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Sports Betting: an application of neural networks and modern portfolio theory to the English Premier League

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Listed:
  • V'elez Jim'enez
  • Rom'an Alberto
  • Lecuanda Ontiveros
  • Jos'e Manuel
  • Edgar Possani

Abstract

This paper presents a novel approach for optimizing betting strategies in sports gambling by integrating Von Neumann-Morgenstern Expected Utility Theory, deep learning techniques, and advanced formulations of the Kelly Criterion. By combining neural network models with portfolio optimization, our method achieved remarkable profits of 135.8% relative to the initial wealth during the latter half of the 20/21 season of the English Premier League. We explore complete and restricted strategies, evaluating their performance, risk management, and diversification. A deep neural network model is developed to forecast match outcomes, addressing challenges such as limited variables. Our research provides valuable insights and practical applications in the field of sports betting and predictive modeling.

Suggested Citation

  • V'elez Jim'enez & Rom'an Alberto & Lecuanda Ontiveros & Jos'e Manuel & Edgar Possani, 2023. "Sports Betting: an application of neural networks and modern portfolio theory to the English Premier League," Papers 2307.13807, arXiv.org.
    • Handle: RePEc:arx:papers:2307.13807
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    References listed on IDEAS

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    1. Enzo Busseti & Ernest K. Ryu & Stephen Boyd, 2016. "Risk-Constrained Kelly Gambling," Papers 1603.06183, arXiv.org.
    2. Hubáček, Ondřej & Šourek, Gustav & Železný, Filip, 2019. "Exploiting sports-betting market using machine learning," International Journal of Forecasting, Elsevier, vol. 35(2), pages 783-796.
    3. Chris Whitrow, 2007. "Algorithms for optimal allocation of bets on many simultaneous events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(5), pages 607-623, November.
    4. Jakobsson, Robin & Karlsson, Niklas, 2007. "Testing Market Efficiency in a Fixed Odds Betting Market," Working Papers 2007:12, Örebro University, School of Business.
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