Take a sport where teams play in leagues and have a "final standing" each year, typically the proportion of games won, in which case the average over all teams must be 0.5. The regression effect predicts that, for a team with above average performance this year, say a final standing of 0.6, its final standing next year is likely to be >less than this year's 0.6.
(Analogously, for a team with below average performance this year, say a final standing of 0.4, its final standing next year is likely to be >more than this year's 0.4)
This effect will be most noticeable for the best and worst teams of the year. The table shows data over 25 years. If we had made this prediction each year for each of the top 3 teams, or each of the bottom 3 teams, the final column shows how many of these 75 predictions would have been correct.
|Sport||Games||Teams||Predictions for||Proportion correct|
|U.K. Premier||38||20||Top 3||60%|
|Italy. Serie A||38||20||Top 3||68%|
|Spain. Primera||38||20||Top 3||60%|
|Germany. Bundesliga||34||18||Top 3||71%|
|Portugal. Europa||30||16||Top 3||53%|
|France. Ligue 1||38||20||Top 3||71%|
|Netherlands. Eredivisie||34||18||Top 3||64%|
Why does this imply a sucker bet? A self-proclaimed expert in the sport who has not paid attention to freshman statistics might have some specific information -- recruiting a top player in the off-season -- leading them to believe a top team's performance is likely to improve in the upcoming season. This is of course possible, but is more likely to be outweighed by the regression effect.