The first 10 matchdays of the current season in the Bundesliga revealed some clear disadvantages of my Poisson model. The predictor variables attack and defence strength respond too slowly to performance changes of single teams. This was clearly shown by the loss produced by the poorly performing FC Cologne. A normal SMA (simple moving average) does not use a weight. So latest results, which represent the current form, have not a higher priority over older results. As I looked for solution for this problem I stumbled over the EMA (exponential moving average). This post will explain the use of the EMA and how you can implement it inside the Exasol, so that it is usable as an analytical function for the predictor variables. On top I will show you, how you can analyse the team performance with help of MACD (Moving Average Convergence/Divergence oscillator ).
The first and the second part of this series explained some basic methods to optimise the regression models for the GS & PPG match rating. You have now a set of 3 different regression models (linear, polynomial and polynomial without outliers) for each predictive variable. These models now have to not only compete against each other, but also of course against the Bookie odds and the Poisson prediction model.