AI Project 06 / 07
AI-based Analysis of Plyometric Training in Football
Computer vision and machine learning combine to deliver objective measurements of plyometric exercises.
Overview
About the project
This project focuses on the automatic analysis of plyometric exercises in football using artificial intelligence. By combining computer vision and machine learning, the system provides objective measurements such as jump detection, flight time, and ground contact time.
Capabilities
Key features
Audience
Who is it for?
FAQ
Frequently asked questions
How does AI analyze plyometric exercises?
The system uses computer vision to detect jumps in video and machine learning to measure flight time and ground contact time objectively.
Why is objective measurement important?
Objective data lets coaches compare sessions, monitor progress and adjust training load precisely instead of relying only on visual estimation.
About the author
Anas Skiti
Licensed football coach in Switzerland and IT professional. This project is part of ScrumSoccer’s research and development in AI-driven football analysis. The ideas behind it are explored in depth in the book Modern Football Intelligence, and coaches can learn the ScrumSoccer method in the ScrumSoccer course.
Interested in this project?
Book a call to discuss a demo, consulting or a research collaboration for your club, academy or organization.
Research & development
Other AI football projects
AI-Supported Visualization of Defensive and Offensive Spaces
AI-Assisted Tactical Scenario Simulation
AI-Supported Detection and Evaluation of Tactical Triangles in Gameplay
Predictive AI: Injury Risk Prediction in Intensive Plyometric Loads
AI-Driven Personalization Of Plyometric-Workout-Plans
