- Bayesian Anal.
- Volume 4, Number 4 (2009), 631-652.
Hierarchical Bayesian modeling of hitting performance in baseball
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.
Bayesian Anal., Volume 4, Number 4 (2009), 631-652.
First available in Project Euclid: 22 June 2012
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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
- 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.