Originally published at nbaproplab.com/learn/model-postmortem.
A model that looks excellent offline and random in production is usually blamed on the serving code. Ours was not: the two computations matched to 2e-16. The cause was a calibration snapshot whose export query nobody wrote down.
What we built
We score NBA and WNBA player props through seven independent blocks: player form, matchup, game context, market line, teammate synergy, analysis quality and external signals. Each returns a 0-100 score with a confidence index, and a confidence-weighted mean produces the final score that decides whether a pick is published.
On top of that engine we fitted a logistic meta-learner. It takes the seven block scores plus the pick direction and outputs a single calibrated probability that the pick hits. The idea was sound: a weighted mean treats every block as positively predictive, while signed coefficients let an anti-predictive block subtract instead.
