Optimal sports betting strategies in practice: an experimental review
We investigate the most popular approaches to the problem of sports betting investment based on modern portfolio theory and the Kelly criterion. We define the problem setting, the formal investment strategies, and review their common modifications used in practice. The underlying purpose of the reviewed modifications is to mitigate the additional risk stemming from the unrealistic mathematical assumptions of the formal strategies. We test the resulting methods using a unified evaluation protocol for three sports: horse racing, basketball and soccer. The results show the practical necessity of the additional risk-control methods and demonstrate their individual benefits. Particularly, we show that an adaptive variant of the popular ``fractional Kelly'' method is a very suitable choice across a wide range of settings.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
XGBoost Learning of Dynamic Wager Placement for In-Play Betting on an Agent-Based Model of a Sports Betting Exchange
We present first results from the use of XGBoost, a highly effective machine learning (ML) method, within the Bristol Betting Exchange (BBE), an open-source agent-based model (ABM) designed to simulate a contemporary spo…
Sports Betting: an application of neural networks and modern portfolio theory to the English Premier League
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 C…
ManagementPortfolio OptimizationBeating the House: Identifying Inefficiencies in Sports Betting Markets
Inefficient markets allow investors to consistently outperform the market. To demonstrate that inefficiencies exist in sports betting markets, we created a betting algorithm that generates above market returns for the NF…
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions
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 t…
Anomaly DetectionFairnessFraud DetectionManagementOnchain Sports Betting using UBET Automated Market Maker
The paper underscores how decentralization in sports betting addresses the drawbacks of traditional centralized platforms, ensuring transparency, security, and lower fees. Non-custodial solutions empower bettors with own…
Decision Making