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Machine-Learning-Based Statistical Arbitrage Football Betting

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Abstract

Across countries and continents, football (soccer) has drawn increasingly more attention over the last decades and developed into a huge commercial complex. Consequently, the market of bookmakers providing the possibility to bet on the result of football matches grew rapidly, especially with the appearance of the internet. With a high number of games every week in multiple countries, football league matches hold enormous potential for generating profits over time with the use of advanced betting strategies. In this paper, we use machine learning for predicting the outcome of football league matches by exploiting data about match characteristics. Based on insights from the field of statistical arbitrage stock market trading, we show that one could generate meaningful profits over time by betting accordingly. A simulation study analyzing the matches of the five top European football leagues from season 2013/14 to 2017/18 presented economically and statistically significant returns achieved by exploiting large data sets with modern machine learning algorithms. In contrast to these modern algorithms, the break-even point could not be reached with an ordinary linear regression approach or simple betting strategies, e.g. always betting on the home team.

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Notes

  1. We thank https://www.sportal.de/ for providing the data.

  2. We thank https://www.football-data.co.uk/data.php for providing the data.

  3. Please contact the authors if you are interested in the data and the code.

  4. Without loss of generality, our model can also be used for matches without home advantage, e.g., FIFA World Cup and UEFA Euro Cup. In this case both teams would be neutral teams.

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Acknowledgements

We are grateful to two anonymous referees for many helpful suggestions on this topic.

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Authors and Affiliations

  1. Hochschule für Oekonomie and Management, Nuremberg, Germany

    Julian Knoll

  2. Friedrich-Alexander-Universität Erlangen-Nürnberg, Nuremberg, Germany

    Johannes Stübinger

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  1. Julian Knoll
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Correspondence to Julian Knoll.

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Knoll, J., Stübinger, J. Machine-Learning-Based Statistical Arbitrage Football Betting. Künstl Intell 34, 69–80 (2020). https://doi.org/10.1007/s13218-019-00610-4

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