Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911399608844288 |
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| author | Pipping-Gamón, Jonathan Feng, Tianshu Sabin, R. Paul |
| author_facet | Pipping-Gamón, Jonathan Feng, Tianshu Sabin, R. Paul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer Pipping-Gamón, Jonathan Feng, Tianshu Sabin, R. Paul Applications Machine Learning Image and Video Processing 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. |
| title | Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer |
| topic | Applications Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2512.00203 |