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

Brian Mills and Salaga Steven
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Salaga Steven: University of Michigan

Journal of Quantitative Analysis in Sports, 2011, vol. 7, issue 4, 32

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.

Keywords: hall of fame; random forest; classification; prediction; baseball (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (2)

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DOI: 10.2202/1559-0410.1367

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