This paper introduces an innovative application of Large Event Models (LEMs),
akin to Large Language Models, to the domain of soccer analytics. By learning
the "language" of soccer - predicting variables for subsequent events rather
than words LEMs facilitate the simulation of matches and offer various
applications, including player performance prediction across different team
contexts. We focus on fine-tuning LEMs with the WyScout dataset for the
2017-2018 Premier League season to derive specific insights into player
contributions and team strategies. Our methodology involves adapting these
models to reflect the nuanced dynamics of soccer, enabling the evaluation of
hypothetical transfers. Our findings confirm the effectiveness and limitations
of LEMs in soccer analytics, highlighting the model's capability to forecast
teams' expected standings and explore high-profile scenarios, such as the
potential effects of transferring Cristiano Ronaldo or Lionel Messi to
different teams in the Premier League. This analysis underscores the importance
of context in evaluating player quality. While general metrics may suggest
significant differences between players, contextual analyses reveal narrower
gaps in performance within specific team frameworks.

该研究介绍了大型事件模型（LEMs）在足球分析领域的创新应用，通过学习 “足球语言”，LEM 模型能够模拟比赛、预测球员表现以及探索不同球队情境下的应用，研究方法涉及微调 LEM 模型以反映足球的微妙动态，结果确认了 LEM 模型在足球分析中的有效性和局限性，以及衡量球员素质时上下文的重要性。