Evaluating a soccer player's performance can be challenging due to the high costs and small margins involved in recruitment decisions. Raw observational statistics further complicate an accurate individual skill assessment as they do not abstract from the potentially confounding factor of team strength. We introduce the Soccer Factor Model (SFM), which corrects this bias by isolating a player's true skill from the team's influence. We compile a novel data set, web-scraped from publicly available data sources. Our empirical application draws on information of 144 players, playing a total of over 33,000 matches, in seasons 2000/01 through 2023/24. Not only does the SFM allow for a structural interpretation of a player's skill, but also stands out against more reduced-form benchmarks in terms of forecast accuracy. Moreover, we propose Skill- and Performance Above Replacement as metrics for fair cross-player comparisons. These, for example, allow us to settle the discussion about the GOAT of soccer in the first quarter of the twenty-first century.

翻译：由于招募决策涉及高昂成本与微小边际，评估足球运动员的表现颇具挑战性。原始观测统计数据因未排除球队实力这一潜在混杂因素的影响，进一步增加了准确评估个人技能的难度。本文提出的足球因子模型通过分离球员真实技能与球队影响来校正这一偏差。我们基于公开数据源网络爬取构建了一个新颖数据集，实证研究采用了2000/01至2023/24赛季144名球员共计超过33,000场比赛的信息。该模型不仅能对球员技能进行结构化解读，在预测准确性方面也优于简化形式的基准模型。此外，我们提出"技能替代值增量"与"表现替代值增量"作为公平跨球员比较的度量指标。这些指标使我们能够解决二十一世纪前二十五年关于足球史上最佳球员的争论。