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Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System Technology

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Purpose: 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. Methods: 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. Results: 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. Conclusions: 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.
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Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Note. This article will be published in a forthcoming issue of the
International Journal of Sports Physiology and Performance. The
article appears here in its accepted, peer-reviewed form, as it was
provided by the submitting author. It has not been copyedited,
proofread, or formatted by the publisher.
Section: Original Investigation
Article Title: Methodological Considerations When Quantifying High-Intensity Efforts in
Team Sport Using Global Positioning System Technology
Authors: Matthew C. Varley1, Arne Jaspers2, Werner F. Helsen2, and James J. Malone3
Affiliations: 1Institute of Sport, Exercise and Active Living, Victoria University, Melbourne,
Australia. 2 Department of Kinesiology, Laboratory of Perception and Performance,
Movement Control and Neuroplasticity Research Group, University of Leuven (KU Leuven),
Leuven, Belgium. 3School of Health Sciences, Liverpool Hope University, Liverpool, United
Kingdom.
Journal: International Journal of Sports Physiology and Performance
Acceptance Date: December 5, 2016
©2017 Human Kinetics, Inc.
DOI: http://dx.doi.org/10.1123/ijspp.2016-0534
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Title: Methodological considerations when quantifying high-intensity efforts
in team sport using global positioning system technology
Submission Type: Original Article
Authors: Matthew C. Varley1, Arne Jaspers2, Werner F. Helsen2, James J.
Malone3
Affiliations: 1Institute of Sport, Exercise and Active Living, Victoria University,
Melbourne, Australia
2 Department of Kinesiology, Laboratory of Perception and
Performance, Movement Control and Neuroplasticity Research Group,
University of Leuven (KU Leuven), Leuven, Belgium
3School of Health Sciences, Liverpool Hope University, Liverpool,
United Kingdom
Corresponding Author: Matthew C. Varley
Corresponding Address: Aspire Academy, Aspire Zone, Doha, Qatar
Corresponding Email: matthew.varley@gmail.com
Preferred running head: Determining high-intensity efforts
Abstract word-count: 250
Text only word-count: 4101
Number of figures: 5
Number of tables: 2
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Abstract
Purpose: 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. The study aimed to determine the influence of different techniques and
settings for detecting high-intensity efforts using Global Positioning System (GPS) data.
Methods: Velocity and acceleration data of a professional soccer match was recorded via 10-
Hz GPS. Velocity data was filtered using either a median or exponential filter. Acceleration
data was derived from velocity data over a 0.2 s time interval (with and without an exponential
filter applied) and a 0.3 s time interval. High-speed running (≥4.17 m.s-1), sprint (≥7.00 m.s-1)
and acceleration (≥2.78 m.s-2) efforts were then identified using minimum effort durations (0.1
to 0.9 s) to assess differences in the total number of efforts reported. Results: Different velocity
filtering methods resulted in small to moderate differences (Effect Size; 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 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. Conclusions: 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.
Key words: soccer, football, GPS, acceleration, sprint
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Introduction
Athlete tracking systems allow the quantification of athlete movement during training
or matches by measuring the distance, velocity and acceleration of an athlete. Semi-automated
tracking systems measure the displacement of an athlete over time from which distance,
velocity and acceleration are calculated. Global positioning system (GPS) devices measure
distance travelled via positional differentiation (the change in device location with each
received satellite signal). While velocity can be derived from this distance measure (distance
over time), a greater accuracy and lower error is found when velocity is calculated using the
Doppler-shift method (measured via the change in frequency of the satellite signal).1 Thus, the
majority of GPS manufacturers calculate velocity via the Doppler-shift method from which
acceleration is subsequently derived. Athlete movements are typically recorded as the distance
covered or number of discrete efforts in specific speed or acceleration categories. These
categories are defined using specific speed/acceleration thresholds which may vary between
users and sports. Practitioners and researchers use the distances and number of efforts
performed by athletes to monitor training load,2,3 profile physical performance during
competition4-6 and link these movements to injury7 or match events such as scoring or
conceding points8.
Numerous validation studies have assessed the ability of GPS to measure distance and
velocity which have been summarised in a recent review.9 This is a continuous process as each
new device or upgrade requires new validation. However, there is limited research regarding
the various methods used to determine movement efforts. Typically, a movement effort is
identified when GPS velocity/acceleration enters a specific threshold (e.g. sprint threshold) and
lasts for a minimum duration, referred to as ‘dwell time’ or minimum effort duration (MED).
Often the total count of efforts performed during a training session or match are reported.
Movement efforts are determined independently of GPS distance information and are
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
calculated using purely the velocity and acceleration data. The acceleration data is typically
calculated based on the GPS-derived data and not from the inertial sensors within these devices
which was the case in the present study. This is a common misconception from practitioners
and may cloud judgement on the data reported.10 The most common movement efforts reported
in research and by practitioners are high-speed, sprint and acceleration efforts.4,5,11,12 A recent
survey of practitioners from high-level football clubs around the world found that acceleration
variables were ranked 1st as the most commonly used metric when monitoring athletes during
training.13
There are several methodological considerations when identifying an effort that may
substantially change the number of efforts identified when tracking athletes. To determine a
meaningful effort, practitioners should establish a minimum duration that velocity/acceleration
must exceed the specific movement threshold. For example, if a MED of 0.5 s is set to define
a sprint effort then an athlete would need to maintain a speed greater than the sprint threshold
for at least 0.5 s for an effort to be recorded. This ensures that possible spikes in the GPS data
due to noise, which may last 0.1 s or lower depending on the sampling frequency, are not
recorded as discrete efforts. Additionally, as velocity/acceleration may oscillate around a set
threshold, selecting an appropriate MED will help to ensure that only meaningful efforts are
recorded. The MED for a sprint may be longer than that for an acceleration, as a high rate of
acceleration is likely to be short.14 These considerations will account for the inherent noise in
GPS velocity/acceleration data and increase the likelihood that any efforts identified are real.
Another consideration when using GPS to quantify athlete movement is the use of data
filtering techniques within the manufacturer software. Due to the inherent noise in raw GPS
velocity data, manufacturers apply different filtering techniques to smooth velocity and
acceleration data. The type of filter is often chosen at the discretion of the manufacturer and
may include median, exponential, Butterworth or other filters. Additionally, acceleration data
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
can be smoothed by widening or shortening the time interval over which it is derived from
velocity with a wider interval resulting in a greater smoothening of the data. Thus, acceleration
data can undergo substantial smoothing through a combination of manipulating the interval
over which it was derived and applying a filter to the data as demonstrated in Figure 1. The
development of filtering techniques to improve accuracy is ongoing within the athlete tracking
industry via software and firmware updates. These updates may incorporate different filtering
techniques which can lead to substantial changes in the movement data reported. For example,
following a software upgrade large decreases in the number of acceleration efforts were
detected when the same GPS data was processed.15 Although this was not directly attributed to
changes in data filtering it is likely that these differences were partially due to a change in data
filtering. While some manufacturers will allow the user to customise the filter or the time
interval used to calculate acceleration, in other cases this is fixed and information regarding
these elements may not be available to the user. Alternatively, the raw data can be exported
and analysed in custom-based software such as Matlab or Microsoft excel allowing these
considerations to be defined by the user.
Currently it is unknown how changes to the data filtering and/or MED will directly
affect the number of efforts reported. In a sports setting, any changes to these settings may
substantially alter the reported values which will affect athlete monitoring, training preparation
and the practitioner's interpretation of these results. In research these details are often not
reported limiting both the ability to compare results across the literature and the reproducibility
of the research. The aim of this study was to determine the influence of varying MED to detect
high-intensity efforts in an applied sporting context. This study also examined the influence of
different filtering techniques within GPS manufacturers' software on subsequent high-intensity
effort detection. The practical application of this study is to provide some recommended
guidelines for practitioners using such data for their daily practice.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Methods
Participants
Data were collected from six professional soccer players (23.0 ± 1.8 years,) competing
in the highest league of the Netherlands (Eredivisie). As this study assessed the influence of
different data analysis techniques a large sample size was not essential. Written informed
consent was provided before participation in this study, which was approved by the ethics
committee of KU in line with the requirements stipulated in the Declaration of Helsinki.
Design
To assess the differences of various MED methods and data smoothing filters,
movement data were recorded in two different stages. The first was during controlled sprint
tests of 10, 20 and 40 m under the assumption that during a maximal sprint from a static start
a player should only register a single high-speed, sprint and/or acceleration effort. If more than
one effort was recorded the MED could be adjudged to be too low. Participants were asked to
perform all sprint tests maximally. These tests were all filmed and both the footage and raw
GPS data were visually inspected to ensure the players had performed maximal efforts
throughout the tests. Only one trial for each sprint test (10, 20 and 40 m) was included in the
analysis for each player (n=6). In the second stage, movement data were recorded during a
competitive match in order to demonstrate how the different effort detection methods
influenced the number of efforts identified in a practical way. For both stages, GPS data was
downloaded and processed using two versions of the manufacturer's software, SprintTM and
OpenfieldTM, which each used different filtering techniques.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Methodology
GPS Data Collection
Data was collected using a commercial 10-Hz GPS device (Optimeye S5; firmware
version 7.22, Catapult Sports, Melbourne, Australia) worn inside a custom made garment
positioned between the scapula. Previous research has found such devices to have acceptable
levels of reliability and validity for assessing velocity.16 Prior to data collection, the devices
were left outside in an open area for 30 minutes to allow satellite connection and checked to
ensure a satellite 'lock' had occurred prior to placing on the players. The sprint testing was
conducted on an outdoor natural grass pitch and the match data was collected in the team’s
home stadium. The average ± SD number of satellites and horizontal dilution of position during
the sprint testing was 14.0 ± 0 and 0.74 ± 0.01, respectively, and for the match data collection
was 15.0 ± 0.6 and 0.70 ± 0.10, respectively. These values have been suggestive of being
acceptable for good GPS signal coverage based on the manufacturer’s recommendations.10
GPS Data Analysis
Subsequent data was downloaded and exported using two versions of the
manufacturer's software, SprintTM (version 5.1.7) and OpenfieldTM (version 1.12.0, Catapult
Sports, Melbourne, Australia). The following describes the different filtering techniques
applied by the manufacturer's software in order to calculate the GPS velocity and subsequently
GPS acceleration data that is used to quantify player movement. The raw GPS velocity data is
calculated using the Doppler-Shift method. The SprintTM software filters the raw GPS velocity
data using a median filter (GPS Velsprint), while the OpenfieldTM software filters the raw GPS
velocity data using an exponential filter (GPS Velopenfield).
The GPS acceleration data is derived from GPS velocity data. In the SprintTM software
the user can select the time interval over which acceleration (GPS Accelsprint) is derived from
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
GPS Velsprint (referred to in the software as Smoothing Filter Width). In this study, time
intervals of 0.2 (Accelsprint_0.2) and 0.3 s (Accelsprint_0.3) were used. No additional filters are
applied to GPSAccelsprint after the time interval has been selected. In the OpenfieldTM software
GPS acceleration is derived from GPS Velopenfield using the 0.2 s time interval that is fixed
within the software. Data is then filtered further using an exponential filter (GPS Accelopenfield).
All data was exported for analysis using custom-based software (Microsoft Excel).
Calculation of Movement Efforts
Movement efforts were determined from the aforementioned GPS velocity and
acceleration data using the following thresholds high-speed running (≥4.17 m.s-1), sprinting
(≥7.00 m.s-1) and acceleration (2.78 m.s-2). These thresholds were selected as they are
commonly used amongst the research literature.4,5,17 High-speed running and sprint efforts
were identified using the following MED 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0,7, 0.8, 0.9 and 1 s.
Acceleration efforts were identified using the following MED 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
0.9 and 1 s. All data was analysed in Microsoft Excel duplicating the methods and output from
the respective software. It is worth noting that in the SprintTM software although the minimum
duration for accelerations is an open option there is an error in the software that results in odd
numbers being 'rounded up' to the next decimal place (e.g. 0.1 becomes 0.2, 0.3 becomes 0.4
etc.), therefore practitioners who use this software will find 0.2, 0.4, 0.6, 0.8 and 1 s relevant.
Statistical Analysis
Data in the figures are presented as means and in tables as effect size and 90%
confidence limits (CL). All data were first log-transformed to reduce bias arising from non-
uniformity of error. Differences in the number of efforts recorded between each MED and
differences in the number of efforts recorded between software filters were standardised using
Cohen's effect size principle with 90% CL. Uncertainty in each effect was expressed as 90%
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
CL and as probabilities that the true effect was substantially greater than the smallest important
positive or negative difference. These probabilities were used to make a qualitative
probabilistic mechanistic inference about the true effect using the following scale: >25 75%,
possibly; >75-95%, likely; >95 99%, very likely; >99%, almost certainly.18,19 The magnitude
of a given effect was determined from its observed standardized value (the difference in means
divided by the between subject standard deviation) using the following scale; <0.20, trivial;
0.20-0.59, small; 0.60-1.19, moderate; 1.20-1.99, large; 2.00, very large.18,19 For clarity only
effects with a likelihood >75% are presented.
Results
Efforts detected during 10 -20 - 40m Sprint Tests
There were substantial differences in the number of acceleration efforts detected
between most of the different MED during the 10, 20 and 40 m sprints (Figure 2). Notably,
Accelopenfield resulted in fewer differences across the MED (Figure 2C). A 0.2, 0.3 and 0.4 s
MED resulted in the identification of more than one acceleration effort per sprint when
analysed using Accelsprint_0.2 and Accelsprint_0.3 as did a MED of 0.2 s when using AccelOpenfield
(Figure 2). When comparing differences in the filtering methods, the number of acceleration
efforts detected were greater for shorter MED for both Accelsprint_0.2 and Accelsprint_0.3 compared
to AccelOpenfield and lower for longer MED (Table 1). The number of acceleration efforts
determined using Accelsprint_0.3 was lower for shorter MED compared to when using a 0.2 s
interval, however these differences became unclear as the MED increased (Table 1).
During the 10, 20 and 40 m sprints only one high-speed running effort was detected for
each test regardless of the MED and filtering method. Similarly only one sprint effort was
detected during the 40 m sprint regardless of the MED and filtering method, while no sprint
efforts were detected during the 10 and 20 m sprints.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Efforts detected during a competitive match
There was an exponential decline in the number of acceleration efforts identified during
a match as the MED increased for all filtering methods (Figure 3). The number of acceleration
efforts identified using AccelOpenfield were lower by a large to very-large magnitude for MED
lower than 0.5 s compared to using both Accelsprint_0.2 and Accelsprint_0.3 (Table 2). A greater
number of acceleration efforts were identified for a 0.2 and 0.3 s MED when using Accelsprint_0.2
compared to Accelsprint_0.3 and lower number for a 0.4 and 0.5 MED (Table 2).
The number of high-speed running and sprint efforts identified during a match appeared
to decline exponentially with an increase in the MED for both the SprintTM and OpenfieldTM
filtering (Figure 4). The number of high-speed running and sprint efforts identified during a
match using the OpenfieldTM filtering were higher by a small to large magnitude which
declined with increasing MED from 0.1 to 0.3 s, however, from 0.6 s on the differences were
either unclear or clearly trivial (Table 2).
Discussion
The main finding of this study was that changes in the MED as small as 0.1 s
substantially affected the number of accelerations, high-speed running and sprint efforts
detected during matches. A secondary finding was that the use of different filtering methods
used to smooth velocity and acceleration data changed the number of efforts identified.
Of all efforts, the number of accelerations were most affected by different MED and
filters. The analysis of individual sprints over 10, 20 and 40 m allowed the evaluation of
different MED for acceleration efforts from a practical perspective. The MED resulting in the
detection of more than 1 acceleration effort per sprint (0.2, 0.3 and 0.4 s when using SprintTM
filtering and 0.2 s when using OpenfieldTM filtering) could be suggested to overestimate the
number of acceleration efforts occurring. However, in a competitive match MED greater than
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
0.5 s detected no more than 6 efforts regardless of the filtering method used (Figure 3). The
duration an athlete can sustain a high rate of acceleration is very short 14 and longer MED may
exclude maximal accelerations. An explanation for the detection of multiple accelerations
during the sprint tests is that the manufacturer's software defines the end of an acceleration
effort as when acceleration falls below the specific threshold for a single sample (0.1 s). As
GPS acceleration data is subject to noise, this could result in what would practically be termed
a single acceleration effort being classified as two separate efforts as can be seen in Figure 1.
To test this assumption, the Accelsprint_0.2 and Accelsprint_0.3 data was reanalysed using previously
established methods4 where the end of an acceleration effort was defined as when acceleration
fell below 0 m.s-2 following the detection of an effort. As shown in Figure 5, this resulted in
the detection of multiple acceleration efforts for a MED of 0.2 s only, while all other durations
detected no more than a single effort, confirming the above hypothesis.
The method used to identify the end of the effort is just as important as the MED,
however, this is often overlooked. Various methods can be used such as establishing a
minimum duration for velocity/acceleration to fall below the set threshold or requiring a drop
in velocity/acceleration below a percentage of the set threshold. How the end of an effort is
identified should be based on the user's practical needs of the data. As an individual may
continue to accelerate until their rate of acceleration falls below 0 m.s-2, this may be a more
practical definition for identifying acceleration efforts than purely quantifying the extremely
short duration spent accelerating above the required threshold and may better represent the
perception of an acceleration held by a coach or other support staff. This method also allows
practitioners to use lower MED (e.g. 0.3 or 0.4 s) with confidence that single acceleration
efforts will not be detected as multiple efforts (Figure 5A and 5C). The limitation to this
approach is where an athlete accelerates maximally, their rate of acceleration falls below
threshold but not 0 m.s-2 and then rises again, as this would only be considered a single effort.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Practitioners can either use both methods or choose one based on their needs. An endpoint
where acceleration falls below the maximum threshold may be more relevant for practitioners
interested in when athletes are only working at their most energetically demanding. An
endpoint where acceleration falls below 0 m.s-2 may provide a more practical measure of
acceleration efforts allowing greater contextualisation of the movement.
The additional filtering used by the OpenfieldTM software resulted in a substantially
lower number of accelerations recorded during the match. A large change in the number of
accelerations detected has also been observed following a software upgrade using GPS from
other manufacturers (GPSports).15 The results of this study suggest these changes were due to
the implementation of a more severe smoothing filter on the acceleration data. In this study,
absolute acceleration and velocity thresholds were used to demonstrate the methodological
differences when analysing GPS data. While new filters may provide a more realistic
representation of acceleration and velocity efforts they may also require the user to re-evaluate
the thresholds they have used to define their movements. For example, Figure 1 demonstrates
the different smoothing methods used to determine acceleration result in substantially different
peak acceleration values. Velocity would also show differences in the maximal values if a
smoothing filter is applied, such as the exponential filter used in OpenfieldTM. Thus, for a given
threshold the greater the smoothing applied to velocity and acceleration data, the less an athlete
would be expected to reach a given threshold. A possible way to address this issue may be to
develop device or filter specific thresholds. If movement thresholds are based on athlete
physical testing, athletes could wear the GPS during these tests allowing data to be processed
for each filtering technique. For example, if the sprint threshold is defined as percentage of
Maximal Sprint Speed recorded by GPS during a 40 m sprint,20 GPS data could be reprocessed
when/if a new data filter is used to maintain a consistent threshold. This will reduce the impact
of changing manufacturers/software on longitudinal monitoring.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Regardless of the methods used practitioners should be aware that there is no perfect
combination for detecting acceleration efforts. A lower MED will likely overestimate the
number of acceleration efforts while a higher MED will likely underestimate the number of
efforts. Further, applying a greater smoothing method to the data will allow lower MED to be
used while a lower smoothing method may restrict the user to higher MED. Understanding the
advantages and limitations of each method will allow practitioners to choose the combination
that best suits their needs. It should also be acknowledged that this study has only considered
maximal acceleration efforts, which primarily occur at low velocities.4 The effect of different
methods to identify low and moderate accelerations are likely to be even more pronounced as
athletes are likely to have much greater oscillation around lower rates of acceleration.
The MED used to identify velocity based efforts showed smaller discrepancies than that
of acceleration based efforts. Given that the 10, 20 and 40 m sprints were all maximal it is not
surprising that there was no difference in the number of high-speed running or sprint efforts
detected. However, during a match different MED resulted in the number of efforts decreasing
in a somewhat exponential manner as duration increased (Figure 4). This is likely due to the
intermittent nature of match-running where players may oscillate around specific velocity
thresholds, whereas during sprint tests velocity is linearly increasing. Further, the exponential
filter used in OpenfieldTM resulted in a greater number of efforts being identified compared to
the median filter used in SprintTM. Likewise, there were more and larger differences in the
number of efforts according to MED when analysed with OpenfieldTM compared to SprintTM.
Similar to acceleration, different smoothing filters can have a substantial effect on velocity data
and efforts detected, an issue which is likely to occur regardless of manufacturer where
different filters are used.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Movement categories can be separated into a number of threshold bands such as
running (e.g. 4.17 to 7.00 m.s-1). However, in this study, the thresholds for high-speed running
(>4.17 m.s-1) and sprinting (>7.00 m.s-1) were both open-ended, therefore high-speed running
efforts also included sprint efforts. The use of threshold bands may be more appropriate when
determining the distances covered within each band rather than the number of efforts within
each band. It is difficult to determine a MED required within each band as an athlete will pass
through all bands when sprinting from a low speed. Depending on the rate of acceleration this
may result in multiple efforts for what is ultimately a single sprint effort. The use of efforts
according to threshold bands may have limited practical application for practitioners. A similar
argument could be made when considering banded rates of acceleration effort, especially as
the higher the rate of acceleration, the shorter the maximal acceleration is likely to be. There is
currently no consensus on how the total number of high speed or high-intensity efforts should
be defined. For example, if an athlete performs 30 sprint efforts (>7.00 m.s-1) and 50 running
efforts (4.17 to 7.00 m.s-1), 30 of which ultimately lead to sprints, should these be considered
separately (i.e. 80 independent high-speed efforts) or in combination (i.e. 30 sprints and 20
running efforts)? This is an important topic with regards to profiling high-intensity movements
and practitioners should make their decision based on how the information will be used.
Practical Applications
Different data filtering methods and MED can substantially effect the number of high-
intensity movements detected using GPS devices
Practitioners and researchers should include detailed information regarding the filtering
techniques and settings used to determine movement efforts in practical reports and
research publications.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
If velocity or acceleration thresholds are based on physical capacities determined from
physical testing, practitioners should establish a set of reference data which can be
reprocessed using different smoothing filters to adjust these thresholds accordingly.
When defining acceleration efforts practitioners may consider defining the end of an
effort as when acceleration falls below 0 m.s-2 to provide a more practical measure
Practitioners should use a consistent method when analysing athlete velocity and
acceleration data during a season, and any changes to this method should be done at the
end of the season and may be applied to retrospective data
Conclusion
Different filtering techniques and MED substantially affected the number of high-
intensity efforts detected with GPS. While this study provides novel insights into this area, it
is difficult to provide a recommendation for the appropriate filtering and MED to be used with
high-speed running, sprinting and acceleration efforts based on the results. It is unlikely that
practitioners using manufacturer software will be able to select the type of filter used, and may
be restricted in their choice of MED. Practitioners and researchers should be aware that changes
to filtering and MED are likely to affect reported data. The key recommendation is that
practitioners maintain consistency as much as possible in their data processing. Also following
a software or firmware update that affects data filtering, practitioners may consider re-
analysing retrospective data to allow ongoing comparison of the data. Finally, the different
filtering of velocity and acceleration data will also effect the distances athletes cover at specific
thresholds and this should be explored in future research.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
References
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Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
17. Aughey RJ. Increased high-intensity activity in elite Australian football finals matches. Int. J.
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Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Figure 1. GPS velocity and acceleration data during a 40 m sprint effort. The graph
demonstrates the smoothing effect when acceleration is derived from velocity using a different
intervals (0.2 and 0.3 s) and when data is processed using an exponential filter (acceleration
was derived using a 0.2 s interval). The threshold used to identify an acceleration effort is
indicated by the line running parallel to the x axis at 2.78 m.s-2.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Figure 2. The number of acceleration efforts detected during 10, 20 and 40 m sprints when using different minimum effort durations and different
filtering methods. The SprintTM software derives acceleration from velocity data over a 0.2 (Figure A) or a 0.3 s interval (Figure B) and OpenfieldTM
software derives acceleration from velocity data over a 0.2 s interval and then applies an exponential filter (Figure C). For each sprint test
n=6._Quantitative chances of higher or lower differences between minimum effort durations are evaluated according to thresholds identified in
statistical analysis; normal text = Likely, underlined text = Very likely, bold text = Almost certainly. T = Trivial effect size, S = small effect size,
M = moderate effect size, L = large effect size, vL = very large effect size. 2, 3, 4, 5, 6, 7, 8 indicate an effect compared to a minimum duration of
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, respectively.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Figure 3. The number of acceleration efforts performed by players (n=6) during a competitive match when detected using different minimum
effort durations and different filtering methods. The SprintTM software derives acceleration from velocity data over a 0.2 (Figure A) or a 0.3 s
interval (Figure B) and OpenfieldTM software derives acceleration from velocity data over a 0.2 s interval and then applies an exponential filter
(Figure C). Quantitative chances of higher or lower differences between minimum effort durations are evaluated according to thresholds
identified in statistical analysis; normal text = Likely, underlined text = Very likely, bold text = Almost certainly. S = small effect size, M =
moderate effect size, L = large effect size, vL = very large effect size. 2, 3, 4, 5, 6, 7, 8 indicate an effect compared to a minimum duration of
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, respectively.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Figure 4. The number of high-speed running (Figure A and B) and sprint efforts (Figure C and
D) performed by players (n=6) during a competitive match when detected using different
minimum effort durations and different filtering methods. The SprintTM software uses a median
filter and the OpenfieldTM software uses an exponential filter. Quantitative chances of higher
or lower differences between minimum effort durations are evaluated according to thresholds
identified in statistical analysis; normal text = Likely, underlined text = Very likely, bold text
= Almost certainly. S = small effect size, M = moderate effect size, L = large effect size, vL =
very large effect size. 2, 3, 4, 5, 6, 7, 8, indicate an effect compared to a minimum duration of
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, respectively.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Figure 5. The number of acceleration efforts detected during 10, 20 and 40 m sprints (Figure
A and C)and a competitive game (Figure B and D) when using different minimum durations.
Acceleration is derived from velocity using a 0.2 s (Figure A and B) and 0.3 s (Figure C and
D) interval and the end of the acceleration effort is identified when acceleration falls below or
is equal to 0 m.s-2
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System
Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Table 1. Differences in the number of acceleration efforts detected during 10, 20 and 40 m
sprints according to the filtering method used. For each sprint test n=6. Data is effect size and
90% confidence limits
SprintTM 0.2
vs OpenfieldTM
SprintTM 0.3
vs OpenfieldTM
SprintTM 0.2
vs Sprint 0.3
Minimum duration
Acceleration efforts
Acceleration efforts
Acceleration efforts
10 m Sprint
0.2
-1.13 (-2.49 to 0.22)*
-0.64 (-1.85 to 0.57)*
-0.49 (-0.99 to 0.00)*
0.3
-0.64 (-1.59 to 0.31)*
-0.12 (-0.29 to 0.04)*
-0.51 (-1.51 to 0.49)
0.4
0.32 (-0.37 to 1.01)
-0.10 (-0.22 to 0.03)
0.42 (-0.23 to 1.07)
0.5
0.65 (-0.20 to 1.51)*
-0.05 (-0.15 to 0.06)**
0.70 (-0.11 to 1.52)*
0.6
0.62 (-0.19 to 1.43)*
0.31 (-0.35 to 0.97)
0.31 (-0.35 to 0.97)
0.7
0.99 (0.13 to 1.86)*
0.66 (-0.20 to 1.53)*
0.33 (-0.37 to 1.04)
0.8
1.18 (0.15 to 2.20)*
0.78 (-0.24 to 1.81)*
0.39 (-0.44 to 1.23)
0.9
NA
NA
NA
1
NA
NA
NA
20 m Sprint
0.2
-1.74 (-2.32 to -1.17)***
-1.26 (-2.12 to -0.40)**
-0.48 (-1.00 to 0.03)*
0.3
-1.99 (-2.48 to -1.50)***
-1.34 (-2.57 to -0.12)*
-0.64 (-1.70 to 0.41)*
0.4
-0.39 (-1.23 to 0.44)
-1.18 (-2.20 to -0.15)*
0.78 (-0.24 to 1.81)*
0.5
0.36 (-0.59 to 1.32)
0.43 (-0.48 to 1.34)
-0.06 (-1.51 to 1.38)
0.6
0.78 (-0.24 to 1.81)*
0.39 (-0.44 to 1.23)
0.39 (-0.44 to 1.23)
0.7
NA
0.51 (-0.57 to 1.58)
NA
0.8
NA
2.02 (0.95 to 3.10)**
NA
0.9
NA
1.07 (-0.45 to 2.60)*
NA
1
NA
1.07 (-0.45 to 2.60)*
NA
40 m Sprint
0.2
-2.26 (-3.19 to -1.33)***
-0.88 (-1.69 to -0.07)*
-1.38 (-2.19 to -0.57)**
0.3
-2.06 (-2.95 to -1.18)**
-0.67 (-2.1 to 0.76)
-1.4 (-2.33 to -0.46)**
0.4
0.61 (-0.69 to 1.91)
-0.18 (-0.42 to 0.06)
0.79 (-0.67 to 2.25)*
0.5
0.77 (-0.23 to 1.77)*
0.33 (-0.53 to 1.19)
0.44 (-0.36 to 1.24)
0.6
0.62 (-0.19 to 1.43)*
0.31 (-0.35 to 0.97)
0.31 (-0.93 to 1.55)
0.7
0.99 (0.13 to 1.86)*
0.33 (-0.37 to 1.04)
0.66 (-0.75 to 2.07)
0.8
0.99 (0.13 to 1.86)*
0.33 (-0.37 to 1.04)
0.66 (-0.75 to 2.07)
0.9
NA
0.64 (-0.20 to 1.48)*
NA
1
NA
0.64 (-0.20 to 1.48)*
NA
Negative values indicate a lower number of efforts were reported using the second software name in each column.
Quantitative chances of higher or lower differences between filtering methods are evaluated according to
thresholds identified in statistical analysis; * = Likely, ** = Very likely, *** = Almost certainly. NA indicates
that no efforts were detected during one of the filtering methods.
Methodological Considerations When Quantifying High-Intensity Efforts in Team Sport Using Global Positioning System Technology” by Varley MC, Jaspers A, Helsen WF, Malone JJ
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Table 2. Differences in the number of high-speed running, sprint and acceleration efforts performed by players (n=6) during a competitive match
when detected according to the filtering method used. Data is effect size and 90% confidence limits
SprintTM
vs OpenfieldTM
SprintTM 0.2
vs OpenfieldTM
Sprint 0.3
vs OpenfieldTM
Minimum duration
High-speed running efforts
Sprint efforts
Acceleration efforts
0.1
1.09 (0.82 to 1.35)***
1.06 (0.41 to 1.70)**
NA
NA
0.2
0.68 (0.5 to 0.85)***
0.50 (0.11 to 0.89)*
-5.69 (-6.51 to -4.88)***
-4.6 (-5.36 to -3.83)***
0.3
0.39 (0.25 to 0.53)**
0.35 (-0.03 to 0.73)*
-5.30 (-6.12 to -4.47)***
-4.47 (-5.28 to -3.66)***
0.4
0.28 (0.17 to 0.39)*
0.04 (-0.22 to 0.30)*
-2.26 (-3.03 to -1.48)***
-3.81 (-4.58 to -3.04)***
0.5
0.17 (0.05 to 0.3)
0.008 (-0.21 to 0.37)
-0.78 (-1.29 to -0.26)**
-1.37 (-1.98 to -0.76)**
0.6
0.04 (-0.03 to 0.12)**
-0.19 (-0.48 to 0.10)
0.55 (-0.09 to 1.20)*
0.30 (-0.48 to 1.07)
0.7
-0.04 (-0.09 to 0.01)***
-0.22 (-0.51 to 0.06)
0.44 (0.03 to 0.85)*
0.27 (-0.19 to 0.74)
0.8
-0.05 (-0.07 to -0.04)***
-0.31 (-0.59 to -0.02)
NA
0.18 (0.03 to 0.32)
0.9
-0.05 (-0.15 to 0.05)**
-0.06 (-0.18 to 0.06)**
NA
1.06 (0.18 to 1.93)*
1
-0.08 (-0.23 to 0.06)*
-0.22 (-0.45 to 0.01)
NA
NA
Negative values indicate a lower number of efforts were reported using the second software name in each column. Quantitative chances of higher or lower differences between
filtering methods are evaluated according to thresholds identified in statistical analysis; * = Likely, ** = Very likely, *** = Almost certainly. NA indicates that no efforts were
detected during one of the filtering methods.
... In soccer, research often focuses on the distance covered at high intensity, and several authors assert that high-intensity actions are considered the best indicator of performance [24,52,54]. Although some studies consider an absolute threshold around 18 km/h to determine the distance covered at high speed, others use a threshold of 19.8 km/h, indicating a clear lack of consensus in the current literature regarding the categorization of these actions [4,10,[63][64][65][66]. Some researchers use the term "high intensity" to encompass both high-intensity and sprint segments, combining the distances covered in both ranges, which further complicates potential comparisons among authors [55]. ...
... Some researchers use the term "high intensity" to encompass both high-intensity and sprint segments, combining the distances covered in both ranges, which further complicates potential comparisons among authors [55]. Within high intensity, the distance covered during sprints is even less defined, with differences of more than 4 km/h in the two most commonly used fixed thresholds of 21 and 25.2 km/h; some authors even use a threshold of 24 km/h [8,9,12,64,[66][67][68][69]. The lack of clarity in sprint thresholds arises from how they are recorded; they can be counted numerically or by the distance covered. ...
... The lack of clarity in sprint thresholds arises from how they are recorded; they can be counted numerically or by the distance covered. Generally, a sprint is recorded as an effort that involves a minimum movement of 1 m, maintained for at least 1 s, and reaching a defined speed [66]. Therefore, when sprints are recorded numerically, an action can fall into the high-intensity zone (speed > 21 km/h) or very-high-intensity zone (>24 or 25 km/h, depending on the authors) [55]. ...
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To enhance athletic performance and reduce the risk of injury, load quantification has allowed for a better understanding of the individual characteristics of the physical demands on soccer players during training or competition. In this regard, it appears crucial to summarize scientific evidence to provide useful information and future directions related to the speed and acceleration profiles of male soccer players. This review aims to evaluate the findings reflected in the available literature on both profiles in football, synthesizing and discussing data from scientific articles, while providing insights into quantification methods, employed thresholds, tracking systems, terminology, playing position, and microcycle day. Therefore, it is hoped that this narrative review can support objective decision-making in practice for coaches, sports scientists, and medical teams regarding individualized load management and the appropriate selection of metrics, to explore current trends in soccer player profiles.
... Two previous studies of a limited number of matches (n = 10) of a single team indicated that a range of physical metrics vary between some specific phases of play (Bortnik et al., 2022(Bortnik et al., , 2023. Furthermore, accelerations and decelerations which occur during match play involve distinct physiological and biomechanical demands (Caldbeck et al., 2022;Dalen et al., 2016;Harper et al., 2019;Varley et al., 2017). For example, acceleration involves a high rate of muscular work and metabolic stress (Hader et al., 2016), whereas deceleration involves high musculoskeletal loading, a likely elevated risk of injury (e.g., anterior cruciate ligament injuries) (Alentorn-Geli et al., 2009;Cochrane et al., 2007;Harper et al., 2022;Johnston et al., 2018) and eccentric contractions that initiate muscle damage leading to delayed onset muscle soreness (Lieber, 2018). ...
... Periods of sustained acceleration (>2 m⋅s −2 ) or deceleration (< −2 m⋅s −2 ) for a minimum effort duration of 0.7 s were classified as acceleration and deceleration (in accordance with previously published recommendations) (Varley et al., 2017). The end of an acceleration/deceleration effort was determined when the rate of acceleration/deceleration first went below/above 0 m⋅s −2 (Varley et al., 2012). ...
... This study also uniquely found that forwards and defenders spent a high proportion of time decelerating during chance creation (6.6%) and low-block (6.2%), respectively, which may be indicative of frequent and sudden directional changes (involving a deceleration) as players' maneuver to find space in congested areas of the pitch (e.g., penalty box). Consequently, during these phases, these positions may be exposed to higher muscle and joint loading, as well as eccentric contractions that tend to initiate muscle damage and elevate injury risk (Dalen et al., 2016;Hader et al., 2016;Harper et al., 2019Harper et al., , 2022Lieber, 2018;Varley et al., 2017). Future work examining the physical intensity associated with specific technical-tactical actions performed during different match phases would be pertinent (Ju et al., 2023). ...
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... Therefore, quantifying these acceleration-based movements in AF are of interest to coaches and practitioners. Manufacturer data processing can have a large influence on GNSS acceleration data and corresponding summary metrics such as number of efforts [11,12]. Different data processes can alter the quantification of player movement, which may affect a practitioner's interpretation of the data and training programs. ...
... Different data processes can alter the quantification of player movement, which may affect a practitioner's interpretation of the data and training programs. For example, applying a data processing method with strong smoothing, can cause a reduction in the number of acceleration efforts recorded during a match [11], potentially changing a practitioners interpretation of players' workload. Valid GNSS acceleration data are needed to correctly quantify player movement with summary metrics. ...
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... Analyzing specific demands, several studies have pointed out that indicators based solely on the speed profile do not reveal the full picture of the load, because efforts with high accelerations require more energy and a higher muscle demand than speed efforts [23]. Incorporating accelerometry through GPS calculation factors into the workload has highlighted a 6 to 8% difference in load estimation, compared to monitoring techniques derived solely from speed calculated by positioning systems (e.g., GPS) [16,[23][24][25][26][27]. Furthermore, players rarely have the time and space to reach maximum speeds, relying instead on their ability to accelerate rapidly [19,28,29]. ...
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... It can be quantified, for instance, as the total distance (TD) covered, accelerations (ACC), deceleration (DCC), or metabolic power (1,26,31). Using Inertial Measurement Units (IMU) technology and satellites, GPS devices use positional differentiation to evaluate external effort and cadence, calculate athlete locations with variable precision (120,132), and quantify sports and team activity metrics using accelerometers and power meters. Accelerometers, gyroscopes, and magnetometers, particularly accelerometers, quantify external workload during low-speed, short-distance activities with high reliability and validity (116). ...
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... The use of this technology can provide data about distances covered in different velocities and bouts of acceleration (ACC) and deceleration (DEC) at different intensities and running velocities during training sessions and games [6]. Based on scientific evidence, the total distance (TD) covered, the distance covered at high-speed running measured between 19.8 and 25.2 km/h (HSRD), the distance covered at sprint running (SPR) measured over 25.2 km/h, the specific maximal speed (e.g., to record in the game), the number of ACC ≥ +3 m/s 2 , and the number DEC ≤ −3 m/s 2 seem to be relevant GPS parameters to monitor the external TL in professional soccer [6][7][8][9]. ...
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... Actually, several indicators have been used to monitor soccer players considering both physiological (e.g., heart rate, maximal oxygen consumption and lactate blood concentration) (Rampinini et al., 2007a;Stølen et al., 2005) and match-demand-related 55 components. Recently, this information has been derived from Electronic Performance and Tracking Systems (EPTS) (Maughan et al., 2021) devices such as Global Positioning System (GPS) (Pillitteri et al., 2023b;Varley et al., 2017) and inertial measurement unit (IMU) (Pillitteri et al., 2023a). 60 Noteworthy, in order to better understand the TL to which soccer players are subjected an integrative approach that considers both external and internal loads is guaranteed by conceptual frameworks (F. . ...
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Athlete tracking devices that include global positioning system (GPS) and micro electrical mechanical system (MEMS) components are now commonplace in sport research and practice. These devices provide large amounts of data that are used to inform decision-making on athlete training and performance. However, the data obtained from these devices are often provided without clear explanation of how these metrics are obtained. At present, there is no clear consensus regarding how these data should be handled and reported in a sport context. Therefore, the aim of this review was to examine the factors that affect the data produced by these athlete tracking devices to provide guidelines for collecting, processing, and reporting of data. Many factors including device sampling rate, positioning and fitting of devices, satellite signal and data filtering methods can affect the measures obtained from GPS and MEMS devices. Therefore researchers are encouraged to report device brand/model, sampling frequency, number of satellites, horizontal dilution of precision (HDOP) and software/firmware versions in any published research. Additionally, details of data inclusion/exclusion criteria for data obtained from these devices are also recommended. Considerations for the application of speed zones to evaluate the magnitude and distribution of different locomotor activities recorded by GPS are also presented, alongside recommendations for both industry practice and future research directions. Through a standard approach to data collection and procedure reporting, researchers and practitioners will be able to make more confident comparisons from their data, which will improve the understanding and impact these devices can have on athlete performance.
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Abstract Training load (TL) is monitored with the aim of making evidence-based decisions on appropriate loading schemes to reduce injuries and enhance team performance. However little is known in detail about the variables of load and methods analysis used in high level football. Therefore the aim of this study was to provide information on the practices and perceptions of monitoring in professional clubs. Eighty two high-level football clubs from Europe, the United States and Australia were invited to answer questions relating to (1) how TL is quantified; (2) how players’ responses are monitored, and (3) their perceptions of the effectiveness of monitoring. Forty one responses were received. All teams used GPS and heart rate monitors during all training sessions and 28 used RPE. The top 5 ranking TL variables were; acceleration (various thresholds), total distance, distance covered above 5.5 m·s-1,estimated metabolic power, and heart rate exertion. Players’ responses to training are monitored using questionnaires (68% of clubs) and submaximal exercise protocols (41%). Differences in expected vs. actual effectiveness of monitoring were 23% and 20% for injury prevention and performance enhancement respectively (P<0.001 d=1.0 to 1.4). Of the perceived barriers to effectiveness, “limited human resources” scored highest, followed by “coach buy-in”. The discrepancy between expected and actual effectiveness appears to be due to suboptimal integration with coaches, insufficient human resources and concerns over the reliability of assessment tools. Future approaches should critically evaluate the usefulness of current monitoring tools and explore methods of reducing the identified barriers to effectiveness.
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Purpose: The aim of the current study was to identify the external-training-load markers that are most influential on session rating of perceived exertion (RPE) of training load (RPE-TL) during elite soccer training. Methods: Twenty-two elite players competing in the English Premier League were monitored. Training-load data (RPE and 10-Hz GPS integrated with a 100-Hz accelerometer) were collected during 1892 individual training sessions over an entire in-season competitive period. Expert knowledge and a collinearity r < .5 were used initially to select the external training variables for the final analysis. A multivariate adjusted within-subjects model was employed to quantify the correlations of RPE and RPE-TL (RPE × duration) with various measures of external training intensity and training load. Results: Total high-speed-running (HSR; >14.4 km/h) distance and number of impacts and accelerations >3 m/s2 remained in the final multivariate model (P < .001). The adjusted correlations with RPE were r = .14, r = .09, and r = .25 for HSR, impacts, and accelerations, respectively. For RPE-TL, the correlations were r = .11, r = .45, and r = .37, respectively. Conclusions: The external-load measures that were found to be moderately predictive of RPE-TL in soccer training were HSR distance and the number of impacts and accelerations. These findings provide new evidence to support the use of RPE-TL as a global measure of training load in elite soccer. Furthermore, understanding the influence of characteristics affecting RPE-TL may help coaches and practitioners enhance training prescription and athlete monitoring.
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Purpose: To examine the nature and frequency of rugby league repeated high-intensity effort (RHIE) activity in relation to tries scored and conceded in successful and unsuccessful teams. Methods: One-hundred and eighty-five semi-professional rugby league players (mean ± SD age: 23.7 ± 3.2 yr) from 11 teams participated in this study. Global positioning system (GPS) data was collected during 21 matches. Data were analysed for the total number of RHIE bouts, efforts per bout, duration of efforts and recovery between efforts. Using notational analysis, a RHIE bout frequency distribution, representing 0-60s, 61-120s, 121-180s, 181-240s, and 241-300s prior to scoring and conceding a try was established. Results: Over 50% of RHIE bouts occurred within five minutes of a try. Bottom 4 teams performed a greater proportion of bouts within five minutes of a try than Top 4 teams (61.5% vs. 48.2%, effect size, ES = 0.69 ± 0.28, p=0.0001). Top 4 teams performed a greater number of RHIE bouts per conceded try (3.0 ± 2.1 vs. 1.6 ± 0.7, ES = 0.74 ± 0.51, p<0.05), while Bottom 4 teams performed a greater number of RHIE bouts per try scored (3.6 ± 2.5 vs. 2.1 ± 1.7, ES = 0.70 ± 0.71, p=0.10). Conclusion: The majority of rugby league RHIE bouts occur at critical periods during match-play. Successful rugby league teams perform more RHIE bouts prior to conceding tries, while unsuccessful teams perform more bouts prior to scoring tries. These findings demonstrate that unsuccessful teams are required to work harder to score tries while successful teams work harder to prevent tries.
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This study assessed the positional and temporal movement patterns of professional rugby union players during competition using global positioning system (GPS) units. GPS data were collected from 33 professional rugby players from 13 matches throughout the 2012-2013 season sampling at 10 Hz. Players wore GPS units from which information on distances, velocities, accelerations, exertion index, player load, contacts, sprinting and repeated high-intensity efforts (RHIE) were derived. Data files from players who played over 60 min (n = 112) were separated into five positional groups (tight and loose forwards; half, inside and outside backs) for match analysis. A further comparison of temporal changes in movement patterns was also performed using data files from those who played full games (n = 71). Significant positional differences were found for movement characteristics during performance (P < 0.05). Results demonstrate that inside and outside backs have greatest high-speed running demands; however, RHIE and contact demands are greatest in loose forwards during match play. Temporal analysis of all players displayed significant differences in player load, cruising and striding between halves, with measures of low- and high-intensity movement and acceleration/deceleration significantly declining throughout each half. Our data demonstrate significant positional differences for a number of key movement variables which provide a greater understanding of positional requirements of performance. This in turn may be used to develop progressive position-specific drills that elicit specific adaptations and provide objective measures of preparedness. Knowledge of performance changes may be used when developing drills and should be considered when monitoring and evaluating performance.
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Purpose: To quantify the seasonal training load completed by professional soccer players of the English Premier League. Methods: Thirty players were sampled (using GPS, heart rate, and rating of perceived exertion [RPE]) during the daily training sessions of the 2011-12 preseason and in-season period. Preseason data were analyzed across 6×1-wk microcycles. In-season data were analyzed across 6×6-wk mesocycle blocks and 3×1-wk microcycles at start, midpoint, and end-time points. Data were also analyzed with respect to number of days before a match. Results: Typical daily training load (ie, total distance, high-speed distance, percent maximal heart rate [%HRmax], RPE load) did not differ during each week of the preseason phase. However, daily total distance covered was 1304 (95% CI 434-2174) m greater in the 1st mesocycle than in the 6th. %HRmax values were also greater (3.3%, 1.3-5.4%) in the 3rd mesocycle than in the first. Furthermore, training load was lower on the day before match (MD-1) than 2 (MD-2) to 5 (MD-5) d before a match, although no difference was apparent between these latter time points. Conclusions: The authors provide the 1st report of seasonal training load in elite soccer players and observed that periodization of training load was typically confined to MD-1 (regardless of mesocycle), whereas no differences were apparent during MD-2 to MD-5. Future studies should evaluate whether this loading and periodization are facilitative of optimal training adaptations and match-day performance.
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Purpose: To investigate the influence of score line, level of opposition, and timing of substitutes on the activity profile of rugby sevens players and describe peak periods of activity. Methods: Velocity and distance data were measured via 10-Hz GPS from 17 international-level male rugby sevens players on 2-20 occasions over 4 tournaments (24 matches). Movement data were reported as total distance (TD), high-speed-running distance (HSR, 4.17-10.0 m/s), and the occurrence of maximal accelerations (Accel, ≥2.78 m/s2). A rolling 1-min sample period was used. Results: Regardless of score line or opponent ranking there was a moderate to large reduction in average and peak TD and HSR between match halves. A close halftime score line was associated with a greater HSR distance in the 1st minute of the 1st and 2nd halves compared with when winning. When playing against higher- compared with lower-ranked opposition, players covered moderately greater TD in the 1st minute of the 1st half (difference = 26%; 90% confidence limits = 6, 49). Compared with players who played a full match, substitutes who came on late in the 2nd half had a higher average HSR and Accel by a small magnitude (31%; 5, 65 vs 34%; 6, 69) and a higher average TD by a moderate magnitude (16%; 5, 28). Conclusions: Match score line, opposition, and substitute timing can influence the activity profile of rugby sevens players. Players are likely to perform more running against higher opponents and when the score line is close. This information may influence team selection.
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The aims of the current study were to examine the magnitude of between-GPS-models differences in commonly reported running-based measures in football, examine between-units variability, and assess the effect of software updates on these measures. Fifty identical-brand GPS units (15 SPI-proX and 35 SPI-proX2, 15 Hz, GPSports, Canberra, Australia) were attached to a custom-made plastic sled towed by a player performing simulated match running activities. GPS data collected during training sessions over 4 wk from 4 professional football players (N = 53 files) were also analyzed before and after 2 manufacturer-supplied software updates. There were substantial differences between the different models (eg, standardized difference for the number of acceleration >4 m/s2 = 2.1; 90% confidence limits [1.4, 2.7], with 100% chance of a true difference). Between-units variations ranged from 1% (maximal speed) to 56% (number of deceleration >4 m/s2). Some GPS units measured 2-6 times more acceleration/deceleration occurrences than others. Software updates did not substantially affect the distance covered at different speeds or peak speed reached, but 1 of the updates led to large and small decreases in the occurrence of accelerations (-1.24; -1.32, -1.15) and decelerations (-0.45; -0.48, -0.41), respectively. Practitioners are advised to apply care when comparing data collected with different models or units or when updating their software. The metrics of accelerations and decelerations show the most variability in GPS monitoring and must be interpreted cautiously.