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Elite 1k, Analytic 128, FPL-modelling 🥝 21/22 1.8k MD 61st // 22/23 MD 394th // 23/24 ID 486

Jul 4, 2022, 7 tweets

Finally made 5k 🎉

Thanks all, been sitting at 4.9 for a while 🙇‍♂️.

Probably have @AnalystNik11 to thank for the latest influx even though all I did was show how to use a simple macro 😅.

To celebrate here's a short thread, new followers I promise they're usually better 👇🥝!

Ever wondered how the 🥝 calculates team strengths but been too afraid to ask?

Well some other people asked for you, so here's what I told them!

(I will eventually get round to a nicer more detailed thread but I figured this would still be useful)

#FPL #modelling

Here is my explanation in DMs. There are a few more tweets worth of screenshots 😅!

The main thing I anticipate questions about is "how do you account for X change using only past data?"

Every question of that format is answered by "using odds" 🎲🙃.

There are lots of 🥝

things that models like these with only basic public data will find hard to account for. Impacts of new managers, transfers, formations/styles, injuries, fatigue from cups, etc.

Even ideas like depth to cover future injury, Jan window moves, manager/style change mid-season... 🥝

In theory, ALL public information is contained in odds. If it wasn't, you could make money by betting 💰.

They aren't perfect, but as long as there is money to be made & the most informed people want to make money, the odds should represent them 🥝.

🔞 begambleaware.org

One thing I have noticed in my model though is that over a season it tends to underrate top teams & overrate bottom teams (compared to odds).

To help offset this I am considering adding a "club wealth" factor, which should help with things like depth/future transfers 🤔🥝.

And the final piece...

Feel free to ask questions, I may have forgotten something or just don't choose to model everything (or goofed)!

(PS my team strengths for the new season will come after the game releases and I add all the new player data - maybe in a week or so)

🎤🥝

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