Changing gears: data-driven velocity zones to support monitoring and research in men's rugby league

Abstract

<p><b>Objectives:</b> The study aimed to (1) apply a data-mining approach to league-wide microtechnology data to identify absolute velocity zone thresholds and (2) apply the respective velocity zones to microtechnol-ogy data to examine the locomotor demands of elite match-play.</p><p> <b>Methods:</b> League-wide microtechnology data were collected from elite male rugby league players representing all National Rugby League (NRL) teams (n = 16 teams, one excluded due to a different microtechnology device; n = 4836 files) over one season. To identify four velocity zones, spectral cluster-ing with a beta smoothing cut-off of 0.1 was applied to each players' instantaneous match-play velocity data. Velocity zones for each player were calculated as the median while the overarching velocity zones were determined through an incremental search to minimise root mean square error.</p><p> <b>Results:</b> The velocity zones identified through spectral clustering were 0–13.99 km · h⁻¹ (i.e., low velocity), 14.00–20.99 km · h⁻¹ (i.e., moderate velocity), 21.00–24.49 km · h⁻¹ (i.e., high velocity) and >24.50 km · h⁻¹ (i.e., very-high velocity).</p><p> <b>Conclusions:</b> The application of spectral clustering (i.e., a data-mining method) to league-wide rugby league microtechnology data yielded insights into the distribution of velocity data, thereby informing the cut-off values which best place similar data points into the same velocity zones. As the identified zones are representative of the intensities of locomotion achieved by elite male rugby league players, it is suggested that when absolute zones are used, the consistent application of the identified zones would facilitate standardisation, longitudinal athlete monitoring as well as comparisons between teams, leagues and published literature.</p>