2025/11/28 by Jonathan Pipping, Pipping, Jonathan, Tianshu Feng +3
Economics, Econometrics and Finance · Engineering · Medicine · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Sports Analytics and Performance #Sports Dynamics and Biomechanics #Sports Performance and Training #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2512.00203
openalex publication_date 2025/11/28 · openalex created_date 2025/12/03 · openalex updated_date 2026/07/31
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.