Bayesian Analysis

Hierarchical Bayesian modeling of hitting performance in baseball

Shane T. Jensen, Blakeley B. McShane, and Abraham J. Wyner

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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 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.

Article information

Source
Bayesian Anal., Volume 4, Number 4 (2009), 631-652.

Dates
First available in Project Euclid: 22 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1340369815

Digital Object Identifier
doi:10.1214/09-BA424

Mathematical Reviews number (MathSciNet)
MR2570079

Zentralblatt MATH identifier
1330.62451

Keywords
baseball hidden Markov model hierarchical Bayes

Citation

Jensen, Shane T.; McShane, Blakeley B.; Wyner, Abraham J. Hierarchical Bayesian modeling of hitting performance in baseball. Bayesian Anal. 4 (2009), no. 4, 631--652. doi:10.1214/09-BA424. https://projecteuclid.org/euclid.ba/1340369815


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See also

  • Related item: Jim Albert, Phil Birnbaum, Robert McCulloch. Comment on article by Jensen et al. Bayesian Anal., Vol. 4, Iss. 4(2009), 653-660.
  • Related item: Mark E. Glickman. Comments on article by Jensen, et al. Bayesian Anal., Vol. 4, Iss. 4 (2009), 661-664.
  • Related item: Peter Müller, Fernando A. Quintana. Comments on article by Jensen et al. Bayesian Anal., Vol. 4, Iss. 4 (2009), 665-668.
  • Related item: Shane T. Jensen, Blakeley B. McShane, Abraham J. Warner. Rejoinder. Bayesian Anal., Volume 4, Number 4 (2009), 669-674.