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Mets Run Support by Starting Pitcher
*August 1, 2014*

*Posted by tomflesher in Baseball.*

Tags: Jacob deGrom, Mets, pitching, run support, Zack Wheeler

2 comments

Tags: Jacob deGrom, Mets, pitching, run support, Zack Wheeler

2 comments

Yesterday’s post discussed distributional wins and losses based on the Mets’ inconsistent bunching of runs together. Since the boys didn’t play last night, I had a pretty stable dataset to work with, and the opportunity to crunch some numbers to see if the hypothesis that we’re working with is true. In addition, I took a look at each of our current starting rotation’s run support numbers and found some surprising things.

First of all, no pitcher had a statistically significant run support number than any other. Although Dillon Gee‘s run support is .77 lower than the average pitcher, for example, the p-value is .44, meaning the probablity that that’s statistically different from 0 is just about 56%. Jacob deGrom has a similar number – .796 runs below the average, but a .42 p-value. The only pitcher with a positive effect on run support is Bartolo Colon, but his p-value is a whopping .72, meaning it’s more likely than not that his number is a statistical artifact.

The runs allowed are a bit more stable – deGrom allows 1.18 runs fewer than average with a .2 p-value – but Gee, Jonathon Niese, Colon, and Zack Wheeler all have statistically 0 effect on runs allowed. Their ps are, respectively, .91, .84, .64, and .79. Basically, this means that an effect would have to be really big to show up in such a small sample size, not even all 108 games are covered in the sample.

Another way of tracking pitcher run support is to track team wins and losses in the games started by those pitchers and compare it to the team’s Pythagorean expectation in those games. This is a bit more revealing; for example, the Mets are 6-8 in starts by deGrom, but would have a Pythagorean expectation of about .568, or about 8-6, in those games. Wheeler also ends up with a Pythagorean expectation better than his record, predicting the Mets would have won 11 rather than 10 of his 22 games. The other pitchers are more or less in line with their expectations, although, like Zack, the pitchers don’t always get credit for the wins they pitched in.

Behind the cut is the table of regression results for a linear model with a dummy variable for each pitcher’s starts, plus a totally useless Away game dummy to look for home field advantage. (Surprise: There is none for the Mets, but all pitchers do allow roughly .74 more runs on the road than at home.)