As a data-driven sports analyst raised in Los Angeles, I witnessed something rare: 100 fans queued for hours to play chess with me at Fanatics Fest—not basketball, not draft picks, but a game of Bayesian precision. I analyzed each move like a play clock: not luck, but expected value. This wasn’t entertainment. It was probability in motion. The crowd didn’t cheer for dunks—they cheered for optimal lines on the board. My moves weren’t emotional; they were calibrated. And when the model was right? They knew it before you did.