NBA Stats Pipeline

Overview

A data and modeling pipeline for NBA game outcomes. Ingests games, players, coaches, per-quarter stats, and sportsbook odds. Trains an XGBRegressor on roughly 169 features to predict home-team margin, then calibrates a sigmoid over it for win probability. Quarter-level models (Q1 through Q4) run alongside the full-game model. A bet-result table and HTML viewer let me compare predicted vs actual outcomes. The FastAPI backend and React frontend are designed but not yet wired; right now it runs from scripts.

Highlights

• 169-feature XGBRegressor predicting home-team margin per game.
• Quarter-level sub-models (Q1, Q2, Q3, Q4) trained separately alongside the full-game model.
• Calibrated sigmoid converting predicted margin into a win probability.
• Odds integration (bet365, 1xBet, Betfair Exchange) plus a bet-results table for backtesting.

Challenges and approach

• Feature engineering across games, players, coaches, and per-quarter stats required a strict ingestion schema so new seasons could be added without rewriting downstream models.
• Calibration: raw margin predictions are not probabilities. Fitting a sigmoid over a held-out set produced calibrated win probabilities that actually compared to implied odds.