Master's Theses or Doctor of Nursing Practice

Department

Health and Human Performance

Degree Name

Master of Science (MS)

Abstract

Sprint performance is a foundational determinant of success across multiple track and field events, yet many of the biomechanical and physiological factors underlying performance are typically assessed using laboratory-based tools that are not accessible to most coaches. The purpose of this study was to develop and evaluate a practical, field-based testing model capable of predicting 60 m sprint performance and identifying key performance determinants in sprint and jump athletes.

A cohort of 40 collegiate track and field athletes (ages 18–24) completed a comprehensive testing battery including 30 m acceleration, 30 m fly sprint, standing long jump, standing five-bound, vertical jump, reactive strength index (RSI), and overhead shot put. Performance data were analyzed using Pearson correlations and multiple linear regression models to examine relationships between field-based measures and sprint performance.

Strong relationships were observed between sprint-specific variables and 60 m performance, with 30 m acceleration (r = .965, p < .001) and 30 m fly time (r = .946, p < .001) demonstrating the highest associations. Jump-based measures such as standing five-bound, standing long jump, and vertical jump were also significantly correlated with sprint performance (r = −.890 to −.882, p < .001), indicating the importance of horizontal and vertical power qualities. The final regression model, incorporating sprint-specific and select field-based variables, explained 95.1% of the variance in 60 m performance (R² = .951), with a standard error of estimate of 0.126 s.

These findings suggest that while sprint-specific measures are the strongest predictors of sprint performance, field-based tests provide valuable insight into the underlying mechanical and neuromuscular qualities contributing to performance. The integration of both approaches allows coaches to not only predict sprint outcomes with high accuracy but also identify individual performance limitations. This study supports the use of accessible field-based testing as a practical tool for athlete profiling, individualized training prescription, and performance optimization in track and field environments.

Keywords

Testing, Acceleration, Prediction, Performance

Advisor

Dr. Justin Monteny

Date of Award

Spring 2026

Document Type

Thesis

Rights

© The Author

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