ScoutGPT: Capturing Player Impact from Team Action Sequences Using GPT-Based Framework

• URL: http://arxiv.org/abs/2512.17266v2
• Date: Tue, 23 Dec 2025 06:52:01 GMT
• Title: ScoutGPT: Capturing Player Impact from Team Action Sequences Using GPT-Based Framework
• Abstract summary: We introduce EventGPT, a player-conditioned, value-aware next-event prediction model built on a GPT-style autoregressive transformer.<n>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.<n>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.
• Score: 5.36551574565267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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