An AI-powered Rocket League coaching platform that analyzes your gameplay, identifies weaknesses, and creates personalized training routines to help you rank up faster.
RocketTrainer uses machine learning to analyze your Rocket League replays and provides:
- Smart Weakness Detection: AI identifies your specific skill gaps
- Personalized Training: Custom training pack recommendations
- Progress Tracking: Detailed analytics and improvement metrics
- Rank Prediction: Forecasts your rank progression based on improvement
- Docker & Docker Compose
- Node.js 18+ (for local development)
- Python 3.9+ (for local development)
git clone <repository-url>
cd RocketTrainer
make setupmake start- Frontend: http://localhost:3000
- API: http://localhost:8000
- API Docs: http://localhost:8000/docs
- Weakness Detection: Upload replays and get AI-powered analysis of your gameplay weaknesses
- Skill Breakdown: Detailed scoring across 8 skill categories (mechanical, positioning, game sense, etc.)
- Training Recommendations: Personalized training pack suggestions based on your weaknesses
- Progress Tracking: Monitor improvement over time with confidence scoring
- Environment-aware configuration (lower requirements for testing)
- Comprehensive error handling with user-friendly messages
- Redis caching for improved performance
- Rate limiting for API protection
Once the development server is running, visit:
- Swagger UI: http://localhost:8000/docs
- ReDoc: http://localhost:8000/redoc
Copy .env.example to .env and configure:
# Required for full functionality
BALLCHASING_API_KEY=your-ballchasing-api-key
STEAM_API_KEY=your-steam-api-key
# Database
DATABASE_URL=postgresql://postgres:password@localhost:5432/rockettrainer
# Security
SECRET_KEY=your-secret-key-hereThis project is licensed under the MIT License - see the LICENSE file for details.
- Ballchasing.com for replay data API
- Rocket League community for training pack creation
- Psyonix for creating a W game i've spent years on