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Every aggregated MLB stat had its question baked in years ago. Raw events flip the model — every pitch is a row with 20+ tags. Seven example formulas show what's possible when you write the question yourself instead of consuming someone else's.
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Everyone's building AI sports prediction tools in 24 hours. But there's a difference between a model that looks like it's finding edge and one that actually is — and a viral experiment involving Dwayne Johnson explains exactly why.
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Building a sports prediction model is actually three problems: building something predictive, figuring out how to bet on it profitably, and proving the edge is real. Most tools solve one. Here's how Moddy solves all three — and what's happening under the hood when you build a model.
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A look inside one of Moddy's NBA models — and what it reveals about why winning bets doesn't always mean making money
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