Sports Betting: an application of neural networks and modern portfolio theory to the English Premier League
This work addresses sports betting optimization for gamblers and analysts, but it is incremental as it combines existing methods in a new application.
The paper tackled the problem of optimizing sports betting strategies by integrating neural networks with portfolio theory, achieving a 135.8% profit relative to initial wealth during the latter half of the 20/21 English Premier League season.
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.