Dynamic Graph-Based Forecasts of Bookmakers' Odds in Professional Tennis

TL;DR

This paper introduces a dynamic graph-based model that forecasts bookmaker odds for tennis matches, enabling early predictions and outperforming traditional ranking methods.

Contribution

The novel dynamic graph-based approach allows for pre-tournament odds forecasting, improving prediction timeliness and accuracy over existing models.

Findings

Model achieves comparable accuracy to bookmakers and literature models.

Outperforms rankings-based predictions significantly.

Effective for detailed pre-tournament analysis.

Abstract

Bookmakers' odds consistently provide one of the most accurate methods for predicting the results of professional tennis matches. However, these odds usually only become available shortly before a match takes place, limiting their usefulness as an analysis tool. To ameliorate this issue, we introduce a novel dynamic graph-based model which aims to forecast bookmaker odds for any match on any surface, allowing effective and detailed pre-tournament predictions to be made. By leveraging the high-quality information contained in the odds, our model can keep pace with new innovations in tennis modelling. By analysing major tennis championships from 2024 and 2025, we show that our model achieves comparable accuracy both to the bookmakers and other models in the literature, while significantly outperforming rankings-based predictions.