RESEARCH ARTICLE
Area per player in small-sided games to
replicate the external load and estimated
physiological match demands in elite soccer
players
Andrea RiboliID1,2*, Giuseppe Coratella2, Susanna Rampichini2, Emiliano Ce´ ID2,
Fabio EspositoID2
1 Performance Department, Atalanta B.C., Bergamo, Italy, 2 Department of Biomedical Sciences for Health,
Università degli Studi di Milano, Milano, Italy
* riboliandrea@outlook.com
Abstract
The current study determined the area-per-player during small- or large-sided games
with or without goalkeeper that replicates the relative (mmin-1) total distance, high-inten-
sity running distance, sprint distance and metabolic power covered during official
matches. Time-motion analysis was performed on twenty-five elite soccer-players during
26 home-matches. A total of 2565 individual samples for SSGs using different pitch sizes
and different number of players were collected and classified as SSGs with (SSG-G) or
without goalkeeper (SSG-P). A between-position comparison was also performed. The
area-per-player needed to replicate the official match demands was largely higher in
SSG-G vs SSG-P for total distance [187±53 vs 115±35 m2, effect size (ES): 1.60 95%CI
0.94/2.21], high-intensity running distance [262±72 vs 166±39 m2, ES: 1.66(0.99/2.27)]
and metabolic power [177±42 vs 94±40, ES: 1.99(1.31/2.67)], but similar for sprint dis-
tance [(316±75 vs 295±99 m2, ES: 0.24(-0.32/0.79)] with direction of larger area-per-
player for sprint distance > high-intensity running > total distance metabolic power for
both SSG-G and SSG-P. In SSG-G, forwards required higher area-per-player than cen-
tral-defenders [ES: 2.96(1.07/4.35)], wide-midfielders [ES: 2.45(0.64/3.78)] and wide-
defenders [ES: 3.45(1.13/4.99)]. Central-midfielders required higher area-per-player
than central-defenders [ES: 1.69(0.20/2.90)] and wide-midfielders [ES: 1.35(-0.13/
2.57)]. In SSG-P, central defenders need lower area-per-player (ES: -6.01/-0.92) to
overall replicate the match demands compared to all other positions. The current results
may be used to gain knowledge of the SSGs relative to the match demands. This imply
manipulating SSGs using higher or lower ApP, the presence of the goalkeeper or design
specific rules to increase or decrease the position-specific demands with respect to the
desired external load outcomes.
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OPEN ACCESS
Citation: Riboli A, Coratella G, Rampichini S, Ce´ E,
Esposito F (2020) Area per player in small-sided
games to replicate the external load and estimated
physiological match demands in elite soccer
players. PLoS ONE 15(9): e0229194. https://doi.
org/10.1371/journal.pone.0229194
Editor: Luca Paolo Ardigò, Universita degli Studi di
Verona, ITALY
Received: January 31, 2020
Accepted: September 4, 2020
Published: September 23, 2020
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0229194
Copyright: © 2020 Riboli et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files.
Introduction
Small- or large-sided games are frequently used to replicate the soccer-specific match demands
in terms of technical proficiency, tactical awareness, speed, acceleration/deceleration, and
endurance performance [1]. To assess these demands, contemporary player-tracking technolo-
gies such as global positioning system (GPS) or semi-automatic video-based multi-camera
image system (MCIS), are typically used [2]. In small- or large-sided games (SSGs), the manip-
ulation of pitch size, number of players per team, goalkeeper presence and technical rules
modulate the soccer-specific demands depending on the aims of each practice session [1, 3].
Increments in pitch size or reduction in the number of players increases total distance (TD)
covered, total high-intensity running distance (HIRD) and total sprint distance (TSD) [4, 5].
Conversely, when pitch size is reduced or the number of players is increased, players get more
ball touches but they have not the space to reach the high-speed running, and the total distance
covered is rather characterized by acceleration and deceleration (Acc/Dec) [5, 6]. To possibly
combine the pitch size and number of players, the area per player (ApP, expressed as m2 
player-1) has been introduced [1]. Lastly, SSGs can be performed with (SSG-G) or without
goalkeepers (SSG-P), when the aim is to out-score the opponent team or to maintain ball pos-
session as long as possible, respectively [1].Some authors reported higher TD and distances
covered at different speed-thresholds during 2-, 3- and 4-a-side SSG-P than SSG-G [7]. Simi-
larly, higher HIRD was found comparing 3-a-side [8], 5- and 7-a-side SSG-P than SSG-G [9].
Although TD, HIRD and TSD were found to be higher in SSG-P than SSG-G using the same
pitch size [1, 10], other studies found lower HIRD in 3-a-side SSG-P than SSG-G [9], no differ-
ences in TSD in 3-a-side vs 5-a-side SSG-P than SSG-G [9] or higher TSD and lower Acc/Dec
in SSG-G compared to SSG-P [6]. These conflicting findings suggest that further investigation
is needed [11].
The metabolic power (Pmet) approach has been recently proposed as a tool to estimate the
energetic demands of variable-speed and accelerated/decelerated locomotion activities typi-
cally seen in team sports [12, 13]. While it is difficult to measure directly the exact energy cost
of changing speed, a metabolic power calculation based on a theoretical model has been used
to estimate the energy cost of locomotion in team sports [12, 14]. However, this model was
questioned since it may underestimate the actual net energy demand of soccer-specific exer-
cises [15–17]. Additionally, the traditional speed-threshold approach was shown to provide
similar external load compared to Pmet [18, 19]. Nevertheless, the metabolic power approach
could capture the high-demanding locomotor activities independently of the actual speed reg-
istered by GPS [16, 20], and it was shown to be a useful tool for the classification of the loco-
motion intensity in team sports [21]. Previous studies have provided evidence for concurrent
ecological validity to this approach, reporting correlations between Pmet and aerobic fitness
variables during professional soccer matches [22] and with time above 85% of the maximal
heart-rate in elite hockey matches [21]. Moreover, Pmet can be sensitive to decrements in run-
ning performance during competition [23–25] and it could be used to account for positional
differences [23, 25]. Therefore, the combination of the Pmet approach and the traditional
speed-threshold metrics should be used to provides a more comprehensive assessment of the
intermittent running demands typically occurring in team sports [15–17, 21, 24, 26–28].
An accurate comparison of the match vs training loads may help to plan the training ses-
sions to condition the locomotor activities typically required during the official match and to
optimize performance goals [5, 29]. Quantifying TD, HIRD, TSD, Acc/Dec and Pmet training
loads relative to the game demands was suggested to be an important strategy when attempting
to optimize position-specific loads in elite soccer practice [29]. Particularly, the locomotor
activities during different SSGs compared to official matches are still under investigation.
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Funding: The author(s) received no specific
funding for this work
Competing interests: The authors have declared
that no competing interests exist
Additionally, discriminating such locomotor activities by position could help to tailor the
training session. Therefore, the present study aimed to: i) determine the ApP that could be
used to replicate the official matches TD, HIRD, TSD, Acc/Dec (normalized as meters covered
in one minute) and Pmet (normalized as Wkg-1) during both SSG-P and SSG-G; and ii) differ-
entiate the ApP according to playing position. To increase the ecological validity, this was
assessed in elite Serie A soccer players.
Materials and methods
Participants
Twenty-five elite soccer players competing in Italian Serie A were involved in the present
study (age: 27 ± 5 yrs; body mass: 79 ± 7 kg; body height: 1.84 ± 0.06 m). All participants were
classified according to their position: central-defenders (n = 6), wide-defenders (n = 4), cen-
tral-midfielders (n = 5), wide-midfielders (n = 5) and forwards (n = 5). The goalkeepers were
excluded from data collection. The club’s medical staff certified the health status of each player.
An injured player was excluded from data collection for at least one month after their return
to full training. All participants gave their written consent after a full explanation of the pur-
pose of the study and the experimental design. The Ethics Committee of the Università degli
Studi di Milano approved the study and was performed in accordance with the principles of
the Declaration of Helsinki (1975).
Design
The present investigation was carried out during the competition period across two seasons
(August 2014 –May 2016). The participants undertook their traditional weekly training rou-
tine. All sessions were performed on two grass pitches preserved by qualified operators and
were conducted at the same time of day to limit the effects of circadian variation. A specialized
and high-qualified physician staff recommended and monitored the diet regime of each player
before and after every training session.
Two different formats of SSGs were analyzed: SSG-G and SSG-P. A total of 2565 (1033 and
1532, respectively) individual GPS samples with a median of 37 (range = 12 to 62) and 56
(range = 25 to 86) in SSG-G and SSG-P respectively were undertaken for each player. The
number of players ranged from 5vs5 to 10vs10, with a pitch area ranging from 800 m2 to 6825
m2 for SSG-G and 3v3 to 10vs10 with a pitch area from 400 m2 to 4550 m2 for SSG-P. Hence,
ApP ranged from 67 m2 to 341 m2 for SSG-G and from 43 m2 to 341 m2 for SSG-P (for a
detailed description of these parameters, see S1 and S2 Tables). ApP was calculated excluding
the goalkeepers in SSG-G. Both small- or large-sided games were abbreviated as SSGs and
specified by ApP. The SSGs were performed under the supervision and motivation of several
coaches to keep up a high work-rate [3]. For the same reason, a ball was always available by
prompt replacement when it went out-of-play [1]. In SSG-G, the corners were replaced by a
prompt ball-in-game from the goalkeeper [9]. The SSGs were completed after a standardized
20-min warm-up under the guidance of club staff. Only official home matches (N = 26; indi-
vidual samples = 228; individual sample range = 6 to 24) were assessed to ensure data consis-
tency [11]. The home-match pitch size was 105 x 66 m, with a grass surface.
To determine the ApP in both SSG-G and SSG-P that replicates the normalized TD, HIRD,
TSD, Acc/Dec (mmin-1) and Pmet (Wkg-1) recorded during the official matches, we first
recorded these variables during the official matches. Thereafter, we separately plotted each
relationship between ApP and the normalized TD, HIRD, TSD, Acc/Dec and Pmet during
SSG-G or SSG-P. Then, the mean values recorded during the official matches were used to
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intersect each ApP/ TD, HIRD, TSD, Acc/Dec or Pmet relationship recorded in SSG-G or
SSG-P to calculate the ApP that corresponded to the official match demands (Fig 1).
Procedures
For the aims of this study, the interchangeability of GPS and MCIS for TD, HIRD, TSD, Acc/
Dec and Pmet needed to be calculated as first step. A 10Hz GPS (K-Sport, Montelabbate, Italy)
unit was used to collect data during the training sessions [30]. The GPS unit was placed within
a dedicated pouch between the player’s shoulder blades (upper thoracic-spine) in a sports vest
and worn under the playing jersey. Each device was turned on at least 15-min before each ses-
sion to allow for acquisition of the satellite signal [6]. To reduce the inter-unit differences, each
player wore the same unit for every training session over the whole investigation [31]. The
locomotor activities during the official matches were collected using a computerized semi-
automated MCIS (STATS LLC, Chicago, Illinois, USA) and processed by a dedicated software
(K-SportOnline, K-Sport, Montelabbate, Italy). The system has previously been shown to pro-
vide valid and reliable measurements of the match activity in soccer [32, 33].
During both training sessions and home-matches, total distance, total high-intensity run-
ning distance (>15 kmh-1), total sprint distance (>24 kmh-1) [3, 11, 33] were measured.
Fig 1. Graphical representation of the procedures used to determine the area per player in SSG-G or SSG-P that matches the
official match demands. X-axis: the area per player in SSG-G or SSG-P; Y-axis: the SSG-G or SSG-P demands. The regression line
shows how the area per player influences the SSGs demands. The horizontal dashed line represents the official match demands.
From the intersection point of the regression line with the horizontal line (i.e. when the SSGs demands equate the official match
demands), a vertical dotted line is drawn to the X-axis. The intersection point between the X-axis and the vertical dotted line is the
calculated area per player in SSGs necessary to replicate the official match demands.
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Additionally, the total distance of velocity changes calculated using >2 ms-2 accelerations and
decelerations (Acc/Dec) were measured [4, 5]. The average metabolic power (Pmet) was calcu-
lated following previous procedures [13, 27]. TD, HIRD, TSD and Acc/Dec were normalized
as relative distance covered in one minute (mmin-1), while Pmet were normalized as watt per
kilogram (Wkg-1); then all parameters were inserted into the data analysis.
TD, HIRD, TSD and Acc/Dec were measured using either GPS or the MCIS. Therefore, to
check the interchangeability of these two tracking technologies, a 10-min simulated match was
monitored using both GPS and MCIS simultaneously [2, 34, 35]. All data were collected in the
stadium where the official matches were played. For each dependent locomotor activity, a cali-
bration equation was calculated to compare GPS and MCIS, as previously proposed [2, 34].
Statistical analysis
Statistical analysis was performed using a statistical software package (SigmaPlot v-12.5, Systat
Software Inc., San Jose, CA, USA). To check the normal distribution of the sampling, a Sha-
piro-Wilk test was used. A Bland-Altman analysis was used to display the degrees of bias and
the limits of agreement between the GPS and the MCIS. A linear regression analysis was used
to calculate the correlation between GPS and MCIS. The Pearson’s product moment and the
typical error of the estimate (TEE) were calculated to determine the relationship between the
GPS and the MCIS. The correlation coefficient was interpreted as follows: r = 0.00–0.09 trivial,
0.10–0.29 small, 0.30–0.49 moderate, 0.50–0.69 large, 0.70–0.89 very large, 0.90–0.99 nearly per-
fect; the threshold values for the TEE were interpreted as follows: >0.2 small, >0.6 moderate,
>1.2 large and >2 very large [36]. A linear regression analysis was used to calculate the correla-
tion between TD, HIRD, TSD, Acc/Dec, Pmet and the ApP during both SSG-G and SSG-P.
Thereafter, a two-way ANOVA was used to calculate the difference in the optimal ApP in TD,
HIRD, TSD, Acc/Dec, Pmet calculated for SSG (SSG-G vs SSG-P) and position (central-defend-
ers, wide-defenders, central-midfielders, wide-midfielders and forwards). A post-hoc analysis
(Holm-Sidak correction) was used to calculate the differences in the independent factors. The
effect size with 95% confidence intervals (CI) was calculated and interpreted as follows: <0.20:
trivial; 0.20–0.59: small; 0.60–1.19: moderate; 1.20–1.99: large; 2.00: very large [36]. Statistical
significance was set at α < 0.05. Unless otherwise stated, all values are presented as
mean ± standard deviation (SD).
Results
The magnitude of the GPS vs MCIS bias is shown in Fig 2. For each dependent parameter,
Bland-Altman analysis and correlation graph with the respective calibration equation are
shown. The bias between GPS vs MCIS were trivial for TD (-3.0 ± 1.3%, ES = -0.18, CI: -0.80/
0.44), HIRD (-3.3 ± 1.6%, ES = -0.12, CI: -0.74/0.51), TSD (-3.9 ± 10.9%, ES = -0.11, CI: -0.44/
0.22) and Acc/Dec (-4.1 ± 6.3%, ES = -0.19, CI: -0.80/0.44) and small for Pmet (-4.0 ± 0.6%, ES
= -0.38, CI: -1.27/0.49). A small TEE was found between the MCIS and GPS for TD (TEE:
0.09, CI: 0.07/0.14), HIRD (TEE: 0.04, CI: 0.03/0.06), TSD (TEE: 0.08, CI: 0.07/0.10) and Pmet
(TEE: 0.07, CI: 0.04/0.13), while a moderate TEE was found for Acc/Dec (TEE: 0.75, CI: 0.56/
1.10). In addition, a nearly perfect correlation was observed for TD, HIRD, TSD and Pmet and a
moderate correlation for Acc/Dec measured using GPS and MCIS (Fig 2).
As shown in Fig 3, in SSG-P a very large correlation between the relative distance and the
ApP was found for TD, HIRD, TSD and Pmet while a moderate correlation was found for Acc/
Dec. In SSG-G, a very large correlation between the relative distance and the ApP for TD,
HIRD and TSD, a large correlation for Pmet and a moderate negative correlation for Acc/Dec
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were found. Because of the moderate correlations observed for Acc/Dec in both SSG-G and
SSG-P, we did not perform the calculation or the ApP for Acc/Dec, given the high risk of bias.
For both SSG-P and SSG-G, the ApP necessary to replicate the relative distance recorded
during the matches for TD, HIRD, TSD and Pmet is shown in Table 1. No SSG × position inter-
action was found (p = 0.674) for ApP for TD. A main effect for SSG (p < 0.001) and position
(p = 0.024) was detected. The between-SSG post-hoc analysis is reported in Table 1. In SSG-P,
a larger ApP is required for forwards vs central-defenders (p = 0.023; ES = 4.35, CI: 1.93/6.01),
with no other between-position differences. In SSG-G, no between-position difference
occurred.
No SSG × position interaction was found (p = 0.065) for ApP for HIRD. A main effect for
SSG (p < 0.001) and position (p < 0.001) was detected. The between-exercise post-hoc analy-
sis is reported in Table 1. In SSG-P, a higher ApP is required for forwards vs central-defenders
(p = 0.024; ES = 2.92, CI: 1.04/4.29), with no other between-position differences. In SSG-G,
forwards required higher ApP than central-defenders (p < 0.001; ES = 2.96, CI: 1.07/4.35),
wide-midfielders (p = 0.002; ES = 2.45, CI: 0.64/3.78) and wide-defenders (p = 0.029,
ES = 3.45, CI: 1.13/4.99). Central-midfielders required a higher ApP than central-defenders
(p = 0.002; ES = 1.69, CI: 0.20/2.90) and wide-midfielders (p = 0.019, ES = 1.35, CI: 0.13/2.57).
No SSG × position interaction was found (p = 0.803) for ApP for TSD, not even a main
effect for exercise (p = 0.415). A main effect for position (p = 0.049) was detected. The
between-exercise post-hoc analysis is reported in Table 1. In both SSG-P and SSG-G, no
between-position difference occurred.
No SSG × position interaction was found (p = 0.167) for ApP for Pmet. A main effect for
SSG (p < 0.001) and position (p = 0.002) was detected. The between-SSG post-hoc analysis is
reported in Table 1. In SSG-P, a lower ApP is required for central-defenders vs wide-defenders
(p = 0.031; ES = -2.69, CI: -4.32/-1.05), wide-midfielders (p < 0.001; ES = -2.64, CI: -4.35/-
0.92), central-midfielders (p = 0.028; ES = -5.10, CI: -7.53/-2.66), forwards (p = 0.024; ES =
-1.89, CI: -3.32/0.47). In SSG-G, no between-position difference occurred.
Discussion
The first novel finding observed in the present study was a detailed calculation of the ApP in
SSG-P or SSG-G necessary to replicate the TD, HIRD, TSD or Pmet recorded during the official
matches. It is shown here that, irrespective of the SSG type, the higher the speed threshold, the
larger the ApP required (i.e., TSD > HIRD > TD  Pmet). Secondly, the inclusion of the goal-
keeper increases the ApP for TD, HIRD and Pmet, while no difference was observed in SSG-P
vs SSG-G for TSD. Additionally, central defenders required the lowest ApP compared to all
other positions, both in SSG-P and SSG-G. Lastly, both central-midfielders and forwards need
the highest ApP compared to all other positions, both in SSG-G and SSG-P, to replicate the
match demands.
During official matches, total high-intensity running distance covered [37], technical skills
to maintain greater ball possession [38], the total distance covered with ball possession [39]
and tactical behaviours [40] are key factors for success in soccer performance. Within weekly
training routines, SSGs are largely used to elicit high-intensity running [1], a high number of
technical drills with the ball possession [41] and to improve tactical behaviours [40].
Fig 2. Bland-Altman analysis and linear regression analysis with calibration equation for the GPS vs MCIS bias
for each locomotor activity. The linear regression analysis is shown with 95% confidence bands. Panels A-B: total
distance; C-D: high-intensity running distance; E-F: total sprint distance; G-H: acceleration/deceleration; I-L:
metabolic power.
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Interestingly, SSGs were shown to lead to similar enhancement in aerobic fitness than high-
intensity running interval training [42]. In SSGs, manipulating the number of players, the
pitch size and the goalkeeper presence results in different physiological, technical and tactical
outcomes [1, 40]. For example, while increments in ApP was shown to increases TD, HIRD
and TSD [4, 5], decreasing ApP leads to more ball touches and Acc/Dec [5, 6]. Determining
the ApP that replicates the match external-load demands may help sport physiologists and
practitioners to properly plan SSGs for specific performance objectives [5]. Therefore, the cur-
rent results may be used to gain knowledge of the SSGs relative to the match demands. Unsur-
prisingly, both in SSG-P and in SSG-G, higher ApP leads to greater distance covered whatever
the speed threshold [5]. Accordingly, the present findings highlight that the ApP in SSGs to
replicate the TSD match demands is very close to the official match ApP ( 340 m2). In line
with the present outcomes, it was shown that the larger the pitch size, the greater the distance
covered at speed >18 kmh-1 [43]. Other authors found that TD and the distance covered at
19.8–25.2 kmh-1 and >25.2 kmh-1 increased proportionally with the pitch size [44]. A recent
study reported that ApP  311 m2 was able to replicate the high-speed match demands during
SSG-G [5]. The exposure to high-demanding activities was shown to improve the players’
Fig 3. The relationship between area per player (m2player) and relative speed distance (mmin-1) or estimated metabolic power (Wkg-1) for each locomotor
activity. The linear regression analysis with 95% confidence bands and the correlation between the area per player and the relative distance or metabolic power are
also reported. SSG-P, closed circles: small-sided games possession-play without goalkeepers; SSG-G, open circles: small-sided games with goalkeepers. Panel A: total
distance; B: high-intensity running distance; C: total sprint distance, D: acceleration/deceleration; E: metabolic power.
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Table 1. Area per player (m2player) to replicate official-match load using SSGs for relative speed distances or estimated metabolic power. Data are presented as
mean(SD), effect size (95% confidence intervals).
TD
HIRD
TSD
Pmet
Position
SSG-P
SSG-G
p
ES (CI)
SSG-P
SSG-G
p
ES (CI)
SSG-P
SSG-G
p
ES (CI)
SSG-P
SSG-G
p
ES (CI)
Total
115
(35)
187
(53)a
<0.001
-1.60
(-2.21/-
0.94)
166
(39)
262
(72)a
<0.001
-1.66
(-2.27/-
0.99)
295
(99)
316
(75)
0.415
-0.24
(-0.79/
0.32)
94(40)
177
(42)a
<0.001
-1.99
(-2.67/-
1.31)
CD
65
(24)b
165
(26)a
<0.001
-4.00
(-5.55/-
1.83)
122
(30)b
205
(57)abc
<0.001
-1.82
(-3.00/-
0.37)
257
(76)
278
(51)
0.672
-0.32
(-1.44/
0.84)
31(11)
151
(23)a
<0.001
-6.14
(-8.85/-
3.44)
WD
121
(21)
193
(71)a
0.023
-1.38
(-2.70/
0.31)
163
(30)
246
(36)ab
0.003
-2.50
(-3.92/-
0.43)
297
(26)
274
(67)
0.696
0.45
(-1.01/
1.79)
106
(31)d
183
(27)a
0.003
-2.39
(-4.01/-
0.77)
CM
119(9)
184
(41)a
0.021
-2.12
(-3.41/-
0.42)
174
(28)
311
(69)a
<0.001
-2.60
(-3.96/-
0.74)
329
(66)
340
(33)
0.834
-0.21
(-1.43/
1.05)
107
(13)d
191
(25)a
<0.001
-3.81
(-5.88/-
1.73)
WM
135
(20)
183
(81)a
0.079
-0.81
(-2.02/
0.55)
172
(19)
222
(72)abc
0.031
-0.95
(-2.15/
0.44)
264
(52)
281
(62)
0.758
-0.30
(-1.51/
0.98)
132
(12)d
180
(61)a
0.047
-0.95
(-2.71/
0.51)
FW
147(9)
214
(51)a
0.018
-1.83
(-3.09/-
0.22)
207
(28)
333
(12)a
<0.001
-5.85
(-7.91/-
2.66)
334
(92)
407
(68)
0.176
-0.90
(-2.10/
0.48)
99(2)d
201
(66)a
<0.001
-1.97
(-3.48/-
0.46)
TD, total distance; HIRD, high intensity running distance; TSD, sprint distance; Pmet, average metabolic power; SSG-P, small-sided games without goalkeepers; SSG-G,
small-side games with goalkeepers; Total, team average; CD, central defenders; WD, wide defenders; CM, central midfielders; WM, wide midfielders; FW, forwards; ES,
effect size; CI, confidence interval.
a Significantly different (p < 0.05) from SSG-P
b Significantly different (p < 0.05) from forwards
c Significantly different (p < 0.05) from central midfielders
d Significantly different (p < 0.05) from central defenders
https://doi.org/10.1371/journal.pone.0229194.t001
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fitness level, to prepare the players to the match workload and to result in greater protection
against non-contact injuries [45–47]. Therefore, manipulating ApP allows for training loads in
SSGs to be managed with respect to the desired external load outcomes, both for performance
and prevention purposes.
The current findings also highlight that training using SSGs with or without goalkeeper
affects the ApP necessary to replicate the match demands. Particularly, with the exception of
TSD, the goalkeeper presence increases the ApP for TD, HIRD and Pmet, i.e. SSG-G > SSG-P.
Partially in contrast with the present outcomes, it was reported that SSG-G resulted in higher
TSD than found in SSG-P [6]. However, the authors investigated a maximum ApP of 135 m2,
hence, this does not allow an appropriate comparison. Other researchers reported that TD and
the time spent in high-intensity running (>17 kmh-1) was higher with goalkeepers [48].
Although the authors argued that the goalkeeper presence might have motivated the players,
several authors found higher high-intensity running without goalkeepers in different 3-, 4-, 5-,
and 7-a-side SSGs [7–9]. Moreover, two subsequent reviews [1, 10] consistently remarked that
the goalkeeper presence could improve the players’ organization, thus decreasing the SSGs
demands. Indeed, during SSG-G, the two teams’ aim is to outscore the opponent team, while
maintaining a match-like tactical organization. In contrast, since during SSG-P the aim is to
maintain the ball possession as long as possible, the players are free to move across the selected
pitch size. This rule-difference seems to account for the largest ApP in SSG-G necessary to rep-
licate the TD, HIRD or Pmet recorded during the official matches. Interestingly, the current
results come with moderate correlation between Acc/Dec and ApP in SSG-P, while no correla-
tion was observed between Acc/Dec and ApP in SSG-G. Previous results suggested that lower
pitch size induced increments in Acc/Dec [5, 49]. In line with the present outcomes, other
authors retrieved no differences for high-demand (>2 ms2) Acc/Dec with the increment in
pitch size during 3-, 5- and 7-a-side [9] or 3-, 5-, and 10-a-side SSG-G or SSG-P [6]. Compar-
ing SSG-P vs SSG-G, higher Acc/Dec were reported during SSG-G than SSG-P using an ApP
of ~210 m2 [9], while no difference in Acc/Dec between SSG-G vs SSG-P were found using an
ApP from 73-to-135 m2 [6]. Despite the greater stimulation of accelerations in SSG-G vs
SSG-P might be accounted for the players’ need to overpass the opponent or defensive lines in
order to achieve the rival goal in larger ApP, a controversy still exists.
To our knowledge, the calculation of the ApP across positions was used here for the first
time. No between-position difference in ApP was found for TSD, neither in SSG-P nor SSG-G.
In SSG-P, it was observed that central defenders need lower ApP than forwards for TD, and
HIRD, while lower ApP than all other position for Pmet. In SSG-G, no between-position differ-
ence in ApP was observed for TD, TSD and Pmet, while forwards and central midfielders need
larger ApP than central defenders and wide midfielders for HIRD suggesting that these posi-
tions might undergo different stimuli during similar SSGs. Defenders tend to move within a
“defined” space over the official match, while central-midfielders and forwards tend to cover a
greater area of the pitch in order to gain possession of the ball, marking the opponent or creat-
ing space to score [33]. This might be considered for the lower ApP needed to accumulate the
match demands in central defenders than forwards/central-midfielders. However, the sprint-
ing activities are not influenced by position, since these appear to need large pitch areas avail-
able anyhow. The different ApP recorded across position offers the possibility to tailor the
training load to enhance the performance adaptations. It was previously suggested that similar
high-intensity training load could lead to overload or underload different positions, so affect-
ing the competition performance or possibly increasing the risk of injury [29]. Interestingly,
high-intensity activities were shown to be underloaded during the training routines compared
to the official matches, with a high variability across positions [29]. The present results suggest
that some positions need higher or lower ApP to replicate the HIRD or TSD accumulated over
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the matches. Furthermore, position-specific rule modifications within SSGs or additional exer-
cises could be integrated to technical/tactical exercises to individualize high-intensity training
activities.
Some limitations accompany the present investigation. For replication purposes, the inter-
changeability between the GPS and MCIS needs to be carefully checked, especially when
recording high-speed or non-linear movements [2]. The present results are based on the trivial
differences in the metrics recorded using either the GPS or MCIS and a calibration equation
was provided to partially account for these differences. Secondly, due to technological limita-
tion during the official matches, no internal load parameter (e.g. heart rate) was assessed.
However, it was reported that Pmet maintains a strong and consistent relationship with the
measures of internal load during low-to-high intensity locomotor activities [21]. Therefore,
Pmet could be a satisfactory way to estimate with accuracy the training and match demands
[12, 22] and to classify the locomotion intensity in team sports [21, 28].
Conclusions
The current results suggest that soccer players need a specific ApP during SSGs with or with-
out goalkeeper to replicate the match demands, especially to perform each locomotor activity
(i.e., TSD > HIRD > TD  Pmet). Moreover, SSG-G need higher ApP than SSG-P to replicate
the match demands. Lastly, position-difference in ApP were found, so that central defenders
need lower and forwards and central midfielders higher ApP.
These results allow managing the training loads towards the desired players’ fitness compo-
nent to maximize transfer to the game-like and performance goal using SSGs. Indeed, soccer
training methodology are evolving to an alternation of the training objectives with the aim to
overload the desired fitness component relative to the match demands [5, 29]. When aware of
the training/matches differences in locomotor activities, coaches could design SSGs with the
intent to replicate, underload or overload the match demands. This imply manipulating SSGs
using higher or lower ApP, the presence of the goalkeeper or design specific rules to increase
or decrease the position-specific demands. To synthetize, the present outcomes could be used
in practice to: i) calculate an ApP that replicate an estimated match demand using Pmet for
both SSG-P and SSG-G; ii) replicate the official relative match demands using the specific min-
imal ApP to HIRD or TSD be accumulated during the SSG-G/P performed in the training ses-
sions; iii) differentiate the ApP when SSG-P or SSG-G are performed according to the aim of
the training session (e.g. replicate, overload or underload specific training objectives); iv) add
SSGs with position-specific ApP to the training routines when needed or propose specific
additional exercises or rules to overload or underload each player.
Supporting information
S1 Table. Small-sided games with goalkeepers. The small-sided games with goalkeepers are
split for the number of players and pitch size (width x length). The total pitch area and area per
player have been calculated. The average number of observations per player for each condition
are also reported as mean (max-min).
(DOCX)
S2 Table. Small-sided games without goalkeepers. The small-sided games without goalkeep-
ers are split for the number of players and pitch size (width x length). The total pitch area and
area per player have been calculated. The average number of observations per player for each
condition are also reported as mean (max-min).
(DOCX)
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S1 Data.
(XLSX)
Author Contributions
Conceptualization: Andrea Riboli.
Data curation: Andrea Riboli.
Formal analysis: Emiliano Ce´.
Investigation: Andrea Riboli.
Methodology: Andrea Riboli, Giuseppe Coratella.
Software: Susanna Rampichini.
Supervision: Fabio Esposito.
Visualization: Giuseppe Coratella.
Writing – original draft: Andrea Riboli, Giuseppe Coratella.
Writing – review & editing: Andrea Riboli, Giuseppe Coratella, Fabio Esposito.
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Area per player in small-sided games to replicate the external load and estimated physiological match demands in elite soccer players.
Riboli, Andrea,Coratella, Giuseppe,Rampichini, Susanna,Cé, Emiliano,Esposito, Fabio