Introduction

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.

Continue reading “A Prediction Model for Sports Match Results – Part 5” →