Inferring Team Strengths Using a Discrete Markov Random Field
This work addresses team strength inference for sports analytics, but it appears incremental as it applies existing methods like EM and Loopy Belief Propagation to a specific domain.
The authors tackled the problem of estimating historical offensive and defensive strengths of teams in sports like soccer by proposing a discrete Markov Random Field model, and they demonstrated its performance on English Premier League data.
We propose an original model for inferring team strengths using a Markov Random Field, which can be used to generate historical estimates of the offensive and defensive strengths of a team over time. This model was designed to be applied to sports such as soccer or hockey, in which contest outcomes take value in a limited discrete space. We perform inference using a combination of Expectation Maximization and Loopy Belief Propagation. The challenges of working with a non-convex optimization problem and a high-dimensional parameter space are discussed. The performance of the model is demonstrated on professional soccer data from the English Premier League.