A full-stack web application designed to provide advanced statistics and data-driven insights for the WNBA. This project showcases a complete development lifecycle, including a Python/FastAPI backend, a React frontend, a PostgreSQL database, a machine learning model for player similarity, and a professional development workflow using Docker, CI/CD, and automated testing.

• Full CRUD API: A robust RESTful API to Create, Read, Update, and Delete players and their seasonal stats.
• Modern Frontend: A responsive user interface built with React and Material-UI (MUI), featuring client-side routing, a polished design, and a light/dark mode theme.
• Player Similarity Engine: A machine learning model that finds the most statistically similar players for any given player-season, providing unique analytical insights that go beyond standard box scores.
• Automated Data Seeding: The application uses clean JSON files, generated by a separate data pipeline, to reliably seed the database with real-world player data.
• Professional DevOps Workflow:
  • Containerized with Docker for consistent development and deployment environments.
  • Full-stack CI/CD pipeline with GitHub Actions to lint and run backend & frontend tests on every push.
  • Comprehensive test suite using Pytest and React Testing Library, including advanced techniques like mocking.
  • Streamlined development workflow with an automated Makefile.

• Docker Desktop installed and running.
• A git client.
• The make command-line tool (pre-installed on macOS/Linux).

1. Clone the repository:

   git clone https://github.com/aseseri/wnba-analytics.git
   cd wnba-analytics

2. Build and start all services: This command will build the Docker images and start the backend, frontend, and database containers in the background.

   make build

3. Populate the database and create the ML model: This command runs the necessary scripts inside the backend container to seed your database with real stats and generate the similarity model files.

   make setup

   Your application is now running!

   • Frontend (React App): http://localhost:3000
   • Backend (API Docs): http://localhost:8000/docs

To run the entire suite of backend and frontend tests and then clean up the containers, use the following command:

make test

The data for this project is collected and cleaned using a custom Python pipeline that scrapes publicly available statistics from basketball-reference.com. The code for this data pipeline is maintained in a separate, private repository to decouple data acquisition from the application logic. The cleaned data is stored as JSON files which are then used to seed the application's database.