Assessing the agreement
between a global navigation
satellite system and an optical-
tracking system for measuring
total, high-speed running, and
sprint distances in ofﬁcial
soccer matches
Piotr Makar1, Ana Filipa Silva2,3,4,
Rafael Oliveira4,5,6
, Marcin Janusiak7,
Przemysław Parus8, Małgorzata Smoter9
and Filipe Manuel Clemente2,10
1Gdańsk University of Physical Education and Sport, Gdańsk, Poland
2Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do
Castelo, Viana do Castelo, Portugal
3Research Center in Sports Performance, Recreation, Innovation and
Technology (SPRINT), Melgaço, Portugal
4The Research Centre in Sports Sciences, Health Sciences and Human
Development (CIDESD), Vila Real, Portugal
5Sports Science School of Rio Maior–Polytechnic Institute of Santarém,
Rio Maior, Portugal
6Life Quality Research Centre, Rio Maior, Portugal
7Śląsk Wrocław Basketball, Physiology Department, Wrocław, Poland
8FC WKS Śląsk Wrocław, Physical Performance Department, Wrocław,
Poland
9Department of Basics of Physiotherapy, Gdansk University of Physical
Education and Sport, Gdańsk, Poland
10Instituto de Telecomunicações, Delegação da Covilhã, Lisboa, Portugal
Abstract
This study aimed to compare the agreement of total distance (TD), high-speed running (HSR) dis-
tance, and sprint distance during 16 ofﬁcial soccer matches between a global navigation satellite
Corresponding author:
Filipe Manuel Clemente, Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua
Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal.
Email: ﬁlipe.clemente5@gmail.com
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons
Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction
and distribution of the work without further permission provided the original work is attributed as speciﬁed on the SAGE and
Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Sports Sciences, Exercise, and Health – Original
Manuscript
SCIENCE PROGRESS
Science Progress
2023, Vol. 106(3) 1–14
© The Author(s) 2023
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/00368504231187501
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system (GNSS) and an optical-tracking system. A total of 24 male soccer players, who are actively
participating in the Polish Ekstraklasa professional league, were included in the analysis conducted
during ofﬁcial competitions. The players were systematically monitored using Catapult GNSS (10-
Hz, S7) and Tracab optical-tracking system (25-Hz, ChyronHego). TD, HSR distance, sprint dis-
tance, HSR count (HSRC), and sprint count (SC) were collected. The data were extracted in 5-
min epochs. A statistical approach was employed to visually examine the relationship between
the systems based on the same measure. Additionally, R2 was utilized as a metric to quantify
the proportion of variance accounted for by a variable. To assess agreement, Bland–Altman
plots were visually inspected. The data from both systems were compared using the estimates
derived from the intraclass correlation (ICC) test and Pearson product–moment correlation.
Finally, a paired t-test was employed to compare the measurements obtained from both systems.
The interaction between Catapult and Tracab systems revealed an R2 of 0.717 for TD, 0.512 for
HSR distance, 0.647 for sprint distance, 0.349 for HSRC, and 0.261 for SC. The ICC values for
absolute agreement between the systems were excellent for TD (ICC = 0.974) and good for
HSR distance (ICC = 0.766), sprint distance (ICC = 0.822). The ICC values were not good for
HSRCs (ICC = 0.659) and SCs (ICC = 0.640). t-test revealed signiﬁcant differences between
Catapult and Tracab for TD (p < 0.001; d = −0.084), HSR distance (p < 0.001; d = −0.481), sprint
distance (p < 0.001; d = −0.513), HSRC (p < 0.001; d = −0.558), and SC (p < 0.001; d = −0.334).
Although both systems present acceptable agreement in TD, they may not be perfectly inter-
changeable, which sports scientists and coaches must consider when using them.
Keywords
: Football, athletic performance, player tracking systems, training load monitoring, locomotor
demands
Introduction
Monitoring locomotor demands through technological devices has become a widespread
and recurring practice in soccer training. A survey conducted among 94 coaches and 88
practitioners belonging to elite English soccer found that tracking systems (e.g., global
navigation satellite system [GNSS]) were used more than other different training load
monitoring methods (e.g., blood lactates, ratings of perceived exertion, heart rates).1 In
another survey conducted among 82 high-level soccer teams competing in the top
leagues of countries such as the United Kingdom, Spain, France, Germany, and Italy,
the ﬁndings revealed that approximately 40% of the teams utilized time motion analysis
and accelerometers as the primary tools for quantifying training load.2 It is noteworthy to
acknowledge that the term “training load” has been a topic of discussion regarding its
accuracy. This is primarily due to the conventional association of the term “load” with
a mechanical variable measured in newtons within the International System of Units.3
However, it is crucial to emphasize that in the speciﬁc context of training load, it
serves as a scientiﬁc construct rather than a direct “measurement” per se.4 Therefore,
its usage does not contravene any scientiﬁc principles.4
To enhance the efﬁcacy of collecting sports science training data, it is crucial to
acknowledge the inherent value of coaches and performance staff. This recognition
plays a pivotal role in facilitating the widespread adoption and seamless integration of
meticulously engineered tracking systems that are purposefully designed to augment
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Science Progress 106(3)
training load practices. Supporting this notion, a comprehensive survey was conducted
among 176 soccer coaches and performance coaches, revealing that sport science data
holds substantial importance in guiding their practice, being perceived as both somewhat
important and very important.5
In light of the proliferation of tracking systems, it is evident that a multitude of options
are now available on the market. However, it becomes increasingly challenging to
conduct fair comparisons and establish the interchangeability of data among these
diverse alternatives.6 Among the various technological options, GNSS, ultra-wideband
technology, and optical video tracking systems are prominent examples. The usability
of these options is contingent upon speciﬁc contexts and objectives. GNSS, while cost-
effective, is primarily applicable in outdoor facilities. Conversely, ultra-wideband tech-
nology, despite its higher cost, offers versatility by functioning effectively in both
outdoor and indoor settings.7
In the context of outdoor facilities, GNSS remains widely utilized, potentially owing
to its user-friendly nature compared to installation-dependent optical video tracking
systems, as well as its relatively lower cost in comparison to ultra-wideband technology.
It is important to note, however, that optical video tracking systems can present an alter-
native and intriguing solution for sports scientists and players in outdoor facilities. These
systems ensure high-quality data collection and provide the opportunity to combine time-
motion analysis with notational analysis, all without necessitating any devices to be
placed on the players. Having in mind the multiplicity of the options and technical
aspects of the devices, it is particularly essential to focus on the agreement between
such systems,7 considering that data collected can vary from system to system with a
remarkable impact on data interpretation.
Taking that into consideration, different studies have focused on testing absolute agree-
ment between different tracking systems.6,8,9 For example, a 10-Hz multi-GNSS GNSS
device (vector, Catapult) and two optical tracking systems (25-Hz Tracab and Second
Spectrum) were compared. The results indicated that in comparison to GNSS, Tracab
revealed signiﬁcantly higher values for most locomotor measures followed by the other
optical system (Second Spectrum).6 Another study comparing two 10-Hz GNSS systems
(Viper, StatSports; and Apex, StatSports) with Tracab optical tracking system demonstrated
signiﬁcant differences between GNSS and the optical tracking systems for locomotor mea-
sures such as total distance (TD), high-speed running (HSR) distance, and sprint distance.8
However, the different systems presented very large correlations.8 Another comparison of
Tracab and a 10-Hz GNSS (Wimu) revealed that the optical tracking system slightly overes-
timated most locomotor measures compared to GNSS.9
These aforementioned studies6,8,9 suggest notable differences in locomotor measures
when comparing different technologies, speciﬁcally GNSS and optical video tracking
systems. However, they also demonstrate a strong correlation between the two, indicating
the potential for interchangeability. Despite these ﬁndings, the current research has not
focused on analyzing peak demands within 5-min epoch periods. Peak demands, or
worst-case scenarios, have recently garnered attention,10 raising concerns about the
accuracy, precision, and interchangeability of different systems.
Although Ellens’ study6 investigated the interchangeability between a 10-Hz GNSS
Vector and Tracab (an optical tracking system with 25-Hz), the analysis did not
Makar et al.
3
speciﬁcally examine the interchangeability of epoch periods. Therefore, further research
is needed to determine whether interchangeability can also be achieved within 5-min
epochs. Such research would provide another independent assessment of absolute agree-
ment between different systems and models, particularly comparing new GNSS systems
like the Catapult S7 with Tracab (an optical tracking system with 25-Hz).
Testing for interchangeability holds signiﬁcant importance for several reasons. Firstly, it
allows for better control over the comparisons made between scientiﬁc articles and bench-
marks conducted on players. By establishing the interchangeability of devices and technolo-
gies, it becomes possible to ensure the validity and reliability of such comparisons.
Secondly, considering that clubs often change their devices and technologies, having
access to interchangeability values becomes crucial. It provides clubs with the necessary
information to determine whether fair and accurate comparisons can be made or if caution
should be exercised due to potential discrepancies between different measurement
systems. This knowledge empowers clubs to make informed decisions regarding the
compatibility and comparability of data collected from different sources.
Therefore, the objective of this study is to assess the absolute agreement between the 10-Hz
Catapult S7 GNSS and the 25-Hz Tracab optical video tracking system in terms of TD, HSR
distance, sprint distance, as well as the number of HSR and sprints. These measures were spe-
ciﬁcally chosen due to their signiﬁcance in quantifying training load within the given context.
TD serves as a comprehensive measure of locomotor demand, which is closely asso-
ciated with internal load responses.11 It provides valuable insights into the magnitude of
demands imposed on players. HSR and sprint distances were selected as they represent
the most demanding locomotor demands observed during matches. Moreover, these mea-
sures are known to have traditionally lower values of precision,12 necessitating further
examination to ensure optimal accuracy and precision of the collected data.
Methods
Study design
This study employed a longitudinal design, focusing on a group of twenty-one soccer
players from a single professional team. Over a period of 16 ofﬁcial soccer matches,
which took place outdoors in stadium facilities, the players were consecutively observed.
The observation period spanned from July 15, 2022, to November 13, 2022, correspond-
ing to the competitive phase of the season. For the analysis, only data from ofﬁcial
matches in the domestic competition, including league matches and cup matches, were
considered. The players were monitored using two tracking systems: (a) a GNSS and
(b) an optical-tracking system. The study aimed to test the agreement between both
systems for monitoring locomotor demands of the players during the match.
Participants
We used nonprobabilistic convenience sampling. A group of 21 male professional foot-
ball players (231 observations) from the ﬁrst team of one of the Polish Ekstraklasa clubs
(age: 25 ± 3 years, body height: 179.6 ± 5.5 cm, body mass: 76.1 ± 5.0 kg) participated in
4
Science Progress 106(3)
the research. The data were collected over 16 matches played in the autumn round of the
2022/2023 season and were recorded simultaneously during each of the observed games.
Goalkeepers were not included in this study due to the unique nature of their position.
Considering the speciﬁc movements and actions performed by goalkeepers during
matches, the use of GNSS instruments may potentially cause damage or interfere with
their typical movements. Hence, to ensure the integrity of the study and avoid any poten-
tial harm to the goalkeepers, their data was not collected or analyzed as part of this
research. All data were created as a condition of employment where players were rou-
tinely monitored throughout league play.
In order to uphold ethical standards, all players involved in the study were provided
with detailed information about the study design, the associated risks, and the potential
beneﬁts of participation. Only after obtaining their informed consent was the study con-
ducted. The informed consent process ensured that the players were fully aware of the
study’s objectives, procedures, and potential implications before agreeing to participate.
This study adhered to the ethical guidelines outlined in the Declaration of Helsinki for
research involving human participants. Conﬁdentiality was strictly maintained through-
out the study by anonymizing all data prior to analysis.
Methodological procedures
Two tracking systems were used simultaneously: (a) a GNSS unit (Vector S7, Catapult
Innovations, Melbourne, Australia; 81mm×43mm×16mm), operating at a frequency of
10-Hz and (b) an optical tracking system (TRACAB, ChyronHego, New York, USA)
using two multicamera units (each containing three HD-SDI cameras with a resolution
of 1920 × 1080 pixels) with a sampling frequency of 25-Hz. On average, the number
of satellites connected during data collection was 15, and the average horizontal dilution
of precision (HDOP) was 0.7. Vector S7 was preliminarily tested for its ability to assess a
force-velocity proﬁle.13 Furthermore, the Tracab system underwent a validation process
to assess its accuracy and precision in measuring locomotor demands across various
running speed thresholds.14
The players always used the same GNSS unit to reduce inter-unit variability.15 The
GNSS units were placed between the players’ shoulder blades and were activated accord-
ing to a manufacturer’s guidelines before kickoff. To avoid potential unit differences, the
players wore the same GNSS unit for each match.8 The data recorded by the units were
downloaded after each match for further analysis using Catapult OpenField Cloud
Analytics (OpenField 3.9.0 Catapult Sports, Melbourne, Australia). The following vari-
ables were selected for analysis during this study: ﬁeld time, deﬁned as the time spent on
the ﬁeld (FT; min), TD (m), distance in HSR, deﬁned as a running speed between 19.81
and 25.2 km/h (HSR; m), sprint, deﬁned as velocity greater than 25.2 km/h (SPR; m),
High-speed running count (HSRC) and sprint count (SC). The HSR speed threshold of
19.81 km/h was determined based on the research conducted by Abt and Lovell,16
who identiﬁed this value as the reference for the second ventilatory threshold. This spe-
ciﬁc threshold has gained broad acceptance and is widely used as a prevalent measure to
deﬁne arbitrary speed thresholds in soccer players. The selection of the 25.2 km/h speed
threshold for sprints aligns with established conventions based on previous research
Makar et al.
5
conducted on sprinting in soccer players.17 The velocity thresholds chosen are those
deﬁned by both tracking system providers. All data from the Tracab system were pro-
vided by ChyronHego as a match report. Data from both the Catapult and Tracab
systems were extracted in 5-min epochs, which involved dividing the ofﬁcial match
time into consecutive 5-min time periods. This approach ensured that all 5-min epochs
within the match time were considered and included in the analysis.
Statistical procedures
Descriptive statistics are presented in the form of mean and standard deviation. Plotting
data was performed to visually inspect the relationship between the systems for the same
measure. At the same time, R2 was used as a measure to represent the proportion of the
variance for a variable. Measuring agreement was visually inspected by Bland–Altman
plots with a 95% conﬁdence interval using the mean difference between measures.
The estimates of the intraclass correlation (ICC) test and their 95% conﬁdence intervals
were calculated by means of SPSS statistical package (28.0.0.0, IBM, Chicago, IL) based
on a mean-rating (k = 2), absolute-agreement, a two-way mixed effects model. The clas-
siﬁcation of the agreement18 was considered good between ICC = 0.75 and ICC = 0.90,
while the values above ICC = 0.90 were considered excellent. The Pearson–product cor-
relation test was executed on SPSS (version 28.0.0., IBM, Chicago, USA) for a p-value
less than 0.05 to analyze the strength of the relationship between the systems. The cor-
relation coefﬁcients19 between r = 0.50 and r = 0.7 were considered large, between r =
0.7 and r = 0.9 very large, and above r = 0.9 nearly perfect. The paired t-test was used
to compare the measures obtained for both systems, followed by the standardized
effect size (Cohen’s d) that was interpreted as20: 0.0–0.2, trivial; 0.2–0.5, medium;
0.5–0.8, large; and >0.8, very large. The statistical procedures were executed in SPSS
(version 28.0.0., IBM, Chicago, USA) for a p < 0.05.
Results
Figure 1 displays a scatter plot comparing the Catapult and Tracab systems for the
various running-based measures extracted during the matches. It is important to note
that all data presented in the results correspond to the values obtained for the 5-min
epochs. The interaction between Catapult and Tracab systems revealed an R2 of 0.717
for TD, 0.512 for HSR distance, 0.647 for sprint distance, 0.349 for HSRC, and 0.261
for SC.
Table 1 presents the ICC and the Pearson–product correlation tests comparing both
systems for the different running-based measures. The ICC values for absolute agreement
between the systems were excellent for TD (ICC = 0.974) and good for HSR distance
(ICC = 0.766), and sprint distance (ICC = 0.822). The ICC values were not good for
HSRCs (ICC = 0.659) and SCs (ICC = 0.640).
The measurement agreement was performed by visual inspection of the data. Figure 2
presents the Bland–Altman plots for the different running-based measurements. The
mean difference for TD (for 5-min epochs) was −11.5 [95% CI: −165; 142], while for
HSR distance was −11 [95% CI: −44; 22] and for sprinting −8.0 [95% CI: −26; 11].
6
Science Progress 106(3)
Figure 1. Scatter plot between Catapult and Tracab for TD, HSR distance, sprint distance, HSRC,
and SC. The data displayed in the ﬁgure represents the aggregation of values over 5-min epochs.
Makar et al.
7
Regarding HSR and SCs, the mean difference was −1 [95% CI: −3; 2] and 0 [95% CI:
−1; 1].
Table 2 presents descriptive statistics of the running-based measures collected in both
the Catapult and Tracab systems. t-test revealed signiﬁcant differences between Catapult
and Tracab for TD (p < 0.001; d = −0.084), HSR distance (p < 0.001; d = −0.481), sprint
distance (p < 0.001; d = −0.513), HSRC (p < 0.001; d = −0.558), and SC (p < 0.001; d =
−0.334).
Discussion
The objective of this study was to evaluate the absolute agreement of TD, HSR distance,
and sprint distance between the 10-Hz Catapult S7 GNSS and the 25-Hz Tracab optical
video tracking system. This evaluation was conducted using data collected in 5-min
epochs. The main ﬁndings showed excellent absolute agreement between the systems
for TD and good absolute agreement for HSR distance, sprint distance, HSRC, and
SCs. Nonetheless, it was noted that Catapult underestimated the values in comparison
to Tracab, particularly for HSR and sprint distances/counts. This corresponds with the
ﬁndings of previous studies that revealed higher values for tracking systems when com-
pared with GNSS6
Regarding TD, the present study showed higher values for Tracab in comparison to
Vector S7, which was also previously conﬁrmed.6 Moreover, the ﬁndings concerning
speed values, higher for the tracking system when compared to GNSS, are similar to
the ﬁndings of previous research.7 The observed discrepancies may arise from disparities
in data ﬁltering methodologies employed by device software. Notably, the implementa-
tion of ﬁlters such as moving averages has been shown to yield more reﬁned speed data.
However, it is crucial to elucidate that these ﬁltering techniques do not affect the funda-
mental principle of peak velocity. Peak velocity signiﬁes the utmost speed attained within
a speciﬁed timeframe, irrespective of any averaged HSR or sprinting encompassed during
the said interval. It is noteworthy that the present study adopted 5-min epochs as the tem-
poral units for data analysis.
This study also found that the above differences tend to increase signiﬁcantly with
higher speed distances and counts, which was found in the previous study that analyzed
Tracab and GNSS (GPEXE®, Exelio, Udine, Italia) with different epochs (15, 30, and 45
min).21 Hence, it can be inferred that as the velocity achieved during high-speed distances
Table 1. ICC and Pearson-product correlation tests comparing both systems for the different
running-based measures.
Intraclass correlation
r Pearson
Total distance (m)
0.906 [95% CI: 0.897; 0.914]
0.851 [0.841; 0.860] | p < 0.001
High-speed running (m)
0.766 [95% CI: 0.522; 0.864]
0.716 [0.697; 0.734] | p < 0.001
Sprint (m)
0.822 [95% CI: 0.445; 0.917]
0.804 [0.780; 0.826] | p < 0.001
High-speed running (n)
0.659 [95% CI: 0.399; 0.784]
0.590 [0.564; 0.615] | p < 0.001
Sprint (n)
0.640 [95% CI: 0.542; 0.712]
0.523 [0.468; 0.574] | p < 0.001
8
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Table 2. Descriptive statistics (mean ± standard deviation) for the different running-based measures collected by 5-min epoch and inferential
comparisons.
Catapult
Tracab
Mean difference (Catapult − Tracab)
p-value
d
Total distance (m)
for a 5-min epoch
491.0 ± 116.6
502.7 ± 144.8
−11.8 [95% CI: −14.4; −9.2]
<0.001
−0.084 [95% CI: −0.103; −0.065]
High-speed running
(m) for a 5-min
epoch
27.5 ± 19.1
38.2 ± 24.0
−10.8 [95% CI: −11.4; −10.1]
<0.001
−0.481 [95% CI: −0.512; −0.450]
Sprint (m) for a
5-min epoch
18.0 ± 13.5
25.7 ± 15.9
−7.7 [95% CI: −8.4; −7.1]
<0.001
−0.513 [95% CI: −0.560; −0.465]
High-speed running
(n) for a 5-min
epoch
2.1 ± 1.2
2.9 ± 1.5
−0.8 [95% CI: −0.8; −0.7]
<0.001
−0.558 [95% CI: −0.597; −0.519]
Sprint (n) for a
5-min epoch
1.2 ± 0.4
1.4 ± 0.6
−0.2 [95% CI: −0.2; −0.1]
<0.001
−0.334 [95% CI: −0.408; −0.261]
Makar et al.
9
Figure 2. Difference against mean for running-based measures.
10
Science Progress 106(3)
increases, the likelihood of encountering greater differences between the systems also
ampliﬁes.
In addition, the number of HSR and sprint efforts detected was the greatest for
TRACAB, which is also in line with a previous study.6 In this regard, it is important
to acknowledge that the count detection of speed distances requires a minimum duration
above a ﬁxed velocity. For instance, Varley et al.22 showed moderate to large differences
when different minimum effort durations were applied in the number of HSR detected
with 10-Hz GNSS during a soccer match (∼150 efforts detected for 0.1 s duration com-
pared to ∼90 efforts detected for 1 s duration). Thus, when comparing different systems,
it is relevant to consider ﬁltering technology differences to understand the pros and cons
of each system23 as well as other factors, such as the sampling rate, the number of satel-
lites, HDOP, and the software analysis of different systems.7
A limitation of the study is a small sample size of participants and, consequently,
speculation that a larger sample size could potentially provide a different result when
comparing both systems. However, in the context of professional soccer matches, collect-
ing data by means of two different systems is complex and limits the opportunity to gather
larger sample sizes. Nonetheless, this had also been pointed as a limitation due to
non-ecological environments by the simulation of circuits or matches.24 Besides, such
devices are expensive and not all teams have access to them. The lack of analysis of
accelerometer-based
variables
is
another
limitation
that
may
provide
further
state-of-the-art knowledge, considering that accelerating or decelerating with or
without changing direction has been reported as very important in soccer.25 Therefore,
the investigation with a similar design but with a larger sample size and analysis of
accelerometer-based variables is recommended for future studies.
Conclusions
Although both systems present a strong relationship and acceptable agreement for TD,
the interchangeability should be considered cautiously, mainly regarding signiﬁcant dif-
ferences in the collected measures. Coaches and sports scientists should be mindful of the
differences between both systems when comparing data and avoid using them
interchangeably.
Acknowledgments
The authors appreciate the study participants. All authors have read and agreed to the published
version of the manuscript.
Author contributions
Conceptualization by P.M. and F.M.C.; methodology by P.M. and F.M.C.; formal analysis by
F.M.C.; investigation by P.M., M.J., P.P., F.M.C.; data curation by F.M.C.; writing—original
draft preparation by P.M., A.F.S., R.O., M.J., P.P., M.S., F.M.C.; writing—review and editing
by P.M., A.F.S., R.O., M.J., P.P., M.S., F.M.C.; project administration by F.M.C.
Makar et al.
11
Availability of data and materials
All data generated or analyzed during this study are available at the request of the corresponding author.
Consent for publication
Not applicable.
Declaration of conﬂicting interests
The author(s) declared no potential conﬂicts of interest with respect to the research, authorship, and/
or publication of this article.
Ethics approval
Medical Ethic Committee in University of Gdańsk (Decision number 62/2022).
Funding
The author(s) disclosed receipt of the following ﬁnancial support for the research, authorship, and/
or publication of this article: Filipe Manuel Clemente and this work are funded by the Fundação
para a Ciência e Tecnologia/Ministério da Ciência, Tecnologia e Ensino Superior through national
funds, and when applicable, co-funded by EU funds under the project UIDB/50008/2020.
Informed consent
All participants were informed about the study and signed free consent.
ORCID iDs
Rafael Oliveira
https://orcid.org/0000-0001-6671-6229
Filipe Manuel Clemente
https://orcid.org/0000-0001-9813-2842
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Author biographies
Piotr Makar is Head of Department at Gdansk University of Physical Education and Sport (Poland).
Ana Filipa Silva is an assistant professor at Instituto Politécnico de Viana do Castelo (Portugal).
Rafael Oliveira is an assistant professor at Instituto Politénico de Santarém.
Makar et al.
13
Marcin Janusiak is professor at Ś ląsk Wrocław Basketball, Physiology Department, Wrocław,
Poland.
Przemysław Parus is sport scientist at FC WKS Ś ląsk Wrocław, Physical Performance Department,
Wrocław, Poland.
Małgorzata Smoter is professor at Department of Basics of Physiotherapy, Gdansk University of
Physical Education and Sport, Gdańsk, Poland.
Filipe Manuel Clemente is an assistant professor at Instituto Politécnico de Viana do Castelo,
Portugal.
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Science Progress 106(3)

Assessing the agreement between a global navigation satellite system and an optical-tracking system for measuring total, high-speed running, and sprint distances in official soccer matches.
Makar, Piotr,Silva, Ana Filipa,Oliveira, Rafael,Janusiak, Marcin,Parus, Przemysław,Smoter, Małgorzata,Clemente, Filipe Manuel