A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics
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Blakeley B. McShane
Numerous statistics have been proposed to measure offensive ability in Major League Baseball. While some of these measures may offer moderate predictive power in certain situations, it is unclear which simple offensive metrics are the most reliable or consistent. We address this issue by using a hierarchical Bayesian variable selection model to determine which offensive metrics are most predictive within players across time. Our sophisticated methodology allows for full estimation of the posterior distributions for our parameters and automatically adjusts for multiple testing, providing a distinct advantage over alternative approaches. We implement our model on a set of fifty different offensive metrics and discuss our results in the context of comparison to other variable selection techniques. We find that a large number of metrics demonstrate signal. However, these metrics are (i) highly correlated with one another, (ii) can be reduced to about five without much loss of information, and (iii) these five relate to traditional notions of performance (e.g., plate discipline, power, and ability to make contact).
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Articles in the same Issue
- Letter from the Editor
- The Next Step
- Article
- A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics
- Ups and Downs: Team Performance in Best-of-Seven Playoff Series
- The Penalty Shot/Optional Minor Choice in Ice Hockey
- Using Local Correlation to Explain Success in Baseball
- Exploring Competition Performance in Decathlon Using Semi-Parametric Latent Variable Models
- Going for the Green: A Simulation Study of Qualifying Success Probabilities in Professional Golf
- Rule of Tangent for Win-By-Two Games
- Effect of Differences in Kicking Legs, Kick Directions, and Kick Skill on Kicking Accuracy in Soccer Players
- The Methodology of Officially Recognized International Sports Rating Systems
- Scoring Strategies for the Underdog: A General, Quantitative Method for Determining Optimal Sports Strategies
- Using Tree Ensembles to Analyze National Baseball Hall of Fame Voting Patterns: An Application to Discrimination in BBWAA Voting
- An Estimate of How Hitting, Pitching, Fielding, and Basestealing Impact Team Winning Percentages in Baseball
Articles in the same Issue
- Letter from the Editor
- The Next Step
- Article
- A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics
- Ups and Downs: Team Performance in Best-of-Seven Playoff Series
- The Penalty Shot/Optional Minor Choice in Ice Hockey
- Using Local Correlation to Explain Success in Baseball
- Exploring Competition Performance in Decathlon Using Semi-Parametric Latent Variable Models
- Going for the Green: A Simulation Study of Qualifying Success Probabilities in Professional Golf
- Rule of Tangent for Win-By-Two Games
- Effect of Differences in Kicking Legs, Kick Directions, and Kick Skill on Kicking Accuracy in Soccer Players
- The Methodology of Officially Recognized International Sports Rating Systems
- Scoring Strategies for the Underdog: A General, Quantitative Method for Determining Optimal Sports Strategies
- Using Tree Ensembles to Analyze National Baseball Hall of Fame Voting Patterns: An Application to Discrimination in BBWAA Voting
- An Estimate of How Hitting, Pitching, Fielding, and Basestealing Impact Team Winning Percentages in Baseball
