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Transaction Oracle — A Timely Look at Transactions as They Happen Monday, July 13, 2009Barry Zito, Consistency, and the 2009 GiantsIn a recent column, Tim Kawakami suggested that Barry Zito was worse than his statistics suggest due to his inconsistency, that his higher percentage of “dud games” hurt the San Francisco Giants winloss record.
So, is it true? It’s a quick, simple question, so I went and did a quick look at the distribution of Giant runscoring and Zito runscoring to try to ascertain if Zito’s really hurting the Giants aboveandbeyond having mediocre statistics. For the purposes of this exercise, I’m considering Zito’s ERA as being 5.00 instead of 5.01.
The first thing to do is make a quickie model of the Giant offense and Zito’s run allowance. I don’t want to consider past ability or any concept of “true ability” here, which makes making a model of both these things considerably more problematic. However, you also run into the problem that we have very small sample sizes that can also cause problems in our models. For example, the Giants have scored 4 or 6 runs on 10 occasions and 5 runs on 8 occasions, so we can’t simply choose a random Giant run/9 outing and a random Zito run/9 outing match them and call it a day.
To make a long story short, we want to extrapolate the likeliest model of the 2009 Giant offense and 2009 Zito outings. Using STATISTICA, the math nerd’s version of a sex shop, we can extrapolate a bell curve with the skewness and kurtosis that we see from the data, and sample the data so we can easily see the conclusions.
After doing this, I simulated 50,000 Giant offense outings and 50,000 Zito defense outings. So, how did theoretical Zito do with the theoretical Giants?
As one can see, a pitcher with an ERA of 5 and Zito’s distribution of runs allowed theoretically allows a team that scores like the 2009 Giants to win more often than the generic distribution that the James socalled Pythagorean Theorem predicts. How about some other distributions (all distributions below had a simulated ERA within 1/50th of a run of the ideal 5.00).
 Mr. Consistent, a pitcher that allows exactly 5 runs every 9 innings.
So, how does this Rogue’s Gallery fare?
So, why does this happen?
In essence, we’re looking at a phenomenon caused by the hard floor of zero. A team cannot score or allow fewer than zero runs, and given the patterns in scoring in baseball, in which teams frequently score and allow 5 more runs a game more than average in a single outing but cannot score or allow more than 5 runs less than their average, you run into the issue in which the depreciating values of high numbers of runs cannot be counterbalanced by the depreciating values of low numbers of runs.
As an extreme example, imagine if Barry Zito threw 2 complete games this season and allowed 30 and 29 runs in those games respectively. Those games were essentially 100% losses for the Giants and the chances don’t improve all that much for the Giants if he cut those runs by a third. But outside of those 2 losses, every other game Barry threw 5 innings in would be a win for the Giants and he’d be one of the most valuable pitchers in baseball.
Now, depending on the quality of the team and the quality of the pitcher, the breakeven points for value shift considerably. 
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1. Zoppity Zoop Posted: July 13, 2009 at 07:00 PM (#3252039) MWE
He clearly doesn't know how to win.
It isn't?
I am pretty sure when I die and go to H.E. double hockey sticks, Tommy Lasorda will be there waiting for me in the "Clockwork Orange" screening room with the video.
This would seem to bode well for the Rockies, who appear to have two such jekyll/hyde types in Jason Marquis and Jorge de la Rosa. And maybe a 3rd in Jason Hammel, who is Hyde in home games (7.62 ERA) and Jekyll in road games (1.97).
Does this work for hitters, too? I wonder if the feast or famine type player tends to put together more WPA than a more consistent one.
Re #2, I could see how aboveaverage hitters are more valuable when they're consistent, but with starting pitchers, is it ever true? I think if you defined "likely" starts more realistically, you'd find that, no matter the true talent level, you'd rather have Jekyll and Hyde. How about an assessment based on the win probabilities of these nine outcomes:
ER = 9  IP and IP = n, where n = 0 through 9.
A 5.00 ERA Sam Stochastinator would actually be: Nine 9.0 IP, 0 ER starts and Five 0.0 IP, 9 ER starts. Based on your numbers, that'd work out to a .640ish winning percentage. A 4.00 ERA Sam S. would generate a .690ish WP with nine shutouts and four 0.0 IP, 9 ER duds. Does it get much better than that?
SF IP 781, Zito IP 106, Zito RA 59
Extrapolating to SF IP, RA becomes 435
SF RS 368
Basic Pythag I get .417
Yes. Mainstream fans and sportswriters. How many times do we hear sportswriters whining about a pitcher being "inconsistent," without realizing that being inconsistent is preferable to being consistent?
I actually liked the comment in post 2. It's a lot closer to the way that I think it should be portrayed.
2. Mike Emeigh Posted: July 13, 2009 at 03:05 PM (#3252052)
This is a fairly wellknown phenomenon, with a fairly simple conclusion: Teams prefer consistency from betterthanaverage performers and inconsistency from worsethanaverage performers.
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