A Prediction Model for Sports Match Results – Part 5


I am quite happy to have arrived at the final post of the series – not because I disliked writing the previous posts, but because I have worked for four years with the models presented in this series. I have become accustomed to their weaknesses and strengths, as well as those of the methods used for comparison. This post, however, is new and fresh. Finally, I have the change to put on paper (sort of) all the ideas I have had over the years on how to improve the model.

I don’t think it will surprise anyone reading this to know that I love modeling data. One of the reasons I love it is that modeling is not an exact science, as one might expect it to be. Modeling is just as much an art as a science; there are no certainties when we work with Probability Theory. Dealing with probabilities means making your peace with the fact that you will always be uncertain about your conclusions.

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