Toward interpretable expected goals modeling using Bayesian mixed models.

Empowered by technological progress, sports teams and bookmakers strive to understand relationships between player and team activity and match outcomes. For this purpose, the probability of an event to succeed (e.g., the probability of a goal to be scored, namely, xG for eXpected Goals) provides insightful information on team and player performance and helps statistical and machine learning approaches predict match outcomes. However, recent approaches require powerful but complex models that need more inherent interpretability for practitioners. This study uses a Bayesian generalized linear mixed-effects model to introduce a simple and interpretable xG modeling approach. The model provided similar performance when compared to the StatsBomb model (property of the StatsBomb company) using only seven variables relating to shot type and position, and surrounding opponents (AUC = 0.781 and 0.801, respectively). Pre-trained models through transfer learning are suitable for identifying teams' strengths and weaknesses using small sample sizes and enable interpretation of the model's predictions.