Learning Team Synergy from Team Composition with a Siamese Transformer

Predicting match outcomes typically depends on rich performance statistics. This work investigates whether it is possible to extract team synergy from only team composition and use it for sports outcome prediction. We model each player as a token and use a Siamese transformer to learn within‑team interactions and compare team representations to predict the win probability. Across multiple datasets, the model consistently outperforms classical baselines such as multilayer perceptrons and non‑Siamese transformers. Results suggest that synergy between teammates can be captured from team composition alone. When optional auxiliary information is available, a parallel Siamese branch yields additional performance gains. Overall, this study shows that team composition alone contains meaningful predictive structure that modern attention‑based models can effectively extract.