Novel Use of Generalizability Theory to Optimize Countermovement Jump Data Collection

Huebner, Alan; Lever, Jonathon R.; Clark, Thomas W.; Suchomel, Timothy J.; Metoyer, Casey J.; Hauenstein, Jonathan D.; Wagle, John P.

doi:10.3390/sports13030085

Open AccessArticle

Novel Use of Generalizability Theory to Optimize Countermovement Jump Data Collection

by

Alan Huebner

^1,2

,

Jonathon R. Lever

^1,*

,

Thomas W. Clark

²

,

Timothy J. Suchomel

³

,

Casey J. Metoyer

¹,

Jonathan D. Hauenstein

²

and

John P. Wagle

¹

Sports Performance, University of Notre Dame, Notre Dame, IN 46556, USA

²

Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA

³

Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, PA 15260, USA

^*

Author to whom correspondence should be addressed.

Sports 2025, 13(3), 85; https://doi.org/10.3390/sports13030085

Submission received: 7 February 2025 / Revised: 24 February 2025 / Accepted: 6 March 2025 / Published: 12 March 2025

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Abstract

This study aimed to evaluate the reliability of countermovement jump (CMJ) performance metrics across five NCAA Division I varsity sports using Generalizability Theory (G-Theory). Three hundred male athletes from football, hockey, baseball, soccer, and lacrosse performed three or more CMJs on dual-force platforms. G-Theory was applied to identify variance components and determine reliability coefficients (Φ) for 14 key metrics. Metrics requiring more than three jumps to achieve Φ 0.80 were deemed unreliable. Metric reliability varied by sport and phase of movement. Metrics associated with the eccentric phase (e.g., Eccentric Duration, Deceleration Rate of Force Development Asymmetry) demonstrated lower reliability, often requiring >3 jumps. Reliable metrics across sports included Phase 1 Concentric Impulse and Scaled Power, requiring three trials or fewer. CMJ reliability is sport- and metric-specific. Practitioners should prioritize reliable metrics and adjust protocols to balance data quality and practicality, particularly when monitoring eccentric characteristics.

Keywords: CMJ; team sport; testing; monitoring; force plate

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MDPI and ACS Style

Huebner, A.; Lever, J.R.; Clark, T.W.; Suchomel, T.J.; Metoyer, C.J.; Hauenstein, J.D.; Wagle, J.P. Novel Use of Generalizability Theory to Optimize Countermovement Jump Data Collection. Sports 2025, 13, 85. https://doi.org/10.3390/sports13030085

AMA Style

Huebner A, Lever JR, Clark TW, Suchomel TJ, Metoyer CJ, Hauenstein JD, Wagle JP. Novel Use of Generalizability Theory to Optimize Countermovement Jump Data Collection. Sports. 2025; 13(3):85. https://doi.org/10.3390/sports13030085

Chicago/Turabian Style

Huebner, Alan, Jonathon R. Lever, Thomas W. Clark, Timothy J. Suchomel, Casey J. Metoyer, Jonathan D. Hauenstein, and John P. Wagle. 2025. "Novel Use of Generalizability Theory to Optimize Countermovement Jump Data Collection" Sports 13, no. 3: 85. https://doi.org/10.3390/sports13030085

APA Style

Huebner, A., Lever, J. R., Clark, T. W., Suchomel, T. J., Metoyer, C. J., Hauenstein, J. D., & Wagle, J. P. (2025). Novel Use of Generalizability Theory to Optimize Countermovement Jump Data Collection. Sports, 13(3), 85. https://doi.org/10.3390/sports13030085

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Novel Use of Generalizability Theory to Optimize Countermovement Jump Data Collection

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