This post will be the start of a new series, where I explain, how to implement another predictive model at the TripleA DWH architecture. When starting developing predictive models with R, I was a little bit overstrained by the different plots provided by R, which can be used to analyse and optimize your predictive model. That’s why I wanted to learn and understand the whole optimizing process in R on base of a simple predictive model. Football-data.co.uk provides an explanation for a small rating system, which uses a linear regression to predict the probability for a home-win, draw or away win. I have chosen this linear regression model, as linear regression is a frequent used and easy to understand predictive method. With a linear regression you can investigate the relationship of the variable, which should be predicted, and one or more features.
I don’t know, how many of you know Kicktipp. Kicktipp is a very popular betting game in Germany, where everybody can start an own small betting community and can invite people to this community. This is very popular with friends and in companies especially during the big tournaments like the world cup. My company has also a yearly betting game for the German Bundesliga. The rules are very simple: You have to tip every match and you get 4 points for the correct result, 3 points for the correct goal difference and 2 points for the correct trend. In this post I will show you different betting strategies for Kicktipp and test, whether they are useful to win your personal Kicktipp betting game.
While browsing the internet and looking for some new inspiration to build an own predictive model, I came upon a very interesting possible feature: the Brier score.
The Brier score is a possibility to measure the accuracy of a predictive model. It gets often used to measure the accuracy for weather forecasts. First I thought, I could use it as a kind of calibration feature for a predictive model. So that a predictive model recognizes, when it was too inaccurate in the past. But using it as a feature to detect teams, which can be predicted well by the bookies or which could cause unexpected results, seems to be a more promising approach. Therefor I want to explain in this post, how to calculate the Brier score based on the last betting odds for a specific team.
In the last post the prototype of the Poisson prediction model has proven, that the optimised model is suitable to beat the bookie – at least for the German Bundesliga. The next step in the predictive model development process consists of implementing the model for forecasting the current fixtures. Regarding this model this part is very easy, as you need not to implement a trained model, just the prediction logic.