A Point-Mass Mixture Random Effects Model for Pitching Metrics
A plethora of statistics have been proposed to measure the effectiveness of pitchers in Major League Baseball. While many of these are quite traditional (e.g., ERA, wins), some have gained currency only recently (e.g., WHIP, K/BB). Some of these metrics may have predictive power, but it is unclear which are the most reliable or consistent. We address this question by constructing a Bayesian random effects model that incorporates a point mass mixture and fitting it to data on twenty metrics spanning approximately 2,500 players and 35 years. Our model identifies FIP, HR/9, ERA, and BB/9 as the highest signal metrics for starters and GB%, FB%, and K/9 as the highest signal metrics for relievers. In general, the metrics identified by our model are independent of team defense. Our procedure also provides a relative ranking of metrics separately by starters and relievers and shows that these rankings differ quite substantially between them. Our methodology is compared to a Lasso-based procedure and is internally validated by detailed case studies.
Year of publication: |
2010
|
---|---|
Authors: | James, Piette ; Alexander, Braunstein ; B, McShane Blakeley ; Jensen Shane T. |
Published in: |
Journal of Quantitative Analysis in Sports. - De Gruyter, ISSN 1559-0410. - Vol. 6.2010, 3, p. 1-17
|
Publisher: |
De Gruyter |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics
McShane Blakeley B., (2011)
-
Estimating Fielding Ability in Baseball Players Over Time
James, Piette, (2012)
-
Alexander, Braunstein, (2010)
- More ...