An important step for treatment of a particular injury etiology is the appropriate application of a treatment targeted to the population at risk. An anterior cruciate ligament (ACL) injury risk algorithm has been defined that employs field-based techniques in lieu of laboratory-based motion analysis systems to identify athletes with high ACL injury risk landing strategies. The resultant field-based assessment techniques, in combination with the developed prediction algorithm, allow for low-cost identification of athletes who may be at increased risk of sustaining ACL injury. The combined simplicity and accuracy of the field-based tool facilitate its use to identify specific factors that may increase risk of injury in female athletes. The purpose of this report is to demonstrate novel algorithmic techniques to accurately capture and analyze measures of knee valgus motion, knee flexion range of motion, body mass, tibia length and quadriceps to hamstrings ratio with video analysis software typically used by coaches, strength and conditioning specialists, and athletic trainers. The field-based measurements and software analyses were used in a prediction algorithm to identify those at potential risk of noncontact ACL injury that may directly benefit from neuromuscular training.
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