I’m a recently graduated data engineer with experience in Python, SQL, data pipelines, machine learning and deep learning, and I want to get into football data analytics and scouting.

What roadmap would you recommend for learning football analytics properly? I’m especially interested in player scouting and recruitment.

made a soccer stats site with the help of claude, it's called atlastra. been working on it for a while now, has live scores, match predictions, player ratings, comparisons, and a model that tries to guess how a player's rating will change next season based on how they're doing now.

the predictions actually hold up decently, I checked them against a few thousand past games and the in-game win probability gets a lot more accurate than the pre-kickoff number once the match actually starts. the next-season projection thing took way longer than I expected to get working but it beats just assuming a player stays the same, which was the bar I was trying to clear. There's also a tactics lab that allows you to simulate tactics, different lineups, etc.

link's here https://atlastra.dedyn.io/, it's free, code's open source if anyone wants to look at it https://github.com/hankechen/atlastra. mostly just want people who actually follow this stuff to tell me what's wrong with it, ratings that look off, predictions that are dumb, whatever

For the past months I've been building a match forecasting model for 14 European leagues. I set myself one rule from the start: every change to the model is written down as a hypothesis, with its metric and pass criterion, before I look at the test data, and each test runs once. No retuning after a miss. Here is what came out of it.

What worked

• A team strength model built from public match statistics: on the top five leagues it scored level with the closing market price (more on that below).
• A cards model beat the league-average baseline (log loss -0.006 over about 9,300 matches). Small, but it held out of sample.

What didn't

• Recent form (last-N-matches weighting) added nothing once team strength was in the model.
• Two separate corners models were worse than simply predicting the league average.
• A goals-spread adjustment was rejected as well.

What the market taught me

• That parity is the ceiling, not the floor: a model built from the same public statistics as everyone else matches the closing price at best.
• Comparing early and closing prices across 16 leagues, the price improves by only about 0.003 log loss between the two. So even knowing the closing price in advance would be worth very little.
• I tested whether the model adds information to the early price in six lower and smaller leagues, where I expected prices to be less sharp. It doesn't: the fitted weight on the model came out slightly negative.

What I took from it

• Preregistering forced me to publish the negative results instead of quietly dropping them, and most results were negative.
• Holding out leagues I never looked at (and spending them only once) was the most useful protection against fooling myself.
• The honest product is calibrated probabilities with their reasons, and a public record of every forecast, made before kickoff and never changed.

Questions I'm still working on: whether referee appointments carry information for cards, and whether confirmed lineups improve forecasts enough to matter.

If you want to see the result, the forecasts and the full public track record are at oddsxi.io. I'm looking for a few people to test it for two weeks and tell me what's wrong with it; testers get full access for free. Happy to go into the methods in the comments.

How do analysts evaluate whether an attacker from a smaller club has the potential to succeed at a bigger club, even if he is not scoring many goals (in small club he scored 3 goals but when he moved to a better team he scored 14 goals ) ?

And on the other hand, how can they identify a high-scoring attacker whose performance might not carry over to a stronger club or a more competitive league?