TYPE Original Research
PUBLISHED 01 August 2022
DOI 10.3389/fpubh.2022.966578
OPEN ACCESS
EDITED BY
Juel Jarani,
Sports University of Tirana, Albania
REVIEWED BY
Mücahit Fi¸sne,
Sivas Cumhuriyet University, Turkey
Ugur Ödek,
Nev¸sehir Haci Bekta¸s Veli
University, Turkey
*CORRESPONDENCE
Marko Joksimovi´c
nicifor007@outlook.com
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This article was submitted to
Aging and Public Health,
a section of the journal
Frontiers in Public Health
RECEIVED 11 June 2022
ACCEPTED 11 July 2022
PUBLISHED 01 August 2022
CITATION
Goranovi´c K, Hadži´c R, Petkovi´c J and
Joksimovi´c M (2022) Exploring trends
of running performance during
matches of professional soccer players
in Montenegro: A longitudinal study.
Front. Public Health 10:966578.
doi: 10.3389/fpubh.2022.966578
COPYRIGHT
© 2022 Goranovi´c, Hadži´c, Petkovi´c
and Joksimovi´c. This is an
open-access article distributed under
the terms of the Creative Commons
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distribution or reproduction in other
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original author(s) and the copyright
owner(s) are credited and that the
original publication in this journal is
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academic practice. No use, distribution
or reproduction is permitted which
does not comply with these terms.
Exploring trends of running
performance during matches of
professional soccer players in
Montenegro: A longitudinal
study
Kosta Goranovi´c1, Rašid Hadži´c1, Jovica Petkovi´c1 and
Marko Joksimovi´c1,2*
1Department of Physical Education, Faculty of Sports and Physical Education, University of
Montenegro, Podgorica, Montenegro, 2Institute of Sports and Sports Medicine, Podgorica,
Montenegro
The practical value of monitoring is that well-chosen performance indicators
can help coaches identify the good and bad performance of individuals
or teams. External monitoring of matches is useful in establishing the
physiological requirements of the sport and assessing how a player compares
to the requirements of the event in this regard. This study aimed to analyze
the trend component of running performance during a game of professional
soccer in Montenegro. The research included a sample of 82 professional
soccer players. The ﬁrst subsample included 44 professional soccer players
of the club Budu´cnost from Podgorica, height 185.89 ± 6.29 cm, mass
81.06 ± 5.47 kg, BMI 23.47 ± 0.96 kg/m², age 28.86 ± 3.85 yrs. The second
subsample included 38 professional soccer players from the Sutjeska club from
Nikši´c, height 181.88 ± 6.35 cm, mass 77.28 ± 6.78 kg, BMI 23.32 ± 1.08 kg/m²,
age 29.43 ± 5.68 yrs. The InStat kinematic system captured the outﬁeld players
by using six cameras placed around the perimeter of the ﬁeld at the minimal
height of 12 m. The frame frequency was 25 frames per second; data were
centralized for further analysis. Statistically signiﬁcant diferences were noted
only in the variable sprint distance in the 2017 season. The results of the current
research indicate that the soccer players who compete in Montenegro are
below the values achieved by those who compete in Europe.
KEYWORDS
performance analysis, external monitoring, time-motion analysis, high intensity
running, soccer
Introduction
Soccer is one of the most complex sports in the world; players need technical, tactical,
and physical skills to achieve successful performance and eventually win a game. The
cooperative relationships between players who play diﬀerent positions are critical to a
team’s success. For instance, the main role of midﬁelders is to organize the oﬀense with
proper ball control and passes, while the main duties of defenders are to win aerial duels
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10.3389/fpubh.2022.966578
and tackles or to perform interceptions of the balls passed
to attackers. Understanding these position-speciﬁc demands is
crucial in the evaluation of players’ achievements (1). Modern
soccer requires a high level of endurance, speed, strength, and
coordination (2). Therefore, players must have well-developed
physical ﬁtness. Given that the energy used by soccer players is
mainly produced by aerobic metabolism (3, 4), it is essential that
players have well-developed aerobic ﬁtness.
Running in-game performance is a set of variables used in
soccer performance analysis and is deﬁned “as the choice and
combination of variables that deﬁne an aspect of performance
and help achieve sporting success” (5), in which the player’s
duties are passing, shooting, throwing the ball, dribbling, etc.
Currently, several video-based platforms are available to track
player performance indicators; some of the most commonly
used platforms are InStat, Optasport, and Wyscout. Such
platforms quickly and accurately provide a wide range of data
on game performance indicators, enabling simultaneous analysis
of physical eﬀort, movement patterns, and technical actions of
players, with and without the ball (6).
Various studies have examined these characteristics and
requirements within a soccer team (7). Yi et al. (8) explored
the technical requirements of diﬀerent playing positions for
play in the UEFA Champions League. In contrast, Modri´c
et al. (6) identiﬁed running performance speciﬁc to each
playing position in professional soccer players. Dellal et al. (9)
identiﬁed positional requirements from technical and physical
aspects in the French premier league. All studies indicated high
applicability of running performance in evaluating team-speciﬁc
achievements and team position. It is known that running
performance during the game is an essential determinant
of success in professional soccer, which has been studied
repeatedly, although some studies have been done with diﬀerent
aims (10–12). However, due to its importance, more research
is required in diﬀerent countries according to diﬀerent levels
of players and leagues. This is the ﬁrst study to monitor
the performance of running during the game in the ﬁrst
Telekom Montenegrin league. In this study, we hypothesized
that examining the diﬀerences in the variables mentioned in
diﬀerent matches could provide a useful, practical report to
coaches and trainers in Montenegro. Therefore, this study aimed
to explore trends of running performance during the match in
professional soccer players in Montenegro in three competitive
matches of diﬀerent seasons.
Materials and methods
Participants
The research included a sample of 82 professional soccer
players. The ﬁrst subsample included 44 professional soccer
players of the soccer club Budu´cnost from Podgorica, height
185.89 ± 6.29 cm, mass 81.06 ± 5.47 kg, BMI 23.47 ± 0.96
kg/m², age 28.86 ± 3.85 yrs. The second subsample included
38 professional soccer players from the Sutjeska soccer club
from Nikši´c, height 181.88 ± 6.35 cm, mass 77.28 ± 6.78 kg,
BMI 23.32 ± 1.08 kg/m², age 29.43 ± 5.68 yrs. All soccer
players compete in the ﬁrst Telekom Montenegrin league,
the highest competitive rank in Montenegro. The study is
longitudinal in nature, and testing was done in three seasons:
2014/2015, 2016/2017, and 2019/2020, where derby matches
between Budu´cnost and Sutjeska were observed each season.
The criteria for inclusion were that the ﬁrst team’s players
had been team members for at least 6 months, that all the
players went through the preparation period with the team,
were without injuries in the previous 6 months, and that they
played one half-season before testing. Exclusion criteria were
athletes in the recovery phase from some form of acute or
chronic injury and athletes who did not complete the entire
preparation period. All respondents were ﬁrst informed about
the study and the purpose and goal of the research; the possible
consequences were explained to them. Also, the procedure and
the course of the testing itself were explained to the respondents.
Prior to the survey, each respondent signed a consent form to
participate. For this research, the consent and approval of the
head coach and the club president were obtained, and testing was
started. The research was in accordance with the Declaration of
Helsinki (13).
Study design
InStat Kinematic System—“Currently, various video-based
systems track performance indicators of soccer players (InStat,
Optasport, Wyscout). Such platforms quickly and accurately
provide a large range of match-related performance measures,
allowing the simultaneous analysis of the physical eﬀorts,
movement patterns, and technical actions of players, both with
and without the ball” (6). “The match performance indicators
for each player were determined by the position-speciﬁc InStat
system. The InStat tracking system was previously employed
to analyze the association between running performance and
game performance indicators in professional soccer players” (6).
“The InStat kinematic system captured the outﬁeld players using
six cameras placed around the perimeter of the ﬁeld at the
minimal height of 12 m. The frame frequency was 25 frames
per second; data were centralized for further analysis. InStat
Autocrop allows ﬁlming matches without a cameraman. The
footage covered every player on the ﬁeld. There is minimum
human involvement in the process; a person is only needed
to set up a panoramic camera at the required height, connect
it to a computer, and check the Internet connection before
the start of the match. An Autocrop camera is set at a height
of 8–10 meters and 23–24 meters away from the sideline. A
special algorithm allows the camera to cover the entire ﬁeld. The
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program analyzes every frame and centers the image depending
on the players’ positions, without any sudden zooming. The
following parameters of running performance were selected
to estimate the match performance of players: total distance
covered per match and during each half (m), the average speed
per match and during each half (km/h), maximal speed (km/h);
the total distance covered at high-intensity (m) (speed range
19.8–25.2 km/h) per match and for each half, the total distance
covered sprinting (m) (speed above 25.2 km/h) per match and
for each half, and the number of sprints. The speed thresholds
for each category are similar to those reported previously” (6)
and have been universally accepted.
Statistical analysis
All data collected by the survey were processed using
descriptive and comparative statistics. Regarding descriptive
statistics,
mean
and
standard
deviation
were
measured
for
each
variable.
Regarding
comparative
statistics,
a
discriminant
parametric
procedure
was
used:
analysis
of variance with one-factor Anova and Post Hoc, which
determined the diﬀerences in running performance every
year
separately.
The
statistical
program
for
personal
computers SPSS for Windows version 20.0 was used for
data processing.
TABLE 1 Descriptive data of performance running.
Variables
Team
2015
2017
2020
F
Sig.
Mean ± SD
Mean ± SD
Mean ± SD
TD (m)
Budu´cnost
8.274 ± 3.87
8.041 ± 3.40
9.129 ± 2.46
0.760
0.541
Sutjeska
9.441 ± 3.11
7.081 ± 3.12
7.019 ± 3.45
WD (m)
Budu´cnost
2.776 ± 1.23
2.899 ± 1.23
3.431 ± 0.85
0.004
0.996
Sutjeska
3.194 ± 0.99
2.995 ± 1.04
2.498 ± 1.28
JD (m)
Budu´cnost
3.436 ± 1.67
3.175 ± 1.39
3.589 ± 1.15
1.168
0.422
Sutjeska
3.841 ± 1.46
3.070 ± 1.37
2.795 ± 1.50
RD (m)
Budu´cnost
1.378 ± 0.71
1.281 ± 0.72
1.395 ± 0.53
1.585
0.339
Sutjeska
1.552 ± 0.72
1.283 ± 0.63
1.155 ± 0.64
HSRD(m)
Budu´cnost
719 ± 0.44
583 ± 0.30
617 ± 0.28
5.389
0.102
Sutjeska
794 ± 0.30
538 ± 0.23
461 ± 0.26
SD (m)
Budu´cnost
92.75 ± 93.2
437 ± 0.32†
119 ± 0.09
0.401
0.010
Sutjeska
105 ± 72.1
347 ± 0.23‡
66 ± 0.05
†2017 vs. 2015, 2020; ‡2017 vs. 2015, 2020; TD, total distance; WD, walk distance; JD, jog distance; RD, run distance; HSRD, high speed runs distance; SD, sprint distance.
FIGURE 1
Trend in mean total distance by years.
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FIGURE 2
Trend in mean walk distance by years.
Results
Table 1 shows the basic central and dispersion data on
running performance during in-game soccer players. Analyzing
the results in Table 1, it is evident that the players of both clubs
achieved identical results in running performance during the
game. Analyzing the derby match from 2020, it is evident that
the soccer players of Buducnost ran more (9,129 m) in relation
to the players of Sutjeska (7,019 m). Comparing the derbies from
2015 and 2017, it is clear that in the previous two derbies, the
players from Sutjeska ran a greater distance compared to the
derby from 2020, while the players from Buducnost ran the most
in the derby in 2020. Also, in the derby in 2017, the players of
both teams achieved a higher number of sprints compared to
the derbies in 2015 and 2020. Applying appropriate statistical
procedures, it was found that there are no statistically signiﬁcant
diﬀerences in running performance.
Trends in running performance during the game by year are
shown in the ﬁgures (Figures 1–6). Figure 1 shows the trend of
the total length of running during the match. Unlike the soccer
players of Buducnost, the soccer players of Sutjeska have a sharp
drop in the total length of running in 2017.
Figure 2 shows the trend of walking in the game. The
analysis of the graph shows that the number of meters spent
walking during the game varies from year to year. The soccer
players of Sutjeska reduced the trend of walking, while the soccer
players of Buducnost increased the trend of walking during
the game. Unlike Figure 2, which shows a walk during the
game, Figure 3 shows the total jog distance of the course during
the game. Inspecting Figure 3 shows that the soccer players of
Buducnost have a continuous trend of jogging, while the soccer
players of Sutjeska have a trend of declining jogging in all 3 years.
Figure 4 shows the downward trend in the running among
Buducnost soccer players in all 3 years. The Sutjeska soccer
players have seen a downward trend in all 3 years.
Figure 5 shows the high-speed running distance for the
soccer players of Buducnost and Sutjeska. Looking at Figure 5,
it is evident that the players of both clubs have a downward
trend in the most important zone for success in top soccer
with one characteristic that the players of Buducnost have a
minimal increase in 2020 compared to 2017, while the players
of Sutjeska have a declining trend throughout the analyzed
period. In contrast, Figure 6, which provides an insight into
sprint distance, shows an increase in the number of sprints at
both clubs in 2017, where the players of the Buducnost made a
larger number of sprints, while in 2020 there is a decline and
return to identical values as in 2015.
Discussion
The practical value of such analyses is that well-chosen
performance indicators can help coaches identify the good and
bad performance of individuals or teams. In this regard, match
analyses help identify the physiological requirements of the
sport and in examining how a particular player compares to
the requirements of their event. Understanding the physiological
load imposed on top players in accordance with their positional
role during competitive matches (activity proﬁle, distance
traveled, intensity, energy systems, and muscles involved) is
necessary when developing a sport-speciﬁc training protocol.
Especially with elite athletes, the most important form of
training is the one that corresponds to the use of energy
and biomechanics of the planned competitive eﬀect. Therefore,
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FIGURE 3
Trend in mean jog distance by years.
FIGURE 4
Trend in mean run distance by years.
match analyses are helpful for the development of a speciﬁc
training program that mimics the physiological conditions
imposed by the game. Elite sports performances in soccer are
a composite of the elite characteristics of physical performance,
which in turn depend on several physiological characteristics,
as well as on the training and health status of the individual
athlete (14).
The current study aimed to analyze trends of running
performance in professional soccer players in Montenegro in
three competitive matches of diﬀerent seasons. During the
game, soccer players perform diﬀerent types of movement,
ranging from resting to running at maximum speed, the
intensity of which can change at any time. The distance covered
during the match with elite soccer players is in the range
of 10,000–12,000 m (15). The results of this study indicate
that the trend component for the variable total distance
is on an upward trajectory for Buducnost soccer players,
ranging from 8,274 m in 2015 to 9,129 m in 2020, while for
Sutjeska soccer players, there is a declining trend component
of 9,441 m in 2015 to 7,019 m in 2020. Di Salvo et al. (14)
recorded an average distance of 11,393 m for players competing
in the Spanish Premier League in the 2003/2004 season.
Osgnach et al. (16) recorded an average distance of 10,950 m
for soccer players competing in the Italian Serie A in the
2007/2008 season. Comparing the stated results with the current
research, it is evident that the soccer players who compete
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FIGURE 5
Trend in mean high speed runs distance by years.
FIGURE 6
Trend in mean sprint distance by years.
in Montenegro are below the values achieved by those who
compete in Europe.
In the current study, the distances covered were categorized
into ﬁve levels of intensity. The trend component in the walking
distance variable for Buducnost players ranges from 2,776 m in
2015 to 3,431 m in 2020, while for Sutjeska players, there is a
trend component of declining walking during the game from
3,194 m in 2015 to 2,498 m in 2020. In the variables jog distance
and run distance, there is a continuous trend component
without large oscillations in the players of both clubs. Withers
et al. (17) state that 26.3% of the total game time falls on the
intensity up to 14 km/h, 64.6% on the running intensity of
14.1–19 km/h, and 18.9% on the intensity of 19.1–23> km/h.
Mayhew and Wenger (18) established that a soccer player walks
46.6%, runs slowly 38%, runs quickly or sprints 11.3%, and
stands without moving 2.3% of the total playing time of a
game. During a match, soccer players perform diﬀerent types of
behavior, ranging from standing still to maximum speed runs,
the intensity of which may change at any given time. However,
intensity parameters are not precisely deﬁned in these papers.
“Soccer is a non-cyclical and intermittent sport in which
short-duration maximum-intensity activities, for example,
sprint runs over a distance of 10–20 m, and high-intensity
actions, such as counterattacks, are intertwined with activities
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of low and moderate intensity (marching and jogging) and with
pauses, for example, standing. Sprinting is one of the most
important activities in soccer, although it merely constitutes
between 1 and 12% of the mean total distance covered by a
player during a match, that is, from only 0.5–3% of playing time.
During a competitive game, players perform 2- to 4-s long-sprint
runs every 90–180 s on average. It is assumed that players
of higher ability cover longer sprinting distances with higher
intensity” (19). The results of our study indicate that there is
a downward trend in the most important zone for success in
top soccer (high-speed runs distance), with one characteristic
that the players of Buducnost have a minimal increase in 2020
compared to 2017, while Sutjeska players have a noticeable
declining trend throughout the analyzed period. In contrast, an
increase in the number of sprints at both clubs was recorded in
2017, while in 2020, there is a decline and return to identical
values as in 2015. “The amount of high-speed running is
what distinguishes top-class players from those at a lower
level. Computerized time-motion analysis has demonstrated
that international top-class players perform 28% more high-
intensity running (2.43 vs. 1.90 km) and 58% more sprinting
(650 vs. 410 m) than professional players at a lower level”
(20). Furthermore, Ingebrigtsen et al. (21) “found that top
teams in the Danish League covered 30–40% more high-speed
running distance compared to the middle and bottom teams.”
In contrast, Di Salvo et al. (22) “observed that Championship
players did more high-speed running and sprinting than players
in the Premier League, even though the diﬀerences were small.
Along the same lines, a study comparing the match performance
of players in the top three competitive standards of English
soccer found that players in the second (Championship) and
third (League 1) categories performed more high-speed running
(>19 km/h) than those in the Premier League (803, 881, and
681 m, respectively), which was also the case for sprinting (308,
360, and 248 m, respectively)” (23).
From the physiological aspect, the results of our study can
be explained by the following fact: “During repetitive speed
exercises, the contribution of phosphocreatine hydrolysis to the
meeting of energy the demand of working muscles increases
after each loading. The cool-down phase duration depends not
only on the stimulation of the central nervous system but also on
the rate of recovery of the autonomic nervous system functions
related to the payoﬀ of oxygen debt run up during physical
exercise and on the rate of phosphocreatine resynthesis” (19).
In contrast, soccer players perform signiﬁcantly less
high-intensity activities when they win than when they lose or
when the result is a draw. Also, if the players score a goal in the
early phase of the match, they do not use the maximum of their
capacities during the match. Since winning is a pleasant situation
for the team, it is possible that the players have set a strategy of
keeping the ball, which results in fewer sprints (24).
The limitations of this study are that only two soccer clubs
from the ﬁrst Telekom Montenegrin league were analyzed.
Nevertheless, these two clubs are the most trophy-winning in
the Montenegrin league, so they are included in the analysis.
Future studies are recommended to enlarge the database. Such
studies might be more suitable for detecting evolutionary trends
in match-related variables.
Conclusions
The conclusion of this study provided information on
performance in Montenegrin soccer, which could consequently
improve the applicability of running performance in training
and competitions. Based on the obtained results, the coaches
will be advised in which direction the training process should
go in order to increase the performance of Montenegrin elite
soccer players.
Data availability statement
The original contributions presented in the study are
included in the article/supplementary material, further inquiries
can be directed to the corresponding author.
Ethics statement
Ethical review and approval was not required for the study
of human participants in accordance with the local legislation
and institutional requirements. Written informed consent was
obtained from the participants.
Author contributions
MJ formulated the research goals and aims, developed and
designed the methodology, prepared the published work, and
speciﬁcally wrote the initial draft. KG, JP, RH, and MJ prepared
the published work, speciﬁcally with critical reviews, editing, and
revisions. All authors commented on the draft and contributed
to the ﬁnal version, approved the publication of the manuscript,
and agreed to be accountable for all aspects of the work.
Conﬂict of interest
The authors declare that the research was conducted in the
absence of any commercial or ﬁnancial relationships that could
be construed as a potential conﬂict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their aﬃliated
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organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
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Exploring trends of running performance during matches of professional soccer players in Montenegro: A longitudinal study.
Goranović, Kosta,Hadžić, Rašid,Petković, Jovica,Joksimović, Marko