I think its fine for something like CPOE, which fairly stable after one season of performance Basically a QBs past CPOE tells us something about their future CPOE, so it probably measures something about their ability

But I really don't know what make of RYOE, which is super unstable year to year (min 50 attempts) To me this says the model's error term has a lot more in it than can be explained by the RB, so we shouldn't say RYOE is a RB stat unless we've got a big af sample

Like, even if we only look at RBs with >1000 career carries, yards per attempt and RYOE have a correlation north of 0.60 A single game of RYOE might as well be YPA

A full dive here

@greerreNFL Curious what would happen if you assign RYOE into QB instead

@903124S omg this is diabolical Why can't running backs have nice things

@greerreNFL God in Heaven do I ever think about this. Building narratives out of residuals.

@CoachCClement Im guilty of this, but like, we should try not to do it There is one tweet specifically Im thinking of that is just egregious confirmation bias using an OE metric

@greerreNFL Nobody talks about these choices. With FG kickers it makes sense but in baseball for example example, we only only assign variance to variables that demonstrate it is theirs.

@bachlaw Its a simple requirement, but 100% the correct one and well said

@greerreNFL preach brotha

@greerreNFL Your statement is absolutely true. RYOE & CPOE are aggregate residuals of a model. We are still working through the bridge between our current CNN architecture and what we believe to be a possible solution (mixed effects). Then there is the temporal aspect (change over time).