Validity of Global Positioning System Technology to Measure Maximum Velocity Sprinting in Elite Sprinters
Bibliographic record
Abstract
ABSTRACT: Thome, M, Thorpe, RT, Jordan, MJ, and Nimphius, S. Validity of global positioning system (GPS) technology to measure maximum velocity sprinting in elite sprinters. J Strength Cond Res 37(12): 2438-2442, 2023-The objective of this study was to assess the concurrent validity of 10-Hz wearable Global Positioning System (GPS) technology to measure maximum velocity sprinting (Vmax) relative to Doppler radar in elite sprinters. Data were collected from a single training session performed by elite 100 and 200 m sprinters (males: n = 5; 100 m best times: 10.02 ± 0.07 seconds, range: 9.94-10.10 seconds; 200 m best times: 20.29 ± 0.42 seconds, range: 19.85-20.80 seconds; females: n = 2; age: 28.0 ± 4.2 years; body mass: 65.8 ± 4.6 kg; 100 m best times: 11.18 ± 0.34 seconds; 200 m best times: 22.53 ± 0.04 seconds). Velocity and time data from 16 maximal, 60-m sprint efforts were recorded simultaneously with 10 Hz GPS and 47 Hz radar. Validity was assessed using Bland-Altman 95% limits of agreement (LOA) and intraclass correlation coefficient (ICC), each with respective 95% confidence intervals (CI). Vmax measured with 10 Hz GPS demonstrated a LOA of -0.11 m·s-1 (-0.17, -0.05) and an ICC of 0.99 (0.98, 1.0) relative to the radar device.10 Hz GPS overestimated Vmax by 0.11 m·s-1 relative to the radar but could still be considered a suitable tool for monitoring external load in elite sprinters. However, the much smaller average annual improvement in this population (∼0.1-0.2%) in comparison with the ∼1% overestimation reduces the utility of 10 Hz GPS to detect meaningful performance changes in maximum velocity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".