Anterior cruciate ligament (ACL) injuries are becoming increasingly common among young athletes, with re-injury rates reaching up to 35%. Managing recovery and determining safe return-to-sport (RTS) timelines after ACL reconstruction is especially complex in pediatric patients, and current RTS criteria are still unclear. As youth sports become more demanding, there is a growing need for accurate, sport-specific assessments that reflect real in-game movements—particularly those linked to non-contact ACL injuries, such as sudden cutting actions in football.

This study presents a novel field-based algorithm called ACL Injury Risk Profile Detection (ACL-IRD), designed to evaluate football-specific movement patterns directly on the pitch. By analyzing players’ biomechanics in realistic settings, the tool can detect high-risk movement profiles even after athletes have been cleared to return to sport. The goal is to translate complex motion data into practical, real-time insights for healthcare professionals, supporting safer RTS decisions and more personalized injury prevention strategies.