arXiv:2510.20454 · v3
Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach
摘要
Intransitive player dominance, where player A beats B, B beats C, but C beats A, is common in competitive tennis. Yet, there are few known attempts to incorporate it within forecasting methods. We address this problem with a graph neural network approach that explicitly models these intransitive relationships through temporal directed graphs, with players as nodes and their historical match outcomes as directed edges. Our model (65.7% accuracy, 0.214 Brier score) forecasts competitively with established rating systems such as Weighted Elo. Although it does not improve on the baseline in unconditional accuracy, a forecast-encompassing test shows that it carries complementary information. A combined forecast significantly outperforms Weighted Elo, and there is some indication that the gain is larger on the intransitive matchups our model targets. A graph-based representation of player interactions thus captures a forecasting signal that transitive rating systems discard, even between players who share no common opponents.
作者备注：42 pages, 7 figures. Accepted author manuscript, forthcoming in International Journal of Forecasting (2027). Revised following peer review, with updated analyses, robustness checks, and expanded appendices