Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer
This work addresses the problem of incomplete shot analysis in soccer analytics for teams and analysts, offering an incremental improvement over existing xG models.
The paper tackles the limitation of expected goals (xG) models, which only operate on observed shots, by proposing xG+, a probabilistic framework that jointly models shot occurrences and quality at the possession level, resulting in improved team-level predictive accuracy and more persistent player skill signals.
Expected goals (xG) models estimate the probability that a shot results in a goal from its context (e.g., location, pressure), but they operate only on observed shots. We propose xG+, a possession-level framework that first estimates the probability that a shot occurs within the next second and its corresponding xG if it were to occur. We also introduce ways to aggregate this joint probability estimate over the course of a possession. By jointly modeling shot-taking behavior and shot quality, xG+ remedies the conditioning-on-shots limitation of standard xG. We show that this improves predictive accuracy at the team level and produces a more persistent player skill signal than standard xG models.