DeepAI AI Chat
Log In Sign Up

Optimal sports betting strategies in practice: an experimental review

by   Matej Uhrín, et al.

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.


page 1

page 2

page 3

page 4


Set-valued classification – overview via a unified framework

Multi-class classification problem is among the most popular and well-st...

Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal control

We offer a historical overview of methodologies for quantifying the noti...

Collocation-Based Output-Error Method for Aircraft System Identification

The output-error method is a mainstay of aircraft system identification ...

Further results and examples for formal mathematical systems with structural induction

In the former article "Formal mathematical systems including a structura...

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

We develop a family of accelerated stochastic algorithms that minimize s...

Learning how to approve updates to machine learning algorithms in non-stationary settings

Machine learning algorithms in healthcare have the potential to continua...

Sequential algorithmic modification with test data reuse

After initial release of a machine learning algorithm, the model can be ...