-Eye, 5 or global position systems (GPS) 6 – 11 among the most typically used for these purposes. Quantifying this information during real or simulated tennis matches helps coaches to provide objective knowledge about the demands of match play, ultimately improving the preparation of more effective
Cesar Gallo-Salazar, Juan Del Coso, David Sanz-Rivas and Jaime Fernandez-Fernandez
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.
John F. Fitzpatrick, Kirsty M. Hicks and Philip R. Hayes
during the 6-week period, using a number of different methods: global positioning system (GPS), heart rate (HR) telemetry, and session rating of perceived exertion (sRPE). External load was measured using GPS units (MinimaxX S4; Catapult Sports, Melbourne, Australia) sampling at a frequency of 10 Hz. GPS
Richard J. Taylor, Dajo Sanders, Tony Myers, Grant Abt, Celia A. Taylor and Ibrahim Akubat
training dose; however, if these TL measures fail to inform a strong enough dose-response relationship the manipulation of training using such measures may not result in expected training outcomes. 7 The availability of microelectromechanical systems/global positioning systems (MEMS/GPS) and heart
Jonathan D. Bartlett, Fergus O’Connor, Nathan Pitchford, Lorena Torres-Ronda and Samuel J. Robertson
The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.
TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.
Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.
This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.
Shannon N. Zenk, Amy J. Schulz, Angela M. Odoms-Young, JoEllen Wilbur, Stephen Matthews, Cindy Gamboa, Lani R. Wegrzyn, Susan Hobson and Carmen Stokes
Global positioning systems (GPS) have emerged as a research tool to better understand environmental influences on physical activity. This study examined the feasibility of using GPS in terms of perceived acceptability, barriers, and ease of use in a racially/ethnically diverse sample of lower socioeconomic position (SEP).
Data were from 2 pilot studies involving a total of 170 African American, Hispanic, and White urban adults with a mean (standard deviation) age of 47.8 (±13.1) years. Participants wore a GPS for up to 7 days. They answered questions about GPS acceptability, barriers (wear-related concerns), and ease of use before and after wearing the GPS.
We found high ratings of GPS acceptability and ease of use and low levels of wear-related concerns, which were maintained after data collection. While most were comfortable with their movements being tracked, older participants (P < .05) and African Americans (P < .05) reported lower comfort levels. Participants who were younger, with higher education, and low incomes were more likely to indicate that the GPS made the study more interesting (P < .05). Participants described technical and wear-related problems, but few concerns related to safety, loss, or appearance.
Use of GPS was feasible in this racially/ethnically diverse, lower SEP sample.
Simon J. MacLeod, Chris Hagan, Mikel Egaña, Jonny Davis and David Drake
. PubMed ID: 23090320 doi:10.1519/JSC.0b013e318277fd21 23090320 10.1519/JSC.0b013e318277fd21 5. Johnston R , Watsford M , Kelly S , Pine M , Spurrs R . Validity and interunit reliability of 10 Hz and 15 Hz GPS units for assessing athlete movement demands . J Strength Cond Res . 2014 ; 28
Harry E. Routledge, Stuart Graham, Rocco Di Michele, Darren Burgess, Robert M. Erskine, Graeme L. Close and James P. Morton
( 20 ), 1858 – 1866 . doi: 10.1080/02640414.2013.823227 Wiseby , B. , Montgomery , P.G. , Pyne , D.B. , & Rattray , B. ( 2010 ). Quantifying movement demands of AFL football using GPS tracking . Journal of Science and Medicine in Sport, 13 (5), 531 – 536 .
Tom Kempton, Anita Claire Sirotic, Ermanno Rampinini and Aaron James Coutts
To describe the metabolic demands of rugby league match play for positional groups and compare match distances obtained from high-speed-running classifications with those derived from high metabolic power.
Global positioning system (GPS) data were collected from 25 players from a team competing in the National Rugby League competition over 39 matches. Players were classified into positional groups (adjustables, outside backs, hit-up forwards, and wide-running forwards). The GPS devices provided instantaneous raw velocity data at 5 Hz, which were exported to a customized spreadsheet. The spreadsheet provided calculations for speed-based distances (eg, total distance; high-speed running, >14.4 km/h; and very-highspeed running, >18.1 km/h) and metabolic-power variables (eg, energy expenditure; average metabolic power; and high-power distance, >20 W/kg).
The data show that speed-based distances and metabolic power varied between positional groups, although this was largely related to differences in time spent on field. The distance covered at high running speed was lower than that obtained from high-power thresholds for all positional groups; however, the difference between the 2 methods was greatest for hit-up forwards and adjustables.
Positional differences existed for all metabolic parameters, although these are at least partially related to time spent on the field. Higher-speed running may underestimate the demands of match play when compared with high-power distance—although the degree of difference between the measures varied by position. The analysis of metabolic power may complement traditional speed-based classifications and improve our understanding of the demands of rugby league match play.
Niels J. Nedergaard, Mark A. Robinson, Elena Eusterwiemann, Barry Drust, Paulo J. Lisboa and Jos Vanrenterghem
To investigate the relationship between whole-body accelerations and body-worn accelerometry during team-sport movements.
Twenty male team-sport players performed forward running and anticipated 45° and 90° side-cuts at approach speeds of 2, 3, 4, and 5 m/s. Whole-body center-of-mass (CoM) accelerations were determined from ground-reaction forces collected from 1 foot–ground contact, and segmental accelerations were measured from a commercial GPS accelerometer unit on the upper trunk. Three higher-specification accelerometers were also positioned on the GPS unit, the dorsal aspect of the pelvis, and the shaft of the tibia. Associations between mechanical load variables (peak acceleration, loading rate, and impulse) calculated from both CoM accelerations and segmental accelerations were explored using regression analysis. In addition, 1-dimensional statistical parametric mapping (SPM) was used to explore the relationships between peak segmental accelerations and CoM-acceleration profiles during the whole foot–ground contact.
A weak relationship was observed for the investigated mechanical load variables regardless of accelerometer location and task (R 2 values across accelerometer locations and tasks: peak acceleration .08–.55, loading rate .27–.59, and impulse .02–.59). Segmental accelerations generally overestimated whole-body mechanical load. SPM analysis showed that peak segmental accelerations were mostly related to CoM accelerations during the first 40–50% of contact phase.
While body-worn accelerometry correlates to whole-body loading in team-sport movements and can reveal useful estimates concerning loading, these correlations are not strong. Body-worn accelerometry should therefore be used with caution to monitor whole-body mechanical loading in the field.