Forecasting Soccer Matches through Distributions
Applications
2025-01-13 v1
Abstract
Forecasting sporting events encapsulate a compelling intellectual endeavor, underscored by the substantial financial activity of an estimated $80 billion wagered in global sports betting during 2022, a trend that grows yearly. Motivated by the challenges set forth in the Springer Soccer Prediction Challenge, this study presents a method for forecasting soccer match outcomes by forecasting the shot quantity and quality distributions. The methodology integrates established ELO ratings with machine learning models. The empirical findings reveal that, despite the constraints of the challenge, this approach yields positive returns, taking advantage of the established market odds.
Keywords
Cite
@article{arxiv.2501.05873,
title = {Forecasting Soccer Matches through Distributions},
author = {Tiago Mendes-Neves and Yassine Baghoussi and Luís Meireles and Carlos Soares and João Mendes-Moreira},
journal= {arXiv preprint arXiv:2501.05873},
year = {2025}
}