NBA Game Outcome Predictor

This project applies machine learning to the exciting domain of sports analytics, specifically predicting NBA game outcomes. By analyzing vast amounts of historical data including team statistics, player performance metrics, and situational variables, the model learns patterns that indicate game outcomes.

The system aggregates data from multiple seasons, performing feature engineering to extract meaningful predictors such as recent team form, offensive/defensive efficiency ratings, pace of play, and head-to-head history. The predictive model serves as both a practical tool and a demonstration of applied ML techniques.

The data pipeline begins with scraping NBA statistics using the nba_api Python library, collecting team stats, player metrics, and game results spanning multiple seasons. Raw data undergoes extensive cleaning and transformation in Pandas, handling missing values and ensuring temporal consistency.

Feature engineering creates derived metrics including moving averages for recent performance (last 5/10 games), offensive/defensive ratings, rest days between games, and home court advantage factors. The feature set includes 50+ variables per matchup.

Multiple ML algorithms were evaluated: Logistic Regression (baseline), Random Forest, Gradient Boosting (XGBoost), and Neural Networks. Models were trained on 70% of historical data with 30% held for testing. Hyperparameter tuning used grid search with cross-validation. The final ensemble model combines Random Forest and XGBoost predictions.

The system outputs win probabilities for each team along with confidence intervals. Model performance metrics and feature importance visualizations are generated using Matplotlib and Seaborn.

The final model achieved 68% prediction accuracy on test data, significantly outperforming baseline random chance (50%) and simple win-rate predictions (62%). The model performs best on games where team strength differentials are clear, achieving 78% accuracy when predicting by 10+ point margins.

Feature importance analysis revealed that recent team form (last 10 games), offensive efficiency rating, and pace differential were the strongest predictors. Home court advantage contributed approximately 3-4% to win probability.

The model successfully predicted 72% of playoff game outcomes, demonstrating robust performance on high-stakes matchups. Future improvements include integrating player injury data, lineup-specific statistics, and betting market odds as features.