Baseball Musings: Probabilistic Model of Range, 2008, Defense Behind Pitchers

November 23, 2008

Probabilistic Model of Range, 2008, Defense Behind Pitchers

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.

Posted by David Pinto at 10:38 AM | Probabilistic Model of Range | TrackBack (0)

Comments

It's weird that Wang, who is a ground ball pitcher is rated so high and Pettitte, who isn't so much a ground ball pitcher, is rated so low. They have the same defense behind them. How can there be such a huge disparity between the two? Does this system take into account the fact that some pitchers just get a much higher percentage of ground balls?

Posted by: sabernar at November 23, 2008 01:08 PM

But Carmona, another ground ball pitcher, is dead last, and Derek Lowe is right in the middle. It's interesting that the Indians' defense did better than either Milwaukee's or Boston's behind Sabathia and Paul Byrd, while failing Carmona. Is it just the difference between outfield and infield defense? Yet the Indians' infielders ranked in the middle or higher in terms of range, except for first base. Hmmm.

Posted by: JJ at November 23, 2008 02:33 PM

PMR does take into account the difference between grounders and flies. But there's always been something weird about Wang; his ERA is much better than his WHIP or K/9 predict. Maybe this is just another facet: Wang grounders are easier to field?

Posted by: James at November 23, 2008 02:56 PM

How much does PMR take into account fielding independent factors like BBs and Ks? Watching the Yanks, Pettitte was getting hit hard. By contrast, Wang induces weak hit balls. It seems that factor is reliant on the pitcher and the best the defense can do is respond. Similarly, watching Wakefield and Matsuzaka when I have they both do a very good job of keeping hitters off-balance.

The more I think about this stats, the less I think it's about team defense. It seems, rather to represent, how well a *pitcher* pitches to that defense.

Posted by: Rob at November 24, 2008 06:19 AM

The more I think about this stats, the less I think it's about team defense. It seems, rather to represent, how well a *pitcher* pitches to that defense.

Posted by: Rob at November 24, 2008 06:19 AM

Walks and strike outs have nothing to do with this stat. It's just fieldable balls in play.

Posted by: David Pinto at November 24, 2008 08:57 AM

David:

Did you run this data for prior years? It would be interesting to see how well this tracks for pitchers from year to year.

Posted by: Mike Emeigh at November 24, 2008 10:10 AM

Mike,

Yes, look at the Probabilistic Model of Range category.

David

Posted by: David Pinto at November 24, 2008 10:36 AM

David:

Thanks. I didn't see 2006 - if you have those handy, could you post them or send them to me? Also, last year you posted Wang's breakdown by ball-in-play type; it might be instructive to do that again.

There is some year-to-year correlation between the predicted DER for the pitchers - CC Sabathia and Wang have both tended to be way down on predicted DER, for example, and Marcum and Maine pretty high - which I would expect, since predicted DER should be a function of the types of balls in play allowed (GB being harder to field, normally, than FB). There are also some differences (notably Kevin Correia). Looking at how these numbers trend - and year-to-year changes in them - might tell us something about the relationship between pitchers and the defense behind them.

Posted by: Mike Emeigh at November 24, 2008 02:00 PM

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