With Östersunds FK starting their Europa League group stage campaign with two straight victories I wanted to assess their chances of going through from their group.
This season I won´t do a regular season summary of Allsvenskan the way I did last year. Instead I will tell the story about GIF Sundsvall, and introduce a new area chart visualization
Since we have two really unexpected teams in the first two spots of Allsvenskan after 5 rounds I thought it would be interesting to look for some explanations in the numbers.
I just wanted to post a brief update of what I have been working on lately. The Swedish leagues are soon starting and the work I’m doing together with ÖFK is making some real progress. So expect a lot more on the blog in the coming months!
While I was at it I made three more projections at more or less the half way mark of the seasons. Continue reading 3 more projections
With most of the European top leagues reaching the half way mark I thought it was time to start looking at my league projections again. Continue reading The how good was my prediction post.
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!
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.
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!