Jim Albert’s Baseball Blog

January 20, 2016

(This article was first published on R – Statistical Modeling, Causal Inference, and Social Science, and kindly contributed to R-bloggers)

Jim Albert has a baseball blog:

I sent a link internally to people I knew were into baseball, to which Andrew replied, “I agree that it’s cool that he doesn’t just talk, he has code.” (No kidding—the latest post as of writing this was on an R package to compute value above replacement players (VAR).)

You may know me from…

You may know Jim Albert from the “Albert and Chib” approach to Gibbs sampling for probit regression. I first learned about him through his fantastic book, Curve Ball, which I recommend at every opportunity (the physical book’s inexpensive and I’m stunned Springer’s selling an inexpensive PDF with no DRM—no reason not to get it). It’s not only very insightful about baseball, it’s a wonderful introduction to statistics via simulation. It starts out analyzing All-Star Baseball, a game based on spinners. This book went a long way in helping me understand statistics, but at a level I could share with friends and family, not just math geeks. It then took Gelman and Hill’s regression book and understanding the BUGS examples until I could make sense of BDA.

In the same vein, Albert has a solo book aimed at undergraduates or their professors—Teaching Statistics Using Baseball. And I just saw from his home page, a book on Analyzing Baseball Data with R.

Little Professor Baseball

I first wrote to Jim Albert way back before I was working with Andrew on Stan. I’d just read Curve Ball and had just created my very simple baseball simulation, Little Professor Baseball. I was very pleased with how I’d made it simple like All-Star Baseball, but included pitching and batting, like Strat-o-Matic Baseball (a more “serious” baseball simulation game). My only contribution was figuring out how to allow both players (offense/defnese) to roll dice, with the resulting being read from the card of the highest roller. I had to solve a quadratic equation to adjust for the bias of taking the highest roller and further adjusting to deal with the Strat-o-Matic-style correction for only reading the results off a player’s card half the time (here’s the derivations with a statistical discussion on getting the expectations right). I analyze the 1970 Major League Baseball season (same one used by Efron and Morris, by the way). I even name-drop Andrew’s hero, Earl Weaver, in the writeup.

The post Jim Albert’s Baseball Blog appeared first on Statistical Modeling, Causal Inference, and Social Science.

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