Back at the beginning of the 2022 flat season a tipping competition popped up on Twitter. Entries had to make two selections in all the flat seasons heritage handicaps. I felt this was a nice opportunity to test a machine learning model designed to run specifically on heritage handicaps so I set about creating such a model. Drilling down into the data for just heritage handicaps might produce too little data to work with so I decided to go for training the model on all races of class 4 and below. I also ended up splitting the task into two models, one for races up to a mile and another for races beyond a mile. Selections would be made simply by posting up on Twitter the top two rated.

Things got off to a pretty surprising start when the model found Johan, a 28/1 sp winner of the Lincoln and generally got better as the year progressed. Here are the results with EW bets settled at whatever place terms were on offer by at least two big bookmakers.

Firstly let me say that the above returns are not likely sustainable but the profit generated does add weight to the historical results and suggests that the model can be profitable going forward especially at these place terms. I will consider posting these up to the MySportsAI email forum next year