YouTube Sports Analyzer - Character Tracking & LangMem Integration Guide

🎯 Overview

The YouTube Sports Analyzer now includes advanced multi-character tracking with LangMem integration and Pydantic-based structured output. This allows you to track individual players/characters and their actions throughout sports videos with intelligent memory storage.

✨ Key Features

👥 Multi-Character Tracking

• Track multiple players/characters simultaneously
• Sport-specific action types (goals, assists, fouls, etc.)
• Individual performance metrics and statistics
• Team-level aggregations and comparisons

🧠 LangMem Integration

• Automatic storage of character data in LangMem memory system
• Cross-session character performance analysis
• Intelligent context for future video analysis
• Session-based memory management with UUID tracking

📊 Pydantic Statistics Output

• Structured, validated data models for all character information
• Type-safe statistics with automatic validation
• Exportable JSON and CSV formats
• Comprehensive error handling and data integrity

🏗️ Data Models

CharacterAction

{
    "timestamp": "15:30",
    "action_type": "goal", 
    "description": "Brilliant solo goal from outside the box",
    "impact_score": 9.5,
    "confidence": 0.95
}

CharacterProfile

{
    "character_id": "player_001",
    "name": "Lionel Messi",
    "team": "Barcelona", 
    "jersey_number": 10,
    "position": "Forward",
    "first_detected": "00:05:30",
    "last_seen": "01:45:20",
    "detection_confidence": 0.95
}

MultiCharacterReport

• Complete analysis report with all characters
• Team statistics and comparisons
• Key moments analysis across all characters
• Performance summaries and insights
• LangMem storage metadata

📋 Action Types Supported

Universal Actions

• GOAL, ASSIST, SHOT, SAVE, FOUL, PENALTY

Sport-Specific Actions

• Soccer: YELLOW_CARD, RED_CARD, CORNER, OFFSIDE
• Basketball: THREE_POINTER, REBOUND, STEAL, BLOCK, TURNOVER
• Football: TOUCHDOWN, INTERCEPTION, SACK, FUMBLE
• Hockey: HIT, FACEOFF_WIN
• Tennis: ACE, WINNER, UNFORCED_ERROR
• Combat Sports: KNOCKDOWN, SUBMISSION_ATTEMPT

⚙️ Configuration Options

Character Tracking Settings

• Enable Multi-Character Tracking: Toggle character tracking on/off
• Track All Characters: Track all detected characters or limit to specific ones
• Store in LangMem: Save character data to LangMem for cross-session analysis
• Min Impact Threshold: Only track actions above this impact score (0-10)
• Max Characters: Maximum number of characters to track simultaneously (1-20)

LangMem Storage Pattern

Following the established LangMem pattern:

Namespace: "sports_videos"
User ID: {session_uuid}  
Collection: "character_tracking"
Content: JSON with character statistics and performance data

📊 Analysis Output

Individual Character Statistics

• Complete action breakdown with timestamps
• Total impact scores and averages
• Action type distribution charts
• Performance timeline visualization

Team-Level Analysis

• Team performance comparisons
• Player distribution by team
• Aggregate impact scores
• Key players identification

Key Moments Analysis

• Top 10 highest impact moments across all characters
• Cross-character event correlation
• Timeline visualization of critical actions

Export Options

• JSON Export: Complete character report in structured format
• CSV Export: Character profiles and summary statistics
• LangMem Session: View stored memory session details

🚀 Usage Example

1. Input Video: Paste YouTube URL
2. Configure Tracking: Enable character tracking in sidebar
3. Set Parameters: Adjust impact threshold and max characters
4. Analyze: Click "Analyze Video"
5. View Results: Browse individual character stats, team comparisons, and key moments
6. Export Data: Download structured reports in JSON/CSV format

💾 LangMem Benefits

Cross-Session Analysis

• Compare character performance across multiple videos
• Track player development over time
• Identify performance patterns and trends

Intelligent Context

• Previously analyzed characters provide context for new videos
• Sport-specific action patterns learned from historical data
• Improved action detection based on stored character profiles

Memory Management

• Automatic session tracking with UUIDs
• Organized storage by video and character
• Efficient retrieval for performance comparisons

🛡️ Error Handling

The system includes comprehensive error handling:

• Graceful fallback when LangMem is unavailable
• Validation of all character data inputs
• Safe data access patterns throughout
• User-friendly error messages with technical details available

📈 Future Enhancements

• Real-time character tracking for live streams
• Machine learning-based action detection
• Cross-video character performance comparisons
• Advanced analytics and predictive modeling
• Integration with sports databases for enhanced player profiles

Ready to track your sports characters with intelligent memory? 🚀