ο»Ώ# MatlaBz - Comprehensive AI-Powered Football Analytics Platform
MatlaBz is now a comprehensive AI-powered football analytics platform that combines:
- Portable MATLAB-Compatible Runtime (Original MatlaBz)
- Weekly Fixtures Dashboard (New AI-powered dashboard)
- Machine Learning Predictions (Advanced prediction models)
- Real-time Data Integration (Live football data)
- Real-time Fixtures Monitoring: Live football fixtures from multiple leagues
- AI-Powered Predictions: Machine learning models for match outcome prediction
- Interactive Dashboard: Modern web interface with Flask
- Automated Operations: Scheduled data updates every 5 minutes
- Advanced Analytics: Team performance metrics and league standings
# Automated setup (recommended)
python auto_setup.py
# Or manual setup
pip install -r requirements.txt
python weekly_fixtures_dashboard.pyDashboard will be available at: http://localhost:5000
- XGBoost Classifier: Primary prediction model
- Random Forest: Ensemble component
- Neural Network: Deep learning predictions
- Ensemble Model: Combined predictions
- Football-Data.org API
- API-Football
- Live-score APIs
- Team websites and sports feeds
- Octave Runtime: GNU Octave with essential packages for MATLAB compatibility
- Integration Hooks: Connectors and bridges for external applications
- AI Modules: Machine learning and deep learning support
- Auto-Launch: Silent integration when flash drive is connected
- Flask Web Application: Modern web interface
- Real-time Data Processing: Live data integration
- Machine Learning Pipeline: Automated model training and prediction
- Scheduling System: Automated updates and maintenance
- Database Layer: Data storage and management
MatlaBz/
βββ weekly_fixtures_dashboard.py # Main dashboard application
βββ auto_setup.py # Automated setup script
βββ requirements.txt # Python dependencies
βββ ai_modules/ # AI and ML modules
β βββ requirements.txt # AI-specific dependencies
β βββ data_integration/ # Data scraping and APIs
β βββ models/ # Trained ML models
βββ config/ # Configuration files
βββ data/ # Application data
βββ logs/ # Application logs
βββ templates/ # HTML templates
βββ static/ # CSS, JS, images
βββ exports/ # Data exports
βββ backups/ # Data backups
βββ octave_runtime/ # GNU Octave installation
βββ integration_hooks/ # App connectors
βββ integrations/ # System integrations
- Flash drive initialized
- Reference apps analyzed
- Octave runtime downloading...
- Folder structure created
- Integration hooks development
- AI modules setup
- Auto-launch configuration
- Testing and deployment
- Flask web application
- AI prediction models
- Real-time data integration
- Automated scheduling
- Interactive dashboard
- Setup automation
- Documentation
- Testing and validation
- Run automated setup:
python auto_setup.py - Start dashboard: Dashboard starts automatically
- Connect MatlaBz runtime: Use integration hooks
- Access both MATLAB compatibility and dashboard
python weekly_fixtures_dashboard.py- Connect flash drive
- Auto-launch will initialize MatlaBz runtime
- Use integration hooks for existing apps
Copy .env.template to .env and configure:
- API keys for football data providers
- Database connection strings
- Email settings
- Security configurations
- Football-Data.org API key
- API-Football API key
- The Odds API API key (optional)
- Python 3.8+
- 2GB RAM
- 1GB storage
- Internet connection
- Python 3.11+
- 4GB RAM
- 2GB storage
- High-speed internet
- Windows 10/11 x64
- 4GB available storage
- Administrative privileges (initial setup)
GET /- Main dashboardGET /api/fixtures- Get fixtures dataGET /api/predictions- Get predictionsGET /api/standings- Get league standingsPOST /api/update-data- Trigger data update
- Multiple system integrations available
- RESTful API architecture
- Real-time data synchronization
- Data Updates: Every 5 minutes
- Model Retraining: Daily at 2 AM
- Data Cleanup: Weekly
- Backup Creation: Daily at 3 AM
- Report Generation: Weekly
- High-confidence predictions
- Important match results
- System errors
- Model performance alerts
- Redis for session management
- Flask-Caching for API responses
- Browser caching for static assets
- Indexed queries
- Connection pooling
- Data archiving
- Performance metrics
- Error tracking
- Resource usage monitoring
- JWT token-based authentication
- Session management
- Rate limiting
- Input validation
- SQL injection prevention
- XSS protection
- CSRF tokens
python weekly_fixtures_dashboard.pygunicorn -w 4 -b 0.0.0.0:5000 weekly_fixtures_dashboard:appFROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:5000", "weekly_fixtures_dashboard:app"]- API Key Issues: Check API key validity and rate limits
- Database Connection: Verify database configuration
- Memory Issues: Monitor memory usage, consider model optimization
- Performance: Check caching configuration and database indexes
- Check logs in
logs/directory - Review system configuration
- Verify API connections
- Monitor resource usage
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE file for details.
- Major Update: Added Weekly Fixtures Dashboard
- AI Integration: Advanced prediction models
- Real-time Data: Live football data integration
- Automated Operations: Scheduling and notifications
- Enhanced UI: Modern web interface
- Comprehensive Setup: Automated installation
- Initial MatlaBz release
- MATLAB-compatible runtime
- Basic AI modules
- Integration hooks
MatlaBz v2.0 - Comprehensive AI-Powered Football Analytics Platform