Transfers play a pivotal role in shaping a football club's success, yet forecasting whether a transfer will succeed remains difficult due to the strong context-dependence of on-field performance. Existing evaluation practices often rely on static summary statistics or post-hoc value models, which fail to capture how a player's contribution adapts to a new tactical environment or different teammates. To address this gap, we introduce EventGPT, a player-conditioned, value-aware next-event prediction model built on a GPT-style autoregressive transformer. Our model treats match play as a sequence of discrete tokens, jointly learning to predict the next on-ball action's type, location, timing, and its estimated residual On-Ball Value (rOBV) based on the preceding context and player identity. A key contribution of this framework is the ability to perform counterfactual simulations. By substituting learned player embeddings into new event sequences, we can simulate how a player's behavioral distribution and value profile would change when placed in a different team or tactical structure. Evaluated on five seasons of Premier League event data, EventGPT outperforms existing sequence-based baselines in next-event prediction accuracy and spatial precision. Furthermore, we demonstrate the model's practical utility for transfer analysis through case studies-such as comparing striker performance across different systems and identifying stylistic replacements for specific roles-showing that our approach provides a principled method for evaluating transfer fit.

翻译：球员转会对于塑造足球俱乐部的成功至关重要，然而由于场上表现具有强烈的上下文依赖性，预测转会能否成功仍然十分困难。现有的评估方法通常依赖于静态汇总统计数据或事后价值模型，这些方法无法捕捉球员的贡献如何适应新的战术环境或不同的队友。为弥补这一不足，我们提出了EventGPT，这是一个基于GPT风格自回归Transformer的、以球员为条件且具有价值感知的下一事件预测模型。我们的模型将比赛过程视为离散标记序列，联合学习基于先前上下文和球员身份来预测下一个持球动作的类型、位置、时间及其估计的残差持球价值。该框架的一个关键贡献在于能够执行反事实模拟。通过将学习到的球员嵌入代入新的事件序列，我们可以模拟当球员被置于不同的球队或战术结构时，其行为分布和价值概况将如何变化。在五个赛季的英超联赛事件数据上的评估表明，EventGPT在下一事件预测准确性和空间精度方面优于现有的基于序列的基线方法。此外，我们通过案例研究（例如比较不同体系中前锋的表现，以及为特定角色识别风格替代者）展示了该模型在转会分析中的实际效用，表明我们的方法为评估转会适配性提供了一种原则性的途径。