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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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Author Info

  • Brian M. Mills

    (University of Michigan)

  • Steven Salaga

    (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.

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File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1367&context=jqas
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Bibliographic Info

Article provided by De Gruyter in its journal Journal of Quantitative Analysis in Sports.

Volume (Year): 7 (2011)
Issue (Month): 4 ()
Pages: 12

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Handle: RePEc:bpj:jqsprt:v:7:y:2011:i:4:n:12

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Web page: http://www.degruyter.com

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Web: http://www.degruyter.com/view/j/jqas

Related research

Keywords: Baseball; hall of fame; random forest; classification; prediction; baseball;

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