Concurrent Validity and Reliability of Different Technologies for Sprint-Derived Horizontal Force-Velocity-Power Profiling
Bibliographic record
Abstract
ABSTRACT: Cormier, P, Tsai, M-C, Meylan, C, Agar-Newman, D, Epp-Stobbe, A, Kalthoff, Z, and Klimstra, M. Concurrent validity and reliability of different technologies for sprint-derived horizontal force-velocity-power profiling. J Strength Cond Res 37(6): 1298-1305, 2023-This study evaluated the validity and reliability of common systems to assess sprint-derived horizontal force-velocity-power ( FVPH ) profile metrics. Two double constellation athlete monitoring systems (STATSports Apex, Catapult Vector S7) and one timing gate system were compared with a radar gun for the computation of FVPH metrics. Intersystem validity was assessed using intraclass correlation coefficients (ICC), Pearson's correlation coefficients ( R2 ), and Bland-Altman plots with absolute and percent agreement. Intrasystem reliability was assessed with agreement bias and ICC. STATSports demonstrated moderate agreement for F0 , Pmax , τ, and Drf (8.62, 6.46, -9.81, and 9.96%, respectively) and good agreement for V0 and MSS (-2.18 and -1.62%). Catapult displayed good agreement across all metrics ( F0 , V0 , Pmax , MSS, τ, and Drf : -0.96, -0.89, -1.85, -0.84, 0.38, and -0.27%, respectively). Timing gates demonstrated good agreement with V0 and MSS (-2.62 and -1.71%) and poor agreement with F0 , Pmax , τ, and Drf (19.17, 16.64, -20.49, and 20.18%, respectively). Intrasystem reliability demonstrated good agreement (<2% bias) with very large to near-perfect ICC (0.84-0.99) for Catapult and STATSports systems. Overall, GPS/GNSS 10 Hz technology is reliable across devices and can provide moderate-to-good accuracy of FVPH metrics in single maximal effort sprints. However, Catapult provided better agreement for more FVPH metrics than STATSports, which may be related to differences in proprietary algorithms. Also, modeling timing gate data using current FVPH profiling techniques results in poor bias that requires greater investigation. GPS/GNSS data can be used for FVPH profiling, which could inform performance and rehabilitation processes.
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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 | 0.025 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".