The Story So Far

The prediction algorithm has been through 2 main versions so far. I unfortunately don’t have the original anymore so can’t quantify how effective it was comparatively, all I can say is that it wasn’t as good as V2 but better than just predicting the most common score from the previous season every week.

Version 1
A simple calculation of: total form diff * a + position diff * b + location form (home teams home form - away teams away form) * c + home advantage.

The values of A, B, C and home advantage were all calculated by searching all values in a range to find the values that produced the highest total number of correct result over all previous games. A very slow process but it gave decent accuracy.

Version 2
The predictive element was replaced with a system to predict the goal difference and another to convert that to a likelihood for each score line, so the one with highest probability can be picked.

A linear regression algorithm is used for the predictive element, where attributes from all previous games (including xG for and against) were put into a matrix, X, as well as the goal difference from each of those into a result matrix, y, and then a calculation is done to obtain a vector, w, to multiply the test game’s attributes by to get a predicted goal difference.

The probability of each score happening is calculated by multiplying together;

  • The probability that the goal difference of a certain score will happen - given the expected GD 
  • The probability of the home team scoring and conceding n goals 
  • The probability of the away team scoring and conceding n goals
 Much quicker as it uses a relatively simple method, if matrices can be handled easily and improves on the previous version.

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