NFL Stats ETL Pipeline
Apache Airflow Orchestrated Data Processing System
Project Overview
Objective: Build a comprehensive ETL pipeline for collecting, processing, and analyzing NFL player statistics using Apache Airflow for workflow orchestration. The system scrapes data from NFL.com, processes player statistics across multiple categories, and provides analytics capabilities including machine learning model evaluation.
Innovation: Historical year parameter support (2000-2025) enables time-series analysis and year-over-year performance comparisons with robust CI/CD integration.
Source Repository
Airflow DAG Architecture
🔍 Extract Phase
- Web Scraping: Selenium-based data collection
- Multi-Category: Passing, Rushing, Receiving, Scoring, Tackles
- Year Selection: Historical data from 2000-2025
- Async Processing: Concurrent data retrieval
- Error Handling: Robust retry mechanisms
⚙️ Transform Phase
- Data Cleaning: Standardized player names
- Normalization: Consistent statistical formats
- Aggregation: Unified dataset creation
- Feature Engineering: ML-ready data preparation
- Validation: Data quality assurance
📤 Load Phase
- AWS S3 Storage: Scalable cloud storage
- Model Evaluation: ML performance metrics
- Report Generation: Analytics summaries
- Data Cataloging: Metadata management
- Version Control: Data lineage tracking
🎯 Historical Year Parameter Feature
Innovation Highlight: The pipeline supports collecting NFL statistics for any year between 2000-2025, enabling comprehensive historical analysis and trend identification.
📅 Usage Examples
python main.py --year 2022python main.py --category rushing --year 2020- Airflow DAG parameter configuration
- Environment variable control
📊 Analysis Capabilities
- Year-over-year performance trends
- Historical player comparisons
- Era-based statistical analysis
- Longitudinal career tracking
🎼 Orchestration
- Apache Airflow 2.7.3+: Workflow orchestration
- Python Operators: Custom task execution
- DAG Scheduling: Automated pipeline runs
- Task Dependencies: Proper execution order
- Monitoring: Web UI and logging
⚡ Data Processing
- Python 3.11+: Core processing language
- Pandas & NumPy: Data manipulation
- Selenium: Web scraping automation
- Async Processing: Concurrent operations
- Machine Learning: Scikit-learn evaluation
💾 Storage & Cloud
- AWS S3: Scalable object storage
- AWS CloudWatch: Monitoring and logging
- JSON/CSV: Structured data formats
- Compression: Optimized storage
- Versioning: Data lineage tracking
🚀 CI/CD & Testing
- GitHub Actions: Automated testing
- Pytest: Comprehensive test suite
- Chrome/Selenium: Browser automation testing
- Code Coverage: Quality metrics
- Artifact Generation: Test reports
🔒 Security & SSL
SSL-secure data collection with proper certificate handling and secure AWS credential management.
📈 Scalable Architecture
Modular design supports easy addition of new statistical categories and data sources.
🔄 Error Recovery
Comprehensive error handling with automatic retries and detailed logging for troubleshooting.
📊 Analytics Ready
Machine learning evaluation framework built-in for predictive analytics and performance modeling.
⏱️ Time Series Support
Historical data collection enables trend analysis and comparative studies across multiple seasons.
🧪 Test Coverage
Extensive testing suite with automated CI/CD pipeline ensuring code quality and reliability.
🎯 Project Impact
Automated NFL data collection pipeline enabling comprehensive statistical analysis across 26 years of historical data. The system supports real-time analytics, predictive modeling, and longitudinal performance studies with robust cloud integration and monitoring capabilities.
Technical Achievement: Successfully integrated modern data engineering practices with sports analytics, creating a scalable and maintainable solution for complex data processing workflows.