Real-Time Sports Analytics Application

A comprehensive sports analytics platform that provides real-time player and ball tracking, tactical analysis, performance feedback, and automated refereeing assistance for live sports events and uploaded video files.

Features

🎯 Real-Time Player Tracking

• Identify and track all players on the field/court from live video feeds
• Visualize player IDs and movement trajectories as overlays
• Support multiple simultaneous player tracking with minimal latency

⚽ Real-Time Ball Tracking

• Accurate ball detection and position tracking
• Real-time ball trajectory visualization
• Ball possession analysis

📊 Tactical Analysis Module

• Team formation detection (4-4-2, 4-3-3, etc.)
• Player spacing and distance analysis
• Heatmaps showing player activity zones
• Passing pattern analysis and ball possession statistics

📈 Real-Time Performance Feedback

• Individual player metrics (speed, distance, sprints, shot accuracy)
• Team performance indicators (possession %, total shots)
• Real-time dashboard with key statistics

🏟️ Automated Refereeing Assistance

• Computer vision algorithms for critical decisions
• Offside detection in football
• Line call assistance for tennis and other sports

📹 Video Upload Analysis

• Process pre-recorded sports videos (MP4, AVI)
• Same analysis capabilities as live feeds
• Video player controls with synchronized analytics

Technical Stack

Backend

• Flask: Web framework with WebSocket support
• OpenCV: Computer vision and video processing
• YOLO (Ultralytics): Real-time object detection
• MediaPipe: Pose estimation and tracking
• NumPy/SciPy: Numerical computations
• Matplotlib/Seaborn: Data visualization

Frontend

• React: Modern UI framework
• Socket.IO: Real-time communication
• Canvas API: Video overlay rendering
• Chart.js: Interactive data visualizations

Installation

1. Clone the repository

git clone <repository-url>
cd sports-analytics

2. Set up Python environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Set up Node.js environment

cd frontend
npm install

4. Download YOLO models

python scripts/download_models.py

Usage

1. Start the backend server

python app.py

2. Start the frontend development server

cd frontend
npm start

3. Access the application

• Open http://localhost:3000 in your browser
• For live video analysis, connect a camera or video stream
• For uploaded video analysis, use the file upload feature

Project Structure

sports-analytics/
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── models/               # AI/ML models
├── utils/                # Utility functions
├── static/               # Static files
├── templates/            # HTML templates
├── frontend/             # React frontend
├── scripts/              # Setup and utility scripts
└── data/                 # Sample data and videos

API Endpoints

• GET /: Main application page
• POST /upload: Video file upload
• GET /api/analysis/<video_id>: Get analysis results
• WebSocket /socket.io: Real-time video processing

Contributing

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

License

MIT License - see LICENSE file for details