A Drone-Based Method to Measure Sprint Force-Velocity Profiles in 30-Meter Sprint Test - A Pilot Study. - Wang, Fahui; Hautier, Christophe; Song, Lin; Zhou, Yong; Guignard, Brice; Glaise, Paul; Zhang, Qingshan (Journal of Sports Science & Medicine, 2026)
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 horizontal 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 ≤ 1.54%) 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 (e.g., 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.