arXiv:2510.20454v2 Announce Type: replace 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. Al
The continuous evolution of AI research, particularly in areas like graph neural networks, allows for more sophisticated modeling of complex, non-linear relationships such as intransitive dominance.
This development indicates a growing capability for AI to model intricate real-world dynamics, moving beyond simple linear correlations to handle more nuanced and 'irrational' human behaviors or competitive outcomes, potentially improving predictive accuracy in sports, finance, or strategic planning.
Traditional rating systems, which often struggle with intransitive relationships, may be surpassed by graph neural network approaches that explicitly model these complex dynamics, leading to more accurate forecasting and potentially new algorithmic competitive strategies.
- · AI/ML researchers
- · Sports analytics industry
- · Algorithmic trading firms
- · Data scientists
- · Traditional statistical modeling firms
- · Human sports analysts relying on intuition
This research provides a more accurate method for forecasting outcomes in competitive environments where intransitive dominance is present.
Improved forecasting could influence betting markets, player recruitment strategies, and competitive game theory, extending beyond sports.
The methodology could be generalized to model other complex systems with non-linear, intransitive relationships, such as supply chain vulnerabilities or geopolitical rivalries, offering new insights into systemic risks.
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