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The aim of the study was to quantify the individual player area (IPA) that emerges during football matches at youth levels, considering different numerical relations and pitch zones. Two hundred and twenty-eight players, divided by U15, U17 and U19, participated in the study. Jonckheete-Terpstra and Kruskal Wallis nonparametric tests were used to compare the IPA according to variations in players' age, numerical relations and pitch zones considered for analysis. All ages and numerical relation results revealed the highest IPA in the zones closer to the goal and were lower in the middle of the pitch. For 3 × 3 to 10 × 10 numerical relations, the IPA was higher in the U15 and lower in the U17. The greater differences between the age groups concerned numerical relations of 6 × 6 to 10 × 10 (p ≤ 0.001). The effect size was moderate between the U15 and U17 in numerical relations of 8 × 8 to 10 × 10. Results suggest that the manipulation of IPA during training sessions should respect players' age and be adjusted considering the numerical relation and the tactical purpose of coaches. ARTICLE HISTORY
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International Journal of Performance Analysis in Sport
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rpan20
Variations in individual player area in youth
football matches: the effects of changes of players’
age, numerical relations, and pitch zones
Nuno Coito, Hugo Folgado, Félix Romero, Nuno Loureiro & Bruno Travassos
To cite this article: Nuno Coito, Hugo Folgado, Félix Romero, Nuno Loureiro & Bruno Travassos
(2022): Variations in individual player area in youth football matches: the effects of changes of
players’ age, numerical relations, and pitch zones, International Journal of Performance Analysis in
Sport, DOI: 10.1080/24748668.2022.2025713
To link to this article: https://doi.org/10.1080/24748668.2022.2025713
Published online: 07 Jan 2022.
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Variations in individual player area in youth football matches:
the eects of changes of players’ age, numerical relations,
and pitch zones
Nuno Coito
a,b,c
, Hugo Folgado
d,e
, Félix Romero
b,c
, Nuno Loureiro
b,c
and Bruno Travassos
a,f,g
a
Department of Sport Sciences, Universidade da Beira Interior, Covilhã, Portugal;
b
Escola Superior de
Desporto Rio Maior (ESDRM-IPSantarém), Rio Maior, Portugal;
c
Life Quality Research Center, CIEQV, Rio
Maior, Portugal;
d
Departamento de Desporto e Universidade de Évora, Évora, Portugal;
e
Comprehensive
Health Research Centre (CHRC), Évora, Portugal;
f
Research Centre in Sports Sciences, Health Sciences and
Human Development, CIDESD, Vila Real, Portugal;
g
Portuguese Football Federation, Lisboa, Portugal
ABSTRACT
The aim of the study was to quantify the individual player area (IPA)
that emerges during football matches at youth levels, considering
dierent numerical relations and pitch zones. Two hundred and
twenty-eight players, divided by U15, U17 and U19, participated in
the study. Jonckheete-Terpstra and Kruskal Wallis nonparametric
tests were used to compare the IPA according to variations in
players’ age, numerical relations and pitch zones considered for
analysis. All ages and numerical relation results revealed the highest
IPA in the zones closer to the goal and were lower in the middle of
the pitch. For 3 × 3 to 10 × 10 numerical relations, the IPA was
higher in the U15 and lower in the U17. The greater dierences
between the age groups concerned numerical relations of 6 × 6 to
10 × 10 (p ≤ 0.001). The eect size was moderate between the U15
and U17 in numerical relations of 8 × 8 to 10 × 10. Results suggest
that the manipulation of IPA during training sessions should respect
players’ age and be adjusted considering the numerical relation and
the tactical purpose of coaches.
ARTICLE HISTORY
Received 29 July 2021
Accepted 31 December 2021
KEYWORDS
Zone; individual player area;
numerical relations; age;
football
1. Introduction
In football, the use of small-sided and conditioned games (SSCG), for training and
teaching at different competitive levels and ages, has been a trend in recent years. This
practice has been accompanied and supported by scientific research that aims to identify
the effects of different SSCG manipulations on players and teams performance, and
compared them with the competition requirements (Aguiar, Botelho, Peñas, Maças &
Sampaio, 2012; Sarmento et al., 2018). In this line, several studies have evaluated the
effect of manipulations related to the playing area. Results showed that the increased
dimension of the playing area often leads players to run more distances (Lemes et al.,
2019) and to variations in their tactical behaviour, particularly in the increased
CONTACT Nuno Coito coitonuno@gmail.com Department of Sport Sciences, Universidade da Beira Interior,
Covilhã, Portugal; Sports Science School of Rio Maior (ESDRM-IPS), Rio Maior, Portugal
INTERNATIONAL JOURNAL OF PERFORMANCE ANALYSIS IN SPORT
https://doi.org/10.1080/24748668.2022.2025713
© 2022 Cardiff Metropolitan University
spatiotemporal relation between teammates or opponents (Folgado et al., 2019; Frencken
et al., 2013; Silva, Duarte et al., 2014), with implications for the number of technical
actions of players (Nunes et al., 2020). Conversely, the reduction in playing area tends to
promote a greater number of ball losses, more physical contacts, more individual duels,
and tackles (Dellal et al., 2012).
For the manipulation of playing areas in SSCG, the dimensions of the football pitch
(105x68 metres) can be used as a reference, allowing for the classification in large (55%),
medium (45%) and small (35%) pitch, according to the percentage of size decrease (Silva,
Aguiar et al., 2014). Similarly, the definition of the playing area can be achieved based on
the individual playing area (IPA), which is calculated through the total area of the pitch
divided by the number of players involved in the training or match situation (Aguiar
et al., 2015; Olthof et al., 2019). However, many of the studies do not present any
theoretical or practical reason such as for example, the age or level of practice of players,
for the manipulations carried out in the IPA (Caro et al., 2019).
In fact, previous studies using SSCG in youth football suggested that older players
perform more passes during a game and present more time spent in ball possession,
using a wider area of the pitch, while younger players tend to play lengthwise in similar
playing areas (Folgado et al., 2014; Olthof et al., 2015). On the other hand, differences
on the use of width and length in particular pitch zones have been revealed for players
of different ages in SSCG and in official games (Caro et al., 2019; Fradua et al., 2013;
Tenga et al., 2015).
These studies suggest that the playing area used is influenced by the ball position on
the pitch and can therefore vary depending on the game phase and on the ball location.
In view of the above stated, the analysis of the area occupied by players during the
game, as well as its variation according to the pitch zones (defensive zone, middle zone
and attacking zone), can make us think about the manipulation of the playing area in
training tasks (Zubillaga et al., 2013). In football, the possibilities of individual and
collective action (affordances) arise from the complementarity between the individual
characteristics of the players and the spatiotemporal dynamics between them on the
pitch, enhanced by the competitive environment (Araújo et al., 2006). Therefore,
a better understanding of the pitch areas manipulations to be used in training is needed
in order to promote the adequate relationship between players and game environment
(Travassos et al., 2013).
Thus, in this study we intend to describe the individual area per player, according to
age, numerical relations, and the pitch zone. For this, the different individual areas per
player in a recreated football match during normal training were quantified, considering
different age groups (U15, U17, U19).
It was expected to measure changes in IPA according to different age groups. Also, it
was expected to observe variations in IPA considering variations in numerical relation-
ships and the location in the field.
2. Methods
A total of two hundred and twenty-eight male players who competed in the national
championships, the highest competitive level for each age group, participated in the
study, divided by U15 (n = 76, age 14.4 ± 0.4 years, height 1.61 ± 0.07 weight 52.2 ± 9.0);
2N. COITO ET AL.
U17 (n = 76, age 15.6 ± 0.5 years, height 1.74 ± 0.05, weight 63.1 ± 7.5) and U19 (n = 76,
age 17.7 ± 0.5 years, height 1.78 ± 0.09, weight 75.3 ± 9.3). Three different teams
participated in each age group (Figure 1). The team composition was defined by the
head coach to ensure balanced and competitive matches. Each game had an average of 25
players. Each game had three to four players as substitutes who came in for other players.
Each team had three training session, lasting ninety-minute, and one official game per
week. Goalkeepers, despite being present during the situation, were not considered for
the calculation of the indicators used in the study, given the specificity of their functions.
All players’ legal guardian were informed of the study and gave their written consent
before the latter began. This study was approved by the Ethics Committee under the
number CE-UBI-Pj-2020-043.
2.1. Data collection
For each age group, a recreated football match in their normal training was played
between each team, with a total of three matches per level. This situation was performed
at the beginning of the session, after twenty minutes of warm-up, consisting of running
and passing exercises. In each recreated match, the coaches distributed the players in two
balanced teams, according to the coach perception, and considering players specific
positions. The game length varied according to age and the official rules of the respective
Figure 1. The team composition.
INTERNATIONAL JOURNAL OF PERFORMANCE ANALYSIS IN SPORT 3
level: U15 – 2 halves of 35 minutes; U17 – 2 halves of 40 minutes; and U19 – 2 halves of
45 minutes. The rest time between the 1st and 2nd half was 10 minutes in all games. The
games were played on artificial turf pitches with the official football measures. Positional
data of all the players were collected using inertial WIMU TM devices (RealTrack
Systems, Almeria, Spain). Data were analysed using the SPRO TM analysis programme
(RealTrack Systems, Almeria, Spain). Following the manufacturer guidelines, the units
were turned on at least 30 minutes before the beginning of each session. Devices were
placed on players, in appropriate vests, before the warm-up. All games were video
recorded through a camera (Panasonic HC-V160) placed at a higher level in the middle
zone of the pitch, for posterior notational analysis.
2.2. Data processing
Based on the collected video, notational analysis was performed considering the follow-
ing ball related actions (Folgado et al., 2019): individual player gaining ball possession;
individual player disposing the ball possession; player touching the ball; ball over the end
line; ball over the side line; ball shooting; ball hitting crossbar/post; goal scoring; fouls.
The software LongoMatch 1.3.7 (Fernandez, 2017) was used for this analysis, capturing
the time of each action, for synchronising the ball events with the GPS positional data
(Figure 2). A visual representation of each simulated match was processed, presenting the
ball position, displacement, and the time of each action. This representation was used for
possible notational errors correction, by comparing it with the original video.
To calculate the IPA by different numerical relations, the rectangle formed by the
players of each team closest to the ball at the time of the pass was considered. The players
in the periphery of the ball area defined the limits (Caro et al., 2019) for each numerical
relation, taking width as the shortest distance that allowed to include all the players of the
numerical relation in the sideline-sideline axis, and length as the smallest distance that
allowed all players to be included in the goal-goal axis (Figure 3). In this study, the IPA
Figure 2. Notational analysis using the software LongoMatch for GPS and technical events
synchronisation.
4N. COITO ET AL.
was determined by dividing the playing area by the number of players (Casamichana &
Castellano, 2010). This means that, in a playing area with 4 players (the 2 players, from
each team, closest to the ball), the division of the playing area by the 4 players was
calculated, and so on, up to a 10 × 10 numerical ratio.
For passing location, the pitch was divided into different six zones, following
existing literature (Fradua et al., 2013; Figure 4). Zone 1 (Z1) corresponds to the
zone closest to the analysed team goal and zone 6 (Z6) corresponds to the zone closest
to the opponent’s goal. The IPA was calculated according to the passing location. Five
thousand and seventy-six game situations were recorded (1379 U15, 2182 U17 and
1515 U19).
2.3. Statistical analysis
Initially, the IPA was calculated for each numerical relation and the results grouped by
age. The normality of data was analysed through the Kolmogorov-Smirnov test, revealing
a non-normal and strongly asymmetric distribution. Thus, a descriptive analysis was
performed through the median, interquartile range. The lack of normality led to the
adoption of the Jonckheere-Terpstra test when comparing age groups. Pairwise compar-
isons between each age group and numerical relations were performed by calculating the
standardised effect sizes (ES; Pallant, 2007). Therefore, the effects were described accord-
ing to the following scale: null (0.00–0.10); weak (0.11–0.29); moderate (0.30–0.49) and
strong (>0.5; Cohen, 1988). In the comparison between the different areas for each level,
the Kruskal-Wallis H test was used. The identification of the differences detected by both
Figure 3. Example of playing area calculation involving 2 × 2 (dashed line), 5 × 5 (dotted-dashed line)
and 10 × 10 (solid line) players, according to the different six pitch zones.
INTERNATIONAL JOURNAL OF PERFORMANCE ANALYSIS IN SPORT 5
non-parametric techniques was performed using Bonferroni Correction. The level of
significance was set at p < 0.05 for multiple tests. For statistical analysis, the following
software was used: IBM SPSS statistic-v.26.0.
3. Results
Figure 5 shows the median of the IPA (m
2
) of each numerical relation in each age
group. The results revealed higher values for U15 and lower values for U17, in all
numerical relations.
Figure 4. The pitch was divided into different six zones.
Table 1. Comparison between ages in each numerical relationship.
Effect size
Players t
jt
z p-value Post- hoc U15 – U17 U15-19 U17-19
2x2 4313886 1.99 0.047 U15
b
>U17
a
U19
b
>U17
c
0.04 0.07
3x3 4206562 .082 .935
4x4 4136618 −1.159 .246
5x5 4018348 −3.258 .001 U15
bc
>U19
ab
>U17
ac
−0.17 (weak) −0.07 (null) 0.11 (weak)
6x6 4012436 −3.36 .000 0.20 (weak) 0.08 (null) 0.13 (weak)
7x7 3978330 −3.97 .000 0.24 (weak) 0.09 (null) 0.17 (weak)
8x8 3960919 −4.28 .000 0.31 (moderate) −0.11 (weak) 0.22 (weak)
9x9 3992537 −3.72 .000 −0.31 (moderate) −0.10 (null) 0.24 (weak)
10x10 3959018 −4.31 .000 −0.36 (moderate) −0.12 (weak) 0.28 (weak)
Legend:
a
significant differences between U15 and other ages;
b
significant differences between U17 and other ages;
c
significant differences between U19 and other agesTjt: Test Statistic; z: standardised Test Statistic; p: significance value
6N. COITO ET AL.
Figure 5. Values IPA (median) in U15, U17 e U19.
INTERNATIONAL JOURNAL OF PERFORMANCE ANALYSIS IN SPORT 7
Table 1 presents the significant differences between numerical relations, namely 2 × 2
(p = 0.047), 5 × 5 (p = 0.001) and 6 × 6 to 10 × 10 (p = 0.000), between the different age
groups. Moderate differences were observed in 8 × 8 to 10 × 10 and weak differences for
other numerical relations between U15 and U17. Between U15 and U19 all the
differences were null to weak and between U17 and U19 all the differences revealed
a weak effect
Figure 6. Median in six zones at each age (U15,U17 e U19).
8N. COITO ET AL.
The results of IPA (m2) for each numerical relation revealed an effect of the pitch zone
(Figure 4). Higher values were observed in the zones closer to the goals (Z1 and Z6) and
lower values in the middle zone of the pitch (Z3 and Z4), with significant differences in all
numerical relations and ages (p = 0.000). An effect of age was also observed in the
different areas analysed. While in U15 the highest values were always in zone 1, in U17
and U19 they were in zone 1 or zone 6 (Figure 6). It is also worth mentioning that despite
variations in age levels or in numerical relations no significant differences were observed
between zones 1 and 6, zones 2 and 5 and zones 3 and 4.
4. Discussion
This study aimed to identify the differences between IPA according to age (U15, U17,
U19), numerical relations and different pitch zones. In general, results revealed differ-
ences between age groups for different numerical relations and considering the pitch
zones of play.
As expected, IPA values revealed general differences between players of different age
groups (U15, U17, U19). However, there was no gradual increase in IPA concerning
age. U15 values revealed the highest IPA values, U17 values corresponded to the lowest
ones, and U19 values were associated to intermediate IPA values. According to pre-
vious studies, variations in age directly influence the way through which players
explore the pitch and, consequently, how they explore own possibilities as well as
their teammates’, depending on the opponent’s behaviour (Menuchi et al., 2018; Nunes
et al., 2020). Probably, this variation in space is related to the ability of players to adjust
their individual performance behaviours to the playing area, according to their team-
mates, opponents and ball placement (Travassos et al., 2018). In opposition to our
expectations, similar IPA results were observed between U15 and U19 players, with the
U17 revealing the lowest values. However, the similarities between the IPA U15 and
U19 are sustained by different reasons. While the higher IPA of U15 could be related
with the need for more time and space to decrease the ball pressure and to ensure
additional time for decision and action (Nunes et al., 2020), the higher IPA for U19
could be related with the higher capability to manage the interacting space with
teammates and opponents according to ball placement and game dynamics (Folgado
et al., 2014; Travassos et al., 2018). The maturation stage in the young U14 and U15,
due to bodily changes that occur as a result of peak height speed, can influence
Figure 6. continued.
INTERNATIONAL JOURNAL OF PERFORMANCE ANALYSIS IN SPORT 9
technical actions and motor skills (Philippaerts et al., 2006), with implications in
tactical behaviour and decision-making (Sevil-Serrano et al., 2017).Also, changing the
game format from football 7 to football 11 can contribute to less game efficiency in the
U14 and U15, creating the need to use more space and time to perform due to the
increase in the number of players and the spatial-temporal relations that they need to
manage (Lapresa et al., 2006).
Regarding the more reduced space occupation by U17, a possible reason may be
related to the time of knowledge acquisition by players of this age. In fact, they are at the
beginning of the specialisation stage and, therefore, their knowledge of space is still under
development (Machado et al., 2015). In this way, players tend to reduce the distance
between them in the attacking stage, resulting in lower IPA values compared to other age
groups. Data suggest that, up to U17, players reveal difficulties in adapting to the constant
changes occurring in the playing area and individually adjust their actions according to
the ball placement. These differences in IPA according to players age suggest that the
manipulations of playing areas in SSCG during training sessions should be adjusted
according to the age level of players to promote most adjusted contexts of learning
(Olthof et al., 2019; Travassos et al., 2018).
Interestingly, the IPA values tend to reveal higher differences between age groups for
higher numerical relations of players. Several studies on SSCG suggest that the greater
number of players involved the less the variability in players’ positioning, making the
game more positional (Silva et al., 2015). Thus, while with low number of players the IPA
seems stable, increasing the number of players that participating on the game tend to
highlight the adaptive behaviours of players to occupy space according to their own
individual and relational tactical capabilities. Further research is required on this topic to
understand the dynamic of such variations according to players’ levels of expertise and
individual tactical, technical, and physical capabilities.
At the end, the analysis of IPA according to the pitch zones of play revealed that, in
general, the zones closest to the goals presented the higher values of IPA. While in the U15,
in all numerical relations, the highest values occurred in the zones closest to the own goal,
in the U17 and U19, the highest values were found in the zone closest to the opponent’s
goal. With the increase in the number of players, the highest values in both echelons tended
to be associated to the U17 in zone 6 and to the U19 in zone 1.
The study also revealed three similar IPA values for all numerical relations and ages:
i) in the zones closest to the goals (Z1 and Z6), values were higher; ii) in Z2 and Z5,
values were intermediate; iii) in the middle zone (Z3 and Z4) values were lower. These
data revealed zones with similar IPA but with different objectives. Although players’
functions depend on their position on the pitch, which leads to different dynamics
within the game (Caro et al., 2019), there were similarities in the playing areas accord-
ing to their relative position on the field in relation to own or opposite goal. For
example, while zone 1 is characterised by the beginning of the attacking stage, zone 6 is
the space where the attack finishes. Zone 2 is characterised by the security actions of
players to continue the attacking stage. Zone 5 is the space of the pitch that is suitable
for the players to risk so as to initiate the imbalance in the opponent’s defensive stage.
Zones 3 and 4 reveal similar objectives, such as preparation of finishing situations. The
difference in values in the IPA between the middle and the zones close to the goal posts
may be due to the constraints of the offside rule, promoting a greater length distance
10 N. COITO ET AL.
between players, since the ones on the defensive line are close to the midfield line when
the ball appears in more advanced areas of the pitch (Tenga et al., 2015). On the other
hand, when teams have ball possession in zones close to the goal, they tend to place
players further from the ball in terms of width and length, to continue the attack,
promoting the distance of the team’s players. When the ball is on the middle zone, the
teams tend to place the players further from the goal, to be more compact, reducing the
distances between players and making IPA smaller in the middle zone.
Current findings suggest differences in IPA in youth football compared to professional
football. These results may help coaches to adjust the dimensions of the SSCG according
to different age groups and to the objectives concerning different field zones. However,
further research is required to link such spatial occupation with the team purposes and
the types of actions that tend to occur in each zone. Thus, it will be possible to better
design SSCG that combine the collective with the individual requirements according to
what happens during the game.
One of the present study limitations was the use of recreated matches during training
sessions instead of regular matches and considering higher number of teams of different level
of practice. Despite ensuring a controlled environment, it lacks the competitive demands
present in a regular match. Future studies should be carried out in regular competitions.
Practical implications
This study suggests the need to vary the playing area according to age level, numerical relations
and the collective goals of each task according to the field location. In other words, the sectorial
training of defenders, midfielders or attackers associated with different objectives must be trained
in different spaces. The design of SSCG should respect the proportionality of space occupied by
players of each team according to their own individual and collective capabilities for action. Thus,
the evaluation of teams’ space of play should be done during the season in order to constantly
promote new adaptations in players’ behaviours according to coaches’ purposes. The use of higher
proportional IPA in comparison with the game should offer additional time of players to perceive
and act during the training sessions, while the use of lower proportional IPA will require faster
perception and more precision in actions. The presented values could be used as reference for the
design of SSCG in the U15, U17 and U19 age levels if they don’t have possibility to measure the
IPA values of their own team. Further research should be developed to link the variation in space
occupied and the game moment, helping coaches to design more representative tasks in relation to
the competitive environment.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
The author(s) reported there is no funding associated with the work featured in this article.
ORCID
Nuno Coito http://orcid.org/0000-0001-7779-8282
Hugo Folgado http://orcid.org/0000-0002-9432-1950
INTERNATIONAL JOURNAL OF PERFORMANCE ANALYSIS IN SPORT 11
Félix Romero http://orcid.org/0000-0002-0472-0799
Nuno Loureiro http://orcid.org/0000-0002-6558-1956
Bruno Travassos http://orcid.org/0000-0002-2165-2687
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INTERNATIONAL JOURNAL OF PERFORMANCE ANALYSIS IN SPORT 13
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