Earlier quoted context omitted.
No, overfitting is a real thing. Overfitted learning algorithms are generally worse at generalizing their ability to broader examples and new situations. The types of candidates who spend the time necessary to memorize algorithm trivia for the sake of passing these exams are exactly like overfitted learning algorithms. What they happen to know is unlikely to generalize well. Of course you could get lucky and hire som…
Totally disagree with the final sentence. Most math/programming olympiad winners are way more than capable of handling anything the Macgyver/Edison type would be good at. At least in my industry, every olympiad winner has been a consistently spectacular performer, and I have absolutely no qualms heavily biasing myself towards that credential.
But I grant this is reasoning just from the anecdata that I have. I can believe that winners perhaps represent a higher degree of skill, but then we're talking about an extremely small number of people.
Generally you're facing a tradeoff where you have to choose between a sort of rustic self-reliance skill set versus a bookworm skill set. People from either group can learn the other over time, but you can't predict how well by testing them solely on trivia that constitutes their current main group. My preference is to hire for self-reliance and learn bookworm stuff later. I used to believe the opposite (e.g. hire someone good at math because they can always learn to be an effective programmer later) but my job experience made me believe the opposite (e.g. actually it's pretty easy to teach people stochastic processes, machine learning, or cryptography, but it's incredibly hard to teach people how to be good at creative software design).