When we see "regression" in fantasy, it& #39;s usually about a player over or underperforming in a stat relative to the league-wide sample (usually over multiple seasons). When a player is way above or below the average of that sample, he& #39;s a positive or negative regression candidate.

It does not mean that every player regresses to the mean of the sample. We have very good reasons to believe some players& #39; true expectation over a big sample would be above or below the league-wide average. For example, we very much should believe Mahomes& #39; TD% is higher than avg.

Where people get confused with fantasy football regression is when a very good or very bad player is a "regression candidate". That doesn& #39;t mean they& #39;ll regress to the average, but it does mean they& #39;re projected to regress *closer* to the mean (ex: Mahomes TD% from 2018 to 2019).

Lets look at Amari Cooper, who scored 8 TDs when my model expected him to score 4 based on his usage.

Amari is good and he plays on a good team. His true baseline is above average, so we should expect him to regress to about 6 or 7 TDs instead of 4. It can be that simple.