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

ArXiv ID: 2307.13807 “View on arXiv”

Authors: Unknown

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.

Keywords: Von Neumann-Morgenstern Expected Utility Theory, Deep learning, Kelly Criterion, Portfolio optimization, Sports gambling, Sports betting

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like Von Neumann-Morgenstern utility, deep learning architecture details, and detailed portfolio optimization formulations (Kelly Criterion, Sharpe Ratio), and presents specific backtest results (135.8% profit) on a defined dataset (English Premier League).

  flowchart TD
    A["Research Goal: Optimize Sports Betting Strategy"] --> B["Data: EPL 20/21 Season Match Data"]
    B --> C["Methodology: Deep Neural Network Model"]
    C --> D{"Forecast Match Outcomes"}
    D --> E["Computational Process:<br>Portfolio Optimization &<br>Kelly Criterion Allocation"]
    E --> F["Key Findings:<br>135.8% Profit Increase<br>Risk-Managed Diversification"]