Projecting league positions, part 3 – Allsvenskan!

So, to sum the last two posts up. The lower the delta, the fewer rounds to predict – the better the model is at projecting the table positions and how many points a team will get. The easiest way of visualizing would be to look at how well the model performs in terms of correctly guessed table positions or near table positions. Continue reading Projecting league positions, part 3 – Allsvenskan!

Projecting league positions, part 2

This post turned out to become quite a lot related to programming. As I mentioned in the last post my idea was make my predictive table positions/points model more accurate by adding more data to it. I wanted to use my own football-data parser in order to add shots data, including TSR to all teams included in the model.

Continue reading Projecting league positions, part 2

Projecting league positions, part 1

With the Swedish leagues slowly coming to an end I wanted to look at creating a method for making a qualified, statistically based projection of how football leagues will end up. As the name of this blog now implies, I have approached the problem with machine learning and the results look really promising!

Continue reading Projecting league positions, part 1