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Basketball Player Tracking and Analysis using Computer Vision

Abstract

In this paper, we present a computer vision-based system for tracking and analyzing basketball players' movements on the court. The system utilizes a combination of object detection, tracking, and data analysis to provide insights into player performance. We implemented the system using Python and OpenCV, and deployed it on GitHub Pages.

Step 2: The MVP (Minimum Viable Product)

Instead of tackling the full NBA API on day one, build a "Free Throw Simulator." basketball github io

• HTML: A button that says "Shoot."
• CSS: A red square for a backboard, a white line for the court.
• JS: A random number generator. If Math.random() > 0.7, display "Swish!"; else, display "Clank."

2. Complex Math becomes Tangible

Machine learning is abstract. But drawing a regression line through Steph Curry’s three-point attempts to predict his next hot streak makes data science visceral. HTML: A button that says "Shoot