This is the forth post in the following series:
1.Introduction to Logistic Regression
2.Setting up a model
3.Testing and optimising the model
4.Evaluating the model
In the previous post we created a model as good as we could, we know that model is statistically “fair” but we are not sure if that means the model is long-term profitable (got an edge in the market). So we need to test it on out of sample data!
After the creation of my dataset I divided it onto two parts, one for modelling and one for out of sample simulation. I used 2014 and 2015 data to model, and selected 2016 as simulation set.
I have now implemented the new model in SAS, and start out with the rule: create a back signal IF the offered odds is at least my calculated odds plus 1%. I also have a suspicion that if my model deviates to much from the market price, then there is a flaw in my model and not in the market. I plot the accumulated ROI by different “edges” (or deviation from market price) together with the number of bets induced by the model in 2016.
I use linear regression to get a feel of how to model behaves, and conclude that a higher edge is probably just a sign of me missing some vital information in my model. This model is probably OK for small deviations from market, so I restrict the model to work in the range of edges below 4 %. I choose 4 % because at that point I should get about 1 % ROI (by looking at the graph above and seeing where the regression line crosses 1 % ROI).
Running the model through with this restriction to see how the 118 bets are distributed by month:
It is a small sample, but the model implies a 1.1% ROI when simulated on 2016 (out of sample) data – It might be a model worth going further with!
In “real life” I do much more extensive testing, I look for strange things by splitting my simulated data by different variables and try to find all possible areas where the model doesn’t work. It is not as easy as just finding unprofitable segments, they also need to be logical and explainable. Limiting the model on historical data might risk creating an over adapted model with poor predictive power.
In the next post I will write about the implementation phase.