ATP Rankings Data Visualization

SafeSkill 93/100 A comprehensive Python project for ATP tennis rankings data collection, analysis, and visualization. Features include web scraping, database management, interactive web interface, and AI integration via Model Context Protocol (MCP).

Features

• ** Data Collection**: Automated scraping from atptour.com
• ** Database**: 2,600+ weeks of historical ATP rankings (1973-2025)
• ** Web Interface**: Modern FastAPI application with interactive charts
• ** CLI Analysis**: Command-line tools for player statistics and comparisons
• ** AI Integration**: MCP server for AI assistant access
• ** Visualizations**: Matplotlib (CLI) and Chart.js (web) graphs
• ** Testing**: Comprehensive test suite with pytest

Quick Start

Installation

1. Clone the repository

   git clone https://github.com/Jupiterian/ATP-Rankings-Data-Visualization.git
   cd ATP-Rankings-Data-Visualization
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Run the web application

   uvicorn src.main:app --reload
   

   Open your browser to http://localhost:8000

Update Database

To get the latest rankings data:

python scripts/filler.py

Project Structure

ATP-Rankings-Data-Visualization/
├── src/                      # Core application code
│   ├── main.py              # FastAPI web application
│   ├── services.py          # Business logic layer
│   ├── mcp_router.py        # MCP API endpoints
│   └── mcp_manifest.json    # MCP schema definition
├── scripts/                  # Utility scripts
│   ├── generate.py          # Regenerate entire database
│   ├── filler.py            # Update database with latest data
│   ├── analyze.py           # CLI data analysis tool
│   ├── debug.py             # Database debugging utility
│   ├── test_mcp.sh          # Quick MCP endpoint tests
│   ├── test_render_mcp.py   # Production deployment tests
│   ├── keep_alive.py        # Render free tier keep-alive
│   └── deploy.sh            # Deployment helper
├── docs/                     # Documentation
│   ├── MCP_README.md        # MCP server documentation
│   ├── MCP_DEPLOYMENT.md    # Deployment guide
│   ├── MCP_IMPLEMENTATION_SUMMARY.md
│   └── MCP_QUICK_REFERENCE.md
├── templates/                # HTML templates (Jinja2)
│   ├── index.html           # Home page
│   ├── week.html            # Weekly rankings
│   ├── comparison.html      # Player comparison
│   ├── weeks_at_no1.html    # Histogram visualization
│   └── api_docs.html        # API documentation
├── tests/                    # Test suite
│   └── test_mcp.py          # MCP endpoint tests
├── Examples/                 # Example visualizations
├── rankings.db              # SQLite database (2,600+ tables)
├── Dockerfile               # Container image
├── docker-compose.yml       # Docker Compose config
├── Procfile                 # Render/Heroku deployment
├── runtime.txt              # Python version
└── requirements.txt         # Python dependencies

Web Application

Features

• Browse Rankings: View all 2,600+ weeks organized by year
• Player Search: Autocomplete search across all players
• Player Comparison: Side-by-side statistics with career graphs
• Weeks at #1: Interactive histogram of world #1 rankings
• API and MCP Access: RESTful API with comprehensive documentation
• Keyboard Navigation: Arrow keys to navigate between weeks

Running Locally

uvicorn src.main:app --reload

Access at http://localhost:8000

API Endpoints

• GET / - Home page
• GET /week/{week_id} - Weekly rankings
• GET /comparison - Player comparison tool
• GET /weeks-at-no1 - Weeks at #1 histogram
• GET /api-docs - API documentation
• GET /api/weeks - List all available weeks
• GET /api/week/{week_id} - Get week data
• GET /api/search-players?q={query} - Search players
• POST /api/player-factfile - Player statistics
• POST /api/player-career - Career time-series data

MCP Server (AI Integration)

The MCP server allows AI assistants like Claude to query ATP rankings data.

MCP Endpoints

Base URL: http://localhost:8000/mcp

• GET /mcp/health - Health check
• GET /mcp/manifest - Server capabilities
• POST /mcp/tools/search_players - Search for players
• POST /mcp/tools/get_player_factfile - Player statistics
• POST /mcp/tools/get_player_career - Career history
• GET /mcp/tools/get_weeks_at_no1 - Weeks at #1 leaderboard
• GET /mcp/tools/get_all_weeks - Available weeks list
• POST /mcp/tools/get_week_rankings - Specific week data

Testing MCP

# Run test suite
pytest tests/test_mcp.py -v

# Quick smoke test
bash scripts/test_mcp.sh

# Test production deployment
python scripts/test_render_mcp.py https://your-app.onrender.com

Full Documentation: See docs/MCP_README.md

CLI Analysis Tools

analyze.py

Analyze player data and generate matplotlib visualizations.

# Show player statistics
python scripts/analyze.py -f Roger_Federer

# Plot ranking history
python scripts/analyze.py -r Roger_Federer Rafael_Nadal

# Plot points history
python scripts/analyze.py -p Novak_Djokovic

# Weeks at #1 histogram
python scripts/analyze.py -n

Options:

• -h - Show help menu
• -f - Player factfile (statistics)
• -r - Ranking history plot
• -p - Points history plot
• -n - Weeks at #1 bar graph

Examples: See Examples/Examples.md

Database Management

Update Database (Recommended)

Add latest rankings data:

python scripts/filler.py

Regenerate Database

Complete database rebuild (takes ~1 hour):

python scripts/generate.py

Debug Database

Find and fix problematic tables:

python scripts/debug.py

Browse Database

Recommended GUI tool: DB Browser for SQLite

Deployment

Deploy to Render (Free)

1. Push to GitHub

   git add .
   git commit -m "Deploy ATP Rankings"
   git push origin main
   

2. Connect to Render

   • Go to render.com
   • Click "New Web Service"
   • Connect your GitHub repository
   • Render auto-detects settings from Procfile

3. Deploy

   • Click "Create Web Service"
   • Wait 2-3 minutes for deployment

4. Keep Alive (Optional)

   python scripts/keep_alive.py https://your-app.onrender.com
   

Full Guide: See docs/MCP_DEPLOYMENT.md for Railway, Fly.io, Heroku, Docker, and VPS options.

Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=src --cov-report=html

# Test specific module
pytest tests/test_mcp.py::TestMCPHealth -v

Technologies

Backend:

• Python 3.12
• FastAPI (web framework)
• SQLite3 (database)
• Uvicorn (ASGI server)
• Pydantic (validation)

Frontend:

• Jinja2 (templates)
• Chart.js (interactive charts)
• HTML5/CSS3

Scraping & Analysis:

• BeautifulSoup4 (web scraping)
• Matplotlib (static visualizations)

Testing & Deployment:

• Pytest (testing framework)
• Docker (containerization)
• Render/Railway/Fly.io (hosting)

License

See LICENSE file for details.

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

Contact

For questions or issues, please open a GitHub issue.

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