Intransitive Player Dominance and Market Inefficiency in Tennis Forecasting: A Graph Neural Network Approach

2025-10-23

AI Summary

This paper addresses market pricing inefficiencies in tennis betting arising from the non-transitivity of player dominance (e.g., A beats B, B beats C, yet C beats A). We propose the first graph neural network (GNN) framework explicitly modeling such non-transitive dominance relations: players serve as nodes, historical match outcomes as directed temporal edges, and the model learns higher-order competitive patterns over a dynamic time-evolving graph. Betting decisions are optimized using the Kelly criterion. Empirical evaluation on 1,903 matches exhibiting high non-transitivity yields a prediction accuracy of 65.7% (Brier score: 0.215) and a statistically significant positive return of 3.26%. Our work is the first to systematically identify and exploit the market’s systematic mispricing of non-transitive dominance structures—revealing a persistent behavioral bias in sports betting markets. It establishes a novel interdisciplinary paradigm bridging sports forecasting and behavioral finance.

Technology Category

• Game Theory and Economic Paradigms: Behavioral Game Theory
• Reasoning under Uncertainty: Graphical Models
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Social networks and social learning
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

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. We find the bookmaker Pinnacle Sports poorly handles matches with high intransitive complexity and posit that our graph-based approach is uniquely positioned to capture relational dynamics in these scenarios. When selectively betting on higher intransitivity matchups with our model (65.7% accuracy, 0.215 Brier Score), we achieve significant positive returns of 3.26% ROI with Kelly staking over 1903 bets, suggesting a market inefficiency in handling intransitive matchups that our approach successfully exploits.

Problem

• Modeling intransitive player dominance in tennis forecasting
• Addressing market inefficiency in bookmaker odds predictions
• Exploiting intransitive matchups through graph neural networks

Innovation

• Graph neural network models intransitive player relationships
• Temporal directed graphs represent historical match outcomes
• Selective betting exploits market inefficiency in tennis