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.
Probabilistic Model of Range, 2008, Defense Behind Pitchers Permalink
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
Team
In Play
Actual Outs
Predicted Outs
DER
Predicted DER
Ratio
Chien-Ming Wang
NYY
306
215
200.92
0.703
0.657
107.01
Daisuke Matsuzaka
Bos
449
327
306.07
0.728
0.682
106.84
Jesse Litsch
Tor
569
407
382.14
0.715
0.672
106.51
Tim Wakefield
Bos
539
405
382.33
0.751
0.709
105.93
Ryan Rowland-Smith
Sea
366
262
249.06
0.716
0.680
105.20
Justin Duchscherer
Oak
409
308
292.97
0.753
0.716
105.13
CC Sabathia
Cle
334
228
217.23
0.683
0.650
104.96
Tim Hudson
Atl
435
317
302.69
0.729
0.696
104.73
Scott Kazmir
TB
378
277
264.57
0.733
0.700
104.70
Roy Oswalt
Hou
617
436
416.75
0.707
0.675
104.62
Jeremy Sowers
Cle
409
279
266.73
0.682
0.652
104.60
CC Sabathia
Mil
353
246
235.49
0.697
0.667
104.46
Armando Galarraga
Det
525
391
375.29
0.745
0.715
104.19
Greg Smith
Oak
578
423
406.63
0.732
0.704
104.03
John Lackey
LAA
469
329
316.59
0.701
0.675
103.92
Kyle Kendrick
Phi
560
380
365.69
0.679
0.653
103.91
Glen Perkins
Min
520
357
343.77
0.687
0.661
103.85
Ryan Dempster
ChC
572
406
391.16
0.710
0.684
103.80
Shaun Marcum
Tor
427
317
305.52
0.742
0.716
103.76
Paul Byrd
Cle
446
319
307.48
0.715
0.689
103.75
Brian Moehler
Hou
509
356
343.33
0.699
0.675
103.69
R.A. Dickey
Sea
374
263
253.88
0.703
0.679
103.59
Joe Saunders
LAA
623
445
429.72
0.714
0.690
103.56
Dustin McGowan
Tor
337
228
220.26
0.677
0.654
103.51
Adam Wainwright
StL
404
289
279.63
0.715
0.692
103.35
Josh Beckett
Bos
492
333
322.69
0.677
0.656
103.19
Jorge Campillo
Atl
490
347
336.39
0.708
0.687
103.16
Ben Sheets
Mil
589
417
404.45
0.708
0.687
103.10
John Lannan
Was
560
401
389.21
0.716
0.695
103.03
Zach Miner
Det
385
276
268.08
0.717
0.696
102.95
Kevin Slowey
Min
480
340
330.37
0.708
0.688
102.92
Vicente Padilla
Tex
524
356
345.93
0.679
0.660
102.91
Jake Peavy
SD
459
331
322.53
0.721
0.703
102.63
Jeremy Guthrie
Bal
587
428
417.45
0.729
0.711
102.53
Cole Hamels
Phi
635
464
453.13
0.731
0.714
102.40
David Bush
Mil
567
422
413.33
0.744
0.729
102.10
Paul Maholm
Pit
621
437
428.22
0.704
0.690
102.05
Jeff Francis
Col
469
321
314.83
0.684
0.671
101.96
John Danks
CWS
569
396
388.39
0.696
0.683
101.96
Scott Baker
Min
497
354
347.21
0.712
0.699
101.96
Roy Halladay
Tor
713
501
491.48
0.703
0.689
101.94
Matt Garza
TB
560
399
391.43
0.712
0.699
101.93
Micah Owings
Ari
312
220
215.84
0.705
0.692
101.93
Johan Santana
NYM
668
476
467.22
0.713
0.699
101.88
Scott Feldman
Tex
488
344
337.73
0.705
0.692
101.86
Oliver Perez
NYM
527
380
373.11
0.721
0.708
101.85
Derek Lowe
LAD
644
453
444.85
0.703
0.691
101.83
Scott Olsen
Fla
640
463
454.69
0.723
0.710
101.83
Felix Hernandez
Sea
577
391
384.12
0.678
0.666
101.79
Doug Davis
Ari
457
303
298.15
0.663
0.652
101.63
Edwin Jackson
TB
582
401
394.74
0.689
0.678
101.59
Dan Haren
Ari
610
421
414.60
0.690
0.680
101.54
Aaron Cook
Col
725
489
481.78
0.674
0.665
101.50
Kyle Lohse
StL
650
453
446.33
0.697
0.687
101.49
Jeff Suppan
Mil
589
407
401.18
0.691
0.681
101.45
Hiroki Kuroda
LAD
598
418
412.03
0.699
0.689
101.45
Dana Eveland
Oak
519
351
346.09
0.676
0.667
101.42
Jered Weaver
LAA
513
355
350.05
0.692
0.682
101.41
Todd Wellemeyer
StL
579
419
413.37
0.724
0.714
101.36
Carlos Zambrano
ChC
570
404
398.66
0.709
0.699
101.34
Jamie Moyer
Phi
625
437
431.36
0.699
0.690
101.31
Jon Garland
LAA
684
462
456.10
0.675
0.667
101.29
Braden Looper
StL
653
453
447.51
0.694
0.685
101.23
Miguel Batista
Sea
379
257
254.22
0.678
0.671
101.09
Jair Jurrjens
Atl
589
401
397.07
0.681
0.674
100.99
Matt Cain
SF
630
436
431.93
0.692
0.686
100.94
Kevin Correia
SF
382
248
245.75
0.649
0.643
100.91
Gavin Floyd
CWS
625
450
446.51
0.720
0.714
100.78
Javier Vazquez
CWS
598
405
401.92
0.677
0.672
100.77
Tim Lincecum
SF
562
385
382.12
0.685
0.680
100.75
Jason Marquis
ChC
554
390
387.25
0.704
0.699
100.71
Aaron Harang
Cin
552
379
376.37
0.687
0.682
100.70
Jose Contreras
CWS
402
280
278.09
0.697
0.692
100.69
Johnny Cueto
Cin
500
344
341.79
0.688
0.684
100.65
Joel Pineiro
StL
505
342
339.90
0.677
0.673
100.62
Brad Penny
LAD
311
212
211.06
0.682
0.679
100.45
Jon Lester
Bos
632
438
436.20
0.693
0.690
100.41
Boof Bonser
Min
382
249
248.24
0.652
0.650
100.31
Greg Maddux
SD
511
360
359.16
0.705
0.703
100.23
Aaron Laffey
Cle
316
217
216.67
0.687
0.686
100.15
Manny Parra
Mil
499
322
321.73
0.645
0.645
100.08
Gil Meche
KC
611
420
419.79
0.687
0.687
100.05
Mike Mussina
NYY
613
409
409.01
0.667
0.667
100.00
Jarrod Washburn
Sea
512
350
350.13
0.684
0.684
99.96
Chad Billingsley
LAD
556
370
370.36
0.665
0.666
99.90
Cliff Lee
Cle
670
462
462.50
0.690
0.690
99.89
Zack Greinke
KC
587
399
399.62
0.680
0.681
99.84
Ted Lilly
ChC
574
412
412.67
0.718
0.719
99.84
Tim Redding
Was
572
397
397.98
0.694
0.696
99.75
Wandy Rodriguez
Hou
393
266
266.71
0.677
0.679
99.74
Andy Sonnanstine
TB
632
432
433.24
0.684
0.686
99.71
Chris Sampson
Hou
383
267
267.83
0.697
0.699
99.69
Daniel Cabrera
Bal
594
409
410.64
0.689
0.691
99.60
Bronson Arroyo
Cin
605
408
409.76
0.674
0.677
99.57
Joe Blanton
Oak
440
303
304.31
0.689
0.692
99.57
Jason Bergmann
Was
445
310
311.45
0.697
0.700
99.53
Brandon Webb
Ari
671
458
460.45
0.683
0.686
99.47
Ervin Santana
LAA
605
422
424.34
0.698
0.701
99.45
Zach Duke
Pit
669
445
447.62
0.665
0.669
99.42
Kenny Rogers
Det
598
400
402.42
0.669
0.673
99.40
Ubaldo Jimenez
Col
572
395
397.49
0.691
0.695
99.37
Carlos Silva
Sea
564
365
367.36
0.647
0.651
99.36
Nate Robertson
Det
563
365
367.59
0.648
0.653
99.30
Jo-Jo Reyes
Atl
361
241
242.79
0.668
0.673
99.26
Clayton Kershaw
LAD
306
204
205.65
0.667
0.672
99.20
Ricky Nolasco
Fla
606
432
435.96
0.713
0.719
99.09
James Shields
TB
641
448
452.73
0.699
0.706
98.96
Justin Verlander
Det
598
415
419.47
0.694
0.701
98.93
Mike Pelfrey
NYM
652
446
450.98
0.684
0.692
98.89
Randy Johnson
Ari
531
359
363.22
0.676
0.684
98.84
Kyle Davies
KC
361
248
251.20
0.687
0.696
98.73
Edinson Volquez
Cin
511
350
354.63
0.685
0.694
98.69
Nick Blackburn
Min
658
445
451.38
0.676
0.686
98.59
John Maine
NYM
399
286
290.11
0.717
0.727
98.58
Pedro Martinez
NYM
337
225
228.38
0.668
0.678
98.52
A.J. Burnett
Tor
613
405
411.37
0.661
0.671
98.45
Jorge de la Rosa
Col
361
240
243.99
0.665
0.676
98.37
Mark Hendrickson
Fla
439
302
307.04
0.688
0.699
98.36
Brian Burres
Bal
460
309
314.46
0.672
0.684
98.26
Kevin Millwood
Tex
569
360
366.45
0.633
0.644
98.24
Brian Bannister
KC
603
408
415.51
0.677
0.689
98.19
Luke Hochevar
KC
430
291
297.11
0.677
0.691
97.94
Randy Wolf
SD
348
237
242.07
0.681
0.696
97.91
Brandon Backe
Hou
512
341
348.41
0.666
0.680
97.87
Barry Zito
SF
576
393
401.59
0.682
0.697
97.86
Cha Seung Baek
SD
353
238
243.40
0.674
0.690
97.78
Brett Myers
Phi
554
379
388.08
0.684
0.701
97.66
Mark Buehrle
CWS
699
466
477.66
0.667
0.683
97.56
Odalis Perez
Was
507
337
345.85
0.665
0.682
97.44
Andrew Miller
Fla
336
216
222.12
0.643
0.661
97.24
Tom Gorzelanny
Pit
332
225
231.65
0.678
0.698
97.13
Jonathan Sanchez
SF
442
297
306.08
0.672
0.692
97.03
Livan Hernandez
Min
525
339
349.78
0.646
0.666
96.92
Garrett Olson
Bal
451
295
304.47
0.654
0.675
96.89
Carlos Villanueva
Mil
320
220
228.02
0.688
0.713
96.48
Ian Snell
Pit
522
335
347.60
0.642
0.666
96.38
Andy Pettitte
NYY
641
420
439.26
0.655
0.685
95.62
Darrell Rasner
NYY
387
257
269.56
0.664
0.697
95.34
Adam Eaton
Phi
356
236
248.23
0.663
0.697
95.07
Fausto Carmona
Cle
405
273
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.
Probabilistic Model of Range, 2008, Pitchers Permalink
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.
Probabilistic Model of Range, 2008, Catcher Permalink
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.
Probabilistic Model of Range, 2008, First Basemen Permalink
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.
Probabilistic Model of Range, 2008, Leftfielders Permalink
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.
Probabilistic Model of Range, 2008, Rightfielders Permalink
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.
Probabilistic Model of Range, 2008, Third Basemen Permalink
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.
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.
Probabilistic Model of Range, Centerfielders, 2008 Permalink
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.
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).
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):
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.
Probabilistic Model of Range, 2008, Second Basemen Permalink
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.
Probabilistic Model of Range, 2008, Shortstops Permalink
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.
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.
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.
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!
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
25
4
4
0.000
26
12
14
0.000
27
37
26
0.766
28
118
57
0.898
29
175
118
0.706
30
193
156
0.671
31
148
119
0.844
32
111
82
0.934
33
164
136
0.868
34
114
105
0.585
35
100
74
0.535
36
117
124
0.624
37
101
108
0.617
38
116
150
0.838
39
119
131
0.865
40
163
139
0.764
41
165
174
0.550
42
110
130
0.688
43
61
55
0.847
44
35
40
0.572
45
7
13
0.010
46
5
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 balls
Yankees
Opponents
In Holes
1110
1029
At Fielders
882
895
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.
Probabilistic Model of Range, Pitchers, 2007 Permalink
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.
Probabilistic Model of Range, Catchers, 2007 Permalink
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.
Probabilistic Model of Range, Firstbasemen, 2007 Permalink
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.
Probabilistic Model of Range, Leftfielders, 2007 Permalink
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.
Probabilistic Model of Range, Rightfielders, 2007 Permalink
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.
Probabilistic Model of Range, Second Basemen, 2007 Permalink
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.
Probabilistic Model of Range, Centerfielders, 2007 Permalink
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.
Probabilistic Model of Range, Shortstops Permalink
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.
Probabilistic Model of Range, Defense Behind Pitchers Permalink
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.
Probabilistic Model of Range, 2007, Teams Permalink
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
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.
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!
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.
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):
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.
Probablisitic Model of Range, Catchers, 2006 Permalink
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.
Probabilistic Model of Range, Pitchers, 2006 Permalink
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.
Probabilistic Model of Range, Third Basemen, 2006 Permalink
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.
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.
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.
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.
He had a number of line drives hit right at him this year, and that helped his actual DER.
Probabilistic Model of Range, Second Basemen, 2006 Permalink
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.
Probabilistic Model of Range, Rightfielders, 2006 Permalink
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?
Probabilistic Model of Range, Shortstops, 2006 Permalink
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.
Probabilistic Model of Range, First Basemen, 2006 Permalink
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.
Probabilistic Model of Range, Centerfielders, 2006 Permalink
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.
Probabilistic Model of Range, Leftfielders, 2006 Permalink
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?).
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.
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.
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.
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.
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.
Probabilistic Model of Range, 2005, Runs Created Against First Basemen Permalink
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)
Player
Fieldable Balls In Play
Actual Outs by Fielder
Predicted Outs by Fielder
RCA
Predicted RCA
RCA/27 Outs
Predicted RCA/27
Runs Saved/27 Outs
Jose Hernandez
240
81
65.83
11.35
19.00
3.78
7.79
4.010
John Olerud
311
109
92.16
13.58
23.70
3.36
6.94
3.580
Chad A Tracy
472
159
134.45
29.28
39.32
4.97
7.90
2.924
Ben Broussard
711
223
185.89
37.53
49.87
4.54
7.24
2.698
Paul Konerko
985
294
261.08
51.80
69.55
4.76
7.19
2.435
Doug Mientkiewicz
522
158
134.64
26.84
34.01
4.59
6.82
2.235
Darin Erstad
972
313
272.80
38.36
54.52
3.31
5.40
2.087
Ryan J Howard
474
155
144.41
25.81
35.01
4.50
6.54
2.049
Kevin Millar
681
223
207.71
36.54
48.66
4.42
6.32
1.900
Daryle Ward
608
177
157.24
31.45
37.34
4.80
6.41
1.613
Derrek Lee
1029
298
270.98
52.82
62.70
4.79
6.25
1.462
Justin Morneau
922
263
256.38
43.46
56.16
4.46
5.91
1.453
Mike Lamb
306
87
82.25
17.93
21.22
5.56
6.96
1.400
Nick Johnson
901
294
282.15
35.75
47.62
3.28
4.56
1.273
Mark Teixeira
1121
350
309.40
71.76
77.88
5.54
6.80
1.261
Shea Hillenbrand
445
133
133.27
22.22
28.44
4.51
5.76
1.250
Travis Lee
708
245
214.15
42.10
46.28
4.64
5.83
1.196
Matt Stairs
434
115
110.00
32.99
35.98
7.75
8.83
1.086
Albert Pujols
1002
329
302.94
57.80
64.84
4.74
5.78
1.035
Lyle Overbay
917
273
252.84
47.00
52.53
4.65
5.61
0.961
Chris B Shelton
535
156
150.18
32.11
35.93
5.56
6.46
0.903
Dan R Johnson
641
193
193.02
33.89
40.28
4.74
5.64
0.894
Lance Niekro
394
136
120.38
19.06
20.53
3.78
4.61
0.821
Tony Clark
464
130
128.20
28.51
31.88
5.92
6.71
0.792
Todd Helton
997
301
286.52
69.91
74.27
6.27
7.00
0.728
Scott Hatteberg
344
103
101.56
23.57
25.73
6.18
6.84
0.663
Tino Martinez
575
181
144.70
36.71
32.82
5.48
6.12
0.648
Mike Sweeney
372
112
112.99
19.94
22.53
4.81
5.38
0.577
Hee Seop Choi
545
170
157.74
33.37
34.20
5.30
5.85
0.555
Eric Hinske
681
198
204.16
31.63
36.58
4.31
4.84
0.524
Julio Franco
366
85
92.45
20.99
24.17
6.67
7.06
0.394
Rafael Palmeiro
553
146
157.18
30.40
34.52
5.62
5.93
0.309
Richie Sexson
1067
276
275.00
58.96
60.54
5.77
5.94
0.176
Lance Berkman
531
146
155.13
32.57
35.05
6.02
6.10
0.076
Brad Eldred
256
65
67.08
16.99
17.66
7.06
7.11
0.050
Mark Sweeney
249
81
76.91
12.64
12.08
4.21
4.24
0.029
J.T. Snow
639
197
193.74
46.25
42.14
6.34
5.87
-0.466
Adam LaRoche
875
249
251.10
65.64
61.64
7.12
6.63
-0.490
Sean Casey
875
258
256.57
48.86
43.15
5.11
4.54
-0.573
Eduardo Perez
227
70
65.09
15.90
13.06
6.13
5.42
-0.714
Carlos Delgado
888
263
269.56
65.40
58.01
6.71
5.81
-0.903
Jim Thome
306
78
83.76
19.99
18.60
6.92
5.99
-0.927
Olmedo Saenz
379
81
94.72
28.06
28.60
9.35
8.15
-1.203
Carlos Pena
329
100
108.13
19.68
15.43
5.31
3.85
-1.460
Phil Nevin
457
142
144.26
39.96
31.00
7.60
5.80
-1.795
Jason Giambi
430
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.
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.
Probabilistic Model of Range, 2005, Runs Created Against Third Basemen Permalink
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)
Player
Fieldable Balls In Play
Actual Outs by Fielder
Predicted Outs by Fielder
RCA
Predicted RCA
RCA/27 Outs
Predicted RCA/27
Runs Saved/27 Outs
Wilson Betemit
373
123
102.29
15.55
27.93
3.41
7.37
3.958
Freddy Sanchez
446
180
148.78
21.96
36.21
3.29
6.57
3.278
Geoff Blum
301
118
97.38
15.14
23.50
3.47
6.51
3.050
Chipper Jones
695
235
212.40
41.04
60.57
4.72
7.70
2.984
Corey Koskie
572
214
179.63
34.69
47.40
4.38
7.12
2.748
Pedro Feliz
529
191
155.23
28.34
36.86
4.01
6.41
2.406
Bill Hall
347
121
115.83
20.43
29.68
4.56
6.92
2.361
Morgan Ensberg
1116
400
348.31
70.33
91.17
4.75
7.07
2.319
Jeff Cirillo
304
102
93.89
20.82
26.56
5.51
7.64
2.125
Joe Crede
976
348
306.29
49.61
66.84
3.85
5.89
2.043
Brandon Inge
1397
506
457.59
106.89
131.29
5.70
7.75
2.043
Scott Rolen
433
176
148.12
23.05
30.52
3.54
5.56
2.027
Adrian Beltre
1242
396
357.19
90.94
108.51
6.20
8.20
2.002
Chone Figgins
373
128
103.59
25.21
27.68
5.32
7.21
1.896
Alex S Gonzalez
707
236
214.48
60.74
70.17
6.95
8.83
1.885
Rob Mackowiak
426
158
140.50
30.78
36.93
5.26
7.10
1.838
Bill Mueller
1103
353
312.94
102.45
111.37
7.84
9.61
1.772
Melvin Mora
1149
401
361.65
84.04
99.09
5.66
7.40
1.740
Edwin Encarnacion
404
168
136.40
39.13
40.35
6.29
7.99
1.699
Alex Rodriguez
1328
395
350.67
99.71
109.33
6.82
8.42
1.602
Abraham O Nunez
698
256
225.84
48.33
55.61
5.10
6.65
1.551
Mike Lowell
983
339
308.19
77.92
87.92
6.21
7.70
1.497
Eric Chavez
1222
408
380.75
74.83
89.22
4.95
6.33
1.374
Garrett Atkins
989
333
313.98
81.32
92.57
6.59
7.96
1.367
David Bell
1066
406
360.53
87.56
95.68
5.82
7.17
1.343
Aaron Boone
1174
380
343.69
102.92
106.18
7.31
8.34
1.029
Dallas L McPherson
443
122
102.19
35.41
32.58
7.84
8.61
0.771
Hank Blalock
1304
398
385.85
97.70
103.03
6.63
7.21
0.581
Shea Hillenbrand
404
130
122.75
31.27
31.50
6.49
6.93
0.434
Aramis Ramirez
853
279
267.97
67.17
68.25
6.50
6.88
0.376
Mike Cuddyer
745
243
234.36
58.17
59.33
6.46
6.84
0.373
Vinny Castilla
904
344
337.16
52.72
54.18
4.14
4.34
0.200
Mark T Teahen
1034
340
312.64
101.71
95.84
8.08
8.28
0.200
Edgardo Alfonzo
727
223
219.04
58.44
58.56
7.08
7.22
0.143
Sean Burroughs
581
199
186.94
35.71
34.14
4.84
4.93
0.086
David A Wright
1288
432
408.09
111.65
106.51
6.98
7.05
0.069
Joe Randa
1106
349
340.28
101.14
97.97
7.82
7.77
-0.052
Russell Branyan
360
116
111.71
35.27
33.28
8.21
8.05
-0.164
Troy Glaus
1207
411
402.21
114.59
105.61
7.53
7.09
-0.438
Jorge L Cantu
438
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?
Probabilistic Model of Range, 2005, Runs Created Against Rightfielders Permalink
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)
Player
Fieldable Balls In Play
Actual Outs by Fielder
Predicted Outs by Fielder
RCA
Predicted RCA
RCA/27 Outs
Predicted RCA/27
Runs Saved/27 Outs
Jose Cruz
228
110
90.55
21.94
37.87
5.39
11.29
5.905
Jeff B Francoeur
281
131
116.89
35.38
46.79
7.29
10.81
3.516
Chad A Tracy
208
86
82.33
26.05
33.98
8.18
11.14
2.966
Jay Gibbons
296
133
126.19
20.37
32.67
4.14
6.99
2.855
Nick T Swisher
447
198
180.06
51.42
65.75
7.01
9.86
2.847
Vladimir Guerrero
516
245
225.72
54.09
66.76
5.96
7.99
2.024
Trot Nixon
567
240
234.77
63.85
79.64
7.18
9.16
1.975
Jeromy Burnitz
664
303
291.75
80.84
97.71
7.20
9.04
1.839
Casey Blake
610
287
280.95
57.37
74.09
5.40
7.12
1.723
Mike Cameron
318
137
122.46
36.28
40.15
7.15
8.85
1.702
Jermaine Dye
619
260
252.18
74.32
85.79
7.72
9.19
1.468
Magglio Ordonez
309
139
136.15
38.65
45.16
7.51
8.96
1.447
Jason Lane
516
225
210.46
67.10
73.21
8.05
9.39
1.340
Ichiro Suzuki
771
383
361.02
63.59
76.00
4.48
5.68
1.201
Shawn Green
522
232
218.32
73.14
75.97
8.51
9.40
0.883
Brian Giles
651
295
277.54
92.24
95.67
8.44
9.31
0.864
Geoff Jenkins
652
307
292.31
64.56
70.79
5.68
6.54
0.861
Bobby Abreu
602
267
261.04
69.81
75.94
7.06
7.85
0.795
Victor I Diaz
339
153
150.52
37.26
39.97
6.57
7.17
0.595
Richard Hidalgo
398
174
169.72
55.95
57.57
8.68
9.16
0.476
Sammy Sosa
289
121
124.99
32.99
35.60
7.36
7.69
0.329
Emil Brown
579
243
252.59
76.05
81.67
8.45
8.73
0.280
Austin Kearns
481
238
234.46
42.62
44.04
4.83
5.07
0.237
Jacque Jones
592
262
256.70
76.89
76.34
7.92
8.03
0.105
Jose Guillen
659
299
298.66
73.21
73.22
6.61
6.62
0.008
Aubrey Huff
470
204
207.52
52.60
53.17
6.96
6.92
-0.044
Alexis I Rios
563
246
256.53
63.82
65.76
7.01
6.92
-0.084
Gary Sheffield
553
240
225.92
71.32
66.33
8.02
7.93
-0.096
Moises Alou
231
90
97.28
28.55
30.27
8.56
8.40
-0.164
Juan Encarnacion
521
216
213.79
68.31
66.12
8.54
8.35
-0.189
Brad B Hawpe
379
148
156.44
60.51
62.62
11.04
10.81
-0.230
Michael Tucker
230
91
94.94
33.88
32.77
10.05
9.32
-0.734
Larry Walker
265
107
107.33
38.20
35.34
9.64
8.89
-0.750
Matt Lawton
538
230
240.06
72.21
60.57
8.48
6.81
-1.664
Craig Monroe
280
132
138.13
35.85
27.34
7.33
5.34
-1.989
Wily Mo Pena
235
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?
Probabilistic Model of Range, 2005, Runs Created Against Centerfielders Permalink
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)
Player
Fieldable Balls In Play
Actual Outs by Fielder
Predicted Outs by Fielder
RCA
Predicted RCA
RCA/27 Outs
Predicted RCA/27
Runs Saved/27 Outs
Joey R Gathright
375
181
167.23
23.76
48.36
3.54
7.81
4.263
Jerry Hairston
200
90
84.03
22.04
31.70
6.61
10.19
3.574
Jason Ellison
403
197
178.25
36.49
54.52
5.00
8.26
3.257
Andruw Jones
800
365
337.56
99.20
131.84
7.34
10.55
3.208
Jim Edmonds
673
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 Sizemore
796
373
370.07
69.49
103.55
5.03
7.56
2.525
Jason Michaels
334
161
150.73
28.36
40.42
4.76
7.24
2.485
Willy Taveras
771
332
322.83
81.34
107.70
6.61
9.01
2.393
Shawn Green
212
81
84.00
30.23
38.16
10.08
12.26
2.188
Aaron Rowand
811
388
362.99
70.62
95.24
4.91
7.08
2.170
Nook P Logan
581
282
270.92
45.57
63.75
4.36
6.35
1.990
Curtis Granderson
237
119
110.91
31.15
37.00
7.07
9.01
1.940
Mark Kotsay
691
299
306.87
73.41
95.91
6.63
8.44
1.810
Tike Redman
355
158
143.77
52.38
56.29
8.95
10.57
1.621
Jeremy T Reed
834
384
384.13
63.96
86.41
4.50
6.07
1.576
Corey Patterson
526
240
232.53
59.95
71.17
6.74
8.26
1.520
Vernon Wells
832
351
356.22
91.64
112.47
7.05
8.52
1.476
Luis Terrero
268
121
121.69
21.72
28.23
4.85
6.26
1.416
Laynce Nix
355
160
159.88
40.08
48.39
6.76
8.17
1.409
Brady Clark
823
399
380.69
75.00
91.32
5.07
6.48
1.402
Damon J Hollins
472
198
197.37
56.59
66.43
7.72
9.09
1.371
Jason Repko
240
97
105.29
20.41
27.29
5.68
7.00
1.314
Luis Matos
642
299
286.93
82.44
91.75
7.44
8.63
1.189
Johnny Damon
878
396
402.01
106.64
122.95
7.27
8.26
0.986
Randy Winn
382
184
182.71
39.69
44.62
5.82
6.59
0.769
Torii Hunter
510
218
220.35
56.90
62.46
7.05
7.65
0.606
Carlos Beltran
806
378
372.03
76.84
83.59
5.49
6.07
0.578
Kenny Lofton
458
201
207.17
47.87
53.71
6.43
7.00
0.570
Cory Sullivan
438
172
179.90
67.78
74.36
10.64
11.16
0.519
Lew Ford
348
140
150.24
46.02
51.86
8.87
9.32
0.446
Juan Pierre
790
332
337.90
107.44
111.48
8.74
8.91
0.170
Brad Wilkerson
509
234
230.76
61.26
60.46
7.07
7.07
0.006
Dave Roberts
579
234
240.18
73.35
73.60
8.46
8.27
-0.190
David DeJesus
672
306
313.16
87.76
86.92
7.74
7.49
-0.250
Milton Bradley
416
181
183.19
56.16
54.76
8.38
8.07
-0.307
Chone Figgins
296
131
134.46
34.32
32.73
7.07
6.57
-0.502
Bernie Williams
556
226
245.61
63.33
63.29
7.57
6.96
-0.609
Steve Finley
598
266
279.55
82.21
78.10
8.34
7.54
-0.801
Preston Wilson
652
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 Cruz
224
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?
Probabilistic Model of Range, 2005, Runs Created Against Second Basemen Permalink
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)
Player
Fieldable Balls In Play
Actual Outs by Fielder
Predicted Outs by Fielder
RCA
Predicted RCA
RCA/27 Outs
Predicted RCA/27
Runs Saved/27 Outs
Alex Cora
351
138
113.32
14.41
23.19
2.82
5.53
2.707
Nick Punto
616
246
213.29
24.94
37.36
2.74
4.73
1.992
Chase Utley
1203
481
429.21
52.90
73.81
2.97
4.64
1.674
Craig Counsell
1416
519
492.13
73.53
97.86
3.83
5.37
1.543
Orlando Hudson
1316
520
442.17
70.44
85.16
3.66
5.20
1.543
Placido Polanco
1009
370
346.28
55.75
70.93
4.07
5.53
1.463
Tony Graffanino
689
227
219.28
32.54
43.20
3.87
5.32
1.450
Mark Ellis
1055
400
381.12
52.80
68.62
3.56
4.86
1.297
Ronnie Belliard
1373
503
485.33
61.42
79.35
3.30
4.41
1.117
Mark Grudzielanek
1283
467
460.13
59.68
76.36
3.45
4.48
1.030
Nick Green
822
257
262.85
42.14
53.00
4.43
5.44
1.017
Adam Kennedy
1285
424
411.48
69.56
82.99
4.43
5.45
1.016
Kazuo Matsui
664
228
236.34
30.81
39.56
3.65
4.52
0.871
Marcus Giles
1469
531
523.20
95.36
106.20
4.85
5.48
0.631
Tadahito Iguchi
1337
441
451.36
75.69
86.27
4.63
5.16
0.526
Craig Biggio
1358
440
461.25
77.33
89.20
4.75
5.22
0.476
Luis Castillo
1094
418
396.59
63.31
65.47
4.09
4.46
0.368
Jerry Hairston
361
138
128.03
18.54
18.75
3.63
3.95
0.327
Mark Bellhorn
954
311
325.10
50.91
56.61
4.42
4.70
0.282
Miguel Cairo
780
264
264.95
53.05
55.59
5.43
5.67
0.240
Ray Durham
1268
394
409.18
71.85
77.21
4.92
5.09
0.171
Jose Castillo
969
322
330.00
63.84
66.87
5.35
5.47
0.118
Rich Aurilia
594
214
202.49
33.56
32.60
4.23
4.35
0.113
Damion Easley
357
135
131.99
21.89
21.73
4.38
4.45
0.067
Rickie Weeks
991
292
315.43
62.10
67.43
5.74
5.77
0.030
Jeff Kent
1352
460
468.51
75.94
77.78
4.46
4.48
0.025
Brian Roberts
1384
486
470.33
86.12
82.72
4.78
4.75
-0.036
Deivi Cruz
358
112
130.55
19.20
22.07
4.63
4.57
-0.063
Freddy Sanchez
427
137
137.43
33.17
32.79
6.54
6.44
-0.096
Alfonso Soriano
1601
512
554.60
90.57
95.21
4.78
4.64
-0.141
Jose C Lopez
536
197
186.96
31.47
28.25
4.31
4.08
-0.235
Ryan Freel
463
162
150.43
26.41
22.71
4.40
4.08
-0.325
Jamey Carroll
462
161
162.69
30.62
28.80
5.13
4.78
-0.354
Junior Spivey
625
223
210.83
41.50
36.08
5.02
4.62
-0.403
Ruben A Gotay
868
290
290.27
63.28
58.77
5.89
5.47
-0.425
Bret Boone
935
292
315.56
55.07
53.46
5.09
4.57
-0.518
Todd Walker
895
290
307.00
56.18
52.92
5.23
4.65
-0.576
Chone Figgins
399
113
137.60
26.01
27.79
6.21
5.45
-0.761
Aaron Miles
744
246
252.32
54.50
48.62
5.98
5.20
-0.779
Jose Vidro
724
240
252.81
48.14
42.86
5.42
4.58
-0.838
Omar Infante
610
205
207.93
39.24
32.96
5.17
4.28
-0.889
Robinson Cano
1401
474
513.25
91.74
80.14
5.23
4.22
-1.010
Luis A Gonzalez
684
225
245.12
54.73
50.39
6.57
5.55
-1.017
Mark Loretta
973
324
335.52
63.99
52.78
5.33
4.25
-1.085
Luis Rivas
388
127
130.44
27.40
22.30
5.82
4.62
-1.209
Jorge L Cantu
764
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.
Probabilistic Model of Range, 2005, Runs Created Against Fielders Permalink
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)
Player
Fieldable Balls In Play
Actual Outs by Fielder
Predicted Outs by Fielder
RCA
Predicted RCA
RCA/27 Outs
Predicted RCA/27
Runs Saved/27 Outs
John McDonald
521
178
172.92
27.41
36.40
4.16
5.68
1.527
Adam Everett
1490
517
498.79
71.05
95.41
3.71
5.16
1.454
Omar Infante
529
191
173.53
30.93
37.40
4.37
5.82
1.446
Bobby Crosby
925
312
304.63
42.34
56.74
3.66
5.03
1.365
Rafael Furcal
1648
596
576.97
84.81
109.11
3.84
5.11
1.264
Clint Barmes
859
306
279.67
49.78
56.65
4.39
5.47
1.077
Yuniesky Betancourt
610
177
174.31
39.99
45.58
6.10
7.06
0.961
Juan Uribe
1729
537
557.52
82.57
103.72
4.15
5.02
0.872
Julio Lugo
1761
560
540.03
122.06
134.05
5.88
6.70
0.817
Jimmy Rollins
1625
510
519.18
89.33
106.01
4.73
5.51
0.784
David Eckstein
1737
615
617.90
97.98
114.19
4.30
4.99
0.688
Jack Wilson
1734
600
610.94
104.81
121.02
4.72
5.35
0.632
Orlando Cabrera
1613
469
481.07
87.65
99.85
5.05
5.60
0.558
Oscar M Robles
554
172
179.32
31.03
35.19
4.87
5.30
0.427
Russ M Adams
1511
401
437.66
91.76
106.33
6.18
6.56
0.381
Neifi Perez
1269
445
448.29
65.47
71.20
3.97
4.29
0.316
Wilson Valdez
516
155
153.97
27.33
28.84
4.76
5.06
0.297
Miguel Tejada
1846
572
590.91
122.90
132.45
5.80
6.05
0.251
Juan Castro
774
264
260.08
59.78
61.02
6.11
6.33
0.221
Jhonny Peralta
1603
509
549.61
94.74
106.72
5.03
5.24
0.218
Jason A Bartlett
769
281
270.60
47.53
47.88
4.57
4.78
0.211
J.J. Hardy
1085
346
359.91
66.62
71.75
5.20
5.38
0.184
Omar Vizquel
1620
538
558.31
86.16
91.63
4.32
4.43
0.107
Alex Gonzalez
1273
452
441.85
91.18
88.58
5.45
5.41
-0.033
Derek Jeter
1913
561
602.13
115.11
122.15
5.54
5.48
-0.063
Carlos Guillen
834
262
270.95
54.67
55.58
5.63
5.54
-0.095
Khalil Greene
1246
399
409.60
74.67
74.98
5.05
4.94
-0.110
Jose Reyes
1865
522
569.16
115.79
123.88
5.99
5.88
-0.113
Cesar Izturis
1175
366
386.49
74.27
76.18
5.48
5.32
-0.157
Royce Clayton
1528
473
502.95
99.61
98.82
5.69
5.30
-0.381
Bill Hall
609
196
205.22
47.93
46.59
6.60
6.13
-0.473
Edgar Renteria
1773
491
499.45
128.25
120.64
7.05
6.52
-0.531
Marco Scutaro
846
259
282.20
50.54
48.34
5.27
4.62
-0.644
Michael Young
1930
534
580.38
134.80
131.21
6.82
6.10
-0.711
Felipe Lopez
1467
459
493.34
95.85
89.31
5.64
4.89
-0.750
Cristian Guzman
1333
417
438.14
83.54
75.53
5.41
4.65
-0.754
Mike Morse
581
156
170.02
47.09
43.64
8.15
6.93
-1.220
Angel Berroa
1818
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)
Player
Fieldable Balls In Play
Actual Outs by Fielder
Predicted Outs by Fielder
RCA
Predicted RCA
RCA/27 Outs
Predicted RCA/27
Runs Saved/27 Outs
Chris A Burke
272
120
101.65
26.59
41.11
5.98
10.92
4.935
Coco Crisp
617
294
261.44
66.21
96.11
6.08
9.93
3.846
Reed Johnson
282
134
112.67
34.77
41.96
7.01
10.05
3.049
Bobby Kielty
242
99
96.77
21.15
30.47
5.77
8.50
2.733
Carl Crawford
717
341
309.93
58.46
84.48
4.63
7.36
2.731
Matt T Holliday
558
236
214.67
72.13
87.09
8.25
10.95
2.701
Pedro Feliz
300
138
131.98
29.23
40.98
5.72
8.38
2.663
Jay Payton
245
107
94.67
24.01
30.14
6.06
8.60
2.537
Eric Byrnes
433
209
185.38
34.82
43.41
4.50
6.32
1.824
Carlos Lee
708
307
289.83
90.47
104.77
7.96
9.76
1.803
Kevin Mench
521
231
213.63
51.08
61.35
5.97
7.75
1.784
Moises Alou
284
132
123.81
25.23
31.83
5.16
6.94
1.781
Scott Podsednik
557
260
240.02
55.04
66.05
5.72
7.43
1.715
Craig Monroe
255
99
102.25
35.00
42.14
9.54
11.13
1.582
Raul Ibanez
255
106
103.57
22.14
27.17
5.64
7.08
1.443
Kelly A Johnson
356
166
154.90
42.52
47.68
6.92
8.31
1.394
Rondell White
270
119
118.43
30.45
36.12
6.91
8.24
1.327
Luis Gonzalez
619
270
246.42
95.06
97.93
9.51
10.73
1.225
Randy Winn
497
226
209.19
47.81
53.67
5.71
6.93
1.215
Cliff Floyd
654
283
267.59
76.59
82.93
7.31
8.37
1.061
Hideki Matsui
520
218
204.33
65.97
69.62
8.17
9.20
1.029
Adam Dunn
616
246
243.81
88.16
95.94
9.68
10.62
0.948
Frank Catalanotto
389
163
160.10
49.82
54.50
8.25
9.19
0.939
Jason Bay
625
266
257.22
83.97
89.04
8.52
9.35
0.824
Todd Hollandsworth
251
103
101.45
36.08
37.60
9.46
10.01
0.549
Shannon Stewart
555
249
237.55
74.67
75.06
8.10
8.53
0.434
Garret Anderson
531
201
208.94
59.21
61.67
7.95
7.97
0.016
Pat Burrell
627
236
247.60
82.06
85.00
9.39
9.27
-0.118
David Dellucci
228
84
90.32
29.17
30.85
9.38
9.22
-0.152
Marlon Byrd
210
100
102.84
21.98
21.78
5.93
5.72
-0.215
Terrence Long
423
166
163.61
76.66
73.49
12.47
12.13
-0.342
Reggie Sanders
268
108
105.77
47.56
43.95
11.89
11.22
-0.671
Ryan Klesko
504
204
202.51
76.35
69.22
10.11
9.23
-0.877
Miguel Cabrera
495
188
208.43
72.91
72.74
10.47
9.42
-1.049
Manny Ramirez
689
243
254.92
125.73
118.56
13.97
12.56
-1.412
Larry Bigbie
232
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.
Probabilistic Model of Range, 2005, Team Runs Saved Permalink
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
Team
InPlay
RCA
Predicted RCA
RCA per 27 Outs
Predicted RCA/27
Runs Saved per 27 Outs
Astros
4204
424.18
510.24
3.71
4.63
0.9163
Athletics
4286
395.81
482.57
3.34
4.23
0.8872
Cardinals
4414
428.09
514.59
3.52
4.40
0.8816
Indians
4385
420.76
508.22
3.50
4.37
0.8669
White Sox
4457
420.22
503.66
3.42
4.25
0.8290
Phillies
4211
442.77
517.76
3.89
4.71
0.8170
Braves
4559
488.44
569.82
3.99
4.80
0.8048
Blue Jays
4511
464.49
543.76
3.81
4.59
0.7811
Twins
4545
458.47
532.69
3.71
4.44
0.7269
Pirates
4467
480.44
538.91
3.98
4.58
0.5962
Angels
4383
465.62
519.73
3.95
4.51
0.5613
Red Sox
4575
552.63
605.03
4.59
5.13
0.5363
Orioles
4377
473.93
526.22
4.05
4.59
0.5344
Tigers
4527
478.16
532.86
3.91
4.44
0.5286
Diamondbacks
4571
547.20
594.48
4.53
5.02
0.4885
Giants
4520
491.96
542.16
4.06
4.55
0.4875
Mariners
4546
472.36
521.65
3.86
4.33
0.4708
Devil Rays
4560
557.49
602.93
4.68
5.14
0.4515
Cubs
4117
434.25
475.70
3.92
4.36
0.4344
Brewers
4252
467.77
499.93
4.12
4.45
0.3310
Rangers
4697
559.00
590.74
4.54
4.84
0.2981
Mets
4424
460.25
488.90
3.86
4.15
0.2877
Rockies
4537
583.74
611.55
4.97
5.24
0.2767
Dodgers
4392
467.44
487.51
3.95
4.15
0.2003
Marlins
4367
526.65
532.17
4.56
4.64
0.0812
Nationals
4538
482.25
486.34
3.96
4.00
0.0335
Padres
4423
513.08
514.33
4.38
4.39
0.0119
Yankees
4483
509.11
497.02
4.28
4.16
-0.1157
Royals
4611
612.92
591.70
5.16
4.94
-0.2189
Reds
4650
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.
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.
Probabilistic Model of Range, 2005, Centerfielders, No Out Penalties Permalink
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
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Jason Ellison
1867
197
147.52
0.106
0.079
0.02650
Joey R Gathright
1587
181
139.15
0.114
0.088
0.02637
Jason Michaels
1621
161
121.64
0.099
0.075
0.02428
Nook P Logan
2730
282
222.69
0.103
0.082
0.02172
Curtis Granderson
1044
119
96.64
0.114
0.093
0.02141
Randy Winn
1603
184
151.43
0.115
0.094
0.02032
Tike Redman
1613
158
125.28
0.098
0.078
0.02029
Jerry Hairston
1100
90
67.70
0.082
0.062
0.02027
Jeremy T Reed
3692
384
309.33
0.104
0.084
0.02023
Gary Matthews Jr.
2822
258
201.33
0.091
0.071
0.02008
Brady Clark
3765
399
325.40
0.106
0.086
0.01955
Aaron Rowand
4128
388
307.33
0.094
0.074
0.01954
Willy Taveras
3646
332
263.79
0.091
0.072
0.01871
Damon J Hollins
2010
198
162.20
0.099
0.081
0.01781
Andruw Jones
4309
365
289.87
0.085
0.067
0.01743
Jim Edmonds
3538
319
258.07
0.090
0.073
0.01722
Luis Matos
3017
299
249.80
0.099
0.083
0.01631
Grady Sizemore
4136
373
305.68
0.090
0.074
0.01628
Luis Terrero
1310
121
100.02
0.092
0.076
0.01602
Laynce Nix
1674
160
133.31
0.096
0.080
0.01594
Mark Kotsay
3519
299
243.40
0.085
0.069
0.01580
Jason Repko
1128
97
79.24
0.086
0.070
0.01575
Dave Roberts
2715
234
191.74
0.086
0.071
0.01557
Corey Patterson
2799
240
197.48
0.086
0.071
0.01519
Kenny Lofton
2167
201
168.93
0.093
0.078
0.01480
Carlos Beltran
3967
378
320.30
0.095
0.081
0.01454
Johnny Damon
3952
396
338.79
0.100
0.086
0.01448
Vernon Wells
4239
351
290.97
0.083
0.069
0.01416
Torii Hunter
2575
218
181.55
0.085
0.071
0.01415
Chone Figgins
1184
131
114.47
0.111
0.097
0.01396
Brad Wilkerson
2414
234
200.75
0.097
0.083
0.01378
Milton Bradley
1969
181
154.02
0.092
0.078
0.01370
David DeJesus
3304
306
263.92
0.093
0.080
0.01274
Juan Pierre
4171
332
280.23
0.080
0.067
0.01241
Lew Ford
1677
140
121.05
0.083
0.072
0.01130
Cory Sullivan
1935
172
151.11
0.089
0.078
0.01079
Steve Finley
2691
266
242.14
0.099
0.090
0.00887
Preston Wilson
3362
267
237.40
0.079
0.071
0.00880
Bernie Williams
2689
226
204.58
0.084
0.076
0.00797
Ken Griffey Jr.
3439
286
259.60
0.083
0.075
0.00768
Jose Cruz
1317
87
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
Player
Predicted Outs
Predicted Outs No Hogs
Difference
Diff 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.
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
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Wilson Valdez
1198
147
108.97
0.123
0.091
0.03174
Jason A Bartlett
1766
257
201.14
0.146
0.114
0.03163
Clint Barmes
2209
276
206.61
0.125
0.094
0.03141
Omar Infante
1233
171
133.05
0.139
0.108
0.03078
Juan Uribe
3946
494
378.25
0.125
0.096
0.02933
Adam Everett
3748
469
360.11
0.125
0.096
0.02905
John McDonald
1223
163
128.61
0.133
0.105
0.02812
Rafael Furcal
4111
539
423.58
0.131
0.103
0.02808
Julio Lugo
4297
523
404.03
0.122
0.094
0.02769
Jimmy Rollins
3994
473
365.01
0.118
0.091
0.02704
Yuniesky Betancourt
1426
161
122.81
0.113
0.086
0.02678
Bobby Crosby
2163
277
221.03
0.128
0.102
0.02588
Neifi Perez
3026
410
331.69
0.135
0.110
0.02588
Juan Castro
1775
243
197.45
0.137
0.111
0.02566
Orlando Cabrera
3706
425
335.17
0.115
0.090
0.02424
Carlos Guillen
1934
240
193.33
0.124
0.100
0.02413
Oscar M Robles
1313
157
126.13
0.120
0.096
0.02351
Omar Vizquel
4024
500
406.57
0.124
0.101
0.02322
J.J. Hardy
2805
316
251.53
0.113
0.090
0.02298
Bill Hall
1447
183
151.08
0.126
0.104
0.02206
Khalil Greene
3123
365
297.32
0.117
0.095
0.02167
Jack Wilson
4240
543
451.46
0.128
0.106
0.02159
Miguel Tejada
4280
526
433.75
0.123
0.101
0.02155
Edgar Renteria
4119
452
367.66
0.110
0.089
0.02048
Cesar Izturis
2859
338
279.59
0.118
0.098
0.02043
Alex Gonzalez
3291
404
337.51
0.123
0.103
0.02020
Derek Jeter
4231
525
440.39
0.124
0.104
0.02000
Russ M Adams
3433
372
303.72
0.108
0.088
0.01989
Jhonny Peralta
3736
465
392.67
0.124
0.105
0.01936
David Eckstein
4109
550
470.45
0.134
0.114
0.01936
Cristian Guzman
3605
381
314.00
0.106
0.087
0.01858
Marco Scutaro
1980
238
201.91
0.120
0.102
0.01823
Jose Reyes
4308
479
405.05
0.111
0.094
0.01717
Felipe Lopez
3804
418
353.72
0.110
0.093
0.01690
Michael Young
4398
489
415.35
0.111
0.094
0.01675
Mike Morse
1437
144
120.59
0.100
0.084
0.01629
Angel Berroa
4438
505
434.84
0.114
0.098
0.01581
Royce Clayton
3711
430
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
Player
Predicted Outs
Predicted Outs No Hogs
Difference
Diff 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.
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
Location
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Away
2027
140
136.64
0.069
0.067
0.00166
Home
1929
103
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.
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.
Probabilistic Model of Range, 2005, Pitchers Permalink
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
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Kirk Saarloos
553
41
23.33
0.074
0.042
0.03196
Kenny Rogers
665
49
33.01
0.074
0.050
0.02405
Greg Maddux
728
52
38.90
0.071
0.053
0.01799
Chan Ho Park
501
37
29.08
0.074
0.058
0.01581
Jake Westbrook
693
53
42.09
0.076
0.061
0.01574
Brad Radke
651
31
23.04
0.048
0.035
0.01223
Brett Myers
587
37
30.33
0.063
0.052
0.01136
Kip Wells
562
34
27.70
0.060
0.049
0.01121
Barry Zito
654
36
28.92
0.055
0.044
0.01082
Mark Mulder
659
48
41.34
0.073
0.063
0.01011
Roger Clemens
577
35
30.24
0.061
0.052
0.00825
Jose Lima
597
23
18.66
0.039
0.031
0.00727
Livan Hernandez
796
45
39.53
0.057
0.050
0.00687
Mark Redman
574
34
30.47
0.059
0.053
0.00614
Dontrelle Willis
716
39
34.64
0.054
0.048
0.00609
Aaron Harang
643
27
23.41
0.042
0.036
0.00558
Ramon Ortiz
567
25
22.09
0.044
0.039
0.00513
Tomo Ohka
596
27
24.19
0.045
0.041
0.00472
Johan Santana
604
29
26.28
0.048
0.044
0.00450
Carlos Silva
641
30
27.16
0.047
0.042
0.00443
Jake Peavy
521
24
21.87
0.046
0.042
0.00409
Zack Z Greinke
625
26
23.44
0.042
0.038
0.00409
Jamie Moyer
683
27
24.32
0.040
0.036
0.00392
Josh Fogg
571
26
23.86
0.046
0.042
0.00374
Javier Vazquez
626
34
31.74
0.054
0.051
0.00361
Josh Towers
706
32
29.86
0.045
0.042
0.00304
Woody Williams
513
19
17.45
0.037
0.034
0.00302
Matt Clement
582
21
19.28
0.036
0.033
0.00295
Kyle Lohse
607
26
24.28
0.043
0.040
0.00283
Runelvys Hernandez
523
16
14.53
0.031
0.028
0.00281
Derek Lowe
700
53
51.13
0.076
0.073
0.00267
John Patterson
543
16
14.59
0.029
0.027
0.00260
Jon Garland
706
38
36.20
0.054
0.051
0.00255
Pedro Martinez
564
19
17.64
0.034
0.031
0.00241
Noah Lowry
599
29
27.59
0.048
0.046
0.00235
Rodrigo Lopez
702
31
29.45
0.044
0.042
0.00220
Andy Pettitte
643
38
36.71
0.059
0.057
0.00200
Brandon Webb
689
43
41.95
0.062
0.061
0.00152
Brian Moehler
538
28
27.19
0.052
0.051
0.00151
John Smoltz
690
35
33.96
0.051
0.049
0.00151
Jeff Suppan
626
29
28.13
0.046
0.045
0.00139
Mark Buehrle
758
45
44.07
0.059
0.058
0.00122
Brian Lawrence
656
33
32.37
0.050
0.049
0.00095
Cory Lidle
607
30
29.65
0.049
0.049
0.00057
Freddy Garcia
708
28
27.87
0.040
0.039
0.00019
Tim Wakefield
678
24
24.27
0.035
0.036
-0.00040
Mike Mussina
545
27
27.33
0.050
0.050
-0.00061
Mark Hendrickson
633
23
23.51
0.036
0.037
-0.00080
Kris Benson
565
30
30.55
0.053
0.054
-0.00098
Chris Carpenter
667
42
42.99
0.063
0.064
-0.00148
Scott E Kazmir
522
15
15.87
0.029
0.030
-0.00166
Brett Tomko
625
20
21.07
0.032
0.034
-0.00172
Horacio Ramirez
667
43
44.16
0.064
0.066
-0.00174
Brandon Claussen
522
20
20.93
0.038
0.040
-0.00179
Paul Byrd
682
22
23.24
0.032
0.034
-0.00182
Bruce Chen
594
23
24.31
0.039
0.041
-0.00220
Matt Morris
633
26
27.67
0.041
0.044
-0.00264
Joel Pineiro
629
20
21.73
0.032
0.035
-0.00274
Joe Mays
564
23
24.63
0.041
0.044
-0.00289
Tom Glavine
720
42
44.09
0.058
0.061
-0.00290
Gustavo G Chacin
652
24
25.95
0.037
0.040
-0.00299
Chris Capuano
639
27
28.92
0.042
0.045
-0.00301
Jose Contreras
595
23
25.28
0.039
0.042
-0.00383
Bronson Arroyo
688
22
24.66
0.032
0.036
-0.00386
Doug Davis
615
27
29.53
0.044
0.048
-0.00411
Eric Milton
633
17
19.67
0.027
0.031
-0.00422
Brad A Halsey
550
21
23.39
0.038
0.043
-0.00435
John Lackey
598
24
26.70
0.040
0.045
-0.00452
Jason Johnson
718
33
36.44
0.046
0.051
-0.00480
Jason Marquis
664
23
26.42
0.035
0.040
-0.00514
Mike Maroth
683
19
22.62
0.028
0.033
-0.00530
Carlos Zambrano
592
34
37.18
0.057
0.063
-0.00536
Jamey Wright
563
22
25.48
0.039
0.045
-0.00618
Scott Elarton
585
11
14.74
0.019
0.025
-0.00640
Jeff Weaver
677
27
31.35
0.040
0.046
-0.00642
Esteban Loaiza
661
32
36.33
0.048
0.055
-0.00655
Victor Zambrano
532
22
25.55
0.041
0.048
-0.00668
Roy Oswalt
744
34
39.04
0.046
0.052
-0.00678
Tim Hudson
608
39
43.19
0.064
0.071
-0.00689
Doug Waechter
533
9
12.68
0.017
0.024
-0.00690
Bartolo Colon
678
17
22.27
0.025
0.033
-0.00777
Jarrod Washburn
567
16
20.83
0.028
0.037
-0.00852
Randy Johnson
618
23
29.00
0.037
0.047
-0.00970
Danny Haren
649
20
27.02
0.031
0.042
-0.01082
Joe M Blanton
624
18
24.81
0.029
0.040
-0.01091
Ryan Franklin
642
13
20.02
0.020
0.031
-0.01093
Jeff W Francis
594
17
23.50
0.029
0.040
-0.01095
Joe Kennedy
515
14
19.77
0.027
0.038
-0.01120
Cliff Lee
621
11
18.06
0.018
0.029
-0.01138
Jon Lieber
684
28
36.25
0.041
0.053
-0.01206
David Wells
622
15
23.11
0.024
0.037
-0.01304
C.C. Sabathia
574
17
24.76
0.030
0.043
-0.01351
Kevin Millwood
576
18
26.09
0.031
0.045
-0.01405
Brad Penny
555
25
32.86
0.045
0.059
-0.01417
Nate Robertson
624
24
33.38
0.038
0.053
-0.01504
Jeremy Bonderman
574
15
24.26
0.026
0.042
-0.01614
A.J. Burnett
577
19
28.56
0.033
0.049
-0.01657
Probabilistic Model of Range, Pitchers, 2005, Smoothed Visiting Player Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Kirk Saarloos
553
41
23.64
0.074
0.043
0.03140
Kenny Rogers
665
49
33.21
0.074
0.050
0.02374
Chan Ho Park
501
37
28.34
0.074
0.057
0.01729
Greg Maddux
728
52
39.57
0.071
0.054
0.01708
Jake Westbrook
693
53
42.60
0.076
0.061
0.01501
Kip Wells
562
34
26.31
0.060
0.047
0.01368
Mark Mulder
659
48
39.08
0.073
0.059
0.01354
Brett Myers
587
37
30.63
0.063
0.052
0.01085
Barry Zito
654
36
28.99
0.055
0.044
0.01072
Brad Radke
651
31
24.71
0.048
0.038
0.00966
Livan Hernandez
796
45
38.49
0.057
0.048
0.00818
Mark Redman
574
34
29.52
0.059
0.051
0.00781
Jose Lima
597
23
18.61
0.039
0.031
0.00736
Jamie Moyer
683
27
22.17
0.040
0.032
0.00707
Ramon Ortiz
567
25
21.04
0.044
0.037
0.00699
Roger Clemens
577
35
30.99
0.061
0.054
0.00696
Mark Buehrle
758
45
39.93
0.059
0.053
0.00669
Jake Peavy
521
24
21.18
0.046
0.041
0.00541
Matt Clement
582
21
18.00
0.036
0.031
0.00515
Johan Santana
604
29
25.97
0.048
0.043
0.00502
Aaron Harang
643
27
23.78
0.042
0.037
0.00501
Dontrelle Willis
716
39
35.54
0.054
0.050
0.00484
Carlos Silva
641
30
27.28
0.047
0.043
0.00425
John Smoltz
690
35
32.28
0.051
0.047
0.00395
John Patterson
543
16
14.05
0.029
0.026
0.00359
Josh Fogg
571
26
23.96
0.046
0.042
0.00357
Javier Vazquez
626
34
31.83
0.054
0.051
0.00347
Woody Williams
513
19
17.22
0.037
0.034
0.00346
Zack Z Greinke
625
26
23.84
0.042
0.038
0.00346
Jon Garland
706
38
35.57
0.054
0.050
0.00344
Pedro Martinez
564
19
17.09
0.034
0.030
0.00339
Kyle Lohse
607
26
24.39
0.043
0.040
0.00266
Brandon Webb
689
43
41.27
0.062
0.060
0.00251
Josh Towers
706
32
30.29
0.045
0.043
0.00242
Tomo Ohka
596
27
25.58
0.045
0.043
0.00238
Runelvys Hernandez
523
16
14.79
0.031
0.028
0.00231
Horacio Ramirez
667
43
41.63
0.064
0.062
0.00206
Andy Pettitte
643
38
36.72
0.059
0.057
0.00199
Derek Lowe
700
53
51.70
0.076
0.074
0.00185
Rodrigo Lopez
702
31
29.79
0.044
0.042
0.00173
Noah Lowry
599
29
28.30
0.048
0.047
0.00117
Cory Lidle
607
30
29.53
0.049
0.049
0.00078
Mike Mussina
545
27
26.90
0.050
0.049
0.00018
Kris Benson
565
30
30.01
0.053
0.053
-0.00001
Gustavo G Chacin
652
24
24.10
0.037
0.037
-0.00016
Freddy Garcia
708
28
28.17
0.040
0.040
-0.00024
Brian Lawrence
656
33
33.40
0.050
0.051
-0.00060
Jeff Suppan
626
29
29.48
0.046
0.047
-0.00076
John Lackey
598
24
24.48
0.040
0.041
-0.00080
Chris Carpenter
667
42
42.73
0.063
0.064
-0.00109
Brian Moehler
538
28
28.60
0.052
0.053
-0.00112
Joel Pineiro
629
20
20.71
0.032
0.033
-0.00113
Paul Byrd
682
22
22.91
0.032
0.034
-0.00133
Scott E Kazmir
522
15
15.88
0.029
0.030
-0.00168
Mark Hendrickson
633
23
24.22
0.036
0.038
-0.00192
Jose Contreras
595
23
24.28
0.039
0.041
-0.00215
Brett Tomko
625
20
21.38
0.032
0.034
-0.00221
Matt Morris
633
26
27.69
0.041
0.044
-0.00267
Bruce Chen
594
23
24.79
0.039
0.042
-0.00301
Tim Wakefield
678
24
26.07
0.035
0.038
-0.00305
Chris Capuano
639
27
29.02
0.042
0.045
-0.00316
Brad A Halsey
550
21
22.76
0.038
0.041
-0.00321
Bronson Arroyo
688
22
24.45
0.032
0.036
-0.00355
Joe Mays
564
23
25.02
0.041
0.044
-0.00358
Brandon Claussen
522
20
21.92
0.038
0.042
-0.00368
Tom Glavine
720
42
44.70
0.058
0.062
-0.00375
Eric Milton
633
17
19.66
0.027
0.031
-0.00420
Roy Oswalt
744
34
37.16
0.046
0.050
-0.00424
Mike Maroth
683
19
22.11
0.028
0.032
-0.00455
Jason Marquis
664
23
26.23
0.035
0.040
-0.00486
Doug Davis
615
27
30.06
0.044
0.049
-0.00497
Carlos Zambrano
592
34
36.98
0.057
0.062
-0.00503
Tim Hudson
608
39
42.16
0.064
0.069
-0.00521
Scott Elarton
585
11
14.19
0.019
0.024
-0.00546
Victor Zambrano
532
22
24.96
0.041
0.047
-0.00556
Jeff Weaver
677
27
30.80
0.040
0.045
-0.00561
Jason Johnson
718
33
37.09
0.046
0.052
-0.00569
Jarrod Washburn
567
16
19.47
0.028
0.034
-0.00612
Jamey Wright
563
22
25.91
0.039
0.046
-0.00694
Doug Waechter
533
9
12.99
0.017
0.024
-0.00748
Randy Johnson
618
23
27.83
0.037
0.045
-0.00781
Bartolo Colon
678
17
22.31
0.025
0.033
-0.00783
Esteban Loaiza
661
32
37.66
0.048
0.057
-0.00857
Nate Robertson
624
24
29.58
0.038
0.047
-0.00894
Danny Haren
649
20
26.04
0.031
0.040
-0.00931
Joe M Blanton
624
18
24.14
0.029
0.039
-0.00984
Ryan Franklin
642
13
19.53
0.020
0.030
-0.01017
Joe Kennedy
515
14
19.33
0.027
0.038
-0.01035
Cliff Lee
621
11
18.37
0.018
0.030
-0.01187
Jeff W Francis
594
17
24.07
0.029
0.041
-0.01191
Jon Lieber
684
28
36.35
0.041
0.053
-0.01221
C.C. Sabathia
574
17
24.24
0.030
0.042
-0.01261
Brad Penny
555
25
32.70
0.045
0.059
-0.01388
Jeremy Bonderman
574
15
23.30
0.026
0.041
-0.01445
Kevin Millwood
576
18
26.42
0.031
0.046
-0.01462
David Wells
622
15
24.53
0.024
0.039
-0.01533
A.J. Burnett
577
19
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.
Probabilistic Model of Range, 2005, Catchers, Groundballs Permalink
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)
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Dioner F Navarro
603
16
8.03
0.027
0.013
0.01321
Javier Valentin
754
13
8.51
0.017
0.011
0.00596
Vance Wilson
626
7
4.53
0.011
0.007
0.00395
Danny Ardoin
876
12
9.15
0.014
0.010
0.00325
Ivan Rodriguez
1566
17
11.96
0.011
0.008
0.00322
Johnny Estrada
1307
24
20.66
0.018
0.016
0.00256
Chris R Snyder
1435
17
13.80
0.012
0.010
0.00223
Yorvit Torrealba
768
10
8.31
0.013
0.011
0.00220
Mike Piazza
1161
17
14.84
0.015
0.013
0.00186
Gregg Zaun
1603
13
10.42
0.008
0.006
0.00161
Jason LaRue
1309
21
18.93
0.016
0.014
0.00158
Sal Fasano
648
6
5.16
0.009
0.008
0.00130
Jose Molina
635
6
5.19
0.009
0.008
0.00128
Yadier B Molina
1554
18
16.20
0.012
0.010
0.00116
Geronimo Gil
502
4
3.53
0.008
0.007
0.00093
Mike Redmond
608
2
1.61
0.003
0.003
0.00065
JD Closser
852
9
8.63
0.011
0.010
0.00044
John R Buck
1500
11
10.37
0.007
0.007
0.00042
Rod Barajas
1592
10
9.47
0.006
0.006
0.00033
Jason Phillips
1204
14
13.63
0.012
0.011
0.00030
Chris Widger
503
4
3.90
0.008
0.008
0.00020
Jorge Posada
1721
20
19.66
0.012
0.011
0.00020
A.J. Pierzynski
1545
9
8.99
0.006
0.006
0.00001
Mike Matheny
1582
14
14.28
0.009
0.009
-0.00018
Mike Lieberthal
1413
15
15.34
0.011
0.011
-0.00024
Gary Bennett
690
9
9.21
0.013
0.013
-0.00030
Jason Kendall
1753
12
12.54
0.007
0.007
-0.00031
Humberto Cota
990
13
13.34
0.013
0.013
-0.00034
Henry Blanco
556
5
5.39
0.009
0.010
-0.00071
Miguel Olivo
963
8
8.78
0.008
0.009
-0.00081
Jason Varitek
1592
7
8.44
0.004
0.005
-0.00091
Sandy Alomar Jr.
516
4
4.53
0.008
0.009
-0.00103
Joe Mauer
1431
7
9.28
0.005
0.006
-0.00160
Chad Moeller
655
6
7.07
0.009
0.011
-0.00163
Brad Ausmus
1537
18
20.51
0.012
0.013
-0.00163
Ryan M Doumit
614
7
8.01
0.011
0.013
-0.00164
Victor Martinez
1757
10
13.02
0.006
0.007
-0.00172
Damian Miller
1228
12
14.17
0.010
0.012
-0.00177
Brian M McCann
693
12
13.31
0.017
0.019
-0.00189
Brian Schneider
1256
10
13.03
0.008
0.010
-0.00241
Bengie Molina
1154
4
7.20
0.003
0.006
-0.00277
Einar Diaz
508
4
5.50
0.008
0.011
-0.00296
Toby Hall
1417
6
10.50
0.004
0.007
-0.00317
Todd Pratt
595
6
7.93
0.010
0.013
-0.00324
Michael Barrett
1489
13
17.82
0.009
0.012
-0.00324
Paul Lo Duca
1537
12
17.56
0.008
0.011
-0.00362
Ramon R Castro
830
5
8.80
0.006
0.011
-0.00458
Ramon Hernandez
1127
7
12.32
0.006
0.011
-0.00472
Javy Lopez
903
0
5.67
0.000
0.006
-0.00628
Probabilistic Model of Range, Catchers, 2005, Smoothed Visiting Player Model, Ground Balls Only (Grounders + Bunts)
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Dioner F Navarro
603
16
8.37
0.027
0.014
0.01265
Javier Valentin
754
13
7.15
0.017
0.009
0.00775
Danny Ardoin
876
12
7.69
0.014
0.009
0.00492
Jason LaRue
1309
21
15.95
0.016
0.012
0.00386
Vance Wilson
626
7
4.74
0.011
0.008
0.00361
Ivan Rodriguez
1566
17
11.59
0.011
0.007
0.00345
Johnny Estrada
1307
24
19.76
0.018
0.015
0.00324
Yorvit Torrealba
768
10
7.56
0.013
0.010
0.00318
Chris R Snyder
1435
17
12.80
0.012
0.009
0.00293
Yadier B Molina
1554
18
14.51
0.012
0.009
0.00225
Chris Widger
503
4
3.14
0.008
0.006
0.00171
Mike Lieberthal
1413
15
12.84
0.011
0.009
0.00153
Gregg Zaun
1603
13
10.80
0.008
0.007
0.00138
Mike Redmond
608
2
1.21
0.003
0.002
0.00131
JD Closser
852
9
7.89
0.011
0.009
0.00130
Jose Molina
635
6
5.19
0.009
0.008
0.00127
Sal Fasano
648
6
5.19
0.009
0.008
0.00125
Jason Phillips
1204
14
12.86
0.012
0.011
0.00095
Geronimo Gil
502
4
3.56
0.008
0.007
0.00088
Gary Bennett
690
9
8.73
0.013
0.013
0.00039
Jorge Posada
1721
20
19.51
0.012
0.011
0.00028
Henry Blanco
556
5
4.87
0.009
0.009
0.00024
Mike Piazza
1161
17
16.73
0.015
0.014
0.00023
Jason Kendall
1753
12
12.56
0.007
0.007
-0.00032
A.J. Pierzynski
1545
9
9.51
0.006
0.006
-0.00033
John R Buck
1500
11
11.59
0.007
0.008
-0.00040
Sandy Alomar Jr.
516
4
4.21
0.008
0.008
-0.00040
Brian M McCann
693
12
12.44
0.017
0.018
-0.00063
Rod Barajas
1592
10
11.22
0.006
0.007
-0.00077
Ryan M Doumit
614
7
7.49
0.011
0.012
-0.00080
Damian Miller
1228
12
13.03
0.010
0.011
-0.00084
Todd Pratt
595
6
6.54
0.010
0.011
-0.00090
Joe Mauer
1431
7
8.37
0.005
0.006
-0.00095
Mike Matheny
1582
14
15.64
0.009
0.010
-0.00104
Victor Martinez
1757
10
12.01
0.006
0.007
-0.00114
Humberto Cota
990
13
14.30
0.013
0.014
-0.00131
Miguel Olivo
963
8
9.27
0.008
0.010
-0.00132
Jason Varitek
1592
7
9.22
0.004
0.006
-0.00140
Brad Ausmus
1537
18
20.37
0.012
0.013
-0.00154
Bengie Molina
1154
4
6.67
0.003
0.006
-0.00231
Chad Moeller
655
6
7.54
0.009
0.012
-0.00236
Einar Diaz
508
4
5.26
0.008
0.010
-0.00248
Michael Barrett
1489
13
17.22
0.009
0.012
-0.00283
Brian Schneider
1256
10
13.91
0.008
0.011
-0.00312
Toby Hall
1417
6
10.66
0.004
0.008
-0.00329
Paul Lo Duca
1537
12
17.12
0.008
0.011
-0.00333
Ramon Hernandez
1127
7
10.86
0.006
0.010
-0.00343
Ramon R Castro
830
5
8.32
0.006
0.010
-0.00400
Javy Lopez
903
0
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.
Probabilistic Model of Range, 2005, Catchers Permalink
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
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Dioner F Navarro
1307
28
20.87
0.021
0.016
0.00546
Vance Wilson
1324
18
10.87
0.014
0.008
0.00539
Kelly Stinnett
1038
18
13.11
0.017
0.013
0.00472
Mike Matheny
3552
52
38.35
0.015
0.011
0.00384
Geronimo Gil
1038
14
11.13
0.013
0.011
0.00277
Ryan M Doumit
1327
20
16.35
0.015
0.012
0.00275
Yorvit Torrealba
1637
28
23.66
0.017
0.014
0.00265
Danny Ardoin
1851
25
20.87
0.014
0.011
0.00223
Jason Varitek
3536
48
40.28
0.014
0.011
0.00218
Jason Kendall
3802
41
36.12
0.011
0.009
0.00128
Gary Bennett
1631
26
23.93
0.016
0.015
0.00127
Humberto Cota
2127
27
24.74
0.013
0.012
0.00106
JD Closser
1822
23
21.21
0.013
0.012
0.00098
Gregg Zaun
3357
33
29.72
0.010
0.009
0.00098
Ivan Rodriguez
3203
37
33.93
0.012
0.011
0.00096
Jason LaRue
2978
45
42.37
0.015
0.014
0.00088
Brad Ausmus
3095
43
40.33
0.014
0.013
0.00086
Joe Mauer
3074
33
30.66
0.011
0.010
0.00076
Chris Widger
1059
9
8.42
0.008
0.008
0.00055
Johnny Estrada
2607
34
32.64
0.013
0.013
0.00052
Mike Piazza
2442
31
29.85
0.013
0.012
0.00047
Chris R Snyder
2848
33
32.12
0.012
0.011
0.00031
Yadier B Molina
2872
31
30.12
0.011
0.010
0.00030
Jorge Posada
3446
47
46.84
0.014
0.014
0.00005
Ramon Hernandez
2459
28
27.87
0.011
0.011
0.00005
A.J. Pierzynski
3360
35
34.97
0.010
0.010
0.00001
Rod Barajas
3325
34
34.15
0.010
0.010
-0.00004
John R Buck
3231
35
35.24
0.011
0.011
-0.00007
Sal Fasano
1324
11
11.22
0.008
0.008
-0.00016
Jose Molina
1413
15
15.26
0.011
0.011
-0.00018
Henry Blanco
1141
14
14.46
0.012
0.013
-0.00040
Sandy Alomar Jr.
1041
11
11.49
0.011
0.011
-0.00047
Javier Valentin
1644
18
18.94
0.011
0.012
-0.00057
Matt A Treanor
1109
14
14.94
0.013
0.013
-0.00085
Mike Lieberthal
2959
30
32.58
0.010
0.011
-0.00087
Michael Barrett
2976
29
31.88
0.010
0.011
-0.00097
Miguel Olivo
2132
19
21.30
0.009
0.010
-0.00108
Damian Miller
2778
35
38.00
0.013
0.014
-0.00108
Jason Phillips
2401
25
27.71
0.010
0.012
-0.00113
Victor Martinez
3728
30
34.37
0.008
0.009
-0.00117
John Flaherty
1016
14
15.23
0.014
0.015
-0.00121
Brian Schneider
2883
29
33.06
0.010
0.011
-0.00141
Chad Moeller
1474
16
18.09
0.011
0.012
-0.00142
Toby Hall
3359
30
35.02
0.009
0.010
-0.00149
Todd Pratt
1252
12
13.97
0.010
0.011
-0.00158
Brian M McCann
1410
18
20.40
0.013
0.014
-0.00171
Mike Redmond
1219
5
7.23
0.004
0.006
-0.00183
Bengie Molina
2635
23
28.49
0.009
0.011
-0.00208
Paul Lo Duca
3110
31
37.68
0.010
0.012
-0.00215
Ramon R Castro
1825
16
21.06
0.009
0.012
-0.00277
Javy Lopez
1916
13
20.55
0.007
0.011
-0.00394
Pat Borders
1016
8
13.13
0.008
0.013
-0.00505
Probabilistic Model of Range, Catchers, 2005, Smoothed Visiting Player Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Kelly Stinnett
1038
18
11.36
0.017
0.011
0.00640
Dioner F Navarro
1307
28
20.67
0.021
0.016
0.00561
Vance Wilson
1324
18
11.08
0.014
0.008
0.00522
Geronimo Gil
1038
14
10.35
0.013
0.010
0.00351
Mike Matheny
3552
52
39.57
0.015
0.011
0.00350
Ryan M Doumit
1327
20
15.63
0.015
0.012
0.00329
Yorvit Torrealba
1637
28
22.61
0.017
0.014
0.00329
Danny Ardoin
1851
25
20.36
0.014
0.011
0.00251
Gary Bennett
1631
26
22.06
0.016
0.014
0.00241
Chris Widger
1059
9
6.52
0.008
0.006
0.00234
Jason Varitek
3536
48
40.91
0.014
0.012
0.00200
Jason LaRue
2978
45
39.39
0.015
0.013
0.00188
Jason Kendall
3802
41
35.43
0.011
0.009
0.00147
Brad Ausmus
3095
43
38.81
0.014
0.013
0.00135
Gregg Zaun
3357
33
29.22
0.010
0.009
0.00113
Ivan Rodriguez
3203
37
33.75
0.012
0.011
0.00102
Johnny Estrada
2607
34
31.74
0.013
0.012
0.00087
Ramon Hernandez
2459
28
25.92
0.011
0.011
0.00085
JD Closser
1822
23
21.83
0.013
0.012
0.00064
Jorge Posada
3446
47
44.83
0.014
0.013
0.00063
Humberto Cota
2127
27
25.77
0.013
0.012
0.00058
Chris R Snyder
2848
33
31.50
0.012
0.011
0.00053
Joe Mauer
3074
33
31.53
0.011
0.010
0.00048
Sandy Alomar Jr.
1041
11
10.50
0.011
0.010
0.00048
Henry Blanco
1141
14
13.67
0.012
0.012
0.00029
Matt A Treanor
1109
14
13.80
0.013
0.012
0.00018
Mike Lieberthal
2959
30
29.59
0.010
0.010
0.00014
Sal Fasano
1324
11
10.92
0.008
0.008
0.00006
Javier Valentin
1644
18
17.94
0.011
0.011
0.00003
Yadier B Molina
2872
31
31.00
0.011
0.011
0.00000
Mike Piazza
2442
31
31.01
0.013
0.013
-0.00000
Jose Molina
1413
15
15.12
0.011
0.011
-0.00009
Todd Pratt
1252
12
12.14
0.010
0.010
-0.00011
Victor Martinez
3728
30
31.08
0.008
0.008
-0.00029
A.J. Pierzynski
3360
35
36.01
0.010
0.011
-0.00030
John Flaherty
1016
14
14.37
0.014
0.014
-0.00037
John R Buck
3231
35
37.01
0.011
0.011
-0.00062
Jason Phillips
2401
25
26.58
0.010
0.011
-0.00066
Rod Barajas
3325
34
36.57
0.010
0.011
-0.00077
Michael Barrett
2976
29
31.63
0.010
0.011
-0.00088
Damian Miller
2778
35
37.68
0.013
0.014
-0.00097
Brian Schneider
2883
29
32.30
0.010
0.011
-0.00115
Toby Hall
3359
30
34.78
0.009
0.010
-0.00142
Bengie Molina
2635
23
26.81
0.009
0.010
-0.00145
Mike Redmond
1219
5
6.89
0.004
0.006
-0.00155
Miguel Olivo
2132
19
22.53
0.009
0.011
-0.00165
Paul Lo Duca
3110
31
36.72
0.010
0.012
-0.00184
Chad Moeller
1474
16
18.98
0.011
0.013
-0.00202
Brian M McCann
1410
18
21.56
0.013
0.015
-0.00252
Ramon R Castro
1825
16
20.91
0.009
0.011
-0.00269
Javy Lopez
1916
13
21.27
0.007
0.011
-0.00431
Pat Borders
1016
8
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.
Probabilistic Model of Range, 2005, First Basemen Permalink
It's time for the statistics for the first basemen:
Probabilistic Model of Range, First Basemen, 2005, Original Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Tino Martinez
2398
178
141.40
0.074
0.059
0.01526
Jose Hernandez
1021
77
63.94
0.075
0.063
0.01279
John Olerud
1341
107
90.47
0.080
0.067
0.01233
Ben Broussard
3160
220
181.42
0.070
0.057
0.01221
Chad A Tracy
2034
155
131.35
0.076
0.065
0.01163
Travis Lee
2952
240
208.94
0.081
0.071
0.01052
Mark Teixeira
4407
347
301.54
0.079
0.068
0.01032
Doug Mientkiewicz
2055
153
132.12
0.074
0.064
0.01016
Darin Erstad
3822
304
266.06
0.080
0.070
0.00993
Lance Niekro
1636
132
117.41
0.081
0.072
0.00892
Paul Konerko
3860
288
255.66
0.075
0.066
0.00838
Daryle Ward
2779
171
153.58
0.062
0.055
0.00627
Ryan J Howard
2013
154
141.68
0.077
0.070
0.00612
Derrek Lee
3959
288
265.84
0.073
0.067
0.00560
Albert Pujols
4150
318
294.88
0.077
0.071
0.00557
Lyle Overbay
3764
269
248.13
0.071
0.066
0.00554
Kevin Millar
2581
218
203.73
0.084
0.079
0.00553
Hee Seop Choi
2084
165
154.19
0.079
0.074
0.00519
Mark Sweeney
1017
79
75.04
0.078
0.074
0.00389
Eduardo Perez
1058
68
63.94
0.064
0.060
0.00384
Todd Helton
3943
293
278.32
0.074
0.071
0.00372
Mike Lamb
1255
85
80.40
0.068
0.064
0.00367
J.T. Snow
2617
197
188.76
0.075
0.072
0.00315
Nick Johnson
3447
286
276.94
0.083
0.080
0.00263
Chris B Shelton
2337
152
146.52
0.065
0.063
0.00234
Matt Stairs
1679
109
106.46
0.065
0.063
0.00151
Justin Morneau
3621
256
251.36
0.071
0.069
0.00128
Scott Hatteberg
1335
101
99.32
0.076
0.074
0.00126
Sean Casey
3663
255
251.80
0.070
0.069
0.00087
Mike Sweeney
1377
112
111.02
0.081
0.081
0.00071
Dan R Johnson
2560
190
188.77
0.074
0.074
0.00048
Shea Hillenbrand
1835
130
129.88
0.071
0.071
0.00007
Richie Sexson
4177
269
269.29
0.064
0.064
-0.00007
Tony Clark
2011
124
124.19
0.062
0.062
-0.00009
Phil Nevin
1884
139
140.51
0.074
0.075
-0.00080
Brad Eldred
1268
64
65.58
0.050
0.052
-0.00125
Adam LaRoche
3240
241
245.85
0.074
0.076
-0.00150
Carlos Delgado
3649
257
264.15
0.070
0.072
-0.00196
Eric Hinske
2676
194
199.28
0.072
0.074
-0.00197
Lance Berkman
2121
144
151.70
0.068
0.072
-0.00363
Jim Thome
1337
76
81.29
0.057
0.061
-0.00396
Rafael Palmeiro
2281
144
153.34
0.063
0.067
-0.00410
Julio Franco
1318
84
90.10
0.064
0.068
-0.00463
Carlos Pena
1363
98
105.67
0.072
0.078
-0.00563
Jason Giambi
1797
94
107.90
0.052
0.060
-0.00773
Olmedo Saenz
1426
77
91.72
0.054
0.064
-0.01032
Probabilistic Model of Range, First Baseman, 2005, Smoothed Visiting Player Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Tino Martinez
2398
178
140.67
0.074
0.059
0.01557
Jose Hernandez
1021
77
62.11
0.075
0.061
0.01458
John Olerud
1341
107
88.32
0.080
0.066
0.01393
Chad A Tracy
2034
155
128.16
0.076
0.063
0.01320
Doug Mientkiewicz
2055
153
126.32
0.074
0.061
0.01298
Mark Teixeira
4407
347
290.41
0.079
0.066
0.01284
Ben Broussard
3160
220
180.81
0.070
0.057
0.01240
Travis Lee
2952
240
206.10
0.081
0.070
0.01148
Darin Erstad
3822
304
261.19
0.080
0.068
0.01120
Lance Niekro
1636
132
115.97
0.081
0.071
0.00980
Paul Konerko
3860
288
257.63
0.075
0.067
0.00787
Lyle Overbay
3764
269
242.11
0.071
0.064
0.00714
Derrek Lee
3959
288
260.24
0.073
0.066
0.00701
Eduardo Perez
1058
68
61.40
0.064
0.058
0.00624
Albert Pujols
4150
318
292.89
0.077
0.071
0.00605
Kevin Millar
2581
218
202.57
0.084
0.078
0.00598
Daryle Ward
2779
171
154.99
0.062
0.056
0.00576
Ryan J Howard
2013
154
142.50
0.077
0.071
0.00571
Todd Helton
3943
293
272.69
0.074
0.069
0.00515
Hee Seop Choi
2084
165
155.34
0.079
0.075
0.00464
Mike Lamb
1255
85
79.57
0.068
0.063
0.00433
Mark Sweeney
1017
79
74.87
0.078
0.074
0.00406
J.T. Snow
2617
197
187.48
0.075
0.072
0.00364
Scott Hatteberg
1335
101
97.26
0.076
0.073
0.00280
Nick Johnson
3447
286
276.62
0.083
0.080
0.00272
Chris B Shelton
2337
152
147.75
0.065
0.063
0.00182
Tony Clark
2011
124
121.13
0.062
0.060
0.00143
Sean Casey
3663
255
249.97
0.070
0.068
0.00137
Justin Morneau
3621
256
251.30
0.071
0.069
0.00130
Matt Stairs
1679
109
107.03
0.065
0.064
0.00117
Mike Sweeney
1377
112
111.16
0.081
0.081
0.00061
Brad Eldred
1268
64
64.05
0.050
0.051
-0.00004
Dan R Johnson
2560
190
190.41
0.074
0.074
-0.00016
Phil Nevin
1884
139
139.54
0.074
0.074
-0.00028
Richie Sexson
4177
269
273.34
0.064
0.065
-0.00104
Carlos Delgado
3649
257
261.78
0.070
0.072
-0.00131
Shea Hillenbrand
1835
130
133.25
0.071
0.073
-0.00177
Adam LaRoche
3240
241
247.17
0.074
0.076
-0.00191
Eric Hinske
2676
194
201.57
0.072
0.075
-0.00283
Lance Berkman
2121
144
152.07
0.068
0.072
-0.00381
Jim Thome
1337
76
82.13
0.057
0.061
-0.00459
Rafael Palmeiro
2281
144
154.66
0.063
0.068
-0.00467
Julio Franco
1318
84
90.24
0.064
0.068
-0.00473
Jason Giambi
1797
94
107.12
0.052
0.060
-0.00730
Carlos Pena
1363
98
108.12
0.072
0.079
-0.00742
Olmedo Saenz
1426
77
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.
Probabilistic Model of Range, 2005, Leftfielders Permalink
Here are the results for the left fielders.
Probabilistic Model of Range, Leftfielders, 2005, Original Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Reed Johnson
1838
134
112.67
0.073
0.061
0.01160
B.J. Surhoff
1132
76
64.82
0.067
0.057
0.00987
Chris A Burke
1861
120
101.65
0.064
0.055
0.00986
Eric Byrnes
2450
209
185.38
0.085
0.076
0.00964
Coco Crisp
3623
294
261.44
0.081
0.072
0.00899
Jay Payton
1379
107
94.67
0.078
0.069
0.00894
Brian Jordan
1114
75
65.13
0.067
0.058
0.00886
Carl Crawford
4004
341
309.93
0.085
0.077
0.00776
Jayson Werth
1033
84
76.11
0.081
0.074
0.00764
Ryan Langerhans
1202
91
83.01
0.076
0.069
0.00664
Randy Winn
2564
226
209.19
0.088
0.082
0.00656
Matt T Holliday
3371
236
214.67
0.070
0.064
0.00633
Scott Podsednik
3200
260
240.02
0.081
0.075
0.00625
Luis Gonzalez
4115
270
246.42
0.066
0.060
0.00573
Kevin Mench
3188
231
213.63
0.072
0.067
0.00545
Kelly A Johnson
2056
166
154.90
0.081
0.075
0.00540
Moises Alou
1786
132
123.81
0.074
0.069
0.00459
Hideki Matsui
3024
218
204.33
0.072
0.068
0.00452
Ryan M Church
1052
77
72.59
0.073
0.069
0.00419
Carlos Lee
4151
307
289.83
0.074
0.070
0.00414
Cliff Floyd
3867
283
267.59
0.073
0.069
0.00398
Shannon Stewart
3503
249
237.55
0.071
0.068
0.00327
Pedro Feliz
1951
138
131.98
0.071
0.068
0.00308
Jason Bay
3662
266
257.22
0.073
0.070
0.00240
Bobby Kielty
1320
99
96.77
0.075
0.073
0.00169
Raul Ibanez
1463
106
103.57
0.072
0.071
0.00166
Larry Bigbie
1464
98
96.20
0.067
0.066
0.00123
Frank Catalanotto
2383
163
160.10
0.068
0.067
0.00122
Tony Womack
1090
72
70.71
0.066
0.065
0.00119
Reggie Sanders
1902
108
105.77
0.057
0.056
0.00117
Terrence Long
2599
166
163.61
0.064
0.063
0.00092
Todd Hollandsworth
1746
103
101.45
0.059
0.058
0.00089
Adam Dunn
3517
246
243.81
0.070
0.069
0.00062
Ryan Klesko
2849
204
202.51
0.072
0.071
0.00052
Rondell White
1644
119
118.43
0.072
0.072
0.00035
Craig Monroe
1630
99
102.25
0.061
0.063
-0.00199
Marlon Byrd
1195
100
102.84
0.084
0.086
-0.00238
Garret Anderson
2776
201
208.94
0.072
0.075
-0.00286
Manny Ramirez
3956
243
254.92
0.061
0.064
-0.00301
Pat Burrell
3846
236
247.60
0.061
0.064
-0.00302
Ricky Ledee
1230
57
61.47
0.046
0.050
-0.00363
David Dellucci
1247
84
90.32
0.067
0.072
-0.00507
Miguel Cabrera
3336
188
208.43
0.056
0.062
-0.00612
Probabilistic Model of Range, Leftfielders, 2005, Smoothed Visiting Player Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Reed Johnson
1838
134
112.70
0.073
0.061
0.01159
Eric Byrnes
2450
209
184.60
0.085
0.075
0.00996
Jay Payton
1379
107
93.36
0.078
0.068
0.00989
B.J. Surhoff
1132
76
65.32
0.067
0.058
0.00944
Chris A Burke
1861
120
103.38
0.064
0.056
0.00893
Brian Jordan
1114
75
65.31
0.067
0.059
0.00870
Coco Crisp
3623
294
262.57
0.081
0.072
0.00868
Carl Crawford
4004
341
311.50
0.085
0.078
0.00737
Jayson Werth
1033
84
76.52
0.081
0.074
0.00724
Matt T Holliday
3371
236
214.13
0.070
0.064
0.00649
Randy Winn
2564
226
209.68
0.088
0.082
0.00637
Moises Alou
1786
132
120.91
0.074
0.068
0.00621
Ryan Langerhans
1202
91
83.77
0.076
0.070
0.00601
Scott Podsednik
3200
260
241.70
0.081
0.076
0.00572
Luis Gonzalez
4115
270
246.89
0.066
0.060
0.00562
Kelly A Johnson
2056
166
155.95
0.081
0.076
0.00489
Kevin Mench
3188
231
218.15
0.072
0.068
0.00403
Cliff Floyd
3867
283
268.14
0.073
0.069
0.00384
Carlos Lee
4151
307
291.09
0.074
0.070
0.00383
Ryan M Church
1052
77
73.00
0.073
0.069
0.00380
Hideki Matsui
3024
218
206.61
0.072
0.068
0.00377
Bobby Kielty
1320
99
94.48
0.075
0.072
0.00342
Shannon Stewart
3503
249
238.42
0.071
0.068
0.00302
Pedro Feliz
1951
138
132.16
0.071
0.068
0.00299
Frank Catalanotto
2383
163
157.64
0.068
0.066
0.00225
Larry Bigbie
1464
98
95.08
0.067
0.065
0.00200
Jason Bay
3662
266
259.91
0.073
0.071
0.00166
Raul Ibanez
1463
106
104.80
0.072
0.072
0.00082
Adam Dunn
3517
246
243.41
0.070
0.069
0.00074
Reggie Sanders
1902
108
107.67
0.057
0.057
0.00017
Todd Hollandsworth
1746
103
103.68
0.059
0.059
-0.00039
Terrence Long
2599
166
167.45
0.064
0.064
-0.00056
Tony Womack
1090
72
72.73
0.066
0.067
-0.00067
Ryan Klesko
2849
204
205.93
0.072
0.072
-0.00068
Rondell White
1644
119
120.57
0.072
0.073
-0.00096
Garret Anderson
2776
201
207.61
0.072
0.075
-0.00238
Marlon Byrd
1195
100
103.25
0.084
0.086
-0.00272
Manny Ramirez
3956
243
258.33
0.061
0.065
-0.00388
Craig Monroe
1630
99
105.91
0.061
0.065
-0.00424
Ricky Ledee
1230
57
63.60
0.046
0.052
-0.00536
Pat Burrell
3846
236
257.78
0.061
0.067
-0.00566
Miguel Cabrera
3336
188
211.55
0.056
0.063
-0.00706
David Dellucci
1247
84
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.
Probabilistic Model of Range, 2005, Rightfielders Permalink
Time to look at the range of the rightfielders.
Probabilistic Model of Range, Rightfielders, 2005, Original Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Dustan Mohr
1216
103
80.81
0.085
0.066
0.01825
Jose Cruz
1296
110
90.55
0.085
0.070
0.01501
Mike Cameron
1846
137
122.46
0.074
0.066
0.00788
Jeff B Francoeur
1837
131
116.89
0.071
0.064
0.00768
Vladimir Guerrero
3140
245
225.72
0.078
0.072
0.00614
J.D. Drew
1219
83
75.56
0.068
0.062
0.00610
Nick T Swisher
2993
197
179.73
0.066
0.060
0.00577
Ryan Langerhans
1138
75
69.32
0.066
0.061
0.00499
Ichiro Suzuki
4432
383
361.02
0.086
0.081
0.00496
Brian Giles
3704
295
277.51
0.080
0.075
0.00472
Jason Lane
3253
225
210.46
0.069
0.065
0.00447
Shawn Green
3225
232
218.32
0.072
0.068
0.00424
Gary Sheffield
3415
240
225.92
0.070
0.066
0.00412
Geoff Jenkins
3673
307
292.30
0.084
0.080
0.00400
Jay Gibbons
1710
133
126.19
0.078
0.074
0.00398
Chad A Tracy
1173
86
82.33
0.073
0.070
0.00313
Jeromy Burnitz
3867
303
291.75
0.078
0.075
0.00291
Jermaine Dye
3718
260
252.18
0.070
0.068
0.00210
Richard Hidalgo
2288
174
169.72
0.076
0.074
0.00187
Trot Nixon
2991
240
234.77
0.080
0.078
0.00175
Casey Blake
3592
287
280.95
0.080
0.078
0.00168
Raul Mondesi
1070
67
65.26
0.063
0.061
0.00163
Jacque Jones
3396
262
256.70
0.077
0.076
0.00156
Bobby Abreu
4018
267
261.04
0.066
0.065
0.00148
Magglio Ordonez
2154
139
136.15
0.065
0.063
0.00132
Austin Kearns
2891
238
234.45
0.082
0.081
0.00123
Victor I Diaz
2024
153
150.52
0.076
0.074
0.00123
Juan Encarnacion
3355
216
213.79
0.064
0.064
0.00066
Kevin Mench
1029
60
59.72
0.058
0.058
0.00027
Jose Guillen
3708
299
298.66
0.081
0.081
0.00009
Larry Walker
1959
107
107.33
0.055
0.055
-0.00017
Aubrey Huff
2583
204
207.52
0.079
0.080
-0.00136
Sammy Sosa
1763
121
124.99
0.069
0.071
-0.00226
Emil Brown
3597
243
252.59
0.068
0.070
-0.00267
Michael Tucker
1372
91
94.94
0.066
0.069
-0.00287
Craig Monroe
1952
132
138.13
0.068
0.071
-0.00314
Alexis I Rios
3310
246
256.53
0.074
0.078
-0.00318
Matt Lawton
2984
230
240.06
0.077
0.080
-0.00337
Brad B Hawpe
2259
148
156.37
0.066
0.069
-0.00371
Moises Alou
1316
90
97.28
0.068
0.074
-0.00553
Wily Mo Pena
1290
92
105.82
0.071
0.082
-0.01071
Probabilistic Model of Range, Rightfielders, 2005, Smoothed Visiting Player Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Dustan Mohr
1216
103
82.93
0.085
0.068
0.01650
Jose Cruz
1296
110
91.20
0.085
0.070
0.01451
Mike Cameron
1846
137
121.54
0.074
0.066
0.00837
Brian Giles
3704
295
270.20
0.080
0.073
0.00670
Nick T Swisher
2993
197
177.02
0.066
0.059
0.00668
Vladimir Guerrero
3140
245
224.45
0.078
0.071
0.00654
J.D. Drew
1219
83
75.82
0.068
0.062
0.00589
Ryan Langerhans
1138
75
68.59
0.066
0.060
0.00564
Jeff B Francoeur
1837
131
120.85
0.071
0.066
0.00552
Ichiro Suzuki
4432
383
359.66
0.086
0.081
0.00527
Jason Lane
3253
225
208.68
0.069
0.064
0.00502
Jay Gibbons
1710
133
124.72
0.078
0.073
0.00484
Geoff Jenkins
3673
307
290.57
0.084
0.079
0.00447
Shawn Green
3225
232
218.55
0.072
0.068
0.00417
Gary Sheffield
3415
240
226.94
0.070
0.066
0.00382
Chad A Tracy
1173
86
82.09
0.073
0.070
0.00333
Trot Nixon
2991
240
231.46
0.080
0.077
0.00286
Casey Blake
3592
287
278.14
0.080
0.077
0.00247
Jeromy Burnitz
3867
303
294.00
0.078
0.076
0.00233
Jermaine Dye
3718
260
252.11
0.070
0.068
0.00212
Jacque Jones
3396
262
254.88
0.077
0.075
0.00210
Austin Kearns
2891
238
232.83
0.082
0.081
0.00179
Bobby Abreu
4018
267
260.54
0.066
0.065
0.00161
Richard Hidalgo
2288
174
170.57
0.076
0.075
0.00150
Magglio Ordonez
2154
139
136.09
0.065
0.063
0.00135
Victor I Diaz
2024
153
150.93
0.076
0.075
0.00102
Raul Mondesi
1070
67
66.91
0.063
0.063
0.00009
Juan Encarnacion
3355
216
215.77
0.064
0.064
0.00007
Jose Guillen
3708
299
300.89
0.081
0.081
-0.00051
Larry Walker
1959
107
108.86
0.055
0.056
-0.00095
Kevin Mench
1029
60
61.04
0.058
0.059
-0.00101
Sammy Sosa
1763
121
123.60
0.069
0.070
-0.00148
Aubrey Huff
2583
204
209.20
0.079
0.081
-0.00201
Michael Tucker
1372
91
93.77
0.066
0.068
-0.00202
Brad B Hawpe
2259
148
154.40
0.066
0.068
-0.00283
Matt Lawton
2984
230
239.00
0.077
0.080
-0.00302
Alexis I Rios
3310
246
256.82
0.074
0.078
-0.00327
Craig Monroe
1952
132
139.11
0.068
0.071
-0.00364
Emil Brown
3597
243
256.30
0.068
0.071
-0.00370
Moises Alou
1316
90
96.96
0.068
0.074
-0.00529
Wily Mo Pena
1290
92
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.
Probabilistic Model of Range, 2005, Third Basemen and Grounders Permalink
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
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Pedro Feliz
804
140
113.01
0.174
0.141
0.03357
Chone Figgins
584
93
74.12
0.159
0.127
0.03233
Freddy Sanchez
715
134
111.00
0.187
0.155
0.03216
Scott Rolen
799
144
118.75
0.180
0.149
0.03161
Wilson Betemit
684
95
76.06
0.139
0.111
0.02769
Corey Koskie
1034
157
130.17
0.152
0.126
0.02594
Edwin Encarnacion
682
115
97.66
0.169
0.143
0.02543
David Bell
1796
299
256.81
0.166
0.143
0.02349
Dallas L McPherson
615
84
69.57
0.137
0.113
0.02347
Aaron Boone
1764
291
250.30
0.165
0.142
0.02307
Morgan Ensberg
1848
297
256.48
0.161
0.139
0.02193
Abraham O Nunez
1195
202
176.25
0.169
0.147
0.02155
Joe Crede
1552
247
214.99
0.159
0.139
0.02062
Bill Mueller
1716
263
228.79
0.153
0.133
0.01994
Rob Mackowiak
665
119
106.21
0.179
0.160
0.01923
Melvin Mora
1907
297
260.49
0.156
0.137
0.01915
Adrian Beltre
1862
274
240.36
0.147
0.129
0.01806
Eric Chavez
1846
297
267.03
0.161
0.145
0.01624
Alex Rodriguez
2153
288
253.18
0.134
0.118
0.01617
Alex S Gonzalez
1070
170
153.13
0.159
0.143
0.01576
Brandon Inge
2135
375
341.64
0.176
0.160
0.01563
David A Wright
2023
330
298.97
0.163
0.148
0.01534
Aramis Ramirez
1449
216
194.59
0.149
0.134
0.01478
Garrett Atkins
1781
261
234.69
0.147
0.132
0.01477
Mike Lowell
1648
242
222.66
0.147
0.135
0.01173
Mike Cuddyer
1272
190
175.91
0.149
0.138
0.01108
Sean Burroughs
902
143
133.98
0.159
0.149
0.01000
Mark T Teahen
1639
238
221.79
0.145
0.135
0.00989
Chipper Jones
1281
166
153.38
0.130
0.120
0.00985
Bill Hall
607
87
81.13
0.143
0.134
0.00967
Shea Hillenbrand
641
95
89.06
0.148
0.139
0.00926
Hank Blalock
2180
298
283.48
0.137
0.130
0.00666
Russell Branyan
584
81
77.44
0.139
0.133
0.00610
Troy Glaus
1949
313
305.81
0.161
0.157
0.00369
Edgardo Alfonzo
1183
157
154.04
0.133
0.130
0.00250
Vinny Castilla
1582
208
207.62
0.131
0.131
0.00024
Joe Randa
1704
232
232.50
0.136
0.136
-0.00029
Jorge L Cantu
638
89
91.77
0.139
0.144
-0.00434
Probabilistic Model of Range, Third Baseman, 2005, Smoothed Visiting Player Model, Ground Balls Only
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Pedro Feliz
804
140
110.42
0.174
0.137
0.03679
Freddy Sanchez
715
134
108.00
0.187
0.151
0.03636
Scott Rolen
799
144
117.29
0.180
0.147
0.03342
Chone Figgins
584
93
74.16
0.159
0.127
0.03226
Wilson Betemit
684
95
75.35
0.139
0.110
0.02873
David Bell
1796
299
252.02
0.166
0.140
0.02616
Dallas L McPherson
615
84
67.98
0.137
0.111
0.02605
Edwin Encarnacion
682
115
98.20
0.169
0.144
0.02463
Corey Koskie
1034
157
133.23
0.152
0.129
0.02298
Bill Mueller
1716
263
223.71
0.153
0.130
0.02290
Aaron Boone
1764
291
250.89
0.165
0.142
0.02274
Abraham O Nunez
1195
202
175.09
0.169
0.147
0.02252
Morgan Ensberg
1848
297
255.55
0.161
0.138
0.02243
Joe Crede
1552
247
213.18
0.159
0.137
0.02179
Rob Mackowiak
665
119
105.84
0.179
0.159
0.01979
Adrian Beltre
1862
274
237.41
0.147
0.128
0.01965
Alex Rodriguez
2153
288
246.82
0.134
0.115
0.01913
Melvin Mora
1907
297
261.04
0.156
0.137
0.01886
Alex S Gonzalez
1070
170
151.51
0.159
0.142
0.01728
Eric Chavez
1846
297
266.80
0.161
0.145
0.01636
Brandon Inge
2135
375
344.59
0.176
0.161
0.01424
Garrett Atkins
1781
261
235.96
0.147
0.132
0.01406
Aramis Ramirez
1449
216
195.81
0.149
0.135
0.01393
David A Wright
2023
330
302.82
0.163
0.150
0.01343
Chipper Jones
1281
166
151.03
0.130
0.118
0.01169
Mike Cuddyer
1272
190
175.29
0.149
0.138
0.01156
Mike Lowell
1648
242
223.36
0.147
0.136
0.01131
Bill Hall
607
87
80.54
0.143
0.133
0.01064
Mark T Teahen
1639
238
221.57
0.145
0.135
0.01002
Hank Blalock
2180
298
279.49
0.137
0.128
0.00849
Shea Hillenbrand
641
95
89.71
0.148
0.140
0.00825
Sean Burroughs
902
143
136.63
0.159
0.151
0.00707
Edgardo Alfonzo
1183
157
151.30
0.133
0.128
0.00481
Russell Branyan
584
81
78.22
0.139
0.134
0.00476
Troy Glaus
1949
313
312.76
0.161
0.160
0.00012
Joe Randa
1704
232
232.83
0.136
0.137
-0.00049
Vinny Castilla
1582
208
214.58
0.131
0.136
-0.00416
Jorge L Cantu
638
89
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.
Probabilistic Model of Range, 2005,Third Basemen Permalink
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
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Edwin Encarnacion
1538
159
128.09
0.103
0.083
0.02010
Chone Figgins
1334
121
97.95
0.091
0.073
0.01728
Pedro Feliz
1812
176
144.87
0.097
0.080
0.01718
Wilson Betemit
1353
118
96.76
0.087
0.072
0.01570
Freddy Sanchez
1488
162
138.96
0.109
0.093
0.01549
Scott Rolen
1447
160
138.73
0.111
0.096
0.01470
Corey Koskie
2121
196
169.20
0.092
0.080
0.01263
David Bell
3786
388
340.31
0.102
0.090
0.01260
Morgan Ensberg
3738
374
327.06
0.100
0.087
0.01256
Abraham O Nunez
2249
239
211.24
0.106
0.094
0.01234
Brandon Inge
4416
474
426.52
0.107
0.097
0.01075
Rob Mackowiak
1391
147
132.27
0.106
0.095
0.01059
Aaron Boone
3776
364
324.05
0.096
0.086
0.01058
Alex S Gonzalez
2522
228
202.89
0.090
0.080
0.00996
Joe Crede
3378
324
290.40
0.096
0.086
0.00995
Alex Rodriguez
4338
373
330.67
0.086
0.076
0.00976
Bill Mueller
3859
334
296.75
0.087
0.077
0.00965
Dallas L McPherson
1431
111
97.37
0.078
0.068
0.00953
Melvin Mora
3939
378
340.87
0.096
0.087
0.00943
Adrian Beltre
4246
375
337.14
0.088
0.079
0.00892
Chipper Jones
2600
223
201.98
0.086
0.078
0.00809
Eric Chavez
3965
389
359.65
0.098
0.091
0.00740
Mark T Teahen
3464
321
295.69
0.093
0.085
0.00731
Mike Lowell
3376
312
288.76
0.092
0.086
0.00688
David A Wright
4325
414
386.30
0.096
0.089
0.00641
Aramis Ramirez
2920
268
251.97
0.092
0.086
0.00549
Garrett Atkins
3714
315
295.69
0.085
0.080
0.00520
Sean Burroughs
1938
187
177.09
0.096
0.091
0.00511
Jeff Cirillo
1039
94
88.87
0.090
0.086
0.00494
Shea Hillenbrand
1369
122
115.98
0.089
0.085
0.00439
Bill Hall
1338
114
108.45
0.085
0.081
0.00415
Edgardo Alfonzo
2588
215
206.13
0.083
0.080
0.00343
Mike Cuddyer
2589
230
221.44
0.089
0.086
0.00331
Russell Branyan
1341
109
104.90
0.081
0.078
0.00306
Troy Glaus
4010
392
379.78
0.098
0.095
0.00305
Hank Blalock
4500
378
364.42
0.084
0.081
0.00302
Joe Randa
3850
330
322.49
0.086
0.084
0.00195
Vinny Castilla
3651
325
320.17
0.089
0.088
0.00132
Jorge L Cantu
1557
108
119.34
0.069
0.077
-0.00728
Probabilistic Model of Range, Third Baseman, 2005, Smoothed Visiting Player Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Pedro Feliz
1812
176
141.92
0.097
0.078
0.01881
Edwin Encarnacion
1538
159
131.03
0.103
0.085
0.01819
Freddy Sanchez
1488
162
135.54
0.109
0.091
0.01778
Chone Figgins
1334
121
97.83
0.091
0.073
0.01737
Wilson Betemit
1353
118
96.20
0.087
0.071
0.01611
Scott Rolen
1447
160
136.97
0.111
0.095
0.01592
David Bell
3786
388
334.73
0.102
0.088
0.01407
Abraham O Nunez
2249
239
209.33
0.106
0.093
0.01319
Morgan Ensberg
3738
374
327.66
0.100
0.088
0.01240
Bill Mueller
3859
334
287.50
0.087
0.075
0.01205
Rob Mackowiak
1391
147
131.06
0.106
0.094
0.01146
Corey Koskie
2121
196
172.56
0.092
0.081
0.01105
Alex Rodriguez
4338
373
325.12
0.086
0.075
0.01104
Alex S Gonzalez
2522
228
200.31
0.090
0.079
0.01098
Dallas L McPherson
1431
111
95.51
0.078
0.067
0.01082
Aaron Boone
3776
364
323.99
0.096
0.086
0.01060
Joe Crede
3378
324
289.27
0.096
0.086
0.01028
Brandon Inge
4416
474
430.06
0.107
0.097
0.00995
Chipper Jones
2600
223
198.93
0.086
0.077
0.00926
Adrian Beltre
4246
375
335.79
0.088
0.079
0.00923
Melvin Mora
3939
378
342.39
0.096
0.087
0.00904
Mike Lowell
3376
312
285.64
0.092
0.085
0.00781
Eric Chavez
3965
389
360.02
0.098
0.091
0.00731
Mark T Teahen
3464
321
295.83
0.093
0.085
0.00727
David A Wright
4325
414
391.13
0.096
0.090
0.00529
Bill Hall
1338
114
107.69
0.085
0.080
0.00472
Garrett Atkins
3714
315
297.58
0.085
0.080
0.00469
Aramis Ramirez
2920
268
254.57
0.092
0.087
0.00460
Edgardo Alfonzo
2588
215
204.03
0.083
0.079
0.00424
Jeff Cirillo
1039
94
89.88
0.090
0.087
0.00397
Mike Cuddyer
2589
230
220.11
0.089
0.085
0.00382
Shea Hillenbrand
1369
122
116.87
0.089
0.085
0.00375
Sean Burroughs
1938
187
179.89
0.096
0.093
0.00367
Hank Blalock
4500
378
362.63
0.084
0.081
0.00342
Russell Branyan
1341
109
104.64
0.081
0.078
0.00325
Joe Randa
3850
330
322.20
0.086
0.084
0.00203
Troy Glaus
4010
392
387.22
0.098
0.097
0.00119
Vinny Castilla
3651
325
327.58
0.089
0.090
-0.00071
Jorge L Cantu
1557
108
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.
Probabilistic Model of Range, 2005, Centerfielders Permalink
Without much further ado, the centerfielders.
Probabilistic Model of Range, Centerfielders, 2005, Original Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Jason Ellison
1867
197
178.25
0.106
0.095
0.01004
Tike Redman
1613
158
143.70
0.098
0.089
0.00887
Joey R Gathright
1587
181
167.23
0.114
0.105
0.00868
Curtis Granderson
1044
119
110.91
0.114
0.106
0.00775
Andruw Jones
4309
365
337.56
0.085
0.078
0.00637
Jason Michaels
1621
161
150.73
0.099
0.093
0.00634
Jim Edmonds
3538
319
297.13
0.090
0.084
0.00618
Aaron Rowand
4128
388
362.99
0.094
0.088
0.00606
Gary Matthews Jr.
2822
258
242.31
0.091
0.086
0.00556
Jerry Hairston
1100
90
84.03
0.082
0.076
0.00542
Brady Clark
3765
399
380.69
0.106
0.101
0.00486
Nook P Logan
2730
282
270.92
0.103
0.099
0.00406
Luis Matos
3017
299
286.93
0.099
0.095
0.00400
Corey Patterson
2799
240
232.53
0.086
0.083
0.00267
Willy Taveras
3646
332
322.83
0.091
0.089
0.00252
Carlos Beltran
3967
378
372.03
0.095
0.094
0.00151
Brad Wilkerson
2414
234
230.76
0.097
0.096
0.00134
Randy Winn
1603
184
182.71
0.115
0.114
0.00080
Grady Sizemore
4136
373
370.07
0.090
0.089
0.00071
Damon J Hollins
2010
198
197.37
0.099
0.098
0.00031
Laynce Nix
1674
160
159.88
0.096
0.096
0.00007
Jeremy T Reed
3692
384
384.13
0.104
0.104
-0.00003
Luis Terrero
1310
121
121.69
0.092
0.093
-0.00053
Torii Hunter
2575
218
220.35
0.085
0.086
-0.00091
Milton Bradley
1969
181
183.11
0.092
0.093
-0.00107
Vernon Wells
4239
351
356.22
0.083
0.084
-0.00123
Juan Pierre
4171
332
337.90
0.080
0.081
-0.00141
Johnny Damon
3952
396
402.01
0.100
0.102
-0.00152
David DeJesus
3304
306
313.16
0.093
0.095
-0.00217
Mark Kotsay
3519
299
306.87
0.085
0.087
-0.00224
Dave Roberts
2715
234
240.18
0.086
0.088
-0.00228
Kenny Lofton
2167
201
207.17
0.093
0.096
-0.00285
Chone Figgins
1184
131
134.46
0.111
0.114
-0.00292
Cory Sullivan
1935
172
179.74
0.089
0.093
-0.00400
Preston Wilson
3362
267
283.81
0.079
0.084
-0.00500
Steve Finley
2691
266
279.55
0.099
0.104
-0.00503
Lew Ford
1677
140
150.24
0.083
0.090
-0.00610
Jose Cruz
1317
87
96.22
0.066
0.073
-0.00700
Bernie Williams
2689
226
245.61
0.084
0.091
-0.00729
Jason Repko
1128
97
105.29
0.086
0.093
-0.00735
Ken Griffey Jr.
3439
286
321.33
0.083
0.093
-0.01027
Probabilistic Model of Range, Centerfielders, 2005, Smoothed Visiting Player Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Jason Ellison
1867
197
176.89
0.106
0.095
0.01077
Joey R Gathright
1587
181
165.35
0.114
0.104
0.00986
Tike Redman
1613
158
142.11
0.098
0.088
0.00985
Andruw Jones
4309
365
330.56
0.085
0.077
0.00799
Curtis Granderson
1044
119
111.13
0.114
0.106
0.00753
Jim Edmonds
3538
319
292.73
0.090
0.083
0.00743
Aaron Rowand
4128
388
360.04
0.094
0.087
0.00677
Jason Michaels
1621
161
151.37
0.099
0.093
0.00594
Gary Matthews Jr.
2822
258
241.64
0.091
0.086
0.00580
Brady Clark
3765
399
378.87
0.106
0.101
0.00535
Luis Matos
3017
299
288.54
0.099
0.096
0.00347
Jerry Hairston
1100
90
86.37
0.082
0.079
0.00330
Nook P Logan
2730
282
273.04
0.103
0.100
0.00328
Corey Patterson
2799
240
232.61
0.086
0.083
0.00264
Willy Taveras
3646
332
324.32
0.091
0.089
0.00211
Brad Wilkerson
2414
234
230.16
0.097
0.095
0.00159
Carlos Beltran
3967
378
372.21
0.095
0.094
0.00146
Randy Winn
1603
184
181.93
0.115
0.113
0.00129
Grady Sizemore
4136
373
368.15
0.090
0.089
0.00117
Laynce Nix
1674
160
158.40
0.096
0.095
0.00096
Damon J Hollins
2010
198
196.96
0.099
0.098
0.00052
Jeremy T Reed
3692
384
382.67
0.104
0.104
0.00036
Torii Hunter
2575
218
218.23
0.085
0.085
-0.00009
Vernon Wells
4239
351
355.19
0.083
0.084
-0.00099
Luis Terrero
1310
121
122.38
0.092
0.093
-0.00105
Johnny Damon
3952
396
401.06
0.100
0.101
-0.00128
Mark Kotsay
3519
299
303.73
0.085
0.086
-0.00134
Dave Roberts
2715
234
238.58
0.086
0.088
-0.00169
Juan Pierre
4171
332
339.18
0.080
0.081
-0.00172
Milton Bradley
1969
181
184.58
0.092
0.094
-0.00182
David DeJesus
3304
306
314.56
0.093
0.095
-0.00259
Chone Figgins
1184
131
134.10
0.111
0.113
-0.00262
Kenny Lofton
2167
201
206.75
0.093
0.095
-0.00265
Cory Sullivan
1935
172
180.98
0.089
0.094
-0.00464
Steve Finley
2691
266
279.04
0.099
0.104
-0.00485
Preston Wilson
3362
267
284.16
0.079
0.085
-0.00510
Lew Ford
1677
140
148.71
0.083
0.089
-0.00519
Jason Repko
1128
97
104.79
0.086
0.093
-0.00691
Jose Cruz
1317
87
96.29
0.066
0.073
-0.00705
Bernie Williams
2689
226
247.78
0.084
0.092
-0.00810
Ken Griffey Jr.
3439
286
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.
Probabilistic Model of Range, 2005, Second Basemen and Grounders Permalink
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)
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Nick Punto
823
169
152.10
0.205
0.185
0.02054
Ryan Freel
540
113
102.38
0.209
0.190
0.01966
Junior Spivey
752
156
143.90
0.207
0.191
0.01609
Jamey Carroll
581
120
111.36
0.207
0.192
0.01487
Adam Kennedy
1422
302
280.93
0.212
0.198
0.01482
Chase Utley
1675
325
300.24
0.194
0.179
0.01478
Orlando Hudson
1604
333
309.35
0.208
0.193
0.01474
Craig Counsell
1920
369
341.11
0.192
0.178
0.01453
Luis Castillo
1509
291
271.70
0.193
0.180
0.01279
Jose C Lopez
615
134
126.40
0.218
0.206
0.01237
Brian Roberts
1784
353
332.84
0.198
0.187
0.01130
Mark Grudzielanek
1901
365
347.33
0.192
0.183
0.00930
Mark Ellis
1334
273
262.67
0.205
0.197
0.00774
Rich Aurilia
801
148
142.17
0.185
0.177
0.00728
Placido Polanco
1417
255
246.01
0.180
0.174
0.00635
Tony Graffanino
873
157
151.50
0.180
0.174
0.00629
Jeff Kent
1835
352
341.60
0.192
0.186
0.00567
Ronnie Belliard
1795
354
343.99
0.197
0.192
0.00558
Tadahito Iguchi
1637
311
302.03
0.190
0.185
0.00548
Omar Infante
890
150
145.16
0.169
0.163
0.00544
Marcus Giles
2006
401
391.69
0.200
0.195
0.00464
Nick Green
974
169
164.83
0.174
0.169
0.00428
Ray Durham
1596
282
283.31
0.177
0.178
-0.00082
Craig Biggio
1725
327
331.95
0.190
0.192
-0.00287
Ruben A Gotay
1046
200
203.16
0.191
0.194
-0.00302
Jose Vidro
889
170
173.04
0.191
0.195
-0.00342
Luis Rivas
524
88
90.19
0.168
0.172
-0.00418
Todd Walker
1136
214
219.18
0.188
0.193
-0.00456
Mark Bellhorn
1056
227
231.99
0.215
0.220
-0.00473
Luis A Gonzalez
907
174
178.52
0.192
0.197
-0.00498
Kazuo Matsui
791
161
165.74
0.204
0.210
-0.00599
Miguel Cairo
966
176
182.46
0.182
0.189
-0.00668
Aaron Miles
869
173
179.06
0.199
0.206
-0.00697
Jose Castillo
1293
211
223.38
0.163
0.173
-0.00958
Robinson Cano
1747
333
352.20
0.191
0.202
-0.01099
Alfonso Soriano
2136
383
406.87
0.179
0.190
-0.01118
Mark Loretta
1236
217
231.12
0.176
0.187
-0.01142
Freddy Sanchez
522
83
89.90
0.159
0.172
-0.01322
Rickie Weeks
1121
200
214.88
0.178
0.192
-0.01327
Jorge L Cantu
915
151
165.19
0.165
0.181
-0.01551
Bret Boone
1101
197
221.76
0.179
0.201
-0.02249
Probabilistic Model of Range, Second Basemen, 2005, Smoothed Visiting Player Model, Groundballs Only (Grounders + Bunt Grounders)
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Nick Punto
823
169
151.23
0.205
0.184
0.02159
Ryan Freel
540
113
102.83
0.209
0.190
0.01883
Craig Counsell
1920
369
336.93
0.192
0.175
0.01670
Brian Roberts
1784
353
324.04
0.198
0.182
0.01623
Orlando Hudson
1604
333
308.16
0.208
0.192
0.01548
Jamey Carroll
581
120
111.40
0.207
0.192
0.01481
Junior Spivey
752
156
144.88
0.207
0.193
0.01479
Adam Kennedy
1422
302
281.31
0.212
0.198
0.01455
Chase Utley
1675
325
301.94
0.194
0.180
0.01377
Jose C Lopez
615
134
125.67
0.218
0.204
0.01355
Luis Castillo
1509
291
272.98
0.193
0.181
0.01194
Mark Grudzielanek
1901
365
344.48
0.192
0.181
0.01079
Placido Polanco
1417
255
242.23
0.180
0.171
0.00901
Mark Ellis
1334
273
261.59
0.205
0.196
0.00856
Marcus Giles
2006
401
387.54
0.200
0.193
0.00671
Rich Aurilia
801
148
142.73
0.185
0.178
0.00658
Jeff Kent
1835
352
340.13
0.192
0.185
0.00647
Omar Infante
890
150
144.57
0.169
0.162
0.00610
Ronnie Belliard
1795
354
343.25
0.197
0.191
0.00599
Tony Graffanino
873
157
151.84
0.180
0.174
0.00591
Tadahito Iguchi
1637
311
302.37
0.190
0.185
0.00527
Nick Green
974
169
165.16
0.174
0.170
0.00394
Luis Rivas
524
88
88.57
0.168
0.169
-0.00109
Ray Durham
1596
282
284.05
0.177
0.178
-0.00128
Craig Biggio
1725
327
329.43
0.190
0.191
-0.00141
Jose Vidro
889
170
172.51
0.191
0.194
-0.00282
Ruben A Gotay
1046
200
203.59
0.191
0.195
-0.00343
Luis A Gonzalez
907
174
177.58
0.192
0.196
-0.00395
Todd Walker
1136
214
220.94
0.188
0.194
-0.00611
Aaron Miles
869
173
178.71
0.199
0.206
-0.00658
Mark Bellhorn
1056
227
234.07
0.215
0.222
-0.00670
Jose Castillo
1293
211
221.39
0.163
0.171
-0.00804
Miguel Cairo
966
176
184.52
0.182
0.191
-0.00882
Robinson Cano
1747
333
348.52
0.191
0.199
-0.00888
Kazuo Matsui
791
161
168.52
0.204
0.213
-0.00950
Mark Loretta
1236
217
231.37
0.176
0.187
-0.01162
Rickie Weeks
1121
200
213.42
0.178
0.190
-0.01197
Alfonso Soriano
2136
383
409.21
0.179
0.192
-0.01227
Freddy Sanchez
522
83
89.62
0.159
0.172
-0.01268
Jorge L Cantu
915
151
164.63
0.165
0.180
-0.01489
Bret Boone
1101
197
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 Type
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Fly
1019
127
74.31
0.125
0.073
0.05170
Grounder
1554
330
305.95
0.212
0.197
0.01547
Liner
687
30
29.66
0.044
0.043
0.00050
Bunt Fly
8
0
0.00
0.000
0.000
0.00000
Bunt Grounder
50
3
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.
Probabilistic Model of Range, 2005, Shortstops and Grounders, Revised Permalink
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)
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Omar Infante
567
131
125.07
0.231
0.221
0.01046
Clint Barmes
1042
211
200.24
0.202
0.192
0.01033
Jason A Bartlett
827
205
197.36
0.248
0.239
0.00924
Rafael Furcal
2050
437
418.74
0.213
0.204
0.00891
John McDonald
611
135
129.68
0.221
0.212
0.00870
Juan Castro
891
201
194.14
0.226
0.218
0.00770
Carlos Guillen
950
203
197.69
0.214
0.208
0.00559
Adam Everett
1855
373
365.12
0.201
0.197
0.00425
Bobby Crosby
999
221
216.87
0.221
0.217
0.00414
Julio Lugo
1813
372
365.06
0.205
0.201
0.00383
Neifi Perez
1547
339
333.43
0.219
0.216
0.00360
Wilson Valdez
535
104
103.25
0.194
0.193
0.00139
Jack Wilson
1991
439
439.30
0.220
0.221
-0.00015
Jimmy Rollins
1914
365
365.63
0.191
0.191
-0.00033
Cesar Izturis
1464
281
282.54
0.192
0.193
-0.00105
Yuniesky Betancourt
637
114
115.77
0.179
0.182
-0.00278
Alex Gonzalez
1608
315
319.65
0.196
0.199
-0.00289
Miguel Tejada
2065
417
425.11
0.202
0.206
-0.00393
Khalil Greene
1417
280
285.95
0.198
0.202
-0.00420
Edgar Renteria
1858
344
353.88
0.185
0.190
-0.00532
David Eckstein
2209
453
466.16
0.205
0.211
-0.00596
J.J. Hardy
1253
240
248.74
0.192
0.199
-0.00698
Omar Vizquel
1829
382
396.82
0.209
0.217
-0.00810
Bill Hall
630
138
143.11
0.219
0.227
-0.00811
Cristian Guzman
1585
278
291.23
0.175
0.184
-0.00835
Orlando Cabrera
1642
314
328.01
0.191
0.200
-0.00853
Royce Clayton
1845
354
373.81
0.192
0.203
-0.01074
Derek Jeter
2088
399
425.52
0.191
0.204
-0.01270
Marco Scutaro
921
189
200.96
0.205
0.218
-0.01298
Juan Uribe
1820
363
387.20
0.199
0.213
-0.01330
Oscar M Robles
598
116
125.03
0.194
0.209
-0.01510
Felipe Lopez
1707
322
348.31
0.189
0.204
-0.01541
Angel Berroa
2088
379
411.17
0.182
0.197
-0.01541
Jose Reyes
2032
357
389.20
0.176
0.192
-0.01585
Russ M Adams
1646
280
308.72
0.170
0.188
-0.01745
Jhonny Peralta
1729
352
387.25
0.204
0.224
-0.02039
Michael Young
2139
371
417.67
0.173
0.195
-0.02182
Mike Morse
603
100
113.99
0.166
0.189
-0.02320
Probabilistic Model of Range, Shortstops, 2005, Smoothed Visiting Player Model, Groundballs Only (Grounders + Bunt Grounders)
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Jason A Bartlett
827
205
193.52
0.248
0.234
0.01388
Omar Infante
567
131
124.74
0.231
0.220
0.01104
Clint Barmes
1042
211
199.69
0.202
0.192
0.01085
John McDonald
611
135
128.52
0.221
0.210
0.01061
Rafael Furcal
2050
437
417.08
0.213
0.203
0.00972
Juan Castro
891
201
193.57
0.226
0.217
0.00834
Julio Lugo
1813
372
359.76
0.205
0.198
0.00675
Bobby Crosby
999
221
214.88
0.221
0.215
0.00612
Adam Everett
1855
373
362.23
0.201
0.195
0.00581
Neifi Perez
1547
339
330.04
0.219
0.213
0.00579
Carlos Guillen
950
203
197.88
0.214
0.208
0.00539
Jimmy Rollins
1914
365
359.98
0.191
0.188
0.00262
Cesar Izturis
1464
281
279.05
0.192
0.191
0.00133
Jack Wilson
1991
439
436.69
0.220
0.219
0.00116
Wilson Valdez
535
104
104.31
0.194
0.195
-0.00058
Alex Gonzalez
1608
315
316.50
0.196
0.197
-0.00093
Edgar Renteria
1858
344
346.50
0.185
0.186
-0.00135
Yuniesky Betancourt
637
114
115.43
0.179
0.181
-0.00225
Miguel Tejada
2065
417
422.26
0.202
0.204
-0.00255
Khalil Greene
1417
280
284.17
0.198
0.201
-0.00294
David Eckstein
2209
453
459.82
0.205
0.208
-0.00309
Bill Hall
630
138
140.51
0.219
0.223
-0.00399
Cristian Guzman
1585
278
286.55
0.175
0.181
-0.00540
J.J. Hardy
1253
240
247.55
0.192
0.198
-0.00603
Orlando Cabrera
1642
314
327.24
0.191
0.199
-0.00807
Omar Vizquel
1829
382
399.22
0.209
0.218
-0.00942
Marco Scutaro
921
189
198.83
0.205
0.216
-0.01067
Juan Uribe
1820
363
383.41
0.199
0.211
-0.01122
Royce Clayton
1845
354
376.96
0.192
0.204
-0.01244
Jose Reyes
2032
357
384.22
0.176
0.189
-0.01340
Derek Jeter
2088
399
428.51
0.191
0.205
-0.01413
Angel Berroa
2088
379
408.49
0.182
0.196
-0.01413
Felipe Lopez
1707
322
348.13
0.189
0.204
-0.01531
Russ M Adams
1646
280
307.02
0.170
0.187
-0.01641
Jhonny Peralta
1729
352
381.59
0.204
0.221
-0.01712
Oscar M Robles
598
116
126.43
0.194
0.211
-0.01744
Michael Young
2139
371
412.43
0.173
0.193
-0.01937
Mike Morse
603
100
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.
Probabilistic Model of Range, 2005, Shortstops, Revised Permalink
The tables here correct the tables presented in this post.
Probabilistic Model of Range, Shortstops, 2005, Original Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Omar Infante
1233
171
157.18
0.139
0.127
0.01121
Clint Barmes
2209
276
254.21
0.125
0.115
0.00986
Jason A Bartlett
1766
257
245.86
0.146
0.139
0.00631
Julio Lugo
4297
523
496.20
0.122
0.115
0.00624
John McDonald
1223
163
157.90
0.133
0.129
0.00417
Wilson Valdez
1198
147
142.19
0.123
0.119
0.00402
Juan Castro
1775
243
236.77
0.137
0.133
0.00351
Adam Everett
3748
469
457.97
0.125
0.122
0.00294
Rafael Furcal
4111
539
527.12
0.131
0.128
0.00289
Yuniesky Betancourt
1426
161
159.52
0.113
0.112
0.00104
Alex Gonzalez
3291
404
403.74
0.123
0.123
0.00008
Bobby Crosby
2163
277
277.56
0.128
0.128
-0.00026
Neifi Perez
3026
410
411.18
0.135
0.136
-0.00039
Jimmy Rollins
3994
473
475.97
0.118
0.119
-0.00074
Omar Vizquel
4024
500
506.26
0.124
0.126
-0.00156
Edgar Renteria
4119
452
461.67
0.110
0.112
-0.00235
Jack Wilson
4240
543
553.07
0.128
0.130
-0.00238
Miguel Tejada
4280
526
538.20
0.123
0.126
-0.00285
Juan Uribe
3946
494
505.90
0.125
0.128
-0.00302
Bill Hall
1447
183
187.69
0.126
0.130
-0.00324
Carlos Guillen
1934
240
247.06
0.124
0.128
-0.00365
David Eckstein
4109
550
565.01
0.134
0.138
-0.00365
Oscar M Robles
1313
157
162.74
0.120
0.124
-0.00437
J.J. Hardy
2805
316
328.53
0.113
0.117
-0.00447
Khalil Greene
3123
365
379.76
0.117
0.122
-0.00473
Orlando Cabrera
3706
425
443.34
0.115
0.120
-0.00495
Cristian Guzman
3605
381
399.91
0.106
0.111
-0.00525
Cesar Izturis
2859
338
353.90
0.118
0.124
-0.00556
Derek Jeter
4231
525
555.71
0.124
0.131
-0.00726
Royce Clayton
3711
430
459.89
0.116
0.124
-0.00805
Russ M Adams
3433
372
400.54
0.108
0.117
-0.00831
Jhonny Peralta
3736
465
496.59
0.124
0.133
-0.00846
Mike Morse
1437
144
156.18
0.100
0.109
-0.00848
Angel Berroa
4438
505
543.44
0.114
0.122
-0.00866
Michael Young
4398
489
528.27
0.111
0.120
-0.00893
Felipe Lopez
3804
418
454.44
0.110
0.119
-0.00958
Marco Scutaro
1980
238
257.00
0.120
0.130
-0.00959
Jose Reyes
4308
479
522.85
0.111
0.121
-0.01018
Probabilistic Model of Range, Shortstops, 2005, Smoothed Visiting Player Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Omar Infante
1233
171
155.51
0.139
0.126
0.01256
Clint Barmes
2209
276
250.44
0.125
0.113
0.01157
Jason A Bartlett
1766
257
242.41
0.146
0.137
0.00826
Julio Lugo
4297
523
492.52
0.122
0.115
0.00709
John McDonald
1223
163
154.84
0.133
0.127
0.00667
Juan Castro
1775
243
235.64
0.137
0.133
0.00415
Adam Everett
3748
469
456.50
0.125
0.122
0.00334
Wilson Valdez
1198
147
143.00
0.123
0.119
0.00334
Rafael Furcal
4111
539
525.78
0.131
0.128
0.00322
Jimmy Rollins
3994
473
468.01
0.118
0.117
0.00125
Yuniesky Betancourt
1426
161
159.54
0.113
0.112
0.00103
Neifi Perez
3026
410
407.24
0.135
0.135
0.00091
Alex Gonzalez
3291
404
401.73
0.123
0.122
0.00069
Bobby Crosby
2163
277
277.52
0.128
0.128
-0.00024
Edgar Renteria
4119
452
455.84
0.110
0.111
-0.00093
Bill Hall
1447
183
185.15
0.126
0.128
-0.00149
Juan Uribe
3946
494
501.75
0.125
0.127
-0.00196
Miguel Tejada
4280
526
534.62
0.123
0.125
-0.00201
Omar Vizquel
4024
500
508.53
0.124
0.126
-0.00212
David Eckstein
4109
550
559.90
0.134
0.136
-0.00241
Jack Wilson
4240
543
555.09
0.128
0.131
-0.00285
Khalil Greene
3123
365
375.78
0.117
0.120
-0.00345
Carlos Guillen
1934
240
247.11
0.124
0.128
-0.00367
J.J. Hardy
2805
316
326.37
0.113
0.116
-0.00370
Cesar Izturis
2859
338
350.31
0.118
0.123
-0.00430
Orlando Cabrera
3706
425
442.65
0.115
0.119
-0.00476
Oscar M Robles
1313
157
163.31
0.120
0.124
-0.00481
Cristian Guzman
3605
381
398.46
0.106
0.111
-0.00484
Angel Berroa
4438
505
529.83
0.114
0.119
-0.00560
Mike Morse
1437
144
153.39
0.100
0.107
-0.00653
Derek Jeter
4231
525
554.10
0.124
0.131
-0.00688
Russ M Adams
3433
372
398.01
0.108
0.116
-0.00758
Michael Young
4398
489
522.48
0.111
0.119
-0.00761
Jhonny Peralta
3736
465
493.58
0.124
0.132
-0.00765
Royce Clayton
3711
430
463.25
0.116
0.125
-0.00896
Marco Scutaro
1980
238
256.00
0.120
0.129
-0.00909
Felipe Lopez
3804
418
455.16
0.110
0.120
-0.00977
Jose Reyes
4308
479
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.
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.
Probabilistic Model of Range, 2005, Second Basemen 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.
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
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Orlando Hudson
3318
488
412.37
0.147
0.124
0.02279
Alex Cora
1006
125
105.10
0.124
0.104
0.01978
Nick Punto
1742
228
198.86
0.131
0.114
0.01673
Chase Utley
3490
448
402.41
0.128
0.115
0.01306
Ryan Freel
1216
151
141.56
0.124
0.116
0.00776
Craig Counsell
3908
488
457.89
0.125
0.117
0.00771
Jose C Lopez
1406
184
173.83
0.131
0.124
0.00723
Junior Spivey
1767
207
196.22
0.117
0.111
0.00610
Rich Aurilia
1792
201
190.12
0.112
0.106
0.00607
Mark Ellis
2880
372
354.87
0.129
0.123
0.00595
Adam Kennedy
3277
401
387.19
0.122
0.118
0.00422
Placido Polanco
3001
333
321.62
0.111
0.107
0.00379
Ronnie Belliard
3772
460
445.89
0.122
0.118
0.00374
Tony Graffanino
1907
209
202.97
0.110
0.106
0.00316
Brian Roberts
3693
448
436.85
0.121
0.118
0.00302
Luis Castillo
3068
377
368.15
0.123
0.120
0.00289
Marcus Giles
4038
496
487.73
0.123
0.121
0.00205
Mark Grudzielanek
3525
433
428.40
0.123
0.122
0.00131
Luis Rivas
1073
123
121.95
0.115
0.114
0.00098
Jeff Kent
3693
441
439.06
0.119
0.119
0.00053
Ruben A Gotay
2185
270
269.61
0.124
0.123
0.00018
Freddy Sanchez
1189
126
125.96
0.106
0.106
0.00003
Mark Loretta
2772
312
314.82
0.113
0.114
-0.00102
Aaron Miles
1946
234
235.98
0.120
0.121
-0.00102
Jose Castillo
2662
303
305.71
0.114
0.115
-0.00102
Omar Infante
1810
190
192.47
0.105
0.106
-0.00137
Jamey Carroll
1350
153
154.85
0.113
0.115
-0.00137
Craig Biggio
3464
424
430.44
0.122
0.124
-0.00186
Tadahito Iguchi
3533
417
423.67
0.118
0.120
-0.00189
Ray Durham
3575
377
383.87
0.105
0.107
-0.00192
Miguel Cairo
2035
242
247.85
0.119
0.122
-0.00287
Kazuo Matsui
1683
215
219.93
0.128
0.131
-0.00293
Jose Vidro
2062
227
236.43
0.110
0.115
-0.00457
Nick Green
2327
233
243.90
0.100
0.105
-0.00469
Todd Walker
2276
271
284.36
0.119
0.125
-0.00587
Mark Bellhorn
2341
281
300.77
0.120
0.128
-0.00845
Bret Boone
2505
269
291.21
0.107
0.116
-0.00887
Rickie Weeks
2532
271
294.57
0.107
0.116
-0.00931
Alfonso Soriano
4411
471
514.25
0.107
0.117
-0.00980
Luis A Gonzalez
1831
210
229.03
0.115
0.125
-0.01040
Jorge L Cantu
2169
201
226.94
0.093
0.105
-0.01196
Robinson Cano
3555
420
475.02
0.118
0.134
-0.01548
Deivi Cruz
1046
104
121.39
0.099
0.116
-0.01663
Chone Figgins
1001
110
128.09
0.110
0.128
-0.01807
Probabilistic Model of Range, Second Baseman, 2005, Smoothed Visiting Player Model
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Orlando Hudson
3231
482
398.69
0.149
0.123
0.02579
Nick Punto
1708
228
199.52
0.133
0.117
0.01668
Chase Utley
3384
444
393.82
0.131
0.116
0.01483
Jose C Lopez
1374
184
166.21
0.134
0.121
0.01294
Craig Counsell
3848
488
460.59
0.127
0.120
0.00712
Ryan Freel
1169
149
141.07
0.127
0.121
0.00679
Rich Aurilia
1728
201
189.96
0.116
0.110
0.00639
Mark Ellis
2808
367
350.26
0.131
0.125
0.00596
Brian Roberts
3617
444
423.49
0.123
0.117
0.00567
Placido Polanco
2932
332
317.45
0.113
0.108
0.00496
Junior Spivey
1722
206
197.59
0.120
0.115
0.00489
Tony Graffanino
1865
207
200.27
0.111
0.107
0.00361
Marcus Giles
3925
493
481.19
0.126
0.123
0.00301
Adam Kennedy
3229
401
391.41
0.124
0.121
0.00297
Luis Castillo
2987
374
366.95
0.125
0.123
0.00236
Mark Grudzielanek
3477
433
425.60
0.125
0.122
0.00213
Luis Rivas
1049
123
121.28
0.117
0.116
0.00164
Ronnie Belliard
3705
458
453.32
0.124
0.122
0.00126
Craig Biggio
3326
417
412.86
0.125
0.124
0.00124
Jeff Kent
3647
440
439.32
0.121
0.120
0.00019
Ruben A Gotay
2145
270
270.26
0.126
0.126
-0.00012
Omar Infante
1754
190
191.31
0.108
0.109
-0.00075
Jose Castillo
2598
300
302.53
0.115
0.116
-0.00097
Aaron Miles
1915
233
235.82
0.122
0.123
-0.00147
Mark Loretta
2723
311
316.51
0.114
0.116
-0.00202
Tadahito Iguchi
3412
415
422.02
0.122
0.124
-0.00206
Ray Durham
3458
374
384.45
0.108
0.111
-0.00302
Miguel Cairo
1988
239
246.05
0.120
0.124
-0.00354
Freddy Sanchez
1158
125
129.60
0.108
0.112
-0.00397
Jose Vidro
1928
218
229.16
0.113
0.119
-0.00579
Nick Green
2270
230
244.11
0.101
0.108
-0.00622
Todd Walker
2239
268
282.03
0.120
0.126
-0.00627
Jamey Carroll
1269
148
156.25
0.117
0.123
-0.00650
Kazuo Matsui
1646
212
223.34
0.129
0.136
-0.00689
Bret Boone
2456
269
289.91
0.110
0.118
-0.00851
Mark Bellhorn
2287
280
303.18
0.122
0.133
-0.01014
Rickie Weeks
2471
268
294.17
0.108
0.119
-0.01059
Jorge L Cantu
2132
200
222.99
0.094
0.105
-0.01078
Alfonso Soriano
4314
470
523.90
0.109
0.121
-0.01249
Luis A Gonzalez
1800
209
233.30
0.116
0.130
-0.01350
Robinson Cano
3495
417
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.
Probabilistic Model of Range, Shortstops, 2005, Original Model, Groundballs Only (Grounders + Bunt Grounders)
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
John McDonald
611
135
128.09
0.221
0.210
0.01131
Omar Infante
567
131
124.90
0.231
0.220
0.01076
Bobby Crosby
999
226
216.78
0.226
0.217
0.00923
Clint Barmes
1042
208
199.37
0.200
0.191
0.00828
Neifi Perez
1547
340
330.11
0.220
0.213
0.00639
Jason A Bartlett
827
201
195.84
0.243
0.237
0.00624
Wilson Valdez
535
105
102.46
0.196
0.192
0.00476
Yuniesky Betancourt
637
119
116.07
0.187
0.182
0.00459
Adam Everett
1855
371
363.80
0.200
0.196
0.00388
Cesar Izturis
1464
286
282.07
0.195
0.193
0.00269
Rafael Furcal
2050
420
414.68
0.205
0.202
0.00259
Juan Castro
891
194
192.20
0.218
0.216
0.00202
Julio Lugo
1813
361
360.32
0.199
0.199
0.00038
Miguel Tejada
2065
421
421.96
0.204
0.204
-0.00046
Alex Gonzalez
1608
317
317.88
0.197
0.198
-0.00055
Jack Wilson
1991
433
437.56
0.217
0.220
-0.00229
Jimmy Rollins
1914
351
356.95
0.183
0.186
-0.00311
David Eckstein
2209
458
465.27
0.207
0.211
-0.00329
Carlos Guillen
950
193
197.13
0.203
0.208
-0.00435
Khalil Greene
1417
278
284.50
0.196
0.201
-0.00459
Cristian Guzman
1585
275
286.96
0.174
0.181
-0.00755
Orlando Cabrera
1642
314
327.09
0.191
0.199
-0.00797
Omar Vizquel
1829
376
392.82
0.206
0.215
-0.00919
J.J. Hardy
1253
233
246.02
0.186
0.196
-0.01039
Oscar M Robles
598
117
124.69
0.196
0.209
-0.01285
Russ M Adams
1646
285
306.68
0.173
0.186
-0.01317
Juan Uribe
1820
361
386.61
0.198
0.212
-0.01407
Royce Clayton
1845
346
372.43
0.188
0.202
-0.01432
Jose Reyes
2032
358
388.18
0.176
0.191
-0.01485
Angel Berroa
2088
377
409.76
0.181
0.196
-0.01569
Felipe Lopez
1707
317
345.10
0.186
0.202
-0.01646
Michael Young
2139
381
418.01
0.178
0.195
-0.01730
Jhonny Peralta
1729
356
386.12
0.206
0.223
-0.01742
Bill Hall
630
129
141.28
0.205
0.224
-0.01950
Marco Scutaro
921
181
199.97
0.197
0.217
-0.02060
Edgar Renteria
1858
308
346.29
0.166
0.186
-0.02061
Mike Morse
603
97
113.40
0.161
0.188
-0.02720
Derek Jeter
2088
346
417.89
0.166
0.200
-0.03443
Probabilistic Model of Range, Shortstops, 2005, Smoothed Visiting Player Model, Groundballs Only (Grounders + Bunt Grounders)
Player
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Jason A Bartlett
814
201
190.15
0.247
0.234
0.01333
John McDonald
605
135
126.97
0.223
0.210
0.01327
Neifi Perez
1519
340
323.44
0.224
0.213
0.01090
Bobby Crosby
981
225
214.36
0.229
0.219
0.01085
Omar Infante
560
130
124.48
0.232
0.222
0.00986
Clint Barmes
1032
208
197.93
0.202
0.192
0.00976
Yuniesky Betancourt
629
118
113.60
0.188
0.181
0.00699
Cesar Izturis
1450
286
277.41
0.197
0.191
0.00592
Julio Lugo
1781
361
353.57
0.203
0.199
0.00417
Adam Everett
1795
363
356.25
0.202
0.198
0.00376
Juan Castro
882
194
191.78
0.220
0.217
0.00251
Wilson Valdez
530
105
103.71
0.198
0.196
0.00244
Alex Gonzalez
1579
317
314.25
0.201
0.199
0.00174
Rafael Furcal
2003
415
412.15
0.207
0.206
0.00142
Miguel Tejada
2038
418
415.75
0.205
0.204
0.00111
Khalil Greene
1390
278
280.11
0.200
0.202
-0.00152
David Eckstein
2181
458
461.60
0.210
0.212
-0.00165
Jack Wilson
1951
433
440.34
0.222
0.226
-0.00376
Cristian Guzman
1511
273
279.15
0.181
0.185
-0.00407
Jimmy Rollins
1861
347
355.79
0.186
0.191
-0.00473
Carlos Guillen
937
193
197.89
0.206
0.211
-0.00522
Orlando Cabrera
1622
313
325.76
0.193
0.201
-0.00787
J.J. Hardy
1227
233
244.14
0.190
0.199
-0.00908
Omar Vizquel
1781
376
393.31
0.211
0.221
-0.00972
Juan Uribe
1774
355
372.71
0.200
0.210
-0.00998
Jose Reyes
1999
357
379.67
0.179
0.190
-0.01134
Russ M Adams
1612
284
303.51
0.176
0.188
-0.01210
Jhonny Peralta
1706
356
380.57
0.209
0.223
-0.01440
Oscar M Robles
590
117
125.54
0.198
0.213
-0.01447
Michael Young
2099
376
406.67
0.179
0.194
-0.01461
Bill Hall
620
128
137.43
0.206
0.222
-0.01521
Edgar Renteria
1813
307
335.25
0.169
0.185
-0.01558
Angel Berroa
2064
375
408.13
0.182
0.198
-0.01605
Royce Clayton
1810
346
376.25
0.191
0.208
-0.01671
Marco Scutaro
911
180
196.65
0.198
0.216
-0.01828
Felipe Lopez
1668
317
349.72
0.190
0.210
-0.01962
Mike Morse
589
97
111.71
0.165
0.190
-0.02497
Derek Jeter
2059
345
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.
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.
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.
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.
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:
Season
In Play
Actual Outs
Pred. Outs
DER
Pred. DER
Difference
2002
131915
91661
92748.99
0.695
0.703
-0.00825
2003
133657
92756
91800.78
0.694
0.687
0.00715
2004
132952
91912
93408.06
0.691
0.703
-0.01125
2005
133589
92647
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.
Season
Batted Ball Type
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
2002
Bunt Fly
249
227
227.50
0.912
0.914
-0.00201
2003
Bunt Fly
301
284
282.33
0.944
0.938
0.00554
2004
Bunt Fly
281
257
258.17
0.915
0.919
-0.00415
2005
Bunt Fly
272
260
260.00
0.956
0.956
0.00000
2002
Bunt Grounder
2861
2144
2137.95
0.749
0.747
0.00211
2003
Bunt Grounder
2998
2229
2257.07
0.743
0.753
-0.00936
2004
Bunt Grounder
2931
2262
2245.54
0.772
0.766
0.00562
2005
Bunt Grounder
2909
2209
2203.44
0.759
0.757
0.00191
2002
Fly
43037
38924
39043.76
0.904
0.907
-0.00278
2003
Fly
41673
38088
37349.57
0.914
0.896
0.01772
2004
Fly
45136
38575
39530.60
0.855
0.876
-0.02117
2005
Fly
42742
37634
37297.06
0.880
0.873
0.00788
2002
Grounder
57916
43378
43953.03
0.749
0.759
-0.00993
2003
Grounder
58673
44371
43818.41
0.756
0.747
0.00942
2004
Grounder
59719
43861
44493.05
0.734
0.745
-0.01058
2005
Grounder
59891
44273
43618.51
0.739
0.728
0.01093
2002
Liner
27852
6988
7386.75
0.251
0.265
-0.01432
2003
Liner
29779
7574
7883.40
0.254
0.265
-0.01039
2004
Liner
24885
6957
6880.70
0.280
0.277
0.00307
2005
Liner
27775
8271
7639.14
0.298
0.275
0.02275
2003
Pop (Not used)
233
210
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.
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.
Pitcher
Team
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Rich Harden
Oak
341
247
228.27
0.724
0.669
0.05494
Jon Garland
CWS
706
514
477.48
0.728
0.676
0.05172
Claudio Vargas
Ari
362
257
238.87
0.710
0.660
0.05009
Roy Halladay
Tor
408
297
276.93
0.728
0.679
0.04918
Bruce Chen
Bal
594
431
403.52
0.726
0.679
0.04626
Roger Clemens
Hou
577
426
399.50
0.738
0.692
0.04593
Tim Wakefield
Bos
678
495
466.14
0.730
0.688
0.04256
Horacio Ramirez
Atl
667
479
451.43
0.718
0.677
0.04134
Kameron D Loe
Tex
307
218
205.50
0.710
0.669
0.04072
Barry Zito
Oak
654
486
460.30
0.743
0.704
0.03930
Wandy E Rodriguez
Hou
400
277
261.30
0.692
0.653
0.03924
Kirk Saarloos
Oak
553
392
370.70
0.709
0.670
0.03851
Pedro Martinez
NYM
564
422
400.38
0.748
0.710
0.03833
Brett Myers
Phi
587
418
396.01
0.712
0.675
0.03745
Vicente Padilla
Phi
447
315
298.28
0.705
0.667
0.03740
Carlos Zambrano
ChC
592
433
411.41
0.731
0.695
0.03646
Mark Mulder
StL
659
457
433.24
0.693
0.657
0.03605
Jorge Sosa
Atl
416
300
285.02
0.721
0.685
0.03600
Andy Pettitte
Hou
643
463
440.27
0.720
0.685
0.03535
Chris Carpenter
StL
667
476
452.75
0.714
0.679
0.03485
Dave T Bush
Tor
438
313
298.02
0.715
0.680
0.03421
Jose Contreras
CWS
595
436
416.49
0.733
0.700
0.03279
John Smoltz
Atl
690
490
467.42
0.710
0.677
0.03272
Jason Jennings
Col
397
271
258.02
0.683
0.650
0.03269
Kirk Rueter
SF
404
279
266.38
0.691
0.659
0.03123
Joe M Blanton
Oak
624
462
442.80
0.740
0.710
0.03076
Jon Lieber
Phi
684
485
464.49
0.709
0.679
0.02998
Mark Prior
ChC
425
297
284.67
0.699
0.670
0.02901
Brandon Backe
Hou
465
328
314.65
0.705
0.677
0.02871
Carlos Silva
Min
641
445
427.56
0.694
0.667
0.02720
Scott Elarton
Cle
585
418
402.32
0.715
0.688
0.02680
Casey Fossum
TB
498
338
324.83
0.679
0.652
0.02644
D.J. Carrasco
KC
394
270
259.62
0.685
0.659
0.02635
Jason Marquis
StL
664
477
459.60
0.718
0.692
0.02620
Paul Byrd
LAA
682
481
463.20
0.705
0.679
0.02609
Freddy Garcia
CWS
708
501
482.60
0.708
0.682
0.02599
Cliff Lee
Cle
621
442
426.01
0.712
0.686
0.02574
Javier Vazquez
Ari
626
430
414.46
0.687
0.662
0.02482
Kenny Rogers
Tex
665
465
448.78
0.699
0.675
0.02440
C.C. Sabathia
Cle
574
403
389.06
0.702
0.678
0.02429
Jeff Weaver
LAD
677
484
467.62
0.715
0.691
0.02420
Jake Westbrook
Cle
693
479
462.50
0.691
0.667
0.02381
Ted Lilly
Tor
386
271
261.83
0.702
0.678
0.02376
Danny Haren
Oak
649
453
437.68
0.698
0.674
0.02361
Jarrod Washburn
LAA
567
396
383.49
0.698
0.676
0.02206
Matt Morris
StL
633
434
420.28
0.686
0.664
0.02167
Seth McClung
TB
319
226
219.17
0.708
0.687
0.02141
Kevin Millwood
Cle
576
400
387.72
0.694
0.673
0.02133
Jerome Williams
ChC
334
243
235.90
0.728
0.706
0.02126
Ervin R Santana
LAA
412
287
278.25
0.697
0.675
0.02123
Shawn Estes
Ari
408
285
276.43
0.699
0.678
0.02101
Greg Maddux
ChC
728
505
489.74
0.694
0.673
0.02096
Kip Wells
Pit
562
386
374.28
0.687
0.666
0.02085
Cory Lidle
Phi
607
403
390.49
0.664
0.643
0.02062
Johan Santana
Min
604
438
425.63
0.725
0.705
0.02047
Bartolo Colon
LAA
678
481
467.31
0.709
0.689
0.02019
Tim Hudson
Atl
608
425
412.90
0.699
0.679
0.01991
Dave Williams
Pit
426
303
294.54
0.711
0.691
0.01985
Nate Robertson
Det
624
436
423.62
0.699
0.679
0.01985
Jason Johnson
Det
718
499
484.83
0.695
0.675
0.01973
Jamie Moyer
Sea
683
473
459.72
0.693
0.673
0.01944
Tony Armas Jr.
Was
318
232
225.90
0.730
0.710
0.01918
Victor Santos
Mil
466
323
314.10
0.693
0.674
0.01911
Brandon Webb
Ari
689
470
456.91
0.682
0.663
0.01900
Gustavo G Chacin
Tor
652
449
437.03
0.689
0.670
0.01835
Matt Clement
Bos
582
400
389.36
0.687
0.669
0.01829
John Thomson
Atl
330
218
212.09
0.661
0.643
0.01791
Randy Johnson
NYY
618
432
421.36
0.699
0.682
0.01722
Josh Fogg
Pit
571
394
384.25
0.690
0.673
0.01707
Brad Radke
Min
651
462
450.96
0.710
0.693
0.01696
Scott E Kazmir
TB
522
356
347.31
0.682
0.665
0.01666
Jake Peavy
SD
521
371
362.45
0.712
0.696
0.01641
Dontrelle Willis
Fla
716
505
493.58
0.705
0.689
0.01596
David Wells
Bos
622
413
403.21
0.664
0.648
0.01574
Brett Tomko
SF
625
437
427.24
0.699
0.684
0.01562
Ben Sheets
Mil
446
318
311.26
0.713
0.698
0.01511
Josh Beckett
Fla
484
340
332.94
0.702
0.688
0.01458
Mark Hendrickson
TB
633
418
409.04
0.660
0.646
0.01415
Kris Benson
NYM
565
409
401.26
0.724
0.710
0.01370
Chien-Ming Wang
NYY
392
279
273.77
0.712
0.698
0.01334
Chris Capuano
Mil
639
442
433.56
0.692
0.679
0.01320
Joe Mays
Min
564
377
369.64
0.668
0.655
0.01305
Noah Lowry
SF
599
418
410.44
0.698
0.685
0.01263
Joel Pineiro
Sea
629
426
418.23
0.677
0.665
0.01235
Roy Oswalt
Hou
744
509
500.00
0.684
0.672
0.01210
Livan Hernandez
Was
796
545
535.54
0.685
0.673
0.01189
Orlando Hernandez
CWS
397
274
269.42
0.690
0.679
0.01155
Jose Lima
KC
597
401
394.25
0.672
0.660
0.01131
Brad M Hennessey
SF
386
272
267.85
0.705
0.694
0.01075
Runelvys Hernandez
KC
523
364
358.53
0.696
0.686
0.01046
Bronson Arroyo
Bos
688
489
481.93
0.711
0.700
0.01028
Kyle Lohse
Min
607
414
408.12
0.682
0.672
0.00969
Brian Lawrence
SD
656
454
448.43
0.692
0.684
0.00850
Woody Williams
SD
513
359
354.67
0.700
0.691
0.00844
Russ Ortiz
Ari
418
283
279.64
0.677
0.669
0.00804
Tom Glavine
NYM
720
496
490.23
0.689
0.681
0.00801
Ramon Ortiz
Cin
567
390
385.46
0.688
0.680
0.00801
Rodrigo Lopez
Bal
702
482
476.50
0.687
0.679
0.00783
Erik Bedard
Bal
409
276
272.81
0.675
0.667
0.00780
Mike Wood
KC
382
260
257.48
0.681
0.674
0.00659
John Patterson
Was
543
388
385.11
0.715
0.709
0.00533
Gil Meche
Sea
462
320
317.70
0.693
0.688
0.00498
Doug Waechter
TB
533
361
358.58
0.677
0.673
0.00455
Mike Maroth
Det
683
472
468.90
0.691
0.687
0.00454
Jeff W Francis
Col
594
386
383.33
0.650
0.645
0.00450
Jeff Suppan
StL
626
432
429.24
0.690
0.686
0.00441
Doug Davis
Mil
615
430
427.49
0.699
0.695
0.00408
Daniel A Cabrera
Bal
447
312
310.20
0.698
0.694
0.00403
Aaron Sele
Sea
406
274
272.41
0.675
0.671
0.00392
Mark Redman
Pit
574
397
394.86
0.692
0.688
0.00372
Aaron Cook
Col
307
208
206.91
0.678
0.674
0.00356
Brandon Claussen
Cin
522
363
361.32
0.695
0.692
0.00322
Odalis Perez
LAD
338
235
234.04
0.695
0.692
0.00284
Mark Buehrle
CWS
758
521
520.04
0.687
0.686
0.00127
Brad Penny
LAD
555
383
382.32
0.690
0.689
0.00123
Brian Moehler
Fla
538
355
354.45
0.660
0.659
0.00102
Derek Lowe
LAD
700
489
488.42
0.699
0.698
0.00083
Chan Ho Park
Tex
354
228
227.98
0.644
0.644
0.00005
A.J. Burnett
Fla
577
391
391.17
0.678
0.678
-0.00029
Brad A Halsey
Ari
550
370
370.21
0.673
0.673
-0.00038
Byung-Hyun Kim
Col
450
304
304.59
0.676
0.677
-0.00132
Tomo Ohka
Mil
416
280
280.85
0.673
0.675
-0.00205
Ryan Franklin
Sea
642
453
454.56
0.706
0.708
-0.00243
Josh Towers
Tor
706
477
480.37
0.676
0.680
-0.00477
Jeremy Bonderman
Det
574
389
392.27
0.678
0.683
-0.00571
Aaron Harang
Cin
643
441
445.17
0.686
0.692
-0.00649
John Lackey
LAA
598
397
401.04
0.664
0.671
-0.00676
D.J. Houlton
LAD
407
279
281.98
0.686
0.693
-0.00732
Zack Z Greinke
KC
625
407
411.96
0.651
0.659
-0.00794
Sidney Ponson
Bal
460
297
300.83
0.646
0.654
-0.00833
Adam Eaton
SD
405
271
274.55
0.669
0.678
-0.00876
Jason Schmidt
SF
485
333
337.52
0.687
0.696
-0.00932
Jamey Wright
Col
563
370
375.61
0.657
0.667
-0.00996
Glendon Rusch
ChC
476
305
310.14
0.641
0.652
-0.01081
Victor Zambrano
NYM
532
361
368.64
0.679
0.693
-0.01436
Hideo Nomo
TB
344
232
238.13
0.674
0.692
-0.01781
Mike Mussina
NYY
545
362
371.75
0.664
0.682
-0.01789
Chris Young
Tex
492
342
351.47
0.695
0.714
-0.01926
Eric Milton
Cin
633
427
441.81
0.675
0.698
-0.02339
Joe Kennedy
Col
327
207
215.86
0.633
0.660
-0.02708
Esteban Loaiza
Was
661
444
462.19
0.672
0.699
-0.02751
Carl Pavano
NYY
343
225
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:
Probabilistic Model of Range, Defense Behind Pitchers 2005. Unsmoothed Park Model. Easiest to Field.
Pitcher
Team
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Chris Young
Tex
492
342
351.47
0.695
0.714
-0.01926
Tony Armas Jr.
Was
318
232
225.90
0.730
0.710
0.01918
Kris Benson
NYM
565
409
401.26
0.724
0.710
0.01370
Pedro Martinez
NYM
564
422
400.38
0.748
0.710
0.03833
Joe M Blanton
Oak
624
462
442.80
0.740
0.710
0.03076
John Patterson
Was
543
388
385.11
0.715
0.709
0.00533
Ryan Franklin
Sea
642
453
454.56
0.706
0.708
-0.00243
Jerome Williams
ChC
334
243
235.90
0.728
0.706
0.02126
Johan Santana
Min
604
438
425.63
0.725
0.705
0.02047
Barry Zito
Oak
654
486
460.30
0.743
0.704
0.03930
Bronson Arroyo
Bos
688
489
481.93
0.711
0.700
0.01028
Jose Contreras
CWS
595
436
416.49
0.733
0.700
0.03279
Esteban Loaiza
Was
661
444
462.19
0.672
0.699
-0.02751
Chien-Ming Wang
NYY
392
279
273.77
0.712
0.698
0.01334
Eric Milton
Cin
633
427
441.81
0.675
0.698
-0.02339
Ben Sheets
Mil
446
318
311.26
0.713
0.698
0.01511
Derek Lowe
LAD
700
489
488.42
0.699
0.698
0.00083
Jason Schmidt
SF
485
333
337.52
0.687
0.696
-0.00932
Jake Peavy
SD
521
371
362.45
0.712
0.696
0.01641
Doug Davis
Mil
615
430
427.49
0.699
0.695
0.00408
Carlos Zambrano
ChC
592
433
411.41
0.731
0.695
0.03646
Daniel A Cabrera
Bal
447
312
310.20
0.698
0.694
0.00403
Brad M Hennessey
SF
386
272
267.85
0.705
0.694
0.01075
Victor Zambrano
NYM
532
361
368.64
0.679
0.693
-0.01436
D.J. Houlton
LAD
407
279
281.98
0.686
0.693
-0.00732
Brad Radke
Min
651
462
450.96
0.710
0.693
0.01696
Odalis Perez
LAD
338
235
234.04
0.695
0.692
0.00284
Roger Clemens
Hou
577
426
399.50
0.738
0.692
0.04593
Aaron Harang
Cin
643
441
445.17
0.686
0.692
-0.00649
Hideo Nomo
TB
344
232
238.13
0.674
0.692
-0.01781
Brandon Claussen
Cin
522
363
361.32
0.695
0.692
0.00322
Jason Marquis
StL
664
477
459.60
0.718
0.692
0.02620
Dave Williams
Pit
426
303
294.54
0.711
0.691
0.01985
Woody Williams
SD
513
359
354.67
0.700
0.691
0.00844
Jeff Weaver
LAD
677
484
467.62
0.715
0.691
0.02420
Dontrelle Willis
Fla
716
505
493.58
0.705
0.689
0.01596
Bartolo Colon
LAA
678
481
467.31
0.709
0.689
0.02019
Brad Penny
LAD
555
383
382.32
0.690
0.689
0.00123
Mark Redman
Pit
574
397
394.86
0.692
0.688
0.00372
Josh Beckett
Fla
484
340
332.94
0.702
0.688
0.01458
Scott Elarton
Cle
585
418
402.32
0.715
0.688
0.02680
Gil Meche
Sea
462
320
317.70
0.693
0.688
0.00498
Tim Wakefield
Bos
678
495
466.14
0.730
0.688
0.04256
Seth McClung
TB
319
226
219.17
0.708
0.687
0.02141
Mike Maroth
Det
683
472
468.90
0.691
0.687
0.00454
Mark Buehrle
CWS
758
521
520.04
0.687
0.686
0.00127
Cliff Lee
Cle
621
442
426.01
0.712
0.686
0.02574
Carl Pavano
NYY
343
225
235.27
0.656
0.686
-0.02996
Jeff Suppan
StL
626
432
429.24
0.690
0.686
0.00441
Runelvys Hernandez
KC
523
364
358.53
0.696
0.686
0.01046
Noah Lowry
SF
599
418
410.44
0.698
0.685
0.01263
Jorge Sosa
Atl
416
300
285.02
0.721
0.685
0.03600
Andy Pettitte
Hou
643
463
440.27
0.720
0.685
0.03535
Brett Tomko
SF
625
437
427.24
0.699
0.684
0.01562
Brian Lawrence
SD
656
454
448.43
0.692
0.684
0.00850
Jeremy Bonderman
Det
574
389
392.27
0.678
0.683
-0.00571
Mike Mussina
NYY
545
362
371.75
0.664
0.682
-0.01789
Randy Johnson
NYY
618
432
421.36
0.699
0.682
0.01722
Freddy Garcia
CWS
708
501
482.60
0.708
0.682
0.02599
Tom Glavine
NYM
720
496
490.23
0.689
0.681
0.00801
Josh Towers
Tor
706
477
480.37
0.676
0.680
-0.00477
Dave T Bush
Tor
438
313
298.02
0.715
0.680
0.03421
Ramon Ortiz
Cin
567
390
385.46
0.688
0.680
0.00801
Bruce Chen
Bal
594
431
403.52
0.726
0.679
0.04626
Paul Byrd
LAA
682
481
463.20
0.705
0.679
0.02609
Tim Hudson
Atl
608
425
412.90
0.699
0.679
0.01991
Jon Lieber
Phi
684
485
464.49
0.709
0.679
0.02998
Nate Robertson
Det
624
436
423.62
0.699
0.679
0.01985
Chris Carpenter
StL
667
476
452.75
0.714
0.679
0.03485
Rodrigo Lopez
Bal
702
482
476.50
0.687
0.679
0.00783
Roy Halladay
Tor
408
297
276.93
0.728
0.679
0.04918
Orlando Hernandez
CWS
397
274
269.42
0.690
0.679
0.01155
Chris Capuano
Mil
639
442
433.56
0.692
0.679
0.01320
Ted Lilly
Tor
386
271
261.83
0.702
0.678
0.02376
A.J. Burnett
Fla
577
391
391.17
0.678
0.678
-0.00029
Adam Eaton
SD
405
271
274.55
0.669
0.678
-0.00876
C.C. Sabathia
Cle
574
403
389.06
0.702
0.678
0.02429
Shawn Estes
Ari
408
285
276.43
0.699
0.678
0.02101
John Smoltz
Atl
690
490
467.42
0.710
0.677
0.03272
Byung-Hyun Kim
Col
450
304
304.59
0.676
0.677
-0.00132
Horacio Ramirez
Atl
667
479
451.43
0.718
0.677
0.04134
Brandon Backe
Hou
465
328
314.65
0.705
0.677
0.02871
Jarrod Washburn
LAA
567
396
383.49
0.698
0.676
0.02206
Jon Garland
CWS
706
514
477.48
0.728
0.676
0.05172
Ervin R Santana
LAA
412
287
278.25
0.697
0.675
0.02123
Jason Johnson
Det
718
499
484.83
0.695
0.675
0.01973
Tomo Ohka
Mil
416
280
280.85
0.673
0.675
-0.00205
Kenny Rogers
Tex
665
465
448.78
0.699
0.675
0.02440
Brett Myers
Phi
587
418
396.01
0.712
0.675
0.03745
Danny Haren
Oak
649
453
437.68
0.698
0.674
0.02361
Mike Wood
KC
382
260
257.48
0.681
0.674
0.00659
Victor Santos
Mil
466
323
314.10
0.693
0.674
0.01911
Aaron Cook
Col
307
208
206.91
0.678
0.674
0.00356
Kevin Millwood
Cle
576
400
387.72
0.694
0.673
0.02133
Brad A Halsey
Ari
550
370
370.21
0.673
0.673
-0.00038
Jamie Moyer
Sea
683
473
459.72
0.693
0.673
0.01944
Josh Fogg
Pit
571
394
384.25
0.690
0.673
0.01707
Livan Hernandez
Was
796
545
535.54
0.685
0.673
0.01189
Doug Waechter
TB
533
361
358.58
0.677
0.673
0.00455
Greg Maddux
ChC
728
505
489.74
0.694
0.673
0.02096
Kyle Lohse
Min
607
414
408.12
0.682
0.672
0.00969
Roy Oswalt
Hou
744
509
500.00
0.684
0.672
0.01210
Aaron Sele
Sea
406
274
272.41
0.675
0.671
0.00392
John Lackey
LAA
598
397
401.04
0.664
0.671
-0.00676
Kirk Saarloos
Oak
553
392
370.70
0.709
0.670
0.03851
Gustavo G Chacin
Tor
652
449
437.03
0.689
0.670
0.01835
Mark Prior
ChC
425
297
284.67
0.699
0.670
0.02901
Rich Harden
Oak
341
247
228.27
0.724
0.669
0.05494
Kameron D Loe
Tex
307
218
205.50
0.710
0.669
0.04072
Matt Clement
Bos
582
400
389.36
0.687
0.669
0.01829
Russ Ortiz
Ari
418
283
279.64
0.677
0.669
0.00804
Jake Westbrook
Cle
693
479
462.50
0.691
0.667
0.02381
Vicente Padilla
Phi
447
315
298.28
0.705
0.667
0.03740
Jamey Wright
Col
563
370
375.61
0.657
0.667
-0.00996
Carlos Silva
Min
641
445
427.56
0.694
0.667
0.02720
Erik Bedard
Bal
409
276
272.81
0.675
0.667
0.00780
Kip Wells
Pit
562
386
374.28
0.687
0.666
0.02085
Scott E Kazmir
TB
522
356
347.31
0.682
0.665
0.01666
Joel Pineiro
Sea
629
426
418.23
0.677
0.665
0.01235
Matt Morris
StL
633
434
420.28
0.686
0.664
0.02167
Brandon Webb
Ari
689
470
456.91
0.682
0.663
0.01900
Javier Vazquez
Ari
626
430
414.46
0.687
0.662
0.02482
Jose Lima
KC
597
401
394.25
0.672
0.660
0.01131
Joe Kennedy
Col
327
207
215.86
0.633
0.660
-0.02708
Claudio Vargas
Ari
362
257
238.87
0.710
0.660
0.05009
Kirk Rueter
SF
404
279
266.38
0.691
0.659
0.03123
Zack Z Greinke
KC
625
407
411.96
0.651
0.659
-0.00794
D.J. Carrasco
KC
394
270
259.62
0.685
0.659
0.02635
Brian Moehler
Fla
538
355
354.45
0.660
0.659
0.00102
Mark Mulder
StL
659
457
433.24
0.693
0.657
0.03605
Joe Mays
Min
564
377
369.64
0.668
0.655
0.01305
Sidney Ponson
Bal
460
297
300.83
0.646
0.654
-0.00833
Wandy E Rodriguez
Hou
400
277
261.30
0.692
0.653
0.03924
Casey Fossum
TB
498
338
324.83
0.679
0.652
0.02644
Glendon Rusch
ChC
476
305
310.14
0.641
0.652
-0.01081
Jason Jennings
Col
397
271
258.02
0.683
0.650
0.03269
David Wells
Bos
622
413
403.21
0.664
0.648
0.01574
Mark Hendrickson
TB
633
418
409.04
0.660
0.646
0.01415
Jeff W Francis
Col
594
386
383.33
0.650
0.645
0.00450
Chan Ho Park
Tex
354
228
227.98
0.644
0.644
0.00005
Cory Lidle
Phi
607
403
390.49
0.664
0.643
0.02062
John Thomson
Atl
330
218
212.09
0.661
0.643
0.01791
I wonder if San Diego knew about this when they traded for Chris Young. They probably didn't if they were willing to take Chan Ho Park. It's a good thing John Thomson and Corey Lidle had good defenses backing them up.
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.
I didn't want to throw out perfectly good data, especially with small sample sizes.
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.
Probabilistic Model of Range, 2005. Model Includes Parks, Smoothed Visiting Team Fielding
Team
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Astros
4204
2963
2845.45
0.705
0.677
0.02796
Athletics
4286
3064
2944.68
0.715
0.687
0.02784
White Sox
4457
3175
3052.08
0.712
0.685
0.02758
Phillies
4211
2962
2846.84
0.703
0.676
0.02735
Indians
4385
3108
2988.57
0.709
0.682
0.02724
Cardinals
4414
3101
2991.45
0.703
0.678
0.02482
Braves
4559
3162
3059.99
0.694
0.671
0.02238
Blue Jays
4511
3156
3058.15
0.700
0.678
0.02169
Twins
4545
3193
3094.64
0.703
0.681
0.02164
Angels
4383
3070
2987.00
0.700
0.681
0.01894
Giants
4520
3152
3070.46
0.697
0.679
0.01804
Orioles
4377
3032
2953.85
0.693
0.675
0.01786
Red Sox
4575
3127
3053.44
0.683
0.667
0.01608
Pirates
4467
3095
3023.38
0.693
0.677
0.01603
Mariners
4546
3184
3111.16
0.700
0.684
0.01602
Devil Rays
4560
3112
3044.55
0.682
0.668
0.01479
Diamondbacks
4571
3118
3062.57
0.682
0.670
0.01213
Brewers
4252
2960
2908.65
0.696
0.684
0.01208
Tigers
4527
3152
3099.48
0.696
0.685
0.01160
Cubs
4117
2871
2825.48
0.697
0.686
0.01106
Rangers
4697
3200
3152.10
0.681
0.671
0.01020
Dodgers
4392
3073
3031.40
0.700
0.690
0.00947
Rockies
4537
3043
3008.62
0.671
0.663
0.00758
Mets
4424
3094
3061.94
0.699
0.692
0.00725
Padres
4423
3051
3043.61
0.690
0.688
0.00167
Yankees
4483
3087
3085.86
0.689
0.688
0.00025
Marlins
4367
2965
2965.36
0.679
0.679
-0.00008
Nationals
4538
3161
3167.85
0.697
0.698
-0.00151
Royals
4611
3068
3099.55
0.665
0.672
-0.00684
Reds
4650
3148
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?
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
Team
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Astros
4204
2963
2854.17
0.705
0.679
0.02589
Indians
4385
3108
2995.26
0.709
0.683
0.02571
Phillies
4211
2962
2853.80
0.703
0.678
0.02570
Athletics
4286
3064
2954.86
0.715
0.689
0.02546
White Sox
4457
3175
3066.86
0.712
0.688
0.02426
Cardinals
4414
3101
3007.96
0.703
0.681
0.02108
Blue Jays
4511
3156
3063.16
0.700
0.679
0.02058
Braves
4559
3162
3073.91
0.694
0.674
0.01932
Twins
4545
3193
3107.42
0.703
0.684
0.01883
Angels
4383
3070
2998.12
0.700
0.684
0.01640
Giants
4520
3152
3080.03
0.697
0.681
0.01592
Orioles
4377
3032
2964.67
0.693
0.677
0.01538
Pirates
4467
3095
3032.44
0.693
0.679
0.01400
Diamondbacks
4571
3118
3059.45
0.682
0.669
0.01281
Red Sox
4575
3127
3068.95
0.683
0.671
0.01269
Devil Rays
4560
3112
3054.72
0.682
0.670
0.01256
Cubs
4117
2871
2819.97
0.697
0.685
0.01239
Mariners
4546
3184
3128.12
0.700
0.688
0.01229
Tigers
4527
3152
3097.51
0.696
0.684
0.01204
Brewers
4252
2960
2916.77
0.696
0.686
0.01017
Rangers
4697
3200
3158.10
0.681
0.672
0.00892
Dodgers
4392
3073
3036.02
0.700
0.691
0.00842
Mets
4424
3094
3058.20
0.699
0.691
0.00809
Rockies
4537
3043
3013.43
0.671
0.664
0.00652
Padres
4423
3051
3047.08
0.690
0.689
0.00089
Marlins
4367
2965
2965.42
0.679
0.679
-0.00010
Yankees
4483
3087
3092.01
0.689
0.690
-0.00112
Nationals
4538
3161
3166.79
0.697
0.698
-0.00128
Royals
4611
3068
3099.97
0.665
0.672
-0.00693
Reds
4650
3148
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
Team
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Phillies
4211
2962
2812.44
0.703
0.668
0.03552
Athletics
4286
3064
2921.09
0.715
0.682
0.03334
Indians
4385
3108
2970.70
0.709
0.677
0.03131
Astros
4204
2963
2835.95
0.705
0.675
0.03022
Braves
4559
3162
3043.69
0.694
0.668
0.02595
White Sox
4457
3175
3061.04
0.712
0.687
0.02557
Cardinals
4414
3101
2992.97
0.703
0.678
0.02447
Blue Jays
4511
3156
3066.66
0.700
0.680
0.01981
Giants
4520
3152
3062.55
0.697
0.678
0.01979
Dodgers
4392
3073
2992.05
0.700
0.681
0.01843
Cubs
4117
2871
2799.86
0.697
0.680
0.01728
Nationals
4538
3161
3082.57
0.697
0.679
0.01728
Orioles
4377
3032
2960.89
0.693
0.676
0.01625
Diamondbacks
4571
3118
3051.28
0.682
0.668
0.01460
Angels
4383
3070
3007.42
0.700
0.686
0.01428
Twins
4545
3193
3130.04
0.703
0.689
0.01385
Pirates
4467
3095
3034.07
0.693
0.679
0.01364
Mariners
4546
3184
3124.61
0.700
0.687
0.01306
Tigers
4527
3152
3101.99
0.696
0.685
0.01105
Brewers
4252
2960
2913.06
0.696
0.685
0.01104
Mets
4424
3094
3051.37
0.699
0.690
0.00964
Devil Rays
4560
3112
3068.61
0.682
0.673
0.00951
Rangers
4697
3200
3165.60
0.681
0.674
0.00732
Red Sox
4575
3127
3104.20
0.683
0.679
0.00498
Padres
4423
3051
3039.75
0.690
0.687
0.00254
Rockies
4537
3043
3035.26
0.671
0.669
0.00171
Marlins
4367
2965
2958.27
0.679
0.677
0.00154
Reds
4650
3148
3155.28
0.677
0.679
-0.00157
Yankees
4483
3087
3135.64
0.689
0.699
-0.01085
Royals
4611
3068
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
Team
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Phillies
4211
2962
2833.12
0.703
0.673
0.03061
Athletics
4286
3064
2937.98
0.715
0.685
0.02940
Indians
4385
3108
2982.98
0.709
0.680
0.02851
Astros
4204
2963
2845.06
0.705
0.677
0.02805
White Sox
4457
3175
3063.95
0.712
0.687
0.02492
Cardinals
4414
3101
3000.46
0.703
0.680
0.02278
Braves
4559
3162
3058.80
0.694
0.671
0.02264
Blue Jays
4511
3156
3064.91
0.700
0.679
0.02019
Giants
4520
3152
3071.29
0.697
0.679
0.01786
Twins
4545
3193
3118.73
0.703
0.686
0.01634
Orioles
4377
3032
2962.78
0.693
0.677
0.01581
Angels
4383
3070
3002.77
0.700
0.685
0.01534
Cubs
4117
2871
2809.92
0.697
0.683
0.01484
Pirates
4467
3095
3033.25
0.693
0.679
0.01382
Diamondbacks
4571
3118
3055.36
0.682
0.668
0.01370
Dodgers
4392
3073
3014.04
0.700
0.686
0.01343
Mariners
4546
3184
3126.36
0.700
0.688
0.01268
Tigers
4527
3152
3099.75
0.696
0.685
0.01154
Devil Rays
4560
3112
3061.67
0.682
0.671
0.01104
Brewers
4252
2960
2914.92
0.696
0.686
0.01060
Mets
4424
3094
3054.78
0.699
0.691
0.00886
Red Sox
4575
3127
3086.58
0.683
0.675
0.00884
Rangers
4697
3200
3161.85
0.681
0.673
0.00812
Nationals
4538
3161
3124.68
0.697
0.689
0.00800
Rockies
4537
3043
3024.35
0.671
0.667
0.00411
Padres
4423
3051
3043.42
0.690
0.688
0.00171
Marlins
4367
2965
2961.85
0.679
0.678
0.00072
Reds
4650
3148
3169.14
0.677
0.682
-0.00455
Yankees
4483
3087
3113.83
0.689
0.695
-0.00598
Royals
4611
3068
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.
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):
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.
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.
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.
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
Team
InPlay
Actual Outs
Predicted Outs
DER
Predicted DER
Difference
Angels
4360
2990
3080.32
0.686
0.706
-0.02072
Royals
4643
3127
3211.12
0.673
0.692
-0.01812
Yankees
4488
3081
3158.71
0.686
0.704
-0.01732
Tigers
4524
3091
3169.20
0.683
0.701
-0.01729
Orioles
4458
3058
3125.39
0.686
0.701
-0.01512
Pirates
4326
2959
3023.71
0.684
0.699
-0.01496
Reds
4590
3155
3220.04
0.687
0.702
-0.01417
Twins
4491
3083
3140.70
0.686
0.699
-0.01285
Mariners
4490
3140
3183.09
0.699
0.709
-0.00960
Brewers
4416
3049
3086.30
0.690
0.699
-0.00845
Rockies
4620
3138
3174.15
0.679
0.687
-0.00782
Expos
4421
3067
3100.04
0.694
0.701
-0.00747
Astros
4151
2843
2866.27
0.685
0.691
-0.00561
Indians
4490
3069
3093.60
0.684
0.689
-0.00548
Rangers
4551
3124
3148.34
0.686
0.692
-0.00535
Athletics
4499
3127
3148.70
0.695
0.700
-0.00482
Diamondbacks
4320
2939
2955.30
0.680
0.684
-0.00377
Braves
4489
3088
3102.32
0.688
0.691
-0.00319
Blue Jays
4478
3097
3108.56
0.692
0.694
-0.00258
Padres
4393
3040
3050.63
0.692
0.694
-0.00242
Giants
4541
3148
3157.22
0.693
0.695
-0.00203
Devil Rays
4471
3127
3135.05
0.699
0.701
-0.00180
Marlins
4263
2991
2995.97
0.702
0.703
-0.00117
Mets
4557
3166
3170.73
0.695
0.696
-0.00104
Phillies
4452
3127
3129.24
0.702
0.703
-0.00050
Dodgers
4333
3089
3089.39
0.713
0.713
-0.00009
White Sox
4375
3038
3028.95
0.694
0.692
0.00207
Cubs
4124
2873
2861.76
0.697
0.694
0.00273
Red Sox
4391
3041
3028.85
0.693
0.690
0.00277
Cardinals
4387
3112
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:
Direction of hit (a vector).
The type of hit (Fly, ground, line drive, bunt).
How hard the ball was hit (slow, medium, hard).
The park.
The handedness of the pitcher.
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.