Implement Model: Poisson distribution

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.

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Validate model: Poisson distribution (part 2)

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.

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Validate model: Poisson distribution (part 1)

The first model I tested is based on the predictive models of Maher [1] and Dixon / Coles [2]. 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.

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Define variables: attack & defence strength

During my first investigations for predicting football scores I came across the predictive models of Maher [1] and Dixon / Coles [2]. Maher modelled the number of goals a team scores during a match as two independent Poisson distributed variables, for the home team and the away team. He assumed that each team has an attacking strength and a defence strength. Dixon / Coles extended this model by adjusting some disadvantages of the Poisson distribution and by using a time dependent attack and defence strength. Both papers are the base of my first predictive model.

In this Post I want to describe, how the attack and defence strength are calculated and how you add this calculation to the existing Data Vault model. The predictive model itself will be explained in another post.

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