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Sports Teams as Superorganisms: Implications of Sociobiological Models of Behaviour for Research and Practice in Team Sports Performance Analysis


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Significant criticisms have emerged on the way that collective behaviours in team sports have been traditionally evaluated. A major recommendation has been for future research and practice to focus on the interpersonal relationships developed between team players during performance. Most research has typically investigated team game performance in subunits (attack or defence), rather than considering the interactions of performers within the whole team. In this paper, we offer the view that team performance analysis could benefit from the adoption of biological models used to explain how repeated interactions between grouping individuals scale to emergent social collective behaviours. We highlight the advantages of conceptualizing sports teams as functional integrated 'super-organisms' and discuss innovative measurement tools, which might be used to capture the superorganismic properties of sports teams. These tools are suitable for revealing the idiosyncratic collective behaviours underlying the cooperative and competitive tendencies of different sports teams, particularly their coordination of labour and the most frequent channels of communication and patterns of interaction between team players. The principles and tools presented here can serve as the basis for novel approaches and applications of performance analysis devoted to understanding sports teams as cohesive, functioning, high-order organisms exhibiting their own peculiar behavioural patterns.
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Sports Teams as Superorganisms
Implications of Sociobiological Models of Behaviour for Research
and Practice in Team Sports Performance Analysis
Ricardo Duarte,
Duarte Arau
Vanda Correia
and Keith Davids
1 Laboratory of Expertise in Sports, Faculty of Human Kinetics, Technical University of Lisbon,
Lisbon, Portugal
2 CIPER Interdisciplinary Centre for the Study of Human Performance, Lisbon, Portugal
3 School of Education and Communication, University of Algarve, Faro, Portugal
4 School of Human Movement Studies, Queensland University of Technology, Brisbane, QLD, Australia
Abstract Significant criticisms have emerged on the way that collective behaviours
in team sports have been traditionally evaluated. A major recommendation
has been for future research and practice to focus on the interpersonal re-
lationships developed between team players during performance. Most re-
search has typically investigated team game performance in subunits (attack
or defence), rather than considering the interactions of performers within the
whole team. In this paper, we offer the view that team performance analysis
could benefit from the adoption of biological models used to explain how
repeated interactions between grouping individuals scale to emergent social
collective behaviours. We highlight the advantages of conceptualizing sports
teams as functional integrated ‘super-organisms’ and discuss innovative mea-
surement tools, which might be used to capture the superorganismic proper-
ties of sports teams. These tools are suitable for revealing the idiosyncratic
collective behaviours underlying the cooperative and competitive tendencies
of different sports teams, particularly their coordination of labour and the
most frequent channels of communication and patterns of interaction be-
tween team players. The principles and tools presented here can serve as the
basis for novel approaches and applications of performance analysis devoted
to understanding sports teams as cohesive, functioning, high-order organisms
exhibiting their own peculiar behavioural patterns.
1. Introduction
Nature provides evidence that groups of co-
operating individuals can gain many functional
advantages in coordinating their actions when
working and living together. Research has dem-
onstrated the superior performance of groups
over single organisms in a wide range of human
social phenomena.
Sports teams are also com-
posed of different interacting individuals who
develop cooperative relations to achieve success-
ful performance outcomes. The collective per-
formance of sports teams has been extensively
investigated by a range of analytical performance
For example, it has been demon-
strated that the percentage of ball possession in
association football teams changes as a function
of the evolving match status, game location and
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level of opposition,
and that the percentage of
ball possession in the opposition penalty box re-
mained high when teams used a counter-attacking
However, the view that some game events
are more important than others, and that the prev-
alent methodology of notating discrete actions or
events fails to provide enough information about
the performance context, is challenging sports sci-
entists to rethink their research strategies.
With reference to tracked positional data, re-
cent studies have begun to reveal how players and
teams continuously interact during competition.
For example, teams tend to be tightly synchronized
in their lateral and longitudinal movements,
a counter-phase relation regarding their collec-
tive expansion and contraction movement pat-
commonly caused by changes in ball
This type of investigatory approach
shares some conceptual similarities with models
of behaviour in biological systems, which have
revealed emergent social collective behaviours
in human groups and animal societies.
we offer the view that sports teams also exhibit
emergent collective behavioural tendencies that
differ from the sum of individual aggregated
performances. Analysis of patterns of behaviour
atthecollectivesystemlevel in team sports requires
a reformation of notational analysis methods
used to study performance.
Like agents in other collective systems, sports
team performers often need to make decisions about
where to move and when, and which actions to
perform in uncertain and shifting environmental
conditions. It has been suggested both in biology
and in team sports analyses
that individuals base
their movement decisions on locally acquired
information sources such as the relative position-
ing, motion direction or changing motion direc-
tion of significant others operating in a system,
making a collective response all the more re-
markable. This finding implies an intertwined
relationship between perception, action and the
intentions of individuals functioning in a complex
biological system in order to intimately coordi-
nate their patterned behaviours. Next we discuss
the potential advantages of integrating socio-
biological models to study emergent collective
behaviours of performers in team sports.
2. A Brief Incursion into Sociobiology
Studies in biology have shown how the repeated
interactions among grouping animals (including
humans) scale to global collective system behav-
These social interactive behaviours
within a group lead to the emergence of a ‘collec-
tive’, which can be understood as a ‘new organism’
within the animal-environment system.
this sense, the actions of individual organisms
(e.g. team players) constrain and are constrained
by the actions of neighbouring organisms (e.g.
team-mates and opponents) toward the mutually
exclusive goals of the ‘collective’. The coordina-
tion and reorganization of these ongoing inter-
actions occur via externally controlled feedback
processes sustained by the continuous exchange
of information between the grouping individuals.
For example, when an ant finds a food source it
deposits a pheromone so that other members
of the colony can locate it, or when a fish swims in
a specific direction, its nearest neighbours in a
school soon follow to remain within one ‘fish’
length and preserve group security. As such re-
cruitment behaviours continue, the number of
individuals engaged in a goal-directed activity
Evidence is beginning to reveal that
similar processes seem to emerge during compet-
itive performance in team games in which a
player’s motions can functionally influence the
spatial-temporal characteristics of patterned move-
ments in team-mates and opponents, creating a
purposeful aggregation during specific perfor-
mance subphases.
What does a commitment to viewing sports
teams as complex superorganismic systems
imply? The sensitivity of biological systems to in-
formation provided in feedback loops can help us
to better understand the postulate that ‘a system
is more than the sum of its parts’. The functional
integration of individuals in highly related group-
ing organisms, such as social insects (e.g. ants,
bees, wasps), is a central aspect to consider. This
feature has been attributed to natural multi-
level selection mechanisms acting at the level of
between-group colonies and not just at the level
of genetic selection.
The evolutionary ad-
vantages of functional (re)organization through
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cooperative activities have led some animal societies
to develop tightly coordinated and complementary
behaviours among group members, which im-
prove the likelihood of the entire group to be se-
lected, or to attain some functional adaptations
to their goal-directed activities.
In the perfor-
mance context of team sports, these advantages
may act to promote functional group adaptations
as a means of symbolic group ‘selection’. Here,
the term ‘selection’ can be equated with a team
succeeding when competing against another group
of individuals in sport, even though the outcomes
of performance might differ from those pursued
by a biological system like a colony, flock or
school. Despite the need for functional integra-
tion, each individual in a group is different in
terms of genetic heritage, previous experience and
specific roles in the group. It is widely accepted
that interindividual variation is a valuable process
that can lead to system variability and yield a
continual supply of new solutions to the behav-
ioural challenges that groups face.
complex biological systems face a complementary
interplay between functional specialization ten-
dencies (based on interindividual variation) and
functional integration tendencies.
These prop-
erties have led some scientists to advocate that
highly coordinated groups behave like ‘super-
since individuals possessing
high levels of interindividual variability can co-
operate together to perform as a single social
entity in order to achieve specific higher-order
task goals. For instance, team games players with
different attributes, unique skills and varied roles
(functional specialization) may work together to
collectively regain ball possession by restricting
space on the field and pressurizing the opposition
(functional integration).
2.1 Viewing Teams as ‘Superorganisms’
The ‘superorganism’ concept was first proposed
by William Morton Wheeler to describe the de-
gree to which ant colony members appear to op-
erate as a single functional unit.
The concept
has been extensively used in sociobiology al-
though some criticism has pointed to the absence
of experimental and mathematical support for this
Recent formal descriptions of group
and intergroup competition
proved its utility and tempered the criticisms. An
example of successful collective system behaviours
was demonstrated by fire ants self-assembling
waterproof rafts as an adaptive evolutionary
strategy to survive floods.
The cooperative com-
plementary relations of the conspecific individuals
allowed the emergence of superorganismic behav-
iours based on the trapping of ants at the raft
edge by their neighbours. These data suggested
that the ‘superorganism’ concept can be defined
as ‘‘a group of individuals self-organized by divi-
sion of labour and united by a closed system of
(p. 84, in italics our emphasis).
These two main features of highly coordinated
grouping organisms division of labour and sys-
tem of communication might also be of interest
for performance analysis in sports teams, func-
tioning as integrated organisms.
Division of labour has been considered in stud-
ies of team sports as a key aspect expressing the
functional integration, complementarity of be-
haviour and coordination among team-mates.
The existence of a communication system is an-
other central issue also present in team sports
research. Coaches, performance analysts and re-
searchers have begun to enhance understanding
of the communication channels used by players
to support the effectiveness of teamwork.
mon actions in team games, such as passing the
ball or switching positions with team-mates, are
founded on a platform of communication or in-
formation exchange. Such actions imply the ex-
istence of informational links between players,
which allow them to detect the appropriate en-
vironmental conditions to successfully cooperate
during performance.
It has been suggested that
an important feature of superorganisms is the
existence of altruistic cooperation,
which op-
poses the existence of internal conflicts between
system agents during performance.
Despite the
absence of data on this latter issue, it is clear that
sports teams do possess altruistic forms of co-
operation between their team members. This idea
has been exemplified many times in team sports
by the unselfish play of an individual who passes
the ball to a better-placed team-mate to score,
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while eschewing the personal glory of scoring
a deciding goal. Extending understanding from
other social neurobiological systems, we argue
that considering sports teams as functional inte-
grated ‘superorganisms’ might allow us to capture
the self-organized dynamics of complex social
interactions that shape collective behaviours in
teams. In order to progress understanding of sports
teams as superorganisms, sport science needs to
develop specific analysis methods that provide
insights into the functional collective behaviours
of such social neurobiological systems. How has
recent work developed methodological and anal-
ysis tools that can capture the superorganismic
properties of sports teams?
3. Capturing the ‘Superorganismic’
Properties of Sports Teams
The interactions of agents in sports teams,
defined as collective social systems, reveal common
underlying principles. In this respect, emergent
interactions between team players are sustained
by informational flow fields that specify each in-
dividual’s opportunities for action.
For ex-
ample, in the team sport of rugby union, Passos
and colleagues
showed that an attacking sub-
group with a ball, generated information for each
individual to decrease interpersonal distances with
other players and act as a single cohesive social unit,
during its approach to a subgroup of defenders.
However, each performer possessed different char-
acteristics that influenced his/her action capa-
which constrained each individual
to display his/her own idiosyncratic behaviours.
Thus, conceptualizing sports teams as super-
organisms requires dedicated methodological tools
suitable for capturing the ‘division of labour’ and
the ‘communication systems’ of each collective
during the interplay between ‘interindividual var-
iation’ and ‘functional integration’ processes.
3.1 Tools to Assess ‘Division of Labour’ and
‘Communication Systems’
One approach to characterize the ‘division of
labour’ amongst individual agents in sports teams
involves measuring the area of a performer’s in-
terventions onfield (known as the major range).
The predominant area of each individual’s inter-
ventions during performance is defined by an
ellipse centred at the 2-dimensional mean loca-
tion of each performer, with semi-axes being the
standard deviations in X and Y directions, re-
spectively. Figure 1 displays the major ranges for
four backs (figure 1a) and three forward players
(figure 1b) of a goalkeeper+4+3+3 team forma-
tion during a football match, classified by de-
fending and attacking phases (as a function of
ball possession).
Defending phase
50 40 30 20 10 0 1020304050
X (m)
Attacking phase
Y (m)
Defending phase
50 40 30 20 10 0 1020304050
X (m)
Attacking phase
Y (m)
Fig. 1. Major ranges for two subgroups of football players in defending and attacking phases. (a) Shows the four defensive players; (b) shows
the three forwards of the same team. Reproduced from Yue Z, et al.,
with permission.
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These spatial data revealed an inverse trend
between the two subgroups of players. While de-
fenders increased their individual covered area
(i.e. range of displacement trajectories) in at-
tacking phases, the forwards enlarged their cov-
ered areas in the defending phases. This tool can
also be used to assess the coordination of labour
during performance as the game unfolds. Figure 2
presents exemplar data from a change in the
global trend of team performance, from smaller
and more proportional individual areas of inter-
vention (a) in the first 5 minutes of the match, to
highly narrow and elongated ranges (b) in the
next 5 minutes of the game.
Data from figure 2 exemplify how the co-
ordination of labour can change during perfor-
mance under the influence of natural variations
in competitive constraints, such as the opposi-
tion’s style of play
or the dynamics of ball dis-
placement onfield.
For instance, the changes
observed in figure 2 can be interpreted as indicat-
ing that the opposing team has started to impose
a direct playing style in the sport of football, with
predominant longitudinal displacements of the
Concerning the ‘communication system’ of
teams, an approach to capture the tendencies in
the relationships of team players is provided by
small-world networks.
A ball passing action
exemplifies a functional relationship between team-
mates and trends of passing relations can reveal
preferred channels of communication within a
team. Figure 3 shows an application of network
analysis to passing trends during a water polo
competition, based on simple notational data.
The strength of the relationships between pairs
of players was expressed by the width of the ar-
rows. Dominant relations and probabilities of
interaction among team-mates can be assessed
using this method. Deeper understanding of team-
work effectiveness can be obtained by constructing
Fig. 2. Major ranges of ten outfield players from a football team.
(a) Shows values from the first 5 minutes of the game. (b) Displays
values from the next 5-minute segment. A grey colour was used to
distinguish the midfielders from the defenders and forwards. Re-
produced from Duarte et al.,
with permission.
Player 2
Player 3
Player 6
Goalkeeper of
opposite team
Player 4 Player 5
Player 1
Player 2
Player 3 Player 6
Goalkeeper of
opposite team
Player 4 Player 5
Player 1
Fig. 3. (a) and (b) display trends for each team. Grey circles represent players involved in the units of attack. Orientation of the black arrows
indicates pass direction. Origin of the arrow represents the player who passed the ball and the arrowhead represents the player who received
the ball. Width of the black arrows denotes quantity of passes from one player to another during performance (i.e. the thicker the arrows, the
more passes occurred between specific players). Reproduced from Passos et al.,
with permission.
Sports Teams as Superorganisms 637
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networks according to passing accuracy.
actions, such as switching positions between two
players, if understood as communication pro-
cesses, can also be studied in these analyses. Thus,
networks provide a useful method to qualitatively
and quantitatively describe the communication-
based interactions that emerge among players in
team sports.
3.2 Compound Positional Variables
Based on individual positional data, other
variables have been proposed to assess specific
functional collective behaviours of sports teams.
These have been termed compound positional
variables because they integrate the individual
positions of each team player into a meaningful
description of a collective team pattern.
Examples of such compound positional vari-
ables include:
i) the surface area occupied by teams,
represents the covered area of the field by the
whole team in each time frame;
ii) the geometrical centre of teams,
represents a kind of ‘centre of mass’ of a team;
iii) the stretch index of teams
(also known as
), which represents the mean dispersion
value of the players around the centre of each
team (i.e. the geometrical centre);
iv) the team ranges (also known as length and
width of the team
), which represent the size of
the team in the longitudinal and lateral field
These innovative compound measures of team
performance reveal meaningful collective behav-
iours from a practical perspective and can be
used to assess the idiosyncratic performance val-
ues of each team.
Table 1 in the Supplemental
Digital Content illustrates these compound posi-
tional variables from two competing teams
can be found online at http://links.adisonline.
com/SMZ/A10. These collective measures can
assist understanding of interactions between agents
in sports teams as having ‘superorganismic’ qual-
ities. Observing their changes on different time-
scales, due to variations in performance constraints,
such as the evolving score line or different offen-
sive playing styles, is an important aspect to
consider in future performance evaluations. An-
other important issue to consider when measuring
team performance behaviours is to discriminate
values for compound positional variables during
defending and attacking performance phases.
Measurement functions to discriminate data be-
tween these phases have been reported previously
in the literature.
Despite the merits and poten-
tial of these collective measures, they are based on
the assumption that each individual agent’s be-
haviour equally contributes to functional collec-
tive performance. However, team players may
not always have the same weight in the emergence
of the social collective system behaviours.
weighting of the contribution of each player may
change as a function of the evolving game context
(e.g. the place where the ball is located onfield)
and the action capabilities of each individual (e.g.
maximum movement displacement speed). The
next section proposes alternative methods that
account for the weighted contribution of each
team player during competitive performance.
4. Emerging Alternative Approaches and
Future Directions
4.1 Cluster Phase
The cluster phase method was recently pro-
posed in order to analyse synchrony within
systems with a small number of oscillating com-
This method is based on the Kur-
amoto order parameter,
which has been used
to investigate phase synchronization in sys-
tems with large numbers of oscillating compo-
nents (e.g. emergence of collective clapping in
theatre audiences).
The Kuramoto model de-
scribes the synchronization of oscillatory move-
ment components (e.g. team players) in a single
collective parameter. Investigators have adapted
this model and showed its applicability using
a rocking chair paradigm with only six oscilla-
tory units (i.e. six individuals coordinating rock-
ing chair movements).
Specific measures of
individual and whole-group synchrony can be
obtained, which can be a useful means to quan-
tify the contribution of each team player to the
global behaviour of a team, as well as changes in
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the global synchronization tendencies within
a team.
4.2 Dominant Region
The dominant region is a method for group
motion analysis proposed to analyse the spatial
interactions in team sports.
The dominant re-
gion of a team player is defined as a region of the
field space where a particular performer is likely
to arrive earlier than other players.
This method
determines a high functional and dynamic sphere
of influence around each individual by integrat-
ing data on position, speed, direction and accel-
eration. Comparing those kinematic data of all
the performers, this method specifies their move-
ment possibilities and the functional area of in-
tervention behind the control of each specific
player. The weighted contribution of each player
interacting together with his/her team-mates and
opponents originates a purposeful aggregation
(functional integration), which can collectively
express the space-time relations between teams.
Dominance diagrams integrating the sphere of
influence of all players can be visualized in a
frame-by-frame manner depending on the sam-
pling rate of the positional data.
Figure 4 presents
an illustration of the dominant region method for
a single frame. The measures provided are time
series of individual and collective dominant re-
gion areas, time occupancy rate per specific zones
and number of links with near neighbours of each
4.3 Modelling
Common underlying principles of team sports
and animal collectives can form the basis for
a formal description (mathematical modelling)
of the collective behavioural dynamics of sports
teams, allowing scientists and practitioners to
make accurate predictions about team behaviours.
More than 30 years ago, biologists
computer simulations, known as self-propelled
particle (SPP) models, that captured the collec-
tive behaviours of animal groups in terms of local
interactions developed.
For example, Couzin
and colleagues
proposed a model in which in-
dividual animals follow only three simple rules of
thumb: (i) move away from very nearby neigh-
bours; (ii) adopt the same direction as those that
are close by; and (iii) avoid becoming isolated.
Biological systems such as schools of fish are able
to produce different complex patterns due to
small changes in these simple localized rules. SPP
models have also been used to formalize phe-
nomena in human crowds. Treating humans as
particles that interact according to a set of ‘social
forces’, these models have been successful in
predicting specific collective behaviours such as
escape panic, walking in a busy street, the for-
mation of Mexican waves in football stadiums
and the emergence of traffic jams.
It is likely
that adaptations of these models can be success-
fully applied to capture the time-evolving dy-
namics of sports teams as functional integrated
entities or ‘superorganisms’. For example, these
models could help coaches predict how attacking
and defensive formations might change during
the course of a match as specific individuals be-
come fatigued, if weather conditions deteriorate
or if the competitive performance constraints of a
game changes from beginning to end.
might be an important advance for sports per-
formance analysis, given the recent criticism that
it is overly concerned with documenting discrete
performance statistics, often in specific perfor-
mance subphases.
These innovative collective
system analysis methods may support simula-
tions and accurate theoretically principled pre-
dictions about the collective behaviours of whole
teams under competitive conditions.
5. Concluding Remarks
This paper has attempted to conceptualize sports
teamsasfunctionallyintegrated ‘superorganisms’,
proposing an explanation of how highly co-
ordinated collaborating players might collectively
operate as a single social unit. From a performance
analysis perspective, coordination tendencies un-
derlying the emergence of team behaviours seem
to be governed by locally-generated informa-
tion sources from the relative positioning of other
team players, motion directions and changes in
motion. The ‘superorganism’ proposal, more
than focusing attention on compiling discrete
Sports Teams as Superorganisms 639
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action frequencies, suggests the need to regard the
meaningful and synergistic (inter)actions within
sports teams as the appropriate focus of analysis.
The present framework suggests the reconceptu-
alization of research approaches for studying team
sport collectives, as well as performance analysis
applications. For instance, simple notation data
typically collected by performance analysts can
be interpreted in reference to small-world net-
works. Notational variables should also contain
contextual information regarding the performance
constraints surrounding the players’ behaviours,
such as their relative position on the pitch, nu-
merical relations between players in opposing
teams and relative dispersion between teams. The
development of player tracking systems, such as
electronic portable devices or multi-player video-
based systems
offer a novel opportunity to
improve research and sports performance ana-
lyses. The innovative tools presented here might
support these novel approaches to performance
analysis, devoted to the understanding of sports
510 2
510 2
Fig. 4. Visualization of the dominant region method in a single frame. (a) Shows players’ onfield positions with tracked courses. (b) Displays
individual (boundary lines) and each team’s dominant regions (colour contrasts), as well as intra-team links among players sharing direct/
immediate space (Taki T, exemplar unpublished data).
640 Duarte et al.
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teams as functionally integrated high-order
organisms that exhibit their own idiosyncratic
The conceptual approach presented here might
also have some important applications concern-
ing learning and training design. Changing the
local available information that guides players’
decisions and actions during practice games,
coaches can manipulate the local rules governing
interactions between neighbour team-mates, in-
ducing the emergence of new patterns of collec-
tive movement solutions. For example, changing
the numbers of players involved in different team
game practices is likely to promote functional
adaptations in the way team players coordinate
their labours, allowing them to explore different
channels of communication, which should lead to
the emergence of a distinct pattern of collective
In a practical and functional way,
this conceptualization can help coaches and sport
scientists gain a better understanding of processes
of team cohesion related to task and social
orientation. For example, in many team sports
there is a current tendency to use a large squad of
players to enhance and refresh the competitive
performance of the team throughout the season.
The team-as-superorganism concept could help
coaches and practitioners to understand how to
foster ‘group thinking’ so that teams can cope
with issues like injuries, illness and loss of form,
rather than to allow team players to adopt an
individualized focus, throughout such a long
performance season.
A challenging future task for researchers and
practitioners is the formal description of social
collective behaviours of sports teams. Mathemat-
ical models adapted from other biological sys-
tems, such as SPP models, may provide computer
simulations to undertake performance predic-
tions without the need to experimentally test a
whole range of team patterns onfield.
This research was partially supported by a grant
(SFRH/BD/43994/2008) awarded to Ricardo Duarte by the
Foundation for Science and Technology (Portugal). The au-
thors wish to thank Hugo Foldado, Telmo Frias and Tsuyoshi
Taki for their valuable help in some computation procedures.
The authors have no conflicts of interest to declare that are
directly relevant to the content of this review.
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642 Duarte et al.
Adis ª2012 Springer International Publishing AG. All rights reserved. Sports Med 2012; 42 (8)
... For example, in decision making (Hristovski et al., 2006), injuries , intra-and interpersonal coordination (Ric et al., 2017;Vázquez et al., 2021), subjective experience dynamics Garcia et al., 2015) or motor creativity (Torrents et al., 2014). Such understanding has redefined, and is still redefining, the conception of athletes/teams and the training process itself, and subsequently, the usefulness and applications of monitoring tools as well Davids et al., 2003;Duarte et al., 2012;Hristovski et al., 2014;Pol et al., 2020). ...
... The adaptability to environmental changes characterizes successful performers, because their system's stability is reflected in their capacity to negotiate the induced perturbations through stable but flexible coordinated movements (Davids et al., 2012;Santuz et al., 2018). Even good performers need compensatory movement variability (Davids et al., 2012). ...
... The adaptability to environmental changes characterizes successful performers, because their system's stability is reflected in their capacity to negotiate the induced perturbations through stable but flexible coordinated movements (Davids et al., 2012;Santuz et al., 2018). Even good performers need compensatory movement variability (Davids et al., 2012). Such performance conditions involve a coordinated Frontiers in Psychology | ...
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Sports monitoring, based on excessively simplistic theoretical assumptions and methodological techniques, has limitations for capturing and assessing athletes’ behaviour. This thesis, conceptualizing athletes as complex adaptive systems (CAS), aims to propose methods and data analysis techniques for assessing CAS’ properties, and approach sport-related phenomena accordingly. Four published research articles are included, studying the properties of hysteresis, variability and synergies in diverse phenomena: workload stress and tolerance, fatigue-induced exhaustion, exercising flow state, and the relation between intra- and interpersonal synergies in a dyadic task. The applied methods and techniques have shown their potential to capture: a) the psychobiological stress and exercise tolerance through the hysteresis area of heart rate and the rate of perceived exertion, b) the fatigue-induced exhaustion and the exercising flow state through the time-variability of acceleration, and c) the multilevel coordination of dyads through the analysis of synergies. The used time series analysis techniques, taken at individual level, supposed an actionable and effective way to assess athlete’s behaviour and improve the understanding of the studied phenomena. Therefore, this thesis proposes updating, on the basis of a complex systems approach, the current theoretical assumptions and methodological techniques of sports monitoring.
... For example, the critical tensile forces that produce muscle rupture in vitro cannot be directly transferred to the complex muscle contraction in vivo [23,32]. Over the past two decades, the science of complex systems, and particularly the nonlinear dynamic systems theory, has begun to percolate into various branches of sports science [22,23,[33][34][35]. Recently, the network physiology of exercise-a framework studying the nested dynamics of vertical and horizontal physiological network interactions to understand how physiological states and functions emerge-has been introduced to exercise [22,26]. ...
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Current trends in sports monitoring are characterized by the massive collection of tech-based biomechanical, physiological and performance data, integrated through mathematical algorithms. However, the application of algorithms, predicated on mechanistic assumptions of how athletes operate, cannot capture, assess and adequately promote athletes’ health and performance. The objective of this paper is to reorient the current integrative proposals of sports monitoring by re-conceptualizing athletes as complex adaptive systems (CAS). CAS contain higher-order perceptual units that provide continuous and multilevel integrated information about performer–environment interactions. Such integrative properties offer exceptional possibilities of subjective monitoring for outperforming any objective monitoring system. Future research should investigate how to enhance this human potential to contribute further to athletes’ health and performance. This line of argument is not intended to advocate for the elimination of objective assessments, but to highlight the integrative possibilities of subjective monitoring.
... Interestingly, enhancing creativity is likely to be based in inducing highly variable technical-tactical actions performed during the training program, and this leads to an emphasis on diverse training stimuli. As has been highlighted, a high number of stimuli during training tasks remains necessary, as continuous tactical thinking is mandatory for optimal performance, due to the necessity of adapting to continuous organization and re-organization in an unpredictable environment (Delgado-Bordonau & Mendez-Villanueva, 2012;Duarte et al., 2012;Vilar et al., 2012). ...
Novel viewpoints have led to an understanding that good soccer performers are capable of continuous decision-making and performing excellent motor skills in a well conditioned mental state. Our aims in this review were to: (a) summarize the effects of different conditions and constraints on a soccer player’s response and (b) identify potential training designs for varied soccer tasks from a multivariate perspective, emphasizing tactical training. We performed a systematic literature review according to PRISMA guidelines and identified multiple different player constraints, including model strategies for play, drills designed for varied conditions, and training regimens for the dimensions of the physical demands soccer players will face. The use of match-sized training spaces may improve physical fitness and collective tactical behavior, while smaller spaces may contribute to improving tactical behavior from micro-structures (e.g., 1 vs. 1). Pre-session exercises that accelerate the appearance of fatigue during training may help delay the onset of match fatigue and boost players´ creativity. Pitch modifications (dimensions or boundary modifications), modification of game principles (defending strategies or team formations), and altering the number of players involved or coach instructions may contribute to different players improvements. Differential learning, as a non-linear pedagogy, may induce improvements in all dimensions, but especially in creative thinking.
... To date, studies have identified that subtle differences in collective behavior metrics can be obtained by manipulating constraints such as pitch dimensions, number of players and player formations [11,12,17,19,44,[54][55][56][57]. However, due to the lack of understanding of ideal values for metrics, it is unclear whether these adaptations are desirable [57][58][59][60][61][62][63][64][65][66][67][68][69][70][71]. Moreover, understanding how these manipulations translate from training into matches is a further abstraction that at present there is no evidence for . ...
Extensive research has been conducted to investigate collective behavior of football players using spatial-temporal data. The purpose of this systematic review was to synthesize and evaluate the applicability of this research by reviewing information presented in previous studies and its capacity to clearly describe the analysis approaches and practical applications of findings. 85 studies were included in the review with approaches assigned to 4 categories of metrics (1: Spaces; 2: Distances; 3: Position; 4: Numerical relations) and 2 analysis methods (1: Predictability 2: Synchronization). The review identified that authors descriptions of metrics generally focused on operationalized definitions and provided limited translation to game scenarios or coaching strategies. Similarly, a substantive percentage of studies (22%) did provide any practical applications, and where these were provided, they were generally broad and provided limited actionable information that could be used directly by practitioners to inform training. Where specific applications were provided these were consistent with a dynamic systems perspective of collective behavior and focused on organismic, environmental and task constraints that could be manipulated. The findings of the present review highlight the innovative practices of the research base and identify several areas for development to increase understanding and uptake in practice.
This study describes an approach to evaluate the off-ball behaviour of attacking players in association football. The aim was to implement a defensive pressure model to examine an offensive player's ability to create separation from a defender using 1411 high-intensity off-ball actions including 988 Deep Runs (DRs) DRs and 423 Change of Directions (CODs). Twenty-two official matches (14 competitive matches and 8 friendlies) of the German National Team were included in the research. To validate the effectiveness of the pressure model, each pass (n = 25,418) was evaluated for defensive pressure on the receiver at the moment of the pass and for the pass completion rate (R = -.34, p < .001). Next, after assessing the inter-rater reliability (Fleiss Kappa of 80 for DRs and 78 for CODs), three expert raters annotated all DRs and CODs that met the pre-set criteria. A time-series analysis of each DR and COD was calculated to the nearest 0.1 second, finding a slight increase in pressure from the start to the end of the off-ball actions as defenders re-established proximity to the attacker after separation was created. A linear mixed model using run type (DR or COD) as a fixed effect with the local maximum as a fixed effect on a continuous scale resulted in p < 0.001, d = 4.81, CI = 0.63 to 0.67 for the greatest decrease in pressure, p < 0.001, d = 0.143, CI = 9.18 to 10.61 for length of the longest decrease in pressure, and p < 0.001, d = 1.13, CI = 0.90 to 1.11 for the fastest rate of decrease in pressure. As these values pertain to the local maximum, situations with greater starting pressure on the attacker often led to greater subsequent decreases. Furthermore, there was a significant (p < .0001) difference between offensive and defensive positions and the number of off-ball actions. Results suggest the model can be applied to quantify and visualise the pressure exerted on non-ball-possessing players. This approach can be combined with other methods of match analysis, providing practitioners with new opportunities to measure tactical performance in football.
The aim of this systematic review is to provide a base of knowledge from studies that have dealt with the description of collective behaviour in young footballers according to the level of competence associated to that age group, taking representative tasks from positional data as our starting point. Following the PRISMA statement a systematic revision was carried out on three meta-search engines (PubMed, Web of Science and SportDiscus). The following key words were used in the search: football, tactical behaviour, positional data and age-group, together with their equivalents. Of the 423 articles identified, 11 fulfilled the inclusion requirements. The main results suggest that: the variables made up of the joining of two points with a line (Width, Length and distance between dyads) and the collective area covered increase with age; however, the individual area tends to reduce. The increase in level of competence appears to require a greater functional variability in order to generate uncertainty and to counteract that of the opposing team. These results could allow trainers to identify on which tactical behaviour to focus intervention with the aim of fostering optimal development according the age.
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With the rapid growth of information technology and sports, a large amount of sports social network data has emerged. Sports social network data contains rich entity information about athletes, coaches, sports teams, football, basketball, and other sports. Understanding the interaction among these entities is meaningful and challenging. To this end, we first introduce the background of sports social networks. Secondly, we review and categorize the recent research efforts in sports social networks and sports social network analysis based on passing networks, from the centrality and its variants to entropy, and several other metrics. Thirdly, we present and compare different sports social network models that have been used for sports social network analysis, modeling, and prediction. Finally, we present promising research directions in the rapidly growing field, including mining the genes of sports team success with multiview learning, evaluating the impact of sports team collaboration with motif-based graph networks, finding the best collaborative partners in a sports team with attention-aware graph networks, and finding the rising star for a sports team with attribute-based convolutional neural networks. This paper aims to provide the researchers with a broader understanding of the sports social networks, especially valuable as a concise introduction for budding researchers interested in this field.
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El análisis del rendimiento está relacionado con la pedagogía del deporte. Las bases teóricas que sustentan las selecciones metodológicas deben estar bien establecidas para que haya una convergencia entre los instrumentos de evaluación y el proceso de enseñanza y entrenamiento. Este ensayo pretende presentar y discutir la importancia y representatividad del análisis de redes sociales para evaluar el rendimiento en los deportes de equipo. El objetivo principal del análisis de redes sociales es estudiar la relación entre los jugadores para identificar las posibles causas y consecuencias de los eventos durante el partido. Así, el análisis de redes sociales es diferente de los análisis tradicionales, en los que el foco principal está en el sujeto, o de los análisis notacionales, que son más utilizados y acumulan la frecuencia de los eventos ocurridos (por ejemplo, los goles marcados, la posesión del balón, las zonas de remate). Esta herramienta de evaluación, posicionada teóricamente en un enfoque ecológico, se muestra eficaz para la identificación de los patrones de interacción en un grupo y la comprensión de los artificios sociales que ayudan a entender el rendimiento de un equipo. Así, los equipos pasan a ser analizados como grupos sociales y no como sujetos aislados. En este ensayo, también se exponen las principales aplicaciones prácticas de esta herramienta de evaluación en diferentes deportes de equipo, como el fútbol, el fútbol sala, el balonmano, el baloncesto y el voleibol.
This systematic review with a meta-analysis was conducted to compare the effects of small-sided games (SSGs)-based interventions with the effects of running-based high-intensity interval training (HIIT) interventions on soccer players’ repeated sprint ability (RSA). The data sources utilized were Web of Science, Scopus, SPORTDiscus, and PubMed. The study eligibility criteria were: (i) parallel studies (SSG-based programs vs. running-based HIIT) conducted in soccer players with no restrictions on age, sex, or competitive level; (ii) isolated intervention programs (i.e., only SSG vs. only running-based HIIT as individual forms) with no restrictions on duration; (iii) a pre–post outcome for RSA; (iv) original, full-text, peer-reviewed articles written in English. An electronic search yielded 513 articles, four of which were included in the present study. There was no significant difference between the effects of SSG-based and HIIT-based training interventions on RSA (effect size (ES) = 0.30; p = 0.181). The within-group analysis revealed no significant effect of SSG-based training interventions (ES = −0.23; p = 0.697) or HIIT-based training interventions (ES = 0.08; p = 0.899) on RSA. The meta-comparison revealed that neither SSGs nor HIIT-based interventions were effective in improving RSA in soccer players, and no differences were found between the two types of training. This suggests that complementary training may be performed to improve the effects of SSGs and HIIT. It also suggests that different forms of HIIT can be used because of the range of opportunities that such training affords.
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RESUMEN El portero como jugador de campo viene siendo utilizado por la mayoría de entrenadores de futsal en situaciones de emergencia, relacionadas con un marcador adverso en los momentos finales del partido. El objetivo del presente estudio fue identificar como el impacto alcanzado por determinadas variables contextuales puede dejar instalado un estado de criticidad durante el juego que puede ser coincidente con el portero jugador. La muestra estuvo compuesta por 11.446 acciones, correspondientes a 1.325 partidos de la Liga Española de Futsal durante 5 temporadas (2010-2015). Se realizó un análisis descriptivo utilizando tablas de frecuencias que hicieron posible definir la escala de nivel crítico coincidente con portero jugador. Los resultados enfatizaron la relación de simultaneidad que parece existir entre la utilización del portero como jugador de campo y un marcador desfavorable cuando queda poco tiempo para finalizar, lo que es sinónimo de tener que afrontar una situación crítica a través de un procedimiento táctico de riesgo. Las tendencias aquí identificadas podrían ayudarán a los entrenadores a hacer un uso más racional y equilibrado del procedimiento. Palabras Clave: análisis de rendimiento, futsal, portero como jugador de campo, criticidad, variables contextuales ABSTRACT The goalkeeper as an outfield player is being used by most of the futsal coaches in emergency situations, related to an adverse scoreboard in the final moments of the game. The aim of the present study was to identify how the impact achieved by certain contextual variables can leave a criticality state installed during the game that may be coincident with the goalkeeper as an outfield player. The sample consisted of 11,446 actions, corresponding to 1,325 Spanish Futsal League matches from seasons 2010-2015. A descriptive analysis was made using frequency tables that made it possible to define the critical level scale coinciding with goalkeeper as an outfield player. The results emphasized the simultaneity relationship that seems to exist between the use of the goalkeeper as an outfield player and an unfavourable scoreboard, when there is little time left to finish, which is synonymous with having to face a critical situation through a procedure tactical risk. The trends identified here could help coaches to make a more rational and balanced use of the procedure.
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We report on a series of measurements aimed to characterize the development and the dynamics of the rhythmic applause in concert halls. Our results demonstrate that while this process shares many characteristics of other systems that are known to synchronize, it also has features that are unexpected and unaccounted for in many other systems. In particular, we find that the mechanism lying at the heart of the synchronization process is the period doubling of the clapping rhythm. The characteristic interplay between synchronized and unsynchronized regimes during the applause is the result of a frustration in the systems. All results are understandable in the framework of the Kuramoto model.
The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game, especially due to the increased physical demands of match-play. The monitoring of players’ work rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university-based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role that motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, and identify practical implications of the established body of knowledge, while highlighting areas that require further exploration.
Understanding how the actions of members of sports teams are organised and coordinated is a key challenge for sport psychology and, until recently, extant theory within sport psychology has allowed few insights into this topic. This article considers how the labour in sports teams is organised, why the organisational structure of sports teams introduces an acute need for team coordination, and why coordination in teams is difficult to achieve. It also considers the team-level social-cognitive states and processes required to achieve coordination. Implications of the conceptual framework outlined here are presented for current theory and future research on team functioning within sport psychology as well as for applied practitioners working with sports teams.
The role of feedback is central in the performance improvement process, and by inference, so is the need for accuracy and precision of such feedback. The provision of this accurate and precise feedback can only be facilitated if performance and practice is subjected to a vigorous process of analysis. Recent research has reformed our ideas on reliability, performance indicators and performance profiling in notational analysis - also statistical processes have come under close scrutiny, and have generally been found wanting. These are areas that will continue to develop to the good of the discipline and the confidence of the sports scientist, coach and athlete. If we consider the role of a performance analyst in its general sense in relation the to the data that the analyst is collecting, processing and analysing, then there a number of mathematical skills that will be required to facilitate the steps in the processes:- i) defining performance indicators, ii) establishing the reliability of the data collected, iii) ensuring that enough data have been collected to define stable performance profiles, iv) determining which are important, v) comparing sets of data, vi) modelling performances and vii) prediction. The mathematical and statistical techniques commonly used and required for these processes will be discussed and evaluated in this paper.
This paper proposes a basic feature for quantitative measurement and evaluation of group behavior of persons. This feature called 'dominant region' is a kind of sphere of influence for each person in the group. The dominant region is defined as a region in where the person can arrive earlier than any other persons and can be formulated as Voronoi region modified by replacing the distance function with a time function. This time function is calculated based on a computational model of moving ability of the person. As an application of the dominant region, we present a motion analysis system of soccer games. The purpose of this system is to evaluate the teamwork quantitatively based on movement of all the players in the game. From experiments using motion pictures of actual games, it is suggested that the proposed feature is useful for measurement and evaluation of group behavior in team sports. This basic feature may be applied to other team ball games, such as American football, basketball, handball and water polo.
Background and objective: Field invasion games, such as rugby union, can be conceptualised as dynamic social systems in which the agents continuously interact to contest ball possession and territorial gain. Accordingly, this study aimed to identify the collective system dynamics of rugby union phases-of-play near the try line by investigating whether ball displacement trajectory on the playing field provides insights on successful team performance. Methods: Five rugby union matches were videotaped involving teams at a national league performance level. From these matches, 22 second phases-of-play were selected and digitized for analysis. The variable "distance gained" was investigated as a potential coordination variable describing functional coordination between players and teams. This variable concerned the distance between ball initial position and ball current position over time and was used to define the degree of territory gained by an attacking team. Results: Analysis of distance gained dynamics in attacking sub-phases demonstrated the intermittent character of rugby union performers displacement trajectories on the playing field. Amplitude of ball movements was revealed as a distinguishing feature related to attacking effectiveness. Successful attacking phases displayed lower distances of positional retreat, with the maximum retreat distance achieved sooner in successful compared to unsuccessful phases-of-play. Autocorrelation and ApEn analyses suggested low system variability within time series data concerning both performance outcomes. However, evidence of less regularity and more complexity was found in unsuccessful phases-of-play. Conclusion: Results suggested that distance gained dynamics manifests a characteristic collective behaviour pattern that captures the macroscopic functional order of multi-player attack-defence systems in team sports like rugby union.