On Betfair, most horse racing events offer both win and place markets, where place means the horse comes first, second or third. It is natural to ask what the relationship is between both markets - given the win odds for each horse, how well can we predict the probability that each horse will place?
In this post I will look at a natural way of modelling this mathematically, called the normal ranking model.
In this post I will explain cross-market arbitrage on Betfair, including how to determine whether arbitrage opportunities exist, and how to exploit them by placing bets of the right size.
In general, arbitrage refers to the simultaneous buying and selling of something in different markets, exploiting small price differences to make a profit. On Betfair, it means buying positions that collectively cover all possible outcomes of an event, where the prices guarantee a small but certain profit (here I use price to refer to the decimal odds).
There’s a well-known Bloomberg piece The Gambler Who Cracked the Horse-Racing Code that has attracted renewed interest recently. The world of algorithmic betting has come a long way since then - data is more widely available and advances in machine learning have made many traditional quantitative models redundant.
Access to detailed data is often the difference between making a profit and not. Accurate, clean data is expensive and valuable. Without it, you are unlikely to stumble upon a system that works, because all such systems have been (or soon will be) made redundant by data-driven machine learning techniques.
I have been spending more time trading horse racing markets the past couple of months. They are attractive because the liquidity is high (they have the highest trading volume on the exchanges by a large margin), but the markets are also more efficient meaning it’s harder to find an edge.
Favourite-longshot bias Any time you buy a bet, you are implicitly making a statement of your belief about the probability of an event occuring.
Applied Prediction Markets