Informed, uninformed, smart, mug call it what you will many researchers have referred to these two different kinds of money forcing a price down. What would be uninformed money, perhaps that last winner for Frankies Ascot 7 or maybe just simply when a horse won last time out and has a top jockey up today against a bunch who are not quite sparkling form wise. It does not have to be rank stupid it only has to be a case of what does the public know that most of the public do not and in many cases its not a a lot. You should always ask yourself this question with a bet. What do I know that the public does not?. Do not take the last part as meaning, its a complete secret. They only have to be taking little notice of something that is available to them but they just do not have the time or inclination to find it out or perhaps confidence to act on.
With all this in mind I was pondering whether it would be possible to use Machine Learning to analyse the performance of smart money vs not so smart. I set about the task using MySportsAI, software by the way that allows you to carry out this kind of analysis provided you have the mental capacity and dexterity to click a mouse. I first looked through all the features that were strongly correlated with the price dropping on a horse. OK how did I do this and what the hell are features. There are around 90 features in MySportsAI and growing, it also allows you to engineer your own features. A feature is simply a characteristic of a horse running in a race, for example the trainer strike rate of that horse going into each race. Now we have cleared that up what do I mean by correlated and price drop. For price drop I used the feature PriceDrift, so yes I am going to predict horse that drift in price which is kind of the flip side to dropping in price, more or less. What does this mean, well PriceDrift is the pre race average Betfair price divided by the Betfair SP. In MysportsAI you can see which features influence (that means correlation) the PriceDrift target feature. To do this we first make PriceDrift the target feature and then use the correlation matrix in MySportsAI, you have seen this before in previous blog posts. I have purposely left out the names of the input features I used, hey its all there try it yourself.
Once we have chosen some features that have some sort of punch in predicting PriceDrift (in the above I have selected 5 that have a correlation between -0.08 to +0.07), we can use them to create a model to predict PriceDrift. I first created a model using data from 2011 to 2015 for Handicap races, training on the first 0.8 of the data and then testing on the last 0.2. Once I had done this I saved the results of the 0.2 test data to a csv file so that I could load it up into Excel. I repeated the exercise this time training on all 2011 to 2015 data but testing on 2016/17.
Once I had loaded the two csv result files into Excel I could now take a look at how horses faired if they were top rated within a race to be a drifter BUT actually dropped in price. You hopefully see where I am going with this. Conventional form features are indicating a drift but the market is saying no. I confined my selections to those under 10.0 Betfair SP simply because I felt that drop and drifts over longer prices can be less meaningful unless they are perhaps huge drops.
The test data from 2011 to 2015 ( about a seasons worth) produced 389 bets and an after commission profit of +34.22 points ROI +8.79%
The test data for 2016/17 produced 411 bets and a profit of +66.45 ROI% +16.16%
From the above it is quite possible that not all droppers are equal, perhaps some are more informed and the market does not quite drop them enough. I think this is quite possible. There are of course other possibilities with this avenue of research, trading being the most obvious.
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