Live Site: https://basketball-position-analytics.onrender.com/analytics

An analytics web app being built with Flask and SQLite that uses historical NBA data to recommend positions and training plans. By analyzing patterns and performance in NBA, the program identifies where a player's physical attributes and skill sets align with professional players.

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Tech Stack

• Backend: Flask (Python)
• Database: SQLite3
• Frontend: HTML5, CSS, JavaScript
• Data Source: NBA Player Stats

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The Problem & Solution

The Challenge: Most amateur players train without a clear understanding of their natural strengths and weaknesses and where they fit on the court.

The Solution: Pipeline that digests NBA performance trends (Points, Rebounds, Assists, True Shooting %, etc.). The system uses these data points to calculate a "Similarity Score" between the user and professional player profiles, outputting a data-backed training roadmap based on successful NBA patterns.

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How to Launch

py -m venv .venv

..venv\Scripts\Activate.ps1

py -m pip install --upgrade pip py -m pip install -r my_flask_app/requirements.txt

python init_db.py (this creates/updates basketball.db)

Start the web app from the root folder: python my_flask_app/app.py

If PowerShell blocks activation, run .\.venv\Scripts\activate.bat from Command Prompt instead.

Open http://127.0.0.1:5000/ in your browser

Deploy on Render

1. Push the repo to GitHub.
2. Log in to Render and create a new Web Service from the GitHub repo.
3. Let Render use the render.yaml blueprint in the repo root.
4. The build command installs dependencies and rebuilds the SQLite database with init_db.py.
5. The start command runs gunicorn my_flask_app.app:app.
6. Render will create the SECRET_KEY environment variable automatically from the blueprint.
7. Deploy the service and open the generated Render URL.

If you deploy manually instead of using the blueprint, use:

• Build command: python -m pip install -r requirements.txt && python init_db.py
• Start command: gunicorn --chdir . my_flask_app.app:app