Some thoughts on Machine Learning and Football Analytics

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

Damallsvenskan 2015 – season summary

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!

Continue reading Damallsvenskan 2015 – season summary

The importance of scoring the first goal

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

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

Impact of Game State to my Expected Goals/ExpG model

So, I forgot one thing when I made my ExpG model and posted the results here. I guess this is what is so great about trying to contribute to a community and be open about what one is doing.  11tegen11 gave me some feedback and I got curious too. Game State, the current state of the game regarding score should make an impact, I mean who hasn’t seen a player missing out on some clear opportunities when the game already is decided? Tonight I added game state and this is what happened… Continue reading Impact of Game State to my Expected Goals/ExpG model

My ExpG model evaluated and explained

So, I got some great feedback on my first blog post! Now I feel that I have to explain the model in-depth and make some validations. One thing first, I will try to never write long blog posts, Google Analytics tells me that the average visitor reads my blog for 3 minutes so the posts I write will all be readable in that time.

Continue reading My ExpG model evaluated and explained