If the purpose of bets is measure your model against the world, it seems to me that the more valuable lesson is how often you are surprised in the process of evaluating the bet than how often you are correct. If you put 40 or 65% on the hypothesis that the restaurant falls in a particular bin, you aren’t surprised either way by the answer, but you both erred in believing that there were just two bins.
If the purpose of bets is measure your model against the world, it seems to me that the more valuable lesson is how often you are surprised in the process of evaluating the bet than how often you are correct. If you put 40 or 65% on the hypothesis that the restaurant falls in a particular bin, you aren’t surprised either way by the answer, but you both erred in believing that there were just two bins.