The unprecedented growth in participation in collegiate athletics has been accompanied by an increase in injury burden. The complex and multifactorial nature of sports injuries highlights the importance of monitoring athletes prospectively using a novel and holistic biopsychosocial approach, as opposed to contemporary practices that silo these facets of health. Data collected over two competitive, basketball seasons were used in a principal component analysis (PCA) model with the following objectives: i) Determine if on-court, sensor-derived and force-plate-derived countermovement jump (CMJ) biomechanics were correlated, ii) determine the reliability of the biomechanical principal components (PCs) and psychological state metrics (e.g., self-reported pain, etc.) across five preseason weeks, iii) investigate whether biomechanical PCs were correlated with psychological state across a season, and iv) explore whether subject-specific meaningful fluctuations could be detected using minimum detectable change statistics. Weekly CMJ (force plates) and on-court data (inertial measurement units), as well as psychological state (questionnaire) data were collected on the women’s basketball team at McMaster University for two seasons. It was found that on-court and CMJ biomechanics were correlated both between and within systems (r = |0.10, 0.94|; p < 0.05), suggesting that PCA would be an effective method to summarize data. The derived PCs displayed excellent reliability (ICC > 0.9), while psychological state metrics displayed moderate-to-good reliability (ICC = 0.71 – 0.89). While many relationships (n = 27) were identified between biomechanical PCs and psychological state metrics, no overarching associations were identified at the group level. However, subject-specific relationships were identified in case-studies, highlighting the potential utility of “red-flagging” meaningful fluctuations from normative biomechanical and psychological patterns. Overall, this work demonstrates the potential of advanced analytical modeling to characterize components of student-athlete performance, health, and well-being, and the need for more tailored and patient-centered athletic monitoring practices. / Thesis / Master of Science (MSc)