Monday, July 11, 2011

Doing Some WERC During the Break

The limitations of ERA are well known in the blogosphere.  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.

(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.

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 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.  The runs created model is not used so much now and linear weights are better, so I wanted to find a similar statistic based on linear weights.

J.T. Jordan at Hardball Times got us part of the way there.  He used the Baseball -Reference data on batting against pitchers to calculate wOBA against (or wOBAA).  wOBAA for pitchers is calculated the same as wOBA for hitters.  The MLB leaders for 2011 are shown in Table 1 below.

Table 1: AL wOBAA Leaders in 2011

Player
Team
IP
wOBAA
Jered Weaver
LAA
140.1
.238
Justin Verlander
DET
151.0
.242
Josh Beckett
BOS
111.0
.251
Dan Haren
LAA
134.1
.258
Michael Pineda
SEA
113.0
.268
James Shields
TBR
142.2
.268
Alexi Ogando
TEX
104.2
.270
Philip Humber
CHW
107.1
.272
CC Sabathia*
NYY
145.2
.276
Jason Vargas*
SEA
121.1
.283


One good feature of wOBA is that it can easily be translated into runs above average (wRAA or RAA).  To calculate RAA, subtract league average wOBAA from a player's wOBAA, divide by 1.19 (that number changes from year to year but is usually between 1.15 and 1.25) and multiply by plate appearances.  The 2011 leaders are listed in Table 2.  Tigers ace Justin Verlander tops the list with 37 RAA meaning that he has saved the Tigers an estimated 37 runs compared to an average pitcher.   Jered Weaver of the Angels is a close second with 36 RAA. 



Table 2: AL RAA Leaders in 2011

Player
Team
IP
RAA
Justin Verlander
DET
151.0
37
Jered Weaver
LAA
140.1
36
Dan Haren
LAA
134.1
27
Josh Beckett
BOS
111.0
24
James Shields
TBR
142.2
23
CC Sabathia*
NYY
145.2
21
Michael Pineda
SEA
113.0
19
Alexi Ogando
TEX
104.2
17
Philip Humber
CHW
107.1
17
Felix Hernandez
SEA
144.0
16


So, we are almost there.  All we need to do is turn RAA into an ERA.  Here are the steps:

(1) Calculate MLB average runs scored per nine innings (4.2 in 2011)

(2) Subtract a pitcher' runs above average per nine innings pitched from the league average:
4.2- 9 x RAA/IP

(3) About 93% of runs are earned, so multiply the result in step (2) by .93.  The final result is a linear weights component ERA.  I'll call it WERC.

Table 3 shows that Weaver leads the league with a 1.87 WERC in 2011.  


Table 3: AL WERC Leaders in 2011


Player
Team
IP
WERC
Jered Weaver
LAA
140.1
1.87
Justin Verlander
DET
151.0
2.01
Josh Beckett
BOS
111.0
2.23
Dan Haren
LAA
134.1
2.39
Michael Pineda
SEA
113.0
2.68
Alexi Ogando
TEX
104.2
2.73
James Shields
TBR
142.2
2.75
Philip Humber
CHW
107.1
2.79
CC Sabathia*
NYY
145.2
2.88
Jason Vargas*
SEA
121.1
3.12

 WERC is useful because it gives us an intermediate step between ERA and FIP.  For example, Rangers right hander Alexi Ogando has a fairly large discrepancy between his ERA (2.92) and FIP (3.51).  His WERC is 2.73 which is closer to his ERA.  This suggests that a large amount of the difference between Ogando's FIP and ERA is due to batted balls in play rather than sequencing.  We could have figured this out by examining other numbers such as BABIP and LOB%, but it's more convenient to compare three stats on the ERA scale. 

Another example is Athletics fire baller Gio Gonzalez.  Gonzalez's ERA (2.47) was lower than his FIP (3.57).  However, his WERC was 3.62 which hints that the discrepancy was more due to sequencing than hits allowed.

I will apply these statistics to Tigers pitchers in a later post.

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