The next 10 match days are played in the German Bundesliga and so it is time for the next summary of the current betting history of my Poisson prediction model. During my summary about the first 10 match days I faced a really big loss. This trend continued also for the next match days, but some interesting observations can be made.
After 10 games played in the German Bundesliga, it is a good time to draw a small summary about the current stats of the betting history. If you follow my blog, you should know, I publish every pick at Pyckio. Until now I only publish the picks of the Poisson model. I am still investigating new prediction models.
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.
In the first part of this post I described, how a Poisson distribution can be used to predict football scores and why it is not sufficient to beat the bookie. The second part will now explain, how I balanced the disadvantages of the poisson distribution. This turned the model to an efficient predictive model, which can be used to gain profit against the bookie.
The first model I tested is based on the predictive models of Maher  and Dixon / Coles . Maher modelled the expected goals for a specific match as two independent Poisson distributions. After that, Dixon / Coles improved this model to balance some disadvantages.
In the previous post I described, how you can easily calculate the features of these models for any football match in the past. The first part of this post will show you, how to calculate the odds with the help of these features and why a simple Poisson distribution is not enough to beat the bookie. How I solved these problems will be the central element of the second part.
While learning something about sports betting, it is essential to compile betting odds to probabilities and probabilities to betting odds.
There are many ways to beat a bookie. One of the well known methods is arbitrage betting, where you try to find price differences between different bookies. Some years ago this was a really good method, but today, as every single information about sports is available throw the internet, it is hard to find difference between bookies.
I will mainly focus on the so-called Value Betting. But what is Value Betting and how does it work?