Sunday, November 11, 2012

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. 

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