CourtSense

Automated computer vision system analyzing 3x3 basketball gameplay to provide strategic insights for Canada Basketball.

Overview

CourtSense is an automated system developed for Canada Basketball that leverages computer vision to analyze 3x3 basketball gameplay from broadcast videos. The goal is to provide coaches with actionable strategic insights that were previously difficult or time-consuming to obtain manually.

Key Features

• Automated Event Detection: Uses machine learning to detect key game events such as shots, rebounds, and turnovers.
• Player Tracking: Tracks player movements and positioning to analyze spacing and defensive rotations.
• Strategic Insights: Generates reports highlighting team strengths and weaknesses tailored for 3x3 play.
• Full-Stack Implementation: Includes a backend for processing video data and a frontend dashboard for visualizing results.

Technologies Used

• Python: Core logic and data processing.
• Computer Vision: OpenCV and deep learning models (e.g., YOLO, Pose Estimation) for visual analysis.
• Machine Learning: Random Forest or similar classifiers to categorize game state.
• Full Stack: Interface for coaches to upload video and view analytics.

Impact

This tool dramatically reduces the time required for video analysis, allowing coaches to focus more on strategy and player development. It provides a competitive edge by uncovering patterns that might be missed by the human eye.