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Analytics in Football - Official Thread

A good read. Interesting to City have the top 2 players in this analysis.

Haven't checked, but perhaps they don't necessarily overlap on the pitch anywhere near as much as guys who typically pop at the top of these lists. So are job-sharing to some degree.

 
Encouraging, but not surprising, to see Wirtz appear near the top of these playmaker charts


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Interesting development: Fotmob are doing xG for League of Ireland Premier Division. Only started last week so won't be too comprehensive for this season, as a whole, but hopefully will continue for a long time to come.

In a totally related matter, how would you be at automating the collection of per shot xG data from that site? @Philby

I've tried to get AI to help me (reluctantly for environmental reasons), but I don't think my prompts have been precise enough to convey just how tick I am with this scripting stuff. It seems to think I can work Python, when I can't.
 
Scraping from sites like FotMob would likely be fairly achievable using Python alright.

Obviously scraping shot-by-shot, game-by-game data would be a bigger undertaking than scraping averages (e.g. player X averages 0.25 xG per 90 across season 2024/25), because you'd need to build a list of games and scrape shot data game-by-game.

What's the use case for granular shot-by-shot data?

What have you tried so far scraping-wise?

(FYI I don't think it'll be achieveable to craft a prompt for an AI tool, e.g. ChatGPT, which will actually scrape the data directly and return it within the ChatGPT window. But if the thought is to create a script in Python (or other technology) then there are ways to use ChatGPT to help you interatively develop versions of that script. But you'd need somewher eto run that script - e.g. on your local machine or even Google Colab)
 
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