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

arXiv: https://arxiv.org/abs/2410.21484 · HTML: https://mlsanity.com/paper/2410.21484/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 237 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning](https://mlsanity.com/paper/1809.09813/) (2018)
- [Machine Learning for Soccer Match Result Prediction](https://mlsanity.com/paper/2403.07669/) (2024)
- [Predicting the NFL using Twitter](https://mlsanity.com/paper/1310.6998/) (2013)
- [Using Machine Learning to Predict the Outcome of English County twenty over Cricket Matches](https://mlsanity.com/paper/1511.05837/) (2015)
- [Machine Learning Modeling to Evaluate the Value of Football Players](https://mlsanity.com/paper/2207.11361/) (2022)
- [Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach](https://mlsanity.com/paper/2209.06346/) (2022)
- [Betting the system: Using lineups to predict football scores](https://mlsanity.com/paper/2210.06327/) (2022)
- [Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks](https://mlsanity.com/paper/2306.01740/) (2023)
- [XGBoost Learning of Dynamic Wager Placement for In-Play Betting on an Agent-Based Model of a Sports Betting Exchange](https://mlsanity.com/paper/2401.06086/) (2024)
- [Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysis](https://mlsanity.com/paper/2402.02623/) (2024)

## Abstract (arXiv)

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.