If you look at the data from the NCAA four factors analysis (in my prior posts and in David Hess' posts) you might be thinking, correctly:
"This data explains where a team's points come from, but does not explain precisely how they could improve."
Then someone might respond:
"Now wait a second, doesn't this tell us how a team could improve? I mean, all Michigan State needs to do is take better care of the ball to win their games; the numbers say so!"
While the above answer is correct, it is important to realize that most teams don't have data points in Four-Factors ratings as striking as Michigan State's poor ball-handling.
What I will suggest is a continuation of what I have done in the past: figuring out how variable a team's factors are, and what causes this. For example, one might assume that a team thriving off 3-pointers (cough *Northwestern*) has much more variability in predicting offensive rating than one who thrives off 2-pointers, under the old adage, "si on vie par le trois, on mort par le trois." And I suppose it would make more sense to say that we can predict how a team's overall efficiency decreases against certain opponents via Four-Factor regression of individual games*.
Coming soon! (Gotta finish exams first...)
* By this I mean to run a linear/logistic regression to see how much an opponent's factors influence the team's factors.
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