Swish Report is a full-stack web app for basketball player analysis across the high school, college, and NBA levels. It aggregates data, generates AI scouting reports, and lets basketball scouts build and compare lineups and players.
- Frontend: React + Next.js (TypeScript)
- Backend: Python + FastAPI
- Playwright (web scraping)
- OpenAI + Gemini (LLM integrations)
- OpenCV (generating highlight reels and basketball player scouting videos)
- Database: MySQL (relational modeling)
- Infrastructure: Docker (containerization)
- AI powered scouting reports for players at any level (OpenAI & Gemini powered)
- AI generated highlight reels for players at every level of basketball (OpenCV & Gemini powered)
- Player pages: Scouting reports, player related content and highlight reels, and forums to discuss player skillset and potential
- Auth: Email/password + Google OAuth signup and login
- Lineup builder with interactive team composition and lineup analysis
- Community: Compare lineups, post reports, discuss player scouting analysis
- Expand scraping coverage for college players and international players
- More interactive features (salary cap drafts, scenario simulators, player projections and fit on different teams and schemes)
- Deploy backend and database for the application on a cloud service like AWS, GCP, or Azure
- Next.js (app or pages router) serves the UI + API proxy wherever needed
- FastAPI exposes REST endpoints for player search, reports, lineups, and other fatures
- MySQL stores player master data and metadata, evaluations, sources, users, and player content
- Playwright scrapers to act as ETL pipelines and feed normalized data into MySQL database and respective tables
- LLM layer (OpenAI/Gemini) summarizes scouting text and produces structured insights
- Docker for containerization and separation of development and production environment