This study established the reliability of an exponential function to model the change in velocity during the speed skating sprint start and the validity of associated model parameters in a group of subelite and elite long track speed skaters. Long track speed skaters (n = 38) performed maximal effort 50-m on-ice accelerations from a standing start while tethered to a horizontal robotic resistance device that sampled position and time data continuously. An exponential function was applied to the raw data to model the change in velocity throughout the acceleration phase and compute the maximal skating speed (MSS), maximal acceleration capacity (MAC), maximum relative net horizontal power (PMax), and an acceleration-time constant (t). All constructed models provided a sufficient fit of the raw data (R-squared > 0.95, mean bias <2%). Intraday reliability of all model parameters ranged from good to excellent (intraclass correlation coefficient >0.8 and coefficient of variation <5%). Strong negative correlations (r: -0.72 to -0.96) were observed between MSS and PMax and the 10 and 20 m split times measured with the robotic resistance and with 100 split times obtained from 500 m races. Moderate-to-large between-group differences were observed in MSS, MAC, and PMax between the elite vs. subelite speed skaters (Cohen d effect sizes: 1.18-3.53). Our results indicate that monoexponential modeling is a valid and reliable method of monitoring initial acceleration performance in elite level long track speed skaters.
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