用语言模型模拟足球比赛，预测换人后的实际效果。
Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Football
- 把比赛事件当语句，用Transformer建模序列规律。
- 换人后进攻推进和进球概率明显变化，验证模型有效性。
- 适合足球俱乐部做转会决策或球员评估的人参考。
评估足球球员转会难度大，因球员表现受战术体系、队友及比赛情境强烈影响。然而，当前引援决策多依赖静态数据和主观判断，未能充分考虑这些上下文因素。根本原因在于缺乏能模拟假设情境下结果的反事实机制。为此，我们提出ScoutGPT，一种将足球比赛事件视为序列标记的语言建模方法。基于NanoGPT的Transformer架构，通过预测下一个事件进行训练，学习比赛事件序列动态。利用该模型，结合蒙特卡洛采样实现反事实模拟，可评估未发生的情境。在K联赛数据上的实验表明，模拟转会后进攻推进速度与进球概率均显著变化，证明ScoutGPT能捕捉球员在特定情境下的真实影响，超越传统静态指标。
原文摘要 · Abstract (English)
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.
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