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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Steve Rigdon, PhD

4 Issues per year


CiteScore 2017: 0.67

SCImago Journal Rank (SJR) 2017: 0.290
Source Normalized Impact per Paper (SNIP) 2017: 0.853

Online
ISSN
1559-0410
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Volume 7, Issue 4

Issues

Volume 1 (2005)

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

Brian M. Mills / Steven Salaga
Published Online: 2011-10-27 | DOI: https://doi.org/10.2202/1559-0410.1367

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

About the article

Published Online: 2011-10-27


Citation Information: Journal of Quantitative Analysis in Sports, Volume 7, Issue 4, ISSN (Online) 1559-0410, DOI: https://doi.org/10.2202/1559-0410.1367.

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