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.

翻译：本文提出了一种将大规模事件模型（LEMs，类似于大语言模型）创新应用于足球分析领域的方法。通过"学习足球语言"——预测后续事件变量而非词汇——LEMs能够模拟比赛并提供多种应用场景，包括评估球员在不同球队环境中的表现预测。我们聚焦于使用2017-2018赛季英超联赛的WyScout数据集对LEMs进行精调，以深入解析球员贡献与球队策略。该方法通过适配模型以反映足球运动的动态复杂性，实现了对假设性转会的评估。研究结果验证了LEMs在足球分析中的有效性与局限性，展现了模型预测球队预期排名及探讨高关注度场景的能力——例如C罗或梅西转会至英超不同球队的潜在影响。该分析强调了情境在评估球员质量中的关键作用：尽管通用指标可能显示球员间存在显著差异，但情境分析揭示了特定战术体系下的表现差距实际更小。