# What AI can do for horse-racing ?

arXiv: https://arxiv.org/abs/2207.04981 · HTML: https://mlsanity.com/paper/2207.04981/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016)
- [Pretraining boosts out-of-domain robustness for pose estimation](https://mlsanity.com/paper/1909.11229/) (2019)
- [Game Plan: What AI can do for Football, and What Football can do for AI](https://mlsanity.com/paper/2011.09192/) (2020)
- [Tree-Transformer: A Transformer-Based Method for Correction of Tree-Structured Data](https://mlsanity.com/paper/1908.00449/) (2019)
- [baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling](https://mlsanity.com/paper/2102.03291/) (2021)

## Abstract (arXiv)

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.