Player Tracking Metrics to Predict Risk of Anterior Cruciate Ligament Injuries During Change-of-Direction Scenarios in the National Football League.

Abstract

Background: National Football League (NFL) athletes face a substantial risk for anterior cruciate ligament (ACL) injuries, particularly during special team plays. ACL injuries commonly occur during change-of-direction (CoD) scenarios. Player tracking is standardized for all NFL games and can be used to quantify player motion intensity during CoD injury scenarios.

Purpose: The purpose was to identify ACL injuries during CoD scenarios in the NFL. We investigated whether player tracking metrics derived from on-field play can predict an increased ACL injury risk during CoD scenarios.

Study design: Descriptive epidemiology study.

Methods: For all ACL injuries (n = 216) occurring in games during the 2018 to 2022 NFL seasons, the injury timing and injury scenario were identified through a video review. Motion characteristics of ACL injuries during CoD scenarios were identified from player tracking data, and a generalized linear mixed model (GLMM) was developed to quantify whether player tracking metrics were predictive of the ACL injury risk during CoD scenarios.

Results: Among the ACL injuries reviewed, 32% were noncontact, 42% were indirect contact, and 46% were classified as CoD scenarios. Of the athletes involved in a CoD scenario, 98% were decelerating at the time of their ACL injury. Maximum speed (odds ratio, 1.52 per 1-m/s increase in maximum speed) and normalized maximum deceleration power (odds ratio, 1.08 per 1-W/kg increase in maximum deceleration power) were both significant predictors of the CoD ACL injury risk. Punt and kickoff returns had a significantly increased CoD ACL injury risk only when maximum speed and normalized maximum deceleration power metrics were excluded from the GLMM.

Conclusion: ACL injuries in NFL games primarily occurred during CoD scenarios. Player tracking data analyzed for CoD ACL injuries demonstrated a consistent movement pattern involving high speeds and deceleration at the time of the injury. Both a player's maximum speed and normalized maximum deceleration power were significant predictors of an increased CoD ACL injury risk. The inclusion of these metrics in a GLMM helped to explain the variation in CoD ACL injury rates observed across different play types.