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

TL;DR

This paper introduces a graph neural network method to model intransitive dominance in tennis, revealing market inefficiencies and achieving profitable betting strategies by capturing complex relational dynamics.

Contribution

It presents a novel graph-based approach to incorporate intransitive relationships in tennis forecasting, improving prediction accuracy and exploiting market inefficiencies.

Findings

Achieved 65.7% accuracy in intransitive match predictions

Generated 3.26% ROI through strategic betting

Identified market inefficiencies in handling intransitive matchups

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.