Jouko Kylmäoja

I believe foo.csv and bar.xslx are de facto industry standards.

Absolutely I do - we have an internal tool within the FA that utilizes mplsoccer for the things that are relevant for the analysts! Whereabouts do you live, maybe I could show you what we're up to?

Jee voitto

Tomorrow: second leg of FIN-MNE in the UEFA EURO 2025 playoffs. Let's go Helmarit!

The future: here. Me: over there. What an amazing piece of machinery this is. And the best part: no more bloody oil money for mr. Putin's war chest.

That was a bitter blow yesterday. Finland lost to Kazakhstan at home in the Euro 2024 qualifications with two late goals from the Kazakhs. Splendid first half for the Finns, not so great second half. Oh well, onwards to the Nation's league qualifications and fingers crossed for a ticket to Germany!

Matchday! European qualifiers, Slovenia - Finland. Vamos Huuhkajat!

Phew, that's quite a lot of content for one go. I feel exhausted, I'd better go and watch some football. Let's see if anyone finds my ramblings from this new platform I just joined - more may follow if anyone's interested! Cheerio!

b) all throwins from couple of matches from the women's A national team.

Couple of examples: a) shots and goals from the women's A national team against Romania in the UEFA Nation's B league:

Mplsoccer is an excellent soccer data visualization library I have been using for various use cases. Not only does it enable pretty visualizations, but it can handle the oddities of different coordinate systems used by different data providers as well, among many other useful features. Good stuffs!

A few libraries I would like to point out: streamlit.io is a very nice rapid devepment library. You will have your web app running in no time. Performance can be meh and it has its own quirks, but I would say using Streamlit has been a godsend when having the need to prototype data stuff.

Why Python? Yeah well, the main reason for using Python with football data analysis is the huge selection of quite well maintained utility libraries. Pycharm is a good IDE for developing Python as well. R could be used quite as easily, but for the heck of it, I just chose Python.

Recently I have been tinkering with tools for analyzing the different aspects of the game, focusing on the matches of the Finnish youth and adult national teams, both in women and men's side. My data source has been Hudl/Wyscout, and the development language I have been using has been Python.

Fiddling with tools to be used in football analysis (and doing the analysis work too, of course) consists some 25% of my time spent. It's not much, but hey, it's honest work. Honestly.

Yeah so, content, let me see: first a wee bit of background: I am currently working for the FA of Finland. My responsibilites are spread between acting as the analyst for the womens national A team (Helmarit!), training new analysts for the FA, and creating tools for knowledge based management.

Hello, world!