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A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions

René Manassé Galekwa, Jean Marie Tshimula, Etienne Gael Tajeuna, Kyamakya Kyandoghere

TL;DR

This systematic review explores various ML techniques, including support vector machines, random forests, and neural networks, as applied in different sports, as applied in different sports, and highlights both the potential and the limitations of these technologies.

Abstract

The sports betting industry has experienced rapid growth, driven largely by technological advancements and the proliferation of online platforms. Machine learning (ML) has played a pivotal role in the transformation of this sector by enabling more accurate predictions, dynamic odds-setting, and enhanced risk management for both bookmakers and bettors. This systematic review explores various ML techniques, including support vector machines, random forests, and neural networks, as applied in different sports such as soccer, basketball, tennis, and cricket. These models utilize historical data, in-game statistics, and real-time information to optimize betting strategies and identify value bets, ultimately improving profitability. For bookmakers, ML facilitates dynamic odds adjustment and effective risk management, while bettors leverage data-driven insights to exploit market inefficiencies. This review also underscores the role of ML in fraud detection, where anomaly detection models are used to identify suspicious betting patterns. Despite these advancements, challenges such as data quality, real-time decision-making, and the inherent unpredictability of sports outcomes remain. Ethical concerns related to transparency and fairness are also of significant importance. Future research should focus on developing adaptive models that integrate multimodal data and manage risk in a manner akin to financial portfolios. This review provides a comprehensive examination of the current applications of ML in sports betting, and highlights both the potential and the limitations of these technologies.

A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions

TL;DR

This systematic review explores various ML techniques, including support vector machines, random forests, and neural networks, as applied in different sports, as applied in different sports, and highlights both the potential and the limitations of these technologies.

Abstract

The sports betting industry has experienced rapid growth, driven largely by technological advancements and the proliferation of online platforms. Machine learning (ML) has played a pivotal role in the transformation of this sector by enabling more accurate predictions, dynamic odds-setting, and enhanced risk management for both bookmakers and bettors. This systematic review explores various ML techniques, including support vector machines, random forests, and neural networks, as applied in different sports such as soccer, basketball, tennis, and cricket. These models utilize historical data, in-game statistics, and real-time information to optimize betting strategies and identify value bets, ultimately improving profitability. For bookmakers, ML facilitates dynamic odds adjustment and effective risk management, while bettors leverage data-driven insights to exploit market inefficiencies. This review also underscores the role of ML in fraud detection, where anomaly detection models are used to identify suspicious betting patterns. Despite these advancements, challenges such as data quality, real-time decision-making, and the inherent unpredictability of sports outcomes remain. Ethical concerns related to transparency and fairness are also of significant importance. Future research should focus on developing adaptive models that integrate multimodal data and manage risk in a manner akin to financial portfolios. This review provides a comprehensive examination of the current applications of ML in sports betting, and highlights both the potential and the limitations of these technologies.

Paper Structure

This paper contains 33 sections, 22 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Data selection
  4. Inclusion and exclusion criteria
  5. Data extraction
  6. Related work
  7. Machine learning in sport betting
  8. Soccer
  9. Basketball
  10. Tennis
  11. Cricket
  12. American Football
  13. Baseball
  14. Horse Racing
  15. Rugby
  16. ...and 18 more sections

Figures (22)

  • Figure 1: Preferred reporting items for systematic reviews and meta-analyses flowchart of article screening process.
  • Figure 2: General overview of how to construct machine learning models for predicting sports outcomes, calculating odds, recommending betting options, and more.
  • Figure 3: The best performances in Soccer analytics based on accuracy, F1 and RPS
  • Figure 4: Histogram showing the number of articles per year in soccer betting.
  • Figure 5: The best performances in basketball analytics based on accuracy, F1 and RPS
  • ...and 17 more figures