high-intensity running completed, quantified by GPS technology, and PlayerLoad, calculated from triaxial accelerometers, can be used to quantify external match and training loads. 1 – 3 An imbalance between training/competition loads and recovery over extended periods of time may contribute to
Phillip M. Bellinger, Cameron Ferguson, Tim Newans, and Clare L. Minahan
Marni J. Simpson, David G. Jenkins, and Vincent G. Kelly
. Using relative PlayerLoad (PL per minute·), 18 Young et al 17 reported that a total netball training session for elite netballers had significantly lower PL per minute than in matches. However, time spent resting (eg, tactical discussion with coaches or a drink break) was included as “training” and
Aaron T. Scanlan, Robert Stanton, Charli Sargent, Cody O’Grady, Michele Lastella, and Jordan L. Fox
. Garments were fitted to each player prior to games. The garments held microsensors (OptimEye s5; Catapult Innovations, Melbourne, Australia) on the posterior, upper surface of the torso to measure external workload variables. PlayerLoad and inertial movement analysis (IMA) variables were used to represent
Adam Douglas, Michael A. Rotondi, Joseph Baker, Veronica K. Jamnik, and Alison K. Macpherson
recordings of the gyroscope and magnetometer, can successfully quantify sport-specific movements. 7 One such method to quantify the workload performed by an athlete is PlayerLoad, which sums the individual triaxial accelerometer vectors to produce an instantaneous measure of work rate, expressed in
Marni J. Simpson, David G. Jenkins, Aaron T. Scanlan, and Vincent G. Kelly
rates (100 Hz) that use accelerometers, gyroscopes, and magnetometers to quantify external workload. 5 The accelerometer component of IMUs has been used in a number of sports to quantify the movements performed by athletes. In this regard, PlayerLoad (PL) is a variable that uses raw ACCEL data in
Billy T. Hulin, Tim J. Gabbett, Rich D. Johnston, and David G. Jenkins
), mediolateral ( x -axis), and vertical ( z -axis). 1 – 3 Triaxial vector-magnitude PlayerLoad (PL VM ) is calculated as the sum of the squared instantaneous rate of change in acceleration in each of the 3 vectors ( x -, y - and z -axes), which is then squared and divided by 100. 3 PlayerLoad in each
Will Vickery, Ben Dascombe, and Rob Duffield
To examine the relationship between session rating of perceived exertion (sRPE) and measures of internal and external training load (TL) in cricket batsmen and medium-fast bowlers during net-based training sessions.
The internal (heart rate), external (movement demands, PlayerLoad), and technical (cricket-specific skills) loads of 30 male cricket players (age 21.2 ± 3.8 y, height 1.82 ± 0.07 m, body mass 79.0 ± 8.7 kg) were determined from net-based cricket-training sessions (n = 118). The relationships between sRPE and measures of TL were quantified using Pearson product–moment correlations respective to playing position. Stepwise multiple-regression techniques provided key internal- and external-load determinants of sRPE in cricket players.
Significant correlations were evident (r = -.34 to .87, P < .05) between internal and external measures of TL and sRPE, with the strongest correlations (r ≥ .62) for GPS-derived measures for both playing positions. In batsmen, stepwise multiple-regression analysis revealed that 67.8% of the adjusted variance in sRPE could be explained by PlayerLoad and high-intensity distance (y = 27.43 + 0.81 PlayerLoad + 0.29 high-intensity distance). For medium-fast bowlers, 76.3% of the adjusted variance could be explained by total distance and mean heart rate (y = 101.82 + total distance 0.05 + HRmean – 0.48).
These results suggest that sRPE is a valid method of reporting TL among cricket batsmen and medium-fast bowlers. Position-specific responses are evident and should be considered when monitoring the TL of cricket players.
Eirik H. Wik, Live S. Luteberget, and Matt Spencer
Team handball matches place diverse physical demands on players, which may result in fatigue and decreased activity levels. However, previous speed-based methods of quantifying player activity may not be sensitive for capturing short-lasting team-handball-specific movements.
To examine activity profiles of a women’s team handball team and individual player profiles, using inertial measurement units.
Match data were obtained from 1 women’s national team in 9 international matches (N = 85 individual player samples), using the Catapult OptimEye S5. PlayerLoad/min was used as a measure of intensity in 5- and 10-min periods. Team profiles were presented as relative to the player’s match means, and individual profiles were presented as relative to the mean of the 5-min periods with >60% field time.
A high initial intensity was observed for team profiles and for players with ≥2 consecutive periods of play. Substantial declines in PlayerLoad/min were observed throughout matches for the team and for players with several consecutive periods of field time. These trends were found for all positional categories. Intensity increased substantially in the final 5 min of the first half for team profiles. Activity levels were substantially lower in the 5 min after a player’s most intense period and were partly restored in the subsequent 5-min period.
Possible explanations for the observed declines in activity profiles for the team and individual players include fatigue, situational factors, and pacing. However, underlying mechanisms were not accounted for, and these assumptions are therefore based on previous team-sport studies.
Dean J. McNamara, Tim J. Gabbett, Paul Chapman, Geraldine Naughton, and Patrick Farhart
The use of wearable microtechnology to monitor the external load of fast bowling is challenged by the inherent variability of bowling techniques between bowlers. This study assessed the between-bowlers variability in PlayerLoad, bowling velocity, and performance execution across repeated bowling spells.
Seven national-level fast bowlers completed two 6-over bowling spells at a batter during a competitive training session. Key dependent variables were PlayerLoad calculated with a MinimaxX microtechnology unit, ball velocity, and bowling execution based on a predetermined bowling strategy for each ball bowled. The between-bowlers coefficient of variation (CV), repeated-measures ANOVA, and smallest worthwhile change were calculated over the 2 repeated 6-over bowling spells and explored across 12-over, 6-over, and 3-over bowling segments.
From the sum of 6 consecutive balls, the between-bowlers CV for relative peak PlayerLoad was 1.2% over the 12-over bowling spell (P = .15). During this 12-over period, bowling-execution (P = .43) scores and ball-velocity (P = .31) CVs were calculated as 46.0% and 0.4%, respectively.
PlayerLoad was found to be stable across the repeated bowling spells in the fast-bowling cohort. Measures of variability and change across the repeated bowling spells were consistent with the performance measure of ball velocity. The stability of PlayerLoad improved when assessed relative to the individual’s peak PlayerLoad. Only bowling-execution measures were found to have high variability across the repeated bowling spells. PlayerLoad provides a stable measure of external workload between fast bowlers.
Steve Barrett, Adrian Midgley, and Ric Lovell
The study aimed to establish the test–retest reliability and convergent validity of PlayerLoad™ (triaxial-accelerometer data) during a standardized bout of treadmill running.
Forty-four team-sport players performed 2 standardized incremental treadmill running tests (7–16 km/h) 7 d apart. Players’ oxygen uptake (VO2; n = 20), heart rate (n = 44), and triaxialaccelerometer data (PlayerLoad; n = 44) measured at both the scapulae and at the center of mass (COM), were recorded. Accelerometer data from the individual component planes of PlayerLoad (anteroposterior [PLAP], mediolateral [PLML], and vertical [PLV]) were also examined.
Moderate to high test–retest reliability was observed for PlayerLoad and its individual planes (ICC .80–.97, CV 4.2–14.8%) at both unit locations. PlayerLoad was significantly higher at COM vs scapulae (223.4 ± 42.6 vs 185.5 ± 26.3 arbitrary units; P = .001). The percentage contributions of individual planes to PlayerLoad were higher for PLML at the COM (scapulae 20.4% ± 3.8%, COM 26.5% ± 4.9%; P = .001) but lower for PLV (scapulae 55.7% ± 5.3%, COM 49.5% ± 6.9%; P = .001). Between-subjects correlations between PlayerLoad and VO2, and between PlayerLoad and heart rate were trivial to moderate (r = –.43 to .33), whereas within-subject correlations were nearly perfect (r = .92–.98).
PlayerLoad had a moderate to high degree of test–retest reliability and demonstrated convergent validity with measures of exercise intensity on an individual basis. However, caution should be applied in making between-athletes contrasts in loading and when using recordings from the scapulae to identify lower-limb movement patterns.