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Improved component predictions of batting and pitching measures

Author

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  • Albert Jim

    (Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH, 43403, USA)

Abstract

Standard measures of batting performance such as batting average and on-base percentage can be decomposed into component rates such as strikeout rates and home run rates. The likelihood of hitting data for a group of players can be expressed as a product of likelihoods of the component probabilities and this motivates the use of Bayesian random effects models to estimate the groups of component rates. By combining the separate component rates, the aggregate predictions of batting performance for subsequent seasons improve upon standard shrinkage methods. This “separate and aggregate” approach is also illustrated for estimating on-base probabilities and fielding independent pitching (FIP) abilities of pitchers.

Suggested Citation

  • Albert Jim, 2016. "Improved component predictions of batting and pitching measures," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(2), pages 73-85, June.
    • Handle: RePEc:bpj:jqsprt:v:12:y:2016:i:2:p:73-85:n:4
    DOI: 10.1515/jqas-2015-0063
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    References listed on IDEAS

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    1. McShane Blakeley B. & Braunstein Alexander & Piette James & Jensen Shane T., 2011. "A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-26, October.
    2. Albert James, 2006. "Pitching Statistics, Talent and Luck, and the Best Strikeout Seasons of All-Time," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(1), pages 1-32, January.
    3. Neal Dan & Tan James & Hao Feng & Wu Samuel S, 2010. "Simply Better: Using Regression Models to Estimate Major League Batting Averages," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-14, July.
    4. Piette James & Braunstein Alexander & McShane Blakeley B & Jensen Shane T., 2010. "A Point-Mass Mixture Random Effects Model for Pitching Metrics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-17, July.
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    Cited by:

    1. Gerber Eric A. E. & Craig Bruce A., 2021. "A mixed effects multinomial logistic-normal model for forecasting baseball performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(3), pages 221-239, September.
    2. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.

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