Computer Vision for Sports: Analytics and Visualization 2025

About this course

Ever wonder how professional sports teams get their edge? How analysts track player performance with pinpoint accuracy, visualizing every movement to uncover winning strategies? The answer is Computer Vision.

From the Premier League to the NBA, AI-driven analytics has revolutionized the world of sports. The ability to automatically track players, detect the ball, and analyze game-flow from video footage is one of the most exciting and in-demand skills in the AI industry today.

But while many tutorials show you how to detect an object in a single image, they stop there. The real magic happens when you track that object, understand its context on the field of play, and visualize its movement in a way that provides powerful insights. This is the gap between a simple script and a professional-grade sports analytics system.

This course is designed to bridge that gap.

In this comprehensive, hands-on project, you will build a complete, end-to-end tennis analytics system from scratch. We won't just learn theory; we will implement a full pipeline using a state-of-the-art technology stack, including Python, Ultralytics YOLOv8, DeepSORT, Grounding DINO, and OpenCV. You will learn how to combine multiple advanced AI models to create a single, cohesive application that turns raw video into actionable data.

By the end of this course, you will not only have a deep understanding of modern computer vision techniques, but you will also have a stunning, portfolio-worthy project that demonstrates your ability to build real-world AI solutions.

What you'll learn:

• Real-Time Ball Detection: Train and implement the state-of-the-art YOLOv8 model to accurately detect a tennis ball in video footage.
• Zero-Shot Player Detection: Use the powerful Grounding DINO model to detect players using te

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.