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.

翻译：预期进球（xG）已成为足球分析中评估射门技术的常用工具。该方法通过比较球员累计xG与实际进球数，持续超额表现被视为具备优秀射门能力的标志。然而，由于球员难以持续超越累计xG，基于xG评估足球射门技术仍存在争议。本文旨在探讨使用xG统计评估射门能力的局限性及细微差异。具体而言，我们验证三个假设：（1）受限于射门结果的高方差和小样本量，实际进球与预期进球的差值并非合适的度量指标；（2）将全部射门纳入累计xG计算可能欠妥；（3）xG模型存在由数据相互依赖关系导致的偏差，影响技术评估。研究发现：持续超越累计xG需要高射门次数与卓越射门技术的结合；包含所有射门类型会掩盖优秀前锋的射门能力；部分xG模型存在系统性偏差，使优秀射手的实际进球与预期进球差距小于真实值。总体而言，我们的分析表明需要更精细的定量方法来研究球员射门能力——我们借鉴人工智能公平性技术构建了针对多类球员群体校准的xG模型。具体应用案例显示：（1）标准有偏xG模型将梅西的GAX低估17%；（2）梅西的GAX比典型精英级高射门量攻击手高出27%，表明其射门能力远超普遍认知。