AI betting accuracy is commonly defined as the percentage of picks that result in a win. This definition is wrong — and using it leads to systematically bad decisions about which AI prediction systems to use. A 90% accuracy system can lose money; a 55% accuracy system can be highly profitable. Accuracy without reference to odds, sample size, and calibration tells you nothing about a system's profitability. The correct measure of AI betting accuracy is calibration: do the system's stated confidence percentages match its actual win rates across large samples?
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