Modeling collegiate student-athlete sport performance via self-report measures
Purpose: Optimizing athlete performance is the central focus of players, coaches, and support staffs alike. For years, monitoring the stressors encumbering athletes has focused on the injury-risk dimension and has failed to look at sport-specific performance, the ultimate end goal. Self-report wellness measures have shown great promise in this realm and were implemented to track a wider range of metrics, including subjective performance. This study focused on mapping a combination of variables to each athlete’s performance data to better understand the key indicators of our outcome variable on an individual basis. A secondary aim of this study was to uncover trends amongst the team in which certain variables behaved similarly in their relationships with performance. Methods: Female collegiate volleyball student-athletes (N=16) completed daily wellness monitoring via an online questionnaire. Data from the fall competitive season was collected via Qualtrics© and later regression analysis was performed using R. Results: Performance of the regression models ranged from an explained variance (i.e., adjusted-R2) of 0.23 to 0.90 (i.e., 23-90%) indicating poor to strong results, dependent on the specific athlete as expected. Match-specific players averaged an explained variance in performance (adjusted-R2) of 0.66 (66%) while practice-specific players averaged 0.44 (44%). Sleep duration appeared in half of all athlete models though with both positive and negative coefficients. RPE-based training load metrics, daily locus of control, and physical fatigue appeared at the next highest frequencies, respectively, though again the coefficients were not uniformly positive or negative for every athlete. Heart rate variability (HRV) was projected to play a prominent, positive role in athlete performance yet only appeared in two of the regression equations. Conclusions: As expected, the regression models were quite varied across the athletes. The approach worked better for match-specific players, with nearly two-thirds of the variance in match performance explained by the models on average. This study supports the adoption of a wider range of stressor metrics with specific emphasis on adding a locus of control dimension to monitoring systems. An expanded list of questions may be required to better encapsulate and map second order markers of athlete performance and this work provides additional rationale for tracking stressors outside of the sport-specific context as well as deeper use of cost-effective monitoring tools such as self-report measures to model performance in collegiate student- athletes.
Wellness monitoring, Athlete, Stress, Performance, Heart rate variability, Sleep
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign