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THE BOOK--Playing The Percentages In Baseball

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Wednesday, March 17, 2010

How much year-to-year variability do we find in ERA for a star pitcher?

This is what I did.  I started with the list of the 12 best starting pitchers born between 1962 (Clemens) and 1971 (Pedro).  I took out Smoltz because of his relief stint in-between his starting stints, leaving me with 11.

For each of those pitchers, I figured out his first quality year, which I simply defined as having allowed runs at most 80% of the league average while facing at least 500 batters.  I then looked for his last quality year, and set his “last season” as one greater than his last quality year.

I get this chart:


minYear    minAge    maxYear    maxAge    playerID
1990    23    1997    30    appieke01
1995    30    2003    38    brownke01
1986    24    2005    43    clemero02
1988    25    1999    36    coneda01
1991    25    2002    36    glavito02
1993    30    2004    41    johnsra05
1992    26    2002    36    maddugr01
1994    23    2005    34    martipe02
1992    24    2003    35    mussimi01
1985    21    1994    30    saberbr01
1992    26    2004    38    schilcu01

There were 141 pitching seasons in there, from Pedro’s 34% of league average 2000 season, to David Cone’s 135% also of the 2000 season. Basically what I have are 141 seasons where we had reason to believe that our pitchers were at their peak.  (I did the “plus 1” on the last great year because entering that year, we still thought he was great.)

The average of these great seasons from these great starters was 73% of the league average.  So, in a league where the average ERA is 4.30, these eleven guys put up, on average, for those 141 seasons, an ERA of around 3.14.

What was the RANGE of their posted ERA?  With a mean of 73% of league average, one standard deviation was 16% (roughly 0.69 runs).  The 10th percentile was 54% and the 90th percentile was 96%.  The 50th percentile was the same as the mean.  So, 80% of the time, even if you KNOW you’ve got yourself a 73% pitcher, he’s going to post an ERA that is 54% to 96% for whatever reason (good luck, bad luck, temporary injuries, temporary loss of talent, etc).  That is a range of 42% (96 minus 54) of the league average ERA or roughly 1.80 runs per 9 IP!  That is, 80% of the time, a pitcher will post an ERA +/- 1 run from what his talent level is.

This is for pitchers who we “knew” were great, and we only started the “great” clock once they actually performed at a great level.

So, what to expect of Tim Lincecum and his forecasted 73% RA according to Chone (a match to our group of 11 stars)?  What is the chance he will post an ERA of 2.50 or less (58% of league average)?  That happened 20 times, or 14% of the time.  And if he posts an ERA that is 92% or worse of the league average, that also has happened 20 times with our star pitchers.  That’s an ERA of 3.96.  And if Lincecum is actually a bit better than Chone thinks (Marcel says 67%).  Well, that 3.96 is going to come down a bit.

And, what did the Fans say in a recent poll on my site?  3.88.

Give yourselves a fantastic pat on the bat boys.  You figured out in a blink of an eye, what took me thirty minutes to code.

(12) Comments • 2010/03/18 • SabermetricsPitchersTalent_Distribution
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