manufacturers, the interunit reliability and the between-manufacturer variation of variables should be investigated. There are continual improvements in GPS technology occurring, with advancements in microprocessors, software, and data processing techniques, 4 which overall may improve the validity and
Heidi R. Thornton, André R. Nelson, Jace A. Delaney, Fabio R. Serpiello and Grant M. Duthie
Matthew C. Varley, Arne Jaspers, Werner F. Helsen and James J. Malone
Sprints and accelerations are popular performance indicators in applied sport. The methods used to define these efforts using athlete-tracking technology could affect the number of efforts reported. This study aimed to determine the influence of different techniques and settings for detecting high-intensity efforts using global positioning system (GPS) data.
Velocity and acceleration data from a professional soccer match were recorded via 10-Hz GPS. Velocity data were filtered using either a median or an exponential filter. Acceleration data were derived from velocity data over a 0.2-s time interval (with and without an exponential filter applied) and a 0.3-second time interval. High-speed-running (≥4.17 m/s2), sprint (≥7.00 m/s2), and acceleration (≥2.78 m/s2) efforts were then identified using minimum-effort durations (0.1–0.9 s) to assess differences in the total number of efforts reported.
Different velocity-filtering methods resulted in small to moderate differences (effect size [ES] 0.28–1.09) in the number of high-speed-running and sprint efforts detected when minimum duration was <0.5 s and small to very large differences (ES –5.69 to 0.26) in the number of accelerations when minimum duration was <0.7 s. There was an exponential decline in the number of all efforts as minimum duration increased, regardless of filtering method, with the largest declines in acceleration efforts.
Filtering techniques and minimum durations substantially affect the number of high-speed-running, sprint, and acceleration efforts detected with GPS. Changes to how high-intensity efforts are defined affect reported data. Therefore, consistency in data processing is advised.
Malachy P. McHugh, Tom Clifford, Will Abbott, Susan Y. Kwiecien, Ian J. Kremenic, Joseph J. DeVita and Glyn Howatson
these numbers represent actual changes in jump mechanics or are systematic errors in accelerometer data processing. Regardless, from a practical perspective, the flight height data seem to be more sensitive than jump height for measuring performance impairment. Inertial Sensor Versus Optoelectric System
Heidi R. Thornton, Jace A. Delaney, Grant M. Duthie and Ben J. Dascombe
, absolute thresholds permit comparisons between sports and/or competitions 16 ; however, variations in GPS manufacturers, sampling frequencies, and data processing methods influence such comparisons. 16 , 17 Altogether, practitioners should consider which method is most appropriate to evaluate data that
Dustin J. Oranchuk, André R. Nelson, Adam G. Storey and John B. Cronin
deformation of muscle architecture. The probe was oriented perpendicular to the skin and parallel to the estimated fascicle direction. 13 On each occasion, 2 samples were captured and averaged to obtain mean MT, PA, and FL. Data Processing and Analysis Images were analyzed via digitizing software (ImageJ
Richard Latzel, Olaf Hoos, Sebastian Stier, Sebastian Kaufmann, Volker Fresz, Dominik Reim and Ralph Beneke
Düsseldorf, Düsseldorf, Germany), 26 while all other data-processing procedures and statistics were computed using SPSS 20.0 (IBM, Chicago, IL) and Origin 9.0 (OriginLab, Northampton, MA). All measures are presented as mean (SD). The normality of distribution was verified using Kolmogorov–Smirnov testing
Pedro G. Morouço, Tiago M. Barbosa, Raul Arellano and João P. Vilas-Boas
, all participants underwent familiarization sessions (n = 3) with the tethered testing apparatus. Data processing were carried out on a signal processing software (AcqKnowledge, v.4.0; BIOPAC Systems, Santa Barbara, CA); a low-pass filter and cutoff values were chosen based on residual analysis
Owen Jeffries, Mark Waldron, Stephen D. Patterson and Brook Galna
exchanges were recorded to assess oxygen consumption (VO 2 ) (Oxycon Pro; Erich Jaeger GmbH, Hoechberg, Germany). Data Processing Power output data were sampled at 1 Hz and variability examined in several ways. First, the distribution of power output for both conditions was calculated by creating a
Ian N. Bezodis, David G. Kerwin, Stephen-Mark Cooper and Aki I.T. Salo
recorded during the maximal velocity phase of a sprint, at least 40 m from the start. The coach used photocell times on most occasions to give feedback immediately after each run. Data Processing Video data were imported into Target (Loughborough Innovations Ltd, Loughborough, UK) for digitizing. The last
Live S. Luteberget, Benjamin R. Holme and Matt Spencer
analysis consisted therefore of only active periods, which accounted for 63.8 (7.2) minutes. Data Processing The OptimEye S5 (firmware 6.109; Catapult Sports) device is 96 × 52 × 13 mm and weighs 66.8 g. It contains a triaxial accelerometer, gyroscope, and magnetometer, which all sample at a frequency of