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arXiv · 2510.20454

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach

Lawrence Clegg·John Cartlidge
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Abstract

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 grows more strongly 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.

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Lawrence Clegg, John Cartlidge. 2025-10-23. Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach. https://arxiv.org/abs/2510.20454

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