
Luis Castillo 3663 416 403.31 12.69 103.15
Placido Polanco 2838 373 363.35 9.65 102.66
Chris A Burke 1012 128 125.30 2.70 102.16
Brandon Phillips 3791 404 397.11 6.89 101.73
Jose Castillo 3832 387 384.20 2.80 100.73
Tadahito Iguchi 3782 428 425.35 2.65 100.62
Dan C Uggla 3935 485 482.86 2.14 100.44
Brian Roberts 3634 398 396.92 1.08 100.27
Josh L Barfield 3755 442 441.53 0.47 100.11
Ian M Kinsler 3288 424 426.80 -2.80 99.34
Adam Kennedy 3386 406 409.98 -3.98 99.03
Marcus Giles 3589 412 417.71 -5.71 98.63
Ray Durham 3525 393 401.46 -8.46 97.89
Craig Biggio 3162 360 369.43 -9.43 97.45
Jeff Kent 2811 325 335.51 -10.51 96.87
Aaron Miles 2016 238 246.01 -8.01 96.74
Mark Loretta 3578 401 415.16 -14.16 96.59
Hector Luna 1487 151 157.64 -6.64 95.79
Rickie Weeks 2402 263 275.27 -12.27 95.54
Ronnie Belliard 3860 448 472.37 -24.37 94.84
Kaz Matsui 1403 169 179.09 -10.09 94.36
Jose Vidro 2905 305 327.48 -22.48 93.13
Jorge L Cantu 2859 283 307.27 -24.27 92.10
Ty Wigginton 1075 105 116.62 -11.62 90.04
Todd Walker 1279 128 144.79 -16.79 88.40
Posted by StatsGuru at 07:07 PM | Comments (0) | TrackBack (0)
December 05, 2006
Probabilistic Model of Range, Firstbase, 2006
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Here's the table for the first basemen.

Probabilistic Model of Range, First Basemen. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player InPlay Actual Outs Predicted Outs Out Difference Out Ratio
Lance Niekro 1313 98 82.96 15.04 118.13
Albert Pujols 3864 306 265.59 40.41 115.21
Kendry Morales 1338 124 109.59 14.41 113.15
Derrek Lee 1104 85 76.11 8.89 111.69
Doug Mientkiewicz 2350 159 145.03 13.97 109.63
John Mabry 1031 94 87.37 6.63 107.59
Dan R Johnson 2199 163 151.59 11.41 107.52
Ben Broussard 2097 148 137.99 10.01 107.25
Mark Teixeira 4436 310 293.87 16.13 105.49
Ryan N Shealy 1473 81 77.23 3.77 104.88
Mark Sweeney 1227 99 94.63 4.37 104.62
Jeff Conine 1400 91 87.01 3.99 104.58
Shea Hillenbrand 1858 141 135.02 5.98 104.43
Lyle Overbay 3738 307 294.40 12.60 104.28
Adrian Gonzalez 4031 306 294.88 11.12 103.77
Nick Johnson 4014 319 307.69 11.31 103.68
Andy A Phillips 1635 109 105.25 3.75 103.57
Kevin E Youkilis 3123 233 225.01 7.99 103.55
Nomar Garciaparra 3199 194 188.70 5.30 102.81
Howie Kendrick 1017 73 71.18 1.82 102.55
Rich Aurilia 1024 70 68.35 1.65 102.41
Chris B Shelton 2737 179 175.49 3.51 102.00
Lance Berkman 2722 198 194.72 3.28 101.68
Scott Hatteberg 3415 220 216.73 3.27 101.51
Prince G Fielder 3989 269 265.60 3.40 101.28
Kevin Millar 2478 158 156.17 1.83 101.17
Mike Lamb 1488 98 97.62 0.38 100.39
Travis Lee 2794 218 217.25 0.75 100.35
Paul Konerko 3679 215 214.36 0.64 100.30
Justin Morneau 4046 266 268.21 -2.21 99.18
Richie Sexson 4023 291 294.42 -3.42 98.84
Adam LaRoche 3633 262 265.65 -3.65 98.62
Nick T Swisher 2214 153 155.66 -2.66 98.29
Todd Helton 4025 270 279.64 -9.64 96.55
Ty Wigginton 1008 66 68.89 -2.89 95.81
Ryan F Garko 1291 76 79.74 -3.74 95.30
Carlos Delgado 3696 253 266.75 -13.75 94.85
Robb Quinlan 1151 67 70.79 -3.79 94.64
Wes Helms 1305 79 84.03 -5.03 94.02
Craig A Wilson 1819 93 99.06 -6.06 93.88
Mike Jacobs 2949 191 208.47 -17.47 91.62
Ryan J Howard 4301 275 301.23 -26.23 91.29
Conor S Jackson 3295 231 256.35 -25.35 90.11
Sean Casey 2806 168 189.24 -21.24 88.77
Jason Giambi 1467 71 87.61 -16.61 81.05
Posted by StatsGuru at 06:14 PM | Comments (3) | TrackBack (0)
Probablisitic Model of Range, Catchers, 2006
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I wanted to put up tables for all the positions using outs difference so there is a complete record. Pitchers and third basemen are already up, so I'll do the rest in position order. Here are the catchers.

Probabilistic Model of Range, Catchers. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player InPlay Actual Outs Predicted Outs Out Difference Out Ratio
Henry Blanco 1503 11 8.20 2.80 134.08
Ivan Rodriguez 3235 33 25.56 7.44 129.09
Toby Hall 2056 19 15.45 3.55 122.99
Paul Lo Duca 3062 36 29.34 6.66 122.72
Rod Barajas 2630 26 21.86 4.14 118.96
Miguel Olivo 2985 40 34.55 5.45 115.76
Mike Rivera 1048 20 17.45 2.55 114.65
Jason LaRue 1620 29 25.58 3.42 113.35
Matt A Treanor 1283 20 17.81 2.19 112.32
Chris R Snyder 1485 14 12.55 1.45 111.55
Mike Redmond 1153 8 7.28 0.72 109.82
Mike Lieberthal 1402 17 15.54 1.46 109.40
A.J. Pierzynski 3512 27 24.76 2.24 109.06
Dioner F Navarro 2084 21 19.87 1.13 105.66
Mike A Napoli 2105 17 16.10 0.90 105.57
Jason Varitek 2505 21 19.93 1.07 105.37
Doug Mirabelli 1397 13 12.49 0.51 104.09
Vance Wilson 1204 20 19.73 0.27 101.38
Brian Schneider 3176 40 39.53 0.47 101.19
Yadier B Molina 3212 30 29.68 0.32 101.09
Brad Ausmus 3292 45 44.59 0.41 100.91
Kenji Johjima 3631 36 35.77 0.23 100.65
Russell N Martin 3094 36 35.80 0.20 100.56
Bengie Molina 2542 35 34.82 0.18 100.52
Jorge Posada 3237 58 58.00 0.00 100.01
Jason Kendall 3885 45 45.24 -0.24 99.46
Gerald Laird 1828 15 15.13 -0.13 99.15
Michael Barrett 2508 33 33.50 -0.50 98.50
Ronny L Paulino 3196 36 36.77 -0.77 97.90
Yorvit Torrealba 1692 21 21.55 -0.55 97.46
Joe Mauer 3167 28 28.79 -0.79 97.25
Paul Bako 1246 15 15.44 -0.44 97.15
Victor Martinez 3611 43 44.73 -1.73 96.13
Dave Ross 1929 26 27.23 -1.23 95.48
Eliezer J Alfonzo 2116 26 27.27 -1.27 95.35
Sal Fasano 1632 18 19.13 -1.13 94.09
Javy Lopez 1000 8 8.51 -0.51 94.03
Josh Bard 1688 26 27.75 -1.75 93.69
Brian M McCann 3129 29 30.97 -1.97 93.63
Jose Molina 1773 16 17.26 -1.26 92.72
Ramon Hernandez 3399 30 32.84 -2.84 91.35
John R Buck 3042 24 26.85 -2.85 89.40
Gregg Zaun 1654 19 21.53 -2.53 88.25
Todd Greene 1048 11 12.51 -1.51 87.93
Danny Ardoin 1009 9 10.39 -1.39 86.61
Mike Matheny 1222 12 14.01 -2.01 85.66
Gary Bennett 1220 21 24.72 -3.72 84.95
Todd Pratt 1115 14 16.68 -2.68 83.95
Damian Miller 2529 23 27.76 -4.76 82.85
Josh Paul 1239 15 18.27 -3.27 82.12
Chris R Coste 1358 13 16.08 -3.08 80.83
Mike Piazza 2107 22 27.27 -5.27 80.66
Johnny Estrada 2866 15 20.73 -5.73 72.35
Posted by StatsGuru at 05:16 PM | Comments (0) | TrackBack (0)
December 04, 2006
Probabilistic Model of Range, Pitchers, 2006
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The last position to examine is pitchers. They play less than other position players, so I'm looking at hurlers on the field for 500 balls in play.

Probabilistic Model of Range, Pitchers. Model is Based on 2006 Data Only. Minimum 500 Balls in Play. Uses Distance for Fly Balls.
Player InPlay Actual Outs Predicted Outs Out Difference Out Ratio
Greg Maddux 688 53 32.45 20.55 163.31
Jae Seo 528 20 14.26 5.74 140.21
Johan Santana 603 35 25.25 9.75 138.60
Justin B Verlander 564 18 13.38 4.62 134.55
Cory Lidle 516 35 28.44 6.56 123.06
Tom Glavine 621 43 35.04 7.96 122.73
Jake Peavy 542 28 22.88 5.12 122.38
Bronson Arroyo 707 35 28.61 6.39 122.32
Kevin Millwood 670 31 25.60 5.40 121.08
John Lackey 635 28 23.44 4.56 119.47
Jon Garland 715 36 30.24 5.76 119.07
Miguel Batista 692 32 27.04 4.96 118.35
Jeff Suppan 634 24 20.29 3.71 118.29
Kenny Rogers 656 40 33.82 6.18 118.26
Jake Westbrook 720 45 38.28 6.72 117.55
Steve Trachsel 552 28 23.93 4.07 117.00
Mark Redman 573 26 22.25 3.75 116.88
Jeff W Francis 626 34 29.19 4.81 116.48
Matt Morris 687 28 24.05 3.95 116.42
Erik Bedard 583 25 21.87 3.13 114.33
John Smoltz 662 37 33.02 3.98 112.07
Dan Haren 668 27 24.14 2.86 111.86
Jamie Moyer 696 32 28.69 3.31 111.52
Brandon Webb 701 44 39.52 4.48 111.35
Josh Beckett 590 26 23.37 2.63 111.26
Felix A Hernandez 551 26 23.43 2.57 110.99
Javier Vazquez 594 30 27.25 2.75 110.08
Ted Lilly 524 24 21.81 2.19 110.03
Kris Benson 595 26 23.67 2.33 109.85
Josh Fogg 581 28 25.67 2.33 109.08
Paul G Maholm 559 37 34.11 2.89 108.47
Mark Buehrle 688 27 25.13 1.87 107.43
Jason Schmidt 607 18 16.86 1.14 106.79
Zach Duke 726 45 42.71 2.29 105.37
Dontrelle Willis 690 43 40.83 2.17 105.31
Carlos Zambrano 563 36 34.22 1.78 105.20
Noah Lowry 521 22 21.13 0.87 104.11
Livan Hernandez 720 35 33.68 1.32 103.92
Tony Armas Jr. 500 21 20.26 0.74 103.66
Claudio Vargas 538 18 17.48 0.52 102.98
Randy Johnson 590 26 25.40 0.60 102.38
Derek Lowe 716 48 47.07 0.93 101.98
Jason Jennings 655 27 26.56 0.44 101.65
Jose Contreras 614 23 22.72 0.28 101.25
Jon Lieber 557 22 21.90 0.10 100.46
Vicente Padilla 608 24 23.92 0.08 100.35
Clay A Hensley 571 27 26.96 0.04 100.16
Paul Byrd 647 17 17.00 -0.00 99.97
Curt Schilling 592 24 24.13 -0.13 99.45
Roy Halladay 686 34 34.22 -0.22 99.34
Brett Myers 549 24 24.71 -0.71 97.12
Chien-Ming Wang 758 44 45.46 -1.46 96.80
Ian D Snell 539 27 27.97 -0.97 96.52
Rodrigo Lopez 616 17 17.62 -0.62 96.49
Chris Carpenter 638 28 29.24 -1.24 95.75
Jamey Wright 507 21 22.00 -1.00 95.47
Brad Radke 549 22 23.15 -1.15 95.03
Aaron Cook 744 50 52.72 -2.72 94.84
Mike Mussina 570 22 23.24 -1.24 94.68
Jarrod Washburn 619 22 23.30 -1.30 94.44
Jason Marquis 648 32 34.47 -2.47 92.84
Gil Meche 539 19 20.53 -1.53 92.56
Aaron Harang 684 37 40.43 -3.43 91.51
Cliff Lee 658 16 17.57 -1.57 91.06
Andy Pettitte 652 28 31.06 -3.06 90.15
Kelvim Escobar 570 19 21.26 -2.26 89.37
Chris Capuano 677 28 31.75 -3.75 88.19
Matt Cain 528 18 20.59 -2.59 87.41
David T Bush 621 27 30.93 -3.93 87.31
Barry Zito 655 18 20.73 -2.73 86.84
Ramon Ortiz 654 21 24.24 -3.24 86.65
Freddy Garcia 695 21 24.27 -3.27 86.53
Mark Hendrickson 537 22 25.79 -3.79 85.30
Esteban Loaiza 520 16 18.77 -2.77 85.25
Carlos Silva 664 22 25.84 -3.84 85.13
Jeremy Bonderman 615 18 21.26 -3.26 84.66
Brad Penny 583 19 22.54 -3.54 84.28
Roy Oswalt 668 29 34.57 -5.57 83.90
Doug Davis 619 24 28.70 -4.70 83.61
Nate Robertson 639 21 25.44 -4.44 82.53
Tim Hudson 705 30 36.92 -6.92 81.25
Ervin R Santana 603 14 17.33 -3.33 80.80
Joel Pineiro 569 18 22.63 -4.63 79.54
C.C. Sabathia 562 18 23.50 -5.50 76.58
Jeff Weaver 572 16 21.82 -5.82 73.34
Joe M Blanton 668 13 18.49 -5.49 70.31

Maddux is head and shoulders above everyone else. To break this down further, he's +14 outs on ground balls, +4 outs on line drives, and +2 outs on fly balls (assume they're pop ups). On line drives, Greg was expected to make 1.74 outs, and he actually made 6. That's four single he likely saved. Based on this, he definitely deserved the gold glove.

Posted by StatsGuru at 07:04 PM | Comments (4) | TrackBack (0)
November 28, 2006
Probabilistic Model of Range, Third Basemen, 2006
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There's been a suggestion to present the data in a different format, so I'm going to try that with the third basemen. I'm also just reporting the mixed velocity/distance model here. People seem to like that model better. At some point, I'll redo the tables for the positions posted ealier. Here's the ranking of the third baseman based on difference in DER.

Probabilistic Model of Range, Third Basemen. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player In Play Actual Outs Predicted Outs DER Predicted DER Difference
Joe Crede 3962 436 397.55 0.110 0.100 0.00971
Freddy Sanchez 2527 285 265.88 0.113 0.105 0.00757
Pedro Feliz 4278 420 391.93 0.098 0.092 0.00656
Brandon Inge 4278 506 479.75 0.118 0.112 0.00614
Adrian Beltre 4159 416 393.60 0.100 0.095 0.00539
Maicer E Izturis 2069 182 171.49 0.088 0.083 0.00508
Scott Rolen 3788 390 371.79 0.103 0.098 0.00481
Mike Lowell 3990 429 411.96 0.108 0.103 0.00427
Morgan Ensberg 2917 289 276.96 0.099 0.095 0.00413
Ryan W Zimmerman 4383 382 365.01 0.087 0.083 0.00388
Andy M Marte 1348 141 135.81 0.105 0.101 0.00385
Corey Koskie 1847 189 182.02 0.102 0.099 0.00378
David Bell 3716 347 334.10 0.093 0.090 0.00347
Willy Aybar 1388 106 102.60 0.076 0.074 0.00245
Eric Chavez 3607 362 353.27 0.100 0.098 0.00242
Nick Punto 2256 217 212.22 0.096 0.094 0.00212
Miguel Cabrera 4010 349 342.51 0.087 0.085 0.00162
Vinny Castilla 1755 161 158.59 0.092 0.090 0.00138
Chad A Tracy 3930 339 337.78 0.086 0.086 0.00031
Hank Blalock 3374 293 292.07 0.087 0.087 0.00027
Melvin Mora 4109 372 372.59 0.091 0.091 -0.00014
David A Wright 4041 356 359.00 0.088 0.089 -0.00074
Troy Glaus 3586 324 326.88 0.090 0.091 -0.00080
Aramis Ramirez 3934 333 336.63 0.085 0.086 -0.00092
Chipper Jones 2811 247 250.06 0.088 0.089 -0.00109
Mark T Teahen 2954 286 289.22 0.097 0.098 -0.00109
Abraham O Nunez 1876 182 184.40 0.097 0.098 -0.00128
B.J. Upton 1326 114 115.79 0.086 0.087 -0.00135
Mark DeRosa 1098 97 99.17 0.088 0.090 -0.00197
Alex Rodriguez 3968 330 338.71 0.083 0.085 -0.00219
Wilson Betemit 1831 142 146.67 0.078 0.080 -0.00255
Garrett Atkins 4385 358 375.87 0.082 0.086 -0.00408
Edwin Encarnacion 2908 252 265.44 0.087 0.091 -0.00462
Aubrey Huff 2133 193 203.79 0.090 0.096 -0.00506
Aaron Boone 2748 221 235.26 0.080 0.086 -0.00519
Tony Batista 1354 114 124.03 0.084 0.092 -0.00741
Rich Aurilia 1109 101 112.09 0.091 0.101 -0.01000

As you can see, Joe Crede earned that gold glove. Now here's the same list using just outs, and sorted by 100*Actual Outs/Predicted Outs.

Probabilistic Model of Range, Third Basemen. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls. Sorted by Out Ratio.
Player InPlay Actual Outs Predicted Outs Out Difference Out Ratio
Joe Crede 3962 436 397.55 38.45 109.67
Freddy Sanchez 2527 285 265.88 19.12 107.19
Pedro Feliz 4278 420 391.93 28.07 107.16
Maicer E Izturis 2069 182 171.49 10.51 106.13
Adrian Beltre 4159 416 393.60 22.40 105.69
Brandon Inge 4278 506 479.75 26.25 105.47
Scott Rolen 3788 390 371.79 18.21 104.90
Ryan W Zimmerman 4383 382 365.01 16.99 104.65
Morgan Ensberg 2917 289 276.96 12.04 104.35
Mike Lowell 3990 429 411.96 17.04 104.14
David Bell 3716 347 334.10 12.90 103.86
Corey Koskie 1847 189 182.02 6.98 103.84
Andy M Marte 1348 141 135.81 5.19 103.82
Willy Aybar 1388 106 102.60 3.40 103.32
Eric Chavez 3607 362 353.27 8.73 102.47
Nick Punto 2256 217 212.22 4.78 102.25
Miguel Cabrera 4010 349 342.51 6.49 101.89
Vinny Castilla 1755 161 158.59 2.41 101.52
Chad A Tracy 3930 339 337.78 1.22 100.36
Hank Blalock 3374 293 292.07 0.93 100.32
Melvin Mora 4109 372 372.59 -0.59 99.84
David A Wright 4041 356 359.00 -3.00 99.17
Troy Glaus 3586 324 326.88 -2.88 99.12
Aramis Ramirez 3934 333 336.63 -3.63 98.92
Mark T Teahen 2954 286 289.22 -3.22 98.89
Chipper Jones 2811 247 250.06 -3.06 98.78
Abraham O Nunez 1876 182 184.40 -2.40 98.70
B.J. Upton 1326 114 115.79 -1.79 98.46
Mark DeRosa 1098 97 99.17 -2.17 97.82
Alex Rodriguez 3968 330 338.71 -8.71 97.43
Wilson Betemit 1831 142 146.67 -4.67 96.82
Garrett Atkins 4385 358 375.87 -17.87 95.25
Edwin Encarnacion 2908 252 265.44 -13.44 94.94
Aubrey Huff 2133 193 203.79 -10.79 94.71
Aaron Boone 2748 221 235.26 -14.26 93.94
Tony Batista 1354 114 124.03 -10.03 91.91
Rich Aurilia 1109 101 112.09 -11.09 90.11

As you can see, the order is almost exactly the same. From this chart, the Indians should be happier with Marte at third than Boone. And Alex Rodriguez must have made up for all those errors someplace else, since he's only down 8 outs. Freddy Sanchez did it all, winning a batting title and playing a great third base. The Joe Randa injury was the best thing to happen to Pittsburgh last year.

Please let me know which presentation you like better in the comments.

Posted by StatsGuru at 06:16 PM | Comments (13) | TrackBack (0)
Hudson's Defense
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There's been some talk in the comments on the second basemen that Orlando Hudson is a pop up hog and that's why he does so well in PMR. The best way I know to examine this is by charting the player's DER by vector against the expected DER. The yellow line is the difference; above 0 the player is doing a better job than expected. Below 0, a worse job than expected. Here's Hudson on ground balls. All data is for 2006, and I'm using the mixed velocity/distance-smoothed visitor model.

HudsonGround.gif

The vector numbers go up as you approach first base. Second base should be at vector 54, first base at 63. As you can see, Hudson does a better job moving to his left than to his right. Is there any evidence he sets up more toward first base than second?

Here's Hudson on fly balls.

HudsonFly.gif

As you can see, he does get more balls on the first base vector than most second basemen, but it's also an easier play for the second baseman if the ball is behind the bag.

Here's line drives. These are always a big random.

HudsonLine.gif

He had a number of line drives hit right at him this year, and that helped his actual DER.

Posted by StatsGuru at 03:29 PM | Comments (6) | TrackBack (0)
November 26, 2006
Probabilistic Model of Range, Second Basemen, 2006
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As with the left fielders, I'll present both the velocity model and the velocity/distance model. We'll start with the old velocity model:

Probabilistic Model of Range, Second Basemen. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Velocity for Fly Balls.
Player In Play Actual Outs Predicted Outs DER Predicted DER Difference
Tony Graffanino 1702 186 161.05 0.109 0.095 0.01466
Neifi Perez 1374 166 152.40 0.121 0.111 0.00990
Jamey Carroll 2806 396 372.04 0.141 0.133 0.00854
Joe S Inglett 1349 157 145.78 0.116 0.108 0.00832
Orlando Hudson 4128 552 522.65 0.134 0.127 0.00711
Aaron W Hill 2777 358 340.66 0.129 0.123 0.00625
Jose Valentin 2367 316 301.96 0.134 0.128 0.00593
Mark Ellis 3407 390 370.15 0.114 0.109 0.00583
Jose C Lopez 4045 473 451.94 0.117 0.112 0.00521
Placido Polanco 2838 373 359.11 0.131 0.127 0.00489
Luis Castillo 3663 416 401.24 0.114 0.110 0.00403
Chase Utley 4151 476 460.10 0.115 0.111 0.00383
Robinson Cano 3160 385 372.91 0.122 0.118 0.00383
Chris A Burke 1012 128 124.23 0.126 0.123 0.00373
Dan C Uggla 3935 485 473.20 0.123 0.120 0.00300
Tadahito Iguchi 3782 428 418.52 0.113 0.111 0.00251
Josh L Barfield 3755 442 435.89 0.118 0.116 0.00163
Jose Castillo 3832 387 381.40 0.101 0.100 0.00146
Brandon Phillips 3791 404 399.50 0.107 0.105 0.00119
Marcus Giles 3589 412 413.47 0.115 0.115 -0.00041
Mark Grudzielanek 3595 367 370.61 0.102 0.103 -0.00100
Mark Loretta 3578 401 410.00 0.112 0.115 -0.00251
Brian Roberts 3634 398 407.17 0.110 0.112 -0.00252
Ray Durham 3525 393 402.11 0.111 0.114 -0.00258
Adam Kennedy 3386 406 415.75 0.120 0.123 -0.00288
Ian M Kinsler 3288 424 433.63 0.129 0.132 -0.00293
Aaron Miles 2016 238 245.21 0.118 0.122 -0.00358
Ronnie Belliard 3860 448 464.43 0.116 0.120 -0.00426
Jeff Kent 2811 325 338.95 0.116 0.121 -0.00496
Craig Biggio 3162 360 376.73 0.114 0.119 -0.00529
Hector Luna 1487 151 159.76 0.102 0.107 -0.00589
Rickie Weeks 2402 263 278.24 0.109 0.116 -0.00634
Kaz Matsui 1403 169 178.92 0.120 0.128 -0.00707
Jose Vidro 2905 305 327.93 0.105 0.113 -0.00789
Jorge L Cantu 2859 283 311.98 0.099 0.109 -0.01014
Ty Wigginton 1075 105 117.30 0.098 0.109 -0.01144
Todd Walker 1279 128 147.72 0.100 0.115 -0.01542

And here's the mixed velocity/distance model:

Probabilistic Model of Range, Second Basemen. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player In Play Actual Outs Predicted Outs DER Predicted DER Difference
Tony Graffanino 1702 186 165.06 0.109 0.097 0.01230
Jamey Carroll 2806 396 370.20 0.141 0.132 0.00920
Aaron W Hill 2777 358 334.22 0.129 0.120 0.00856
Orlando Hudson 4128 552 520.38 0.134 0.126 0.00766
Jose Valentin 2367 316 301.32 0.134 0.127 0.00620
Mark Grudzielanek 3595 367 344.87 0.102 0.096 0.00616
Chase Utley 4151 476 451.34 0.115 0.109 0.00594
Joe S Inglett 1349 157 151.57 0.116 0.112 0.00403
Mark Ellis 3407 390 376.45 0.114 0.110 0.00398
Neifi Perez 1374 166 160.56 0.121 0.117 0.00396
Jose C Lopez 4045 473 457.26 0.117 0.113 0.00389
Robinson Cano 3160 385 373.02 0.122 0.118 0.00379
Luis Castillo 3663 416 403.31 0.114 0.110 0.00346
Placido Polanco 2838 373 363.35 0.131 0.128 0.00340
Chris A Burke 1012 128 125.30 0.126 0.124 0.00267
Brandon Phillips 3791 404 397.11 0.107 0.105 0.00182
Jose Castillo 3832 387 384.20 0.101 0.100 0.00073
Tadahito Iguchi 3782 428 425.35 0.113 0.112 0.00070
Dan C Uggla 3935 485 482.86 0.123 0.123 0.00054
Brian Roberts 3634 398 396.92 0.110 0.109 0.00030
Josh L Barfield 3755 442 441.53 0.118 0.118 0.00013
Ian M Kinsler 3288 424 426.80 0.129 0.130 -0.00085
Adam Kennedy 3386 406 409.98 0.120 0.121 -0.00118
Marcus Giles 3589 412 417.71 0.115 0.116 -0.00159
Ray Durham 3525 393 401.46 0.111 0.114 -0.00240
Craig Biggio 3162 360 369.43 0.114 0.117 -0.00298
Jeff Kent 2811 325 335.51 0.116 0.119 -0.00374
Mark Loretta 3578 401 415.16 0.112 0.116 -0.00396
Aaron Miles 2016 238 246.01 0.118 0.122 -0.00397
Hector Luna 1487 151 157.64 0.102 0.106 -0.00447
Rickie Weeks 2402 263 275.27 0.109 0.115 -0.00511
Ronnie Belliard 3860 448 472.37 0.116 0.122 -0.00631
Kaz Matsui 1403 169 179.09 0.120 0.128 -0.00719
Jose Vidro 2905 305 327.48 0.105 0.113 -0.00774
Jorge L Cantu 2859 283 307.27 0.099 0.107 -0.00849
Ty Wigginton 1075 105 116.62 0.098 0.108 -0.01081
Todd Walker 1279 128 144.79 0.100 0.113 -0.01313

I'm starting to like the mixed model more. Grudzielanek does better in the second chart. He won the gold glove and did very well in John Dewan's +/- system. Both system agree on the bottom five. Among the second basemen who play every day, Orlando Hudson comes out on top in both systems. I believe he came out near the top last year as well.

Posted by StatsGuru at 05:50 PM | Comments (8) | TrackBack (0)
November 24, 2006
Probabilistic Model of Range, Rightfielders, 2006
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It seems every year I run the PMR for rightfielders I encounter the same problem, and it has to do with Ichiro Suzuki:

Probabilistic Model of Range, Rightfielders. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Velocity for Fly Balls.
Player In Play Actual Outs Predicted Outs DER Predicted DER Difference
Reggie Sanders 1942 170 150.73 0.088 0.078 0.00992
Carlos J Quentin 1156 96 85.83 0.083 0.074 0.00879
Casey Blake 2586 210 191.62 0.081 0.074 0.00711
Damon J Hollins 1440 134 124.64 0.093 0.087 0.00650
Mark DeRosa 1654 125 115.00 0.076 0.070 0.00605
Kevin Mench 1541 112 102.93 0.073 0.067 0.00588
Ryan Freel 1122 101 94.94 0.090 0.085 0.00540
Jose Guillen 1774 164 154.51 0.092 0.087 0.00535
Jay Gibbons 1107 97 91.66 0.088 0.083 0.00482
J.D. Drew 3472 284 267.61 0.082 0.077 0.00472
Alex I Rios 2862 218 205.27 0.076 0.072 0.00445
Juan Encarnacion 3085 219 208.81 0.071 0.068 0.00330
Vladimir Guerrero 3258 253 243.64 0.078 0.075 0.00287
Emil Brown 1349 110 106.51 0.082 0.079 0.00259
Jacque Jones 3476 275 266.55 0.079 0.077 0.00243
Austin Kearns 3928 346 337.89 0.088 0.086 0.00206
Moises Alou 2026 154 150.84 0.076 0.074 0.00156
Russell Branyan 1163 87 86.07 0.075 0.074 0.00080
Bobby Abreu 4047 293 292.60 0.072 0.072 0.00010
Trot Nixon 2700 212 211.95 0.079 0.079 0.00002
Joe Borchard 1060 84 84.06 0.079 0.079 -0.00006
Jeff B Francoeur 4434 317 317.93 0.071 0.072 -0.00021
Brad B Hawpe 3769 280 281.06 0.074 0.075 -0.00028
Jay Payton 1173 89 89.42 0.076 0.076 -0.00036
Ichiro Suzuki 3252 250 251.21 0.077 0.077 -0.00037
Shawn Green 3393 220 222.29 0.065 0.066 -0.00068
Jason Lane 2049 155 156.74 0.076 0.076 -0.00085
Randy Winn 1996 184 185.72 0.092 0.093 -0.00086
Milton Bradley 2518 191 194.41 0.076 0.077 -0.00136
Jermaine Dye 3915 305 310.61 0.078 0.079 -0.00143
Nick Markakis 2843 240 244.33 0.084 0.086 -0.00152
Geoff Jenkins 3333 247 254.04 0.074 0.076 -0.00211
Michael Cuddyer 3637 245 259.18 0.067 0.071 -0.00390
Jeromy Burnitz 1988 120 128.64 0.060 0.065 -0.00435
Bernie Williams 1347 98 104.01 0.073 0.077 -0.00446
Jeremy R Hermida 2003 157 166.44 0.078 0.083 -0.00471
Xavier Nady 2560 187 202.29 0.073 0.079 -0.00597
Magglio Ordonez 3893 258 281.26 0.066 0.072 -0.00598
Brian Giles 4169 298 332.48 0.071 0.080 -0.00827

That's right, Ichiro is very slightly negative (actually, I'd call him neutral). But people who watch him disagree with this finding. He ranks at the top in centerfield, indicating he can chase down balls.

My belief is that Ichiro plays deep in rightfield to take away the long hits. He's making a tradeoff between catching balls that might go as doubles, triples or home runs and giving up short singles that a fielder playing at normal depth levels would catch. When he goes to center, he plays more conservatively there since he's not used to the position, but in right he takes chances.

One suggestion over the time I've presented this data is to use the actual distance of balls rather than the velocity of the ball as a parameter for outfielders. I've always felt velocity was a pretty good proxy for distance, and it allowed me to have the same model for infielders and outfielders. But I thought of a way to incorporate the distance without changing the model. I simply divide the distance by 100, except on ground balls and low line drives. Basically, on balls that infielder have a chance to field, use velocity. On balls that are too high for them to field, use distance. Here's a table using a model that mixes the two.

Probabilistic Model of Range, Rightfielders. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play. Uses Distance for Fly Balls.
Player In Play Actual Outs Predicted Outs DER Predicted DER Difference
Ryan Freel 1122 101 92.89 0.090 0.083 0.00723
Carlos J Quentin 1156 96 87.98 0.083 0.076 0.00694
Damon J Hollins 1440 134 125.21 0.093 0.087 0.00610
Jay Payton 1173 89 82.55 0.076 0.070 0.00550
Juan Encarnacion 3085 219 206.96 0.071 0.067 0.00390
Jose Guillen 1774 164 157.88 0.092 0.089 0.00345
Moises Alou 2026 154 147.83 0.076 0.073 0.00305
Reggie Sanders 1942 170 164.27 0.088 0.085 0.00295
Ichiro Suzuki 3252 250 241.19 0.077 0.074 0.00271
Mark DeRosa 1654 125 120.88 0.076 0.073 0.00249
Alex I Rios 2862 218 210.94 0.076 0.074 0.00247
Jacque Jones 3476 275 266.89 0.079 0.077 0.00233
J.D. Drew 3472 284 276.20 0.082 0.080 0.00225
Joe Borchard 1060 84 81.92 0.079 0.077 0.00196
Emil Brown 1349 110 108.41 0.082 0.080 0.00118
Randy Winn 1996 184 181.86 0.092 0.091 0.00107
Vladimir Guerrero 3258 253 249.59 0.078 0.077 0.00105
Austin Kearns 3928 346 342.73 0.088 0.087 0.00083
Casey Blake 2586 210 208.24 0.081 0.081 0.00068
Geoff Jenkins 3333 247 245.22 0.074 0.074 0.00054
Milton Bradley 2518 191 190.14 0.076 0.076 0.00034
Bobby Abreu 4047 293 292.75 0.072 0.072 0.00006
Jermaine Dye 3915 305 305.35 0.078 0.078 -0.00009
Nick Markakis 2843 240 240.60 0.084 0.085 -0.00021
Jeff B Francoeur 4434 317 318.59 0.071 0.072 -0.00036
Brad B Hawpe 3769 280 281.37 0.074 0.075 -0.00036
Trot Nixon 2700 212 214.27 0.079 0.079 -0.00084
Jason Lane 2049 155 157.68 0.076 0.077 -0.00131
Russell Branyan 1163 87 88.64 0.075 0.076 -0.00141
Jeremy R Hermida 2003 157 160.05 0.078 0.080 -0.00152
Michael Cuddyer 3637 245 251.88 0.067 0.069 -0.00189
Xavier Nady 2560 187 191.96 0.073 0.075 -0.00194
Shawn Green 3393 220 226.92 0.065 0.067 -0.00204
Magglio Ordonez 3893 258 268.74 0.066 0.069 -0.00276
Jay Gibbons 1107 97 100.99 0.088 0.091 -0.00361
Kevin Mench 1541 112 119.34 0.073 0.077 -0.00476
Brian Giles 4169 298 318.55 0.071 0.076 -0.00493
Bernie Williams 1347 98 104.84 0.073 0.078 -0.00508
Jeromy Burnitz 1988 120 137.33 0.060 0.069 -0.00872

As you can see, Ichiro moves up the rankings. I'd be curious to know what people think of each of these methods. Does one ranking strike you as more correct that the other?

Posted by StatsGuru at 09:36 PM | Comments (14) | TrackBack (0)
November 21, 2006
Probabilistic Model of Range, Shortstops, 2006
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With the MVP debate raging and the Cincinnati Reds signing Alex Gonzalez to play shortstop, it's a good time to look at how PMR rates the #6 fielders:

Probabilistic Model of Range, Shortstops. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play.
Player In Play Actual Outs Predicted Outs DER Predicted DER Difference
Adam Everett 3801 500 464.88 0.132 0.122 0.00924
Bill Hall 3311 404 375.73 0.122 0.113 0.00854
Craig Counsell 2274 310 290.98 0.136 0.128 0.00836
Yuniesky Betancourt 4225 501 474.18 0.119 0.112 0.00635
Jason A Bartlett 2570 348 333.97 0.135 0.130 0.00546
Julio Lugo 2103 253 241.59 0.120 0.115 0.00542
Ben T Zobrist 1395 173 165.55 0.124 0.119 0.00534
Khalil Greene 3007 352 335.93 0.117 0.112 0.00534
Clint Barmes 3411 404 386.61 0.118 0.113 0.00510
Juan Castro 1743 205 197.03 0.118 0.113 0.00457
Jhonny Peralta 4086 533 516.35 0.130 0.126 0.00408
Rafael Furcal 4257 538 525.08 0.126 0.123 0.00304
Omar Vizquel 3974 441 430.32 0.111 0.108 0.00269
Carlos Guillen 3808 465 455.45 0.122 0.120 0.00251
Jack Wilson 3485 454 447.27 0.130 0.128 0.00193
Juan Uribe 3553 429 424.28 0.121 0.119 0.00133
John McDonald 2024 237 235.34 0.117 0.116 0.00082
Bobby Crosby 2595 307 304.87 0.118 0.117 0.00082
Alex Gonzalez 2991 350 347.62 0.117 0.116 0.00080
David Eckstein 3222 385 383.63 0.119 0.119 0.00043
Orlando Cabrera 3903 433 432.08 0.111 0.111 0.00024
Edgar Renteria 3958 446 445.44 0.113 0.113 0.00014
Michael Young 4307 536 536.41 0.124 0.125 -0.00009
Jimmy Rollins 4206 499 500.05 0.119 0.119 -0.00025
Ronny Cedeno 3258 398 400.25 0.122 0.123 -0.00069
Hanley Ramirez 4016 466 470.25 0.116 0.117 -0.00106
Alex Cora 1338 163 164.76 0.122 0.123 -0.00131
Geoff Blum 1168 149 150.75 0.128 0.129 -0.00150
Royce Clayton 3338 400 405.01 0.120 0.121 -0.00150
Jose Reyes 3887 443 451.38 0.114 0.116 -0.00215
Angel Berroa 3670 412 420.32 0.112 0.115 -0.00227
Miguel Tejada 4027 465 477.25 0.115 0.119 -0.00304
Derek Jeter 4009 450 464.37 0.112 0.116 -0.00358
Stephen Drew 1475 161 170.45 0.109 0.116 -0.00640
Marco Scutaro 1773 207 218.44 0.117 0.123 -0.00645
Felipe Lopez 4245 438 469.73 0.103 0.111 -0.00747
Aaron W Hill 1273 140 152.71 0.110 0.120 -0.00999

It's pretty clear that Everett deserved the Gold Glove at shortstop this season. And as long as Tejada and Jeter keep hitting, they'll stay at shortstop.

I'm sure I'll get an earful from Boston fans about Alex Gonzalez's ranking. He's pretty neutral. I suppose after watching Renteria boot ground balls for a season, Alex looked like Ozzie Smith. Renteria also improved with his move to Atlanta, coming in fairly neutral as well. Gonzalez will be an improvement over both Lopez and Clayton in Cincinnati, but the Reds still need to find some offense.

And just to avoid an argument, if you look at just ground balls, Gonzalez does better, but not a lot better. He makes 8 more outs than expected on ground balls, so he's -6 on other types of balls in play. Everett still comes out on top.

Posted by StatsGuru at 05:21 PM | Comments (7) | TrackBack (0)
November 20, 2006
Probabilistic Model of Range, First Basemen, 2006
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Here's how PMR ranks the first basemen:

Probabilistic Model of Range, First Basemen. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play.
Player In Play Actual Outs Predicted Outs DER Predicted DER Difference
Kendry Morales 1338 124 110.53 0.093 0.083 0.01007
Albert Pujols 3864 306 267.43 0.079 0.069 0.00998
Lance Niekro 1313 98 85.07 0.075 0.065 0.00984
Dan R Johnson 2199 163 150.02 0.074 0.068 0.00590
John Mabry 1031 94 88.09 0.091 0.085 0.00574
Mark Sweeney 1227 99 92.32 0.081 0.075 0.00544
Derrek Lee 1104 85 80.13 0.077 0.073 0.00441
Lyle Overbay 3738 307 290.83 0.082 0.078 0.00433
Ben Broussard 2097 148 140.01 0.071 0.067 0.00381
Kevin E Youkilis 3123 233 221.36 0.075 0.071 0.00373
Andy A Phillips 1635 109 103.14 0.067 0.063 0.00358
Chris B Shelton 2737 179 169.62 0.065 0.062 0.00343
Nick Johnson 4014 319 305.76 0.079 0.076 0.00330
Adrian Gonzalez 4031 306 295.48 0.076 0.073 0.00261
Doug Mientkiewicz 2350 159 153.11 0.068 0.065 0.00251
Shea Hillenbrand 1858 141 136.44 0.076 0.073 0.00246
Scott Hatteberg 3415 220 212.94 0.064 0.062 0.00207
Howie Kendrick 1017 73 70.97 0.072 0.070 0.00200
Jeff Conine 1400 91 88.78 0.065 0.063 0.00158
Justin Morneau 4046 266 260.21 0.066 0.064 0.00143
Lance Berkman 2722 198 194.25 0.073 0.071 0.00138
Nomar Garciaparra 3199 194 189.86 0.061 0.059 0.00130
Rich Aurilia 1024 70 68.82 0.068 0.067 0.00115
Mark Teixeira 4436 310 305.13 0.070 0.069 0.00110
Nick T Swisher 2214 153 151.32 0.069 0.068 0.00076
Ryan N Shealy 1473 81 79.94 0.055 0.054 0.00072
Travis Lee 2794 218 216.62 0.078 0.078 0.00049
Prince G Fielder 3989 269 269.23 0.067 0.067 -0.00006
Mike Lamb 1488 98 98.33 0.066 0.066 -0.00022
Kevin Millar 2478 158 158.90 0.064 0.064 -0.00036
Adam LaRoche 3633 262 266.94 0.072 0.073 -0.00136
Paul Konerko 3679 215 220.18 0.058 0.060 -0.00141
Richie Sexson 4023 291 297.32 0.072 0.074 -0.00157
Carlos Delgado 3696 253 259.44 0.068 0.070 -0.00174
Todd Helton 4025 270 279.58 0.067 0.069 -0.00238
Ryan F Garko 1291 76 79.09 0.059 0.061 -0.00239
Craig A Wilson 1819 93 97.64 0.051 0.054 -0.00255
Wes Helms 1305 79 82.46 0.061 0.063 -0.00265
Ty Wigginton 1008 66 69.91 0.065 0.069 -0.00388
Robb Quinlan 1151 67 72.13 0.058 0.063 -0.00446
Ryan J Howard 4301 275 302.16 0.064 0.070 -0.00631
Mike Jacobs 2949 191 212.24 0.065 0.072 -0.00720
Conor S Jackson 3295 231 254.95 0.070 0.077 -0.00727
Sean Casey 2806 168 191.82 0.060 0.068 -0.00849
Jason Giambi 1467 71 88.40 0.048 0.060 -0.01186

Albert Pujols put together an amazing defensive season. He turned 40 more balls into outs than expected. Contrast that with Beltran, who converted 18 more balls into outs than expected. It's impressive when a good first baseman can beat a good centerfielder is turning balls into outs. The next closest regular was Overbay, who converted 26 more batted balls into outs than expected.

At the other end of the spectrum, to no one's surprise is Jason Giambi. But also down there is Ryan Howard, who picked up 27 fewer outs than expected. That's a huge difference in fielding ability, over two games worth of outs. It's another reason to vote for Pujols over Howard for MVP. I don't know if there are many surprises on this list, but Tiger fans can get into a nice argument over Sean Casey's offense vs. Chris Shelton's defense.

Posted by StatsGuru at 08:17 PM | Comments (17) | TrackBack (0)
Probabilistic Model of Range, Centerfielders, 2006
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Let's take a look at how PMR rates the centerfielders this year:

Probabilistic Model of Range, Centerfielders. Model is Based on 2006 Data Only. Minimum 1000 Balls in Play.
Player In Play Actual Outs Predicted Outs DER Predicted DER Difference
Ichiro Suzuki 1017 114 106.04 0.112 0.104 0.00782
Ryan Freel 1211 127 119.69 0.105 0.099 0.00603
Shane Victorino 1691 161 151.18 0.095 0.089 0.00581
Carlos Beltran 3517 357 338.76 0.102 0.096 0.00519
Alfredo Amezaga 1580 155 146.95 0.098 0.093 0.00509
Coco Crisp 2814 246 232.39 0.087 0.083 0.00484
Corey Patterson 3360 345 329.33 0.103 0.098 0.00466
Joey R Gathright 3272 341 325.89 0.104 0.100 0.00462
Aaron Rowand 2742 251 238.64 0.092 0.087 0.00451
Johnny Damon 3378 306 294.10 0.091 0.087 0.00352
Rocco Baldelli 2368 228 219.80 0.096 0.093 0.00346
Randy Winn 1366 137 132.45 0.100 0.097 0.00333
Jim Edmonds 2471 223 215.35 0.090 0.087 0.00309
Brady Clark 2748 250 241.95 0.091 0.088 0.00293
Willy Taveras 3304 335 325.37 0.101 0.098 0.00292
Reggie D Abercrombie 1833 172 168.04 0.094 0.092 0.00216
Mike Cameron 3723 367 360.50 0.099 0.097 0.00174
Brian N Anderson 2996 305 300.58 0.102 0.100 0.00148
Steve Finley 3013 287 283.68 0.095 0.094 0.00110
Juan Pierre 4103 380 375.88 0.093 0.092 0.00101
Curtis Granderson 4014 385 381.35 0.096 0.095 0.00091
Vernon Wells 3918 332 330.00 0.085 0.084 0.00051
Eric Byrnes 3208 270 268.41 0.084 0.084 0.00050
Andruw Jones 4109 377 375.19 0.092 0.091 0.00044
Choo Freeman 1021 101 100.81 0.099 0.099 0.00018
Chris Duffy 2053 166 165.87 0.081 0.081 0.00006
So Taguchi 1095 90 89.97 0.082 0.082 0.00003
Marlon Byrd 1272 125 125.07 0.098 0.098 -0.00006
Gary Matthews Jr. 3909 333 334.90 0.085 0.086 -0.00049
Chone Figgins 2455 242 243.74 0.099 0.099 -0.00071
Torii Hunter 3715 343 347.24 0.092 0.093 -0.00114
Nate McLouth 1072 84 86.24 0.078 0.080 -0.00209
David DeJesus 1561 149 153.04 0.095 0.098 -0.00258
Mark Kotsay 3261 281 294.51 0.086 0.090 -0.00414
Cory Sullivan 2666 225 236.48 0.084 0.089 -0.00430
Grady Sizemore 4455 409 431.13 0.092 0.097 -0.00497
Ryan M Church 1172 122 128.48 0.104 0.110 -0.00553
Rob Mackowiak 1415 119 127.40 0.084 0.090 -0.00594
Kenny Lofton 2999 241 259.05 0.080 0.086 -0.00602
Jose A Bautista 1323 114 122.09 0.086 0.092 -0.00612
Jay Payton 1196 104 111.90 0.087 0.094 -0.00661
Ken Griffey Jr. 2753 229 256.68 0.083 0.093 -0.01006
Jeremy T Reed 1535 129 146.35 0.084 0.095 -0.01130

The first thing I notice is that Ken Griffey shouldn't be in centerfield any more. Kenny Lofton outlived his usefulness as well at the position. On the other hand, Crisp did provide the Red Sox with good defense in center, better than Johnny Damon. Damon, however, did improve the Yankees as Bernie Williams was a big negative there in 2005.

Somewhat surprising is the neutrallity of Gary Matthews Jr. He actually made two fewer outs than expected. It's the problem of the spectacular catch. That's what we remember, that's what we see on the highlight reels, so we assume he's a great fielder. Sometimes, however, to make those plays you need to play deep, and singles fall in front of you. Can any Texas fans comment on how deep Matthews plays?

The Phillies looked very good at the position with both Victorino and Rowand. And Ichiro did a great job subbing in center this season. And among the regulars, Beltran clearly deserved his gold glove.

Posted by StatsGuru at 07:55 PM | Comments (10) | TrackBack (0)
November 19, 2006
Probabilistic Model of Range, Leftfielders, 2006
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With Soriano looking like he's headed to the Cubs, I thought I'd start off the positions with left fielders.

Probabilistic Model of Range, Leftfielders. Model is Based on 2006 Data Only.
Player InPlay Actual Outs Predicted Outs DER Predicted DER Difference
Brandon W Fahey 1164 101 91.44 0.087 0.079 0.00821
Matt Diaz 1798 163 150.04 0.091 0.083 0.00721
Reed Johnson 1915 129 116.43 0.067 0.061 0.00656
Melky Cabrera 3063 217 199.05 0.071 0.065 0.00586
So Taguchi 1141 87 81.23 0.076 0.071 0.00506
Dave Roberts 2887 239 226.53 0.083 0.078 0.00432
Emil Brown 2359 163 154.92 0.069 0.066 0.00342
Matt Murton 3026 240 229.83 0.079 0.076 0.00336
Ryan Langerhans 2240 156 148.71 0.070 0.066 0.00325
Juan Rivera 1440 126 122.50 0.087 0.085 0.00243
Andre E Ethier 2779 172 165.56 0.062 0.060 0.00232
David DeJesus 1736 138 134.02 0.079 0.077 0.00229
Frank Catalanotto 2308 140 135.18 0.061 0.059 0.00209
Cliff Floyd 2280 148 143.78 0.065 0.063 0.00185
Jason Michaels 3283 214 208.61 0.065 0.064 0.00164
Alfonso Soriano 4405 326 318.93 0.074 0.072 0.00161
Jason Bay 4269 316 309.87 0.074 0.073 0.00143
Marcus Thames 1193 70 68.93 0.059 0.058 0.00090
Garret Anderson 2377 192 190.14 0.081 0.080 0.00078
Kevin Mench 1217 80 79.09 0.066 0.065 0.00075
Barry Bonds 2708 188 187.05 0.069 0.069 0.00035
Jay Payton 1442 119 118.54 0.083 0.082 0.00032
Luke B Scott 1188 81 81.19 0.068 0.068 -0.00016
Brad Wilkerson 2106 139 139.49 0.066 0.066 -0.00023
Jeff Conine 1436 88 88.42 0.061 0.062 -0.00029
Luis Gonzalez 4063 256 257.40 0.063 0.063 -0.00035
Carl Crawford 4006 302 304.14 0.075 0.076 -0.00053
Josh D Willingham 3255 206 209.14 0.063 0.064 -0.00096
Nick T Swisher 2035 170 172.81 0.084 0.085 -0.00138
Craig Monroe 2909 168 172.45 0.058 0.059 -0.00153
Preston Wilson 2639 156 160.40 0.059 0.061 -0.00167
Matt T Holliday 4234 277 285.50 0.065 0.067 -0.00201
Adam Dunn 4132 279 287.54 0.068 0.070 -0.00207
Scott Podsednik 3417 245 255.60 0.072 0.075 -0.00310
Pat Burrell 2990 205 214.40 0.069 0.072 -0.00314
Raul Ibanez 4289 302 315.64 0.070 0.074 -0.00318
Carlos Lee 3883 227 243.70 0.058 0.063 -0.00430
Chris E Duncan 1015 66 71.63 0.065 0.071 -0.00554
Bobby Kielty 1030 80 88.63 0.078 0.086 -0.00838
Manny Ramirez 3151 175 201.96 0.056 0.064 -0.00856

As you can see, Melky Cabrera did a lot for the Yankees defense in 2006. I'd hope New York would try to find a spot for him defensively next season. I've heard talk of Torre wanting to play Melky everyday, and using the DH spot to rest the other three. That strikes me as a smart move.

Soriano did just fine in left. He wasn't outstanding, but he wasn't a joke, either. He actually picked up about 8 more outs than expected. He was probably better in left than he would have been at second. However, Matt Murton is better. There's some talk of playing Sorian in center.

One surprise to me is Barry Bonds. With his reconstructed knees, I expected Bonds to be at the bottom of the rankings. But he about as average as you'd like. The model predicted he'd turn 187 balls into outs, and he got to 188. This actually confirms what I saw last season. He moved surprising well in left field.

Much better than Manny Ramirez. I could see where the Red Sox might be better off defensively playing Ortiz at first, letting Manny DH and finding someone else to play left (move Crisp there, and sign Drew to play center?).

Posted by StatsGuru at 04:20 PM | Comments (7) | TrackBack (0)
Explaining the Model
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Here's a video explanation of the Probabilistic Model of range for your enjoyment.

Posted by StatsGuru at 03:22 PM | Comments (2) | TrackBack (0)
November 18, 2006
Ground and Air Defense
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These tables are replacements for the ones found in this post. They use data just for the 2006 season (see explanation here). So let's look at how teams did fielding balls on the ground:

Probabilistic Model of Range, 2006, Team Ground Balls Only (ground balls and bunt grounders). Based on 2006 data only.
Team InPlay Actual Outs Predicted Outs DER Predicted DER Difference
Tigers 2115 1632 1582.78 0.772 0.748 0.02327
Cardinals 2204 1670 1622.68 0.758 0.736 0.02147
Astros 2100 1634 1595.39 0.778 0.760 0.01839
Twins 2004 1483 1449.98 0.740 0.724 0.01648
Royals 2083 1502 1469.91 0.721 0.706 0.01540
White Sox 2059 1508 1479.83 0.732 0.719 0.01368
Dodgers 2288 1671 1643.93 0.730 0.719 0.01183
Brewers 1958 1437 1416.97 0.734 0.724 0.01023
Mariners 2009 1492 1472.06 0.743 0.733 0.00992
Giants 1972 1490 1470.98 0.756 0.746 0.00964
Padres 1951 1492 1477.20 0.765 0.757 0.00759
Mets 2001 1519 1503.83 0.759 0.752 0.00758
Blue Jays 2123 1585 1570.90 0.747 0.740 0.00664
Rangers 2206 1622 1611.37 0.735 0.730 0.00482
Yankees 2046 1495 1486.00 0.731 0.726 0.00440
Angels 1922 1389 1380.87 0.723 0.718 0.00423
Rockies 2211 1675 1665.86 0.758 0.753 0.00413
Cubs 1879 1394 1386.91 0.742 0.738 0.00377
Athletics 2026 1494 1487.15 0.737 0.734 0.00338
Phillies 2085 1535 1528.07 0.736 0.733 0.00332
Diamondbacks 2223 1651 1644.98 0.743 0.740 0.00271
Red Sox 2062 1527 1526.09 0.741 0.740 0.00044
Braves 2152 1562 1572.09 0.726 0.731 -0.00469
Pirates 2187 1607 1620.88 0.735 0.741 -0.00635
Orioles 2020 1443 1459.35 0.714 0.722 -0.00809
Marlins 1984 1443 1459.41 0.727 0.736 -0.00827
Devil Rays 2045 1428 1446.97 0.698 0.708 -0.00928
Reds 2036 1473 1498.94 0.723 0.736 -0.01274
Indians 2160 1514 1555.64 0.701 0.720 -0.01928
Nationals 1974 1415 1458.48 0.717 0.739 -0.02202

Interesting that the two teams with the best infield defense made it to the World Series. The Royals did a great job of supporting their pitchers, which gives you an idea of just what a poor staff Kansas City sent to the mound. Somewhat shocking is that the Red Sox rank fairly low on this list, turning ground balls into outs as expected.

Here's the data for balls in the air, flys, liners and bunt pop ups.

Probabilistic Model of Range, 2006, Team Air Balls Only (Fly balls, line drives, and bunt pops). Based on 2006 data only.
Team InPlay Actual Outs Predicted Outs DER Predicted DER Difference
Cubs 2273 1509 1478.09 0.664 0.650 0.01360
Blue Jays 2203 1409 1380.54 0.640 0.627 0.01292
Yankees 2426 1608 1579.28 0.663 0.651 0.01184
Braves 2338 1516 1488.60 0.648 0.637 0.01172
Mets 2309 1509 1483.29 0.654 0.642 0.01113
Angels 2379 1581 1559.46 0.665 0.656 0.00905
Indians 2434 1585 1566.38 0.651 0.644 0.00765
Orioles 2415 1570 1552.45 0.650 0.643 0.00727
Giants 2450 1608 1591.32 0.656 0.650 0.00681
Nationals 2620 1758 1744.91 0.671 0.666 0.00499
Diamondbacks 2239 1398 1388.48 0.624 0.620 0.00425
Padres 2435 1624 1616.00 0.667 0.664 0.00328
Brewers 2342 1513 1505.78 0.646 0.643 0.00308
Phillies 2353 1486 1481.20 0.632 0.629 0.00204
Mariners 2422 1562 1557.41 0.645 0.643 0.00190
Cardinals 2244 1426 1422.54 0.635 0.634 0.00154
White Sox 2469 1630 1626.28 0.660 0.659 0.00151
Marlins 2355 1528 1525.93 0.649 0.648 0.00088
Dodgers 2248 1413 1413.75 0.629 0.629 -0.00033
Rangers 2336 1462 1464.32 0.626 0.627 -0.00099
Royals 2535 1618 1623.30 0.638 0.640 -0.00209
Tigers 2324 1480 1486.59 0.637 0.640 -0.00283
Reds 2491 1608 1615.54 0.646 0.649 -0.00303
Red Sox 2401 1501 1515.56 0.625 0.631 -0.00607
Devil Rays 2500 1620 1638.24 0.648 0.655 -0.00730
Twins 2324 1484 1502.31 0.639 0.646 -0.00788
Athletics 2504 1626 1646.41 0.649 0.658 -0.00815
Rockies 2379 1454 1474.13 0.611 0.620 -0.00846
Pirates 2261 1390 1413.40 0.615 0.625 -0.01035
Astros 2242 1405 1429.51 0.627 0.638 -0.01093

Given the Giants age and Barry Bonds' knees, I'm fairly amazed at how high San Francisco ranks on fly balls. It's also clear from the two tables that Yankees pitchers are better off with a ball in the air than on the ground. The Astros are probably the most extreme team on the two lists. They rank third in balls on the ground, dead last in fly balls.

Posted by StatsGuru at 11:40 AM | Comments (5) | TrackBack (0)
November 17, 2006
Probabilistic Model of Range, 2006
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The other day I published the first of the Probabilistic Model of Range tables, looking at overall team play. However, since doing that I noticed something didn't add up. When I looked at individual fielders, I was getting very strange results. It turns out that Baseball Info Solutions made a change to the scoring system this year designed to improve the accuracy of locating balls in play. They increased the size of the graphic they use to capture the data.

This has the nice effect of allowing the reporter to be more precise in marking where the ball landed or was caught. However, the data is somewhat different than the data from previous years, and this was causing my models to exhibit strange behavior.

After spending a day studying the data, I've concluded that indeed, the 2006 data is more accurate. So in order to avoid the pitfalls of mixing the old and new data, I'm going to use just the 2006 data to figure PMR. At some point I may revisit the older data and try to find a way to translate it into this model. But for now, please ignore the previous post.

Probabilistic Model of Range, 2006. Model Includes Parks, Smoothed Visiting Team Fielding. Based on 2006 Season Only.
Team InPlay Actual Outs Predicted Outs DER Predicted DER Difference
Cardinals 4448 3096 3045.22 0.696 0.685 0.01142
Blue Jays 4326 2994 2951.45 0.692 0.682 0.00984
Tigers 4439 3112 3069.37 0.701 0.691 0.00960
Mets 4310 3028 2987.11 0.703 0.693 0.00949
Cubs 4152 2903 2865.00 0.699 0.690 0.00915
Yankees 4472 3103 3065.29 0.694 0.685 0.00843
Giants 4422 3098 3062.31 0.701 0.693 0.00807
White Sox 4528 3138 3106.11 0.693 0.686 0.00704
Angels 4301 2970 2940.33 0.691 0.684 0.00690
Brewers 4300 2950 2922.74 0.686 0.680 0.00634
Dodgers 4536 3084 3057.68 0.680 0.674 0.00580
Royals 4618 3120 3093.21 0.676 0.670 0.00580
Mariners 4431 3054 3029.47 0.689 0.684 0.00554
Padres 4386 3116 3093.20 0.710 0.705 0.00520
Braves 4490 3078 3060.69 0.686 0.682 0.00386
Diamondbacks 4462 3049 3033.47 0.683 0.680 0.00348
Twins 4328 2967 2952.29 0.686 0.682 0.00340
Astros 4342 3039 3024.90 0.700 0.697 0.00325
Phillies 4438 3021 3009.27 0.681 0.678 0.00264
Rangers 4542 3084 3075.69 0.679 0.677 0.00183
Orioles 4435 3013 3011.80 0.679 0.679 0.00027
Rockies 4590 3129 3139.99 0.682 0.684 -0.00239
Athletics 4530 3120 3133.56 0.689 0.692 -0.00299
Red Sox 4463 3028 3041.66 0.678 0.682 -0.00306
Marlins 4339 2971 2985.34 0.685 0.688 -0.00331
Indians 4594 3099 3122.02 0.675 0.680 -0.00501
Nationals 4594 3173 3203.39 0.691 0.697 -0.00662
Reds 4527 3081 3114.48 0.681 0.688 -0.00740
Devil Rays 4545 3048 3085.21 0.671 0.679 -0.00819
Pirates 4448 2997 3034.28 0.674 0.682 -0.00838

There are more changes at the top than at the bottom. The Cardinals rise to number one. The Royals drop to number 12. Still the Royals defense is better than many thought. Their predicted DER was the worst in the majors, meaning the pitching staff was not making it easy on the defense. The Dodgers also do much better under this system, going from a negative to a positive.

The Pirates replace the Nationals as the worst fielding team, with the Devil Rays in the penultimate slot. I guess Tampa can't do anything well, hit, pitch or field. More to come this weekend.

Posted by StatsGuru at 08:04 PM | Comments (8) | TrackBack (0)
November 15, 2006
In the Air and On the Ground
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Update: Please ignore this post. See here.

To follow up on yesterday's team probabilistic model of range post, I wanted to see how teams did with balls on the ground and balls in the air. We'll start with the ground balls.

Probabilistic Model of Range, 2006, Team Ground Balls Only (ground balls and bunt grounders)
Team InPlay Actual Outs Predicted Outs DER Predicted DER Difference
Astros 2100 1634 1565.36 0.778 0.745 0.03269
Tigers 2115 1632 1573.02 0.772 0.744 0.02789
Royals 2083 1502 1446.85 0.721 0.695 0.02648
Mariners 2009 1492 1440.90 0.743 0.717 0.02544
Blue Jays 2123 1585 1534.63 0.747 0.723 0.02373
Cardinals 2204 1670 1618.52 0.758 0.734 0.02336
Twins 2004 1483 1442.90 0.740 0.720 0.02001
Padres 1951 1492 1459.41 0.765 0.748 0.01670
Red Sox 2062 1527 1495.15 0.741 0.725 0.01544
Yankees 2046 1495 1463.92 0.731 0.716 0.01519
Mets 2001 1519 1489.78 0.759 0.745 0.01460
Giants 1972 1490 1463.01 0.756 0.742 0.01369
White Sox 2059 1508 1484.16 0.732 0.721 0.01158
Rockies 2211 1675 1656.12 0.758 0.749 0.00854
Rangers 2206 1622 1604.17 0.735 0.727 0.00808
Athletics 2026 1494 1480.91 0.737 0.731 0.00646
Brewers 1958 1437 1426.49 0.734 0.729 0.00537
Diamondbacks 2223 1651 1641.68 0.743 0.738 0.00419
Phillies 2085 1535 1526.82 0.736 0.732 0.00393
Cubs 1879 1394 1389.39 0.742 0.739 0.00245
Angels 1922 1389 1384.71 0.723 0.720 0.00223
Devil Rays 2045 1428 1424.81 0.698 0.697 0.00156
Dodgers 2288 1671 1668.07 0.730 0.729 0.00128
Pirates 2187 1607 1609.11 0.735 0.736 -0.00096
Orioles 2020 1443 1448.28 0.714 0.717 -0.00261
Braves 2152 1562 1578.07 0.726 0.733 -0.00747
Marlins 1984 1443 1460.09 0.727 0.736 -0.00861
Reds 2036 1473 1501.86 0.723 0.738 -0.01418
Indians 2160 1514 1550.01 0.701 0.718 -0.01667
Nationals 1974 1415 1465.44 0.717 0.742 -0.02555

I'm not surprised that the Astros, with Everett at shortstop, were the best in the majors.
As you can see, the Red Sox infield did a pretty good job of turning balls in play into outs, especially considering that only the Devil Rays were given tougher balls to field. Let's look at flyballs now:

Probabilistic Model of Range, 2006, Team Air Balls Only (Fly balls, line drives, and bunt pops)
Team InPlay Actual Outs Predicted Outs DER Predicted DER Difference
Braves 2338 1516 1483.12 0.648 0.634 0.01407
Blue Jays 2203 1409 1380.22 0.640 0.627 0.01306
Yankees 2426 1608 1577.01 0.663 0.650 0.01278
Cubs 2273 1509 1485.08 0.664 0.653 0.01052
Mets 2309 1509 1487.02 0.654 0.644 0.00952
Angels 2379 1581 1562.76 0.665 0.657 0.00767
Brewers 2342 1513 1496.99 0.646 0.639 0.00684
Giants 2450 1608 1592.20 0.656 0.650 0.00645
Indians 2434 1585 1569.49 0.651 0.645 0.00637
Diamondbacks 2239 1398 1384.16 0.624 0.618 0.00618
Royals 2535 1618 1604.08 0.638 0.633 0.00549
Padres 2435 1624 1613.70 0.667 0.663 0.00423
Nationals 2620 1758 1755.09 0.671 0.670 0.00111
Reds 2491 1608 1605.91 0.646 0.645 0.00084
Cardinals 2244 1426 1426.20 0.635 0.636 -0.00009
Marlins 2355 1528 1528.87 0.649 0.649 -0.00037
Mariners 2422 1562 1563.02 0.645 0.645 -0.00042
White Sox 2469 1630 1632.33 0.660 0.661 -0.00095
Orioles 2415 1570 1573.97 0.650 0.652 -0.00165
Red Sox 2401 1501 1505.14 0.625 0.627 -0.00173
Tigers 2324 1480 1489.34 0.637 0.641 -0.00402
Rockies 2379 1454 1463.81 0.611 0.615 -0.00412
Devil Rays 2500 1620 1633.87 0.648 0.654 -0.00555
Phillies 2353 1486 1500.10 0.632 0.638 -0.00599
Twins 2324 1484 1500.26 0.639 0.646 -0.00700
Rangers 2336 1462 1480.25 0.626 0.634 -0.00781
Dodgers 2248 1413 1431.70 0.629 0.637 -0.00832
Athletics 2504 1626 1653.48 0.649 0.660 -0.01097
Astros 2242 1405 1437.28 0.627 0.641 -0.01440
Pirates 2261 1390 1427.93 0.615 0.632 -0.01678

I'm amazed that the Giants rank as high as they do, given the age of their team. But you can also see that the Yankees benefitted from trying to improve their outfield defense. When we run individual numbers, we'll see just who contributed to this performance. The other thing that strikes me about this data is just how tough it is to cover ground in the higher elevations. Look at the expected DER for the Rockies and Diamondbacks. Each had a tough set of balls to catch, and the Diamondbacks did a much better job getting to them.

Correction: Fixed caption on the second table.

Posted by StatsGuru at 04:59 PM | Comments (4) | TrackBack (0)
November 14, 2006
Probabilistic Model of Range, 2006
Permalink

Update: Please ignore this post. See here.

It's time to start the yearly look at defense using the Probabilistic Model of Range. My explanation of this model is here. I've settled on the smoothed visiting model this season. As always, let's start off with the teams.

Probabilistic Model of Range, 2006. Model Includes Parks, Smoothed Visiting Team Fielding
Team InPlay Actual Outs Predicted Outs DER Predicted DER Difference
Blue Jays 4326 2994 2914.85 0.692 0.674 0.01830
Royals 4618 3120 3050.93 0.676 0.661 0.01496
Yankees 4472 3103 3040.92 0.694 0.680 0.01388
Mets 4310 3028 2976.80 0.703 0.691 0.01188
Cardinals 4448 3096 3044.72 0.696 0.685 0.01153
Mariners 4431 3054 3003.92 0.689 0.678 0.01130
Tigers 4439 3112 3062.36 0.701 0.690 0.01118
Padres 4386 3116 3073.11 0.710 0.701 0.00978
Giants 4422 3098 3055.20 0.701 0.691 0.00968
Astros 4342 3039 3002.64 0.700 0.692 0.00837
Cubs 4152 2903 2874.47 0.699 0.692 0.00687
Red Sox 4463 3028 3000.30 0.678 0.672 0.00621
Brewers 4300 2950 2923.48 0.686 0.680 0.00617
Twins 4328 2967 2943.16 0.686 0.680 0.00551
Angels 4301 2970 2947.46 0.691 0.685 0.00524
Diamondbacks 4462 3049 3025.84 0.683 0.678 0.00519
White Sox 4528 3138 3116.50 0.693 0.688 0.00475
Braves 4490 3078 3061.18 0.686 0.682 0.00375
Rockies 4590 3129 3119.94 0.682 0.680 0.00197
Rangers 4542 3084 3084.42 0.679 0.679 -0.00009
Phillies 4438 3021 3026.91 0.681 0.682 -0.00133
Orioles 4435 3013 3022.25 0.679 0.681 -0.00209
Devil Rays 4545 3048 3058.68 0.671 0.673 -0.00235
Athletics 4530 3120 3134.39 0.689 0.692 -0.00318
Dodgers 4536 3084 3099.77 0.680 0.683 -0.00348
Marlins 4339 2971 2988.96 0.685 0.689 -0.00414
Indians 4594 3099 3119.50 0.675 0.679 -0.00446
Reds 4527 3081 3107.77 0.681 0.686 -0.00591
Pirates 4448 2997 3037.04 0.674 0.683 -0.00900
Nationals 4594 3173 3220.53 0.691 0.701 -0.01035

I've been told that Ricciardi isn't overly concerned with defense, but the Blue Jays led the pack in 2006, turning 90 more balls into outs than expected. And that with the loss of Hudson and Troy Glaus at third base.

It looks like having Johnny Damon and Melky Cabrera in the outfield helped. And despite A-Rod error prone ways at third, the Yankees did a very good job of turning balls into outs in 2006. And although the Royals defense came in for much criticism this year, it looks like it was much more the fault of the pitching staff. They Royas fielders did a fine job with what they were given.

A big surprise on the downside was the Cleveland Indians. The Tribe ranked near the top in 2005 as a team, but fell to near the bottom in 2006 with pretty much the same team. They gave up nearly three games worth of outs more than expected.

You can also see just how much the Red Sox defense helped them. Only Kansas City's DER was predicted to be lower.

The top three National League teams each won a division. The Dodgers and Athletics each made the post-season giving away more outs that expected.

There will be a lot more to come in the following days and weeks.

Update: I neglected to mention that the model is based on the 2002-2006 seasons.

Posted by StatsGuru at 10:25 PM | Comments (10) | TrackBack (0)
March 01, 2006
PMR at Sports Illustrated
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I didn't see an e-mail address on his page, so here's a public thanks to Jason Luft for mentioning the Probabilistic Model of Range in his Quick Fixes column.

Baseball Musings is conducting a pledge drive in March. Click here for details.

Posted by StatsGuru at 02:32 PM | Comments (1) | TrackBack (0)
February 28, 2006
Probabilistic Model of Range, 2005, Runs Created Against First Basemen
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Here's how the first basemen do at preventing runs on balls in play:

Probabilistic Model of Range, 2005, Runs Created Against First Basemen, Original Model (minimum 200 fieldable balls in play)
PlayerFieldable Balls In PlayActual Outs by FielderPredicted Outs by FielderRCAPredicted RCARCA/27 OutsPredicted RCA/27Runs Saved/27 Outs
Jose Hernandez240 81 65.83 11.35 19.00 3.78 7.79 4.010
John Olerud311 109 92.16 13.58 23.70 3.36 6.94 3.580
Chad A Tracy472 159 134.45 29.28 39.32 4.97 7.90 2.924
Ben Broussard711 223 185.89 37.53 49.87 4.54 7.24 2.698
Paul Konerko985 294 261.08 51.80 69.55 4.76 7.19 2.435
Doug Mientkiewicz522 158 134.64 26.84 34.01 4.59 6.82 2.235
Darin Erstad972 313 272.80 38.36 54.52 3.31 5.40 2.087
Ryan J Howard474 155 144.41 25.81 35.01 4.50 6.54 2.049
Kevin Millar681 223 207.71 36.54 48.66 4.42 6.32 1.900
Daryle Ward608 177 157.24 31.45 37.34 4.80 6.41 1.613
Derrek Lee1029 298 270.98 52.82 62.70 4.79 6.25 1.462
Justin Morneau922 263 256.38 43.46 56.16 4.46 5.91 1.453
Mike Lamb306 87 82.25 17.93 21.22 5.56 6.96 1.400
Nick Johnson901 294 282.15 35.75 47.62 3.28 4.56 1.273
Mark Teixeira1121 350 309.40 71.76 77.88 5.54 6.80 1.261
Shea Hillenbrand445 133 133.27 22.22 28.44 4.51 5.76 1.250
Travis Lee708 245 214.15 42.10 46.28 4.64 5.83 1.196
Matt Stairs434 115 110.00 32.99 35.98 7.75 8.83 1.086
Albert Pujols1002 329 302.94 57.80 64.84 4.74 5.78 1.035
Lyle Overbay917 273 252.84 47.00 52.53 4.65 5.61 0.961
Chris B Shelton535 156 150.18 32.11 35.93 5.56 6.46 0.903
Dan R Johnson641 193 193.02 33.89 40.28 4.74 5.64 0.894
Lance Niekro394 136 120.38 19.06 20.53 3.78 4.61 0.821
Tony Clark464 130 128.20 28.51 31.88 5.92 6.71 0.792
Todd Helton997 301 286.52 69.91 74.27 6.27 7.00 0.728
Scott Hatteberg344 103 101.56 23.57 25.73 6.18 6.84 0.663
Tino Martinez575 181 144.70 36.71 32.82 5.48 6.12 0.648
Mike Sweeney372 112 112.99 19.94 22.53 4.81 5.38 0.577
Hee Seop Choi545 170 157.74 33.37 34.20 5.30 5.85 0.555
Eric Hinske681 198 204.16 31.63 36.58 4.31 4.84 0.524
Julio Franco366 85 92.45 20.99 24.17 6.67 7.06 0.394
Rafael Palmeiro553 146 157.18 30.40 34.52 5.62 5.93 0.309
Richie Sexson1067 276 275.00 58.96 60.54 5.77 5.94 0.176
Lance Berkman531 146 155.13 32.57 35.05 6.02 6.10 0.076
Brad Eldred256 65 67.08 16.99 17.66 7.06 7.11 0.050
Mark Sweeney249 81 76.91 12.64 12.08 4.21 4.24 0.029
J.T. Snow639 197 193.74 46.25 42.14 6.34 5.87 -0.466
Adam LaRoche875 249 251.10 65.64 61.64 7.12 6.63 -0.490
Sean Casey875 258 256.57 48.86 43.15 5.11 4.54 -0.573
Eduardo Perez227 70 65.09 15.90 13.06 6.13 5.42 -0.714
Carlos Delgado888 263 269.56 65.40 58.01 6.71 5.81 -0.903
Jim Thome306 78 83.76 19.99 18.60 6.92 5.99 -0.927
Olmedo Saenz379 81 94.72 28.06 28.60 9.35 8.15 -1.203
Carlos Pena329 100 108.13 19.68 15.43 5.31 3.85 -1.460
Phil Nevin457 142 144.26 39.96 31.00 7.60 5.80 -1.795
Jason Giambi430 98 110.00 32.18 24.67 8.87 6.06 -2.809

Kevin Millar is not known for his glove, but he does fine in this analysis. From watching him last year, I remember a few times he ranged to his right for balls. Maybe all that time with Olerud rubbed off on him.

And please notice that as bad as Jason Giambi plays the position, he only cost the Yankees eight runs with his glove. That's less than a game.

Posted by StatsGuru at 07:45 PM | Comments (3) | TrackBack (0)
Q and Defense
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Keith Isley posts a very interesting discussion at The Hardball Times on using the Q methedology to measure defense. One of his conclusions is that Zone Rating better agrees with subjective defensive ratings than the Probabilistic Model of Range.

We could then sanity check the defensive metric by testing for correlation with the Tools and Skills factor scores. Zone Rating, in fact, does correlate significantly (P<.01) with my Skills factor. This agrees with Tom Tippett’s assessment that ZR places more emphasis on soft hands (Skill) than range (Tool) because the system basically counts only balls that a player is expected to reach, as I understand it.

Extending or improving ZR, it seems, requires better capture of range and throwing performance without compromising ZR’s skillful measure of Skills ability. One such system that I tested—the PMR family—does not seem to have achieved this goal yet. PMR’s shortstop ratings are uncorrelated (that is, basically random) with either Tools or Skills. At the risk of being redundant, if the Two Dimensional Model of Fielding Ability is a reasonable approximation of reality, then metrics that inadequately measure either tools or skills will tend to be unpredictable and fail to converge into general agreement with other metrics. Analytically and anecdotally, Zone Rating does seem to capture certain aspects of Skill.

Something new to explore. PMR should be an improvement on zone ratings, since the zones are created by the fielders ability, rather than some set piece of property. Time to engage the Q.

Posted by StatsGuru at 10:19 AM | Comments (1) | TrackBack (0)
February 25, 2006
Probabilistic Model of Range, 2005, Runs Created Against Third Basemen
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Here's the runs saved data for the third basemen:

Probabilistic Model of Range, 2005, Runs Created Against Third Basemen, Original Model (minimum 300 fieldable balls in play)
PlayerFieldable Balls In PlayActual Outs by FielderPredicted Outs by FielderRCAPredicted RCARCA/27 OutsPredicted RCA/27Runs Saved/27 Outs
Wilson Betemit373 123 102.29 15.55 27.93 3.41 7.37 3.958
Freddy Sanchez446 180 148.78 21.96 36.21 3.29 6.57 3.278
Geoff Blum301 118 97.38 15.14 23.50 3.47 6.51 3.050
Chipper Jones695 235 212.40 41.04 60.57 4.72 7.70 2.984
Corey Koskie572 214 179.63 34.69 47.40 4.38 7.12 2.748
Pedro Feliz529 191 155.23 28.34 36.86 4.01 6.41 2.406
Bill Hall347 121 115.83 20.43 29.68 4.56 6.92 2.361
Morgan Ensberg1116 400 348.31 70.33 91.17 4.75 7.07 2.319
Jeff Cirillo304 102 93.89 20.82 26.56 5.51 7.64 2.125
Joe Crede976 348 306.29 49.61 66.84 3.85 5.89 2.043
Brandon Inge1397 506 457.59 106.89 131.29 5.70 7.75 2.043
Scott Rolen433 176 148.12 23.05 30.52 3.54 5.56 2.027
Adrian Beltre1242 396 357.19 90.94 108.51 6.20 8.20 2.002
Chone Figgins373 128 103.59 25.21 27.68 5.32 7.21 1.896
Alex S Gonzalez707 236 214.48 60.74 70.17 6.95 8.83 1.885
Rob Mackowiak426 158 140.50 30.78 36.93 5.26 7.10 1.838
Bill Mueller1103 353 312.94 102.45 111.37 7.84 9.61 1.772
Melvin Mora1149 401 361.65 84.04 99.09 5.66 7.40 1.740
Edwin Encarnacion404 168 136.40 39.13 40.35 6.29 7.99 1.699
Alex Rodriguez1328 395 350.67 99.71 109.33 6.82 8.42 1.602
Abraham O Nunez698 256 225.84 48.33 55.61 5.10 6.65 1.551
Mike Lowell983 339 308.19 77.92 87.92 6.21 7.70 1.497
Eric Chavez1222 408 380.75 74.83 89.22 4.95 6.33 1.374
Garrett Atkins989 333 313.98 81.32 92.57 6.59 7.96 1.367
David Bell1066 406 360.53 87.56 95.68 5.82 7.17 1.343
Aaron Boone1174 380 343.69 102.92 106.18 7.31 8.34 1.029
Dallas L McPherson443 122 102.19 35.41 32.58 7.84 8.61 0.771
Hank Blalock1304 398 385.85 97.70 103.03 6.63 7.21 0.581
Shea Hillenbrand404 130 122.75 31.27 31.50 6.49 6.93 0.434
Aramis Ramirez853 279 267.97 67.17 68.25 6.50 6.88 0.376
Mike Cuddyer745 243 234.36 58.17 59.33 6.46 6.84 0.373
Vinny Castilla904 344 337.16 52.72 54.18 4.14 4.34 0.200
Mark T Teahen1034 340 312.64 101.71 95.84 8.08 8.28 0.200
Edgardo Alfonzo727 223 219.04 58.44 58.56 7.08 7.22 0.143
Sean Burroughs581 199 186.94 35.71 34.14 4.84 4.93 0.086
David A Wright1288 432 408.09 111.65 106.51 6.98 7.05 0.069
Joe Randa1106 349 340.28 101.14 97.97 7.82 7.77 -0.052
Russell Branyan360 116 111.71 35.27 33.28 8.21 8.05 -0.164
Troy Glaus1207 411 402.21 114.59 105.61 7.53 7.09 -0.438
Jorge L Cantu438 113 126.32 50.88 49.23 12.16 10.52 -1.636

Interestingly, Chipper Jones makes a big move up when you look at runs vs. just outs. Maybe there's a tendancy for balls down the line in Atlanta to go for doubles and triples, so anything you stop saves a lot of bases. On the other hand, Figgins drops a lot. I wonder if positioning of the left fielder has anything to do with it? Or maybe Chipper just plays closer to the line all the time?

Posted by StatsGuru at 04:24 PM | Comments (5) | TrackBack (0)
February 23, 2006
Probabilistic Model of Range, 2005, Runs Created Against Rightfielders
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We continue the run through the positions with the rightfielders:

Probabilistic Model of Range, 2005, Runs Created Against Rightfielders, Original Model (minimum 200 fieldable balls in play)
PlayerFieldable Balls In PlayActual Outs by FielderPredicted Outs by FielderRCAPredicted RCARCA/27 OutsPredicted RCA/27Runs Saved/27 Outs
Jose Cruz228 110 90.55 21.94 37.87 5.39 11.29 5.905
Jeff B Francoeur281 131 116.89 35.38 46.79 7.29 10.81 3.516
Chad A Tracy208 86 82.33 26.05 33.98 8.18 11.14 2.966
Jay Gibbons296 133 126.19 20.37 32.67 4.14 6.99 2.855
Nick T Swisher447 198 180.06 51.42 65.75 7.01 9.86 2.847
Vladimir Guerrero516 245 225.72 54.09 66.76 5.96 7.99 2.024
Trot Nixon567 240 234.77 63.85 79.64 7.18 9.16 1.975
Jeromy Burnitz664 303 291.75 80.84 97.71 7.20 9.04 1.839
Casey Blake610 287 280.95 57.37 74.09 5.40 7.12 1.723
Mike Cameron318 137 122.46 36.28 40.15 7.15 8.85 1.702
Jermaine Dye619 260 252.18 74.32 85.79 7.72 9.19 1.468
Magglio Ordonez309 139 136.15 38.65 45.16 7.51 8.96 1.447
Jason Lane516 225 210.46 67.10 73.21 8.05 9.39 1.340
Ichiro Suzuki771 383 361.02 63.59 76.00 4.48 5.68 1.201
Shawn Green522 232 218.32 73.14 75.97 8.51 9.40 0.883
Brian Giles651 295 277.54 92.24 95.67 8.44 9.31 0.864
Geoff Jenkins652 307 292.31 64.56 70.79 5.68 6.54 0.861
Bobby Abreu602 267 261.04 69.81 75.94 7.06 7.85 0.795
Victor I Diaz339 153 150.52 37.26 39.97 6.57 7.17 0.595
Richard Hidalgo398 174 169.72 55.95 57.57 8.68 9.16 0.476
Sammy Sosa289 121 124.99 32.99 35.60 7.36 7.69 0.329
Emil Brown579 243 252.59 76.05 81.67 8.45 8.73 0.280
Austin Kearns481 238 234.46 42.62 44.04 4.83 5.07 0.237
Jacque Jones592 262 256.70 76.89 76.34 7.92 8.03 0.105
Jose Guillen659 299 298.66 73.21 73.22 6.61 6.62 0.008
Aubrey Huff470 204 207.52 52.60 53.17 6.96 6.92 -0.044
Alexis I Rios563 246 256.53 63.82 65.76 7.01 6.92 -0.084
Gary Sheffield553 240 225.92 71.32 66.33 8.02 7.93 -0.096
Moises Alou231 90 97.28 28.55 30.27 8.56 8.40 -0.164
Juan Encarnacion521 216 213.79 68.31 66.12 8.54 8.35 -0.189
Brad B Hawpe379 148 156.44 60.51 62.62 11.04 10.81 -0.230
Michael Tucker230 91 94.94 33.88 32.77 10.05 9.32 -0.734
Larry Walker265 107 107.33 38.20 35.34 9.64 8.89 -0.750
Matt Lawton538 230 240.06 72.21 60.57 8.48 6.81 -1.664
Craig Monroe280 132 138.13 35.85 27.34 7.33 5.34 -1.989
Wily Mo Pena235 92 105.82 37.26 25.36 10.93 6.47 -4.464

There's a lot of good right field defense in the AL West as Swisher and Guerrero lead the regulars. And given Griffey's poor ratings, putting Willy Mo Pena next to him makes the Reds defense pretty poor. Maybe the Cincinnati pitching is a bit better than we think.

Update: In helping out a friend, I was looking at Sheffield's probability of getting an out vs. runs saved. As you can see, Sheffield does well if you just look at getting outs vs. not, but is negative when you look at runs saved. My first thought on this is that Gary is poor at cutting off balls in the gap. Does anyone have thoughts on this?
Posted by StatsGuru at 04:04 PM | Comments (10) | TrackBack (0)
February 20, 2006
Probabilistic Model of Range, 2005, Runs Created Against Centerfielders
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One question that came up recently is what the RCA number represents. Basically, imagine that every batter put a ball in play that was catchable by the particular fielder. For a centerfielder, imagine players just keep hitting line drives and fly balls to centerfield. No homers, no walks, no strikeouts. That's how many runs you'd expect the team to score before the CF made 27 outs.

Here are the numbers for centerfielders.

Probabilistic Model of Range, 2005, Runs Created Against Centerfielders, Original Model (minimum 200 fieldable balls in play)
PlayerFieldable Balls In PlayActual Outs by FielderPredicted Outs by FielderRCAPredicted RCARCA/27 OutsPredicted RCA/27Runs Saved/27 Outs
Joey R Gathright375 181 167.23 23.76 48.36 3.54 7.81 4.263
Jerry Hairston200 90 84.03 22.04 31.70 6.61 10.19 3.574
Jason Ellison403 197 178.25 36.49 54.52 5.00 8.26 3.257
Andruw Jones800 365 337.56 99.20 131.84 7.34 10.55 3.208
Jim Edmonds673 319 297.21 69.83 98.88 5.91 8.98 3.072
Gary Matthews Jr.586 258 242.31 71.66 91.31 7.50 10.17 2.675
Grady Sizemore796 373 370.07 69.49 103.55 5.03 7.56 2.525
Jason Michaels334 161 150.73 28.36 40.42 4.76 7.24 2.485
Willy Taveras771 332 322.83 81.34 107.70 6.61 9.01 2.393
Shawn Green212 81 84.00 30.23 38.16 10.08 12.26 2.188
Aaron Rowand811 388 362.99 70.62 95.24 4.91 7.08 2.170
Nook P Logan581 282 270.92 45.57 63.75 4.36 6.35 1.990
Curtis Granderson237 119 110.91 31.15 37.00 7.07 9.01 1.940
Mark Kotsay691 299 306.87 73.41 95.91 6.63 8.44 1.810
Tike Redman355 158 143.77 52.38 56.29 8.95 10.57 1.621
Jeremy T Reed834 384 384.13 63.96 86.41 4.50 6.07 1.576
Corey Patterson526 240 232.53 59.95 71.17 6.74 8.26 1.520
Vernon Wells832 351 356.22 91.64 112.47 7.05 8.52 1.476
Luis Terrero268 121 121.69 21.72 28.23 4.85 6.26 1.416
Laynce Nix355 160 159.88 40.08 48.39 6.76 8.17 1.409
Brady Clark823 399 380.69 75.00 91.32 5.07 6.48 1.402
Damon J Hollins472 198 197.37 56.59 66.43 7.72 9.09 1.371
Jason Repko240 97 105.29 20.41 27.29 5.68 7.00 1.314
Luis Matos642 299 286.93 82.44 91.75 7.44 8.63 1.189
Johnny Damon878 396 402.01 106.64 122.95 7.27 8.26 0.986
Randy Winn382 184 182.71 39.69 44.62 5.82 6.59 0.769
Torii Hunter510 218 220.35 56.90 62.46 7.05 7.65 0.606
Carlos Beltran806 378 372.03 76.84 83.59 5.49 6.07 0.578
Kenny Lofton458 201 207.17 47.87 53.71 6.43 7.00 0.570
Cory Sullivan438 172 179.90 67.78 74.36 10.64 11.16 0.519
Lew Ford348 140 150.24 46.02 51.86 8.87 9.32 0.446
Juan Pierre790 332 337.90 107.44 111.48 8.74 8.91 0.170
Brad Wilkerson509 234 230.76 61.26 60.46 7.07 7.07 0.006
Dave Roberts579 234 240.18 73.35 73.60 8.46 8.27 -0.190
David DeJesus672 306 313.16 87.76 86.92 7.74 7.49 -0.250
Milton Bradley416 181 183.19 56.16 54.76 8.38 8.07 -0.307
Chone Figgins296 131 134.46 34.32 32.73 7.07 6.57 -0.502
Bernie Williams556 226 245.61 63.33 63.29 7.57 6.96 -0.609
Steve Finley598 266 279.55 82.21 78.10 8.34 7.54 -0.801
Preston Wilson652 267 283.89 95.84 93.25 9.69 8.87 -0.822
Ken Griffey Jr.695 286 321.33 114.51 101.19 10.81 8.50 -2.308
Jose Cruz224 87 96.22 37.26 32.86 11.56 9.22 -2.343

Notice Curtis Granderson and Nook Logan are very close. However, the balls in play vs. Granderson seem to have a higher run value than the balls in play vs. Logan. Logan played twice as much, so maybe it's just sample size. Looking at the opponents Granderson faced vs. the opponents Logan faced, I'd say a higher proportion of Granderson's opponents were stronger teams. Logan faced all the NL West teams, Granderson none. Maybe Granderson was in behind worse pitching?

Posted by StatsGuru at 03:11 PM | Comments (5) | TrackBack (0)
February 19, 2006
Probabilistic Model of Range, 2005, Runs Created Against Second Basemen
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I'll be going through all the positions as I did with straight probability model. You probably want to read this post first. Here's runs created against (RCA) for second basemen

:
Probabilistic Model of Range, 2005, Runs Created Against Second Basemen, Traditional Model (minimum 300 fieldable balls in play)
PlayerFieldable Balls In PlayActual Outs by FielderPredicted Outs by FielderRCAPredicted RCARCA/27 OutsPredicted RCA/27Runs Saved/27 Outs
Alex Cora351 138 113.32 14.41 23.19 2.82 5.53 2.707
Nick Punto616 246 213.29 24.94 37.36 2.74 4.73 1.992
Chase Utley1203 481 429.21 52.90 73.81 2.97 4.64 1.674
Craig Counsell1416 519 492.13 73.53 97.86 3.83 5.37 1.543
Orlando Hudson1316 520 442.17 70.44 85.16 3.66 5.20 1.543
Placido Polanco1009 370 346.28 55.75 70.93 4.07 5.53 1.463
Tony Graffanino689 227 219.28 32.54 43.20 3.87 5.32 1.450
Mark Ellis1055 400 381.12 52.80 68.62 3.56 4.86 1.297
Ronnie Belliard1373 503 485.33 61.42 79.35 3.30 4.41 1.117
Mark Grudzielanek1283 467 460.13 59.68 76.36 3.45 4.48 1.030
Nick Green822 257 262.85 42.14 53.00 4.43 5.44 1.017
Adam Kennedy1285 424 411.48 69.56 82.99 4.43 5.45 1.016
Kazuo Matsui664 228 236.34 30.81 39.56 3.65 4.52 0.871
Marcus Giles1469 531 523.20 95.36 106.20 4.85 5.48 0.631
Tadahito Iguchi1337 441 451.36 75.69 86.27 4.63 5.16 0.526
Craig Biggio1358 440 461.25 77.33 89.20 4.75 5.22 0.476
Luis Castillo1094 418 396.59 63.31 65.47 4.09 4.46 0.368
Jerry Hairston361 138 128.03 18.54 18.75 3.63 3.95 0.327
Mark Bellhorn954 311 325.10 50.91 56.61 4.42 4.70 0.282
Miguel Cairo780 264 264.95 53.05 55.59 5.43 5.67 0.240
Ray Durham1268 394 409.18 71.85 77.21 4.92 5.09 0.171
Jose Castillo969 322 330.00 63.84 66.87 5.35 5.47 0.118
Rich Aurilia594 214 202.49 33.56 32.60 4.23 4.35 0.113
Damion Easley357 135 131.99 21.89 21.73 4.38 4.45 0.067
Rickie Weeks991 292 315.43 62.10 67.43 5.74 5.77 0.030
Jeff Kent1352 460 468.51 75.94 77.78 4.46 4.48 0.025
Brian Roberts1384 486 470.33 86.12 82.72 4.78 4.75 -0.036
Deivi Cruz358 112 130.55 19.20 22.07 4.63 4.57 -0.063
Freddy Sanchez427 137 137.43 33.17 32.79 6.54 6.44 -0.096
Alfonso Soriano1601 512 554.60 90.57 95.21 4.78 4.64 -0.141
Jose C Lopez536 197 186.96 31.47 28.25 4.31 4.08 -0.235
Ryan Freel463 162 150.43 26.41 22.71 4.40 4.08 -0.325
Jamey Carroll462 161 162.69 30.62 28.80 5.13 4.78 -0.354
Junior Spivey625 223 210.83 41.50 36.08 5.02 4.62 -0.403
Ruben A Gotay868 290 290.27 63.28 58.77 5.89 5.47 -0.425
Bret Boone935 292 315.56 55.07 53.46 5.09 4.57 -0.518
Todd Walker895 290 307.00 56.18 52.92 5.23 4.65 -0.576
Chone Figgins399 113 137.60 26.01 27.79 6.21 5.45 -0.761
Aaron Miles744 246 252.32 54.50 48.62 5.98 5.20 -0.779
Jose Vidro724 240 252.81 48.14 42.86 5.42 4.58 -0.838
Omar Infante610 205 207.93 39.24 32.96 5.17 4.28 -0.889
Robinson Cano1401 474 513.25 91.74 80.14 5.23 4.22 -1.010
Luis A Gonzalez684 225 245.12 54.73 50.39 6.57 5.55 -1.017
Mark Loretta973 324 335.52 63.99 52.78 5.33 4.25 -1.085
Luis Rivas388 127 130.44 27.40 22.30 5.82 4.62 -1.209
Jorge L Cantu764 218 242.20 59.61 47.59 7.38 5.31 -2.077

The Phillies have a very nice player in Chase Utley at second. He gave them good offense and great defense in 2005. At the other end of the scale, the Red Sox acquistion of Mark Loretta doesn't look like it's part of their better defense model.

Posted by StatsGuru at 07:51 PM | Comments (2) | TrackBack (0)
Probabilistic Model of Range, 2005, Runs Created Against Fielders
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A few days ago I made my first attempt to calculate runs saved by teams based on the Probabilistic Model of Range (PMR). I'm using a modified version of the runs created formula that appeared in The Bill James Handbook 2005. That formula is designed for batters. I've modified it in the following ways:

  • Count any time a fielder fails to get an out as a time on base. So if there is a failed fielder's choice, or the batter reaches on an error, it's a time on base. Since we're looking at defenses, this seems appropriate.
  • Total bases are based on the number of bases achieved by the batter when he earns a time on base. So a two base error in this system counts the same as a double. The weights used for the various types of hits are the same as in the Handbook.

So that makes the formula (Times On Base - GDP)* (Weighted Total Base)/(Balls in Play). I'd like to hear what you think about the formula, but I believe it's a good first approximation. It was easy to apply to teams; you're just looking at all balls in play, and the likelihood that a particular ball will end in a particular result. But I wasn't quite sure how to then apply it to individual fielders.

When I was looking just at the probability of catching the ball, I wanted to look at all balls in play. I was looking at the piece of team DER that belonged to a particular fielder. But here, I'm trying to predict runs, so I made the decision to only look at balls in play in which the fielder had a non-zero chance of making the play. If you will, I used the probabilities of various balls in play to define the zone for the fielder, and the results of those balls to define runs created against (RCA).

The results made me wish I had worked on this last year. They're conveying information much more clearly than simply looking at the probability of catching the ball. Let's look at the shortstops first:

Probabilistic Model of Range, 2005, Runs Created Against Shortstops, Original Model (minimum 400 fieldable balls in play)
PlayerFieldable Balls In PlayActual Outs by FielderPredicted Outs by FielderRCAPredicted RCARCA/27 OutsPredicted RCA/27Runs Saved/27 Outs
John McDonald521 178 172.92 27.41 36.40 4.16 5.68 1.527
Adam Everett1490 517 498.79 71.05 95.41 3.71 5.16 1.454
Omar Infante529 191 173.53 30.93 37.40 4.37 5.82 1.446
Bobby Crosby925 312 304.63 42.34 56.74 3.66 5.03 1.365
Rafael Furcal1648 596 576.97 84.81 109.11 3.84 5.11 1.264
Clint Barmes859 306 279.67 49.78 56.65 4.39 5.47 1.077
Yuniesky Betancourt610 177 174.31 39.99 45.58 6.10 7.06 0.961
Juan Uribe1729 537 557.52 82.57 103.72 4.15 5.02 0.872
Julio Lugo1761 560 540.03 122.06 134.05 5.88 6.70 0.817
Jimmy Rollins1625 510 519.18 89.33 106.01 4.73 5.51 0.784
David Eckstein1737 615 617.90 97.98 114.19 4.30 4.99 0.688
Jack Wilson1734 600 610.94 104.81 121.02 4.72 5.35 0.632
Orlando Cabrera1613 469 481.07 87.65 99.85 5.05 5.60 0.558
Oscar M Robles554 172 179.32 31.03 35.19 4.87 5.30 0.427
Russ M Adams1511 401 437.66 91.76 106.33 6.18 6.56 0.381
Neifi Perez1269 445 448.29 65.47 71.20 3.97 4.29 0.316
Wilson Valdez516 155 153.97 27.33 28.84 4.76 5.06 0.297
Miguel Tejada1846 572 590.91 122.90 132.45 5.80 6.05 0.251
Juan Castro774 264 260.08 59.78 61.02 6.11 6.33 0.221
Jhonny Peralta1603 509 549.61 94.74 106.72 5.03 5.24 0.218
Jason A Bartlett769 281 270.60 47.53 47.88 4.57 4.78 0.211
J.J. Hardy1085 346 359.91 66.62 71.75 5.20 5.38 0.184
Omar Vizquel1620 538 558.31 86.16 91.63 4.32 4.43 0.107
Alex Gonzalez1273 452 441.85 91.18 88.58 5.45 5.41 -0.033
Derek Jeter1913 561 602.13 115.11 122.15 5.54 5.48 -0.063
Carlos Guillen834 262 270.95 54.67 55.58 5.63 5.54 -0.095
Khalil Greene1246 399 409.60 74.67 74.98 5.05 4.94 -0.110
Jose Reyes1865 522 569.16 115.79 123.88 5.99 5.88 -0.113
Cesar Izturis1175 366 386.49 74.27 76.18 5.48 5.32 -0.157
Royce Clayton1528 473 502.95 99.61 98.82 5.69 5.30 -0.381
Bill Hall609 196 205.22 47.93 46.59 6.60 6.13 -0.473
Edgar Renteria1773 491 499.45 128.25 120.64 7.05 6.52 -0.531
Marco Scutaro846 259 282.20 50.54 48.34 5.27 4.62 -0.644
Michael Young1930 534 580.38 134.80 131.21 6.82 6.10 -0.711
Felipe Lopez1467 459 493.34 95.85 89.31 5.64 4.89 -0.750
Cristian Guzman1333 417 438.14 83.54 75.53 5.41 4.65 -0.754
Mike Morse581 156 170.02 47.09 43.64 8.15 6.93 -1.220
Angel Berroa1818 551 594.20 168.48 137.92 8.26 6.27 -1.989

Many years ago, Bill James posed a question about shortstops; how many runs does one save with his glove? At the time, someone claimed Ozzie Smith saved 100 runs with his defense. Bill estimated at that time, the difference between the best and worst shortstop in the league was about 25 runs. As you can see here, among regular shortstops, Adam Evertt saved the most runs in 2005, about 24 below expectations. Angel Berroa, the worst regular in the majors, cost the Royals about 31 runs. That puts the difference at 55. Looking at the data, Berroa was an out lier. He was the rare shortstop who contributed nothing offensively while killing the team with his defense. He never should have played a full season at shortstop. Compared to Cristian Guzman, Everett saved about 35 runs, which fits in nicely with Bill's estimate. The magnitude of the numbers looks right to me.

The second feature to look at concerns Derek Jeter and Jose Reyes. There's been discussion as the model developed of how to handle ball in play could be handled by a particular fielder, but are caught by someone else. Runs created against appears to handle this situation quite well. Both Jeter and Reyes allowed fewer runs than the model predicts, despite turning many fewer outs than expected. Why? Others are turning the shortstop misses into outs. What happens, then, is that when calculating RCA, Derek and Jose don't get hurt in the numerator of the equation; they just get bumped up in the denominator.

However, when you look at them in terms of runs per game (27 outs), things change. The outs they don't get matter. They're so many outs behind where they should be, they're actually allowing more runs per game than expected. In other words, the cost of an out by one of these shortstops is high in terms of runs allowed. That I find to be a very cool result, that we can see both team and individual contributions to defense in one line.

The other thing we can see is who plays in tough ballparks. Let's demonstrate this with leftfielders:

Probabilistic Model of Range, 2005, Runs Created Against Leftfielders, Original Model (minimum 200 fieldable balls in play)
PlayerFieldable Balls In PlayActual Outs by FielderPredicted Outs by FielderRCAPredicted RCARCA/27 OutsPredicted RCA/27Runs Saved/27 Outs
Chris A Burke272 120 101.65 26.59 41.11 5.98 10.92 4.935
Coco Crisp617 294 261.44 66.21 96.11 6.08 9.93 3.846
Reed Johnson282 134 112.67 34.77 41.96 7.01 10.05 3.049
Bobby Kielty242 99 96.77 21.15 30.47 5.77 8.50 2.733
Carl Crawford717 341 309.93 58.46 84.48 4.63 7.36 2.731
Matt T Holliday558 236 214.67 72.13 87.09 8.25 10.95 2.701
Pedro Feliz300 138 131.98 29.23 40.98 5.72 8.38 2.663
Jay Payton245 107 94.67 24.01 30.14 6.06 8.60 2.537
Eric Byrnes433 209 185.38 34.82 43.41 4.50 6.32 1.824
Carlos Lee708 307 289.83 90.47 104.77 7.96 9.76 1.803
Kevin Mench521 231 213.63 51.08 61.35 5.97 7.75 1.784
Moises Alou284 132 123.81 25.23 31.83 5.16 6.94 1.781
Scott Podsednik557 260 240.02 55.04 66.05 5.72 7.43 1.715
Craig Monroe255 99 102.25 35.00 42.14 9.54 11.13 1.582
Raul Ibanez255 106 103.57 22.14 27.17 5.64 7.08 1.443
Kelly A Johnson356 166 154.90 42.52 47.68 6.92 8.31 1.394
Rondell White270 119 118.43 30.45 36.12 6.91 8.24 1.327
Luis Gonzalez619 270 246.42 95.06 97.93 9.51 10.73 1.225
Randy Winn497 226 209.19 47.81 53.67 5.71 6.93 1.215
Cliff Floyd654 283 267.59 76.59 82.93 7.31 8.37 1.061
Hideki Matsui520 218 204.33 65.97 69.62 8.17 9.20 1.029
Adam Dunn616 246 243.81 88.16 95.94 9.68 10.62 0.948
Frank Catalanotto389 163 160.10 49.82 54.50 8.25 9.19 0.939
Jason Bay625 266 257.22 83.97 89.04 8.52 9.35 0.824
Todd Hollandsworth251 103 101.45 36.08 37.60 9.46 10.01 0.549
Shannon Stewart555 249 237.55 74.67 75.06 8.10 8.53 0.434
Garret Anderson531 201 208.94 59.21 61.67 7.95 7.97 0.016
Pat Burrell627 236 247.60 82.06 85.00 9.39 9.27 -0.118
David Dellucci228 84 90.32 29.17 30.85 9.38 9.22 -0.152
Marlon Byrd210 100 102.84 21.98 21.78 5.93 5.72 -0.215
Terrence Long423 166 163.61 76.66 73.49 12.47 12.13 -0.342
Reggie Sanders268 108 105.77 47.56 43.95 11.89 11.22 -0.671
Ryan Klesko504 204 202.51 76.35 69.22 10.11 9.23 -0.877
Miguel Cabrera495 188 208.43 72.91 72.74 10.47 9.42 -1.049
Manny Ramirez689 243 254.92 125.73 118.56 13.97 12.56 -1.412
Larry Bigbie232 98 96.20 34.67 28.06 9.55 7.87 -1.677

You can pick out the stadiums where fielding is difficult. Look at Manny Ramirez, Matt Holliday, Luis Gonzalez and Terrance Long. They're all in left fields that generate a lot of runs. And look at Coco Crisp in left, saving 30 runs. Some of that has to translate to center at Fenway.

As always, I'm anxious to hear your criticisms of the model.

Update: I was just reading and responding to an e-mail from Studes at The Hardball Times (see comment below) and I need to clarify something. When I'm talking about runs saved by a fielder (Predicted RCA - RCA), I shouldn't be so liberal in attributing those runs to the fielder. As the Jeter/Reyes comment above shows, those numbers are influenced by teammates. You should think of the runs saved as being attributed to "the fielder and his surrounding teammates."

Update: In order to make things a bit clearer, I've noted in the headings to the tables that the Out columns are outs that are attributed to the fielder, whether actual or predicted.

Posted by StatsGuru at 01:56 PM | Comments (4) | TrackBack (0)
February 15, 2006
Probabilistic Model of Range, 2005, Team Runs Saved
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So far with the range calculations I've been interested in the probability of getting to a batted ball. I'm going to change gears a bit and try to look at runs saved instead. The methodolgy is the same in principle; predict how many runs should be expected given the balls in play, and compare that to how many were actually allowed.

Of course, both these numbers are difficult to calculate. What I'm going to do is use a version of the Bill James runs created formula that appears in The Bill James Handbook 2005. The formula I'm usings is (Times On - GDP)*(Weighted Total Bases)/Balls in Play. I'm not making an adjustment for sac hits or sac flys. I am however, counting all non-out balls in play as a time on base. I'm also counting all non-out balls in play in the total base calculation. In this case, if a player reaches on a 1-base error or a failed fielder's choice, that's the same as a single. If they reach on a three-base error, that's the same as a triple. Since we're measuring range, errors should hurt the teams that commit them.

So I add up all the actual results on balls in play against a team, and calculate actual runs created against (RCA). I also add up all the predictions for the ball (the chance of a single, double, triple, home run, GDP or out) and use that to predict the number of expected runs. Here's the table showing which defenses saved the most runs by this calculation (the traditional calculation is here):

Probabilistic Model of Range, 2005, Runs and Predicted Runs, Original Model Including Parks
TeamInPlayRCAPredicted RCARCA per 27 OutsPredicted RCA/27Runs Saved per 27 Outs
Astros4204 424.18 510.24 3.71 4.63 0.9163
Athletics4286 395.81 482.57 3.34 4.23 0.8872
Cardinals4414 428.09 514.59 3.52 4.40 0.8816
Indians4385 420.76 508.22 3.50 4.37 0.8669
White Sox4457 420.22 503.66 3.42 4.25 0.8290
Phillies4211 442.77 517.76 3.89 4.71 0.8170
Braves4559 488.44 569.82 3.99 4.80 0.8048
Blue Jays4511 464.49 543.76 3.81 4.59 0.7811
Twins4545 458.47 532.69 3.71 4.44 0.7269
Pirates4467 480.44 538.91 3.98 4.58 0.5962
Angels4383 465.62 519.73 3.95 4.51 0.5613
Red Sox4575 552.63 605.03 4.59 5.13 0.5363
Orioles4377 473.93 526.22 4.05 4.59 0.5344
Tigers4527 478.16 532.86 3.91 4.44 0.5286
Diamondbacks4571 547.20 594.48 4.53 5.02 0.4885
Giants4520 491.96 542.16 4.06 4.55 0.4875
Mariners4546 472.36 521.65 3.86 4.33 0.4708
Devil Rays4560 557.49 602.93 4.68 5.14 0.4515
Cubs4117 434.25 475.70 3.92 4.36 0.4344
Brewers4252 467.77 499.93 4.12 4.45 0.3310
Rangers4697 559.00 590.74 4.54 4.84 0.2981
Mets4424 460.25 488.90 3.86 4.15 0.2877
Rockies4537 583.74 611.55 4.97 5.24 0.2767
Dodgers4392 467.44 487.51 3.95 4.15 0.2003
Marlins4367 526.65 532.17 4.56 4.64 0.0812
Nationals4538 482.25 486.34 3.96 4.00 0.0335
Padres4423 513.08 514.33 4.38 4.39 0.0119
Yankees4483 509.11 497.02 4.28 4.16 -0.1157
Royals4611 612.92 591.70 5.16 4.94 -0.2189
Reds4650 586.40 553.75 4.84 4.53 -0.3164

Again, with the line drives fluctuating so much year to year, I'd be more concerned about the order than the magnitude of the runs saved. But Houston and Oakland are impressive teams.

The next trick is to make this work for players. I'm trying to figure out how to split the run elements between fielders who have a chance at catching a given ball. Any suggestions would be welcome.

Correction: Fixed the year of The Bill James Handbook.

Posted by StatsGuru at 04:46 PM | Comments (4) | TrackBack (0)
February 12, 2006
Voting on Range
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Lee Panas at Tiger Tales is combining various range models using a voting system. Here's his first post on the subject, but if you go to his main page you can scroll through the other positions.

When I was working at the Center for Intelligent Information Retrieval at the University of Massachusetts, we were using methods like this to retrieve results from multiple search engines and combine them into a single ordered list. Thanks to Lee for trying this out.

Posted by StatsGuru at 12:32 PM | Comments (2) | TrackBack (0)
February 10, 2006
Probabilistic Model of Range, 2005, Centerfielders, No Out Penalties
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To follow up on the shortstop post from the other day, here's what the centerfielders look like if you don't penalize them for balls other players catch. You may want to compare them to the original here:

Probabilistic Model of Range, Centerfielders, 2005, Original Model, No Penalty for Outs
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Jason Ellison1867197 147.52 0.106 0.079 0.02650
Joey R Gathright1587181 139.15 0.114 0.088 0.02637
Jason Michaels1621161 121.64 0.099 0.075 0.02428
Nook P Logan2730282 222.69 0.103 0.082 0.02172
Curtis Granderson1044119 96.64 0.114 0.093 0.02141
Randy Winn1603184 151.43 0.115 0.094 0.02032
Tike Redman1613158 125.28 0.098 0.078 0.02029
Jerry Hairston110090 67.70 0.082 0.062 0.02027
Jeremy T Reed3692384 309.33 0.104 0.084 0.02023
Gary Matthews Jr.2822258 201.33 0.091 0.071 0.02008
Brady Clark3765399 325.40 0.106 0.086 0.01955
Aaron Rowand4128388 307.33 0.094 0.074 0.01954
Willy Taveras3646332 263.79 0.091 0.072 0.01871
Damon J Hollins2010198 162.20 0.099 0.081 0.01781
Andruw Jones4309365 289.87 0.085 0.067 0.01743
Jim Edmonds3538319 258.07 0.090 0.073 0.01722
Luis Matos3017299 249.80 0.099 0.083 0.01631
Grady Sizemore4136373 305.68 0.090 0.074 0.01628
Luis Terrero1310121 100.02 0.092 0.076 0.01602
Laynce Nix1674160 133.31 0.096 0.080 0.01594
Mark Kotsay3519299 243.40 0.085 0.069 0.01580
Jason Repko112897 79.24 0.086 0.070 0.01575
Dave Roberts2715234 191.74 0.086 0.071 0.01557
Corey Patterson2799240 197.48 0.086 0.071 0.01519
Kenny Lofton2167201 168.93 0.093 0.078 0.01480
Carlos Beltran3967378 320.30 0.095 0.081 0.01454
Johnny Damon3952396 338.79 0.100 0.086 0.01448
Vernon Wells4239351 290.97 0.083 0.069 0.01416
Torii Hunter2575218 181.55 0.085 0.071 0.01415
Chone Figgins1184131 114.47 0.111 0.097 0.01396
Brad Wilkerson2414234 200.75 0.097 0.083 0.01378
Milton Bradley1969181 154.02 0.092 0.078 0.01370
David DeJesus3304306 263.92 0.093 0.080 0.01274
Juan Pierre4171332 280.23 0.080 0.067 0.01241
Lew Ford1677140 121.05 0.083 0.072 0.01130
Cory Sullivan1935172 151.11 0.089 0.078 0.01079
Steve Finley2691266 242.14 0.099 0.090 0.00887
Preston Wilson3362267 237.40 0.079 0.071 0.00880
Bernie Williams2689226 204.58 0.084 0.076 0.00797
Ken Griffey Jr.3439286 259.60 0.083 0.075 0.00768
Jose Cruz131787 82.74 0.066 0.063 0.00323

There's been some speculation in the comments on this site that Andruw Jones lost a step and his left and right fielders were taking balls from him. The Ball Hog Index show that's not true.

Probabilistic Model of Range, Centerfielders, 2005, Original Model, Ball Hogging Index
PlayerPredicted OutsPredicted Outs No HogsDifferenceDiff Per BIP
Jose Cruz 96.22 82.74 13.480 0.0102
Jim Edmonds 297.13 258.07 39.062 0.0110
Andruw Jones 337.56 289.87 47.681 0.0111
Tike Redman 143.70 125.28 18.417 0.0114
Luis Matos 286.93 249.80 37.129 0.0123
Brad Wilkerson 230.76 200.75 30.016 0.0124
Corey Patterson 232.53 197.48 35.044 0.0125
Carlos Beltran 372.03 320.30 51.729 0.0130
Aaron Rowand 362.99 307.33 55.656 0.0135
Curtis Granderson 110.91 96.64 14.261 0.0137
Preston Wilson 283.81 237.40 46.404 0.0138
Juan Pierre 337.90 280.23 57.674 0.0138
Steve Finley 279.55 242.14 37.405 0.0139
Gary Matthews Jr. 242.31 201.33 40.985 0.0145
Brady Clark 380.69 325.40 55.294 0.0147
Cory Sullivan 179.74 151.11 28.630 0.0148
Milton Bradley 183.11 154.02 29.094 0.0148
David DeJesus 313.16 263.92 49.241 0.0149
Jerry Hairston 84.03 67.70 16.335 0.0149
Torii Hunter 220.35 181.55 38.800 0.0151
Bernie Williams 245.61 204.58 41.034 0.0153
Vernon Wells 356.22 290.97 65.252 0.0154
Grady Sizemore 370.07 305.68 64.386 0.0156
Laynce Nix 159.88 133.31 26.572 0.0159
Johnny Damon 402.01 338.79 63.224 0.0160
Willy Taveras 322.83 263.79 59.034 0.0162
Jason Ellison 178.25 147.52 30.735 0.0165
Luis Terrero 121.69 100.02 21.672 0.0165
Chone Figgins 134.46 114.47 19.989 0.0169
Lew Ford 150.24 121.05 29.191 0.0174
Damon J Hollins 197.37 162.20 35.171 0.0175
Kenny Lofton 207.17 168.93 38.240 0.0176
Joey R Gathright 167.23 139.15 28.079 0.0177
Nook P Logan 270.92 222.69 48.233 0.0177
Dave Roberts 240.18 191.74 48.448 0.0178
Ken Griffey Jr. 321.33 259.60 61.721 0.0179
Jason Michaels 150.73 121.64 29.084 0.0179
Mark Kotsay 306.87 243.40 63.470 0.0180
Randy Winn 182.71 151.43 31.281 0.0195
Jeremy T Reed 384.13 309.33 74.803 0.0203
Jason Repko 105.29 79.24 26.056 0.0231

I'm not at all surprised that Griffey scores this low. When I looked at why Griffey was getting low zone ratings in the 1990s, I found that Seattle leftfielders and rightfielders made many more plays than would normally be expected. And that was when Griffey had good legs.

Posted by StatsGuru at 09:04 AM | Comments (8) | TrackBack (0)
February 08, 2006
Ball Hogs
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One criticism that the Probabilistic Model of Range receives is that one player can be penalized by another fielder recording an out. Let me give you a hypothetical example.

Let's take a shallow fly ball behind second base. You can imagine that this ball might be caught by the centerfielder, second baseman or shortstop. The probability of catching the ball would be pretty high, let's say .9. So if we're looking at team numbers, it doesn't matter who catches it. A catch is a .1 reward (1 - 0.9) and a drop is a 0.9 penalty (0 - 0.9).

That 0.9 probability is made up of the probabilities of the CF, 2B and SS catching the ball. For simplicity, let's say they all have an even chance of making the catch; 0.3 each. So, the way the system works, when the shortstop catches the ball, he gets a 0.7 reward (1-0.3) and the other two fielders get 0.3 penalties (0.0 - 0.3). The team is still +0.1 (0.7-.06). Now, if 100 of these balls are hit and all three fielders catch 30 each, everything evens out. Each fielder is expected to get 30 outs, each fielder gets 30 outs, so all are right where they should be. But what if the centerfielder is a ball hog?

On those same 100 balls in play, say the CF catches 50, and the SS and 2B split the remaining 40. The centerfielder would be 20 outs above expectation, but the SS and 2B would each be 10 outs below expectation. The SS and 2B look bad on balls that are caught anyway.

Now, there may be nothing wrong with this. The CF may get to more balls because he has to, not because he's a ball hog. Nevertheless, we can adjust for these outs. Instead of charging a fielder with a penalty on an out he didn't make, we'll just not charge him at all. In that system, the CF still looks above average, but the SS and 2B are even, since we don't count those extra 10 outs each against them.

So let's see how the shortstops stack up when we remove the ball hogs:

Probabilistic Model of Range, Shortstops, 2005, Original Model, No Penalty for Outs
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Wilson Valdez1198147 108.97 0.123 0.091 0.03174
Jason A Bartlett1766257 201.14 0.146 0.114 0.03163
Clint Barmes2209276 206.61 0.125 0.094 0.03141
Omar Infante1233171 133.05 0.139 0.108 0.03078
Juan Uribe3946494 378.25 0.125 0.096 0.02933
Adam Everett3748469 360.11 0.125 0.096 0.02905
John McDonald1223163 128.61 0.133 0.105 0.02812
Rafael Furcal4111539 423.58 0.131 0.103 0.02808
Julio Lugo4297523 404.03 0.122 0.094 0.02769
Jimmy Rollins3994473 365.01 0.118 0.091 0.02704
Yuniesky Betancourt1426161 122.81 0.113 0.086 0.02678
Bobby Crosby2163277 221.03 0.128 0.102 0.02588
Neifi Perez3026410 331.69 0.135 0.110 0.02588
Juan Castro1775243 197.45 0.137 0.111 0.02566
Orlando Cabrera3706425 335.17 0.115 0.090 0.02424
Carlos Guillen1934240 193.33 0.124 0.100 0.02413
Oscar M Robles1313157 126.13 0.120 0.096 0.02351
Omar Vizquel4024500 406.57 0.124 0.101 0.02322
J.J. Hardy2805316 251.53 0.113 0.090 0.02298
Bill Hall1447183 151.08 0.126 0.104 0.02206
Khalil Greene3123365 297.32 0.117 0.095 0.02167
Jack Wilson4240543 451.46 0.128 0.106 0.02159
Miguel Tejada4280526 433.75 0.123 0.101 0.02155
Edgar Renteria4119452 367.66 0.110 0.089 0.02048
Cesar Izturis2859338 279.59 0.118 0.098 0.02043
Alex Gonzalez3291404 337.51 0.123 0.103 0.02020
Derek Jeter4231525 440.39 0.124 0.104 0.02000
Russ M Adams3433372 303.72 0.108 0.088 0.01989
Jhonny Peralta3736465 392.67 0.124 0.105 0.01936
David Eckstein4109550 470.45 0.134 0.114 0.01936
Cristian Guzman3605381 314.00 0.106 0.087 0.01858
Marco Scutaro1980238 201.91 0.120 0.102 0.01823
Jose Reyes4308479 405.05 0.111 0.094 0.01717
Felipe Lopez3804418 353.72 0.110 0.093 0.01690
Michael Young4398489 415.35 0.111 0.094 0.01675
Mike Morse1437144 120.59 0.100 0.084 0.01629
Angel Berroa4438505 434.84 0.114 0.098 0.01581
Royce Clayton3711430 376.02 0.116 0.101 0.01455

You might want to compare the above table to the one in this post. As you see, Jose Reyes is not longer at the bottom. He wasn't making all the plays you would expect from a shortstop, but others were.

The other thing we can do is try to come up with a hog index. By comparing the total predicted outs from the original table with the predicted outs from the No Hog table, we can see which shortstops are having plays taken away the most:

Probabilistic Model of Range, Shortstops, 2005, Original Model, Ball Hogging Index
PlayerPredicted OutsPredicted Outs No HogsDifferenceDiff Per BIP
Omar Infante 157.18 133.05 24.126 0.0196
Alex Gonzalez 403.74 337.51 66.222 0.0201
Julio Lugo 496.20 404.03 92.167 0.0214
Clint Barmes 254.21 206.61 47.600 0.0215
Juan Castro 236.77 197.45 39.313 0.0221
Royce Clayton 459.89 376.02 83.869 0.0226
Edgar Renteria 461.67 367.66 94.012 0.0228
David Eckstein 565.01 470.45 94.562 0.0230
Cristian Guzman 399.91 314.00 85.909 0.0238
John McDonald 157.90 128.61 29.296 0.0240
Jack Wilson 553.07 451.46 101.612 0.0240
Miguel Tejada 538.20 433.75 104.451 0.0244
Angel Berroa 543.44 434.84 108.600 0.0245
Omar Vizquel 506.26 406.57 99.697 0.0248
Mike Morse 156.18 120.59 35.600 0.0248
Rafael Furcal 527.12 423.58 103.544 0.0252
Bill Hall 187.69 151.08 36.606 0.0253
Jason A Bartlett 245.86 201.14 44.724 0.0253
Yuniesky Betancourt 159.52 122.81 36.709 0.0257
Michael Young 528.27 415.35 112.915 0.0257
Cesar Izturis 353.90 279.59 74.319 0.0260
Bobby Crosby 277.56 221.03 56.531 0.0261
Adam Everett 457.97 360.11 97.861 0.0261
Neifi Perez 411.18 331.69 79.495 0.0263
Khalil Greene 379.76 297.32 82.434 0.0264
Felipe Lopez 454.44 353.72 100.715 0.0265
Jose Reyes 522.85 405.05 117.799 0.0273
Derek Jeter 555.71 440.39 115.323 0.0273
J.J. Hardy 328.53 251.53 76.996 0.0274
Wilson Valdez 142.19 108.97 33.213 0.0277
Jimmy Rollins 475.97 365.01 110.960 0.0278
Carlos Guillen 247.06 193.33 53.725 0.0278
Jhonny Peralta 496.59 392.67 103.922 0.0278
Marco Scutaro 257.00 201.91 55.084 0.0278
Oscar M Robles 162.74 126.13 36.609 0.0279
Russ M Adams 400.54 303.72 96.819 0.0282
Orlando Cabrera 443.34 335.17 108.178 0.0292
Juan Uribe 505.90 378.25 127.653 0.0323

Omar Infante doesn't have many outs taken from him, helping to account for his high ranking in the original table. Other fielders get to many balls that Jose Reyes might be able to field, hence his low ranking in the original table. I'd love to hear your comments on this.

Posted by StatsGuru at 06:20 PM | Comments (15) | TrackBack (0)
Manny Being Manny
Permalink

A reader requested a breakdown of Manny Ramirez's range home and away. Here's the result:

Probabilistic Model of Range, Manny Ramirez, LF, 2005, Original Model
LocationInPlayActual OutsPredicted OutsDERPredicted DERDifference
Away2027140 136.64 0.069 0.067 0.00166
Home1929103 118.27 0.053 0.061 -0.00792

He's a normal fielder away from Fenway, but did poorly in his home park. You can also see from the predicted DER that left field in Fenway doesn't yield as many outs as the places the Red Sox visit.

Posted by StatsGuru at 02:47 PM | Comments (11) | TrackBack (1)
Which Model
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Now that all positions are published, I wonder which model people like better? Let me know in the comments if you prefer the original model or the smoothed visiting player model.

The whole PMR thread is here.

Posted by StatsGuru at 02:06 PM | Comments (2) | TrackBack (0)
February 07, 2006
Chronicling Runs
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I've been remiss in not paying more attention to Chronicle of the Lads, who's been busy translating the PMR numbers into expected runs.

Posted by StatsGuru at 09:06 AM | Comments (0) | TrackBack (0)
February 06, 2006
Probabilistic Model of Range, 2005, Pitchers
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To complete the nine position tour of range, here are the tables for pitchers. The minimum here is 500 balls in play. That allows us to capture most of the regular starters.

Probabilistic Model of Range, Pitchers, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Kirk Saarloos55341 23.33 0.074 0.042 0.03196
Kenny Rogers66549 33.01 0.074 0.050 0.02405
Greg Maddux72852 38.90 0.071 0.053 0.01799
Chan Ho Park50137 29.08 0.074 0.058 0.01581
Jake Westbrook69353 42.09 0.076 0.061 0.01574
Brad Radke65131 23.04 0.048 0.035 0.01223
Brett Myers58737 30.33 0.063 0.052 0.01136
Kip Wells56234 27.70 0.060 0.049 0.01121
Barry Zito65436 28.92 0.055 0.044 0.01082
Mark Mulder65948 41.34 0.073 0.063 0.01011
Roger Clemens57735 30.24 0.061 0.052 0.00825
Jose Lima59723 18.66 0.039 0.031 0.00727
Livan Hernandez79645 39.53 0.057 0.050 0.00687
Mark Redman57434 30.47 0.059 0.053 0.00614
Dontrelle Willis71639 34.64 0.054 0.048 0.00609
Aaron Harang64327 23.41 0.042 0.036 0.00558
Ramon Ortiz56725 22.09 0.044 0.039 0.00513
Tomo Ohka59627 24.19 0.045 0.041 0.00472
Johan Santana60429 26.28 0.048 0.044 0.00450
Carlos Silva64130 27.16 0.047 0.042 0.00443
Jake Peavy52124 21.87 0.046 0.042 0.00409
Zack Z Greinke62526 23.44 0.042 0.038 0.00409
Jamie Moyer68327 24.32 0.040 0.036 0.00392
Josh Fogg57126 23.86 0.046 0.042 0.00374
Javier Vazquez62634 31.74 0.054 0.051 0.00361
Josh Towers70632 29.86 0.045 0.042 0.00304
Woody Williams51319 17.45 0.037 0.034 0.00302
Matt Clement58221 19.28 0.036 0.033 0.00295
Kyle Lohse60726 24.28 0.043 0.040 0.00283
Runelvys Hernandez52316 14.53 0.031 0.028 0.00281
Derek Lowe70053 51.13 0.076 0.073 0.00267
John Patterson54316 14.59 0.029 0.027 0.00260
Jon Garland70638 36.20 0.054 0.051 0.00255
Pedro Martinez56419 17.64 0.034 0.031 0.00241
Noah Lowry59929 27.59 0.048 0.046 0.00235
Rodrigo Lopez70231 29.45 0.044 0.042 0.00220
Andy Pettitte64338 36.71 0.059 0.057 0.00200
Brandon Webb68943 41.95 0.062 0.061 0.00152
Brian Moehler53828 27.19 0.052 0.051 0.00151
John Smoltz69035 33.96 0.051 0.049 0.00151
Jeff Suppan62629 28.13 0.046 0.045 0.00139
Mark Buehrle75845 44.07 0.059 0.058 0.00122
Brian Lawrence65633 32.37 0.050 0.049 0.00095
Cory Lidle60730 29.65 0.049 0.049 0.00057
Freddy Garcia70828 27.87 0.040 0.039 0.00019
Tim Wakefield67824 24.27 0.035 0.036 -0.00040
Mike Mussina54527 27.33 0.050 0.050 -0.00061
Mark Hendrickson63323 23.51 0.036 0.037 -0.00080
Kris Benson56530 30.55 0.053 0.054 -0.00098
Chris Carpenter66742 42.99 0.063 0.064 -0.00148
Scott E Kazmir52215 15.87 0.029 0.030 -0.00166
Brett Tomko62520 21.07 0.032 0.034 -0.00172
Horacio Ramirez66743 44.16 0.064 0.066 -0.00174
Brandon Claussen52220 20.93 0.038 0.040 -0.00179
Paul Byrd68222 23.24 0.032 0.034 -0.00182
Bruce Chen59423 24.31 0.039 0.041 -0.00220
Matt Morris63326 27.67 0.041 0.044 -0.00264
Joel Pineiro62920 21.73 0.032 0.035 -0.00274
Joe Mays56423 24.63 0.041 0.044 -0.00289
Tom Glavine72042 44.09 0.058 0.061 -0.00290
Gustavo G Chacin65224 25.95 0.037 0.040 -0.00299
Chris Capuano63927 28.92 0.042 0.045 -0.00301
Jose Contreras59523 25.28 0.039 0.042 -0.00383
Bronson Arroyo68822 24.66 0.032 0.036 -0.00386
Doug Davis61527 29.53 0.044 0.048 -0.00411
Eric Milton63317 19.67 0.027 0.031 -0.00422
Brad A Halsey55021 23.39 0.038 0.043 -0.00435
John Lackey59824 26.70 0.040 0.045 -0.00452
Jason Johnson71833 36.44 0.046 0.051 -0.00480
Jason Marquis66423 26.42 0.035 0.040 -0.00514
Mike Maroth68319 22.62 0.028 0.033 -0.00530
Carlos Zambrano59234 37.18 0.057 0.063 -0.00536
Jamey Wright56322 25.48 0.039 0.045 -0.00618
Scott Elarton58511 14.74 0.019 0.025 -0.00640
Jeff Weaver67727 31.35 0.040 0.046 -0.00642
Esteban Loaiza66132 36.33 0.048 0.055 -0.00655
Victor Zambrano53222 25.55 0.041 0.048 -0.00668
Roy Oswalt74434 39.04 0.046 0.052 -0.00678
Tim Hudson60839 43.19 0.064 0.071 -0.00689
Doug Waechter5339 12.68 0.017 0.024 -0.00690
Bartolo Colon67817 22.27 0.025 0.033 -0.00777
Jarrod Washburn56716 20.83 0.028 0.037 -0.00852
Randy Johnson61823 29.00 0.037 0.047 -0.00970
Danny Haren64920 27.02 0.031 0.042 -0.01082
Joe M Blanton62418 24.81 0.029 0.040 -0.01091
Ryan Franklin64213 20.02 0.020 0.031 -0.01093
Jeff W Francis59417 23.50 0.029 0.040 -0.01095
Joe Kennedy51514 19.77 0.027 0.038 -0.01120
Cliff Lee62111 18.06 0.018 0.029 -0.01138
Jon Lieber68428 36.25 0.041 0.053 -0.01206
David Wells62215 23.11 0.024 0.037 -0.01304
C.C. Sabathia57417 24.76 0.030 0.043 -0.01351
Kevin Millwood57618 26.09 0.031 0.045 -0.01405
Brad Penny55525 32.86 0.045 0.059 -0.01417
Nate Robertson62424 33.38 0.038 0.053 -0.01504
Jeremy Bonderman57415 24.26 0.026 0.042 -0.01614
A.J. Burnett57719 28.56 0.033 0.049 -0.01657

Probabilistic Model of Range, Pitchers, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Kirk Saarloos55341 23.64 0.074 0.043 0.03140
Kenny Rogers66549 33.21 0.074 0.050 0.02374
Chan Ho Park50137 28.34 0.074 0.057 0.01729
Greg Maddux72852 39.57 0.071 0.054 0.01708
Jake Westbrook69353 42.60 0.076 0.061 0.01501
Kip Wells56234 26.31 0.060 0.047 0.01368
Mark Mulder65948 39.08 0.073 0.059 0.01354
Brett Myers58737 30.63 0.063 0.052 0.01085
Barry Zito65436 28.99 0.055 0.044 0.01072
Brad Radke65131 24.71 0.048 0.038 0.00966
Livan Hernandez79645 38.49 0.057 0.048 0.00818
Mark Redman57434 29.52 0.059 0.051 0.00781
Jose Lima59723 18.61 0.039 0.031 0.00736
Jamie Moyer68327 22.17 0.040 0.032 0.00707
Ramon Ortiz56725 21.04 0.044 0.037 0.00699
Roger Clemens57735 30.99 0.061 0.054 0.00696
Mark Buehrle75845 39.93 0.059 0.053 0.00669
Jake Peavy52124 21.18 0.046 0.041 0.00541
Matt Clement58221 18.00 0.036 0.031 0.00515
Johan Santana60429 25.97 0.048 0.043 0.00502
Aaron Harang64327 23.78 0.042 0.037 0.00501
Dontrelle Willis71639 35.54 0.054 0.050 0.00484
Carlos Silva64130 27.28 0.047 0.043 0.00425
John Smoltz69035 32.28 0.051 0.047 0.00395
John Patterson54316 14.05 0.029 0.026 0.00359
Josh Fogg57126 23.96 0.046 0.042 0.00357
Javier Vazquez62634 31.83 0.054 0.051 0.00347
Woody Williams51319 17.22 0.037 0.034 0.00346
Zack Z Greinke62526 23.84 0.042 0.038 0.00346
Jon Garland70638 35.57 0.054 0.050 0.00344
Pedro Martinez56419 17.09 0.034 0.030 0.00339
Kyle Lohse60726 24.39 0.043 0.040 0.00266
Brandon Webb68943 41.27 0.062 0.060 0.00251
Josh Towers70632 30.29 0.045 0.043 0.00242
Tomo Ohka59627 25.58 0.045 0.043 0.00238
Runelvys Hernandez52316 14.79 0.031 0.028 0.00231
Horacio Ramirez66743 41.63 0.064 0.062 0.00206
Andy Pettitte64338 36.72 0.059 0.057 0.00199
Derek Lowe70053 51.70 0.076 0.074 0.00185
Rodrigo Lopez70231 29.79 0.044 0.042 0.00173
Noah Lowry59929 28.30 0.048 0.047 0.00117
Cory Lidle60730 29.53 0.049 0.049 0.00078
Mike Mussina54527 26.90 0.050 0.049 0.00018
Kris Benson56530 30.01 0.053 0.053 -0.00001
Gustavo G Chacin65224 24.10 0.037 0.037 -0.00016
Freddy Garcia70828 28.17 0.040 0.040 -0.00024
Brian Lawrence65633 33.40 0.050 0.051 -0.00060
Jeff Suppan62629 29.48 0.046 0.047 -0.00076
John Lackey59824 24.48 0.040 0.041 -0.00080
Chris Carpenter66742 42.73 0.063 0.064 -0.00109
Brian Moehler53828 28.60 0.052 0.053 -0.00112
Joel Pineiro62920 20.71 0.032 0.033 -0.00113
Paul Byrd68222 22.91 0.032 0.034 -0.00133
Scott E Kazmir52215 15.88 0.029 0.030 -0.00168
Mark Hendrickson63323 24.22 0.036 0.038 -0.00192
Jose Contreras59523 24.28 0.039 0.041 -0.00215
Brett Tomko62520 21.38 0.032 0.034 -0.00221
Matt Morris63326 27.69 0.041 0.044 -0.00267
Bruce Chen59423 24.79 0.039 0.042 -0.00301
Tim Wakefield67824 26.07 0.035 0.038 -0.00305
Chris Capuano63927 29.02 0.042 0.045 -0.00316
Brad A Halsey55021 22.76 0.038 0.041 -0.00321
Bronson Arroyo68822 24.45 0.032 0.036 -0.00355
Joe Mays56423 25.02 0.041 0.044 -0.00358
Brandon Claussen52220 21.92 0.038 0.042 -0.00368
Tom Glavine72042 44.70 0.058 0.062 -0.00375
Eric Milton63317 19.66 0.027 0.031 -0.00420
Roy Oswalt74434 37.16 0.046 0.050 -0.00424
Mike Maroth68319 22.11 0.028 0.032 -0.00455
Jason Marquis66423 26.23 0.035 0.040 -0.00486
Doug Davis61527 30.06 0.044 0.049 -0.00497
Carlos Zambrano59234 36.98 0.057 0.062 -0.00503
Tim Hudson60839 42.16 0.064 0.069 -0.00521
Scott Elarton58511 14.19 0.019 0.024 -0.00546
Victor Zambrano53222 24.96 0.041 0.047 -0.00556
Jeff Weaver67727 30.80 0.040 0.045 -0.00561
Jason Johnson71833 37.09 0.046 0.052 -0.00569
Jarrod Washburn56716 19.47 0.028 0.034 -0.00612
Jamey Wright56322 25.91 0.039 0.046 -0.00694
Doug Waechter5339 12.99 0.017 0.024 -0.00748
Randy Johnson61823 27.83 0.037 0.045 -0.00781
Bartolo Colon67817 22.31 0.025 0.033 -0.00783
Esteban Loaiza66132 37.66 0.048 0.057 -0.00857
Nate Robertson62424 29.58 0.038 0.047 -0.00894
Danny Haren64920 26.04 0.031 0.040 -0.00931
Joe M Blanton62418 24.14 0.029 0.039 -0.00984
Ryan Franklin64213 19.53 0.020 0.030 -0.01017
Joe Kennedy51514 19.33 0.027 0.038 -0.01035
Cliff Lee62111 18.37 0.018 0.030 -0.01187
Jeff W Francis59417 24.07 0.029 0.041 -0.01191
Jon Lieber68428 36.35 0.041 0.053 -0.01221
C.C. Sabathia57417 24.24 0.030 0.042 -0.01261
Brad Penny55525 32.70 0.045 0.059 -0.01388
Jeremy Bonderman57415 23.30 0.026 0.041 -0.01445
Kevin Millwood57618 26.42 0.031 0.046 -0.01462
David Wells62215 24.53 0.024 0.039 -0.01533
A.J. Burnett57719 28.72 0.033 0.050 -0.01684

You can see why Greg Maddux won all those gold gloves. I was expecting to see a differentiation between young and old, stocky and thin, lefty/righty, but I can't discern any of that just by looking at the data.

Posted by StatsGuru at 08:10 PM | Comments (7) | TrackBack (0)
February 05, 2006
Probabilistic Model of Range, 2005, Catchers, Groundballs
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As promised, here's the number of catchers with the popups removed.

Probabilistic Model of Range, Catchers, 2005, Original Model, Groundballs Only (Grounders and Bunts)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Dioner F Navarro60316 8.03 0.027 0.013 0.01321
Javier Valentin75413 8.51 0.017 0.011 0.00596
Vance Wilson6267 4.53 0.011 0.007 0.00395
Danny Ardoin87612 9.15 0.014 0.010 0.00325
Ivan Rodriguez156617 11.96 0.011 0.008 0.00322
Johnny Estrada130724 20.66 0.018 0.016 0.00256
Chris R Snyder143517 13.80 0.012 0.010 0.00223
Yorvit Torrealba76810 8.31 0.013 0.011 0.00220
Mike Piazza116117 14.84 0.015 0.013 0.00186
Gregg Zaun160313 10.42 0.008 0.006 0.00161
Jason LaRue130921 18.93 0.016 0.014 0.00158
Sal Fasano6486 5.16 0.009 0.008 0.00130
Jose Molina6356 5.19 0.009 0.008 0.00128
Yadier B Molina155418 16.20 0.012 0.010 0.00116
Geronimo Gil5024 3.53 0.008 0.007 0.00093
Mike Redmond6082 1.61 0.003 0.003 0.00065
JD Closser8529 8.63 0.011 0.010 0.00044
John R Buck150011 10.37 0.007 0.007 0.00042
Rod Barajas159210 9.47 0.006 0.006 0.00033
Jason Phillips120414 13.63 0.012 0.011 0.00030
Chris Widger5034 3.90 0.008 0.008 0.00020
Jorge Posada172120 19.66 0.012 0.011 0.00020
A.J. Pierzynski15459 8.99 0.006 0.006 0.00001
Mike Matheny158214 14.28 0.009 0.009 -0.00018
Mike Lieberthal141315 15.34 0.011 0.011 -0.00024
Gary Bennett6909 9.21 0.013 0.013 -0.00030
Jason Kendall175312 12.54 0.007 0.007 -0.00031
Humberto Cota99013 13.34 0.013 0.013 -0.00034
Henry Blanco5565 5.39 0.009 0.010 -0.00071
Miguel Olivo9638 8.78 0.008 0.009 -0.00081
Jason Varitek15927 8.44 0.004 0.005 -0.00091
Sandy Alomar Jr.5164 4.53 0.008 0.009 -0.00103
Joe Mauer14317 9.28 0.005 0.006 -0.00160
Chad Moeller6556 7.07 0.009 0.011 -0.00163
Brad Ausmus153718 20.51 0.012 0.013 -0.00163
Ryan M Doumit6147 8.01 0.011 0.013 -0.00164
Victor Martinez175710 13.02 0.006 0.007 -0.00172
Damian Miller122812 14.17 0.010 0.012 -0.00177
Brian M McCann69312 13.31 0.017 0.019 -0.00189
Brian Schneider125610 13.03 0.008 0.010 -0.00241
Bengie Molina11544 7.20 0.003 0.006 -0.00277
Einar Diaz5084 5.50 0.008 0.011 -0.00296
Toby Hall14176 10.50 0.004 0.007 -0.00317
Todd Pratt5956 7.93 0.010 0.013 -0.00324
Michael Barrett148913 17.82 0.009 0.012 -0.00324
Paul Lo Duca153712 17.56 0.008 0.011 -0.00362
Ramon R Castro8305 8.80 0.006 0.011 -0.00458
Ramon Hernandez11277 12.32 0.006 0.011 -0.00472
Javy Lopez9030 5.67 0.000 0.006 -0.00628

Probabilistic Model of Range, Catchers, 2005, Smoothed Visiting Player Model, Ground Balls Only (Grounders + Bunts)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Dioner F Navarro60316 8.37 0.027 0.014 0.01265
Javier Valentin75413 7.15 0.017 0.009 0.00775
Danny Ardoin87612 7.69 0.014 0.009 0.00492
Jason LaRue130921 15.95 0.016 0.012 0.00386
Vance Wilson6267 4.74 0.011 0.008 0.00361
Ivan Rodriguez156617 11.59 0.011 0.007 0.00345
Johnny Estrada130724 19.76 0.018 0.015 0.00324
Yorvit Torrealba76810 7.56 0.013 0.010 0.00318
Chris R Snyder143517 12.80 0.012 0.009 0.00293
Yadier B Molina155418 14.51 0.012 0.009 0.00225
Chris Widger5034 3.14 0.008 0.006 0.00171
Mike Lieberthal141315 12.84 0.011 0.009 0.00153
Gregg Zaun160313 10.80 0.008 0.007 0.00138
Mike Redmond6082 1.21 0.003 0.002 0.00131
JD Closser8529 7.89 0.011 0.009 0.00130
Jose Molina6356 5.19 0.009 0.008 0.00127
Sal Fasano6486 5.19 0.009 0.008 0.00125
Jason Phillips120414 12.86 0.012 0.011 0.00095
Geronimo Gil5024 3.56 0.008 0.007 0.00088
Gary Bennett6909 8.73 0.013 0.013 0.00039
Jorge Posada172120 19.51 0.012 0.011 0.00028
Henry Blanco5565 4.87 0.009 0.009 0.00024
Mike Piazza116117 16.73 0.015 0.014 0.00023
Jason Kendall175312 12.56 0.007 0.007 -0.00032
A.J. Pierzynski15459 9.51 0.006 0.006 -0.00033
John R Buck150011 11.59 0.007 0.008 -0.00040
Sandy Alomar Jr.5164 4.21 0.008 0.008 -0.00040
Brian M McCann69312 12.44 0.017 0.018 -0.00063
Rod Barajas159210 11.22 0.006 0.007 -0.00077
Ryan M Doumit6147 7.49 0.011 0.012 -0.00080
Damian Miller122812 13.03 0.010 0.011 -0.00084
Todd Pratt5956 6.54 0.010 0.011 -0.00090
Joe Mauer14317 8.37 0.005 0.006 -0.00095
Mike Matheny158214 15.64 0.009 0.010 -0.00104
Victor Martinez175710 12.01 0.006 0.007 -0.00114
Humberto Cota99013 14.30 0.013 0.014 -0.00131
Miguel Olivo9638 9.27 0.008 0.010 -0.00132
Jason Varitek15927 9.22 0.004 0.006 -0.00140
Brad Ausmus153718 20.37 0.012 0.013 -0.00154
Bengie Molina11544 6.67 0.003 0.006 -0.00231
Chad Moeller6556 7.54 0.009 0.012 -0.00236
Einar Diaz5084 5.26 0.008 0.010 -0.00248
Michael Barrett148913 17.22 0.009 0.012 -0.00283
Brian Schneider125610 13.91 0.008 0.011 -0.00312
Toby Hall14176 10.66 0.004 0.008 -0.00329
Paul Lo Duca153712 17.12 0.008 0.011 -0.00333
Ramon Hernandez11277 10.86 0.006 0.010 -0.00343
Ramon R Castro8305 8.32 0.006 0.010 -0.00400
Javy Lopez9030 5.84 0.000 0.006 -0.00647

When I saw the result for Javy Lopez, I needed to go back and check the data. Sure enough, Lopez did not make an out on a bunt or ground ball in front of the plate in 2005. With the model estimating 5 to 6 outs, he didn't have many opportunities. Maybe opponents didn't bunt much against the Orioles?

Dodger youngster Dioner Navarro continues to look good, but Mike Matheny takes a big hit. Are popups tougher to catch in San Francisco? I'd imagine that would be true if they were still playing at Candlestick Point.

Posted by StatsGuru at 03:16 PM | Comments (3) | TrackBack (0)
Probabilistic Model of Range, 2005, Catchers
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Range is not a big part of being a catcher, but they still field balls in play, and it's always nice to know who can handle the nubber in front of home plate.

Probabilistic Model of Range, Catchers, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Dioner F Navarro130728 20.87 0.021 0.016 0.00546
Vance Wilson132418 10.87 0.014 0.008 0.00539
Kelly Stinnett103818 13.11 0.017 0.013 0.00472
Mike Matheny355252 38.35 0.015 0.011 0.00384
Geronimo Gil103814 11.13 0.013 0.011 0.00277
Ryan M Doumit132720 16.35 0.015 0.012 0.00275
Yorvit Torrealba163728 23.66 0.017 0.014 0.00265
Danny Ardoin185125 20.87 0.014 0.011 0.00223
Jason Varitek353648 40.28 0.014 0.011 0.00218
Jason Kendall380241 36.12 0.011 0.009 0.00128
Gary Bennett163126 23.93 0.016 0.015 0.00127
Humberto Cota212727 24.74 0.013 0.012 0.00106
JD Closser182223 21.21 0.013 0.012 0.00098
Gregg Zaun335733 29.72 0.010 0.009 0.00098
Ivan Rodriguez320337 33.93 0.012 0.011 0.00096
Jason LaRue297845 42.37 0.015 0.014 0.00088
Brad Ausmus309543 40.33 0.014 0.013 0.00086
Joe Mauer307433 30.66 0.011 0.010 0.00076
Chris Widger10599 8.42 0.008 0.008 0.00055
Johnny Estrada260734 32.64 0.013 0.013 0.00052
Mike Piazza244231 29.85 0.013 0.012 0.00047
Chris R Snyder284833 32.12 0.012 0.011 0.00031
Yadier B Molina287231 30.12 0.011 0.010 0.00030
Jorge Posada344647 46.84 0.014 0.014 0.00005
Ramon Hernandez245928 27.87 0.011 0.011 0.00005
A.J. Pierzynski336035 34.97 0.010 0.010 0.00001
Rod Barajas332534 34.15 0.010 0.010 -0.00004
John R Buck323135 35.24 0.011 0.011 -0.00007
Sal Fasano132411 11.22 0.008 0.008 -0.00016
Jose Molina141315 15.26 0.011 0.011 -0.00018
Henry Blanco114114 14.46 0.012 0.013 -0.00040
Sandy Alomar Jr.104111 11.49 0.011 0.011 -0.00047
Javier Valentin164418 18.94 0.011 0.012 -0.00057
Matt A Treanor110914 14.94 0.013 0.013 -0.00085
Mike Lieberthal295930 32.58 0.010 0.011 -0.00087
Michael Barrett297629 31.88 0.010 0.011 -0.00097
Miguel Olivo213219 21.30 0.009 0.010 -0.00108
Damian Miller277835 38.00 0.013 0.014 -0.00108
Jason Phillips240125 27.71 0.010 0.012 -0.00113
Victor Martinez372830 34.37 0.008 0.009 -0.00117
John Flaherty101614 15.23 0.014 0.015 -0.00121
Brian Schneider288329 33.06 0.010 0.011 -0.00141
Chad Moeller147416 18.09 0.011 0.012 -0.00142
Toby Hall335930 35.02 0.009 0.010 -0.00149
Todd Pratt125212 13.97 0.010 0.011 -0.00158
Brian M McCann141018 20.40 0.013 0.014 -0.00171
Mike Redmond12195 7.23 0.004 0.006 -0.00183
Bengie Molina263523 28.49 0.009 0.011 -0.00208
Paul Lo Duca311031 37.68 0.010 0.012 -0.00215
Ramon R Castro182516 21.06 0.009 0.012 -0.00277
Javy Lopez191613 20.55 0.007 0.011 -0.00394
Pat Borders10168 13.13 0.008 0.013 -0.00505

Probabilistic Model of Range, Catchers, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Kelly Stinnett103818 11.36 0.017 0.011 0.00640
Dioner F Navarro130728 20.67 0.021 0.016 0.00561
Vance Wilson132418 11.08 0.014 0.008 0.00522
Geronimo Gil103814 10.35 0.013 0.010 0.00351
Mike Matheny355252 39.57 0.015 0.011 0.00350
Ryan M Doumit132720 15.63 0.015 0.012 0.00329
Yorvit Torrealba163728 22.61 0.017 0.014 0.00329
Danny Ardoin185125 20.36 0.014 0.011 0.00251
Gary Bennett163126 22.06 0.016 0.014 0.00241
Chris Widger10599 6.52 0.008 0.006 0.00234
Jason Varitek353648 40.91 0.014 0.012 0.00200
Jason LaRue297845 39.39 0.015 0.013 0.00188
Jason Kendall380241 35.43 0.011 0.009 0.00147
Brad Ausmus309543 38.81 0.014 0.013 0.00135
Gregg Zaun335733 29.22 0.010 0.009 0.00113
Ivan Rodriguez320337 33.75 0.012 0.011 0.00102
Johnny Estrada260734 31.74 0.013 0.012 0.00087
Ramon Hernandez245928 25.92 0.011 0.011 0.00085
JD Closser182223 21.83 0.013 0.012 0.00064
Jorge Posada344647 44.83 0.014 0.013 0.00063
Humberto Cota212727 25.77 0.013 0.012 0.00058
Chris R Snyder284833 31.50 0.012 0.011 0.00053
Joe Mauer307433 31.53 0.011 0.010 0.00048
Sandy Alomar Jr.104111 10.50 0.011 0.010 0.00048
Henry Blanco114114 13.67 0.012 0.012 0.00029
Matt A Treanor110914 13.80 0.013 0.012 0.00018
Mike Lieberthal295930 29.59 0.010 0.010 0.00014
Sal Fasano132411 10.92 0.008 0.008 0.00006
Javier Valentin164418 17.94 0.011 0.011 0.00003
Yadier B Molina287231 31.00 0.011 0.011 0.00000
Mike Piazza244231 31.01 0.013 0.013 -0.00000
Jose Molina141315 15.12 0.011 0.011 -0.00009
Todd Pratt125212 12.14 0.010 0.010 -0.00011
Victor Martinez372830 31.08 0.008 0.008 -0.00029
A.J. Pierzynski336035 36.01 0.010 0.011 -0.00030
John Flaherty101614 14.37 0.014 0.014 -0.00037
John R Buck323135 37.01 0.011 0.011 -0.00062
Jason Phillips240125 26.58 0.010 0.011 -0.00066
Rod Barajas332534 36.57 0.010 0.011 -0.00077
Michael Barrett297629 31.63 0.010 0.011 -0.00088
Damian Miller277835 37.68 0.013 0.014 -0.00097
Brian Schneider288329 32.30 0.010 0.011 -0.00115
Toby Hall335930 34.78 0.009 0.010 -0.00142
Bengie Molina263523 26.81 0.009 0.010 -0.00145
Mike Redmond12195 6.89 0.004 0.006 -0.00155
Miguel Olivo213219 22.53 0.009 0.011 -0.00165
Paul Lo Duca311031 36.72 0.010 0.012 -0.00184
Chad Moeller147416 18.98 0.011 0.013 -0.00202
Brian M McCann141018 21.56 0.013 0.015 -0.00252
Ramon R Castro182516 20.91 0.009 0.011 -0.00269
Javy Lopez191613 21.27 0.007 0.011 -0.00431
Pat Borders10168 14.20 0.008 0.014 -0.00610

You get a feeling why the Orioles want to move Javy Lopez to first base. It's also clear that Mike Matheny is properly lauded for his defense. It's also possible Paul DePodseta picked up a cat behind the plate in Dioner Navarro.

I'll run these number shortly on just ground balls and bunts.

Posted by StatsGuru at 01:43 PM | Comments (0) | TrackBack (0)
February 03, 2006
Defensive Comparisons
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David Gassko pits the various Range ratings against UZR in two articles. One, published at The Hardball Times is an overview, while the one on Statistically Speaking does the math. Thanks to David for bringing all this information together!

Posted by StatsGuru at 04:53 PM | Comments (0) | TrackBack (0)
Defensive Review
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Jon Weisman of Dodger Thoughts pens a piece for Sports Illustrated on the latest in defensive statistics. A big thank-you to Jon for mentioning the Probabilistic Model of Range.

Posted by StatsGuru at 01:19 PM | Comments (1) | TrackBack (0)
February 02, 2006
Probabilistic Model of Range, 2005, First Basemen and Grounders
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Here's the data for first basemen, looking only at ground balls (including bunts on the ground). (Data on all balls in play is here.)

Probabilistic Model of Range, First Basemen, 2005, Original Model, Groundballs Only (Grounders and Bunts)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Tino Martinez1200126 99.28 0.105 0.083 0.02227
Doug Mientkiewicz969102 85.57 0.105 0.088 0.01696
John Olerud58172 62.22 0.124 0.107 0.01683
Lance Niekro75285 72.77 0.113 0.097 0.01627
Chad A Tracy1013105 88.90 0.104 0.088 0.01589
Mark Teixeira2131250 218.92 0.117 0.103 0.01458
Ben Broussard1502144 124.02 0.096 0.083 0.01330
Darin Erstad1694202 182.56 0.119 0.108 0.01147
Daryle Ward1307122 108.35 0.093 0.083 0.01044
Hee Seop Choi1054130 119.79 0.123 0.114 0.00969
Albert Pujols2226243 223.51 0.109 0.100 0.00875
Lyle Overbay1658175 161.04 0.106 0.097 0.00842
Ryan J Howard964106 97.91 0.110 0.102 0.00839
Derrek Lee1969203 186.70 0.103 0.095 0.00828
Shea Hillenbrand90997 90.46 0.107 0.100 0.00719
Paul Konerko1799181 169.18 0.101 0.094 0.00657
Mike Lamb62464 60.17 0.103 0.096 0.00613
Justin Morneau1703169 158.65 0.099 0.093 0.00608
Phil Nevin872104 99.95 0.119 0.115 0.00465
Kevin Millar1177155 149.68 0.132 0.127 0.00452
Todd Helton1870218 210.39 0.117 0.113 0.00407
Eric Hinske1270147 142.33 0.116 0.112 0.00368
J.T. Snow1183130 126.05 0.110 0.107 0.00334
Nick Johnson1498195 190.35 0.130 0.127 0.00311
Travis Lee1262136 134.69 0.108 0.107 0.00104
Richie Sexson1835197 195.31 0.107 0.106 0.00092
Matt Stairs79679 78.88 0.099 0.099 0.00015
Tony Clark96082 82.06 0.085 0.085 -0.00006
Dan R Johnson1211119 119.33 0.098 0.099 -0.00028
Scott Hatteberg61571 71.20 0.115 0.116 -0.00032
Jim Thome61258 58.40 0.095 0.095 -0.00066
Brad Eldred60640 40.51 0.066 0.067 -0.00084
Chris B Shelton1116109 110.64 0.098 0.099 -0.00147
Sean Casey1651173 176.48 0.105 0.107 -0.00211
Julio Franco67867 68.73 0.099 0.101 -0.00255
Mike Sweeney66781 82.80 0.121 0.124 -0.00269
Carlos Pena66064 67.44 0.097 0.102 -0.00522
Lance Berkman1052111 116.62 0.106 0.111 -0.00534
Adam LaRoche1576165 174.64 0.105 0.111 -0.00612
Rafael Palmeiro111999 107.12 0.088 0.096 -0.00726
Carlos Delgado1743179 193.49 0.103 0.111 -0.00831
Jason Giambi86463 70.96 0.073 0.082 -0.00922
Olmedo Saenz69659 67.77 0.085 0.097 -0.01260

Probabilistic Model of Range, First Baseman, 2005, Smoothed Visiting Player Model, Ground Balls Only (Grounders + Bunts)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Tino Martinez1200126 100.13 0.105 0.083 0.02156
Doug Mientkiewicz969102 82.53 0.105 0.085 0.02009
John Olerud58172 60.58 0.124 0.104 0.01965
Mark Teixeira2131250 210.20 0.117 0.099 0.01868
Chad A Tracy1013105 87.43 0.104 0.086 0.01735
Lance Niekro75285 72.75 0.113 0.097 0.01629
Darin Erstad1694202 178.07 0.119 0.105 0.01412
Ben Broussard1502144 123.30 0.096 0.082 0.01378
Lyle Overbay1658175 158.44 0.106 0.096 0.00999
Hee Seop Choi1054130 119.67 0.123 0.114 0.00980
Albert Pujols2226243 222.85 0.109 0.100 0.00905
Daryle Ward1307122 110.67 0.093 0.085 0.00867
Derrek Lee1969203 186.17 0.103 0.095 0.00855
Ryan J Howard964106 98.58 0.110 0.102 0.00769
Mike Lamb62464 59.61 0.103 0.096 0.00703
Paul Konerko1799181 169.28 0.101 0.094 0.00652
Kevin Millar1177155 147.56 0.132 0.125 0.00632
Todd Helton1870218 207.21 0.117 0.111 0.00577
Justin Morneau1703169 159.45 0.099 0.094 0.00561
Phil Nevin872104 99.62 0.119 0.114 0.00502
Shea Hillenbrand90997 92.46 0.107 0.102 0.00500
J.T. Snow1183130 124.14 0.110 0.105 0.00496
Nick Johnson1498195 189.23 0.130 0.126 0.00385
Eric Hinske1270147 142.13 0.116 0.112 0.00384
Travis Lee1262136 132.07 0.108 0.105 0.00311
Jim Thome61258 57.10 0.095 0.093 0.00146
Tony Clark96082 80.63 0.085 0.084 0.00143
Matt Stairs79679 78.83 0.099 0.099 0.00022
Scott Hatteberg61571 70.94 0.115 0.115 0.00010
Brad Eldred60640 40.07 0.066 0.066 -0.00011
Richie Sexson1835197 198.28 0.107 0.108 -0.00070
Dan R Johnson1211119 120.02 0.098 0.099 -0.00084
Mike Sweeney66781 82.36 0.121 0.123 -0.00204
Sean Casey1651173 176.58 0.105 0.107 -0.00217
Julio Franco67867 68.92 0.099 0.102 -0.00283
Chris B Shelton1116109 112.73 0.098 0.101 -0.00335
Lance Berkman1052111 114.66 0.106 0.109 -0.00348
Carlos Pena66064 68.48 0.097 0.104 -0.00678
Rafael Palmeiro111999 107.23 0.088 0.096 -0.00736
Adam LaRoche1576165 177.37 0.105 0.113 -0.00785
Carlos Delgado1743179 192.77 0.103 0.111 -0.00790
Jason Giambi86463 70.89 0.073 0.082 -0.00914
Olmedo Saenz69659 67.07 0.085 0.096 -0.01160

That's a big jump for Doug Mientkiewicz.

Posted by StatsGuru at 08:19 AM | Comments (3) | TrackBack (0)
February 01, 2006
Probabilistic Model of Range, 2005, First Basemen
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It's time for the statistics for the first basemen:

Probabilistic Model of Range, First Basemen, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Tino Martinez2398178 141.40 0.074 0.059 0.01526
Jose Hernandez102177 63.94 0.075 0.063 0.01279
John Olerud1341107 90.47 0.080 0.067 0.01233
Ben Broussard3160220 181.42 0.070 0.057 0.01221
Chad A Tracy2034155 131.35 0.076 0.065 0.01163
Travis Lee2952240 208.94 0.081 0.071 0.01052
Mark Teixeira4407347 301.54 0.079 0.068 0.01032
Doug Mientkiewicz2055153 132.12 0.074 0.064 0.01016
Darin Erstad3822304 266.06 0.080 0.070 0.00993
Lance Niekro1636132 117.41 0.081 0.072 0.00892
Paul Konerko3860288 255.66 0.075 0.066 0.00838
Daryle Ward2779171 153.58 0.062 0.055 0.00627
Ryan J Howard2013154 141.68 0.077 0.070 0.00612
Derrek Lee3959288 265.84 0.073 0.067 0.00560
Albert Pujols4150318 294.88 0.077 0.071 0.00557
Lyle Overbay3764269 248.13 0.071 0.066 0.00554
Kevin Millar2581218 203.73 0.084 0.079 0.00553
Hee Seop Choi2084165 154.19 0.079 0.074 0.00519
Mark Sweeney101779 75.04 0.078 0.074 0.00389
Eduardo Perez105868 63.94 0.064 0.060 0.00384
Todd Helton3943293 278.32 0.074 0.071 0.00372
Mike Lamb125585 80.40 0.068 0.064 0.00367
J.T. Snow2617197 188.76 0.075 0.072 0.00315
Nick Johnson3447286 276.94 0.083 0.080 0.00263
Chris B Shelton2337152 146.52 0.065 0.063 0.00234
Matt Stairs1679109 106.46 0.065 0.063 0.00151
Justin Morneau3621256 251.36 0.071 0.069 0.00128
Scott Hatteberg1335101 99.32 0.076 0.074 0.00126
Sean Casey3663255 251.80 0.070 0.069 0.00087
Mike Sweeney1377112 111.02 0.081 0.081 0.00071
Dan R Johnson2560190 188.77 0.074 0.074 0.00048
Shea Hillenbrand1835130 129.88 0.071 0.071 0.00007
Richie Sexson4177269 269.29 0.064 0.064 -0.00007
Tony Clark2011124 124.19 0.062 0.062 -0.00009
Phil Nevin1884139 140.51 0.074 0.075 -0.00080
Brad Eldred126864 65.58 0.050 0.052 -0.00125
Adam LaRoche3240241 245.85 0.074 0.076 -0.00150
Carlos Delgado3649257 264.15 0.070 0.072 -0.00196
Eric Hinske2676194 199.28 0.072 0.074 -0.00197
Lance Berkman2121144 151.70 0.068 0.072 -0.00363
Jim Thome133776 81.29 0.057 0.061 -0.00396
Rafael Palmeiro2281144 153.34 0.063 0.067 -0.00410
Julio Franco131884 90.10 0.064 0.068 -0.00463
Carlos Pena136398 105.67 0.072 0.078 -0.00563
Jason Giambi179794 107.90 0.052 0.060 -0.00773
Olmedo Saenz142677 91.72 0.054 0.064 -0.01032

Probabilistic Model of Range, First Baseman, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Tino Martinez2398178 140.67 0.074 0.059 0.01557
Jose Hernandez102177 62.11 0.075 0.061 0.01458
John Olerud1341107 88.32 0.080 0.066 0.01393
Chad A Tracy2034155 128.16 0.076 0.063 0.01320
Doug Mientkiewicz2055153 126.32 0.074 0.061 0.01298
Mark Teixeira4407347 290.41 0.079 0.066 0.01284
Ben Broussard3160220 180.81 0.070 0.057 0.01240
Travis Lee2952240 206.10 0.081 0.070 0.01148
Darin Erstad3822304 261.19 0.080 0.068 0.01120
Lance Niekro1636132 115.97 0.081 0.071 0.00980
Paul Konerko3860288 257.63 0.075 0.067 0.00787
Lyle Overbay3764269 242.11 0.071 0.064 0.00714
Derrek Lee3959288 260.24 0.073 0.066 0.00701
Eduardo Perez105868 61.40 0.064 0.058 0.00624
Albert Pujols4150318 292.89 0.077 0.071 0.00605
Kevin Millar2581218 202.57 0.084 0.078 0.00598
Daryle Ward2779171 154.99 0.062 0.056 0.00576
Ryan J Howard2013154 142.50 0.077 0.071 0.00571
Todd Helton3943293 272.69 0.074 0.069 0.00515
Hee Seop Choi2084165 155.34 0.079 0.075 0.00464
Mike Lamb125585 79.57 0.068 0.063 0.00433
Mark Sweeney101779 74.87 0.078 0.074 0.00406
J.T. Snow2617197 187.48 0.075 0.072 0.00364
Scott Hatteberg1335101 97.26 0.076 0.073 0.00280
Nick Johnson3447286 276.62 0.083 0.080 0.00272
Chris B Shelton2337152 147.75 0.065 0.063 0.00182
Tony Clark2011124 121.13 0.062 0.060 0.00143
Sean Casey3663255 249.97 0.070 0.068 0.00137
Justin Morneau3621256 251.30 0.071 0.069 0.00130
Matt Stairs1679109 107.03 0.065 0.064 0.00117
Mike Sweeney1377112 111.16 0.081 0.081 0.00061
Brad Eldred126864 64.05 0.050 0.051 -0.00004
Dan R Johnson2560190 190.41 0.074 0.074 -0.00016
Phil Nevin1884139 139.54 0.074 0.074 -0.00028
Richie Sexson4177269 273.34 0.064 0.065 -0.00104
Carlos Delgado3649257 261.78 0.070 0.072 -0.00131
Shea Hillenbrand1835130 133.25 0.071 0.073 -0.00177
Adam LaRoche3240241 247.17 0.074 0.076 -0.00191
Eric Hinske2676194 201.57 0.072 0.075 -0.00283
Lance Berkman2121144 152.07 0.068 0.072 -0.00381
Jim Thome133776 82.13 0.057 0.061 -0.00459
Rafael Palmeiro2281144 154.66 0.063 0.068 -0.00467
Julio Franco131884 90.24 0.064 0.068 -0.00473
Jason Giambi179794 107.12 0.052 0.060 -0.00730
Carlos Pena136398 108.12 0.072 0.079 -0.00742
Olmedo Saenz142677 90.94 0.054 0.064 -0.00978

When Olerud's name is near the top and Giambi's name is near the bottom, I become very comfortable with the data. It will be interesting to see what the ground ball data show us.

Posted by StatsGuru at 04:22 PM | Comments (5) | TrackBack (0)
January 31, 2006
Probabilistic Model of Range, 2005, Leftfielders
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Here are the results for the left fielders.

Probabilistic Model of Range, Leftfielders, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Reed Johnson1838134 112.67 0.073 0.061 0.01160
B.J. Surhoff113276 64.82 0.067 0.057 0.00987
Chris A Burke1861120 101.65 0.064 0.055 0.00986
Eric Byrnes2450209 185.38 0.085 0.076 0.00964
Coco Crisp3623294 261.44 0.081 0.072 0.00899
Jay Payton1379107 94.67 0.078 0.069 0.00894
Brian Jordan111475 65.13 0.067 0.058 0.00886
Carl Crawford4004341 309.93 0.085 0.077 0.00776
Jayson Werth103384 76.11 0.081 0.074 0.00764
Ryan Langerhans120291 83.01 0.076 0.069 0.00664
Randy Winn2564226 209.19 0.088 0.082 0.00656
Matt T Holliday3371236 214.67 0.070 0.064 0.00633
Scott Podsednik3200260 240.02 0.081 0.075 0.00625
Luis Gonzalez4115270 246.42 0.066 0.060 0.00573
Kevin Mench3188231 213.63 0.072 0.067 0.00545
Kelly A Johnson2056166 154.90 0.081 0.075 0.00540
Moises Alou1786132 123.81 0.074 0.069 0.00459
Hideki Matsui3024218 204.33 0.072 0.068 0.00452
Ryan M Church105277 72.59 0.073 0.069 0.00419
Carlos Lee4151307 289.83 0.074 0.070 0.00414
Cliff Floyd3867283 267.59 0.073 0.069 0.00398
Shannon Stewart3503249 237.55 0.071 0.068 0.00327
Pedro Feliz1951138 131.98 0.071 0.068 0.00308
Jason Bay3662266 257.22 0.073 0.070 0.00240
Bobby Kielty132099 96.77 0.075 0.073 0.00169
Raul Ibanez1463106 103.57 0.072 0.071 0.00166
Larry Bigbie146498 96.20 0.067 0.066 0.00123
Frank Catalanotto2383163 160.10 0.068 0.067 0.00122
Tony Womack109072 70.71 0.066 0.065 0.00119
Reggie Sanders1902108 105.77 0.057 0.056 0.00117
Terrence Long2599166 163.61 0.064 0.063 0.00092
Todd Hollandsworth1746103 101.45 0.059 0.058 0.00089
Adam Dunn3517246 243.81 0.070 0.069 0.00062
Ryan Klesko2849204 202.51 0.072 0.071 0.00052
Rondell White1644119 118.43 0.072 0.072 0.00035
Craig Monroe163099 102.25 0.061 0.063 -0.00199
Marlon Byrd1195100 102.84 0.084 0.086 -0.00238
Garret Anderson2776201 208.94 0.072 0.075 -0.00286
Manny Ramirez3956243 254.92 0.061 0.064 -0.00301
Pat Burrell3846236 247.60 0.061 0.064 -0.00302
Ricky Ledee123057 61.47 0.046 0.050 -0.00363
David Dellucci124784 90.32 0.067 0.072 -0.00507
Miguel Cabrera3336188 208.43 0.056 0.062 -0.00612

Probabilistic Model of Range, Leftfielders, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Reed Johnson1838134 112.70 0.073 0.061 0.01159
Eric Byrnes2450209 184.60 0.085 0.075 0.00996
Jay Payton1379107 93.36 0.078 0.068 0.00989
B.J. Surhoff113276 65.32 0.067 0.058 0.00944
Chris A Burke1861120 103.38 0.064 0.056 0.00893
Brian Jordan111475 65.31 0.067 0.059 0.00870
Coco Crisp3623294 262.57 0.081 0.072 0.00868
Carl Crawford4004341 311.50 0.085 0.078 0.00737
Jayson Werth103384 76.52 0.081 0.074 0.00724
Matt T Holliday3371236 214.13 0.070 0.064 0.00649
Randy Winn2564226 209.68 0.088 0.082 0.00637
Moises Alou1786132 120.91 0.074 0.068 0.00621
Ryan Langerhans120291 83.77 0.076 0.070 0.00601
Scott Podsednik3200260 241.70 0.081 0.076 0.00572
Luis Gonzalez4115270 246.89 0.066 0.060 0.00562
Kelly A Johnson2056166 155.95 0.081 0.076 0.00489
Kevin Mench3188231 218.15 0.072 0.068 0.00403
Cliff Floyd3867283 268.14 0.073 0.069 0.00384
Carlos Lee4151307 291.09 0.074 0.070 0.00383
Ryan M Church105277 73.00 0.073 0.069 0.00380
Hideki Matsui3024218 206.61 0.072 0.068 0.00377
Bobby Kielty132099 94.48 0.075 0.072 0.00342
Shannon Stewart3503249 238.42 0.071 0.068 0.00302
Pedro Feliz1951138 132.16 0.071 0.068 0.00299
Frank Catalanotto2383163 157.64 0.068 0.066 0.00225
Larry Bigbie146498 95.08 0.067 0.065 0.00200
Jason Bay3662266 259.91 0.073 0.071 0.00166
Raul Ibanez1463106 104.80 0.072 0.072 0.00082
Adam Dunn3517246 243.41 0.070 0.069 0.00074
Reggie Sanders1902108 107.67 0.057 0.057 0.00017
Todd Hollandsworth1746103 103.68 0.059 0.059 -0.00039
Terrence Long2599166 167.45 0.064 0.064 -0.00056
Tony Womack109072 72.73 0.066 0.067 -0.00067
Ryan Klesko2849204 205.93 0.072 0.072 -0.00068
Rondell White1644119 120.57 0.072 0.073 -0.00096
Garret Anderson2776201 207.61 0.072 0.075 -0.00238
Marlon Byrd1195100 103.25 0.084 0.086 -0.00272
Manny Ramirez3956243 258.33 0.061 0.065 -0.00388
Craig Monroe163099 105.91 0.061 0.065 -0.00424
Ricky Ledee123057 63.60 0.046 0.052 -0.00536
Pat Burrell3846236 257.78 0.061 0.067 -0.00566
Miguel Cabrera3336188 211.55 0.056 0.063 -0.00706
David Dellucci124784 92.88 0.067 0.074 -0.00712

The more I look at the two models, the more I like the visiting smoothed model. Surhoff coming in near the top of this list bothers me a bit, but it is a small sample for him. The visiting model ranks him a little lower. I also like that the visiting model puts Klesko in negative territory.

I didn't expect Miguel Cabrera to be that bad. I guess it's a good thing he's moving to third base.

Posted by StatsGuru at 08:22 PM | Comments (11) | TrackBack (0)
Probabilistic Model of Range, 2005, Rightfielders
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Time to look at the range of the rightfielders.

Probabilistic Model of Range, Rightfielders, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Dustan Mohr1216103 80.81 0.085 0.066 0.01825
Jose Cruz1296110 90.55 0.085 0.070 0.01501
Mike Cameron1846137 122.46 0.074 0.066 0.00788
Jeff B Francoeur1837131 116.89 0.071 0.064 0.00768
Vladimir Guerrero3140245 225.72 0.078 0.072 0.00614
J.D. Drew121983 75.56 0.068 0.062 0.00610
Nick T Swisher2993197 179.73 0.066 0.060 0.00577
Ryan Langerhans113875 69.32 0.066 0.061 0.00499
Ichiro Suzuki4432383 361.02 0.086 0.081 0.00496
Brian Giles3704295 277.51 0.080 0.075 0.00472
Jason Lane3253225 210.46 0.069 0.065 0.00447
Shawn Green3225232 218.32 0.072 0.068 0.00424
Gary Sheffield3415240 225.92 0.070 0.066 0.00412
Geoff Jenkins3673307 292.30 0.084 0.080 0.00400
Jay Gibbons1710133 126.19 0.078 0.074 0.00398
Chad A Tracy117386 82.33 0.073 0.070 0.00313
Jeromy Burnitz3867303 291.75 0.078 0.075 0.00291
Jermaine Dye3718260 252.18 0.070 0.068 0.00210
Richard Hidalgo2288174 169.72 0.076 0.074 0.00187
Trot Nixon2991240 234.77 0.080 0.078 0.00175
Casey Blake3592287 280.95 0.080 0.078 0.00168
Raul Mondesi107067 65.26 0.063 0.061 0.00163
Jacque Jones3396262 256.70 0.077 0.076 0.00156
Bobby Abreu4018267 261.04 0.066 0.065 0.00148
Magglio Ordonez2154139 136.15 0.065 0.063 0.00132
Austin Kearns2891238 234.45 0.082 0.081 0.00123
Victor I Diaz2024153 150.52 0.076 0.074 0.00123
Juan Encarnacion3355216 213.79 0.064 0.064 0.00066
Kevin Mench102960 59.72 0.058 0.058 0.00027
Jose Guillen3708299 298.66 0.081 0.081 0.00009
Larry Walker1959107 107.33 0.055 0.055 -0.00017
Aubrey Huff2583204 207.52 0.079 0.080 -0.00136
Sammy Sosa1763121 124.99 0.069 0.071 -0.00226
Emil Brown3597243 252.59 0.068 0.070 -0.00267
Michael Tucker137291 94.94 0.066 0.069 -0.00287
Craig Monroe1952132 138.13 0.068 0.071 -0.00314
Alexis I Rios3310246 256.53 0.074 0.078 -0.00318
Matt Lawton2984230 240.06 0.077 0.080 -0.00337
Brad B Hawpe2259148 156.37 0.066 0.069 -0.00371
Moises Alou131690 97.28 0.068 0.074 -0.00553
Wily Mo Pena129092 105.82 0.071 0.082 -0.01071

Probabilistic Model of Range, Rightfielders, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Dustan Mohr1216103 82.93 0.085 0.068 0.01650
Jose Cruz1296110 91.20 0.085 0.070 0.01451
Mike Cameron1846137 121.54 0.074 0.066 0.00837
Brian Giles3704295 270.20 0.080 0.073 0.00670
Nick T Swisher2993197 177.02 0.066 0.059 0.00668
Vladimir Guerrero3140245 224.45 0.078 0.071 0.00654
J.D. Drew121983 75.82 0.068 0.062 0.00589
Ryan Langerhans113875 68.59 0.066 0.060 0.00564
Jeff B Francoeur1837131 120.85 0.071 0.066 0.00552
Ichiro Suzuki4432383 359.66 0.086 0.081 0.00527
Jason Lane3253225 208.68 0.069 0.064 0.00502
Jay Gibbons1710133 124.72 0.078 0.073 0.00484
Geoff Jenkins3673307 290.57 0.084 0.079 0.00447
Shawn Green3225232 218.55 0.072 0.068 0.00417
Gary Sheffield3415240 226.94 0.070 0.066 0.00382
Chad A Tracy117386 82.09 0.073 0.070 0.00333
Trot Nixon2991240 231.46 0.080 0.077 0.00286
Casey Blake3592287 278.14 0.080 0.077 0.00247
Jeromy Burnitz3867303 294.00 0.078 0.076 0.00233
Jermaine Dye3718260 252.11 0.070 0.068 0.00212
Jacque Jones3396262 254.88 0.077 0.075 0.00210
Austin Kearns2891238 232.83 0.082 0.081 0.00179
Bobby Abreu4018267 260.54 0.066 0.065 0.00161
Richard Hidalgo2288174 170.57 0.076 0.075 0.00150
Magglio Ordonez2154139 136.09 0.065 0.063 0.00135
Victor I Diaz2024153 150.93 0.076 0.075 0.00102
Raul Mondesi107067 66.91 0.063 0.063 0.00009
Juan Encarnacion3355216 215.77 0.064 0.064 0.00007
Jose Guillen3708299 300.89 0.081 0.081 -0.00051
Larry Walker1959107 108.86 0.055 0.056 -0.00095
Kevin Mench102960 61.04 0.058 0.059 -0.00101
Sammy Sosa1763121 123.60 0.069 0.070 -0.00148
Aubrey Huff2583204 209.20 0.079 0.081 -0.00201
Michael Tucker137291 93.77 0.066 0.068 -0.00202
Brad B Hawpe2259148 154.40 0.066 0.068 -0.00283
Matt Lawton2984230 239.00 0.077 0.080 -0.00302
Alexis I Rios3310246 256.82 0.074 0.078 -0.00327
Craig Monroe1952132 139.11 0.068 0.071 -0.00364
Emil Brown3597243 256.30 0.068 0.071 -0.00370
Moises Alou131690 96.96 0.068 0.074 -0.00529
Wily Mo Pena129092 107.68 0.071 0.083 -0.01216

Mike Cameron's defense certainly translated well to right field. Dustin Mohr covered a lot of ground in spacious Coors Field. I'm always a bit surprised that Ichiro isn't right at the top of these lists. I wonder if he plays deep and lets some balls fall in front of him? Or maybe age caused him to lose a step.

Given this data, the Athletics may not wish to move Swisher to first base. It seems a waste to move a fielder who plays the outfield well to a less important defensive position.

Sammy Sosa's defense is another reason clubs are leary of signing the slugger, but if the Nationals sign him he won't be that much of a downgrade from the injured Jose Guillen.

Posted by StatsGuru at 08:50 AM | Comments (11) | TrackBack (0)
January 29, 2006
Probabilistic Model of Range, 2005, Third Basemen and Grounders
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Here's the followup to yesterday's overall numbers for third basemen. This is just how they did on ground balls.

Probabilistic Model of Range, Third Basemen, 2005, Original Model, Groundballs Only
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Pedro Feliz804140 113.01 0.174 0.141 0.03357
Chone Figgins58493 74.12 0.159 0.127 0.03233
Freddy Sanchez715134 111.00 0.187 0.155 0.03216
Scott Rolen799144 118.75 0.180 0.149 0.03161
Wilson Betemit68495 76.06 0.139 0.111 0.02769
Corey Koskie1034157 130.17 0.152 0.126 0.02594
Edwin Encarnacion682115 97.66 0.169 0.143 0.02543
David Bell1796299 256.81 0.166 0.143 0.02349
Dallas L McPherson61584 69.57 0.137 0.113 0.02347
Aaron Boone1764291 250.30 0.165 0.142 0.02307
Morgan Ensberg1848297 256.48 0.161 0.139 0.02193
Abraham O Nunez1195202 176.25 0.169 0.147 0.02155
Joe Crede1552247 214.99 0.159 0.139 0.02062
Bill Mueller1716263 228.79 0.153 0.133 0.01994
Rob Mackowiak665119 106.21 0.179 0.160 0.01923
Melvin Mora1907297 260.49 0.156 0.137 0.01915
Adrian Beltre1862274 240.36 0.147 0.129 0.01806
Eric Chavez1846297 267.03 0.161 0.145 0.01624
Alex Rodriguez2153288 253.18 0.134 0.118 0.01617
Alex S Gonzalez1070170 153.13 0.159 0.143 0.01576
Brandon Inge2135375 341.64 0.176 0.160 0.01563
David A Wright2023330 298.97 0.163 0.148 0.01534
Aramis Ramirez1449216 194.59 0.149 0.134 0.01478
Garrett Atkins1781261 234.69 0.147 0.132 0.01477
Mike Lowell1648242 222.66 0.147 0.135 0.01173
Mike Cuddyer1272190 175.91 0.149 0.138 0.01108
Sean Burroughs902143 133.98 0.159 0.149 0.01000
Mark T Teahen1639238 221.79 0.145 0.135 0.00989
Chipper Jones1281166 153.38 0.130 0.120 0.00985
Bill Hall60787 81.13 0.143 0.134 0.00967
Shea Hillenbrand64195 89.06 0.148 0.139 0.00926
Hank Blalock2180298 283.48 0.137 0.130 0.00666
Russell Branyan58481 77.44 0.139 0.133 0.00610
Troy Glaus1949313 305.81 0.161 0.157 0.00369
Edgardo Alfonzo1183157 154.04 0.133 0.130 0.00250
Vinny Castilla1582208 207.62 0.131 0.131 0.00024
Joe Randa1704232 232.50 0.136 0.136 -0.00029
Jorge L Cantu63889 91.77 0.139 0.144 -0.00434


Probabilistic Model of Range, Third Baseman, 2005, Smoothed Visiting Player Model, Ground Balls Only
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Pedro Feliz804140 110.42 0.174 0.137 0.03679
Freddy Sanchez715134 108.00 0.187 0.151 0.03636
Scott Rolen799144 117.29 0.180 0.147 0.03342
Chone Figgins58493 74.16 0.159 0.127 0.03226
Wilson Betemit68495 75.35 0.139 0.110 0.02873
David Bell1796299 252.02 0.166 0.140 0.02616
Dallas L McPherson61584 67.98 0.137 0.111 0.02605
Edwin Encarnacion682115 98.20 0.169 0.144 0.02463
Corey Koskie1034157 133.23 0.152 0.129 0.02298
Bill Mueller1716263 223.71 0.153 0.130 0.02290
Aaron Boone1764291 250.89 0.165 0.142 0.02274
Abraham O Nunez1195202 175.09 0.169 0.147 0.02252
Morgan Ensberg1848297 255.55 0.161 0.138 0.02243
Joe Crede1552247 213.18 0.159 0.137 0.02179
Rob Mackowiak665119 105.84 0.179 0.159 0.01979
Adrian Beltre1862274 237.41 0.147 0.128 0.01965
Alex Rodriguez2153288 246.82 0.134 0.115 0.01913
Melvin Mora1907297 261.04 0.156 0.137 0.01886
Alex S Gonzalez1070170 151.51 0.159 0.142 0.01728
Eric Chavez1846297 266.80 0.161 0.145 0.01636
Brandon Inge2135375 344.59 0.176 0.161 0.01424
Garrett Atkins1781261 235.96 0.147 0.132 0.01406
Aramis Ramirez1449216 195.81 0.149 0.135 0.01393
David A Wright2023330 302.82 0.163 0.150 0.01343
Chipper Jones1281166 151.03 0.130 0.118 0.01169
Mike Cuddyer1272190 175.29 0.149 0.138 0.01156
Mike Lowell1648242 223.36 0.147 0.136 0.01131
Bill Hall60787 80.54 0.143 0.133 0.01064
Mark T Teahen1639238 221.57 0.145 0.135 0.01002
Hank Blalock2180298 279.49 0.137 0.128 0.00849
Shea Hillenbrand64195 89.71 0.148 0.140 0.00825
Sean Burroughs902143 136.63 0.159 0.151 0.00707
Edgardo Alfonzo1183157 151.30 0.133 0.128 0.00481
Russell Branyan58481 78.22 0.139 0.134 0.00476
Troy Glaus1949313 312.76 0.161 0.160 0.00012
Joe Randa1704232 232.83 0.136 0.137 -0.00049
Vinny Castilla1582208 214.58 0.131 0.136 -0.00416
Jorge L Cantu63889 92.07 0.139 0.144 -0.00480

This list doesn't look too different to me. The biggest difference is that Encarnacion moves down, and Pedro Feliz takes over the top spot. There is still a huge gap between Koskie and Glaus.

Posted by StatsGuru at 02:25 PM | Comments (2) | TrackBack (0)
January 28, 2006
Probabilistic Model of Range, 2005,Third Basemen
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It was a very good year for third basemen according to both models. Almost all performed above expectation:

Probabilistic Model of Range, Third Basemen, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Edwin Encarnacion1538159 128.09 0.103 0.083 0.02010
Chone Figgins1334121 97.95 0.091 0.073 0.01728
Pedro Feliz1812176 144.87 0.097 0.080 0.01718
Wilson Betemit1353118 96.76 0.087 0.072 0.01570
Freddy Sanchez1488162 138.96 0.109 0.093 0.01549
Scott Rolen1447160 138.73 0.111 0.096 0.01470
Corey Koskie2121196 169.20 0.092 0.080 0.01263
David Bell3786388 340.31 0.102 0.090 0.01260
Morgan Ensberg3738374 327.06 0.100 0.087 0.01256
Abraham O Nunez2249239 211.24 0.106 0.094 0.01234
Brandon Inge4416474 426.52 0.107 0.097 0.01075
Rob Mackowiak1391147 132.27 0.106 0.095 0.01059
Aaron Boone3776364 324.05 0.096 0.086 0.01058
Alex S Gonzalez2522228 202.89 0.090 0.080 0.00996
Joe Crede3378324 290.40 0.096 0.086 0.00995
Alex Rodriguez4338373 330.67 0.086 0.076 0.00976
Bill Mueller3859334 296.75 0.087 0.077 0.00965
Dallas L McPherson1431111 97.37 0.078 0.068 0.00953
Melvin Mora3939378 340.87 0.096 0.087 0.00943
Adrian Beltre4246375 337.14 0.088 0.079 0.00892
Chipper Jones2600223 201.98 0.086 0.078 0.00809
Eric Chavez3965389 359.65 0.098 0.091 0.00740
Mark T Teahen3464321 295.69 0.093 0.085 0.00731
Mike Lowell3376312 288.76 0.092 0.086 0.00688
David A Wright4325414 386.30 0.096 0.089 0.00641
Aramis Ramirez2920268 251.97 0.092 0.086 0.00549
Garrett Atkins3714315 295.69 0.085 0.080 0.00520
Sean Burroughs1938187 177.09 0.096 0.091 0.00511
Jeff Cirillo103994 88.87 0.090 0.086 0.00494
Shea Hillenbrand1369122 115.98 0.089 0.085 0.00439
Bill Hall1338114 108.45 0.085 0.081 0.00415
Edgardo Alfonzo2588215 206.13 0.083 0.080 0.00343
Mike Cuddyer2589230 221.44 0.089 0.086 0.00331
Russell Branyan1341109 104.90 0.081 0.078 0.00306
Troy Glaus4010392 379.78 0.098 0.095 0.00305
Hank Blalock4500378 364.42 0.084 0.081 0.00302
Joe Randa3850330 322.49 0.086 0.084 0.00195
Vinny Castilla3651325 320.17 0.089 0.088 0.00132
Jorge L Cantu1557108 119.34 0.069 0.077 -0.00728


Probabilistic Model of Range, Third Baseman, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Pedro Feliz1812176 141.92 0.097 0.078 0.01881
Edwin Encarnacion1538159 131.03 0.103 0.085 0.01819
Freddy Sanchez1488162 135.54 0.109 0.091 0.01778
Chone Figgins1334121 97.83 0.091 0.073 0.01737
Wilson Betemit1353118 96.20 0.087 0.071 0.01611
Scott Rolen1447160 136.97 0.111 0.095 0.01592
David Bell3786388 334.73 0.102 0.088 0.01407
Abraham O Nunez2249239 209.33 0.106 0.093 0.01319
Morgan Ensberg3738374 327.66 0.100 0.088 0.01240
Bill Mueller3859334 287.50 0.087 0.075 0.01205
Rob Mackowiak1391147 131.06 0.106 0.094 0.01146
Corey Koskie2121196 172.56 0.092 0.081 0.01105
Alex Rodriguez4338373 325.12 0.086 0.075 0.01104
Alex S Gonzalez2522228 200.31 0.090 0.079 0.01098
Dallas L McPherson1431111 95.51 0.078 0.067 0.01082
Aaron Boone3776364 323.99 0.096 0.086 0.01060
Joe Crede3378324 289.27 0.096 0.086 0.01028
Brandon Inge4416474 430.06 0.107 0.097 0.00995
Chipper Jones2600223 198.93 0.086 0.077 0.00926
Adrian Beltre4246375 335.79 0.088 0.079 0.00923
Melvin Mora3939378 342.39 0.096 0.087 0.00904
Mike Lowell3376312 285.64 0.092 0.085 0.00781
Eric Chavez3965389 360.02 0.098 0.091 0.00731
Mark T Teahen3464321 295.83 0.093 0.085 0.00727
David A Wright4325414 391.13 0.096 0.090 0.00529
Bill Hall1338114 107.69 0.085 0.080 0.00472
Garrett Atkins3714315 297.58 0.085 0.080 0.00469
Aramis Ramirez2920268 254.57 0.092 0.087 0.00460
Edgardo Alfonzo2588215 204.03 0.083 0.079 0.00424
Jeff Cirillo103994 89.88 0.090 0.087 0.00397
Mike Cuddyer2589230 220.11 0.089 0.085 0.00382
Shea Hillenbrand1369122 116.87 0.089 0.085 0.00375
Sean Burroughs1938187 179.89 0.096 0.093 0.00367
Hank Blalock4500378 362.63 0.084 0.081 0.00342
Russell Branyan1341109 104.64 0.081 0.078 0.00325
Joe Randa3850330 322.20 0.086 0.084 0.00203
Troy Glaus4010392 387.22 0.098 0.097 0.00119
Vinny Castilla3651325 327.58 0.089 0.090 -0.00071
Jorge L Cantu1557108 120.02 0.069 0.077 -0.00772

It looks like Vinny Castilla can no longer make up for his poor offense with his glove, while moving Chipper Jones back to third cost the Braves some good defense at the position from Benemit. This is the highest I've seen Chone Figgins on any of the charts so far. Third base is his position.

From the rankings here, the Red Sox down graded both defensively and offensively replacing Mueller with Lowell at third. And given how well Orlando Hudson ranked among second basemen, the Glaus trade was a defensive upgrade for the Diamondbacks.

As always, your comments are welcome.

Posted by StatsGuru at 11:44 AM | Comments (13) | TrackBack (0)
January 26, 2006
Probabilistic Model of Range, 2005, Centerfielders
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Without much further ado, the centerfielders.

Probabilistic Model of Range, Centerfielders, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Jason Ellison1867197 178.25 0.106 0.095 0.01004
Tike Redman1613158 143.70 0.098 0.089 0.00887
Joey R Gathright1587181 167.23 0.114 0.105 0.00868
Curtis Granderson1044119 110.91 0.114 0.106 0.00775
Andruw Jones4309365 337.56 0.085 0.078 0.00637
Jason Michaels1621161 150.73 0.099 0.093 0.00634
Jim Edmonds3538319 297.13 0.090 0.084 0.00618
Aaron Rowand4128388 362.99 0.094 0.088 0.00606
Gary Matthews Jr.2822258 242.31 0.091 0.086 0.00556
Jerry Hairston110090 84.03 0.082 0.076 0.00542
Brady Clark3765399 380.69 0.106 0.101 0.00486
Nook P Logan2730282 270.92 0.103 0.099 0.00406
Luis Matos3017299 286.93 0.099 0.095 0.00400
Corey Patterson2799240 232.53 0.086 0.083 0.00267
Willy Taveras3646332 322.83 0.091 0.089 0.00252
Carlos Beltran3967378 372.03 0.095 0.094 0.00151
Brad Wilkerson2414234 230.76 0.097 0.096 0.00134
Randy Winn1603184 182.71 0.115 0.114 0.00080
Grady Sizemore4136373 370.07 0.090 0.089 0.00071
Damon J Hollins2010198 197.37 0.099 0.098 0.00031
Laynce Nix1674160 159.88 0.096 0.096 0.00007
Jeremy T Reed3692384 384.13 0.104 0.104 -0.00003
Luis Terrero1310121 121.69 0.092 0.093 -0.00053
Torii Hunter2575218 220.35 0.085 0.086 -0.00091
Milton Bradley1969181 183.11 0.092 0.093 -0.00107
Vernon Wells4239351 356.22 0.083 0.084 -0.00123
Juan Pierre4171332 337.90 0.080 0.081 -0.00141
Johnny Damon3952396 402.01 0.100 0.102 -0.00152
David DeJesus3304306 313.16 0.093 0.095 -0.00217
Mark Kotsay3519299 306.87 0.085 0.087 -0.00224
Dave Roberts2715234 240.18 0.086 0.088 -0.00228
Kenny Lofton2167201 207.17 0.093 0.096 -0.00285
Chone Figgins1184131 134.46 0.111 0.114 -0.00292
Cory Sullivan1935172 179.74 0.089 0.093 -0.00400
Preston Wilson3362267 283.81 0.079 0.084 -0.00500
Steve Finley2691266 279.55 0.099 0.104 -0.00503
Lew Ford1677140 150.24 0.083 0.090 -0.00610
Jose Cruz131787 96.22 0.066 0.073 -0.00700
Bernie Williams2689226 245.61 0.084 0.091 -0.00729
Jason Repko112897 105.29 0.086 0.093 -0.00735
Ken Griffey Jr.3439286 321.33 0.083 0.093 -0.01027

Probabilistic Model of Range, Centerfielders, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Jason Ellison1867197 176.89 0.106 0.095 0.01077
Joey R Gathright1587181 165.35 0.114 0.104 0.00986
Tike Redman1613158 142.11 0.098 0.088 0.00985
Andruw Jones4309365 330.56 0.085 0.077 0.00799
Curtis Granderson1044119 111.13 0.114 0.106 0.00753
Jim Edmonds3538319 292.73 0.090 0.083 0.00743
Aaron Rowand4128388 360.04 0.094 0.087 0.00677
Jason Michaels1621161 151.37 0.099 0.093 0.00594
Gary Matthews Jr.2822258 241.64 0.091 0.086 0.00580
Brady Clark3765399 378.87 0.106 0.101 0.00535
Luis Matos3017299 288.54 0.099 0.096 0.00347
Jerry Hairston110090 86.37 0.082 0.079 0.00330
Nook P Logan2730282 273.04 0.103 0.100 0.00328
Corey Patterson2799240 232.61 0.086 0.083 0.00264
Willy Taveras3646332 324.32 0.091 0.089 0.00211
Brad Wilkerson2414234 230.16 0.097 0.095 0.00159
Carlos Beltran3967378 372.21 0.095 0.094 0.00146
Randy Winn1603184 181.93 0.115 0.113 0.00129
Grady Sizemore4136373 368.15 0.090 0.089 0.00117
Laynce Nix1674160 158.40 0.096 0.095 0.00096
Damon J Hollins2010198 196.96 0.099 0.098 0.00052
Jeremy T Reed3692384 382.67 0.104 0.104 0.00036
Torii Hunter2575218 218.23 0.085 0.085 -0.00009
Vernon Wells4239351 355.19 0.083 0.084 -0.00099
Luis Terrero1310121 122.38 0.092 0.093 -0.00105
Johnny Damon3952396 401.06 0.100 0.101 -0.00128
Mark Kotsay3519299 303.73 0.085 0.086 -0.00134
Dave Roberts2715234 238.58 0.086 0.088 -0.00169
Juan Pierre4171332 339.18 0.080 0.081 -0.00172
Milton Bradley1969181 184.58 0.092 0.094 -0.00182
David DeJesus3304306 314.56 0.093 0.095 -0.00259
Chone Figgins1184131 134.10 0.111 0.113 -0.00262
Kenny Lofton2167201 206.75 0.093 0.095 -0.00265
Cory Sullivan1935172 180.98 0.089 0.094 -0.00464
Steve Finley2691266 279.04 0.099 0.104 -0.00485
Preston Wilson3362267 284.16 0.079 0.085 -0.00510
Lew Ford1677140 148.71 0.083 0.089 -0.00519
Jason Repko112897 104.79 0.086 0.093 -0.00691
Jose Cruz131787 96.29 0.066 0.073 -0.00705
Bernie Williams2689226 247.78 0.084 0.092 -0.00810
Ken Griffey Jr.3439286 323.44 0.083 0.094 -0.01089

It's nice to see Andruw Jones, Edmonds and Rowand at the top of the list for full time center fielders. I'm also not surprised to see Williams and Griffey near the bottom. Off the top of my head, it looks like Damon will save the Yankees 30 to 35 outs versus having Bernie in center for the full season.

So it is time to move Griffey out of center field? The Reds play in a ballpark that is a home run haven. In that situation, it's important to keep men off base. If Griffe is allowing 40 men more to reach than expected, isn't that a huge hardship on the pitching staff and team? A poor play by Griffey, a bad pitch to the next batter, and it's two runs down for the Reds.

Correction: Fixed the first caption.

Posted by StatsGuru at 03:39 PM | Comments (14) | TrackBack (0)
Probabilistic Model of Range, 2005, Second Basemen and Grounders
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Here's a follow up to the overall ratings for second basemen, this time just looking at ground balls (minimum 500 ground balls in play when on the field):

Probabilistic Model of Range, Second Basemen, 2005, Original Model, Groundballs Only (Grounders + Bunt Grounders)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Nick Punto823169 152.10 0.205 0.185 0.02054
Ryan Freel540113 102.38 0.209 0.190 0.01966
Junior Spivey752156 143.90 0.207 0.191 0.01609
Jamey Carroll581120 111.36 0.207 0.192 0.01487
Adam Kennedy1422302 280.93 0.212 0.198 0.01482
Chase Utley1675325 300.24 0.194 0.179 0.01478
Orlando Hudson1604333 309.35 0.208 0.193 0.01474
Craig Counsell1920369 341.11 0.192 0.178 0.01453
Luis Castillo1509291 271.70 0.193 0.180 0.01279
Jose C Lopez615134 126.40 0.218 0.206 0.01237
Brian Roberts1784353 332.84 0.198 0.187 0.01130
Mark Grudzielanek1901365 347.33 0.192 0.183 0.00930
Mark Ellis1334273 262.67 0.205 0.197 0.00774
Rich Aurilia801148 142.17 0.185 0.177 0.00728
Placido Polanco1417255 246.01 0.180 0.174 0.00635
Tony Graffanino873157 151.50 0.180 0.174 0.00629
Jeff Kent1835352 341.60 0.192 0.186 0.00567
Ronnie Belliard1795354 343.99 0.197 0.192 0.00558
Tadahito Iguchi1637311 302.03 0.190 0.185 0.00548
Omar Infante890150 145.16 0.169 0.163 0.00544
Marcus Giles2006401 391.69 0.200 0.195 0.00464
Nick Green974169 164.83 0.174 0.169 0.00428
Ray Durham1596282 283.31 0.177 0.178 -0.00082
Craig Biggio1725327 331.95 0.190 0.192 -0.00287
Ruben A Gotay1046200 203.16 0.191 0.194 -0.00302
Jose Vidro889170 173.04 0.191 0.195 -0.00342
Luis Rivas52488 90.19 0.168 0.172 -0.00418
Todd Walker1136214 219.18 0.188 0.193 -0.00456
Mark Bellhorn1056227 231.99 0.215 0.220 -0.00473
Luis A Gonzalez907174 178.52 0.192 0.197 -0.00498
Kazuo Matsui791161 165.74 0.204 0.210 -0.00599
Miguel Cairo966176 182.46 0.182 0.189 -0.00668
Aaron Miles869173 179.06 0.199 0.206 -0.00697
Jose Castillo1293211 223.38 0.163 0.173 -0.00958
Robinson Cano1747333 352.20 0.191 0.202 -0.01099
Alfonso Soriano2136383 406.87 0.179 0.190 -0.01118
Mark Loretta1236217 231.12 0.176 0.187 -0.01142
Freddy Sanchez52283 89.90 0.159 0.172 -0.01322
Rickie Weeks1121200 214.88 0.178 0.192 -0.01327
Jorge L Cantu915151 165.19 0.165 0.181 -0.01551
Bret Boone1101197 221.76 0.179 0.201 -0.02249


Probabilistic Model of Range, Second Basemen, 2005, Smoothed Visiting Player Model, Groundballs Only (Grounders + Bunt Grounders)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Nick Punto823169 151.23 0.205 0.184 0.02159
Ryan Freel540113 102.83 0.209 0.190 0.01883
Craig Counsell1920369 336.93 0.192 0.175 0.01670
Brian Roberts1784353 324.04 0.198 0.182 0.01623
Orlando Hudson1604333 308.16 0.208 0.192 0.01548
Jamey Carroll581120 111.40 0.207 0.192 0.01481
Junior Spivey752156 144.88 0.207 0.193 0.01479
Adam Kennedy1422302 281.31 0.212 0.198 0.01455
Chase Utley1675325 301.94 0.194 0.180 0.01377
Jose C Lopez615134 125.67 0.218 0.204 0.01355
Luis Castillo1509291 272.98 0.193 0.181 0.01194
Mark Grudzielanek1901365 344.48 0.192 0.181 0.01079
Placido Polanco1417255 242.23 0.180 0.171 0.00901
Mark Ellis1334273 261.59 0.205 0.196 0.00856
Marcus Giles2006401 387.54 0.200 0.193 0.00671
Rich Aurilia801148 142.73 0.185 0.178 0.00658
Jeff Kent1835352 340.13 0.192 0.185 0.00647
Omar Infante890150 144.57 0.169 0.162 0.00610
Ronnie Belliard1795354 343.25 0.197 0.191 0.00599
Tony Graffanino873157 151.84 0.180 0.174 0.00591
Tadahito Iguchi1637311 302.37 0.190 0.185 0.00527
Nick Green974169 165.16 0.174 0.170 0.00394
Luis Rivas52488 88.57 0.168 0.169 -0.00109
Ray Durham1596282 284.05 0.177 0.178 -0.00128
Craig Biggio1725327 329.43 0.190 0.191 -0.00141
Jose Vidro889170 172.51 0.191 0.194 -0.00282
Ruben A Gotay1046200 203.59 0.191 0.195 -0.00343
Luis A Gonzalez907174 177.58 0.192 0.196 -0.00395
Todd Walker1136214 220.94 0.188 0.194 -0.00611
Aaron Miles869173 178.71 0.199 0.206 -0.00658
Mark Bellhorn1056227 234.07 0.215 0.222 -0.00670
Jose Castillo1293211 221.39 0.163 0.171 -0.00804
Miguel Cairo966176 184.52 0.182 0.191 -0.00882
Robinson Cano1747333 348.52 0.191 0.199 -0.00888
Kazuo Matsui791161 168.52 0.204 0.213 -0.00950
Mark Loretta1236217 231.37 0.176 0.187 -0.01162
Rickie Weeks1121200 213.42 0.178 0.190 -0.01197
Alfonso Soriano2136383 409.21 0.179 0.192 -0.01227
Freddy Sanchez52283 89.62 0.159 0.172 -0.01268
Jorge L Cantu915151 164.63 0.165 0.180 -0.01489
Bret Boone1101197 222.73 0.179 0.202 -0.02337

When you only look at the ability to turn grounders into outs, Orland Hudson loses his top spot among second basemen. You can see how his ability to chase pop ups put him in the overall #1 spot:

Breakdown for Orlando Hudson by Ball in Play Type, as Second Baseman, Original Model
In Play TypeInPlayActual OutsPredicted OutsDERPredicted DERDifference
Fly1019127 74.31 0.125 0.073 0.05170
Grounder1554330 305.95 0.212 0.197 0.01547
Liner68730 29.66 0.044 0.043 0.00050
Bunt Fly80 0.00 0.000 0.000 0.00000
Bunt Grounder503 3.40 0.060 0.068 -0.00800

Also, a bit disturbing for Red Sox fans who are watching the team appear to go for a bit less offense and a bit more defense in 2006, Mark Loretta ranks below both Bellhorn and Walker on ground balls.

Correction: Fixed the caption on the second table.

Posted by StatsGuru at 08:50 AM | Comments (6) | TrackBack (0)
January 25, 2006
Probabilistic Model of Range, 2005, Second Basemen, Revised
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The tables here correct the tables presented in this post.

Probabilistic Model of Range, Second Basemen, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Orlando Hudson3318490 413.33 0.148 0.125 0.02311
Alex Cora1006125 105.69 0.124 0.105 0.01919
Nick Punto1742226 199.09 0.130 0.114 0.01545
Chase Utley3490456 404.40 0.131 0.116 0.01479
Ryan Freel1216153 141.43 0.126 0.116 0.00952
Jose C Lopez1406187 174.21 0.133 0.124 0.00910
Craig Counsell3908490 460.50 0.125 0.118 0.00755
Rich Aurilia1792201 190.00 0.112 0.106 0.00614
Junior Spivey1767207 196.67 0.117 0.111 0.00585
Mark Ellis2880372 355.52 0.129 0.123 0.00572
Luis Castillo3068386 370.42 0.126 0.121 0.00508
Tony Graffanino1907214 204.42 0.112 0.107 0.00502
Adam Kennedy3277401 385.91 0.122 0.118 0.00460
Placido Polanco3001336 322.35 0.112 0.107 0.00455
Brian Roberts3693454 438.41 0.123 0.119 0.00422
Ronnie Belliard3772464 448.16 0.123 0.119 0.00420
Marcus Giles4038497 488.11 0.123 0.121 0.00220
Mark Grudzielanek3525433 428.07 0.123 0.121 0.00140
Luis Rivas1073124 122.55 0.116 0.114 0.00135
Jeff Kent3693439 438.75 0.119 0.119 0.00007
Ruben A Gotay2185269 270.51 0.123 0.124 -0.00069
Freddy Sanchez1189125 126.21 0.105 0.106 -0.00102
Jamey Carroll1350152 153.66 0.113 0.114 -0.00123
Ray Durham3575378 383.19 0.106 0.107 -0.00145
Omar Infante1810190 193.21 0.105 0.107 -0.00177
Mark Loretta2772308 313.81 0.111 0.113 -0.00210
Nick Green2327237 243.98 0.102 0.105 -0.00300
Tadahito Iguchi3533413 423.75 0.117 0.120 -0.00304
Jose Castillo2662298 306.11 0.112 0.115 -0.00305
Aaron Miles1946229 235.37 0.118 0.121 -0.00327
Miguel Cairo2035241 248.01 0.118 0.122 -0.00344
Craig Biggio3464420 432.44 0.121 0.125 -0.00359
Kazuo Matsui1683213 221.05 0.127 0.131 -0.00478
Mark Bellhorn2341292 303.32 0.125 0.130 -0.00483
Todd Walker2276273 284.87 0.120 0.125 -0.00522
Jose Vidro2062225 237.14 0.109 0.115 -0.00589
Alfonso Soriano4411475 515.84 0.108 0.117 -0.00926
Rickie Weeks2532273 296.46 0.108 0.117 -0.00927
Bret Boone2505267 291.94 0.107 0.117 -0.00995
Robinson Cano3555442 477.68 0.124 0.134 -0.01004
Luis A Gonzalez1831210 228.73 0.115 0.125 -0.01023
Jorge L Cantu2169203 227.18 0.094 0.105 -0.01115
Chone Figgins1001109 128.54 0.109 0.128 -0.01952
Deivi Cruz1046100 120.72 0.096 0.115 -0.01981

Probabilistic Model of Range, Second Baseman, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Orlando Hudson3318490 408.26 0.148 0.123 0.02464
Alex Cora1006125 107.33 0.124 0.107 0.01757
Chase Utley3490456 400.46 0.131 0.115 0.01591
Nick Punto1742226 198.51 0.130 0.114 0.01578
Jose C Lopez1406187 170.31 0.133 0.121 0.01187
Ryan Freel1216153 143.14 0.126 0.118 0.00810
Craig Counsell3908490 459.89 0.125 0.118 0.00771
Mark Ellis2880372 352.66 0.129 0.122 0.00672
Brian Roberts3693454 431.41 0.123 0.117 0.00612
Placido Polanco3001336 318.61 0.112 0.106 0.00579
Junior Spivey1767207 198.10 0.117 0.112 0.00504
Rich Aurilia1792201 192.46 0.112 0.107 0.00477
Luis Castillo3068386 371.40 0.126 0.121 0.00476
Tony Graffanino1907214 205.63 0.112 0.108 0.00439
Marcus Giles4038497 482.81 0.123 0.120 0.00352
Adam Kennedy3277401 389.93 0.122 0.119 0.00338
Ronnie Belliard3772464 455.28 0.123 0.121 0.00231
Mark Grudzielanek3525433 426.32 0.123 0.121 0.00190
Luis Rivas1073124 122.41 0.116 0.114 0.00148
Jeff Kent3693439 437.23 0.119 0.118 0.00048
Ruben A Gotay2185269 269.99 0.123 0.124 -0.00045
Omar Infante1810190 191.12 0.105 0.106 -0.00062
Freddy Sanchez1189125 127.57 0.105 0.107 -0.00216
Jamey Carroll1350152 155.01 0.113 0.115 -0.00223
Tadahito Iguchi3533413 421.19 0.117 0.119 -0.00232
Ray Durham3575378 386.29 0.106 0.108 -0.00232
Mark Loretta2772308 315.46 0.111 0.114 -0.00269
Jose Castillo2662298 305.66 0.112 0.115 -0.00288
Craig Biggio3464420 430.09 0.121 0.124 -0.00291
Aaron Miles1946229 235.04 0.118 0.121 -0.00310
Miguel Cairo2035241 249.00 0.118 0.122 -0.00393
Nick Green2327237 246.53 0.102 0.106 -0.00410
Jose Vidro2062225 236.93 0.109 0.115 -0.00578
Todd Walker2276273 286.42 0.120 0.126 -0.00589
Kazuo Matsui1683213 224.94 0.127 0.134 -0.00710
Mark Bellhorn2341292 308.61 0.125 0.132 -0.00710
Rickie Weeks2532273 297.46 0.108 0.117 -0.00966
Bret Boone2505267 291.43 0.107 0.116 -0.00975
Robinson Cano3555442 477.11 0.124 0.134 -0.00988
Alfonso Soriano4411475 523.17 0.108 0.119 -0.01092
Jorge L Cantu2169203 226.69 0.094 0.105 -0.01092
Luis A Gonzalez1831210 230.74 0.115 0.126 -0.01133
Chone Figgins1001109 127.94 0.109 0.128 -0.01892
Deivi Cruz1046100 120.59 0.096 0.115 -0.01968

Not too much of a difference in order here.

Posted by StatsGuru at 08:25 PM | Comments (3) | TrackBack (0)
Probabilistic Model of Range, 2005, Shortstops and Grounders, Revised
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The tables here correct the tables presented in this post. The minimum is 500 balls in play while in the field.

Probabilistic Model of Range, Shortstops, 2005, Original Model, Groundballs Only (Grounders + Bunt Grounders)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Omar Infante567131 125.07 0.231 0.221 0.01046
Clint Barmes1042211 200.24 0.202 0.192 0.01033
Jason A Bartlett827205 197.36 0.248 0.239 0.00924
Rafael Furcal2050437 418.74 0.213 0.204 0.00891
John McDonald611135 129.68 0.221 0.212 0.00870
Juan Castro891201 194.14 0.226 0.218 0.00770
Carlos Guillen950203 197.69 0.214 0.208 0.00559
Adam Everett1855373 365.12 0.201 0.197 0.00425
Bobby Crosby999221 216.87 0.221 0.217 0.00414
Julio Lugo1813372 365.06 0.205 0.201 0.00383
Neifi Perez1547339 333.43 0.219 0.216 0.00360
Wilson Valdez535104 103.25 0.194 0.193 0.00139
Jack Wilson1991439 439.30 0.220 0.221 -0.00015
Jimmy Rollins1914365 365.63 0.191 0.191 -0.00033
Cesar Izturis1464281 282.54 0.192 0.193 -0.00105
Yuniesky Betancourt637114 115.77 0.179 0.182 -0.00278
Alex Gonzalez1608315 319.65 0.196 0.199 -0.00289
Miguel Tejada2065417 425.11 0.202 0.206 -0.00393
Khalil Greene1417280 285.95 0.198 0.202 -0.00420
Edgar Renteria1858344 353.88 0.185 0.190 -0.00532
David Eckstein2209453 466.16 0.205 0.211 -0.00596
J.J. Hardy1253240 248.74 0.192 0.199 -0.00698
Omar Vizquel1829382 396.82 0.209 0.217 -0.00810
Bill Hall630138 143.11 0.219 0.227 -0.00811
Cristian Guzman1585278 291.23 0.175 0.184 -0.00835
Orlando Cabrera1642314 328.01 0.191 0.200 -0.00853
Royce Clayton1845354 373.81 0.192 0.203 -0.01074
Derek Jeter2088399 425.52 0.191 0.204 -0.01270
Marco Scutaro921189 200.96 0.205 0.218 -0.01298
Juan Uribe1820363 387.20 0.199 0.213 -0.01330
Oscar M Robles598116 125.03 0.194 0.209 -0.01510
Felipe Lopez1707322 348.31 0.189 0.204 -0.01541
Angel Berroa2088379 411.17 0.182 0.197 -0.01541
Jose Reyes2032357 389.20 0.176 0.192 -0.01585
Russ M Adams1646280 308.72 0.170 0.188 -0.01745
Jhonny Peralta1729352 387.25 0.204 0.224 -0.02039
Michael Young2139371 417.67 0.173 0.195 -0.02182
Mike Morse603100 113.99 0.166 0.189 -0.02320

Probabilistic Model of Range, Shortstops, 2005, Smoothed Visiting Player Model, Groundballs Only (Grounders + Bunt Grounders)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Jason A Bartlett827205 193.52 0.248 0.234 0.01388
Omar Infante567131 124.74 0.231 0.220 0.01104
Clint Barmes1042211 199.69 0.202 0.192 0.01085
John McDonald611135 128.52 0.221 0.210 0.01061
Rafael Furcal2050437 417.08 0.213 0.203 0.00972
Juan Castro891201 193.57 0.226 0.217 0.00834
Julio Lugo1813372 359.76 0.205 0.198 0.00675
Bobby Crosby999221 214.88 0.221 0.215 0.00612
Adam Everett1855373 362.23 0.201 0.195 0.00581
Neifi Perez1547339 330.04 0.219 0.213 0.00579
Carlos Guillen950203 197.88 0.214 0.208 0.00539
Jimmy Rollins1914365 359.98 0.191 0.188 0.00262
Cesar Izturis1464281 279.05 0.192 0.191 0.00133
Jack Wilson1991439 436.69 0.220 0.219 0.00116
Wilson Valdez535104 104.31 0.194 0.195 -0.00058
Alex Gonzalez1608315 316.50 0.196 0.197 -0.00093
Edgar Renteria1858344 346.50 0.185 0.186 -0.00135
Yuniesky Betancourt637114 115.43 0.179 0.181 -0.00225
Miguel Tejada2065417 422.26 0.202 0.204 -0.00255
Khalil Greene1417280 284.17 0.198 0.201 -0.00294
David Eckstein2209453 459.82 0.205 0.208 -0.00309
Bill Hall630138 140.51 0.219 0.223 -0.00399
Cristian Guzman1585278 286.55 0.175 0.181 -0.00540
J.J. Hardy1253240 247.55 0.192 0.198 -0.00603
Orlando Cabrera1642314 327.24 0.191 0.199 -0.00807
Omar Vizquel1829382 399.22 0.209 0.218 -0.00942
Marco Scutaro921189 198.83 0.205 0.216 -0.01067
Juan Uribe1820363 383.41 0.199 0.211 -0.01122
Royce Clayton1845354 376.96 0.192 0.204 -0.01244
Jose Reyes2032357 384.22 0.176 0.189 -0.01340
Derek Jeter2088399 428.51 0.191 0.205 -0.01413
Angel Berroa2088379 408.49 0.182 0.196 -0.01413
Felipe Lopez1707322 348.13 0.189 0.204 -0.01531
Russ M Adams1646280 307.02 0.170 0.187 -0.01641
Jhonny Peralta1729352 381.59 0.204 0.221 -0.01712
Oscar M Robles598116 126.43 0.194 0.211 -0.01744
Michael Young2139371 412.43 0.173 0.193 -0.01937
Mike Morse603100 112.22 0.166 0.186 -0.02027

To comment on a comment I've seen, I'm presenting infielder just on ground balls to show how we classically think of infielders and range; going after ground balls. For example, Jose Reyes ranks better at going after ground balls than he does overall. You also see Furcal doing very well on grounders. If you primarily think of a shortstop as a ground ball vacuum cleaner, this gives one a better picture of that ability.

Posted by StatsGuru at 07:43 PM | Comments (1) | TrackBack (0)
Probabilistic Model of Range, 2005, Shortstops, Revised
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The tables here correct the tables presented in this post.

Probabilistic Model of Range, Shortstops, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Omar Infante1233171 157.18 0.139 0.127 0.01121
Clint Barmes2209276 254.21 0.125 0.115 0.00986
Jason A Bartlett1766257 245.86 0.146 0.139 0.00631
Julio Lugo4297523 496.20 0.122 0.115 0.00624
John McDonald1223163 157.90 0.133 0.129 0.00417
Wilson Valdez1198147 142.19 0.123 0.119 0.00402
Juan Castro1775243 236.77 0.137 0.133 0.00351
Adam Everett3748469 457.97 0.125 0.122 0.00294
Rafael Furcal4111539 527.12 0.131 0.128 0.00289
Yuniesky Betancourt1426161 159.52 0.113 0.112 0.00104
Alex Gonzalez3291404 403.74 0.123 0.123 0.00008
Bobby Crosby2163277 277.56 0.128 0.128 -0.00026
Neifi Perez3026410 411.18 0.135 0.136 -0.00039
Jimmy Rollins3994473 475.97 0.118 0.119 -0.00074
Omar Vizquel4024500 506.26 0.124 0.126 -0.00156
Edgar Renteria4119452 461.67 0.110 0.112 -0.00235
Jack Wilson4240543 553.07 0.128 0.130 -0.00238
Miguel Tejada4280526 538.20 0.123 0.126 -0.00285
Juan Uribe3946494 505.90 0.125 0.128 -0.00302
Bill Hall1447183 187.69 0.126 0.130 -0.00324
Carlos Guillen1934240 247.06 0.124 0.128 -0.00365
David Eckstein4109550 565.01 0.134 0.138 -0.00365
Oscar M Robles1313157 162.74 0.120 0.124 -0.00437
J.J. Hardy2805316 328.53 0.113 0.117 -0.00447
Khalil Greene3123365 379.76 0.117 0.122 -0.00473
Orlando Cabrera3706425 443.34 0.115 0.120 -0.00495
Cristian Guzman3605381 399.91 0.106 0.111 -0.00525
Cesar Izturis2859338 353.90 0.118 0.124 -0.00556
Derek Jeter4231525 555.71 0.124 0.131 -0.00726
Royce Clayton3711430 459.89 0.116 0.124 -0.00805
Russ M Adams3433372 400.54 0.108 0.117 -0.00831
Jhonny Peralta3736465 496.59 0.124 0.133 -0.00846
Mike Morse1437144 156.18 0.100 0.109 -0.00848
Angel Berroa4438505 543.44 0.114 0.122 -0.00866
Michael Young4398489 528.27 0.111 0.120 -0.00893
Felipe Lopez3804418 454.44 0.110 0.119 -0.00958
Marco Scutaro1980238 257.00 0.120 0.130 -0.00959
Jose Reyes4308479 522.85 0.111 0.121 -0.01018

Probabilistic Model of Range, Shortstops, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Omar Infante1233171 155.51 0.139 0.126 0.01256
Clint Barmes2209276 250.44 0.125 0.113 0.01157
Jason A Bartlett1766257 242.41 0.146 0.137 0.00826
Julio Lugo4297523 492.52 0.122 0.115 0.00709
John McDonald1223163 154.84 0.133 0.127 0.00667
Juan Castro1775243 235.64 0.137 0.133 0.00415
Adam Everett3748469 456.50 0.125 0.122 0.00334
Wilson Valdez1198147 143.00 0.123 0.119 0.00334
Rafael Furcal4111539 525.78 0.131 0.128 0.00322
Jimmy Rollins3994473 468.01 0.118 0.117 0.00125
Yuniesky Betancourt1426161 159.54 0.113 0.112 0.00103
Neifi Perez3026410 407.24 0.135 0.135 0.00091
Alex Gonzalez3291404 401.73 0.123 0.122 0.00069
Bobby Crosby2163277 277.52 0.128 0.128 -0.00024
Edgar Renteria4119452 455.84 0.110 0.111 -0.00093
Bill Hall1447183 185.15 0.126 0.128 -0.00149
Juan Uribe3946494 501.75 0.125 0.127 -0.00196
Miguel Tejada4280526 534.62 0.123 0.125 -0.00201
Omar Vizquel4024500 508.53 0.124 0.126 -0.00212
David Eckstein4109550 559.90 0.134 0.136 -0.00241
Jack Wilson4240543 555.09 0.128 0.131 -0.00285
Khalil Greene3123365 375.78 0.117 0.120 -0.00345
Carlos Guillen1934240 247.11 0.124 0.128 -0.00367
J.J. Hardy2805316 326.37 0.113 0.116 -0.00370
Cesar Izturis2859338 350.31 0.118 0.123 -0.00430
Orlando Cabrera3706425 442.65 0.115 0.119 -0.00476
Oscar M Robles1313157 163.31 0.120 0.124 -0.00481
Cristian Guzman3605381 398.46 0.106 0.111 -0.00484
Angel Berroa4438505 529.83 0.114 0.119 -0.00560
Mike Morse1437144 153.39 0.100 0.107 -0.00653
Derek Jeter4231525 554.10 0.124 0.131 -0.00688
Russ M Adams3433372 398.01 0.108 0.116 -0.00758
Michael Young4398489 522.48 0.111 0.119 -0.00761
Jhonny Peralta3736465 493.58 0.124 0.132 -0.00765
Royce Clayton3711430 463.25 0.116 0.125 -0.00896
Marco Scutaro1980238 256.00 0.120 0.129 -0.00909
Felipe Lopez3804418 455.16 0.110 0.120 -0.00977
Jose Reyes4308479 523.53 0.111 0.122 -0.01034

Omar Infante and Clint Barmes stay at the top. The person who caused the examination that uncovered the data error, Derek Jeter, does make a nice jump, from 38th to 29th or 31st, depending on which model you prefer. He's still not great, but he's not at the bottom. Fear not, New Yorkers! He's replaced in the last spot by Jose Reyes.

Someone hurt by this data change is Bobby Crosby. He looks more average under this model. He appears to have traded places with Rafael Furcal.

More updates to come.

Posted by StatsGuru at 06:38 PM | Comments (5) | TrackBack (0)
Data Problem
Permalink

I really do read your comments. This one was very helpful:

Answer: Because if you watch Derek Jeter everyday, you know that it would be a mistake to move him. I hesitated to even respond to this because these kinds of studies used against Jeter do little else except generate tons of malicious comments by self-aggrandizing people, some who put all their time into knocking Jeter. I heard a guy today from BIS talking about how they need to improve how they measure defensive performance. If someone wants to give me the list of games and plays that show how utterly astounding it is that he hasn't been moved, please email me, and I'd be happy to discuss it further. But, you'll have no case, and you should really move on to something else.

This made me think to actually look at the plays on which Jeter scored poorly and Jeter scored well. I paid my $10 to MLB so I can look at their condensed games and looked at a high probability play that Jeter didn't turn. Lo and behold, Jeter made the play! It turns out the database field I was using wasn't set properly on some fielder's choices.

Baseball Info Solutions promptly gave me a fix. I need to rebuild the models for fielders, although this should not effect the team model from previous posts.

Also, as some in the comments to the second basemen pointed out, the actual outs for second basemen differs between the two models. I've only done a preliminary look at that, and I don't know why yet. However, when I rebuild the model I'll make sure that gets fixed as well.

So for the moment, ignore the shortstop and second basemen posts. I'll be revising these. Given the nature of the data error, however, I don't expect to see a big change in the order.

Update: I found the problem with the data for the second baseman. I didn't use the smoothed model, just the straight visiting model. When I fix the data, and repost, I'll take care of that.

Posted by StatsGuru at 01:57 PM | Comments (5) | TrackBack (0)
January 24, 2006
Probabilistic Model of Range, 2005, Second Basemen
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Update 1/25/2006: I discovered a data error that will likely change the values in these tables. Look for a new post with new data soon.

Here are the tables for second basemen in 2005. I'll do the full model in this post, and just ground balls in another.

Probabilistic Model of Range, Second Basemen, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Orlando Hudson3318488 412.37 0.147 0.124 0.02279
Alex Cora1006125 105.10 0.124 0.104 0.01978
Nick Punto1742228 198.86 0.131 0.114 0.01673
Chase Utley3490448 402.41 0.128 0.115 0.01306
Ryan Freel1216151 141.56 0.124 0.116 0.00776
Craig Counsell3908488 457.89 0.125 0.117 0.00771
Jose C Lopez1406184 173.83 0.131 0.124 0.00723
Junior Spivey1767207 196.22 0.117 0.111 0.00610
Rich Aurilia1792201 190.12 0.112 0.106 0.00607
Mark Ellis2880372 354.87 0.129 0.123 0.00595
Adam Kennedy3277401 387.19 0.122 0.118 0.00422
Placido Polanco3001333 321.62 0.111 0.107 0.00379
Ronnie Belliard3772460 445.89 0.122 0.118 0.00374
Tony Graffanino1907209 202.97 0.110 0.106 0.00316
Brian Roberts3693448 436.85 0.121 0.118 0.00302
Luis Castillo3068377 368.15 0.123 0.120 0.00289
Marcus Giles4038496 487.73 0.123 0.121 0.00205
Mark Grudzielanek3525433 428.40 0.123 0.122 0.00131
Luis Rivas1073123 121.95 0.115 0.114 0.00098
Jeff Kent3693441 439.06 0.119 0.119 0.00053
Ruben A Gotay2185270 269.61 0.124 0.123 0.00018
Freddy Sanchez1189126 125.96 0.106 0.106 0.00003
Mark Loretta2772312 314.82 0.113 0.114 -0.00102
Aaron Miles1946234 235.98 0.120 0.121 -0.00102
Jose Castillo2662303 305.71 0.114 0.115 -0.00102
Omar Infante1810190 192.47 0.105 0.106 -0.00137
Jamey Carroll1350153 154.85 0.113 0.115 -0.00137
Craig Biggio3464424 430.44 0.122 0.124 -0.00186
Tadahito Iguchi3533417 423.67 0.118 0.120 -0.00189
Ray Durham3575377 383.87 0.105 0.107 -0.00192
Miguel Cairo2035242 247.85 0.119 0.122 -0.00287
Kazuo Matsui1683215 219.93 0.128 0.131 -0.00293
Jose Vidro2062227 236.43 0.110 0.115 -0.00457
Nick Green2327233 243.90 0.100 0.105 -0.00469
Todd Walker2276271 284.36 0.119 0.125 -0.00587
Mark Bellhorn2341281 300.77 0.120 0.128 -0.00845
Bret Boone2505269 291.21 0.107 0.116 -0.00887
Rickie Weeks2532271 294.57 0.107 0.116 -0.00931
Alfonso Soriano4411471 514.25 0.107 0.117 -0.00980
Luis A Gonzalez1831210 229.03 0.115 0.125 -0.01040
Jorge L Cantu2169201 226.94 0.093 0.105 -0.01196
Robinson Cano3555420 475.02 0.118 0.134 -0.01548
Deivi Cruz1046104 121.39 0.099 0.116 -0.01663
Chone Figgins1001110 128.09 0.110 0.128 -0.01807

Probabilistic Model of Range, Second Baseman, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Orlando Hudson3231482 398.69 0.149 0.123 0.02579
Nick Punto1708228 199.52 0.133 0.117 0.01668
Chase Utley3384444 393.82 0.131 0.116 0.01483
Jose C Lopez1374184 166.21 0.134 0.121 0.01294
Craig Counsell3848488 460.59 0.127 0.120 0.00712
Ryan Freel1169149 141.07 0.127 0.121 0.00679
Rich Aurilia1728201 189.96 0.116 0.110 0.00639
Mark Ellis2808367 350.26 0.131 0.125 0.00596
Brian Roberts3617444 423.49 0.123 0.117 0.00567
Placido Polanco2932332 317.45 0.113 0.108 0.00496
Junior Spivey1722206 197.59 0.120 0.115 0.00489
Tony Graffanino1865207 200.27 0.111 0.107 0.00361
Marcus Giles3925493 481.19 0.126 0.123 0.00301
Adam Kennedy3229401 391.41 0.124 0.121 0.00297
Luis Castillo2987374 366.95 0.125 0.123 0.00236
Mark Grudzielanek3477433 425.60 0.125 0.122 0.00213
Luis Rivas1049123 121.28 0.117 0.116 0.00164
Ronnie Belliard3705458 453.32 0.124 0.122 0.00126
Craig Biggio3326417 412.86 0.125 0.124 0.00124
Jeff Kent3647440 439.32 0.121 0.120 0.00019
Ruben A Gotay2145270 270.26 0.126 0.126 -0.00012
Omar Infante1754190 191.31 0.108 0.109 -0.00075
Jose Castillo2598300 302.53 0.115 0.116 -0.00097
Aaron Miles1915233 235.82 0.122 0.123 -0.00147
Mark Loretta2723311 316.51 0.114 0.116 -0.00202
Tadahito Iguchi3412415 422.02 0.122 0.124 -0.00206
Ray Durham3458374 384.45 0.108 0.111 -0.00302
Miguel Cairo1988239 246.05 0.120 0.124 -0.00354
Freddy Sanchez1158125 129.60 0.108 0.112 -0.00397
Jose Vidro1928218 229.16 0.113 0.119 -0.00579
Nick Green2270230 244.11 0.101 0.108 -0.00622
Todd Walker2239268 282.03 0.120 0.126 -0.00627
Jamey Carroll1269148 156.25 0.117 0.123 -0.00650
Kazuo Matsui1646212 223.34 0.129 0.136 -0.00689
Bret Boone2456269 289.91 0.110 0.118 -0.00851
Mark Bellhorn2287280 303.18 0.122 0.133 -0.01014
Rickie Weeks2471268 294.17 0.108 0.119 -0.01059
Jorge L Cantu2132200 222.99 0.094 0.105 -0.01078
Alfonso Soriano4314470 523.90 0.109 0.121 -0.01249
Luis A Gonzalez1800209 233.30 0.116 0.130 -0.01350
Robinson Cano3495417 471.84 0.119 0.135 -0.01569

The Arizona Diamondbacks picked up a defensive gem in Orlando Hudson. Counsell was fine at the position, too, which is why they're comfortable moving him back to shortstop. It will be "Death to ground balls up the middle" in Phoenix next year. I'm impressed again that Rich Aurilia does well. He ranked high on the shortstop list last year.

Not so in New York, where Robinson Cano ranks right near the bottom with Derek Jeter. And you can see why the Nationals would rather play Vidro at second than Soriano.

Posted by StatsGuru at 08:00 PM | Comments (12) | TrackBack (1)
Probabilistic Model of Range, 2005, Shortstops and Grounders
Permalink

Update 1/25/2006: I discovered a data error that will likely change the values in these tables. Look for a new post with new data soon.

Update 1/25/2006: Corrected number for this post are here.

As stated in the previous post, here's the data for shortstops on ground balls only. Data with all balls in play is here.

Probabilistic Model of Range, Shortstops, 2005, Original Model, Groundballs Only (Grounders + Bunt Grounders)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
John McDonald611135 128.09 0.221 0.210 0.01131
Omar Infante567131 124.90 0.231 0.220 0.01076
Bobby Crosby999226 216.78 0.226 0.217 0.00923
Clint Barmes1042208 199.37 0.200 0.191 0.00828
Neifi Perez1547340 330.11 0.220 0.213 0.00639
Jason A Bartlett827201 195.84 0.243 0.237 0.00624
Wilson Valdez535105 102.46 0.196 0.192 0.00476
Yuniesky Betancourt637119 116.07 0.187 0.182 0.00459
Adam Everett1855371 363.80 0.200 0.196 0.00388
Cesar Izturis1464286 282.07 0.195 0.193 0.00269
Rafael Furcal2050420 414.68 0.205 0.202 0.00259
Juan Castro891194 192.20 0.218 0.216 0.00202
Julio Lugo1813361 360.32 0.199 0.199 0.00038
Miguel Tejada2065421 421.96 0.204 0.204 -0.00046
Alex Gonzalez1608317 317.88 0.197 0.198 -0.00055
Jack Wilson1991433 437.56 0.217 0.220 -0.00229
Jimmy Rollins1914351 356.95 0.183 0.186 -0.00311
David Eckstein2209458 465.27 0.207 0.211 -0.00329
Carlos Guillen950193 197.13 0.203 0.208 -0.00435
Khalil Greene1417278 284.50 0.196 0.201 -0.00459
Cristian Guzman1585275 286.96 0.174 0.181 -0.00755
Orlando Cabrera1642314 327.09 0.191 0.199 -0.00797
Omar Vizquel1829376 392.82 0.206 0.215 -0.00919
J.J. Hardy1253233 246.02 0.186 0.196 -0.01039
Oscar M Robles598117 124.69 0.196 0.209 -0.01285
Russ M Adams1646285 306.68 0.173 0.186 -0.01317
Juan Uribe1820361 386.61 0.198 0.212 -0.01407
Royce Clayton1845346 372.43 0.188 0.202 -0.01432
Jose Reyes2032358 388.18 0.176 0.191 -0.01485
Angel Berroa2088377 409.76 0.181 0.196 -0.01569
Felipe Lopez1707317 345.10 0.186 0.202 -0.01646
Michael Young2139381 418.01 0.178 0.195 -0.01730
Jhonny Peralta1729356 386.12 0.206 0.223 -0.01742
Bill Hall630129 141.28 0.205 0.224 -0.01950
Marco Scutaro921181 199.97 0.197 0.217 -0.02060
Edgar Renteria1858308 346.29 0.166 0.186 -0.02061
Mike Morse60397 113.40 0.161 0.188 -0.02720
Derek Jeter2088346 417.89 0.166 0.200 -0.03443

Probabilistic Model of Range, Shortstops, 2005, Smoothed Visiting Player Model, Groundballs Only (Grounders + Bunt Grounders)
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Jason A Bartlett814201 190.15 0.247 0.234 0.01333
John McDonald605135 126.97 0.223 0.210 0.01327
Neifi Perez1519340 323.44 0.224 0.213 0.01090
Bobby Crosby981225 214.36 0.229 0.219 0.01085
Omar Infante560130 124.48 0.232 0.222 0.00986
Clint Barmes1032208 197.93 0.202 0.192 0.00976
Yuniesky Betancourt629118 113.60 0.188 0.181 0.00699
Cesar Izturis1450286 277.41 0.197 0.191 0.00592
Julio Lugo1781361 353.57 0.203 0.199 0.00417
Adam Everett1795363 356.25 0.202 0.198 0.00376
Juan Castro882194 191.78 0.220 0.217 0.00251
Wilson Valdez530105 103.71 0.198 0.196 0.00244
Alex Gonzalez1579317 314.25 0.201 0.199 0.00174
Rafael Furcal2003415 412.15 0.207 0.206 0.00142
Miguel Tejada2038418 415.75 0.205 0.204 0.00111
Khalil Greene1390278 280.11 0.200 0.202 -0.00152
David Eckstein2181458 461.60 0.210 0.212 -0.00165
Jack Wilson1951433 440.34 0.222 0.226 -0.00376
Cristian Guzman1511273 279.15 0.181 0.185 -0.00407
Jimmy Rollins1861347 355.79 0.186 0.191 -0.00473
Carlos Guillen937193 197.89 0.206 0.211 -0.00522
Orlando Cabrera1622313 325.76 0.193 0.201 -0.00787
J.J. Hardy1227233 244.14 0.190 0.199 -0.00908
Omar Vizquel1781376 393.31 0.211 0.221 -0.00972
Juan Uribe1774355 372.71 0.200 0.210 -0.00998
Jose Reyes1999357 379.67 0.179 0.190 -0.01134
Russ M Adams1612284 303.51 0.176 0.188 -0.01210
Jhonny Peralta1706356 380.57 0.209 0.223 -0.01440
Oscar M Robles590117 125.54 0.198 0.213 -0.01447
Michael Young2099376 406.67 0.179 0.194 -0.01461
Bill Hall620128 137.43 0.206 0.222 -0.01521
Edgar Renteria1813307 335.25 0.169 0.185 -0.01558
Angel Berroa2064375 408.13 0.182 0.198 -0.01605
Royce Clayton1810346 376.25 0.191 0.208 -0.01671
Marco Scutaro911180 196.65 0.198 0.216 -0.01828
Felipe Lopez1668317 349.72 0.190 0.210 -0.01962
Mike Morse58997 111.71 0.165 0.190 -0.02497
Derek Jeter2059345 419.03 0.168 0.204 -0.03595

It's interesting that Neifi Perez moves up quite a bit between the two models. Neifi Perez was the best regular last season, while Jeter is still at the bottom.

Posted by StatsGuru at 03:37 PM | Comments (5) | TrackBack (1)
Line Drive Variation
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Yesterday these charts showed the variations in different types of balls in play over the last four seasons. The increase in line drive outs led many to wonder if there was some change in the scoring that's causing the difference. Baseball Info Solutions sent me this information in regards to my questions on the subject:

We did some research on this for another customer, and found the difference was real. There were no changes made to our scoring practices to suggest the difference was as a result of scorer decision.

We do believe our scoring has improved each year, so we are confident of the number. What we're not sure of, however, is the normal variance of these numbers since we don't have any data to compare against other than the four years of our data. It will probably take a few more years of data until we can full gauge whether 2005 is an usual year.

So until we know better, I'm willing to trust the data. However, in presenting tables for fielders, a further breakdown in needed. Since line drives are volitile, we'll also look at tables with only the predominant type of ball in play. That's grounders for infielders, fly balls for outfielders. The shortstop data will be available soon.

Posted by StatsGuru at 02:41 PM | Comments (4) | TrackBack (0)
January 23, 2006
Probabilistic Model of Range, 2005, Shortstops
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Update 1/25/2006: I discovered a data error that will likely change the values in these tables. Look for a new post with new data soon.

Update 1/25/2006: Corrected numbers for this post are here.

It's time to start looking at individual players, seeing how the regulars performed in terms of range in 2005. We'll start with the most important defensive position in terms of range, shortstop.

For each set of fielders, I'll present two models; the original based on all plays over the last four years, and another based on visiting players over the last four years. Players included were on the field for 1000 balls in play in 2005.

Probabilistic Model of Range, Shortstops, 2005, Original Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Omar Infante1233171 156.99 0.139 0.127 0.01136
Clint Barmes2209272 253.34 0.123 0.115 0.00845
John McDonald1223163 156.30 0.133 0.128 0.00548
Wilson Valdez1198148 141.46 0.124 0.118 0.00546
Jason A Bartlett1766253 244.34 0.143 0.138 0.00490
Julio Lugo4297512 491.44 0.119 0.114 0.00478
Yuniesky Betancourt1426166 159.83 0.116 0.112 0.00433
Adam Everett3748468 456.62 0.125 0.122 0.00304
Bobby Crosby2163282 276.69 0.130 0.128 0.00246
Alex Gonzalez3291406 401.86 0.123 0.122 0.00126
Neifi Perez3026411 407.35 0.136 0.135 0.00121
Juan Castro1775236 234.83 0.133 0.132 0.00066
Rafael Furcal4111520 521.88 0.126 0.127 -0.00046
Miguel Tejada4280531 535.03 0.124 0.125 -0.00094
Jimmy Rollins3994460 467.81 0.115 0.117 -0.00195
David Eckstein4109555 563.86 0.135 0.137 -0.00216
Omar Vizquel4024493 502.44 0.123 0.125 -0.00235
Jack Wilson4240538 551.57 0.127 0.130 -0.00320
Oscar M Robles1313158 162.38 0.120 0.124 -0.00334
Juan Uribe3946492 505.26 0.125 0.128 -0.00336
Cesar Izturis2859343 353.35 0.120 0.124 -0.00362
Orlando Cabrera3706426 442.40 0.115 0.119 -0.00442
Khalil Greene3123363 378.13 0.116 0.121 -0.00484
Cristian Guzman3605378 395.59 0.105 0.110 -0.00488
J.J. Hardy2805309 325.75 0.110 0.116 -0.00597
Russ M Adams3433377 398.48 0.110 0.116 -0.00626
Michael Young4398499 528.59 0.113 0.120 -0.00673
Jhonny Peralta3736469 495.40 0.126 0.133 -0.00707
Bill Hall1447174 185.81 0.120 0.128 -0.00816
Carlos Guillen1934230 246.47 0.119 0.127 -0.00852
Angel Berroa4438502 541.85 0.113 0.122 -0.00898
Edgar Renteria4119416 454.05 0.101 0.110 -0.00924
Jose Reyes4308479 521.31 0.111 0.121 -0.00982
Felipe Lopez3804413 451.25 0.109 0.119 -0.01006
Royce Clayton3711421 458.43 0.113 0.124 -0.01009
Mike Morse1437141 155.56 0.098 0.108 -0.01013
Marco Scutaro1980229 255.87 0.116 0.129 -0.01357
Derek Jeter4231472 548.13 0.112 0.130 -0.01799

Probabilistic Model of Range, Shortstops, 2005, Smoothed Visiting Player Model
PlayerInPlayActual OutsPredicted OutsDERPredicted DERDifference
Omar Infante1233171 155.90 0.139 0.126 0.01225
Clint Barmes2209272 248.91 0.123 0.113 0.01045
John McDonald1223163 153.31 0.133 0.125 0.00792
Jason A Bartlett1766253 241.27 0.143 0.137 0.00664
Julio Lugo4297512 487.18 0.119 0.113 0.00578
Wilson Valdez1198148 142.28 0.124 0.119 0.00478
Yuniesky Betancourt1426166 159.80 0.116 0.112 0.00435
Adam Everett3748468 455.93 0.125 0.122 0.00322
Neifi Perez3026411 402.01 0.136 0.133 0.00297
Bobby Crosby2163282 277.48 0.130 0.128 0.00209
Alex Gonzalez3291406 400.12 0.123 0.122 0.00179
Juan Castro1775236 233.75 0.133 0.132 0.00127
Miguel Tejada4280531 531.18 0.124 0.124 -0.00004
Rafael Furcal4111520 522.12 0.126 0.127 -0.00052
Jimmy Rollins3994460 463.44 0.115 0.116 -0.00086
David Eckstein4109555 559.91 0.135 0.136 -0.00119
Juan Uribe3946492 501.43 0.125 0.127 -0.00239
Cesar Izturis2859343 350.28 0.120 0.123 -0.00255
Omar Vizquel4024493 504.01 0.123 0.125 -0.00273
Khalil Greene3123363 373.38 0.116 0.120 -0.00332
Jack Wilson4240538 553.77 0.127 0.131 -0.00372
Orlando Cabrera3706426 440.25 0.115 0.119 -0.00384
Oscar M Robles1313158 163.05 0.120 0.124 -0.00385
Cristian Guzman3605378 395.22 0.105 0.110 -0.00478
J.J. Hardy2805309 323.34 0.110 0.115 -0.00511
Russ M Adams3433377 395.73 0.110 0.115 -0.00546
Michael Young4398499 523.59 0.113 0.119 -0.00559
Angel Berroa4438502 529.85 0.113 0.119 -0.00627
Jhonny Peralta3736469 492.80 0.126 0.132 -0.00637
Bill Hall1447174 183.23 0.120 0.127 -0.00638
Edgar Renteria4119416 448.71 0.101 0.109 -0.00794
Mike Morse1437141 153.16 0.098 0.107 -0.00846
Carlos Guillen1934230 247.31 0.119 0.128 -0.00895
Jose Reyes4308479 521.47 0.111 0.121 -0.00986
Felipe Lopez3804413 454.10 0.109 0.119 -0.01080
Royce Clayton3711421 462.03 0.113 0.125 -0.01106
Marco Scutaro1980229 255.28 0.116 0.129 -0.01327
Derek Jeter4231472 546.66 0.112 0.129 -0.01765

Omar Infante is the poster child for a defensive replacement. He has no offensive value, but can flash the leather. Cristian Guzman, who was near the top of the list last year, fell off in 2005. His ability to catch line drives did not hold up, indicating his good range rating was somewhat lucky.

Derek Jeter is back at the bottom of the list after a better showing in 2004. Why the Yankees keep him at that position when there's a better player to his right is beyond me. These numbers also show that the Red Sox will be making a significant defensive upgrade if they end up replacing Renteria with Gonzalez.

As always, comments are welcome.

Posted by StatsGuru at 11:21 PM | Comments (18) | TrackBack (0)
Variation in Predicted DER
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A reader named guy left the following comment to this post:

There are two things being tracked: real DER and pred DER. Real DER changes very little -- approx .691 in 2004 and .694 in 2005 according to David's data. If 3 plays out of 1,000 makes '05 a "better fielding year," well OK (but the difference is w/in the MOE). But the predicted DER goes from .698 last year to .681 this year. That doesn't make sense to me. The distribution of 125,000 BIP can't possibly be that different.

It appears it is that different. Here's how the aggregate for all four seasons break down:

SeasonIn PlayActual OutsPred. OutsDERPred. DERDifference
200213191591661 92748.99 0.695 0.703 -0.00825
200313365792756 91800.78 0.694 0.687 0.00715
200413295291912 93408.06 0.691 0.703 -0.01125
200513358992647 91018.16 0.694 0.681 0.01219

According to this table, balls in play in 2002 and 2004 were relatively easy to field, but were not fielded well. In 2003 and 2005, balls were difficult to field, but defenders picked them just fine. My first guess is to believe it's true, but I want to study the issue more.

Update: Here's a more detailed table, broken down by the type of batted ball and season.

SeasonBatted Ball TypeInPlayActual OutsPredicted OutsDERPredicted DERDifference
2002Bunt Fly249227 227.50 0.912 0.914 -0.00201
2003Bunt Fly301284 282.33 0.944 0.938 0.00554
2004Bunt Fly281257 258.17 0.915 0.919 -0.00415
2005Bunt Fly272260 260.00 0.956 0.956 0.00000
2002Bunt Grounder28612144 2137.95 0.749 0.747 0.00211
2003Bunt Grounder29982229 2257.07 0.743 0.753 -0.00936
2004Bunt Grounder29312262 2245.54 0.772 0.766 0.00562
2005Bunt Grounder29092209 2203.44 0.759 0.757 0.00191
2002Fly4303738924 39043.76 0.904 0.907 -0.00278
2003Fly4167338088 37349.57 0.914 0.896 0.01772
2004Fly4513638575 39530.60 0.855 0.876 -0.02117
2005Fly4274237634 37297.06 0.880 0.873 0.00788
2002Grounder5791643378 43953.03 0.749 0.759 -0.00993
2003Grounder5867344371 43818.41 0.756 0.747 0.00942
2004Grounder5971943861 44493.05 0.734 0.745 -0.01058
2005Grounder5989144273 43618.51 0.739 0.728 0.01093
2002Liner278526988 7386.75 0.251 0.265 -0.01432
2003Liner297797574 7883.40 0.254 0.265 -0.01039
2004Liner248856957 6880.70 0.280 0.277 0.00307
2005Liner277758271 7639.14 0.298 0.275 0.02275
2003Pop (Not used)233210 210.00 0.901 0.901 0.00000

Fielders did a really good job of catching line drives in 2005, and there were a lot more than in 2004.

Update: An explanation for the increase in line drives is posted here.

Posted by StatsGuru at 05:27 PM | Comments (9) | TrackBack (0)
January 22, 2006
Pitchers and Luck
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The Probabilistic Model of Range (PMR) allows us to measure the contributions of a defense to the success of a pitcher, and the contribution of a pitcher to the sucess of the defense. We can see which pitchers had defenses turn more outs than expected (lucky pitchers), and see which pitchers were able to induce balls in the play that were easy to field. We'll start with how defense helped or hurt individual pitchers (minimum 300 balls in play in 2005).

Probabilistic Model of Range, Defense Behind Pitchers 2005. Unsmoothed Park Model. Best Defensive Support.
PitcherTeamInPlayActual OutsPredicted OutsDERPredicted DERDifference
Rich HardenOak341247 228.27 0.724 0.669 0.05494
Jon GarlandCWS706514 477.48 0.728 0.676 0.05172
Claudio VargasAri362257 238.87 0.710 0.660 0.05009
Roy HalladayTor408297 276.93 0.728 0.679 0.04918
Bruce ChenBal594431 403.52 0.726 0.679 0.04626
Roger ClemensHou577426 399.50 0.738 0.692 0.04593
Tim WakefieldBos678495 466.14 0.730 0.688 0.04256
Horacio RamirezAtl667479 451.43 0.718 0.677 0.04134
Kameron D LoeTex307218 205.50 0.710 0.669 0.04072
Barry ZitoOak654486 460.30 0.743 0.704 0.03930
Wandy E RodriguezHou400277 261.30 0.692 0.653 0.03924
Kirk SaarloosOak553392 370.70 0.709 0.670 0.03851
Pedro MartinezNYM564422 400.38 0.748 0.710 0.03833
Brett MyersPhi587418 396.01 0.712 0.675 0.03745
Vicente PadillaPhi447315 298.28 0.705 0.667 0.03740
Carlos ZambranoChC592433 411.41 0.731 0.695 0.03646
Mark MulderStL659457 433.24 0.693 0.657 0.03605
Jorge SosaAtl416300 285.02 0.721 0.685 0.03600
Andy PettitteHou643463 440.27 0.720 0.685 0.03535
Chris CarpenterStL667476 452.75 0.714 0.679 0.03485
Dave T BushTor438313 298.02 0.715 0.680 0.03421
Jose ContrerasCWS595436 416.49 0.733 0.700 0.03279
John SmoltzAtl690490 467.42 0.710 0.677 0.03272
Jason JenningsCol397271 258.02 0.683 0.650 0.03269
Kirk RueterSF404279 266.38 0.691 0.659 0.03123
Joe M BlantonOak624462 442.80 0.740 0.710 0.03076
Jon LieberPhi684485 464.49 0.709 0.679 0.02998
Mark PriorChC425297 284.67 0.699 0.670 0.02901
Brandon BackeHou465328 314.65 0.705 0.677 0.02871
Carlos SilvaMin641445 427.56 0.694 0.667 0.02720
Scott ElartonCle585418 402.32 0.715 0.688 0.02680
Casey FossumTB498338 324.83 0.679 0.652 0.02644
D.J. CarrascoKC394270 259.62 0.685 0.659 0.02635
Jason MarquisStL664477 459.60 0.718 0.692 0.02620
Paul ByrdLAA682481 463.20 0.705 0.679 0.02609
Freddy GarciaCWS708501 482.60 0.708 0.682 0.02599
Cliff LeeCle621442 426.01 0.712 0.686 0.02574
Javier VazquezAri626430 414.46 0.687 0.662 0.02482
Kenny RogersTex665465 448.78 0.699 0.675 0.02440
C.C. SabathiaCle574403 389.06 0.702 0.678 0.02429
Jeff WeaverLAD677484 467.62 0.715 0.691 0.02420
Jake WestbrookCle693479 462.50 0.691 0.667 0.02381
Ted LillyTor386271 261.83 0.702 0.678 0.02376
Danny HarenOak649453 437.68 0.698 0.674 0.02361
Jarrod WashburnLAA567396 383.49 0.698 0.676 0.02206
Matt MorrisStL633434 420.28 0.686 0.664 0.02167
Seth McClungTB319226 219.17 0.708 0.687 0.02141
Kevin MillwoodCle576400 387.72 0.694 0.673 0.02133
Jerome WilliamsChC334243 235.90 0.728 0.706 0.02126
Ervin R SantanaLAA412287 278.25 0.697 0.675 0.02123
Shawn EstesAri408285 276.43 0.699 0.678 0.02101
Greg MadduxChC728505 489.74 0.694 0.673 0.02096
Kip WellsPit562386 374.28 0.687 0.666 0.02085
Cory LidlePhi607403 390.49 0.664 0.643 0.02062
Johan SantanaMin604438 425.63 0.725 0.705 0.02047
Bartolo ColonLAA678481 467.31 0.709 0.689 0.02019
Tim HudsonAtl608425 412.90 0.699 0.679 0.01991
Dave WilliamsPit426303 294.54 0.711 0.691 0.01985
Nate RobertsonDet624436 423.62 0.699 0.679 0.01985
Jason JohnsonDet718499 484.83 0.695 0.675 0.01973
Jamie MoyerSea683473 459.72 0.693 0.673 0.01944
Tony Armas Jr.Was318232 225.90 0.730 0.710 0.01918
Victor SantosMil466323 314.10 0.693 0.674 0.01911
Brandon WebbAri689470 456.91 0.682 0.663 0.01900
Gustavo G ChacinTor652449 437.03 0.689 0.670 0.01835
Matt ClementBos582400 389.36 0.687 0.669 0.01829
John ThomsonAtl330218 212.09 0.661 0.643 0.01791
Randy JohnsonNYY618432 421.36 0.699 0.682 0.01722
Josh FoggPit571394 384.25 0.690 0.673 0.01707
Brad RadkeMin651462 450.96 0.710 0.693 0.01696
Scott E KazmirTB522356 347.31 0.682 0.665 0.01666
Jake PeavySD521371 362.45 0.712 0.696 0.01641
Dontrelle WillisFla716505 493.58 0.705 0.689 0.01596
David WellsBos622413 403.21 0.664 0.648 0.01574
Brett TomkoSF625437 427.24 0.699 0.684 0.01562
Ben SheetsMil446318 311.26 0.713 0.698 0.01511
Josh BeckettFla484340 332.94 0.702 0.688 0.01458
Mark HendricksonTB633418 409.04 0.660 0.646 0.01415
Kris BensonNYM565409 401.26 0.724 0.710 0.01370
Chien-Ming WangNYY392279 273.77 0.712 0.698 0.01334
Chris CapuanoMil639442 433.56 0.692 0.679 0.01320
Joe MaysMin564377 369.64 0.668 0.655 0.01305
Noah LowrySF599418 410.44 0.698 0.685 0.01263
Joel PineiroSea629426 418.23 0.677 0.665 0.01235
Roy OswaltHou744509 500.00 0.684 0.672 0.01210
Livan HernandezWas796545 535.54 0.685 0.673 0.01189
Orlando HernandezCWS397274 269.42 0.690 0.679 0.01155
Jose LimaKC597401 394.25 0.672 0.660 0.01131
Brad M HennesseySF386272 267.85 0.705 0.694 0.01075
Runelvys HernandezKC523364 358.53 0.696 0.686 0.01046
Bronson ArroyoBos688489 481.93 0.711 0.700 0.01028
Kyle LohseMin607414 408.12 0.682 0.672 0.00969
Brian LawrenceSD656454 448.43 0.692 0.684 0.00850
Woody WilliamsSD513359 354.67 0.700 0.691 0.00844
Russ OrtizAri418283 279.64 0.677 0.669 0.00804
Tom GlavineNYM720496 490.23 0.689 0.681 0.00801
Ramon OrtizCin567390 385.46 0.688 0.680 0.00801
Rodrigo LopezBal702482 476.50 0.687 0.679 0.00783
Erik BedardBal409276 272.81 0.675 0.667 0.00780
Mike WoodKC382260 257.48 0.681 0.674 0.00659
John PattersonWas543388 385.11 0.715 0.709 0.00533
Gil MecheSea462320 317.70 0.693 0.688 0.00498
Doug WaechterTB533361 358.58 0.677 0.673 0.00455
Mike MarothDet683472 468.90 0.691 0.687 0.00454
Jeff W FrancisCol594386 383.33 0.650 0.645 0.00450
Jeff SuppanStL626432 429.24 0.690 0.686 0.00441
Doug DavisMil615430 427.49 0.699 0.695 0.00408
Daniel A CabreraBal447312 310.20 0.698 0.694 0.00403
Aaron SeleSea406274 272.41 0.675 0.671 0.00392
Mark RedmanPit574397 394.86 0.692 0.688 0.00372
Aaron CookCol307208 206.91 0.678 0.674 0.00356
Brandon ClaussenCin522363 361.32 0.695 0.692 0.00322
Odalis PerezLAD338235 234.04 0.695 0.692 0.00284
Mark BuehrleCWS758521 520.04 0.687 0.686 0.00127
Brad PennyLAD555383 382.32 0.690 0.689 0.00123
Brian MoehlerFla538355 354.45 0.660 0.659 0.00102
Derek LoweLAD700489 488.42 0.699 0.698 0.00083
Chan Ho ParkTex354228 227.98 0.644 0.644 0.00005
A.J. BurnettFla577391 391.17 0.678 0.678 -0.00029
Brad A HalseyAri550370 370.21 0.673 0.673 -0.00038
Byung-Hyun KimCol450304 304.59 0.676 0.677 -0.00132
Tomo OhkaMil416280 280.85 0.673 0.675 -0.00205
Ryan FranklinSea642453 454.56 0.706 0.708 -0.00243
Josh TowersTor706477 480.37 0.676 0.680 -0.00477
Jeremy BondermanDet574389 392.27 0.678 0.683 -0.00571
Aaron HarangCin643441 445.17 0.686 0.692 -0.00649
John LackeyLAA598397 401.04 0.664 0.671 -0.00676
D.J. HoultonLAD407279 281.98 0.686 0.693 -0.00732
Zack Z GreinkeKC625407 411.96 0.651 0.659 -0.00794
Sidney PonsonBal460297 300.83 0.646 0.654 -0.00833
Adam EatonSD405271 274.55 0.669 0.678 -0.00876
Jason SchmidtSF485333 337.52 0.687 0.696 -0.00932
Jamey WrightCol563370 375.61 0.657 0.667 -0.00996
Glendon RuschChC476305 310.14 0.641 0.652 -0.01081
Victor ZambranoNYM532361 368.64 0.679 0.693 -0.01436
Hideo NomoTB344232 238.13 0.674 0.692 -0.01781
Mike MussinaNYY545362 371.75 0.664 0.682 -0.01789
Chris YoungTex492342 351.47 0.695 0.714 -0.01926
Eric MiltonCin633427 441.81 0.675 0.698 -0.02339
Joe KennedyCol327207 215.86 0.633 0.660 -0.02708
Esteban LoaizaWas661444 462.19 0.672 0.699 -0.02751
Carl PavanoNYY343225 235.27 0.656 0.686 -0.02996

Roger Clemens wishes he got that type of support from his offense. In general, it's good to pitch for Houston or Oakland. At the other end of the scale you have Eric Milton. Not only did he give up a ton of home runs, but his defense added to his trouble by not letting more than their share of batted balls go for hits. Mike Mussina was down at the unlucky end. So it wasn't my imagination that every ball put in play against him seemed to find a hole for a hit.

Now for a look at how pitchers helped or hurt their defenses:

Read More ?


Posted by StatsGuru at 09:34 PM | Comments (8) | TrackBack (0)
Improving the Park Model
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One suggestion for improving the model for the Probabilistic Model of Range was to use just visiting players to construct the park model. The reason for this is that an everyday player can skew the values associated with a park. A really good fielder, accounting for the almost half the model, makes everyone else look worse than they are. The same is true of a very poor fielder making everyone else look better.

My resistance to this idea was two-fold.

  1. I didn't want to throw out perfectly good data, especially with small sample sizes.
  2. I couldn't come up with a good way to smooth the data. Only looking at half the data, there are going to be rare events that aren't covered just by the visiting team.

I decided to try to solve the smoothing problem today. I use the orginal park model (the first table in this post) to agument the data when the visiting team numbers are missing or sparse. What I want is the visiting team model to dominate. For a given set of parameters, if the number of ball in play against the visiting team is greater than or equal to the number of balls in play by the home team, then I just use the visiting team model. Other wise, I use a model weighted like this:

  • 2.0*VisitBallsInPlay/AllBallsInPlay for the visiting team model
  • 1.0-(2.0*VisitBallsInPlay/AllBallsInPlay) for the overall park model.

Let's say there were 100 balls in play for a particular set of parameters. If 60 of those came against visiting teams, I just use the visiting team model. But if 40 of those came against the visiting team, I weight the model 80% visiting team, 20% overall park model.

The following table should be compared to the first table of this post. That's the model used for smoothing.

Probabilistic Model of Range, 2005. Model Includes Parks, Smoothed Visiting Team Fielding
TeamInPlayActual OutsPredicted OutsDERPredicted DERDifference
Astros42042963 2845.45 0.705 0.677 0.02796
Athletics42863064 2944.68 0.715 0.687 0.02784
White Sox44573175 3052.08 0.712 0.685 0.02758
Phillies42112962 2846.84 0.703 0.676 0.02735
Indians43853108 2988.57 0.709 0.682 0.02724
Cardinals44143101 2991.45 0.703 0.678 0.02482
Braves45593162 3059.99 0.694 0.671 0.02238
Blue Jays45113156 3058.15 0.700 0.678 0.02169
Twins45453193 3094.64 0.703 0.681 0.02164
Angels43833070 2987.00 0.700 0.681 0.01894
Giants45203152 3070.46 0.697 0.679 0.01804
Orioles43773032 2953.85 0.693 0.675 0.01786
Red Sox45753127 3053.44 0.683 0.667 0.01608
Pirates44673095 3023.38 0.693 0.677 0.01603
Mariners45463184 3111.16 0.700 0.684 0.01602
Devil Rays45603112 3044.55 0.682 0.668 0.01479
Diamondbacks45713118 3062.57 0.682 0.670 0.01213
Brewers42522960 2908.65 0.696 0.684 0.01208
Tigers45273152 3099.48 0.696 0.685 0.01160
Cubs41172871 2825.48 0.697 0.686 0.01106
Rangers46973200 3152.10 0.681 0.671 0.01020
Dodgers43923073 3031.40 0.700 0.690 0.00947
Rockies45373043 3008.62 0.671 0.663 0.00758
Mets44243094 3061.94 0.699 0.692 0.00725
Padres44233051 3043.61 0.690 0.688 0.00167
Yankees44833087 3085.86 0.689 0.688 0.00025
Marlins43672965 2965.36 0.679 0.679 -0.00008
Nationals45383161 3167.85 0.697 0.698 -0.00151
Royals46113068 3099.55 0.665 0.672 -0.00684
Reds46503148 3191.15 0.677 0.686 -0.00928

The first thing that strikes me is that the Yankees move up. I didn't expect that. One reason readers suggested a visiting team model was that fielders with poor range like Jeter and Williams would bring down the average and would end up being rated higher than they should be. Yet the Yankees get better with a model dominated by the opposition!

Let me suggest that the original model measured something this model isn't; a player against himself as he ages. So this model is comparing the 2005 Bernie Williams vs. the 2002, 2003 and 2004 Williams. My guess is his range is going down as he ages. The same with Jeter. So instead of pulling the averages down, their younger selfs were pulling the averages up.

Even with that, I don't see a big difference between the Models. Does anyone believe that one is really superior to the other?

Posted by StatsGuru at 01:11 PM | Comments (1) | TrackBack (0)
January 21, 2006
Probabilistic Model of Range, 2005
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A number of readers inquired over the last two months if the Probabilistic Range number for 2005 were going to be published this off-season. I'm happy to say I've acquired the data and I'll be presenting tables this week, on teams, defenses behind pitchers, and individual pitchers.

Here's last year's explanation of the model, which I won't repeat here. The idea is to look not just at the balls turned into outs, but how difficult those balls were to turn into outs. Teams or fielders who turn difficult plays into outs do well. Teams or fielders who let easy balls drop for hits (or make errors) do poorly.

One of the hotly debated aspects of this model is how parks are included in the model. The biggest criticism is that home players have too much influence on the model. I'm going to present three tables for the teams that show how parks change the data.

One will be the model as described in the previous work.

One will be the model without parks in the model.

The third will be a combination of the two, 50% of each.

All models are built on data from four years, 2002-2005.

Probabilistic Model of Range, 2005. Model Includes Parks
TeamInPlayActual OutsPredicted OutsDERPredicted DERDifference
Astros42042963 2854.17 0.705 0.679 0.02589
Indians43853108 2995.26 0.709 0.683 0.02571
Phillies42112962 2853.80 0.703 0.678 0.02570
Athletics42863064 2954.86 0.715 0.689 0.02546
White Sox44573175 3066.86 0.712 0.688 0.02426
Cardinals44143101 3007.96 0.703 0.681 0.02108
Blue Jays45113156 3063.16 0.700 0.679 0.02058
Braves45593162 3073.91 0.694 0.674 0.01932
Twins45453193 3107.42 0.703 0.684 0.01883
Angels43833070 2998.12 0.700 0.684 0.01640
Giants45203152 3080.03 0.697 0.681 0.01592
Orioles43773032 2964.67 0.693 0.677 0.01538
Pirates44673095 3032.44 0.693 0.679 0.01400
Diamondbacks45713118 3059.45 0.682 0.669 0.01281
Red Sox45753127 3068.95 0.683 0.671 0.01269
Devil Rays45603112 3054.72 0.682 0.670 0.01256
Cubs41172871 2819.97 0.697 0.685 0.01239
Mariners45463184 3128.12 0.700 0.688 0.01229
Tigers45273152 3097.51 0.696 0.684 0.01204
Brewers42522960 2916.77 0.696 0.686 0.01017
Rangers46973200 3158.10 0.681 0.672 0.00892
Dodgers43923073 3036.02 0.700 0.691 0.00842
Mets44243094 3058.20 0.699 0.691 0.00809
Rockies45373043 3013.43 0.671 0.664 0.00652
Padres44233051 3047.08 0.690 0.689 0.00089
Marlins43672965 2965.42 0.679 0.679 -0.00010
Yankees44833087 3092.01 0.689 0.690 -0.00112
Nationals45383161 3166.79 0.697 0.698 -0.00128
Royals46113068 3099.97 0.665 0.672 -0.00693
Reds46503148 3182.99 0.677 0.685 -0.00753

Unlike 2004, this was a very good defensive year. Seven of the top eight teams in the list made the playoffs or were in contention as late as the last week of the season. Now for the teams with no park adjustment.

Probabilistic Model of Range, 2005. Model Does Not Include Parks
TeamInPlayActual OutsPredicted OutsDERPredicted DERDifference
Phillies42112962 2812.44 0.703 0.668 0.03552
Athletics42863064 2921.09 0.715 0.682 0.03334
Indians43853108 2970.70 0.709 0.677 0.03131
Astros42042963 2835.95 0.705 0.675 0.03022
Braves45593162 3043.69 0.694 0.668 0.02595
White Sox44573175 3061.04 0.712 0.687 0.02557
Cardinals44143101 2992.97 0.703 0.678 0.02447
Blue Jays45113156 3066.66 0.700 0.680 0.01981
Giants45203152 3062.55 0.697 0.678 0.01979
Dodgers43923073 2992.05 0.700 0.681 0.01843
Cubs41172871 2799.86 0.697 0.680 0.01728
Nationals45383161 3082.57 0.697 0.679 0.01728
Orioles43773032 2960.89 0.693 0.676 0.01625
Diamondbacks45713118 3051.28 0.682 0.668 0.01460
Angels43833070 3007.42 0.700 0.686 0.01428
Twins45453193 3130.04 0.703 0.689 0.01385
Pirates44673095 3034.07 0.693 0.679 0.01364
Mariners45463184 3124.61 0.700 0.687 0.01306
Tigers45273152 3101.99 0.696 0.685 0.01105
Brewers42522960 2913.06 0.696 0.685 0.01104
Mets44243094 3051.37 0.699 0.690 0.00964
Devil Rays45603112 3068.61 0.682 0.673 0.00951
Rangers46973200 3165.60 0.681 0.674 0.00732
Red Sox45753127 3104.20 0.683 0.679 0.00498
Padres44233051 3039.75 0.690 0.687 0.00254
Rockies45373043 3035.26 0.671 0.669 0.00171
Marlins43672965 2958.27 0.679 0.677 0.00154
Reds46503148 3155.28 0.677 0.679 -0.00157
Yankees44833087 3135.64 0.689 0.699 -0.01085
Royals46113068 3130.12 0.665 0.679 -0.01347

You can see the big drop in the Red Sox defense if you don't include the park in the calculation of team range. Lots of balls that would be outs other places hit the wall in Fenway. Without the adjustment, the Red Sox defense looks worse than it is.

Here's the smoothed model:

Probabilistic Model of Range, 2005. 50% Model With Parks, 50% Model Without Parks
TeamInPlayActual OutsPredicted OutsDERPredicted DERDifference
Phillies42112962 2833.12 0.703 0.673 0.03061
Athletics42863064 2937.98 0.715 0.685 0.02940
Indians43853108 2982.98 0.709 0.680 0.02851
Astros42042963 2845.06 0.705 0.677 0.02805
White Sox44573175 3063.95 0.712 0.687 0.02492
Cardinals44143101 3000.46 0.703 0.680 0.02278
Braves45593162 3058.80 0.694 0.671 0.02264
Blue Jays45113156 3064.91 0.700 0.679 0.02019
Giants45203152 3071.29 0.697 0.679 0.01786
Twins45453193 3118.73 0.703 0.686 0.01634
Orioles43773032 2962.78 0.693 0.677 0.01581
Angels43833070 3002.77 0.700 0.685 0.01534
Cubs41172871 2809.92 0.697 0.683 0.01484
Pirates44673095 3033.25 0.693 0.679 0.01382
Diamondbacks45713118 3055.36 0.682 0.668 0.01370
Dodgers43923073 3014.04 0.700 0.686 0.01343
Mariners45463184 3126.36 0.700 0.688 0.01268
Tigers45273152 3099.75 0.696 0.685 0.01154
Devil Rays45603112 3061.67 0.682 0.671 0.01104
Brewers42522960 2914.92 0.696 0.686 0.01060
Mets44243094 3054.78 0.699 0.691 0.00886
Red Sox45753127 3086.58 0.683 0.675 0.00884
Rangers46973200 3161.85 0.681 0.673 0.00812
Nationals45383161 3124.68 0.697 0.689 0.00800
Rockies45373043 3024.35 0.671 0.667 0.00411
Padres44233051 3043.42 0.690 0.688 0.00171
Marlins43672965 2961.85 0.679 0.678 0.00072
Reds46503148 3169.14 0.677 0.682 -0.00455
Yankees44833087 3113.83 0.689 0.695 -0.00598
Royals46113068 3115.04 0.665 0.676 -0.01020

I'm open as always to comments on which of these you think is best, or how any of them might be improved. The best suggestions I've heard, however, involve much more complicated programming. I like this simple model.

One thing is very clear, the Yankees, Royals and Reds did not help their pitching staffs in 2005, no matter how you look at the data.

A hat tip to Mitchel Lichtman, who used this idea first in UZR, but has gone on to private practice.

Posted by StatsGuru at 08:28 PM | Comments (11) | TrackBack (0)
March 09, 2005
Charting Range
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Over the last few days I've been chatting with Robert Saunders about presenting data graphically. He pointed me to this post on Edward Tufte's web site, where's he trying to present charts that are the size of words. I'm not there yet, but Robert did get me thinking about presenting the Probabilistic Model of Range graphically. I thought I'd give it a try with David Eckstein, since there were some arguments over whether the data properly reflected his abilities.

What I've done is broken the data down by ball in play type (grounders, flys and liners). Each chart below has the direction of the ball on the X-Axis. The Y-Axis represents the probability of turning those balls into outs. Eckstein's actual probability is compared to the predicted probability. For reference, a vector of -4 (minus 4) represents the thirdbase line, and 8 represents straight away centerfield. Here's Eckstein on grounders in 2004 (click on graphs for a larger image):

Chart of Eckstein's grounders.

As you can see, David is great when the ball is hit right at straight away short. But once he starts moving left or right, he becomes a below average fielder. Nothing terrible, just below average.

Now let's look at fly balls.

Chart of Eckstein's fly balls.

I really love the information this chart conveys. It shows that fly balls are usually caught by shortstops around the normal position, they go down around third base, but pick up again in foul territory. And this shows why David does so poorly. He does not catch pop ups in foul territory. With the Cardinals, he has a great fielding third baseman in Rolen, so Scott will have to go after balls the shortstop usually gets.

Finally, the line drive chart.

Chart of Eckstein's line drives.

He's just way below average to his left. Even at balls hit right at the position, he doesn't do well. Does he not react quickly?

I'll be doing a few more of these. I hope you find them as informative as I do.

Update: Fixed a left-right problem. I said that Eck was below average to his right. I meant left. Thanks, Studes.


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Posted by StatsGuru at 11:40 AM | Comments (33) | TrackBack (4)
January 23, 2005
Probabilistic Model of Range
Permalink

Update: I have improved data for the models, so I've updated the table in a new post. The order changes a little, but not enough to make a big difference.

Sorry, I hit the save instead of the preview button for this post. An explanation will be added shortly.

2004 Probabilistic Model of Range, Totals for Teams
TeamInPlayActual OutsPredicted OutsDERPredicted DERDifference
Angels43602990 3080.32 0.686 0.706 -0.02072
Royals46433127 3211.12 0.673 0.692 -0.01812
Yankees44883081 3158.71 0.686 0.704 -0.01732
Tigers45243091 3169.20 0.683 0.701 -0.01729
Orioles44583058 3125.39 0.686 0.701 -0.01512
Pirates43262959 3023.71 0.684 0.699 -0.01496
Reds45903155 3220.04 0.687 0.702 -0.01417
Twins44913083 3140.70 0.686 0.699 -0.01285
Mariners44903140 3183.09 0.699 0.709 -0.00960
Brewers44163049 3086.30 0.690 0.699 -0.00845
Rockies46203138 3174.15 0.679 0.687 -0.00782
Expos44213067 3100.04 0.694 0.701 -0.00747
Astros41512843 2866.27 0.685 0.691 -0.00561
Indians44903069 3093.60 0.684 0.689 -0.00548
Rangers45513124 3148.34 0.686 0.692 -0.00535
Athletics44993127 3148.70 0.695 0.700 -0.00482
Diamondbacks43202939 2955.30 0.680 0.684 -0.00377
Braves44893088 3102.32 0.688 0.691 -0.00319
Blue Jays44783097 3108.56 0.692 0.694 -0.00258
Padres43933040 3050.63 0.692 0.694 -0.00242
Giants45413148 3157.22 0.693 0.695 -0.00203
Devil Rays44713127 3135.05 0.699 0.701 -0.00180
Marlins42632991 2995.97 0.702 0.703 -0.00117
Mets45573166 3170.73 0.695 0.696 -0.00104
Phillies44523127 3129.24 0.702 0.703 -0.00050
Dodgers43333089 3089.39 0.713 0.713 -0.00009
White Sox43753038 3028.95 0.694 0.692 0.00207
Cubs41242873 2861.76 0.697 0.694 0.00273
Red Sox43913041 3028.85 0.693 0.690 0.00277
Cardinals43873112 3097.10 0.709 0.706 0.00340

Explanation: Last year, I worked on a way of measuring range which I called a Probabilistic Model of Range (see the defense archives). I was basically repeating work done by Mitchel Lichtman which he named the Ultimate Zone Rating (UZR). Since Mitchel's work was more mature than mine, and since I had to write new software because the source of my data changed, I did not puruse these ranking for the 2004 season. However, I just learned that Mr. Lichtman is working for the Cardinals (congratulations, Mike!) and won't be publishing his results anymore. There's a niche to fill, so here it goes.

I calculate the probability of a ball being turned into an out based on six parameters:

  1. Direction of hit (a vector).
  2. The type of hit (Fly, ground, line drive, bunt).
  3. How hard the ball was hit (slow, medium, hard).
  4. The park.
  5. The handedness of the pitcher.
  6. The handedness of the batter.

For each ball in play, the program sums the probability of that ball being turned into an out, and that gives us the expected outs. Dividing that by balls in play yields expected defensive efficiency rating (DER). That is compared to the team's actual DER. A good defensive team should have a better DER than it's expected DER.

There are differences between this year's and last year's calculation. I'm now using three years of data instead of just one. Also, Baseball Info Solution charts balls differently that STATS, Inc. so there are many more vectors that in the previous system. I believe that actually improves the calculation. Finally, the numbers above are approximate; my database is from early October, and BIS had not input every ball in play yet. Still, it should be enough to get a feel for how good teams were on defense in 2004.

The first thing to notice from the table is that it was a poor defensive season overall. Only four teams had a better DER than predicted by the model. The Cardinals and Red Sox were 1-2, and ended up the World Series. The Angels were last, but also made the playoffs. The Yankees continued their abysmal defense, while the Mets high ranking should help explain why so many of their pitchers had better ERAs than DIPS ERAs.

The next step is to use this method to evaluate individual fielders. Watch for that in upcoming posts.

Update: Just in case I wasn't clear on this, the model is built on three years data, but the chart above is just for 2004.

Correction: Corrected the spelling of Mitchel Lichtman's name.
Posted by StatsGuru at 01:38 PM | Comments (24) | TrackBack (5)