Quote:
Originally Posted by Cobby
Using p= 0.05 as the test of significance, here are the traits that mattered for players with POT = 20:
WE..............Very Low...p = 0.046
WE.............. High.........p = 0.043
Greed.......... Low..........p = 0.014 (hmmmm?)
Leadership..Very High..p = 0.018 (hmmmm?)
A few others were close to the significance line and I think a larger sample size might have pushed them over the limit.
These include INT Very High at 0.062, and WIN High at 0.067
But the point is, these differences in development are with players of exactly the same overall Potential, so we can rule out correlation with potential as the cause in these cases at least.
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I'd caution against reaching that conclusion, for two reasons:
- if you look at 30 sets of data that have no correlation with anything, and use a 0.05 significance level to judge if they're correlated with something, you'd naturally expect 5% of your sets of data (so one or two of them) would seem significant, just because of random variance and the definition of a p-value. Finding spurious correlations in that way is what's called "p-hacking". That's very likely the explanation for at least some of your findings, since there's no logical reason they should be true otherwise - for example, there's no logical reason a "Low" Greed rating would be beneficial for development, but a "Very Low" Greed rating would not be. If the extremes of a rating scale are not more obviously beneficial than the non-extremes, that's at least a reason to question the importance of that rating;
- probably more importantly, you pointed out earlier that certain personality ratings are correlated with Potential in your sample. If that is a genuine correlation, it probably persists when you remove the top players from the sample. That is, among the 20 Potential guys, the players with good Personality may be better, in general, than the guys with bad Personality. Guys with 20 Potential are not all alike; some might be rated 50/50/50 and some 40/40/40. The 50/50/50 guy is more likely to reach the Majors. And if that is true, your results might only be indicating which players had the higher initial potential, not how personality ratings influence development after player creation.
Because of that latter issue, I think the correct way to investigate whether Personality ratings have an influence independent of correlations with potential ratings is to do a multiple regression only on the > 20 POT group, with log(potential) and personality ratings as your regressors. Excel will do that for you easily and will give you p-values for the coefficients for each personality variable, and that would tell you if they have an influence independent of Potential.