Aaron J. Coutts
Aaron J. Coutts
Helen Alexiou and Aaron J. Coutts
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.
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.
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).
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.
Thomas Kempton and Aaron J. Coutts
To describe the physical and technical demands of rugby league 9s (RL9s) match play for positional groups.
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.
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.
Positional differences exist for both physical and technical measures, and preparation for RL9s play should incorporate these differences.
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.
Jason C. Laffer, Aaron J. Coutts and Job Fransen
Dynamic motor skills such as volleyball blocking rely on efficient perception–action coupling and are influenced by individual, environmental, and task constraints. However, limited research studies have assessed the effect of an individual constraint such as blocking skill on visual attention during an in-situ volleyball blocking task. Therefore, this study used a cross-sectional, observational design to investigate the gaze behavior of 18 male volleyball players (25.6 ± 4.9 years), of two different levels of blocking skill determined a priori according to success during an on-court blocking task. When compared to relatively unsuccessful players (RUS), the gaze of relatively successful players (RS) was observed to fixate more often (RUS: 0.7 ± 0.7 n, RS: 1.3 ± 0.3 n) and dwell for longer (Total; RUS: 12.2 ± 18.4%, RS: 48.0 ± 37.2%, Phase 4; RUS: 6.6 ± 8.8%, RS: 16.9 ± 12.4%) on the opposition spiker, demonstrating that important perceptual information about an opposing team’s attack lies within the behavior of the opposition spiker. More successful blockers were also observed to be taller (RUS: 181.8 ± 6.6 cm, RS: 192.6 ± 3.9 cm), longer in arm-span (RUS: 185.7 ± 5.6 cm, RS: 195.2 ± 5.6 cm), and heavier (RUS: 78.6 ± 11.4 kg, RS: 90.5 ± 8.5 kg). These results can consequently be used to develop a profile of the visual attention and physical attributes of successful blockers for use in developing talented players.
Patrick Ward, Aaron J. Coutts, Ricard Pruna and Alan McCall
There is a common expression in sports that “there is no ‘I’ in team.” However, collectively, there is actually a very important “I” in sport teams—the individual athlete/player. Each player has his or her own unique characteristics including physical, physiological, and psychological traits. Due to these unique characteristics, each player requires individual provision—whether it be an injury risk profile and targeted prevention strategy or treatment/rehabilitation for injury, dietary regimen, recovery, or psychological intervention. The aim of this commentary is to highlight how 4 high-performance teams from various professional football codes are analyzing individual player data.