Hierarchical Bayesian Modeling of Hitting Performance in Baseball

TL;DR

This paper introduces a hierarchical Bayesian model for predicting baseball players' hitting performance, integrating covariates and sharing information across players and time to improve accuracy over existing sabermetric methods.

Contribution

The paper presents a novel hierarchical Bayesian approach that combines covariate information and mixture distributions for enhanced predictive accuracy in baseball performance modeling.

Findings

Model outperforms current sabermetric methods on 2006 season data

Effective use of mixture distributions for shrinkage and information sharing

Discussion of model strengths and limitations

Abstract

We have developed a sophisticated statistical model for predicting the hitting performance of Major League baseball players. The Bayesian paradigm provides a principled method for balancing past performance with crucial covariates, such as player age and position. We share information across time and across players by using mixture distributions to control shrinkage for improved accuracy. We compare the performance of our model to current sabermetric methods on a held-out season (2006), and discuss both successes and limitations.