CourtCoverage is an AI-powered volleyball analytics system that uses computer vision to automatically track players, ball movement, and game events. The system provides coaches and players with detailed statistics and insights that were previously only available to professional teams with dedicated video analysts.

Current volleyball analytics rely heavily on manual video review and stat tracking, which is time-consuming and prone to human error. Even when stats are tracked, they often lack the spatial context and detailed movement patterns that coaches need to improve team performance.

CourtCoverage aims to democratize access to advanced volleyball analytics by automatically extracting insights from game footage, making professional-level analysis accessible to teams at all levels.

• Player Tracking: Real-time tracking of all players on court with position heatmaps and movement patterns
• Ball Trajectory Analysis: 3D reconstruction of ball flight paths for serve and spike analysis
• Automatic Event Detection: Recognizes kills, digs, blocks, and other key events without manual tagging
• Formation Analysis: Identifies team formations and rotation patterns throughout the match
• Performance Metrics: Comprehensive statistics including attack efficiency, defensive coverage, and more
• Highlight Generation: Automatically creates highlight reels of key plays and moments

1. Court Detection & Homography: Automatically detects court boundaries and establishes perspective transformation for accurate spatial measurements
2. Multi-Object Tracking: Uses state-of-the-art deep learning models (YOLOv8, ByteTrack) to track players and ball across frames
3. Action Recognition: Temporal convolutional networks identify volleyball-specific actions and events
4. Data Pipeline: Processes raw video through detection, tracking, and analysis stages to produce structured data

The core computer vision pipeline is currently in development. Player detection and tracking models have been trained and tested on volleyball footage, with promising results. The system can reliably detect players and track their movement across most camera angles.

Current development focus is on improving ball tracking accuracy in challenging lighting conditions and implementing the action recognition system for automatic event detection. The MVP will focus on basic stat tracking and position heatmaps before expanding to more advanced analytics features.