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Markus N.C. Williams, Vincent J. Dalbo, Jordan L. Fox, Cody J. O’Grady, and Aaron T. Scanlan

locomotive characteristics (eg, activity frequency, distance, acceleration, velocity) have historically been measured using time–motion analysis and microsensors. 1 Furthermore, PlayerLoad ™ (PL) is a popular variable obtained from microsensors to quantify external workload in basketball players. 6 – 9

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Ryan M. Chambers, Tim J. Gabbett, and Michael H. Cole

Commercially available microtechnology devices containing global positioning systems (GPSs) and microsensors (accelerometers, gyroscopes, and magnetometers) are commonly used to quantify the physical demands of rugby union. 1 During match play and training, players are divided into subgroups of

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Markus N.C. Williams, Jordan L. Fox, Cody J. O’Grady, Samuel Gardner, Vincent J. Dalbo, and Aaron T. Scanlan

anthropometric measurements were obtained from each player prior to the first training session including stature using a portable stadiometer (Seca 213; Seca GMBH, Hamburg, Germany) and body mass using electronic scales (BWB-600; Tanita Corporation, Tokyo, Japan). Across the regular season, microsensors

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Cody J. O’Grady, Jordan L. Fox, Vincent J. Dalbo, and Aaron T. Scanlan

annual plan with appropriate external and internal workloads interspersed with suitable recovery periods. 2 , 3 External workload represents the physical stimuli imposed on players and is often measured as speed, distance travelled, movement counts, or accumulated load obtained via microsensors or video

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Dean J. McNamara, Tim J. Gabbett, Paul Chapman, Geraldine Naughton, and Patrick Farhart


Bowling workload is linked to injury risk in cricket fast bowlers. This study investigated the validity of microtechnology in the automated detection of bowling counts and events, including run-up distance and velocity, in cricket fast bowlers.


Twelve highly skilled fast bowlers (mean ± SD age 23.5 ± 3.7 y) performed a series of bowling, throwing, and fielding activities in an outdoor environment during training and competition while wearing a microtechnology unit (MinimaxX). Sensitivity and specificity of a bowling-detection algorithm were determined by comparing the outputs from the device with manually recorded bowling counts. Run-up distance and run-up velocity were measured and compared with microtechnology outputs.


No significant differences were observed between direct measures of bowling and nonbowling events and true positive and true negative events recorded by the MinimaxX unit (P = .34, r = .99). The bowling-detection algorithm was shown to be sensitive in both training (99.0%) and competition (99.5%). Specificity was 98.1% during training and 74.0% during competition. Run-up distance was accurately recorded by the unit, with a percentage bias of 0.8% (r = .90). The final 10-m (–8.9%, r = .88) and 5-m (–7.3%, r = .90) run-up velocities were less accurate.


The bowling-detection algorithm from the MinimaxX device is sensitive to detect bowling counts in both cricket training and competition. Although specificity is high during training, the number of false positive events increased during competition. Additional bowling workload measures require further development.

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Jordan L. Fox, Jesse Green, and Aaron T. Scanlan

), and body mass, using electronic scales (BWB-600; Tanita Corporation, Tokyo, Japan). For all games, the players wore microsensor units (OptimEye s5; Catapult Innovations, Melbourne, Australia) and heart rate (HR) monitors (Polar T31; Polar Electro, Kempele, Finland) to continuously collect data

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Erik Trøen, Bjarne Rud, Øyvind Karlsson, Camilla Høivik Carlsen, Matthias Gilgien, Gøran Paulsen, Ola Kristoffer Tosterud, and Thomas Losnegard

values over 1 minute was taken as VO 2max and HR max , respectively. Figure 3 —Custom-made timing gate with the microsensor (circled) placed over the track. When a subject hits the horizontal bar, a time stamp is registered by the microsensor. Data Analyses Segment times and total 700-m times were

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Jordan L. Fox, Cody J. O’Grady, and Aaron T. Scanlan

sessions per week with 163 and 57 samples obtained for the 2018 and 2019 seasons, respectively. Methodology Prior to each training session, players were fitted with microsensors (OptimEye S5; Catapult Innovations, Melbourne, Australia) held at the upper torso between the scapulae. Heart rate (HR) data were

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Jordan L. Fox, Cody J. O’Grady, and Aaron T. Scanlan

’ responses to a given external workload may depend on the internal workload variable selected during basketball training. However, Scanlan et al 6 only used accelerometer-derived accumulated workload to represent the exercise dose. In turn, many microsensor monitoring systems implemented in basketball

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Aaron T. Scanlan, Robert Stanton, Charli Sargent, Cody O’Grady, Michele Lastella, and Jordan L. Fox

demands reflective of player movements during game-play and are typically quantified using video-based technology or microsensors. 7 Collectively, the existing workload data suggest that basketball players experience high perceptual (∼800 arbitrary units [AU]) 3 and physiological (∼82% of maximum HR [HR