Purpose: Can power law models accurately predict the peak intensities of rugby competition as a function of time?
Methods: Match movement data were collected from 30 elite and 30 subelite rugby union athletes across competitive seasons, using wearable Global Navigation Satellite Systems and accelerometers. Each athlete's peak rolling mean value of each measure (mean speed, metabolic power, and PlayerLoad™) for 8 durations between 5 seconds and 10 minutes was predicted by the duration with 4 power law (log-log) models, one for forwards and backs in each half of a typical match.
Results: The log of peak exercise intensity and exercise duration (5-600 s) displayed strong linear relationships (R2 = .967-.993) across all 3 measures. Rugby backs had greater predicted intensities for shorter durations than forwards, but their intensities declined at a steeper rate as duration increased. Random prediction errors for mean speed, metabolic power, and PlayerLoad were 5% to 6%, 7% to 9%, and 8% to 10% (moderate to large), respectively, for elite players. Systematic prediction errors across the range of durations were trivial to small for elite players, underestimating intensities for shorter (5-10 s) and longer (300-600 s) durations by 2% to 4% and overestimating 20- to 120-second intensities by 2% to 3%. Random and systematic errors were slightly greater for subelites compared to elites, with ranges of 4% to 12% and 2% to 5%, respectively.
Conclusions: Peak intensities of professional rugby union matches can be predicted with adequate precision (trivial to small errors) for prescribing training drills of a given duration, irrespective of playing position, match half, level of competition, or measure of exercise intensity. However, practitioners should be aware of the substantial (moderate to large) prediction errors at the level of the individual player.
Keywords: accelerometers; exercise duration; exercise intensity; football; global positioning systems.