(NOTE: This article is part of an ongoing series documenting an experiment with using AI to fill the NCAA brackets and see how it fares against years of human experience. The original article is as follows.)
A week ago, I wrote about entering an NCAA tournament pool with a more disciplined process than I usually use.
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Instead of relying on mascots, vibes, or whatever team happened to look great on Saturday afternoon, I tried to think about the bracket the way an investor or analyst would: separate raw forecasting from expected value, build one bracket around the highest probability of success, build another around pool dynamics, and make decisions with at least some awareness of uncertainty.
That process produced two brackets. One was the “most likely” bracket, designed to maximize the odds of a strong score if the tournament followed a mostly rational path. The other was an
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