Imagine you had x mates, all experts in a certain field of betting on horse racing. One was an expert on breeding, the other was very knowlegable about draw bias, a third shit hot on trainer jockey combos, I could go on and the topic of expertise does not really matter. The main point is how would you want to synergise their opinions into a race selection. You could put them in a room together and let them debate a selection in the 2.30 at Sandown. The trouble with this is that the value of each may get drowned in the noise of the collective. The optimum way of combining these varied inputs may get lost in the futile attempt to combine them in one fell swoop so to speak.
In the Machine Learning world there is a technique called Ensemble stacking. This is slightly different to the above scenario. With ensemble stacking different ML algorithms are trained on some data and then they make predictions which are then fed into a second stage who’s job is to find out how to combine the predictions to give a super prediction. Going forward this can often result in better predictions especially if the algorithms used are different in nature and therefore discovering slightly different things about the data.
Sound familiar?, well this approach can be used for ML models for horse racing. Instead of throwing the kitchen sink at a model build perhaps results would improve if various models were constructed on tightly related sub fractions of the data. These predictions could then be fed into a second layer predictor that combines them into one final prediction. This also sounds like a close cousin of the two step process I covered in an earlier approach. Unless you are like me (could pick an argument in an empty room) then this may certainly be an approach worth exploring.
If you are interested in exploring Machine Learning for producing your own ratings but do not have any programming skills, don’t worry. I have produced some click and go software for developing ML models for sport. Check out the following