AdamJT13;3632208 said:
If you ignore stats, you wouldn't know that the team commits more penalties than it should or doesn't get as many turnovers as it should. And if you think they're meaningless, then you wouldn't pay attention to the stats about penalties and turnovers anyway.
The stats quantify what happens on the field. And they report both effects and cause, because one stat can affect another, which affects another, which affects another. And collectively, they cause us to lose.
I agree stats are valuable. That is what I do as a real gig. But You have to define all independent variables for the dependent variables (i.e. wins).
As you may be able to prove by yds against and yds for, Dallas is an outlier (reject the null). meaning with an alpha of 5%, we can say that with 95% certainty that these 2 variables are not influencing wins. We can prove it but it is empirical evidence to.
So you can figure in penalties (which you did a good job). but if penalties do not explainit all, it could be net penalty yds, lost yds, missed fgs, plus special teams, 3rd down success, etc.
Ultimately, you could come up with a multiple regression of significant variables that may show yds have nothing to do with it when you factor in others.
you may end up with a mx regression parametric equation: y=b+m1x1+m2x2+...mixi.
i thought about trying all this, but I don't have the time.
Even when you get to the solution, it is a predictive model, but its based on historical data. you can get a good idea, but why Barron IMMEDIATELY grabsand holds on the last play of the game could throw all of the other varaibales away (assuming it is the only penalty of the game).
Stats will get you some insight, but some times its obvious without the work, sometimes you lose man to man battles and it is a domino effect.
I think you have done a good job, but there is always a random variability.
I measure refinance/prepayment behavior. you think that lower rates and low refinance costs would entice borrowers to refi, but there are things like appraisal, and burnout that make it an inexact science. Stats can forecast to the best of the current data, but it cannot predict causes of extreme future events.
The questions is can the outliers produce new data to use as a dependent variable.