Sports analytics tools are becoming more frequently used to help athletes enhance their skills and body strength to perform better and prevent injury. ACL tearing is one of the most common and dangerous injuries in basketball history. This injury occurs most frequently in jumping, landing, and pivoting due to the rapid change of direction and/or sudden deceleration in basketball. Recovering from an ACL injury is a brutal process, can take months – even years – to recover, and significantly decrease the player’s performance after recovery. The goal of this project is to find the relationship between fatigue and different angle measurements in the hips, knees, and back as well as the force applied to the ground to minimize the ACL injury risk. 7 different sensors were attached to a test subject while he conducted the countermovement jump for 10 trials on each leg before and after 2 hours of vigorous exercise. The countermovement jump was chosen due to its ability to assess the ACL injury risk quite well through force and flexion of different body parts. Several statistical tools such as the control chart builder, multivariate correlation, and variable clustering were utilized to discover any general insights between the before and after fatigue state for each exercise (which is related to an increased ACL injury risk). The JMP Multivariate SPC platform provided further biomechanic, time-specific information about how joint flexions differ before and after fatigue at specific time points, giving a more in-depth understanding of how the different joint contributions change when fatigued. The end-to-end experimental and analysis approach can be extended across different sports to prevent injury.

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