将比赛建模为语言：一种生成式Transformer方法用于足球中的反事实球员估值
Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Football
AI总结：
本文提出ScoutGPT，通过将足球比赛事件视为序列标记，利用生成式Transformer模型模拟反事实球员估值，提升对球员转会效果的预测能力。
摘要：
评估足球球员转会极具挑战性，因为球员表现强烈依赖战术体系、队友和比赛情境。尽管复杂性，招聘决策往往依赖静态统计和主观判断，无法充分考虑这些因素。为此，本文提出ScoutGPT，一种将足球比赛事件视为序列标记的生成模型，利用NanoGPT架构训练下一个标记预测，学习比赛事件序列动态，模拟假设阵容下的事件序列，优于现有基线模型。通过蒙特卡洛采样实现反事实模拟，评估未观测场景。实验显示，模拟球员转会导致进攻进展和进球概率的可测量变化，表明ScoutGPT能捕捉球员特定影响，超越传统静态指标。
英文摘要：
Evaluating football player transfers is challenging because player actions depend strongly on tactical systems, teammates, and match context. Despite this complexity, recruitment decisions often rely on static statistics and subjective expert judgment, which do not fully account for these contextual factors. This limitation stems largely from the absence of counterfactual simulation mechanisms capable of predicting outcomes in hypothetical scenarios. To address these challenges, we propose ScoutGPT, a generative model that treats football match events as sequential tokens within a language modeling framework. Utilizing a NanoGPT-based Transformer architecture trained on next-token prediction, ScoutGPT learns the dynamics of match event sequences to simulate event sequences under hypothetical lineups, demonstrating superior predictive performance compared to existing baseline models. Leveraging this capability, the model employs Monte Carlo sampling to enable counterfactual simulation, allowing for the assessment of unobserved scenarios. Experiments on K League data show that simulated player transfers lead to measurable changes in offensive progression and goal probabilities, indicating that ScoutGPT captures player-specific impact beyond traditional static metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
