Hierarchical Bayesian Bradley-Terry for Applications in Major League Baseball
Abstract
A common problem faced in statistical inference is drawing conclusions from paired comparisons, in which two objects compete and one is declared the victor. A probabilistic approach to such a problem is the Bradley-Terry model, first studied by Zermelo in 1929 and rediscovered by Bradley and Terry in 1952. One obvious area of application for such a model is sporting events, and in particular Major League Baseball. With this in mind, we describe a hierarchical Bayesian version of Bradley-Terry suitable for use in ranking and prediction problems, and compare results from these application domains to standard maximum likelihood approaches. Our Bayesian methods outperform the MLE-based analogues, while being simple to construct, implement, and interpret.
Keywords
Cite
@article{arxiv.1712.05879,
title = {Hierarchical Bayesian Bradley-Terry for Applications in Major League Baseball},
author = {Gabriel C. Phelan and John T. Whelan},
journal= {arXiv preprint arXiv:1712.05879},
year = {2017}
}