Search Results

Rugby league coaches often prescribe training to replicate the demands of competition. The intensities of running drills are often monitored in comparison with absolute match-play measures. Such measures may not be sensitive enough to detect fluctuations in intensity across a match or to differentiate between positions.

Purpose: To compare relative and absolute speed and metabolic thresholds for quantifying match output in elite rugby league. Methods: Twenty-six professional players competing in the National Rugby League were monitored with global positioning systems (GPS) across a rugby-league season. Absolute speed (moderate-intensity running [MIR_Th > 3.6 m/s] and high-intensity running [HIR_Th > 5.2 m/s]) and metabolic (>20 W/kg) thresholds were compared with individualized ventilatory (first [VT_1IFT] and second [VT_2IFT]) thresholds estimated from the 30-15 Intermittent Fitness Test (30-15_IFT), as well as the metabolic threshold associated with VT_2IFT (HP_metVT2), to examine difference in match-play demands. Results: VT_2IFT mean values represent 146%, 138%, 167%, and 144% increases in the HIR dose across adjustables, edge forwards, middle forwards, and outside backs, respectively. Distance covered above VT_2IFT was almost certainly greater (ES range = 0.79–1.03) than absolute thresholds across all positions. Trivial to small differences were observed between VT_1IFT and MIR_Th, while small to moderate differences were reported between HP_metVT2 and HP_metTh. Conclusions: These results reveal that the speed at which players begin to run at higher intensities depends on individual capacities and attributes. As such, using absolute HIR speed thresholds underestimates the physical HIR load. Moreover, absolute MIR and high metabolic thresholds may over- or underestimate the work undertaken above these thresholds depending on the respective fitness of the individual. Therefore, using relative thresholds enables better prescription and monitoring of external training loads based on measured individual physical capacities.

High levels of lean mass are important in collision-based sports for the development of strength and power, which may also assist during contact situations. While skinfold-based measures have been shown to be appropriate for cross-sectional assessments of body composition, their utility in tracking changes in lean mass is less clear.

Rugby league involves frequent periods of high-intensity running including acceleration and deceleration efforts, often occurring at low speeds.

Purpose: To investigate whether preseason training plans for Australian football can be computer generated using current training-load guidelines to optimize injury-risk reduction and performance improvement. Methods: A constrained optimization problem was defined for daily total and sprint distance, using the preseason schedule of an elite Australian football team as a template. Maximizing total training volume and maximizing Banister-model-projected performance were both considered optimization objectives. Cumulative workload and acute:chronic workload-ratio constraints were placed on training programs to reflect current guidelines on relative and absolute training loads for injury-risk reduction. Optimization software was then used to generate preseason training plans. Results: The optimization framework was able to generate training plans that satisfied relative and absolute workload constraints. Increasing the off-season chronic training loads enabled the optimization algorithm to prescribe higher amounts of “safe” training and attain higher projected performance levels. Simulations showed that using a Banister-model objective led to plans that included a taper in training load prior to competition to minimize fatigue and maximize projected performance. In contrast, when the objective was to maximize total training volume, more frequent training was prescribed to accumulate as much load as possible. Conclusions: Feasible training plans that maximize projected performance and satisfy injury-risk constraints can be automatically generated by an optimization problem for Australian football. The optimization methods allow for individualized training-plan design and the ability to adapt to changing training objectives and different training-load metrics.