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

TL;DR

This paper introduces a novel sports betting strategy that combines neural networks, portfolio optimization, and utility theory, achieving significant profits in the English Premier League.

Contribution

It presents an innovative integration of deep learning and modern portfolio theory for sports betting, with practical profit results and comprehensive strategy evaluation.

Findings

Achieved 135.8% profit relative to initial wealth

Developed a neural network for match outcome prediction

Evaluated complete and restricted betting strategies

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.