LAUSR

Background: In the National Basketball Association (NBA), lower extremity injuries account for over 70% of games missed, with ankle injuries being the most common. High-quality video analysis has been successful for studying injury mechanism. Purpose: To (1) determine the validity of video-based analysis as a method to evaluate ankle injury mechanisms in NBA players and (2) analyze the circumstances associated with injury, games missed due to injury, and associated costs in player salary due to time missed. Study Design: Case series; Level of evidence, 4. Methods: Ankle injuries were identified using an injury report database, and corresponding videos were searched using YouTube.com to access high-quality video evidence of these injuries during the 2015-2020 NBA regular season. We reviewed 822 injuries, of which 93 had corresponding videos (video subset), in our final analysis. Variables including number of games missed, necessity for surgical treatment, and injury recurrence were reported for the entire cohort. In the video subset, the mechanism of injury and other corresponding situational data were evaluated. Results: The most common mechanism of injury occurred via ankle inversion (83.9%; n = 78; P < .001). These injuries were significantly associated with indirect contact with the player’s ankle (79.6%; n = 74; P < .001). There were significant differences based on player position, within both the video subset (P = .008) and the entire cohort (P < .001), with guards being injured the most frequently. The average number of games missed due to injury was 7 games in the video subset and 5 games in the entire cohort (P = .14). There were significant differences between the groups in average player salary per game ($133,878 [video subset] versus $87,577 [entire cohort]; P < .001). Conclusion: Despite its low yield of 11.3%, video analysis proved to be a useful tool to determine ankle injury mechanisms as well as the distribution of injuries based on player position. However, this methodology was subject to selection bias, as evidenced by a $50,000 increase in player salary among the video cohort. These findings should be considered when using video analysis in future studies.