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Why Calibration Matters More Than Accuracy in Franchise Analytics

A miscalibrated analytics model doesn't just give a franchise the wrong answer. It gives the coaching staff and front office the wrong amount of confidence in the right answer, which is worse. The difference between a model that says "72% chance this prospect becomes a starter" when the true rate is 72% versus when the true rate is 55% is the difference between a sound draft pick and a wasted first-round selection. Most franchise analytics departments optimize their models for accuracy. They sh

April 2026
methodology

We Found a Bug That Made Us Look Better Than We Are. That's the Point.

A few months ago, our NFL simulator was showing 72% ATS accuracy. That's an extraordinary number. If it were real, we'd be printing money. We almost believed it. We didn't ship it. We pressure-tested it. And we found a bug — a subtle data leakage issue that was inflating our results. Once we isolated and fixed it, our validated accuracy came down to 65%. Still strong. Still well above market break-even. But seven points lower than what a less rigorous process would have let us claim. That mome

April 2026
methodology

Why Single-Point Predictions Are Lying to You

Every sports analytics tool you have ever used gives you a number. Team A wins 64% of the time. Player B scores 22.4 points per game. The spread is -3.5. These numbers feel precise. They are not. They are averages — and averages hide the thing that actually determines outcomes in sports: variance. A team that wins 64% of the time still loses 36% of the time. The question is not whether they win — it is under what conditions they lose, and whether those conditions are present this week. This

April 2026