The startup utilizes physics-informed machine learning to analyze player movements and measure forces from video footage, enabling precise performance insights without traditional lab settings. Their technology addresses the need for accurate data in return-to-play protocols, game readiness assessments, and talent identification in professional sports.
Funding
Funding not disclosed
Founders
Product
Problem
Current methods for assessing athletic performance often require specialized lab equipment and controlled environments, limiting the ability to gather data in real-game scenarios. This lack of readily available, context-specific data hinders precise performance analysis, return-to-play protocols, and talent identification in professional sports.
Solution
This startup provides a physics-informed machine learning platform that analyzes player movements and quantifies forces directly from video footage, eliminating the need for traditional lab settings. The technology measures ground reaction forces, collision forces, and joint-level forces with scientific accuracy. This enables sports professionals to gain actionable insights into player performance, optimize training strategies, reduce injury risk, and refine game strategies. The platform also offers simulation capabilities to predict player performance under different conditions and assess injury risks in a virtual environment.
Target Audience
The primary customers are professional sports teams, leagues, coaches, scouts, and analysts who require objective, data-driven insights to optimize player performance, reduce injury risk, and enhance talent identification.
Features
- AI-driven analysis of video footage to measure forces experienced by players on the field.
- In-depth performance insights and comparative analysis across players, games, and seasons.
- Physics-informed simulation technology to predict player performance and assess injury risks.
- Calculation of ground reaction forces, collision forces, and joint-level forces.
- Talent identification and player development tracking.
- Explainable AI models trained and validated against datasets of motion data, collisions, and ground reaction forces.
- Generative AI capabilities to simulate training effects and explore "what if" scenarios.