Your model says a receiver will catch 5.2 passes. The market asks about 6 or more. Those are not the same kind of number. A player prop projection model has to bridge that gap, and this lesson shows how. You take a mean, choose a distribution such as Poisson, and work out the chance of clearing the line. Then you compare several model outputs against a no-vig market benchmark and watch the implied edge swing between them. If you cannot explain that disagreement, the edge is not proven. Finally you learn to test forecasts honestly, freezing them before game time and scoring them with Brier score, log loss and calibration bins.
Learning objectives
- Explain this technique and its assumptions.
- Reproduce the worked numerical example and interpret its limitations.
- Apply a documented research workflow and decide when evidence is insufficient.
