Bayesball: A Bayesian hierarchical model for evaluating fielding in major league baseball
arXiv:0802.4317 · doi:10.1214/08-AOAS228
Abstract
The use of statistical modeling in baseball has received substantial attention recently in both the media and academic community. We focus on a relatively under-explored topic: the use of statistical models for the analysis of fielding based on high-resolution data consisting of on-field location of batted balls. We combine spatial modeling with a hierarchical Bayesian structure in order to evaluate the performance of individual fielders while sharing information between fielders at each position. We present results across four seasons of MLB data (2002--2005) and compare our approach to other fielding evaluation procedures.
Published in at http://dx.doi.org/10.1214/08-AOAS228 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)