Hierarchical bayesian modeling of hitting performance in baseball

Shane T. Jensen; Blakeley B. McShane; Abraham J. Wyner

doi:10.1214/09-BA424

Hierarchical bayesian modeling of hitting performance in baseball

Shane T. Jensen^*, Blakeley B. McShane, Abraham J. Wyner

^*Corresponding author for this work

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17 Scopus citations

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 covari-ates, 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.

Original language	English (US)
Pages (from-to)	631-652
Number of pages	22
Journal	Bayesian Analysis
Volume	4
Issue number	4
DOIs	https://doi.org/10.1214/09-BA424
State	Published - 2009

Keywords

Baseball
Hidden Markov model
Hierarchical Bayes

ASJC Scopus subject areas

Statistics and Probability
Applied Mathematics

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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 covari-ates, 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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AB - 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 covari-ates, 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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Hierarchical bayesian modeling of hitting performance in baseball

Abstract

Keywords

ASJC Scopus subject areas

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