ScoutGPT: Capturing Player Impact from Team Action Sequences Using GPT-Based Framework
This provides a principled method for evaluating transfer fit in football, addressing the context-dependence of player performance, but it is incremental as it applies a known GPT framework to a specific domain problem.
The paper tackles the problem of forecasting football transfer success by developing EventGPT, a GPT-based model that predicts next on-ball actions and their value, enabling counterfactual simulations of player performance in different teams. The model outperforms existing baselines in prediction accuracy and spatial precision on Premier League data, and case studies demonstrate its utility for transfer analysis.
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.