Performance Isn't a Bell Curve

What they were looking at

How does your company rate its people? Most performance systems assume a bell curve, with a big middle, a few stars and a few strugglers, all balanced around the average. Forced rankings, five-point rating scales and quota bands are all built on it.

Ernest O’Boyle and Herman Aguinis, two management professors, asked whether real performance data actually looks like that. They pulled together five studies covering 198 samples and 633,263 people:

• Researchers, counted by journal publications.
• Entertainers, by award nominations such as Emmys.
• Politicians, by how many times they were elected to the US House.
• Amateur and professional athletes.

For each sample they tested whether a bell curve or a power law fit the data better.

What they found

“individual performance is not normally distributed—instead, it follows a Paretian (power law) distribution.” O’Boyle & Aguinis (2012), abstract, p. 79

And it wasn’t close. Of the 198 samples, 186 fit a power law better than a bell curve. A 2017 follow-up by Harry Joo, Herman Aguinis and Kyle Bradley tested 229 samples against more shapes than those two. Only about 1 in 20 looked like a bell curve, and most had a heavy tail that tapers off a little faster than a pure power law, so heavy-tailed is the safer word. The point for a manager doesn’t change. If, as their results suggest, “most performance outcomes are attributable to a small group of elite performers,” then the people who matter most to the result are the few at the top. The paper draws a line between two very different things a manager can focus on:

“the productivity of the majority of workers rather than the workers responsible for the majority of productivity” p. 108

The paper won the journal’s first Best Article Award.

What it means

So who’s the average performer? If output is heavy-tailed, there really isn’t one. Most people sit below the average, a few sit far above it, and rules built for the middle get both ends wrong.

Equal territories, evenly shared leads and a bell-curve rating all assume there’s a middle holding most of the output. In a heavy-tailed team there isn’t, because a minority of performers make up a majority of the results.

Where it shows up in a revenue team

• Your reps’ output is heavy-tailed. A quota set on the average rep fits almost nobody on the team.
• Round-robin routing gives your best rep the same leads as everyone else, and sends your best leads to whoever happens to be free.
• Losing one top performer costs you more than losing several average ones.
• Look at your own team the same way. Rank your reps by the value of what they closed instead of the count, and see how much of the total the top few carry.