Using AI for betting correctly means treating model output as a probability estimate to compare against market odds — not as a guaranteed outcome. The practical workflow: read the model's probability and confidence tier, compute expected value against the bookmaker's de-vigged implied probability, size the stake proportionally using a fractional Kelly approach, and log every result to track real calibration over time. This guide walks through that workflow step by step, and separates realistic AI capability from the exaggerated claims common in this space.
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