Sports Betting: an application of neural networks and modern portfolio
  theory to the English Premier League
Vélez Jiménez Román Alberto Lecuanda Ontiveros José Manuel Edgar Possani
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.