Abstract
Sprint performance is a key determinant of success across many sports (particularly track and field athletes). Many of the biomechanical and physiological factors contributing to sprint 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 as well as anthropomorphic measures (Leg length, Hyper Mobility Screening ). Performance data was analyzed in SPSS 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 were also significantly correlated with sprint performance, including standing five-bound (r = −.890, p < .001), standing long jump (r = −.894, p < .001), and vertical jump (r = −.882, p < .001), indicating the importance of horizontal and vertical power qualities. The final regression model, incorporating sprint-specific variables (30 m acceleration and 30 m fly time) along with select field-based measures (standing five-bound), explained 95.1% of the variance in 60 m performance (R² = .951), with a standard error of estimate of 0.126 s. Prior modeling demonstrated that jump-based variables alone (standing long jump, vertical jump, and overhead shot put) explained 83.2% of the variance (R² = .832), while the addition of sprint-specific variables increased the model’s explanatory power to 83.9% (R² = .839) before the final hierarchical model further improved prediction accuracy.
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. A limitation of this study is potential multi-collinearity; because the predictor variables are highly correlated, the specific structure and entry order of the regression model may have heavily influenced the final weighting of individual performance determinants. 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.
Faculty Advisor
Justin Montney
Department/Program
Health and Human Performance
Submission Type
in-person poster
Date
4-13-2026
Rights
Copyright the Author(s)
Recommended Citation
Bohannon, Peterson; Montney, Justin L.; Washburn, Emily; Bechard, Jenifer; and Frangello, Michael III
(2026)
"Acceleration Battery Predictive Model Utilizing Field-Based Testing,"
SACAD: Scholarly Activities: Vol. 2026, Article 66.
Available at:
https://scholars.fhsu.edu/sacad/vol2026/iss2026/66
