For scientific publishing, I looked into the latest available paper and apparently the data are best fitted by a model where the impact of scientific papers is predicted by Q.p, where p is “intrinsic value” of the project and Q is a parameter capturing the cognitive ability of the researcher. Notably, Q is independent of the total number of papers written by the scientist, and Q and p are also independent. Translating into the language of digging for gold, the prospectors differ in their speed and ability to extract gold from the deposits (Q). The gold in the deposits actually is randomly distributed. To extract exceptional value, you have to have both high Q and be very lucky. What is encouraging in selecting the talent is the Q seems relatively stable in the career and can be usefully estimated after ~20 publications. I would guess you can predict even with less data, but the correct “formula” would be trying to disentangle interestingness of the problems the person is working on from the interestingness of the results.
(As a side note, I was wrong in guessing this is strongly field-dependent, as the model seems stable across several disciplines, time periods, and many other parameters.)
Interesting heuristics about people :)
I agree the problem is somewhat different in areas not that established/institutionalized where you don’t have clear dimensions of competition, or the well measurable dimensions are not that well aligned with what is important. Loooks like another understudied area.
 Quantifying the evolution of individual scientific impact, Sinatra et.al. Science, http://www.sciencesuccess.org/uploads/1/5/5/4/15543620/science_quantifying_aaf5239_sinatra.pdf
I copied this exchange to my blog, and there were an additonal bunch of interesting comments there.