International  Journal  of
Environmental Research
and Public Health
Article
Analysis of the Association between Running
Performance and Game Performance Indicators in
Professional Soccer Players
Toni Modric 1,2, Sime Versic 1,2, Damir Sekulic 1,*
and Silvester Liposek 3,*
1
Faculty of Kinesiology, University of Split, 21000 Split, Croatia; toni.modric@yahoo.com (T.M.);
simeversic@gmail.com (S.V.)
2
HNK Hajduk Split, 21000 Split, Croatia
3
University of Maribor, 2000 Maribor, Slovenia
*
Correspondence: dado@kifst.hr (D.S.); silvester.liposek@um.si (S.L.); Tel.: +385-21-302-440 (D.S.)
Received: 2 October 2019; Accepted: 18 October 2019; Published: 21 October 2019


Abstract:
Running performance (RP) and game performance indicators (GPI) are important
determinants of success in soccer (football), but there is an evident lack of knowledge about
the possible associations between RP and GPI. This study aimed to identify associations between RP
and GPI in professional soccer players and to compare RP and GPI among soccer playing positions.
One hundred one match performances were observed over the course of half of a season at the
highest level of national competition in Croatia. Players (mean ± SD, age: 23.85 ± 2.88 years; body
height: 183.05 ± 8.88 cm; body mass: 78.69 ± 7.17 kg) were classiﬁed into ﬁve playing positions
(central defenders (n = 26), full-backs (n = 24), central midﬁelders (n = 33), wide midﬁelders (n = 10),
and forwards (n = 8). RP, as measured by global positioning system, included the total distance
covered, distance covered in ﬁve speed categories (walking, jogging, running, high-speed running,
and maximal sprinting), total number of accelerations, number of high-intensity accelerations, total
number of decelerations, and number of high-intensity decelerations. The GPI were collected by the
position-speciﬁc performance statistics index (InStat index). The average total distance was 10,298.4
± 928.7 m, with central defenders having the shortest and central midﬁelders having the greatest
covered distances. The running (r = 0.419, p = 0.03) and high-intensity accelerations (r = 0.493,
p = 0.01) were correlated with the InStat index for central defenders. The number of decelerations
of full-backs (r = −0.43, p = 0.04) and the distance covered during sprinting of forwards (r = 0.80,
p = 0.02) were associated with their GPI obtained by InStat index. The speciﬁc correlations between
RP and GPI should be considered during the conditioning process in soccer. The soccer training
should follow the speciﬁc requirements of the playing positions established herein, which will allow
players to meet the game demands and to perform successfully.
Keywords: GPS; football; accelerations; decelerations; eﬃcacy
1. Introduction
Soccer is a highly complex team sport with changing dynamics and multistructural movements
played by two teams. Each team consists of 10 outﬁeld players and a goalkeeper and the ﬁnal game
achievement depends directly on the performance of all 11 players [1,2]. Therefore, performance
analysis is crucial in the evaluation of players’ achievement [3]. The global popularity of soccer has led
to the implementation of scientiﬁc and technological knowledge in everyday use, and this is particularly
evident within the ﬁeld of performance analysis. One of the important aspects of performance analysis
is termed “running performance”, which is nowadays mostly evidenced by global positioning software
systems (GPS) [4].
Int. J. Environ. Res. Public Health 2019, 16, 4032; doi:10.3390/ijerph16204032
www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2019, 16, 4032
2 of 13
GPS technology is known to be highly applicable in evaluation of mobility and physical activity
patterns within the ﬁeld of public health [5–7]. With the improvement of their accuracy/precision,
design, usability and safeness (Figure 1), the GPS-based devices are becoming prevalent even in
competitive sports, including soccer [8,9].
Figure 1. Global positioning device (GPS) used for the measurement of running performances in soccer.
Speciﬁcally, GPS allows collecting data about players’ running performance, such as the total
distance covered, the distance covered at diﬀerent intensities (i.e., speeds), and the number of
accelerations and decelerations. Studies conducted so far have provided playing-position-speciﬁc
evidence with regard to running at diﬀerent intensities, with midﬁelders covering the largest total
distance and wingers performing the most high-intensive sprints [10]. Furthermore, match running
performance in Brazilian professional soccer players indicated that winning teams, home playing
teams and teams that play against “weaker” opponents had the greatest total distance covered [11].
A study performed with <21- and <18-year old soccer players found that a 3–5–2 formation elicited
the highest total distance, with a 4–2–3–1 formation eliciting the highest number of accelerations
and decelerations [12]. The results of the previously cited studies that used GPS technology as
a measurement tool were generally consistent with those from investigations where authors used
diﬀerent video-based computerized match analysis systems in the evaluation of players’ running
performances [13–17].
Game performance indicators are another set of variables that are used in performance analysis
in soccer. Basically, game performance indicators are deﬁned as a “selection and combination of
variables that deﬁne some aspect of performance and that help achieve athletic success” [18]. The most
frequently used game performance indicators are passes, shots, crosses, dribbles, challenges etc. [19].
Currently, numerous video-based platforms that track performance indicators of soccer players are
available (InStat, Optasport, Wyscout). Such platforms quickly and accurately provide a large range of
data about game performance indicators, allowing the simultaneous analysis of the physical eﬀorts,
movement patterns, and technical actions of players, both with and without the ball [20–22].
Previous studies conducted in the ﬁeld of performance analysis in soccer found that both the
physical (i.e., total distance covered, high-intensity running, accelerations and decelerations) and
technical–tactical performances (i.e., shots, crosses, challenges, and dribbles) of players were correlated
with speciﬁc conditions such as match outcome (win/draw/loss), match location (home/away), type of
match (league/cup/friendly), and strength of the opponent team [19,23–28]. Situational variables, such as
ball possession, total shots, shots on target, crosses, dribbles, clearances, challenges, and interceptions,
and their inﬂuence on technical–tactical parameters were mostly evaluated by the variation of counts
of technical match actions, which include shots, passing, tackles, aerial duels, and dribbles [18,27,29,30].
Brieﬂy, situational variables that discriminate among winning, drawing and losing were mostly those
related to ball possession and oﬀensive actions (e.g., total shots, shots on goal, and crosses) [29,30],
Int. J. Environ. Res. Public Health 2019, 16, 4032
3 of 13
while some studies found that indicators of defensive eﬃcacy (e.g., interceptions, clearance, and aerial
challenges) were the variables most related to the match outcome [27].
Although game performance indicators and running performance are often investigated separately,
to the best of our knowledge, there is no study that simultaneously observed both groups of performance
variables during oﬃcial soccer matches. Additionally, there is no information about the relationship that
may exist between these two groups of variables. Therefore, the aim of this study was to identify possible
associations that may exist between running performance and game performance in professional
soccer players. Additionally, running performance and standard soccer performance variables were
compared among playing positions. Authors were of the opinion that a study of this type would
allow a better understanding of the relationships that exist between running performance and game
performance indicators and that such understanding would therefore improve the applicability of both
sets of variables in soccer training and competition.
2. Materials and Methods
2.1. Participants and Design
The participants in this study were professional soccer players from Croatia (mean ± SD, age:
23.85 ± 2.88 years; body height: 183.05 ± 8.88 cm; body mass: 78.69 ± 7.17 kg), and all were members
of one team competing at the highest national. Players were observed over one competitive half
season, resulting in 101 match performances which were used as cases for this study. All data were
collected during 14 matches of the Croatian Soccer League 2018/2019 season, and for the purpose
of this study only the results of those players who participated in the whole game were analyzed.
Players were classiﬁed in ﬁve groups based on playing positions: central defenders (CD; n = 26),
full-backs (FB; n = 24), central midﬁelders (CM; n = 33), wide midﬁelders (WM; n = 10), and forwards
(FW; n = 8), as suggested previously [10]. Sociodemographic and anthropometric data of observed
players are presented in Table 1. In the observed half-season, the team played seven home and seven
guest matches, with three wins, eight draws and three losses. At the end of the observed half-season,
the team ranked 6th of 10 teams which competed in Croatian Soccer League. The investigation was
approved by Ethical Board of the University of Split, Faculty of Kinesiology, Split, Croatia (approval
number: 2181-205-02-05-19-0020).
Table 1. Sociodemographic and anthropometric characteristics of the studied players with diﬀerences
among playing positions (F-test).
Age (years)
Body height (cm)
Body mass (kg)
Mean ± SD
Mean ± SD
Mean ± SD
Total sample (n = 101)
23.85 ± 2.88
183.05 ± 6.88
78.69 ± 7.17
Central Defenders (n = 26)
23.25 ± 2.21
192.25 ± 5.61
87.27 ± 7.38
Full-Backs (n = 24)
23.2 ± 3.56
176.6 ± 3.36
73.4 ± 4.34
Central Midﬁelders (n = 33)
22.66 ± 2.73
175 ± 6.08
76.51 ± 5.02
Wide Midﬁelders (n = 10)
26.0 ± 1.0
183 ± 3.46
76.2 ± 4.17
Forwards (n = 8)
27.0 ± 2.82
181.5 ± 0.7
85 ± 4.1
F-test (p)
1.51 (0.24)
5.92 (0.01)
5.04 (0.01)
2.2. Procedures
The variables in this study were two sets of soccer performance variables (running performance
and game performance indicators) and the ﬁnal game outcome (observed as loss, draw, win).
Data on the running performance of the players were collected by GPS technology (Catapult S5
and X4 devices, Melbourne, Australia) with a sampling frequency of 10 Hz. Such device was already
investigated for metrics, and was found to be appropriately reliable and valid in sport settings (i.e., less
than 1% measurement error, and 80% of common variance with running speed measured by timing
gates) [31,32].
Int. J. Environ. Res. Public Health 2019, 16, 4032
4 of 13
The variables included the following:
total distance covered (m); distance in ﬁve speed
categories (walking (<7.1 km/h), jogging (7.2–14.3 km/h), running (14.4–19.7 km/h), high-speed running
(19.8–25.1 km/h), and maximal sprinting (>25.2 km/h)); number (frequency) of total accelerations
(>0.5 m/s2); number of high-intensity accelerations (>3 m/s2); number of total decelerations (less than
–0.5 m/s2) and number of high-intensity decelerations (less than –3 m/s2).
The game performance indicators for each player were determined by the position-speciﬁc InStat
index (InStat, Moscow, Russia). The InStat index is calculated on the basis of a unique set of key
parameters for each playing position (12–14 performance parameters, depending on the position
during the game), with a higher numerical value indicating better performance. The exact calculations
are trademarked and known only to the manufacturer of the platform. In most general terms, an
automatic algorithm considers the player’s contribution to the team’s success, the signiﬁcance of their
actions, opponent’s level and the level of the competition they play in (i.e., the same performance done
in European Champions League and some national-level ﬁrst division will not be rated with same
values). The rating is created automatically, and each parameter has a factor which changes depending
on the number of actions and events in the match. The weight of the action factors diﬀers depending
on the player’s position. For example, grave mistakes done by CD and their frequency aﬀect InStat
index to a greater extent than those done by FWD. The key factors included in the calculation of the
InStat index are position speciﬁc and include tackling, aerial duels, set pieces in defense, interceptions
(for CD); number of crosses, number of passes to the penalty area, pressing (for FB); playmaking,
number of key passes, ﬁnishing (for CM); pressing, dribbling, ﬁnishing, counterattacking (for WM);
shooting, ﬁnishing, pressing, dribbling (for FWD). In order to calculate the InStat Index, the player
has to spend a certain amount of time on the ﬁeld and perform a minimum number of actions, but in
this study this issue was solved simply by including only those players who played the whole game
(as explained in Section 2.1).
2.3. Statistics
The normality of the distributions was checked by the Kolmogorov–Smirnov test, and the data are
presented as the means ± standard deviations. The homoscedasticity of all variables was conﬁrmed by
Levene’s test. The statistical analyses were performed throughout several phases.
In the ﬁrst phase the data obtained by InStat index were associated with ﬁnal game outcome
by one-way analysis of variance (ANOVA). For this procedure the game outcomes (loss, draw, win)
were considered as the grouping (independent) variable, and diﬀerences were established for total
sample of players, and separately for each playing position. This allowed identiﬁcation of the validity
of the InStat index as an indicator of the ﬁnal game achievement for the total sample, and for the ﬁve
observed playing positions.
The second phase of data analysis comprised calculation of diﬀerences among playing positions
in running performance and InStat index. This was done by ANOVA with a consecutive Scheﬀe
post hoc test. Throughout these analyses the information of running performance speciﬁcs for each
playing position were obtained. Also, the analysis of diﬀerences in InStat allowed identiﬁcation of
the applicability of the InStat index for the analysis of game achievement for each playing position.
To evaluate the eﬀect sizes (ES), partial eta-squared values (η2) were presented (small ES: >0.02; medium
ES: >0.13; large ES: >0.26) [33].
In the third phase, the associations between running performance (obtained by GPS) and game
performance indicators (evaluated by InStat) were identiﬁed by calculating Pearson’s product moment
correlation coeﬃcients.
For all analyses, Statistica 13.0 (TIBCO Software Inc., Greenwood Village, CO, USA) was used,
and a p < 0.05 was applied.
Int. J. Environ. Res. Public Health 2019, 16, 4032
5 of 13
3. Results
The ANOVA indicated signiﬁcant (p < 0.05) association between the InStat index and match
outcome for the total sample (n = 101, F-test: 23.69, η2 = 0.30 (large E)), CD (n = 26, F-test: 3.89,
η2 = 0.24 (medium ES)), FB (n = 24, F-test: 4.98, η2 = 0.31 (large ES)) and CM (n = 33, F-test: 15.71,
η2 = 0.50 (large ES)). The InStat index was not signiﬁcantly associated with game outcomes for WM
(n = 10, F-test: 0.98, η2 = 0.21 (medium ES)), and FW (n = 8, F-test: 2.61, η2 = 0.52 (large ES)) (Figure 2).
Figure 2. InStat index in relation to the outcome of the match for total sample (Total) and diﬀerent
playing positions (CD, central defenders; FB, full-backs; CM, central midﬁelders; WM, wide midﬁelders;
FW, forwards); * indicates statistically signiﬁcant diﬀerences at p < 0.05 derived by analysis of variance
The descriptive parameters for running performances and InStat index in total sample, and for
each playing positions are presented in Table 2. Signiﬁcant ANOVA diﬀerences were found among
playing positions (p < 0.05) in all running performances, with large ES for diﬀerences in: (i) total
distance covered (η2 = 0.59); (ii) distance covered while jogging (η2 = 0.41); (iii) running (η2 = 0.62);
(iv) high-speed running (η2 = 0.53); (v) sprinting (η2 = 0.39); (vi) number of performed accelerations
(η2 = 0.27); (vii) number of decelerations (η2 = 0.45); (viii) number of high-intensity accelerations
(η2 > 0.30); and (ix) number of high-intensity decelerations (η2 = 0.41). Small ES was found for
diﬀerences in distance covered while walking (η2 = 0.11) (Table 3).
Speciﬁcally, CM covered the longest total distance (signiﬁcant post-hoc diﬀerences when compared
to all other playing positions), the longest distance in jogging (signiﬁcant post-hoc diﬀerences when
compared to all other playing positions), and the longest distance while running (signiﬁcantly diﬀerent
from CD and FB). WM covered the longest distance in high-speed running, and in sprinting (signiﬁcant
post-hoc diﬀerences to CD, CM, and FW). CD carried out the highest number of accelerations and
highest number of decelerations (signiﬁcantly diﬀerent from FW). Finally, FW carried out the highest
number of high-intensity accelerations (signiﬁcant post-hoc diﬀerences when compared to CD, FB, and
WM) and high-intensity decelerations (signiﬁcantly diﬀerent to WM) (Table 3).
The total running distance and high-intensity accelerations were correlated with the InStat index
for CD (r = 0.42 and r = 0.49, respectively). Furthermore, the number of decelerations was signiﬁcantly
correlated with the InStat in FB (r = –0.43), while distance covered during sprinting was correlated
with InStat index in FW (r = 0.80). In general, the running performances of players in central and wide
midﬁeld positions were not signiﬁcantly associated with the InStat index (Table 4).
Int. J. Environ. Res. Public Health 2019, 16, 4032
6 of 13
Table 2. Descriptive statistics for running performances and game performance indicator (InStat).
Variables
Total
Central Defenders
Full-Backs
Central Midﬁelders
Wide Midﬁelders
Forwards
Mean ± SD
Mean ± SD
Mean ± SD
Mean ± SD
Mean ± SD
Mean ± SD
Total distance (m)
10,298.4 ± 928.68
9313.5 ± 599.4
10,368 ± 612
11,155.1 ± 635.3
10,264.8 ± 275.2
9796.7 ± 703.7
Walking (m)
4220.57 ± 362.33
4076.6 ± 378.3
4297.9 ± 338.5
4258.5 ± 340.7
4074.8 ± 194.3
4482.1 ± 442.2
Jogging (m)
4092.94 ± 569.73
3859 ± 380.2
3975.4 ± 372.8
4599.7 ± 471.4
3761.2 ± 324.1
3530 ± 729.9
Running (m)
1363.27 ± 339.68
999.2 ± 197.7
1320.7 ± 236.1
1674.9 ± 226.1
1526.5 ± 117.4
1184.4 ± 207.9
High-speed running (m)
461.83 ± 160.15
288.2 ± 63.8
533.9 ± 134.1
492.7 ± 139.9
640.7 ± 105.4
458.7 ± 94.7
Sprinting (m)
155.89 ± 97.13
87.7 ± 59.9
236.6 ± 97.2
123.7 ± 69.5
260.6 ± 68.8
137.1 ± 46.9
Accelerations (count)
716.19 ± 73.15
743.5 ± 56.2
710 ± 66.2
733.4 ± 72.4
688 ± 34.2
610.1 ± 83.7
Decelerations (count)
674.44 ± 69.29
714.1 ± 51.5
672.4 ± 56
681.9 ± 55.8
661.8 ± 36.7
536.6 ± 69
High-intensity accelerations (count)
3.16 ± 2.67
2.5 ± 1.8
3.1 ± 1.7
1.9 ± 2.2
7 ± 2.6
6 ± 2.9
High-intensity decelerations (count)
11.39 ± 6.27
6.1 ± 2.8
13.1 ± 4.9
11.5 ± 5.9
20.8 ± 5.5
11 ± 3.1
InStat (index)
284.5 ± 31.04
247.4 ± 29.2
243 ± 28.7
254.1 ± 29.3
251.1 ± 32.1
242 ± 49.5
Table 3. Diﬀerences among playing positions for running performances and game performance indicator (InStat) determined by analysis of variance (ANOVA), with
Scheﬀe post-hoc test diﬀerences.
Variables
ANOVA
Eﬀect Size
Post hoc
F (p)
η2
Central Defenders
Full-Backs
Central Midﬁelders
Wide Midﬁelders
Forwards
Total distance (m)
35.02 (0.01)
0.59
FB, CM, WM
CD, CM
CD, FB, WM, FW
CD, CM
CM
Walking (m)
3.18 (0.02)
0.11
-
-
-
-
-
Jogging (m)
16.71 (0.01)
0.41
CM
CM
CD, FB, WM, FW
CM
CM
Running (m)
39.30 (0.01)
0.62
FB, CM, WM
CD, CM
CD, FB
CD, FW
CM
High-speed running (m)
29.30 (0.01)
0.53
FB, CM, WM, FW
CD
CD, WM
CD, CM, FW
CD, WM
Sprinting (m)
15.72 (0.01)
0.39
FB, WM
CD, CM, FW
FB, WM
CD, CM, FW
FB, WM
Accelerations (count)
9.06 (0.01)
0.27
FW
FW
FW
CD, CM, FW
Decelerations (count)
20.11 (0.01)
0.45
FW
FW
FW
FW
CD, FB, CM, WM
High-intensity accelerations (count)
8.53 (0.01)
0.30
WM, FW
WM, FW
WM, FW
CD, FB, CM
CD, FB, WM
High-intensity decelerations (count)
16.70 (0.01)
0.41
FB, CM, WM
CD, WM
CD, WM
CD, FB, CM, FW
WM
InStat (index)
0.64 (0.62)
0.03
-
-
-
-
-
Superscripted letters indicate signiﬁcant post-hoc diﬀerences when compared to speciﬁc playing position (CD, central defenders; FB, full-backs; CM, central midﬁelders; WM – wide
midﬁelders; FW, forwards).
Int. J. Environ. Res. Public Health 2019, 16, 4032
7 of 13
Table 4. Pearson’s product moment correlations between running performances and game performance
indicator (InStat) for diﬀerent playing positions.
Variables
Total
(n = 101)
Central
Defenders
(n = 26)
Full-Backs
(n = 24)
Central
Midﬁelders
(n = 33)
Wide
Midﬁelders
(n = 10)
Forwards
(n = 8)
Total distance
0.08
0.18
–0.04
–0.02
–0.17
0.01
Walking
−0.02
0.09
0.01
–0.12
0.07
0.04
Jogging
0.05
0.02
–0.10
0.05
–0.41
–0.05
Running
0.16
0.42 *
0.02
0.12
0.01
–0.13
High-speed running
0.02
–0.04
–0.06
–0.10
0.54
0.17
Sprinting
0.01
–0.24
0.17
–0.04
0.22
0.80 *
Accelerations
–0.01
0.12
–0.39
0.07
–0.24
–0.02
Decelerations
–0.09
0.07
–0.43 *
–0.05
–0.26
–0.33
High-intensity
accelerations
0.18
0.49 *
0.20
0.29
–0.08
0.26
High-intensity
decelerations
0.05
0.29
–0.04
–0.01
0.44
–0.18
* denotes statistical signiﬁcance of p < 0.05.
4. Discussion
With regard to study aims there are two most important ﬁndings. First, the total distance covered
and the intensity of running varied according to the diﬀerent playing positions. Second, running
performance parameters (e.g., the number of accelerations or decelerations and the distance covered
in diﬀerent speed zones) aﬀect successful performance in soccer for some playing positions. Prior to
discussion of these ﬁndings, an overview of the analyses done in order to evaluate the applicability
and validity of InStat index as a measure of ﬁnal match outcome will be provided.
Studies have already investigated the association between diﬀerent variables explaining situational
eﬃcacy (i.e., game performance indicators) and match outcomes. For example, when losing the game,
teams had more ball possession [30,34,35] and performed more crosses and dribbles [27]. Additionally,
when winning, the teams performed more interceptions, clearances and aerial challenges, fewer
passes and dribbles [27], and less high-intensity activities [18,34]. However, previous studies regularly
investigated the performance indicators of the whole team, while there has been limited research
investigating the position-speciﬁc performances in relation to game outcome, even though technical
indicators have been considered good predictors of soccer match success [36]. Also, the quality of
technical skills in real-game performance, which is actually obtained throughout the InStat index and
other similar platforms, has been included as a main component in soccer talent identiﬁcation and
development systems [37,38].
InStat index in soccer is based on wide range of team- and individual-statistics, which are linked
to the supporting video episodes. At the ﬁnal stage, the calculated index should be related to ﬁnal
game outcome, and consequently should be a valid measure of ﬁnal team achievement (i.e., game
outcome). Results of this study indicated signiﬁcant diﬀerences among game outcomes (loss, draw,
or win) in InStat index for the total sample and speciﬁcally for CD, FB and CM. Although the statistical
signiﬁcance of the F-test did not reach statistical signiﬁcance for WM and FW, this may be attributed to
small number of players in these groups (WM: 10 players, FW: 8 players) and consequent small number
of degrees of freedom [39]. Therefore, it might be said that the results presented here conﬁrmed the
validity of InStat in evaluation of ﬁnal game achievement in Croatian professional soccer. It is also
important to note that InStat index is speciﬁcally calculated for diﬀerent positions on the basis of
position-speciﬁc parameters (please see Section 2 for more details). Therefore, the lack of diﬀerences
among playing positions in InStat (please see Tables 2 and 3 for more details) indicates that this index
might be observed as an applicable measure of position-speciﬁc game performance in soccer.
Int. J. Environ. Res. Public Health 2019, 16, 4032
8 of 13
4.1. Running Performances and Diﬀerences Among Playing Positions
Considering the diﬀerent tactical roles of diﬀerent playing positions in soccer games, recent studies
conﬁrm that the distance covered during the match appears to be related to playing position [11,14,16,20].
Results of this study evidenced signiﬁcant diﬀerences in running performance among playing positions,
and such results are generally in agreement with previous studies that investigated these issues in the
English Premier League, the Spanish ﬁrst division, the Italian Serie A, the French League 1, and the
Brazilian ﬁrst division [13,14,16,20,40]. Speciﬁcally, analysis of the Brazilian ﬁrst division evidenced
that the total distance covered by FB, CM and WM was greater than that covered by CD and FW [13].
Supporting this, the lowest total distance was found for CD (9313 m on average). At the same time,
CM covered signiﬁcantly more distance than players in all other positions (11,155 m, on average),
which is known to be related to speciﬁc playing duties (i.e., CM are responsible for the connection
between defense and attack, and such tactical roles require them to achieve greater distances) [14,16].
Previous studies performed indicated 10.7 km as the average total distance covered in Spanish
and English top divisions [10,40,41]. Meanwhile players observed herein covered total distance of
10.3 km in average. Therefore, it seems that the total distance covered is not the factor that distinguishes
Croatian players from those playing in elite European divisions. On the other hand, there is an evident
diﬀerence in the intensity of running. More precisely, top-level European soccer players cover 10% of
the total distance at a high intensity, which includes high-speed running and maximal sprinting [17,42].
Meanwhile, here presented results indicated that Croatian players perform 6.4% of the total distance
covered at a high-intensity running pace.
It is generally accepted that low-intensity activities, such as walking and jogging, are not crucial
in elite soccer performance [43]. However, knowledge of these indicators is important to properly
understand the position-speciﬁc demands. Thus, considering the percentage of the total distance,
the most time spent walking and jogging is observed in CD (an average of 85.2% (7935 m) of their total
distance covered (9313 m)). On the other hand, the least time spent walking and jogging is observed in
WF (76.3%), followed by CM (79.4%), FB (79.8%) and FW (81.8%). Collectively, these ﬁndings support
previous considerations that Croatian ﬁrst division players generally play at a lower game pace when
compared to elite European national division players, who spend a much lower percentage of time in
low-intensity activities (from 74.9% to 79.6% of total distance) [10].
The distance covered while jogging among CM is signiﬁcantly higher than for any other
position. As mentioned before, CM had the greatest total distance, which is directly inﬂuenced
by the distance covered while jogging. Furthermore, CM have the greatest distances in the “running
zones” (e.g., 14.4–19.7 km/h). Therefore, results support the ﬁndings from previous studies in which
authors reported similar ﬁgures and concluded that the physical performance of CM is characterized by
covering a high overall distance, especially at moderate to high speeds such as jogging and running [10].
High-intensity activities are usually deﬁned as all activities with running speeds of 19.8 km/h
and above, and the distance covered at high intensities has been traditionally identiﬁed as a key
performance indicator of physical match performance [44] and one of the crucial elements of success
in soccer [43]. The results showed that the greatest amount of high-intensity running (high-speed
running + sprint running) is covered by WM, while the CD have the lowest values for these indicators.
This is consistent with previous investigations in which authors reported similar results for the English
Premier League and the Spanish ﬁrst division [10,14,20,40].
It is known that outside players (e.g., WM and FB) perform signiﬁcantly more sprints than
players in central playing positions [14]. Supporting this, our results showed that the greatest sprint
distance was covered by WM and FB. However, despite similar diﬀerences among playing positions
between our study and previous studies, values of high-intensity running in Croatian players were
evidently lower than those from the best European national competitions [10,40]. More speciﬁcally,
the mean high-intensity distance covered among all playing positions in the English Premier League
was 936 m, in the Spanish ﬁrst division an average of 821 m was reported, while the average value for
high-intensity running in Croatian players was 652 m.
Int. J. Environ. Res. Public Health 2019, 16, 4032
9 of 13
The highest number of accelerations and decelerations was found for CD and the lowest for FW,
which is consistent with some similar studies on friendly matches in the Spanish ﬁrst division [10].
Speciﬁcally, one of the most important tactical roles of FW is to keep the ball in possession in the
central position, so it is expected that FWs do not cover a large distance. On the other hand, CD
must be constantly prepared for defensive reactions. While trying to ﬁnd appropriate positioning,
they frequently change running directions, but also the type of running (i.e., frontal running to make
a defensive line to catch opposing players in oﬀsides and lateral shuﬄes to obtain better positions
versus FW). This certainly results in a high number of accelerations and decelerations for CD. However,
the kind of accelerometer unit and the way that the data are mathematically treated could have
a signiﬁcant eﬀect on the calculation of accelerations and decelerations, which actually limits the
comparability between diﬀerent studies [10]. Speciﬁcally, while the capacity to accelerate and decelerate
plays a critical role in elite soccer, as it represents high energy demanding activities, the determination
of accelerations might still have unresolved methodological issues [10].
4.2. Associations between Game Performance Indicators and Running Performances— Playing Position
Approach
The results suggest that CD covered the shortest distance while running out of all playing
positions, and this is in agreement with previous reports where authors found that CD exert the fewest
high-intensity eﬀorts compared to all other playing positions [10,14,20,40]. This is understandable
knowing that their technical roles (i.e., aerial duels, tackles, positioning, and interceptions of the balls
passed to the attackers) are generally more focused on the reactions or accelerations and then on
high-speed running. As a result, most of their high-intensity eﬀorts are performed in the zone of
running (14.4–19.7 km/h) simply because they do not have many opportunities to develop running
speeds above the high-intensity zone threshold (>19.8 km/h). However, because of the positive
correlation between the InStat index and the distance covered while running (14.4–19.7 km/h) for
CD, running should be considered an important determinant that aﬀects success for this position.
Furthermore, a positive correlation between the numbers of high-intensity accelerations and the InStat
index among CD shows that a greater number of high-intensity accelerations directly aﬀects real game
performance for this playing position. Speciﬁcally, stepping out to the duels and putting pressure
on opponent players are two of the most important tactical roles of CD. If performed rapidly and
aggressively (in other words, with a high acceleration), the chances of winning a duel increase, which
consequently has positive repercussions on ﬁnal match achievement as well.
The total number of decelerations was inversely associated with the InStat index among FB,
meaning that a higher number of decelerations negatively aﬀected real game eﬃcacy for that playing
position. Although FB are basically defensive players and their starting tactical line-up is in the ﬁrst
third of the pitch, the main technical requirements for FB are the number of entries to the third part of
the pitch (i.e., pressing) and the number of crosses [38,45]. These duties are actually performed on the
opponent’s half. Therefore, some of the most important tactical roles of the FB are actually in attacking.
To create more of these activities, FB frequently have to move away from the starting tactical line-up,
which actually enables them to make crosses and press. Consequently, if FB have a higher number
of stoppings (i.e., decelerations), it probably negatively aﬀects their ability to participate in attacking
actions and to perform crosses and entries to the third part of the pitch. Collectively, it seems that
soccer success of FB is more aﬀected by their attacking activities, regardless of the fact that they are
defensive players.
Previously, it was highlighted that FW had evidently shorter sprinting distances than players in
the same position during games from other European competitions (please see previous discussion
for details). However, results indicated a strong correlation between the InStat index and the sprint
distance for this playing position, which led to conclusion that the sprint distance covered during
the game was a highly important determinant of overall game performance for FW. Indeed, FW are
positioned close to the opponent’s goal, and almost every sprint presents the opportunity to perform
Int. J. Environ. Res. Public Health 2019, 16, 4032
10 of 13
attacking actions. In addition, FW have the lowest number of tackles, interceptions and clearances
compared with other playing positions [38], which suggests that most of their activities are focused on
attacking. With the higher number of attacking actions, there is a growing chance to enter the penalty
area, shoot, and score. As a result, the number of attacking situations increases the likelihood for
positive game outcomes [28–30].
The main role of CM is to organize the oﬀense by proper ball control and passes, rather than
by invasion into the opponent’s area [38]. Considering the lack of signiﬁcant association between
running parameter and the InStat index for this playing position, it seems that CM soccer success is
more inﬂuenced by some variables, other than those obtained by GPS, such as ball possession, number
of key passes, dribbles, and shots. Also, the running indicators obtained by GPS measurements were
not correlated to InStat variables in WM, which may be observed as surprising since WM experience
the greatest physical requirements during the game, both in terms of total distance covered and
high-intensity running [10]. The possible explanation may be the previously discussed ﬁnding of the
small amount of overall distance covered in the studied Croatian players, which actually resulted in
truncated variance and consequently statistically/mathematically decreased the possibility of achieving
signiﬁcant correlations.
4.3. Limitations and Strengths
The main limitation comes from the fact that this study observed only one team which was
observed during one half season. Therefore, some speciﬁc covariates (limited number of observed
players, strength of the opponent, speciﬁc tactical requirements) may inﬂuence reported results. Next,
in this study no data were collected about psycho-physiological responses of the players (e.g., heart rate
and RPE), which are known to be important determinants of overall performance. Further, this study
actually studied relatively simple “game-related outcomes” (i.e., running performances obtained by
GPS and game performance indicators obtained by InStat index), while sport-performances, especially
those in team sports, are far more complex (i.e., include interaction, cooperation, and opposition) [46].
Also, in this study relatively simple methodology was applied, while complex systems like sport games
may ask for more detailed experimental approaches and the use of mixed methods as an observational
methodology [47].
On the other hand, this study has several strengths. First, this is one of the ﬁrst studies which
simultaneously evaluated two sets of performance variables (i.e., running performances and game
performance indicators) and probably the ﬁrst one where associations between these two groups of
performances were analyzed. Also, the data were collected during oﬃcial games, among professional
players, and at the highest national competitive level. Therefore, results are generalizable to similar
samples of participants and levels of competition. Furthermore, the position-speciﬁc approach in
identiﬁcation of the relationships between running performances and game performance indicators is
important strength of the investigation. Therefore, despite the evident limitations, the authors believe
that this study may contribute to the knowledge on this ﬁeld and initiate further research.
5. Conclusions
The total distance covered during the match did not distinguish Croatian ﬁrst division players
from players who compete in elite European divisions (i.e., Spain and Germany). However, the players
studied here achieved total distances at lower running speeds than their peers involved in top-level
European competitions, which clearly indicates the lower game pace in Croatian soccer competition.
These ﬁndings can be useful for determining the physical requirements and proﬁles of the players
in the Croatian ﬁrst division, especially with regard to international competitions (i.e., the European
League, Champions League).
This study conﬁrmed the association between the running performance of players involved in
certain playing positions and overall game performance. Speciﬁcally, it seems that CD distance in
Int. J. Environ. Res. Public Health 2019, 16, 4032
11 of 13
the running zone and number of high-intensity accelerations, FB number of decelerations, and FW
sprinting distance are crucial physical requirements of team success.
Training prescriptions in soccer should be based on established requirements speciﬁc to the
playing positions, thereby ensuring that players are more able to fulﬁll their game duties and tactical
responsibilities over the soccer match. In further studies it would be important to identify possible
associations that might exist between diﬀerent parameters of players’ conditioning status and indicators
of real game performance.
Author Contributions: Conceptualization: T.M.; Data curation: T.M. and S.V.; Formal analysis: T.M.; Funding
acquisition: T.M.; Investigation: T.M., S.V. and S.L.; Methodology: D.S.; Project administration: D.S.; Supervision:
D.S.; Validation: S.V.
Funding: This research received no external funding.
Acknowledgments: Authors are particularly grateful to all players who volunteered to participate in the research.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
References
1.
Leontijevi´c, B.; Jankovi´c, A.; Tomi´c, L. Attacking performance proﬁle of football teams in diﬀerent national
leagues according to uefa rankings for club competitions. Facta Univ. Ser. Phys. Educ. Sport 2019, 697–708.
[CrossRef]
2.
Kubayi, A.; Toriola, A. Physical demands analysis of soccer players during the extra-time periods of the
UEFA Euro 2016. South Afr. J. Sports Med. 2018, 30, 1–3. [CrossRef]
3.
Carling, C.; Williams, A.M.; Reilly, T. Handbook of Soccer Match Analysis: A Systematic Approach to Improving
Performance; Routledge: London, UK, 2007.
4.
Ehrmann, F.E.; Duncan, C.S.; Sindhusake, D.; Franzsen, W.N.; Greene, D.A. GPS and injury prevention in
professional soccer. J. Strength Cond. Res. 2016, 30, 360–367. [CrossRef] [PubMed]
5.
Sanchez, M.; Ambros, A.; Salmon, M.; Bhogadi, S.; Wilson, R.T.; Kinra, S.; Marshall, J.D.; Tonne, C. Predictors
of Daily Mobility of Adults in Peri-Urban South India. Int. J. Environ. Res. Public Health 2017, 14. [CrossRef]
[PubMed]
6.
Tandon, P.S.; Saelens, B.E.; Zhou, C.; Christakis, D.A. A Comparison of Preschoolers' Physical Activity
Indoors versus Outdoors at Child Care. Int. J. Environ. Res. Public Health 2018, 15. [CrossRef] [PubMed]
7.
Mennis, J.; Mason, M.; Coﬀman, D.L.; Henry, K. Geographic Imputation of Missing Activity Space Data
from Ecological Momentary Assessment (EMA) GPS Positions. Int. J. Environ. Res. Public Health 2018, 15.
[CrossRef]
8.
Canton, A.; Torrents, C.; Ric, A.; Goncalves, B.; Sampaio, J.; Hristovski, R. Eﬀects of Temporary Numerical
Imbalances on Collective Exploratory Behavior of Young and Professional Football Players. Front. Psychol.
2019, 10, 1968. [CrossRef]
9.
Sampaio, J.; Macas, V. Measuring tactical behaviour in football. Int. J. Sports Med. 2012, 33, 395–401.
[CrossRef]
10.
Mallo, J.; Mena, E.; Nevado, F.; Paredes, V. Physical demands of top-class soccer friendly matches in relation
to a playing position using global positioning system technology. J. Hum. Kinet. 2015, 47, 179–188. [CrossRef]
11.
Aquino, R.; Carling, C.; Palucci Vieira, L.; Martins, G.; Jabor, G.; Machado, J.; Puggina, E. Inﬂuence of
situational variables, team formation, and playing position on match running performance and social
network analysis in brazilian professional soccer players. J. Strength Cond. Res. 2018. [Epub ahead of print].
[CrossRef]
12.
Tierney, P.J.; Young, A.; Clarke, N.D.; Duncan, M.J. Match play demands of 11 versus 11 professional football
using Global Positioning System tracking: Variations across common playing formations. Hum. Mov. Sci.
2016, 49, 1–8. [CrossRef] [PubMed]
13.
Barros, R.M.; Misuta, M.S.; Menezes, R.P.; Figueroa, P.J.; Moura, F.A.; Cunha, S.A.; Anido, R.; Leite, N.J.
Analysis of the distances covered by ﬁrst division Brazilian soccer players obtained with an automatic
tracking method. J. Sports Sci. Med. 2007, 6, 233–242. [PubMed]
14.
Di Salvo, V.; Baron, R.; Tschan, H.; Montero, F.C.; Bachl, N.; Pigozzi, F. Performance characteristics according
to playing position in elite soccer. Int. J. Sports Med. 2007, 28, 222–227. [CrossRef] [PubMed]
Int. J. Environ. Res. Public Health 2019, 16, 4032
12 of 13
15.
Gregson, W.; Drust, B.; Atkinson, G.; Salvo, V. Match-to-match variability of high-speed activities in premier
league soccer. Int. J. Sports Med. 2010, 31, 237–242. [CrossRef] [PubMed]
16.
Vigne, G.; Gaudino, C.; Rogowski, I.; Alloatti, G.; Hautier, C. Activity proﬁle in elite Italian soccer team. Int. J.
Sports Med. 2010, 31, 304–310. [CrossRef] [PubMed]
17.
Carling, C. Inﬂuence of opposition team formation on physical and skill-related performance in a professional
soccer team. Eur. J. Sport Sci. 2011, 11, 155–164. [CrossRef]
18.
Lago-Peñas, C.; Lago-Ballesteros, J. Game location and team quality eﬀects on performance proﬁles in
professional soccer. J. Sports Sci. Med. 2011, 10, 465–471.
19.
Sarmento, H.; Marcelino, R.; Anguera, M.T.; CampaniÇo, J.; Matos, N.; LeitÃo, J.C. Match analysis in football:
A systematic review. J. Sports Sci. 2014, 32, 1831–1843. [CrossRef]
20.
Dellal, A.; Chamari, K.; Wong, d.P.; Ahmaidi, S.; Keller, D.; Barros, R.; Bisciotti, G.N.; Carling, C. Comparison
of physical and technical performance in European soccer match-play: FA Premier League and La Liga.
Eur. J. Sport Sci. 2011, 11, 51–59. [CrossRef]
21.
Carling, C.; Bloomﬁeld, J.; NELSON, L.; Reilly, T. The role of motion analysis in elite soccer: Contemporary
performance measurement techniques and work rate data. Sports Med. 2012, 38, 389. [CrossRef]
22.
Drust, B.; Atkinson, G.; Reilly, T. Future perspectives in the evaluation of the physiological demands of
soccer. Sports Med. 2007, 37, 783–805. [CrossRef] [PubMed]
23.
Lago-Peñas, C. The role of situational variables in analysing physical performance in soccer. J. Hum. Kinet.
2012, 35, 89–95. [CrossRef] [PubMed]
24.
Moreno, E.; Gómez, M.A.; Lago, C.; Sampaio, J. Eﬀects of starting quarter score, game location, and quality
of opposition in quarter score in elite women’s basketball. Kinesiology 2013, 45, 48–54.
25.
Mackenzie, R.; Cushion, C. Performance analysis in football: A critical review and implications for future
research. J. Sports Sci. 2013, 31, 639–676. [CrossRef] [PubMed]
26.
Taylor, B.J.; Mellalieu, D.S.; James, N.; Barter, P. Situation variable eﬀects and tactical performance in
professional association football. Int. J. Perform. Anal. Sport 2010, 10, 255–269. [CrossRef]
27.
Taylor, J.B.; Mellalieu, S.D.; James, N.; Shearer, D.A. The inﬂuence of match location, quality of opposition,
and match status on technical performance in professional association football. J. Sports Sci. 2008, 26, 885–895.
[CrossRef] [PubMed]
28.
Liu, H.; Yi, Q.; Giménez, J.-V.; Gómez, M.-A.; Lago-Peñas, C. Performance proﬁles of football teams in the
UEFA Champions League considering situational eﬃciency. Int. J. Perform. Anal. Sport 2015, 15, 371–390.
[CrossRef]
29.
Castellano, J.; Casamichana, D.; Lago, C. The use of match statistics that discriminate between successful
and unsuccessful soccer teams. J. Hum. Kinet. 2012, 31, 137–147. [CrossRef]
30.
Lago-Peñas, C.; Lago-Ballesteros, J.; Dellal, A.; Gómez, M. Game-related statistics that discriminated winning,
drawing and losing teams from the Spanish soccer league. J. Sports Sci. Med. 2010, 9, 288–293.
31.
Johnston, R.J.; Watsford, M.L.; Kelly, S.J.; Pine, M.J.; Spurrs, R.W. Validity and interunit reliability of 10 Hz
and 15 Hz GPS units for assessing athlete movement demands. J. Strength Cond. Res. 2014, 28, 1649–1655.
[CrossRef]
32.
Castellano, J.; Casamichana, D.; Calleja-Gonzalez, J.; Roman, J.S.; Ostojic, S.M. Reliability and Accuracy of
10 Hz GPS Devices for Short-Distance Exercise. J. Sports Sci. Med. 2011, 10, 233–234. [PubMed]
33.
Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Lawrence Erlbaum Assosiates: New York, NY,
USA, 1988.
34.
Lago, C. The inﬂuence of match location, quality of opposition, and match status on possession strategies in
professional association football. J. Sports Sci. 2009, 27, 1463–1469. [CrossRef]
35.
Lago, C.; Martín, R. Determinants of possession of the ball in soccer. J. Sports Sci. 2007, 25, 969–974. [CrossRef]
[PubMed]
36.
Lago-Peñas, C.; Lago-Ballesteros, J.; Rey, E. Diﬀerences in performance indicators between winning and
losing teams in the UEFA Champions League. J. Hum. Kinet. 2011, 27, 135–146. [CrossRef]
37.
Vaeyens, R.; Lenoir, M.; Williams, A.M.; Philippaerts, R.M. Talent identiﬁcation and development programmes
in sport. Sports Med. 2008, 38, 703–714. [CrossRef] [PubMed]
38.
Yi, Q.; Jia, H.; Liu, H.; Gómez, M.Á. Technical demands of diﬀerent playing positions in the UEFA Champions
League. Int. J. Perform. Anal. Sport 2018, 18, 926–937. [CrossRef]
Int. J. Environ. Res. Public Health 2019, 16, 4032
13 of 13
39.
Gaddis, M.L. Statistical methodology: IV. Analysis of variance, analysis of co variance, and multivariate
analysis of variance. Acad. Emerg. Med. 1998, 5, 258–265. [CrossRef]
40.
Bradley, P.S.; Sheldon, W.; Wooster, B.; Olsen, P.; Boanas, P.; Krustrup, P. High-intensity running in English
FA Premier League soccer matches. J. Sports Sci. 2009, 27, 159–168. [CrossRef]
41.
Rampinini, E.; Sassi, A.; Azzalin, A.; Castagna, C.; Menaspa, P.; Carlomagno, D.; Impellizzeri, F.M.
Physiological determinants of Yo-Yo intermittent recovery tests in male soccer players. Eur. J. Appl. Physiol.
2010, 108, 401. [CrossRef]
42.
Andrzejewski, M.; Chmura, J.; Pluta, B.; Konarski, J.M. Sprinting activities and distance covered by top level
Europa league soccer players. Int. J. Sports Sci. Coach. 2015, 10, 39–50. [CrossRef]
43.
Di Salvo, V.; Gregson, W.; Atkinson, G.; Tordoﬀ, P.; Drust, B. Analysis of high intensity activity in Premier
League soccer. Int. J. Sports Med. 2009, 30, 205–212. [CrossRef] [PubMed]
44.
Mohr, M.; Krustrup, P.; Bangsbo, J. Match performance of high-standard soccer players with special reference
to development of fatigue. J. Sports Sci. 2003, 21, 519–528. [CrossRef] [PubMed]
45.
Van Lingen, B. Coaching Soccer: The Oﬃcial Coaching Book of the Dutch Soccer Association; Reedswain: Spring
City, PA, USA, 1998.
46.
Pic, M.; Lavega-Burgués, P.; March-Llanes, J. Motor behaviour through traditional games. Educ. Stud. 2018,
45, 1–14. [CrossRef]
47.
Pic, M. Performance and Home Advantage in Handball. J. Hum. Kinet. 2018, 63, 61–71. [CrossRef]
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Analysis of the Association between Running Performance and Game Performance Indicators in Professional Soccer Players.
Modric, Toni,Versic, Sime,Sekulic, Damir,Liposek, Silvester