Research article - (2026)25, 536 - 546
DOI:
https://doi.org/10.52082/jssm.2026.536
A Drone-Based Method to Measure Sprint Force-Velocity Profiles in 30-Meter Sprint Test - A Pilot Study
Fahui Wang1, Christophe Hautier1, Lin Song2, Yong Zhou2, Brice Guignard1, Paul Glaise1, Qingshan Zhang2,1,
1Université Lyon 1, LIBM, UR 7424, Villeurbanne, France
2School of Athletic Performance, Shanghai University of Sport, Shanghai, China

Qingshan Zhang
✉ School of Athletic Performance, Shanghai University of Sport, Shanghai, China
Email: zhang.qingshan@hotmail.com
Received: 29-04-2026 -- Accepted: 31-05-2026
Published (online): 01-06-2026
Narrated in English

ABSTRACT

The objective was to determine the test-retest reliability and concurrent validity of a drone system in comparison to a radar device. Seventeen male collegiate soccer players participated in two maximal 30-meter sprint runs. The test-retest reliability of the drone system was evaluated using intraclass correlation coefficients (ICC3,1), coefficient of variation (CV%), and standard error of measurement (SEM). Subsequently, the systematic bias and consistency of the two devices on various force-velocity (F-V) variables (e.g., maximal velocity [Vmax], theoretical maximal velocity [V0], theoretical maximal horizontal force [F0], the slope of the F-V relationship [SFV]) were evaluated using linear mixed model (LMM) and Bland-Altman analysis. The drone system demonstrated moderate to excellent test-retest reliability across all variables (0.59 ≤ ICC ≤ 0.95; CV% < 10%). While LMM analysis detected significant systematic differences for Vmax (p = 0.013) and V0 (p = 0.012), Bland-Altman analysis confirmed high practical agreement with minimal bias (≤ 1.12%) and narrow limits of agreement (LoA < 10%). Pmax, split times (T5m–T20m) and average accelerations (A10m–A20m) demonstrated greater consistency (%Bias ≤ 0.76%) with no significant systematic bias (p > 0.05). Conversely, early-acceleration and model-derived metrics (Tau, Amax, F0, SFV) exhibited significant bias (p ≤ 0.028) and wide LoA exceeding 10% (e.g., F0: -13.37% to 8.56%; SFV: -11.54% to 18.18%). In conclusion, although the drone system exhibits high monitoring value in the maximum speed phase, early-acceleration metrics (Amax, F0, and T5m) should be interpreted with caution for individual-level monitoring. The tracking instability during the early acceleration phase necessitates further algorithm optimization.

Key words: Computer vision, acceleration, performance, reliability

Key Points
  • The study evaluated a consumer-grade drone system for sprint force-velocity profiling against a radar using multi-faceted statistical analyses (ICC, CV%, SEM, LMM, Bland-Altman).
  • The drone system demonstrated moderate-to-excellent reliability (ICC: 0.59–0.95) and high practical agreement in the maximum speed phase (bias ≤ 1.12%; LoA < 10%), but exhibited significant systematic bias during the initial acceleration phase.
  • While suitable for group-level monitoring of maximum velocity metrics, the identified early-phase tracking instability provides clear targets for algorithm optimization in future drone-based sports technology applications.








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