More Stats Supporting Justin Verlander for Cy Young

Most readers of this blog are aware of the limitations of ERA in evaluating pitcher performance.  Two of the biggest issues are:

(1) ERA gives pitchers full credit/blame for results of batted balls in play despite the fact that they share that responsibility with fielders.  For example, a pitcher with a strong defense behind him will tend to give up fewer hits (and thus fewer runs) than if he had a poor defense behind him and this will deflate his ERA.  .

(2) ERA gives pitchers full responsibility for sequencing or timing of events, that is, it assumes that they can control when they give up hits and walks. For example, if a pitcher pitches extraordinarily well with runners in scoring position in a given year, he will have a lower ERA than if he had a typical year in those situations. Additionally, a pitcher who tends to bunch base runners together in single innings will have a higher ERA than if he had a typical year distributing base runners more evenly.

In reality, pitchers have limited control over both the number of batted balls that drop for hits and sequencing of events.  Thus, Defense Independent Pitching Statistics (DIPS) such as FIP, xFIP, tERA and SIERA have been developed to remove some of the noise of ERA.  DIPS are based on things that pitchers do control for the most part - walks, hit batsmen, strikeouts, home runs and types of batted balls (ground balls , fly balls, line drives, pop flies).

Because they are based on things that pitchers essentially control, the DIPS metrics are said to be better measures of true talent than ERA.  As a result, they are also better than ERA at predicting future performance. However, they only measure a portion of a pitcher's talent and should be used as complements to ERA rather than as replacements.

More and more fans are becoming comfortable with the DIPS theory, but it is still a really difficult concept to get across to the mainstream.  If you ever try to explain FIP or any other DIPS statistic to the uninitiated, you will probably find that they are skeptical of a pitching statistic which ignores hits.  They are not likely to buy into it even if they realize the limitations of ERA. 

So, rather than asking fans to take the big leap from ERA to FIP, why not meet them half way?   Instead of removing hit prevention and sequencing in one step, it might be better to remove one factor at a time.  Bill James did that with his Component ERA (ERC).  Applying the runs created methodology to pitchers, he determined what a pitcher's ERA should have been based on walks, hit batsmen,  strikeouts, homers AND hits allowed.  I'm going to look at some similar statistics here based on more modern measures such as linear weights and Base Runs. 

We often use Weighted On-Base Average (wOBA) to measure overall hitting performance and it can also be used for pitchers.  The American League wOBA Against (wOBAA) leaders are shown in Table 1 below.  Tigers ace Justin Verlander led the league with a .268 wOBAA in 2012.  Teammates Doug Fister (.302) and Max Scherzer (.316) also finished among the top twenty starters.

Table 1: AL Weighted On Base Average Against Leaders, 2012

Player	Team	G	IP	wOBAA
Justin Verlander	DET	33	238.1	.268
Jered Weaver	LAA	30	188.2	.270
David Price*	TBR	31	211.0	.272
Felix Hernandez	SEA	33	232.0	.284
Chris Sale*	CHW	30	192.0	.291
CC Sabathia*	NYY	28	200.0	.292
Jake Peavy	CHW	32	219.0	.295
Jarrod Parker	OAK	29	181.1	.297
James Shields	TBR	33	227.2	.299
Yu Darvish	TEX	29	191.1	.299
Doug Fister	DET	26	161.2	.302
Hiroki Kuroda	NYY	33	219.2	.307
C.J. Wilson*	LAA	34	202.1	.308
Jason Vargas*	SEA	33	217.1	.310
Jeremy Hellickson	TBR	31	177.0	.310
Matt Moore*	TBR	31	177.1	.314
Matt Harrison*	TEX	32	213.1	.315
Max Scherzer	DET	32	187.2	.316
Wei-Yin Chen*	BAL	32	192.2	.317
Scott Diamond*	MIN	27	173.0	.317

Data Source: Baseball-Reference.com

It's always good to convert to runs allowed when trying to evaluate pitchers, so I'll do that next.  The Base Runs measure was created by David Smythe in the early 1990s.  It is based on the idea that we can estimate team runs scored if we know the number of base runners, total bases, home runs and the typical score rate (the score rate is the percentage of base runners that score on average.  Base Runs also works well for individual pitchers.  The complete formula can be found here.

Justin Verlander had 78 Base Runs Against in 238 1/3 innings this year.  This means that he should have allowed an estimated 78 runs based on the number of base runners, total bases and home runs he allowed.  He allowed 81 actual runs, so runs scored against him at a slightly higher rate than you would expect (although it was pretty close).  The small difference could possibly be due to bad defense, unfortunate timing or just bad luck on locations of batted balls.

Verlander had 40 Base Runs Above Average (RAA) which means that he saved the Tigers an estimated 40 runs compared to the average pitcher in the same number of innings. This was the best total in the AL. Note that this number is adjusted for home ballpark (using five-year ballpark factors developed by Brandon Heipp of Walk Like a Sabermetrician).  


Table 2: AL Pitching Runs Above Average Leaders

Player	Team	G	IP	Base Runs	RAA
Justin Verlander	DET	33	238.1	78	40
David Price*	TBR	31	211.0	67	35
Felix Hernandez	SEA	33	232.0	79	33
Jered Weaver	LAA	30	188.2	62	31
Jake Peavy	CHW	32	219.0	86	23
Chris Sale*	CHW	30	192.0	73	22
CC Sabathia*	NYY	28	200.0	81	18
Yu Darvish	TEX	29	191.1	77	18
James Shields	TBR	33	227.2	94	17
Jarrod Parker	OAK	29	181.1	72	17
Hiroki Kuroda	NYY	33	219.2	93	15
Doug Fister	DET	26	161.2	68	12
C.J. Wilson*	LAA	34	202.1	88	11
Matt Harrison*	TEX	32	213.1	96	9
Jason Vargas*	SEA	33	217.1	99	8
Jeremy Hellickson	TBR	31	177.0	81	6
Matt Moore*	TBR	31	177.1	81	6
Scott Diamond*	MIN	27	173.0	80	6
Max Scherzer	DET	32	187.2	88	5
Tommy Milone*	OAK	31	190.0	91	3

Data Source: Baseball-Reference.com

Finally, Table 3 shows that Verlander allowed an AL best 2.91 Base Runs per nine innings (BsR9).  Again, this is adjusted for ballpark. The BsR9 statistic is not a novel idea as Mr. Heipp has been using Base Runs in this way for a while.  About 93% of runs are earned, so you could  multiply this result by .93. to put it on the same scale as ERA if you prefer that.  Verlander's BsR9 was slightly lower than his actual 3.06 runs allowed per nine innings (RA) which indicates that he may have pitched a little better than his RA (or ERA) suggested. 

Table 3: AL Base Runs Per Nine Innings Leaders, 2012

Player	Team	G	IP	BsR9
Justin Verlander	DET	33	238.1	2.91
Jered Weaver	LAA	30	188.2	2.97
David Price*	TBR	31	211.0	2.98
Felix Hernandez	SEA	33	232.0	3.27
Chris Sale*	CHW	30	192.0	3.28
Jake Peavy	CHW	32	219.0	3.37
Yu Darvish	TEX	29	191.1	3.41
CC Sabathia*	NYY	28	200.0	3.55
Jarrod Parker	OAK	29	181.1	3.64
Doug Fister	DET	26	161.2	3.70
Hiroki Kuroda	NYY	33	219.2	3.72
Matt Harrison*	TEX	32	213.1	3.81
James Shields	TBR	33	227.2	3.88
C.J. Wilson*	LAA	34	202.1	3.92
Max Scherzer	DET	32	187.2	4.13
Scott Diamond*	MIN	27	173.0	4.18
Derek Holland*	TEX	29	175.1	4.18
Josh Beckett	BOS-LAD	28	170.1	4.20
Clay Buchholz	BOS	29	189.1	4.22
Wei-Yin Chen*	BAL	32	192.2	4.25

  Data Source: Baseball-Reference.com

That Verlander led in all three of these sequence-independent pitching statistics is more fuel for the argument that he deserves his second consecutive Cy Young award.