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Topic

Jumping Tasks and Athletic Performance

School of Exercise Science, Physical and Health Education

Date & location

• Thursday, March 12, 2026
• 9:00 A.M.
• Virtual Defence

Examining Committee

Supervisory Committee

• Dr. Marc Klimstra, School of Exercise Science, Physical and Health Education, ̽��ϵ�� (Supervisor)
• Dr. Nick Clarke, School of Exercise Science, Physical and Health Education, UVic (Member)
• Dr. Jeremy Sheppard, Performance Coach, Canada Snowboard (Outside Member)

External Examiner

• Dr. Ryan Frayne, School of Health and Human Performance, Dalhousie University

Chair of Oral Examination

• Dr. John Burke, Department of Biochemistry and Microbiology, UVic

Abstract

The assessment and prediction of athletic performance is essential in research and sports science. The following series of studies systematically investigates the relationship between jumping performance and athletic tasks aimed at improving the accuracy and applicability of athletic assessments ranging from lab to field-based tests. Each study builds upon the findings of the previous research, moving from lab-based assessments to field-based applications, and demonstrates how advancements in technology and methodology can enhance athletic evaluation.

The squat jump (SJ) is a commonly used task in athletic assessment and the primary exercise used in vertical force-velocity profiling (v-FVP) of the lower extremities. Study 1 addresses a prevalent issue in SJ assessments, where identifying the unweighting phase before the upward propulsive phase is often subjective. This subjectivity can lead to inaccuracies in evaluating SJ performance and, consequently, in designing training interventions. To resolve this, Study 1 set out to determine a quantitative threshold of unweighting amplitude that resulted in an increased jump height. In a laboratory setting, 56 athletes performed a total of 936 SJs under four different external loads. The SJs were categorized based on the amplitude of unweighting relative to bodyweight (BW) into six groups. Using an Analysis of Covariance with jump height as the dependent variable and external load as the covariate, the study found a significant difference in jump height across unweighting groups (F(5,930) = 13.65, p < 0.01). The results indicated that at a threshold of 2% BW for unweighting amplitude jump height was significantly increased compared to the <1% BW threshold. This finding is critical as it provides a standardized threshold for ensuring the validity of SJ assessments and reduces the subjectivity of previous studies utilizing the squat jump. This threshold not only aids in accurate assessment but also paves the way for the automation of detecting valid SJs.

Study 2 follows from Study 1 by focusing on enhancing the practicality of performance assessments in field settings. While Study 1 established a key threshold for SJ assessments, the next step is to explore how other jumping metrics can be reliably measured using more portable equipment. This study evaluates the predictive validity of the peak-speed measurements from a linear position transducer (LPT) for estimating takeoff speed in hexagonal-bar (hex-bar) jumps. Twenty-one rowing athletes performed hex-bar jumps in accordance with national testing protocols, and the peak-speed data collected from the LPT were compared to criterion measure obtained from force plates. The study demonstrated a high association between peak-speed and takeoff speed (r = 0.99, p < 0.05), with a mean difference of only 0.18%, indicating minimal bias. The Bland-Altman plot further confirmed that there was no systematic bias in the predictions. This study's findings are significant because they show that LPTs, which are more portable and less costly than force plates, can accurately estimate takeoff speed. This capability is crucial for field-based assessments where the availability of “gold standard’ equipment may be limited due to logistical or financial reasons.

Study 3 builds on Study 1 by utilizing a novel exercise for force-velocity profiling of the lower extremities and further advances Study 2 by introducing a computational model to calculate average force, velocity, and power from hex-bar jumps. While Study 2 validated the use of LPTs to calculate takeoff speed, Study 3 aims to extend this by validating a three-factor computational model that utilizes takeoff speed from the hex-bar jumps to derive comprehensive v-FVP. Using 21 university varsity rowing athletes, the study compared the computational model's outputs with criterion measures from force plates. The results confirmed that the model accurately computed theoretical force at zero velocity (F₀), theoretical velocity at zero force (V₀), and maximal mechanical power (P_max) with mean biases for force, velocity, and power being 85.38 N, 0.00 m∙s^-1, and 73.36 W, respectively. This study's findings are important as they demonstrate that a highly accessible and common training exercise can be used in conjunction with a simple computational model to replace expensive force plate measurements. This offers a cost-effective and field-applicable solution for assessing key performance metrics. The ability to calculate these metrics using the hex-bar jump is particularly advantageous for sports where traditional jump tests may be less specific or feasible.

Study 4 follows from Study 3 by focusing on applying these advanced metrics to sport specific contexts. While Study 3 demonstrated the feasibility of a computational model for deriving performance metrics, Study 4 applies these insights to understand how v-FVP metrics can predict sprint performance in female rugby athletes. This study explores the relationship between v-FVP metrics and 40 m sprint times in female university rugby athletes. Data were collected from 50 athletes (mean age 20.30 ± 2.02 years, weight 74.86 ± 12.10 kg, height 1.69 ± 0.05 m, 40 m time 6.15 ± 0.36 s). Pearson correlation coefficients were calculated to examine the relationship between 40 m time and v-FVP metrics, and a linear mixed model was used to analyze the effect of v-FVP variables on 40 m time, accounting for individual differences. Significant correlations (p < 0.01) were found between 40 m time and several v-FVP metrics. Maximal Mechanical Power (P_max, W·kg^-1) and the Slope of the Force-Velocity Relationship (S_FV, N·s·m^-1·kg^-1) were significant predictors of 40 m time in the linear mixed model (p < 0.01). The model explained 93% of the variance, with fixed effects accounting for 46.79%. P_max showed a strong negative relationship with 40 m time, while S_FV had a low positive correlation. These findings suggest that practitioners should focus on improving and monitoring P_max and S_FV to enhance sprint performance over 40 m. The study's application of v-FVP metrics offers a refined approach to enhancing sprinting speed by focusing on individual v-FVP metrics, thus bridging the gap between lab-based measurements and sport-specific training applications.

Study 5 culminates this series by utilizing even simpler jumping tests, addressing a practical challenge in the NFL Combine testing process. While the previous studies have focused on improving measurement techniques and predictive models, Study 5 extends these concepts to a large dataset from the NFL Combine to develop predictive models for sprint performance utilizing simple jumping tasks. Using data from 4,149 NFL Combine athletes, the study developed regression models to predict sprint times for different segments of the 36.58 m sprint based on vertical jump, broad jump, height, and weight. The models demonstrated high accuracy with statistically significant predictions for segmental and overall sprint times. This study’s findings are significant because they provide alternative methods for predicting sprint performance when direct testing utilizing lab-based measures is not possible, thus offering practical solutions for evaluating and training athletes to improve short (36.58 m) sprint speed.

In summary, this series of studies collectively advances the field of athletic performance assessment by progressing from the establishment of standardized testing thresholds to the development of portable and practical measurement solutions. Each study logically builds on the previous one, transitioning from lab-based validations to field-applicable techniques and demonstrating how these advancements can be utilized across various sports contexts.