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Martin Buchheit and Ben Michael Simpson

With the ongoing development of microtechnology, player tracking has become one of the most important components of load monitoring in team sports. The 3 main objectives of player tracking are better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), optimization of training-load patterns at the team level, and decision making on individual players’ training programs to improve performance and prevent injuries (eg, top-up training vs unloading sequences, return to play progression). This paper discusses the basics of a simple tracking approach and the need to integrate multiple systems. The limitations of some of the most used variables in the field (including metabolic-power measures) are debated, and innovative and potentially new powerful variables are presented. The foundations of a successful player-monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and use all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (ie, cost, ease of use, portability, manpower/ability to affect the training program).

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Darren J. Burgess

Research describing load-monitoring techniques for team sport is plentiful. Much of this research is conducted retrospectively and typically involves recreational or semielite teams. Load-monitoring research conducted on professional team sports is largely observational. Challenges exist for the practitioner in implementing peer-reviewed research into the applied setting. These challenges include match scheduling, player adherence, manager/coach buy-in, sport traditions, and staff availability. External-load monitoring often attracts questions surrounding technology reliability and validity, while internal-load monitoring makes some assumptions about player adherence, as well as having some uncertainty around the impact these measures have on player performance This commentary outlines examples of load-monitoring research, discusses the issues associated with the application of this research in an elite team-sport setting, and suggests practical adjustments to the existing research where necessary.

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Kalli A. Reynolds, Emma Haycraft, and Carolyn R. Plateau

been evidenced in the use of social media platforms and fitness tracking technology, such as physical fitness watches and exercise-related mobile applications. 26 , 27 Adolescents often favor fitness tracking technology that allows them to share their physical fitness and body-related progress with

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Alanna Weisberg, Alexandre Monte Campelo, Tanzeel Bhaidani, and Larry Katz

, Mitzner, Fausset, & Rogers, 2017 ; Steinert et al., 2018 ). Preusse et al. ( 2017 ) assessed the usability and acceptance of two activity tracking technologies by older adults: and the Fitbit One+ Fitbit Dashboard. Results indicated similar issues between the two technologies’ webpages

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Marianne I. Clark and Holly Thorpe

Karen Barad ( 2003 , 2007 ) as a particularly useful lens through and with which to rethink women’s relationships with self-tracking technologies and their moving bodies within the material–discursive context of motherhood. Barad, a quantum physicist and feminist theorist, proposes an onto

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Nicholas E. Fears and Jeffrey J. Lockman

use vision during manual tasks can be directly studied with eye-tracking technology. Eye-tracking technology has been used to examine how individuals deployed eye movements as they planned and guided ongoing manual actions during everyday activities ( Gowen & Miall, 2006 ; Hayhoe, 2000 ; Hayhoe

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Semyon Slobounov, William Kraemer, Wayne Sebastianelli, Robert Simon, and Shannon Poole

The primary purpose of this paper was to demonstrate how modem motion tracking technologies, i.e., the Hock of Birds, and computer visualization graphics may be used in a clinical setting. The idea that joint injury reduces proprioception was investigated, and data for injured subjects were compared to data for noninjured subjects (subjects in all experiments were college students). Two experiments showed that there were no significant losses in joint position sense in knee-injured subjects, and both injured and noninjured groups visually overestimated knee movements. However, injured subjects showed no significant differences when visual reproduction data were compared with actual movement data. In addition, these data indicated that injured subjects may have greater potential for apprehension than noninjured subjects, at least in terms of visual estimation of movement ranges. This is an idea that needs further testing.

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Semyon M. Slobounov, Shannon T. Poole, Robert F. Simon, Elena S. Slobounov, Jill A. Bush, Wayne Sebastianelli, and William Kraemer

Assessment and enhancement of joint position sense is an inexact science at best. Anew method of evaluating and improving this sense using motion-tracking technology that incorporates computer visualization graphics was examined. Injured and healthy subjects were evaluated for their abilities to determine shoulder joint position, after abduction, in two tasks. The first was active reproduction of a passively placed angle. The second was visual reproduction of such an angle. A training protocol was added to determine the effectiveness of proprioceptive training in conjunction with 3-D visualization techniques. The primary findings were (a) a significant difference (p = .05) in the level of joint position sense in injured vs. healthy subjects; (b) significantly less accurate reproduction of larger shoulder abduction vs. the smaller movement in the active reproduction task; (c) significantly greater ability to accurately reproduce angles actively vs. visually; and (d) that proprioception training using 3-D visualization techniques significantly increased active and visual reproductions of passively placed angles.

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Brad Millington and Rob Millington

This paper explores the articulations of sport and ‘Big Data’—an important though to date understudied topic. That we have arrived at an ‘Age of Big Data’ is an increasingly accepted premise: the proliferation of tracking technologies, combined with the desire to record/monitor human activity, has radically amplified the volume and variety of data in circulation, as well as the velocity at which data move. Herein, we take initial steps toward addressing the implications of Big Data for sport (and vice versa), first by historicizing the relationship between sport and quantification and second by charting its contemporary manifestations. We then present four overlapping postulates on sport in the Age of Big Data. These go toward both showing and questioning the logic of ‘progress’ said to lie at the core of sport’s nascent statistical turn. We conclude with reflections on how a robust sociology of sport and Big Data might be achieved.

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Kevin J. McQuade, Margaret A. Finley, Michelle Harris-Love, and Sandra McCombe-Waller

The use of magnetic tracking technology has become increasingly popular in recent years for human motion studies. However, there have been few independent evaluations of how these systems perform. The purpose of this study was to develop a dynamic pendulum calibration method to test the performance of magnetic tracking sensors. A nonmetallic pendulum was constructed and instrumented with a rotary potentiometer. A cube was attached to the distal end of the pendulum so that sensors could be mounted orthogonally. In this manner, it was possible to obtain simultaneous recordings of azimuth, elevation, and roll depending on the sensor mounting orientation relative to the axis of rotation of the pendulum. Sensor data, using Flock of Birds™ sensors, and potentiometer data were collected simultaneously during dynamic pendulum motion at two transmitter distances and then were compared. The results showed excellent trial-to-trial repeatability of 2% or better for the sensors, and high correlations between the sensor and potentiometer data. RMS errors range from about 3 to 10 mm depending on the angular velocity of the pendulum. Angular errors were less than 1 degree RMS for all speeds.