What AI can do for horse-racing ?

TL;DR

This paper discusses how advancements in deep learning and the convergence of computer vision, statistical learning, and game theory could revolutionize horse-racing predictions and deepen our understanding of horse-human interactions.

Contribution

It highlights the potential of integrating multiple machine learning fields to improve horse-racing prediction and understanding, proposing horse-racing as a real-world laboratory for AI development.

Findings

Deep learning has achieved state-of-the-art results in related fields.

The convergence of AI fields could transform horse-racing prediction.

Horse-racing can serve as a platform for studying animal-human interactions.

Abstract

Since the 1980s, machine learning has been widely used for horse-racing predictions, gradually expanding to where algorithms are now playing a huge role in the betting market. Machine learning has changed the horse-racing betting market over the last ten years, but main changes are still to come. The paradigm shift of neural networks (deep learning) may not only improve our ability to simply predict the outcome of a race, but it will also certainly shake our entire way of thinking about horse-racing - and maybe more generally about horses. Since 2012, deep learning provided more and more state-of-the-art results in computer vision and now statistical learning or game theory. We describe how the convergence of the three machine learning fields (computer vision, statistical learning, and game theory) will be game-changers in the next decade in our ability to predict and understand horse-racing. We consider that horse-racing is a real world laboratory where we can work on the animal-human interaction and build a non-anthropocentric Artificial Intelligence. We believe that this will lead us to understand the horses better and the interactions between animals and humans in general.