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.

翻译：预期进球（xG）模型通过射门情境（如位置、压迫强度）估计射门转化为进球的概率，但其仅针对已发生的射门进行分析。本文提出xG+框架——一种基于控球时段的分析方法，该框架首先估计未来一秒内发生射门的概率，并预测若射门发生时的对应xG值。我们同时提出了在单次控球过程中聚合该联合概率估计的方法。通过对射门行为与射门质量进行联合建模，xG+弥补了传统xG模型仅针对已发生射门进行条件分析的局限性。研究表明，该方法在球队层面提升了预测准确性，且相较于传统xG模型能产生更具持续性的球员能力评估信号。