After I realized my available data is definitely not enough to beat the bookie, I decided to start a new data journey and take a look at some more advanced statistics. And what could be better suited as Expected Goals (xG). This statistic is used more and more to explain this specific luck / bad luck factor, you feel, when watching a football match. In the first part of this journey I will explain, what are xG and what they tell you about a football match. Continue reading “xG data journey – What are ExpectedGoals?”
BeatTheBookie goes Tensorflow
After gaining much experience over a complete season, it is time to set myself some new goals. Until now I just used or tested predictive models, which were invented or described by other people. Now I want to try something new. I would like to create my first own predictive model, which should of course provide a better performance as the current Poisson model. This is where Tensorflow comes into play.
Define Objectiv: GS & PPG match rating prediction
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.
Continue reading “Define Objectiv: GS & PPG match rating prediction”
Beat the Bookie 