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December 2009 Hierarchical Bayesian modeling of hitting performance in baseball
Shane T. Jensen, Blakeley B. McShane, Abraham J. Wyner
Bayesian Anal. 4(4): 631-652 (December 2009). DOI: 10.1214/09-BA424


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.


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Shane T. Jensen. Blakeley B. McShane. Abraham J. Wyner. "Hierarchical Bayesian modeling of hitting performance in baseball." Bayesian Anal. 4 (4) 631 - 652, December 2009.


Published: December 2009
First available in Project Euclid: 22 June 2012

zbMATH: 1330.62451
MathSciNet: MR2570079
Digital Object Identifier: 10.1214/09-BA424

Keywords: baseball , Hidden Markov model , hierarchical Bayes

Rights: Copyright © 2009 International Society for Bayesian Analysis


Vol.4 • No. 4 • December 2009
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