### Define variables: Brier score for market odds

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.

### Implement Model: Poisson distribution

In theĀ 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.

### How To: Install TensorFlow for Windows

I currently started to test machine learning algorithms to predict the results of football matches. I especially tried to use neural networks. But I soon realized, that the possibilities of R regarding neural networks are a little bit limited. So I want to take a look at TensorFlow. TensorFlow is a machine learning library provided by Google, which was already used for many different use-cases and proved its suitability.

As the installation process for TensorFlow was not self-explanatory, I thought, it would be a good idea to provide a small installation guide. I want to explain, how I installed TensorFlow and the Python GUI PyCharm.

### 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.

I decided to additionally share my sources, which I use to build BeatTheBookie. So everybody is able to re-use them and build his own analytical system. I added a new page to the blog, where you can find the link to the GitHub repository.

### Validate model: Poisson distribution (part 1)

The first model I tested is based on the predictive modelsĀ 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.

### Define variables: attack & defence strength

During my first investigations for predicting football scores I came across the predictive modelsĀ 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.

### Prepare data: football-data.co.uk (part 2)

In the first partĀ Prepare data: football-data.co.uk (part 1)Ā I described how the Data Vault model for the data of football-data.co.uk looks like. In the second part I will now focus on loading data into the Data Vault model. With the overall analytical architecture in mind this equates the data integration process between the stage layer and the raw data layer.

### Prepare data: football-data.co.uk (part 1)

InĀ the postĀ Gather data: football-data.co.ukĀ I described, how you can load CSV data into the Exasol database. As the data is now available at the Stage Layer in the database, IĀ must now prepare the data and persist it at the Raw Data Layer, so that I can easily use it for building predictive models.

With part 1 of this post I want to explain, what Data Vault modeling is and how the Data Vault model for the data structure of football-data.co.uk looks like. With part 2 I will explain, how you load data into the developed Data Vault model.

### Gather data: football-data.co.uk

When I started this project, my biggest problem was to find a source for historic football statistics and historic football odds. Fortunately, I found Joseph Buchdahl’s websiteĀ football-data.co.uk. This website is just great! He offers CSV files for 22 football leagues and about 19 seasons. He updates the data mostly two times a week. So I used this data as the starting point for my analytical system.