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.
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, not many blog posts here recently. Basically that just has been an effect of me having too much to do at my day time job. The good news however is that I will be able to do some analytics the coming weeks at work. My cooperation with, now promoted to Allsvenskan, ÖFK has grown and I’ve managed to convince my employer to start looking at a bigger project. This will include experts in design and UI/UX, to visualize and analyze data and stats. It has a huge potential and I’m really excited. I hope there will be more to come from this on the blog the coming months! Continue reading Some thoughts on Machine Learning and Football Analytics
A while back I listened to the Analytics FC podcast, episode 4 and now recently the episode 14. Among other things they talked about womens football and the lack of available (open) data. When I heard it I thought I’d give a try with the data I have gathered for the Swedish League – Damallsvenskan. So here is the summary of Damallsvenskan 2015!
To quote coach Lars Lagerbäck – “Goals change games”.
At first, this post was just going to be a brief follow up of the preview I posted last week before ÖFK – Sirius. Well, the problem was that when I made the XG map after the game it just didn´t seem to make sense.
Continue reading The importance of scoring the first goal
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!