Hierarchical bayesian modeling of hitting performance in baseball

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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 languageEnglish (US)
Pages (from-to)631-652
Number of pages22
JournalBayesian Analysis
Issue number4
StatePublished - 2009


  • Baseball
  • Hidden Markov model
  • Hierarchical Bayes

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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