Quick note on Four Factors

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.