Is Football Unpredictable? Predicting Matches Using Neural Networks

Luiz E. Luiz, Gabriel Fialho and João P. Teixeira ()
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Luiz E. Luiz: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-253 Braganza, Portugal
Gabriel Fialho: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-253 Braganza, Portugal
João P. Teixeira: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-253 Braganza, Portugal

Forecasting, 2024, vol. 6, issue 4, 1-17

Abstract: The growing sports betting market works on the premise that sports are unpredictable, making it more likely to be wrong than right, as the user has to choose between win, draw, or lose. So could football, the world’s most popular sport, be predictable? This article studies this question using deep neural networks to predict the outcome of football matches using publicly available data. Data from 24,760 matches from 13 leagues over 2 to 10 years were used as input for the neural network and to generate a state-of-the-art validated feature, the pi-rating, and the parameters proposed in this work, such as relative attack, defence, and mid power. The data were pre-processed to improve the network’s interpretation and deal with missing or inconsistent data. With the validated pi-rating, data organisation methods were evaluated to find the most fitting option for this prediction system. The final network has four layers with 100, 80, 5, and 3 neurons, respectively, applying the dropout technique to reduce overfitting errors. The results showed that the most influential features are the proposed relative defending, playmaking, and midfield power, and the home team goal expectancy features, surpassing the pi-rating. Finally, the proposed model obtained an accuracy of 52.8% in 2589 matches, reaching 80.3% in specific situations. These results prove that football can be predictable and that some leagues are more predictable than others.

Keywords: football forecasting; soccer prediction; deep neural network; sports betting; pi-rating; feature importance (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
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