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Aaron J. Coutts

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Thomas Kempton and Aaron J. Coutts

Purpose:

To describe the physical and technical demands of rugby league 9s (RL9s) match play for positional groups.

Methods:

Global positioning system data were collected during 4 games from 16 players from a team competing in the Auckland RL9s tournament. Players were classified into positional groups (pivots, outside backs, and forwards). Absolute and relative physical-performance data were classified as total high-speed running (HSR; >14.4 km/h), very-high-speed running (VHSR; >19.0 km/h), and sprint (>23.0 km/h) distances. Technical-performance data were obtained from a commercial statistics provider. Activity cycles were coded by an experienced video analyst.

Results:

Forwards (1088 m, 264 m) most likely completed less overall and high-speed distances than pivots (1529 m, 371 m) and outside backs (1328 m, 312 m). The number of sprint efforts likely varied between positions, although differences in accelerations were unclear. There were no clear differences in relative total (115.6−121.3 m/min) and HSR (27.8−29.8 m/min) intensities, but forwards likely performed less VHSR (7.7 m/min) and sprint distance (1.3 m/min) per minute than other positions (10.2−11.8 m/min, 3.7−4.8 m/min). The average activity and recovery cycle lengths were ~50 and ~27 s, respectively. The average longest activity cycle was ~133 s, while the average minimum recovery time was ~5 s. Technical involvements including tackles missed, runs, tackles received, total collisions, errors, off-loads, line breaks, and involvements differed between positions.

Conclusions:

Positional differences exist for both physical and technical measures, and preparation for RL9s play should incorporate these differences.

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Helen Alexiou and Aaron J. Coutts

Purpose:

The purpose of this study was to compare the session-RPE method for quantifying internal training load (TL) with various HR-based TL quantification methods in a variety of training modes with women soccer players.

Methods:

Fifteen elite women soccer players took part in the study (age: 19.3 ± 2.0 y and VO2max: 50.8 ± 2.7 mL·kg−1·min−1). Session-RPE, heart rate, and duration were recorded for 735 individual training sessions and matches over a period of 16 wk. Correlation analysis was used to compare session-RPE TLs with three commonly used HR-based methods for assessing TL.

Results:

The mean correlation for session-RPE TL with Banister’s TRIMP, LTzone TL and Edwards’s TL were (r = 0.84, 0.83, and 0.85, all P < .01, respectively). Correlations for session-RPE TL and three HR-based methods separated by session type were all significant (all P < .05). The strongest correlations were reported for technical (r = 0.68 to 0.82), conditioning (r = 0.60 to 0.79), and speed sessions (r = 0.61 to 0.79).

Conclusion:

The session-RPE TL showed a significant correlation with all training types common to soccer. Higher correlations were found with less intermittent, aerobic-based training sessions and suggest that HR-based TLs relate better to session-RPE TLs in less intermittent training activities. These results support previous findings showing that the session-RPE TL compares favorably with HR-based methods for quantifying internal TL in a variety of soccer training activities.

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Alan McCall, Maurizio Fanchini and Aaron J. Coutts

In high-performance sport, science and medicine practitioners employ a variety of physical and psychological tests, training and match monitoring, and injury-screening tools for a variety of reasons, mainly to predict performance, identify talented individuals, and flag when an injury will occur. The ability to “predict” outcomes such as performance, talent, or injury is arguably sport science and medicine’s modern-day equivalent of the “Quest for the Holy Grail.” The purpose of this invited commentary is to highlight the common misinterpretation of studies investigating association to those actually analyzing prediction and to provide practitioners with simple recommendations to quickly distinguish between methods pertaining to association and those of prediction.

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Stephen Crowcroft, Katie Slattery, Erin McCleave and Aaron J. Coutts

Purpose: To assess a coach’s subjective assessment of their athletes’ performances and whether the use of athlete-monitoring tools could improve on the coach’s prediction to identify performance changes. Methods: Eight highly trained swimmers (7 male and 1 female, age 21.6 [2.0] y) recorded perceived fatigue, total quality recovery, and heart-rate variability over a 9-month period. Prior to each race of the swimmers’ main 2 events, the coach (n = 1) was presented with their previous race results and asked to predict their race time. All race results (n = 93) with aligning coach’s predictions were recorded and classified as a dichotomous outcome (0 = no change; 1 = performance decrement or improvement [change +/− > or < smallest meaningful change]). A generalized estimating equation was used to assess the coach’s accuracy and the contribution of monitoring variables to the model fit. The probability from generalized estimating equation models was assessed with receiver operating characteristic curves to identify the model’s accuracy from the area under the curve analysis. Results: The coach’s predictions had the highest diagnostic accuracy to identify both decrements (area under the curve: 0.93; 95% confidence interval, 0.88–0.99) and improvements (area under the curve: 0.89; 95% confidence interval, 0.83–0.96) in performance. Conclusions: These findings highlight the high accuracy of a coach’s subjective assessment of performance. Furthermore, the findings provide a future benchmark for athlete-monitoring systems to be able to improve on a coach’s existing understanding of swimming performance.

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Thiago Oliveira Borges, Nicola Bullock, David Aitken and Aaron J. Coutts

Methods:

This study compared 3 commercially available ergometers for within- and between-brands difference to a first-principle calibration rig.

Results:

All ergometers underestimated true mean power, with errors of 27.6% ± 3.7%, 4.5% ± 3.5%, and 22.5% ± 1.9% for the KayakPro, WEBA, and Dansprint, respectively. Within-brand ergometer power differences ranged from 17 ± 9 to 22 ± 11 W for the KayakPro, 3 ± 4 to 4 ± 4 W for the WEBA, and 5 ± 3 to 5 ± 4 W for the Dansprint. The linear-regression analysis showed that most kayak ergometers have a stable coefficient of variation (0.9–1.7%) with a moderate effect size.

Conclusion:

Taken collectively, these findings show that different ergometers present inconsistent outcomes. Therefore, we suggest that athlete testing be conducted on the same ergometer brand, preferably the same ergometer. Optimally, that ergometer should be calibrated using a first-principle device before any athlete testing block.