NBA Game Outcome Predictor

---

At CodeCurrent LLC, we built an end-to-end machine-learning system that predicts NBA game outcomes and point differentials using multi-season historical data, real-time roster updates, and advanced modeling techniques.

This project demonstrates how our AI pipelines can transform raw, inconsistent sports data into accurate, automated predictions that run at scale — the same type of technology we deliver to clients across finance, telecom, logistics, and healthcare.

Our model ingests play-by-play, team stats, player performance metrics, and injury/active roster data to generate daily predictions for:

• Game winners
• Expected point differential
• Confidence scores
• Player impact metric
• Team momentum indicators
   

We designed a production-grade pipeline that includes:

• Multi-season dataset ingestion (5+ years)
• Automatic API throttling (NBA API rate-safe)
• Roster parsing to handle injuries, trades, and DNPs
• Rolling averages for player and team performance
• Automatic Parquet saving + recovery to prevent data loss
• Full caching system to avoid re-pulling stale API data
   

• Ensemble models (Random Forest, XGBoost, LightGBM)
• Neural network experiments for nonlinear patterns
• Customized features for rest days, travel distance, team fatigue
• Elo-style rating integration
• Hyperparameter tuning & cross-validation
• Model explainability with feature importance
   

• Batch predictions for today's games
• Multi-season training with automated retries
• Clean CSV/Parquet outputs for dashboards
• Modular Python code that plugs into notebooks or production APIs
   

• Python, Pandas, NumPy
• scikit-learn, XGBoost, LightGBM
• FastAPI (optional deployment API)
• Dask for performance scaling
• Docker for reproducible builds
• Excel / Tableau for validation dashboards
   

It’s a real example of how CodeCurrent builds fully automated, ML-driven prediction engines that adapt to new data, scale on demand, and deliver real business value.