Reserve Team Hopeful
February 12, 2022
These are all bits of Python code I'm happy to give away please PM me for any/all. I won't be supporting them, but if they need a csv sheet I will include that so you have the correct format(s). Do not expect this code to be beautifully written either. I'm a novice. Expect functional only. I'm assuming you can download modules as necessary.
I'm also happy to discuss all of the following if you want to approximate some of it using a spreadsheet.
My best ones...
Representative Teams: Gives the score of players needed to build a team capable of vying for promotion this season and beyond (as far as you wish really) this is Machine Learning (ML) based and is well over 99% accurate.
Viability score: Assess players on auction list of for sale on basis of age (with age related changes coded) for next season and if they would be effective in team capable of vying for promotion.
Tactics sheet: Gives number of team/formation tactics/RAT and compartive home/away goal difference, points per games, goals for/against of 2 teams involved.
Team Sheet updater: gives a fully updated team sheet (for Tactics) and so I know how I could expect them to develop or degenerate next year:
Budget Sheet: Full budget details and projections for the team for as far as I wish including all factors you could think of.
Useful and either limited or clunky...
Stadium Dev: This Updates a csv sheet to allow you to model growth with expected crowd size
Following projections: I wanted to know what I can expect my following to be, so I can develop a stadium plan and a financial plan
Results updater: So I can keep my divisions results updated with minimum effort (for Tactics)
Needs further development...
End of season POS predictor: So I know what position a team is likely to being in at the end of the season – it is based on goal difference and is 91% accurate
End of season Points predictor: So I know what average points a team will need to have acrued at the end of the season in order to be in 'X' place, this is 95.2% accurate
Auction Player: So I know the likely price of a sold player at auction. This is 93% accurate.
Auction Non Player: So I know the likely price of a sold staff member at auction. This is 95% accurate.
Just to be clear. I only use Linear Regression in Machine Learning which anyone could realistically approximate using a spreadsheet and 'line of best fit'. I don't have the competence to use anything more sophisticated, which I wouldn't use anyway, since I would consider it cheating: because not everyone can do it.
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