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Using Tree Ensembles to Analyze National Baseball Hall of Fame Voting Patterns: An Application to Discrimination in BBWAA Voting

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  • Mills Brian M.

    (University of Michigan)

  • Salaga Steven

    (University of Michigan)

Abstract

We predict the induction of Major League Baseball hitters and pitchers into the National Baseball Hall of Fame by the Baseball Writers' Association of America. We employ a Random Forest algorithm for binary classification, improving upon past models with a simplistic input approach. Our results suggest that the random forest technique is a fruitful line of research with prediction in the sports world. We find an error rate as low as 0.91% in our most accurate forest, with no out-of-bag Error higher than 2.6% in any tree ensemble. We extend the results to an examination of the possibility of discrimination with respect to BBWAA voting, finding little evidence for exclusions based on race.

Suggested Citation

  • Mills Brian M. & Salaga Steven, 2011. "Using Tree Ensembles to Analyze National Baseball Hall of Fame Voting Patterns: An Application to Discrimination in BBWAA Voting," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-32, October.
    • Handle: RePEc:bpj:jqsprt:v:7:y:2011:i:4:n:12
    DOI: 10.2202/1559-0410.1367
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    References listed on IDEAS

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    Cited by:

    1. Chandler Gabriel & Stevens Guy, 2012. "An Exploratory Study of Minor League Baseball Statistics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(4), pages 1-28, November.
    2. Lock Dennis & Nettleton Dan, 2014. "Using random forests to estimate win probability before each play of an NFL game," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 197-205, June.

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