🎯 CS Pros - Professional Counter-Strike Player & Skin Tracker

A comprehensive data platform that tracks professional Counter-Strike 2 players and their in-game weapon skins through automated demo file analysis and web scraping.

🌟 Project Overview

CS Pros fills a unique gap in the esports ecosystem by providing real-time tracking of professional CS2 players' weapon skin preferences. Unlike existing platforms that offer limited or outdated information, this system automatically analyzes professional match demo files to maintain an up-to-date database of player statistics and skin usage patterns.

✨ Key Features

• Real-time Demo Analysis: Automated parsing of professional CS2 match demos using advanced .NET libraries
• Comprehensive Player Database: Tracks players, teams, and match statistics across Tier-1 professional games
• Skin Usage Analytics: Monitors weapon skin preferences with frequency tracking and trend analysis
• Automated Data Pipeline: Continuous scraping and updating with minimal manual intervention
• Modern Web Interface: Clean, responsive frontend for data visualization and player lookup

📊 Technical Achievements

• 99%+ Match Coverage: Processes nearly every Tier-1 CS2 professional game
• 98% Parse Success Rate: Robust demo file parsing with comprehensive error handling
• Real-time Updates: Data refreshed every few hours as new matches are played
• Scalable Architecture: Designed to handle growing data volumes and user traffic

🏗️ System Architecture

🛠️ Technology Stack

Frontend Frontend Code Here

• Next.js 14 with TypeScript
• Tailwind CSS for modern, responsive design
• Deployed on Vercel for easy maintenance and optimal performance

Backend

• .NET 8 Web API with Entity Framework Core
• RESTful architecture with comprehensive error handling
• Hosted on AWS EC2 with Nginx reverse proxy

Database

• PostgreSQL 15.6 for robust data persistence
• Optimized schema for complex player/match relationships
• Hosted on AWS RDS for reliability and scalability

Data Processing

• Custom demo file parser using DemoFile-Net library
• Selenium-based web scraping for match data
• Automated pipeline running on Raspberry Pi cluster

🚀 Getting Started

Prerequisites

• .NET 8 SDK
• Node.js 18+ and npm
• PostgreSQL 15.6+
• Firefox browser (for web scraping)
• GeckoDriver

Quick Setup

1. Clone the repository

   git clone https://github.com/liamkyoung/cs2-demoanalyzer.git
   cd cs2-demoanalyzer

2. Database Setup

   # Create PostgreSQL database named 'cspros'
   # Update connection strings in appsettings.json
   dotnet ef database update --project ./CSProsLibrary --startup-project ./CS2DemoAnalyzer

3. Environment Configuration

   {
     "ConnectionStrings": {
       "CSPROS_PROD": "Host=server_address;Database=cspros;Username=cspros;Password=YOUR_PASSWORD",
       "CSPROS_DEV": "Host=localhost;Database=cspros;Username=cspros;Password=YOUR_PASSWORD"
     }
   }

4. Set Environment Variables

   export DEMO_DOWNLOAD_DIR=/path/to/downloads
   export DEMO_ANALYZE_DIR=/path/to/analysis
   export NEXT_PUBLIC_BACKEND_HOST=https://localhost:7280

5. Run the Application

   # Start the API
   dotnet run --project ./CSProsAPI
   
   # Start the frontend (in separate terminal)
   cd frontend && npm run dev

📈 Data Pipeline

Automated Workflow

1. Match Discovery: Web scraper identifies new professional matches
2. Demo Download: Automated download of match demo files
3. Data Extraction: Parse demos for player actions, kills, and skin information
4. Database Update: Store processed data with proper player attribution
5. API Serving: Expose data through RESTful endpoints
6. Frontend Display: Present information in user-friendly interface

Data Quality Assurance

• Player Identification: Advanced algorithms to correctly attribute actions to players
• Skin Validation: Cross-reference with CSGOExchange database for accuracy
• Error Recovery: Comprehensive logging and retry mechanisms for failed parses

🎯 Technical Challenges Solved

• Demo File Parsing: Developed robust parsing logic handling various demo file formats and edge cases
• Player Attribution: Created sophisticated matching algorithms to correctly identify players across different naming conventions
• Scalable Data Processing: Designed efficient batch processing system handling large volumes of match data
• Real-time Updates: Implemented automated pipeline maintaining data freshness without manual intervention

🔗 Related Projects

Frontend Repository: CS Pros Frontend

📝 Technical Notes

Current Limitations

• Knife kills are not recorded due to demo file format limitations
• Skin data only available from kills (weapon must be used to eliminate another player)
• ~2% of demo files cannot be parsed due to stream corruption
• Player name matching occasionally requires manual verification

Future Enhancements

• Machine learning models for improved player identification
• Extended weapon tracking beyond kills
• Integration with additional data sources
• Performance optimizations for larger datasets

🤝 Contributing

This project demonstrates full-stack development capabilities, automated data processing, and scalable system design. The codebase showcases modern development practices including:

• Clean architecture patterns
• Automated testing strategies
• CI/CD pipeline implementation
• Performance optimization techniques
• Error handling and logging best practices

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Built with ❤️ for the Counter-Strike community

This project represents a comprehensive solution to a real-world data problem, demonstrating skills in web scraping, data processing, API development, database design, and modern frontend development.