Hierarchical Bayesian Modeling of Hitting Performance in Baseball
Applications
2021-07-21 v1 Methodology
Abstract
We have developed a sophisticated statistical model for predicting the hitting performance of Major League baseball players. The Bayesian paradigm provides a principled method for balancing past performance with crucial covariates, such as player age and position. We share information across time and across players by using mixture distributions to control shrinkage for improved accuracy. We compare the performance of our model to current sabermetric methods on a held-out season (2006), and discuss both successes and limitations.
Cite
@article{arxiv.0902.1360,
title = {Hierarchical Bayesian Modeling of Hitting Performance in Baseball},
author = {Shane T. Jensen and Blake McShane and Abraham J. Wyner},
journal= {arXiv preprint arXiv:0902.1360},
year = {2021}
}