The Missing Variable: Menstrual Cycles, Injury Risk, ​and the Competitive Edge in Data-Driven Sport​

Female athletes experience 2–8x higher ACL injury rates, 2–4x higher bone stress injury rates, and higher concussion rates than their male counterparts—yet hormonal physiology remains largely absent from sports analytics models. Despite tracking workload, sleep, and heart rate variability, most systems reduce the menstrual cycle to calendar counting or ignore it entirely. This approach overlooks dynamic endocrine fluctuations that influence ligament laxity, neuromuscular control, recovery, energy availability, and bone health. Emerging evidence suggests small-to-moderate individual effects across performance and injury domains, with meaningful implications for risk modeling and load management. At the same time, anovulatory cycles and luteal phase deficiencies—undetectable without biological verification—challenge simplistic tracking approaches. This talk reframes menstrual function as a measurable physiologic signal rather than noise, outlining a multilevel model that integrates hormonal, biomechanical, and environmental risk factors to move from prediction toward precision prevention in female athlete health and performance.