Evaluating WHOOP wearable metrics as predictors of Division I collegiate volleyball performance
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Abstract
Wearable devices are increasingly popular among athletes, yet their metrics' predictive value for sports performance remains unclear. Purpose: Evaluate whether WHOOP-derived metrics predict objective and subjective volleyball performance in Division I collegiate athletes. Methods: Fourteen University of Tennessee volleyball players (age 20 ± 1.44 years; playing experience 8.5 ± 3.01 years; WHOOP usage 2.08 ± 1.16 years) participated during off-season. WHOOP metrics included strain, recovery, sleep performance, and sleep debt. Performance outcomes included attacking efficiency, passing efficiency, and weekly Perceived Performance Team Sports Questionnaire (PPTSQ) scores. Linear regression and repeated-measures correlation analyses were conducted (α = .05). Results: Higher strain associated with reduced attacking efficiency (p = .0017, r² = .029), and better sleep performance associated with higher perceived performance (p = .0373, r² = .042). Despite statistical significance, associations showed weak predictive strength, accounting for minimal performance variability. Other metrics showed no significant relationships. Conclusion: WHOOP metrics showed minimal predictive value for volleyball performance in this off-season sample. However, associations involving strain and sleep suggest larger samples, and longer monitoring periods are needed to determine wearable data reliability for performance prediction.
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References
Allen, C. (2016). Using physiological big data to predict cross country performance (Doctoral dissertation).
Bakdash, J. Z., & Marusich, L. R. (2017). Repeated measures correlation. Frontiers in psychology, 8, 456. https://doi.org/10.3389/fpsyg.2017.00456 DOI: https://doi.org/10.3389/fpsyg.2017.00456
Bellenger, C. R., Miller, D. J., Halson, S. L., Roach, G. D., & Sargent, C. (2021). Wrist-based photoplethysmography assessment of heart rate and heart rate variability: Validation of WHOOP. Sensors, 21(10), 3571. https://doi.org/10.3390/s21103571 DOI: https://doi.org/10.3390/s21103571
Berryhill, S., Morton, C. J., Dean, A., Berryhill, A., Provencio-Dean, N., Patel, S. I., … Parthasarathy, S. (2020). Effect of wearables on sleep in healthy individuals: A randomized crossover trial and validation study. Journal of Clinical Sleep Medicine, 16(5), 775-783. https://doi.org/10.5664/jcsm.8340 DOI: https://doi.org/10.5664/jcsm.8356
de Leeuw, A. W., van Baar, R., Knobbe, A., & van der Zwaard, S. (2022). Modeling match performance in elite volleyball players: Importance of jump load and strength training characteristics. Sensors, 22(20), 7996. https://doi.org/10.3390/s22207996 DOI: https://doi.org/10.3390/s22207996
Flatt, A. A., Esco, M. R., & Nakamura, F. Y. (2017). Individual heart rate variability responses to preseason training in high-level female soccer players. Journal of Strength and Conditioning Research, 31(2), 531-538. https://doi.org/10.1519/JSC.0000000000001529 DOI: https://doi.org/10.1519/JSC.0000000000001482
Gershgoren, L., Blatt, A., Sela, T., & Tenenbaum, G. (2021). Does the league table lie? The development and validation of the perceived performance in team sports questionnaire (PPTSQ). Frontiers in Psychology, 11, 615018. https://doi.org/10.3389/fpsyg.2020.615018 DOI: https://doi.org/10.3389/fpsyg.2020.615018
Horvath, E., Kovacs, M. T., Toth, D., & Toth, L. (2022). A study of the relationship between anxiety, cognitive emotion regulation and heart rate variability in athletes. Journal of Physical Education and Sport, 22(2), 528-534. https://doi.org/10.7752/jpes.2022.02067 DOI: https://doi.org/10.7752/jpes.2022.02067
Makivić, B., Nikić Djordjević, M., & Willis, M. S. (2013). Heart rate variability (HRV) as a tool for diagnostic and monitoring performance in sport and physical activities. Journal of Exercise Physiology Online, 16(3), 103-131.
Miller, D. J., Sargent, C., & Roach, G. D. (2022). A validation of six wearable devices for estimating sleep, heart rate and heart rate variability in healthy adults. Sensors, 22(16), 6317. https://doi.org/10.3390/s22166317 DOI: https://doi.org/10.3390/s22166317
Rehman, R. Z. U., Chatterjee, M., Manyakov, N. V., Daans, M., Jackson, A., O'Brisky, A., … Morris, M. (2024). Assessment of physiological signals from photoplethysmography sensors compared to an electrocardiogram sensor: A validation study in daily life. Sensors, 24(21), 6826. https://doi.org/10.3390/s24216826 DOI: https://doi.org/10.3390/s24216826
Sitti, K., & Rangubhet, K. (2025). Volleyball spiking analysis for scoring success: performance indicators in the 2024 VNL women's teams. International Journal of Sports Science and Physical Education, 10(1), 8-17. https://doi.org/10.11648/j.ijsspe.20251001.12 DOI: https://doi.org/10.11648/j.ijsspe.20251001.12
Taber, C. B., Sharma, S., Raval, M. S., Senbel, S., Keefe, A., Shah, J., … Kaya, T. (2024). A holistic approach to performance prediction in collegiate athletics: Player, team, and conference perspectives. Scientific Reports, 14(1), 1162. https://doi.org/10.1038/s41598-024-51658-8 DOI: https://doi.org/10.1038/s41598-024-51658-8
WHOOP. (2023, November 17). How does WHOOP strain work? Retrieved from [Accessed 2026, 12 March]: https://www.whoop.com/us/en/thelocker/how-does-whoop-strain-work-101/
Zadeh, A., Taylor, D., Bertsos, M., Tillman, T., Nosoudi, N., & Bruce, S. (2021). Predicting sports injuries with wearable technology and data analysis. Information Systems Frontiers, 23(5), 1023-1037. https://doi.org/10.1007/s10796-020-10018-3 DOI: https://doi.org/10.1007/s10796-020-10018-3