[Biff]

Originally Posted by Jay Random

It depends what you're after, doesn't it?

If you're satisfied with correlations, and don't give a damn about understanding how the correlations work or what the causative factors are, by all means content yourself with possession proxies.

If you want to figure out how hockey games are actually won and lost, and learn something from data analysis that coaches and managers don't already know, you need more than correlation. You need to do some actual science. To begin with, when there are significant exceptions to a correlation, you can't just classify them as exceptions and shrug them off – because the exceptions disprove your model. You need to look at the exceptions, study additional factors, and refine the model.

The next job for hockey stats people is to explain why there are exceptions that Corsi and Fenwick do not adequately predict. And no, writing it off as ‘luck’ or ‘unsustainable’ is not an explanation; it is a refusal to explain. These people are staring at a golden opportunity right here in Alberta, with two teams that consistently defy their expectations, one in each direction. Instead of studying these phenomena and trying to figure out what additional factors are involved, they screw their eyes shut and chant, ‘Corsi and Fenwick! Corsi and Fenwick! Everything is explained by Corsi and Fenwick!’

This. A thousand times this. I am tired of pseudo-intellectual navel-gazing and data-wonking masquerading itself as enlightenment when it's a classic Emperor's New Clothes-esque denial of a fundamental principle of good science. The Corsi / Fenwick model is broken. Refine it, or stfu. That's science!