A Regularized Adjusted Plus-Minus Model in Soccer with Box Score Prior
Date:
Evaluating the impact of individual players on their team’s performance is an important question to consider when analyzing team sports. Variants of the Adjusted Plus-Minus (APM) model have been viewed as all-in-one player’s performance evaluation matrices, and have been widely utilized in basketball and hockey. However, because of the low number of substitutions and scoring chances in soccer, the APM model has not been shown to be effective in identifying players’ performance. This talk introduces a new kind of Regularized Adjusted Plus-Minus (RAPM) model, which incorporates priors generated from box score statistics into a regularized regression framework, performing point estimation on the player’s contribution to the expected goals per 90 minutes. In particular, using data from the 2021-2022 season of the English Premier League, we show that our RAPM model with box score prior has better predictability and interpretability than the APM model, RAPM model without priors, and RAPM model with FIFA ratings as prior. This model could be further utilized to evaluate the impact of player transfer, simulate teams’ performance, and forecast players’ market value.
Based on joint work with Edvin Tran Hoac and Phong Hoang.