My once a year post on baseball
Dubner worries a lot about whether people comment on our posts, which is definitely evidence that he doesn’t have enough important things to worry about.
Every time I have made a post about baseball, it has unleashed a torrent of comments. So as an Easter gift to Dubner, here is my annual baseball post.
I’ve been working for the last year with a fantastic young man named Ken Kovash. He is unusual in that he got his MBA from the University of Chicago, but while doing so he also took a bunch of economics classes. After getting his MBA, he asked if he could come work with me, and although I told him he was crazy to pass up salaries four times greater than I could offer him, he decided to stick around and work with me anyway. We’ve spent a good part of the last year working on some baseball related projects, as well as some more traditional applications.
While our joint work on baseball is not yet ready for prime time (although baseball genius Nate Silver of Baseball Prospectus makes mention of one piece of it here), Ken has been doing some baseball-related analysis on his own that offers a substantial challenge to the conventional wisdom among sabermetricians. The particular issue Ken has been examining is “protection.” In other words, does it help me as a batter if the person in the on deck circle is really good? The existing research, by excellent folks like J.C. Bradbury who has a nice blog (and has written a book on the economics of baseball which I have not yet read but have heard great things about) argues that protection is a myth. When you look at the outcomes of at bats, you don’t see measurable effects of having a good batter behind you. In Ken’s research, which you can read here, he makes a nice economics-style argument. There is a lot of randomness in how an at bat turns out which makes it hard to detect an impact of protection even if it is really there. There is a lot less noise, however, in some of the inputs to an at bat, like whether the pitcher throws strikes. By focusing on how the pitcher pitches to the batter, rather than how the at bat actually ends, Ken is able to cut through the noise to find strong evidence that it seems to matter who bats behind you.
After a year, Ken is pretty tired of me. So let me make a pitch on his behalf. Here is a guy with great work experience prior to his MBA, an MBA from the U of C, and a set of data skills honed through a year of working side-by-side with some top economists. I think there are very few folks anywhere who have the combination of skills that Ken has. As much as I would like him to stick around, somebody should steal him away from me! Ideally, it would be a major league baseball team that is looking for a guy who lives and breathes baseball, knows the existing sabermetric literature cold, but also brings a business perspective that goes far beyond his ability to manipulate statistics. Failing that, I think just about any firm would want to have Ken on their payroll. If you want more details, you can contact him directly at kkovash[at]gmail[dot]com.