My first reaction was "No fooling!" (Actually, "fooling" is an editorial substitution. I can be a bit cranky when I'm torn from the arms of Morpheus.) Hot on that first reaction was this: "The normal density is not a fractal." Specifically, the right tail of a bell curve is not itself a bell curve ... and if I can recognize that while half asleep, it must be fairly obvious. So where's the beef?
The first figure below shows density functions for Gaussian (red) and Pareto (blue) distributions. The second figure shows a plausible Gaussian distribution for athletic ability among the overall population. Professional athletes, or Division I collegiate athletes, presumably fall in the right tail (shaded). The right tail bears considerably more resemblance to the Pareto distribution in the first figure than it does to a Gaussian distribution.
The research documented in the paper covered five studies (one for each of the fields I listed above) using 198 samples (a variety of performance measures for each field, or subsets of each field), involving a total of 633,263 individuals. As with any statistical study, you can question methods, sample definitions, interpretation of results etc. I've read the paper, and I find the evidence fairly compelling, particularly as it confirms my initial intuition (expressed in my waking reaction).
I'm not sold on Pareto as the correct choice among one-tailed distributions, and I'm not sold on single tail distributions in general. Consider, for instance, a performance measure on a [0,1] scale, such as career batting average for baseball players. The bounded domain precludes a long, thick tail on either side. A friend of mine did in fact download and fit some batting average data. I won't reproduce it here, since I'm not sure how his sample was defined (all players or just selected ones), but the histogram he showed me was a lot closer to Gaussian than to Pareto.
That said, it seems intuitive to me that if performance correlates strongly to ability, if ability has a roughly Gaussian distribution, and if there is a selection mechanism in play that selects the most able to compete, then performance among the competitors will not be Gaussian. This needs to be qualified in a variety of ways, including the following:
- Not everyone with high ability may choose to compete. Athletes may find it more lucrative (and less dangerous) to act in adventure films; people capable of great research may find it more lucrative to work in industry (where opportunities to publish are greatly diminished).
- Not everyone with high ability may have the opportunity to compete. Some star athletes are forced to retire prematurely, or to abandon hope of starting a professional career, due to health concerns. Potential stars in any field may go undiscovered due to where they live or what schools they attend. At the risk of creating a distraction by introducing a somewhat charged topic, women or members of an ethnic (here) or religious (elsewhere) minority may be excluded or discouraged from entering the competition, regardless of their ability.
- Nepotism (particularly but not exclusively in the entertainment field) may introduce competitors who do not reside in the right tail of the ability distribution. To a lesser extent (I think), diligence, hard work or "heart" may allow some people with above median but less than excellent athletic ability to compete in high level events.
- Productivity is not always a monotonic function of talent. A very talented wide receiver may not catch many balls if he is on a team with a relatively poor quarterback, a strong running game, and/or a stellar defense (allowing the team to practice a conservative offense). Conversely, a modestly talented receiver on a team with no running game and a poor defense (so that they are perpetually playing catch-up) may catch a disproportionate number of passes. Similar things can happen to an academic with great research potential working at a "teaching school", or in a department lacking resources and not providing colleagues or doctoral students with whom to collaborate. (Conversely, some faculty are adept at riding the coattails of their more productive colleagues, showing up as coauthors of all sorts of papers.)
Now it is not the case that the response variable (here, the performance measure) need be normally distributed in order for the residuals to be normally distributed. Unless ability is adequately covered by the explanatory variables, though, the effects of ability will be seen in the residuals, and if the distribution of ability among the sample (those who likely were chosen at least in part on the basis of ability) bears any resemblance to a Pareto distribution, it seems fairly unlikely that the residuals will be normally distributed ... and fairly risky to assume that they are. Some academic papers cite specific tests of the normality of residuals, but in my experience it is far from a universal practice.
The authors of the paper point out a second issue related to this. Extremely high values of performance are more likely to occur with Pareto distributions than with Gaussian distributions. Some (many?) authors, taking normality for granted, treat extreme values as outliers, assume the observations are defective, and "sanitize" the data by excluding them.
So consumers of academic papers studying performance may be buying a pig in a poke.
 O'Boyle Jr. and Aguinis, "The Best and the Rest: Revisiting the Norm of Normality of Individual Performances." Personnel Psychology 65 (2012), 79-119.