This project provides a comprehensive platform for collecting, analyzing, and visualizing NBA statistics. It features automated data collection, validation, and analysis tools.
- Automated NBA data collection
- Data validation and verification
- Statistical analysis tools
- RESTful API for data access
- Web-based visualization dashboard
.
├── src/
│ ├── api/ # REST API implementation
│ ├── frontend/ # Web interface
│ ├── data_processing/ # Data processing scripts
│ └── utils/ # Utility functions
├── docs/
│ ├── api/ # API documentation
│ └── user_guide/ # User documentation
└── tests/
├── unit/ # Unit tests
└── integration/ # Integration tests
- Clone the repository
- Install dependencies:
pip install -r requirements.txtThe NBA data collector runs automatically every 10 minutes and performs:
- Player statistics collection
- Team data updates
- Game results processing
Run the validation script to check data integrity:
python src/data_processing/data_validation.py- Python 3.7+
- Uses nba_api for data collection
- FastAPI for REST endpoints
- React for frontend (planned)
- Fork the repository
- Create a feature branch
- Submit a pull request
MIT License
We use a structured, agent-driven workflow to ensure quality, speed, and clarity:
- Rules & Agents: See
AGENTS.mdfor our top-level rules, agent routing, and decision-making system. - Workplans: Every feature or bugfix starts with a workplan in
docs/Plans/(seeWorkplanTemplate.md). - Documentation: All requirements, architecture, UX/UI, and roadmap docs are in the project root or
docs/. - CI/CD: All PRs require a workplan and passing tests. Our CI checks for both.
Key Docs:
- Product:
PRODUCT_REQUIREMENTS.md - Architecture:
ARCHITECTURE.md - UX/UI:
UX_UI_PLAN.md - Roadmap:
PROJECT_OVERVIEW.md - Personas:
elite_persona_stack.md - Rules/Agents:
AGENTS.md - Workplans:
docs/Plans/
New to the project?
- Start by reading
AGENTS.mdandPRODUCT_REQUIREMENTS.md. - Use the workplan template for any new work.
- Ask questions early and often!