Horse Race Analytics Explained

As discussed in the previous post, the aim of my horse race analytics is to estimate the "true odds" for each horse in a race, so that we can compare them with what bookmakers are offering, to identify "good value". I put the term "true odds" in quotes (I did it again!) for a reason. It is quite hard to say what the true odds of anything is - let alone a one-off thing like a race. To measure the true odds accurately we would need to run the same race, with the same horses,  the same health, in the same weather and track conditions, a very large number of times and count the outcomes. But the weather is actually an uncertain factor so maybe we'd need to use a few different seasonally appropriate weather conditions. Anyway, clearly this is completely unfeasible, and the true odds are therefore really quite an abstract concept. Its doubtful that a precise number value for the true odds can even be defined in fact. One thing we can do is work out our way of calculating true odds and test it over a large number of real races. Our three to one (3/1) horses should on average win once for every three losses, our 2/1 should win once for every two losses etc. Incidentally if you are not familiar already, it turns out odds are a little different to probabilities. They are quoted as the number of losses vs. the number of wins. The win probability on the other hand will be a number between 0 and 1 defining the likelihood of a win in each race.

My approach to calculating the true odds has two stages. First, we estimate a "quality score" for each horse. The better the horse, the higher the quality score. We need to decide a standard way of doing this - for example we could look at the six previous races and assign a score of 5 for a win, 4 for second etc. I have a more sophisticated approach that I'll describe in a future post, but for now, you get the general ideas. 

Having calculated a quality score for each horse, we then use this to estimate the odds. In simple, made up cases, this might be easy. If three horses race, for example, and they are all exactly equal in terms of quality score then the true odds are 2/1 for each horse, because, on average, in each 3 races, each horse will win one and loose twice. For any significantly complicated example, it gets a lot more difficult. In fact, it rapidly becomes quite impossible to calculate analytically (i.e. using a formula). The way to do it is to do the kind of thing  merchant banks do a lot when analysing trades - Monte Carlo modelling. More about how this works in the next post.