Monitoring training load and its progression in athletes is important to optimise adaptations to training while simultaneously preventing injury. A recent development in this field is the acute: chronic workload ratio (ACWR), which track s average acute training load against average chronic training load to describe training load progression. Furthermore, a new method of calculating the ACWR has been developed using exponentially weighted moving averages (EWMA) which accounts for the decay of fitness and fatigue. This study sought to investigate the relationship between the EWMA and ACWR (based upon session rating of perceived exertion (sRPE)), and injury risk in intercollegiate female athletes (N=4). Participants performed adductor squeeze tests (ASTs) once a week for 8 week s, while their training and wel lness were monitored with sRPE and a daily questionnaire respectively. A hierarchical regression demonstrated that monitoring of average sleep length, average stress, sRPE work loads and an EWMA provided the best model for predicting injury risk in athletes (R2 = 0.47). The findings indicate that the EWMA may be a effective training load monitoring tool than the ACWR model
