Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer
Abstract
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.
Cite
@article{arxiv.2512.00203,
title = {Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer},
author = {Jonathan Pipping-Gamón and Tianshu Feng and R. Paul Sabin},
journal= {arXiv preprint arXiv:2512.00203},
year = {2026}
}
Comments
18pp main + 3pp appendix; 8 figures, 12 tables. Submitted to the Journal of Quantitative Analysis in Sports (JQAS). Data proprietary to Gradient Sports; we share derived features & scripts (code under MIT/Apache-2.0). Preprint licensed CC BY 4.0