Sorry mate, but as a guy who has some background in Statistics, that model fails on so many levels.Thanks for all PMs
Here's the finished chart. The money spent on each squad plotted against finishing position. According to Excel the correlation is 74%. That means that three quarters of the teams in the EPL finished within 1 standard deviation of their squad cost position. Only one quarter of teams finished away from their determined position.
This means that money is the most important factor in determining where you will finish. But of course everybody knew that anyway. Nobody ever said it's the only factor.
The teams that broke free were West Brom and Burnley who did much better than expected. The worst were Man U and Sunderland who did worse than expected.
So there you have it. Anybody who said it was rubbish @Sammy1887 and @BBF are just talking through their arses
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To begin with, it seems you have taken an error rate of (+-)3 positions or maybe (+-) 4.
In the case of the former, if a team is based on the 10th position, and it finishes 7th or 13th, you are still regarding that acceptable. Which means even without squad cost or any such measure, the probability of getting the positions right is a massive 35% just because of your insane error rate which basically covers 7 positions out of 20
And in the case of the latter (+-4) ,it covers from 6th to 14th. which is 9 positions and has a probability of getting the positions right of 45%.
The overall accuracy or correlation as you called it is 74% which is next to meaningless in the case where your error itself has a probability of 35-45%. Meaning, more than half of your correlation may actually be because of "error". If you could run the same model with an error of +-1 or maybe +-2 and then give the correlation, that would probably be much more indicative of the success or your theory.