Regression To The Mean

In by NBAstuffer Team

Regression to the mean refers to the fact that those with extreme scores on any measure at one point in time will, for purely statistical reasons, probably have less extreme scores the next time they are tested. Scores always involve a little bit of luck. Real situations fall between these two extremes: scores are a combination of skill and luck.

How it applies to basketball statistics is, any athlete who posts a significant outlier, whether as a rookie or particularly after their prime years can be expected to perform more in line with their established standards of performance.

Statistical analysts have long recognized the effect of regression to the mean in sports; they even have a special name for it: the “Sophomore Slump”. For example, Carmelo Anthony had an outstanding rookie season in 2004. It was so outstanding, in fact, that he couldn’t possibly be expected to repeat it in 2005. Anthony’s numbers had slightly dropped from his torrid rookie season. John Hollinger has an alternate name for the law of regression to the mean: the “fluke rule”, while Bill James calls it the “Plexiglass Principle”.

Regression to the mean in sports performance produced the “Sports Illustrated Jinx” superstition, in all probability. Athletes believe that being on the cover of Sports Illustrated jinxes their future performance, where this apparent jinx was an artifact of regression.