[stupac2]

The problem with Sagarin's (and others') predictive models is primarily that they are purely statistical with no adjustments for factors that may have distorted previous results (e.g. lucky plays, officiating errors, personnel changes) or will impact future results (e.g. injuries, weather conditions).

Well, sure, but really the problem is that you can't perfectly predict the world, and college football is a subset of the world. There are systems that do a better or lesser job, but there's not really any way to talk sensibly about the metagame without relying on analytics like these.

[stupac2]

The problem with Sagarin's (and others') predictive models is primarily that they are purely statistical with no adjustments for factors that may have distorted previous results (e.g. lucky plays, officiating errors, personnel changes) or will impact future results (e.g. injuries, weather conditions).

Well, sure, but really the problem is that you can't perfectly predict the world, and college football is a subset of the world. There are systems that do a better or lesser job, but there's not really any way to talk sensibly about the metagame without relying on analytics like these.