Using Tree Ensembles to Analyze National Baseball Hall of Fame Voting Patterns: An Application to Discrimination in BBWAA Voting
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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Volume (Year): 7 (2011)
Issue (Month): 4 (October)
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- Young William A & Holland William S & Weckman Gary R, 2008. "Determining Hall of Fame Status for Major League Baseball Using an Artificial Neural Network," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(4), pages 1-46, October.
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- R. Todd Jewell & Robert Brown & Scott Miles, 2002. "Measuring discrimination in major league baseball: evidence from the baseball hall of fame," Applied Economics, Taylor & Francis Journals, vol. 34(2), pages 167-177.
- Smith Lloyd & Downey James, 2009. "Predicting Baseball Hall of Fame Membership using a Radial Basis Function Network," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-21, January.
- Freiman Michael H., 2010. "Using Random Forests and Simulated Annealing to Predict Probabilities of Election to the Baseball Hall of Fame," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-37, April.
- Lawrence M. Kahn, 1991. "Discrimination in Professional Sports: A Survey of the Literature," ILR Review, Cornell University, ILR School, vol. 44(3), pages 395-418, April.
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