Starting points for implementing ML/AI into sports trading

[Gcampb]

I have developed several machine-learning models for horse-racing markets. They have now been live, rather than in testing, for around seven months, so I am reasonably confident that their real-world performance is broadly in line with what I observed during the training and validation period, which used data from 2015 onwards.

I also try to work closely with others involved in similar projects, and I provide selections for a parimutuel syndicate, so I feel able to speak with some confidence on the subject.

One point that is often misunderstood is that using a generative AI system is not the same thing as building and validating a machine-learning model for statistical prediction. They are fundamentally different approaches.

A major problem with using an AI model for systematic statistical analysis is validation. Unless the entire AI-based process has been recreated and tested across several years of historical data, it is very difficult to know whether the results are genuinely reliable or whether a short run of apparent success is simply noise.

I have yet to see a horse-racing AI project that has been properly backtested over a large historical sample. One likely reason is the cost and complexity involved. Whatever data preparation, prompts, context and query process are being used live would also need to be replicated historically. That could mean tens of thousands of model queries and hundreds of millions of tokens, potentially at significant financial cost.

Any meaningful change to the prompts, data or methodology could then require the entire test to be run again. I have not heard of anyone going to that level of effort with a generative-AI racing system.

There is also a more basic question. If you are prompt-engineering an AI model to analyse a fixed set of data points and produce a consistent, systematic output, why use a generative AI model at all? In many cases, the same logic could be implemented programmatically, with lower cost, greater consistency and much easier testing.

With conventional machine learning, the process is different. Historical testing, calibration and validation are integral parts of model development. A properly designed validation process can give you a reasonable expectation of how the model should perform on genuinely unseen data. You can then run it in shadow mode on live races and compare the observed results with the expected performance before committing capital.

That does not mean machine learning automatically produces an edge. Poor data, leakage, overfitting and weak validation can make an ML model look far better than it really is. However, the framework at least allows those risks to be measured and tested systematically.

Even then, the predictive model is only half the battle. Finding selections with an apparent statistical edge is one problem; executing those selections efficiently in a live market is another. Entry timing, liquidity, slippage, stake size, price movement and trade management can determine whether a theoretical edge survives in practice.

In my view, execution is at least 50% of the challenge.