提出xG+框架，同时预测射门概率和射门质量，突破传统xG仅分析已发生射门的局限。
Beyond Expected Goals: A Probabilistic Framework for Shot Occurrences in Soccer
- 联合建模射门行为与射门质量，从控球层面预测未来1秒内是否射门及射门价值。
- 在球队层面提升预测准确率，且球员能力信号更稳定持久。
- 适合足球数据分析、战术评估与球员表现建模的研究者与俱乐部团队。
预期进球（xG）模型基于上下文（如位置、压力）估算射门进球概率，但仅适用于已发生的射门。本文提出xG+，一种控球层面的框架，首先估计未来1秒内射门发生的概率及其若发生时的xG值。我们还提出对这一联合概率估计在控球过程中的聚合方法。通过联合建模射门行为与射门质量，xG+克服了标准xG仅条件于已发生射门的局限。实验表明，该方法在球队层面提升了预测准确性，并生成比标准xG更持久的球员技能信号。
原文摘要 · Abstract (English)
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.
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