Welcome to Regression Alert, your weekly guide to using regression to predict the future with uncanny accuracy.
Before we get into this week's statistics, let's look back at how previous predictions have fared. Each prediction in this column takes the following form: identify a statistic that is prone to regression, gather all of the players doing best into that statistic into Group A, gather all of the players doing worst in that statistic into Group B, note how much Group A has outperformed Group B to that point, then predict that Group B will outperform Group A going forward.
By and large, I'm not allowed to cherrypick which players I think will regress and which won't. If my process puts Antonio Brown into Group A... well then, Antonio Brown is in Group A. If it puts Isaiah Crowell into Group B, then Isaiah Crowell is in Group B.
(The one exception is for players with atypical roles; short-yardage backs are unlikely to see their ypc increase, change-of-pace backs are unlikely to see their rush attempts increase, etc.)
A key component of this format is accountability. All predictions are trackable and testable. Through immediate feedback, we are able to learn and improve. So here's where things stand at the moment.
In Week 2, I outlined what regression was, what it wasn't, and how it worked. No prediction was made.
In Week 3, I listed running backs with exceptionally high and low yards per carry averages and predicted that the low-ypc cohort would outperform the high-ypc cohort over the next four weeks.
In Week 4, I looked at receivers who were overperforming and underperforming in yards per target and predicted that the underperformers would outperform the overperformers over the next four weeks.
In Week 5, I compared the predictive accuracy of in-season results to the predictive accuracy of preseason ADP. Outside of a general prediction that players would tend to regress in the direction of their preseason ADP, no specific prediction was made.