If no player’s skill ever changes, why does their “best season ever” almost never happen twice?

Athletes who land on a magazine cover after a career-best season often follow it with a quieter one — a predictable pattern, not a jinx.

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Avg. score, their record-breaking season
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Avg. score, the very next season
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League average, both seasons
Slope chart showing top-5% performers' scores dropping from their peak season to the next, while staying above the league average

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There's no jinx anywhere in this code — just a fixed skill number and a fresh random draw each season. The top performers are still above the league average next season; they just aren't as spectacular as their record season made them look.

The same thing happens anywhere you select for an extreme result — a hot sales quarter, a breakout test score, a viral post. Expect the next measurement to look more ordinary. That's a statistical pattern, not a jinx, a slump, or a sign that something changed.

After someone’s career-best year, what should you expect next year?

Why does the top group specifically fall back?

Every simulated player has one fixed true skill level for their entire career. Each season's score is just that fixed skill plus random noise — good luck or bad luck, drawn fresh every time. Nothing decays, nothing is cursed, and nothing "knows" it was on a cover. Pick the top 5% of one season's scores and you've mostly picked the players who got lucky that year, not just the most skilled ones. Next season, that luck resets, so their scores fall back toward their true skill, which was always a bit closer to average than their peak season made it look.

It is the same selection effect as our Survivorship Bias exhibit, pointed the other way: that one hides the cases filtered out entirely, while this one over-weights whichever case happened to land on the extreme end of ordinary luck. Both make a data set look like it is telling a different story than the noise it is actually built from.

Related exhibits

Sources: the "Sports Illustrated cover jinx" is a long-documented piece of sports folklore (see Sports Illustrated's own reporting and Psychology Today, "The Sports Illustrated Cover Jinx," October 2016) that statisticians commonly cite as a real-world illustration of regression to the mean — the same effect Francis Galton first identified in 1886 comparing parents' and children's heights. Rather than rely on unverified real cover statistics, this exhibit simulates the underlying mechanism directly (fixed skill + random noise per season) with a seeded random-number generator. The first numbers shown are exactly reproducible from this page's own code; the "Run a new simulation" button draws fresh random players each time.