Abstract

Several challenges face the U.S. Marine Corps (USMC) and other services in their efforts to design recruit training to augment warfighter mobility and resilience in both male and female recruits as part of an integrated model. Strength and power underpin many of the physical competencies required to meet the occupational demands one might face in military. As the military considers adopting force plate technology to assess indices of strength and power, an opportunity presents itself for the use of machine learning on large datasets to deduce the relevance of variables related to performance and injury risk. The primary aim of this study was to determine whether cluster analysis on baseline strength and power data derived from countermovement jump (CMJ) and isometric mid-thigh pull (IMTP) adequately partitions men and women entering recruit training into distinct performance clusters. The secondary aim of this study is then to assess the between-cluster frequencies of musculoskeletal injury (MSKI).