用图神经网络捕捉网球中非传递性胜败关系，提升预测性能。
Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach
- 构建时序有向图，将球员作为节点，比赛结果作边，显式建模非传递胜败。
- 模型准确率65.7%，Brier得分为0.214，与加权埃洛系统相当。
- 在无共同对手的非传递对局中表现更优，适合复杂对抗场景预测。
非传递性球员优势（如选手A胜B，B胜C，但C胜A）在竞技网球中普遍存在，但现有预测方法极少纳入此类关系。本文提出一种图神经网络方法，通过时序有向图建模玩家间的非传递关系：球员为节点，历史比赛结果为有向边。该模型在预测上达到65.7%准确率和0.214的Brier得分，与加权埃洛系统表现相当。尽管在绝对准确性上未超越基准，但包含性检验表明其携带互补信息；组合预测显著优于加权埃洛，且增益在模型目标的非传递对局中更为明显。这表明基于图的球员互动表示能捕捉到传统传递性评分系统忽略的预测信号，即使在无共同对手的对局中亦有效。
原文摘要 · Abstract (English)
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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