Neural Networks Predictive Modeling for Football Betting

32 Pages Posted: 2 Sep 2020

See all articles by John Sibony

John Sibony

affiliation not provided to SSRN

Abdelfatah Tlemsani

Université Paris Dauphine

Youssef Hamchi

Université Paris Dauphine

Monika Gjergji

Université Paris Dauphine

Evgueny Shurmanov

Ural Federal University

Marius Frunza

Schwarzthal Tech; Schwarzthal Kapital

Date Written: July 19, 2020

Abstract

The aim of this paper is to explore neural network-based modelling strategies for football betting. A neural network model and a challenger model based on a traditional econometric approach introduced by Dixon were estimated on data from five national leagues results including France, Spain, Italy, Germany, England) Our results show that the Neural network-based model has better predictive accuracy compared with the traditional econometric models. Betting strategies were implemented using prediction outputs generated with both econometric and neural networks models. The latter provides with a better return over investment. Nevertheless, both approaches lead to losses in the long run.

Keywords: Sport Betting Football, Machine Learning, Neural Networks, Statistical Models, Forecast

JEL Classification: G12

Suggested Citation

Sibony, John and Tlemsani, Abdelfatah and Hamchi, Youssef and Gjergji, Monika and Shurmanov, Evgueny and Frunza, Marius, Neural Networks Predictive Modeling for Football Betting (July 19, 2020). Available at SSRN: https://ssrn.com/abstract=3655700 or http://dx.doi.org/10.2139/ssrn.3655700

John Sibony

affiliation not provided to SSRN

Abdelfatah Tlemsani

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

Youssef Hamchi

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

Monika Gjergji

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

Evgueny Shurmanov

Ural Federal University ( email )

Russia

HOME PAGE: http://www.urfu.ru

Marius Frunza (Contact Author)

Schwarzthal Tech ( email )

231b Business design Center
Upper Street
London, London N1 0QH
United Kingdom

HOME PAGE: http://www.schwarzthal.tech

Schwarzthal Kapital ( email )

34 quai de Dion Bouton
La Defense Puteaux, 92800
France

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