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Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

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Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany
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Department of Marketing Intelligence, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany
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FOM Hochschule für Oekonomie & Management, Zeltnerstraße 19, 90443 Nürnberg, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(1), 46; https://doi.org/10.3390/app10010046
Received: 1 November 2019 / Revised: 7 December 2019 / Accepted: 13 December 2019 / Published: 19 December 2019
In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team. View Full-Text
Keywords: machine learning; quantitative finance application; football betting; sports forecasting; trading system; statistical arbitrage; profitable investment; time-series prediction machine learning; quantitative finance application; football betting; sports forecasting; trading system; statistical arbitrage; profitable investment; time-series prediction
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MDPI and ACS Style

Stübinger, J.; Mangold, B.; Knoll, J. Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics. Appl. Sci. 2020, 10, 46. https://doi.org/10.3390/app10010046

AMA Style

Stübinger J, Mangold B, Knoll J. Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics. Applied Sciences. 2020; 10(1):46. https://doi.org/10.3390/app10010046

Chicago/Turabian Style

Stübinger, Johannes, Benedikt Mangold, and Julian Knoll. 2020. "Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics" Applied Sciences 10, no. 1: 46. https://doi.org/10.3390/app10010046

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