Expected Goals (xG) has emerged as a popular tool for evaluating finishing skill in soccer analytics. It involves comparing a player's cumulative xG with their actual goal output, where consistent overperformance indicates strong finishing ability. However, the assessment of finishing skill in soccer using xG remains contentious due to players' difficulty in consistently outperforming their cumulative xG. In this paper, we aim to address the limitations and nuances surrounding the evaluation of finishing skill using xG statistics. Specifically, we explore three hypotheses: (1) the deviation between actual and expected goals is an inadequate metric due to the high variance of shot outcomes and limited sample sizes, (2) the inclusion of all shots in cumulative xG calculation may be inappropriate, and (3) xG models contain biases arising from interdependencies in the data that affect skill measurement. We found that sustained overperformance of cumulative xG requires both high shot volumes and exceptional finishing, including all shot types can obscure the finishing ability of proficient strikers, and that there is a persistent bias that makes the actual and expected goals closer for excellent finishers than it really is. Overall, our analysis indicates that we need more nuanced quantitative approaches for investigating a player's finishing ability, which we achieved using a technique from AI fairness to learn an xG model that is calibrated for multiple subgroups of players. As a concrete use case, we show that (1) the standard biased xG model underestimates Messi's GAX by 17% and (2) Messi's GAX is 27% higher than the typical elite high-shot-volume attacker, indicating that Messi is even a more exceptional finisher than people commonly believed.

该研究旨在解决使用 xG 统计评估足球射门能力时的局限性和细微差别问题。研究发现，持续高精准度的累计 xG 需要大量射门和出色的射门技巧，包括所有射门类型会模糊优秀前锋的射门能力，而实际与预期进球之间存在持续偏差，使得出色射手的进球数与预期更接近。因此，我们需要更加细致的定量方法来研究球员的射门能力，通过使用 AI 公平技术学习一个为多个子群体校准的 xG 模型，我们展示了具体案例，即标准的有偏 xG 模型低估了梅西的 GAX 17％，而梅西的 GAX 比典型的高射门量的顶级前锋高出27％，表明梅西是一个比人们普遍认识的更为出色的射手。

预期进球模型中的偏见影响进攻能力