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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.
1. Kerr NL, Tindale RS. Group performance and decision
making. Annu Rev Psychol 2004; 55: 623-55
2. Hughes M, Bartlett R. The use of performance indicators in
performance analysis. J Sports Sci 2002; 20: 739-54
3. Hughes M. Notational analysis: a mathematical perspective.
Int J Perf Anal Sport 2004; 4: 97-139
4. Lago C, Martin R. Determinants of possession of the ball in
soccer. J Sports Sci 2007; 25: 969-74
5. Tenga A, Holme I, Ronglan LT, et al. Effect of playing
tactics on achieving score-box possessions in a random
series of team possessions from Norwegian professional
soccer matches. J Sports Sci 2010; 28: 245-55
6. James N. Notational analysis in soccer: past, present and
future. Int J Perf Anal Sport 2006; 6: 67-81
7. Lames M, Ertmer J, Walter F. Oscillations in football: order
and disorder in spatial interactions between the two teams.
Int J Sport Psychol 2010; 41: 85-6
8. Yue Z, Broich H, Seifriz F, et al. Mathematical analysis of a
soccer game. Part I: individual and collective behaviors.
Stud Appl Math 2008; 121: 223-43
9. Bourbousson G, Se
`ve C, McGarry T. Space-time coordina-
tion dynamics in basketball: part 2. The interaction be-
tween the two teams. J Sports Sci 2010; 28: 349-58
10. Sumpter DJT. The principles of collective animal behaviour.
Phil Trans R Soc B 2006; 361: 5-22
11. Couzin ID. Collective cognition in animal groups. Trends
Cogn Sci 2009; 13: 36-43
12. Grehaigne JF, Bouthier D, David B. Dynamic-system anal-
ysis of opponent relationships in collective actions in soc-
cer. J Sports Sci 1997; 15: 137-49
13. Marsh KL, Richardson MJ, Baron RM, et al. Contrasting
approaches to perceiving and acting with others. Ecol
Psychol 2006; 18: 1-38
14. Marsh KL, Richardson MJ, Schmidt RC. Social connection
through joint action and interpersonal coordination. Top
Cogn Sci 2009; 1: 320-39
15. Krause J, Ruxton GD, Krause S. Swarm intelligence in an-
imals and humans. Trends Ecol Evol 2010; 25: 28-34
16. Passos P, Milho J, Fonseca S, et al. Interpersonal distance
regulates functional grouping tendencies of agents in team
sports. J Motor Behav 2011; 43: 155-63
17. Correia V, Arau´ jo D, Davids K, et al. Territorial gain dy-
namics regulates success in attacking sub-phases of team
sports. Psychol Sport Exerc 2011; 12: 662-9
18. Wilson DS, Wilson EO. Rethinking the theoretical founda-
tion of sociobiology. Q Rev Biol 2007; 82: 327-48
19. Gardner A, Grafen A. Capturing the superorganism: a
formal theory of group adaptation. J Evol Biol 2009; 22:
20. Edelman GM, Gally JA. Degeneracy and complexity in
biological systems. Proc Natl Acad Sci U S A 2001; 98:
21. Seeley TD. The honey bee colony as a superorganism. Amer
Sci 1989; 77: 546-53
Sports Teams as Superorganisms 641
Adis ª2012 Springer International Publishing AG. All rights reserved. Sports Med 2012; 42 (8)
This material is
the copyright of the
original publisher.
Unauthorised copying
and distribution
is prohibited.
22. Seeley TD. Decision making in superorganisms: how col-
lective wisdom arises from the poorly informed masses. In:
Gigerenzer G, Selten R, editors. Bounded rationality: the
adaptive toolbox. Cambridge: MIT Press, 2001: 249-61
23. Wilson DS, Sober E. Reviving the superorganism. J Theor
Biol 1989; 136: 337-56
24. Wheeler WM. The ant-colony as an organism. J Morphol
1911; 22: 307-25
25. Crozier RH. Insect sociobiology: the genetics of social evo-
lution. Science 1989; 245: 313-4
26. Reeve HK, Ho
¨lldobler B. The emergence of a super-
organism through intergroup competition. Proc Natl Acad
Sci U S A 2007; 104: 9736-40
27. Mlot NJ, Tovey CA, Hu DL. Fire ants self-assemble into
waterproof raftsto survive floods. Proc Natl Acad SciU S A
2011; 108: 7669-73
28. Ho
¨lldobler B, Wilson EO. The superorganism: the beauty,
elegance, and strangeness of insect societies. London:
W.W. Norton, 2009
29. Eccles D. The coordination of labour in sports teams. Int
Rev Sport Exerc Psychol 2010; 3: 154-70
30. Duch J, Waitzman JS, Amaral LAN. Quantifying the per-
formance of individual players in a team activity. PLoS
ONE 2010; 5: e10937
31. Passos P, Davids K, Arau´ jo D, et al. Networks as a novel
tool for studying team ball sports as complex social sys-
tems. J Sci Med Sport 2011; 14: 170-6
32. Arau´ jo D, Davids K, Hristovski R. The ecological dynamics of
decision making in sport. Psychol Sport Exerc 2006; 7: 653-76
33. Gibson JJ. The ecological approach to visual perception.
Boston: Houghton Mifflin, 1979
34. Fajen B, Riley MR, Turvey MT. Information, affordances
and control of action in sports. Int J Sport Psychol 2009;
40: 79-107
35. Dicks M, Davids K, Button C. Individual differences in the
visual control of intercepting a penalty kick in association
football. Hum Mov Sci 2010; 29: 401-11
36. Duarte R, Frias T. Collective intelligence: An incursion
into the tactical performance of football teams. In: Jemni
M, Bianco A, Palma A, editors. Proceedings of the First
International Conference in Science and Football; 2011
Apr 15-17; Palermo: 23-28
37. Travassos B, Arau´ jo D, Vilar L, et al. Interpersonal co-
ordination and ball dynamics in futsal (indoor football).
Hum Mov Sci 2011; 30: 1245-59
38. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’
networks. Nature 1998; 393: 440-2
39. Frencken W, Lemmink K, Delleman NJ, et al. Oscillations
of centroid position and surface area of soccer teams in
small-sided games. Eur J Sport Sci 2011; 4: 215-23
40. Duarte R, Arau´ jo D, Davids K, et al. In search for dyna-
mical patterns of teams’ tactical behaviours during the
match 2011 [abstract no. 114 plus oral presentation]. VII
World Congress on Science &Football; 2011 May 26-30;
41. McGarry T. Applied and theoretical perspectives of perfor-
mance analysis in sport: scientific issues and challenges. Int
J Perf Anal Sport 2009; 9: 128-40
42. Frank TD, Richardson MJ. On a test statistic for the Kur-
amoto order parameter of synchronization: an illustration
for group synchronization during rocking chairs. Physica
D 2010; 239: 2084-92
43. Kuramoto Y, Nishikawa I. Statistical macrodynamics of
large dynamical systems: case of a phase transition in os-
cillator communities. J Stat Phys 1987; 49: 569-605
44. Neda Z, Ravasz E, Brechet Y, et al. The sound of many
hands clapping. Nature 2000; 403: 849-50
45. Taki T, Hasegawa J. Quantitative measurement of team-
work in ball games using dominant region. ISPRS J Pho-
togramm 2000; 33: 125-31
46. Taki T, Hasegawa J. Dominant region: a basic feature for
group motion analysis and its application to teamwork
evaluation in soccer games. P SPIE IS&T Elect IM 1998;
3641: 48-57
47. Taki T, Hasegawa J. Visualization of dominant region in
team games and its application to teamwork analysis. In:
Proceedings of the International Conference on Computer
Graphics; 2000 Jun 19-24; Geneva, 227-235
48. Deneubourg JL. Application de l’ordre par fluctuations a
description de certaines e
´tapes de la construction du nid
chez les termites. Insect Soc 1977; 24: 117-30
49. Couzin ID, Krause J, James R, et al. Collective memory
and spatial sorting in animal groups. J Theor Biol 2002;
218: 1-11
50. Glazier P. Game, set and match? Substantive issues and fu-
ture directions in performance analysis. Sports Med 2010;
40: 625-34
51. Vilar L, Arau´ jo D, Davids K, et al. The role of ecological
dynamics in analysing performance in team sports. Sports
Med 2012; 42: 1-10
52. Barris S, Button C. A review of vision-based motion analysis
in sport. Sports Med 2008; 38: 1025-43
53. Carling C, Bloomfield J, Nelson L, et al. The role of motion
analysis in elite soccer: contemporary performance mea-
surement techniques and work rate data. Sports Med 2008;
38: 389-862
54. Correia V, Arau´ jo D, Duarte R, et al. Changes in practice
task constraints shape decision-making behaviours of team
games players. J Sci Med Sport 2012; 15 (3): 244-9
Correspondence: Ricardo Duarte, Faculdade de Motricidade
Humana, Estrada da Costa, 1495-688 Cruz Quebrada,
642 Duarte et al.
Adis ª2012 Springer International Publishing AG. All rights reserved. Sports Med 2012; 42 (8)
... The dynamics of working with a sporting team differ from working with individual athletes (Duarte et al. 2012). Sporting teams comprise of individuals who need to cooperate collectively to achieve successful outcomes in a sport (Duarte et al. 2012). ...
... The dynamics of working with a sporting team differ from working with individual athletes (Duarte et al. 2012). Sporting teams comprise of individuals who need to cooperate collectively to achieve successful outcomes in a sport (Duarte et al. 2012). Within the team, everyone's strengths must be harnessed to ensure optimal individual performance, without compromising the performance of the team (Duarte et al. 2012). ...
... Sporting teams comprise of individuals who need to cooperate collectively to achieve successful outcomes in a sport (Duarte et al. 2012). Within the team, everyone's strengths must be harnessed to ensure optimal individual performance, without compromising the performance of the team (Duarte et al. 2012). There are many advantages for sportsmen and women who are participating as part of a team, such as improved social and psychological health (Anderson, Ottesen & Thing 2018), maintained fitness (Herzog 2018) and improved confidence/self-esteem (Allender, Cowburn & Foster 2006). ...
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Background: Soccer is one of the fastest growing sports in South Africa and the number of physiotherapists working with soccer teams has increased significantly. Despite increased appointments, very little is known regarding the demographic, education and work profiles of these physiotherapists. Objective: To determine the profiles of physiotherapists working with soccer teams in South Africa. Methods: A descriptive, cross-sectional study was used to collect data from physiotherapists employed with soccer teams. Physiotherapists who were employed on a part-time basis and not registered with the Health Professions Council of South Africa and who did not give consent were excluded. A total of 38 physiotherapists working with soccer teams participated in our study. A questionnaire was circulated, and participants were given 4 months to complete and submit it. Results: Results showed that participants had a mean age of 31.35 years and were employed for a mean time of 3.41 years. Most participants were African (89.48%) and worked with amateur soccer teams (52.63%). The education results indicated that 66.67% of participants held bachelor’s degrees. Postgraduate- and undergraduate education were used most frequently by participants to guide clinical decision-making. Job satisfaction was satisfactory, but they were not satisfied with their salaries. Conclusion: Our study is the first to investigate the profiles of physiotherapists working with soccer teams in South Africa. Demographic, education and work profiles for physiotherapists working with soccer teams were compiled, and the lack of information regarding the profiles of these physiotherapists was identified. Clinical implications: Extensive future research is needed to inform and train physiotherapists regarding the management of soccer teams. Keywords: physiotherapy; profile; soccer; football; teams; education; South Africa.
... However, despite differences in dispersion measures, analysis of cooperative network variables from U12s through to U16s revealed no difference in centrality measures in a 5v5 format. Additionally, Costa et al. (138) revealed no systematic advantage for U11-U17 players born early in the selection year vs those born later when considering tactical aspects (126). It was observed that players with birthdays across the span of the selection year had similar movement patterns and tactical performance indexes (126). ...
... Upon the agreeance of a desired outcome, all players involved implicitly become attuned to desirable affordances for action that will allow the achievement of the agreed upon outcome. This collective action provides many functional advantages and has led to superior performance of groups over single organisms in a wide array of human social phenomena (138). As an example, in football, a criterion approach can be incorporated within a small-sided game with vignettes whereby a team (team A) initially begin the game losing to the opposition team (team B) by one goal. ...
... This study has limitations that require acknowledgment. First, all the variables evaluated in this research are based on match statistics, which limits the capacity to capture the complex dynamic of football tactics based on the interactions and synergies between players and teams [28]. Additionally, the study was performed with data from the Spanish football league of professional male players, and the results should not be extrapolated to other leagues, other categories or women's football. ...
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The aim of this study was to compare physical and technical match performance variables in football players who competed in the Spanish second division for one season and were promoted to the top (first) division in the following season. A total of 97 male outfield football players who were promoted from the second to the first division of the Spanish professional football league within the same team were analysed. Data were recorded using the TRACAB (ChyronHego, New York, USA) multicamera computerised optical tracking system during five seasons (2015–2016 to 2019–2020). A one-way ANOVA repeated measures analysis showed that players executed a greater number of high-intensity running (HIR) efforts (P < 0.001; ES: 0.258), as well as covering greater HIR distance (P < 0.010; ES: 0.106) and total running distance (TD) (P < 0.010; ES: 0.080), when they played in the first division compared with the second division. Moreover, players performed a lower number of passes (P < 0.01; ES = 0.116), short passes (P < 0.01; ES = 0.106), long passes (P < 0.05; ES = 0.067), dribbles (P < 0.001; ES = 0.146) and shots (P < 0.01; ES = 0.074) in the first division compared to the second division. No significant differences were found for any of the defensive variables evaluated. In conclusion, being promoted from the second to the first division of professional football requires players to adapt to greater physical demands and a reduced number of technical actions.
... Specifically, in a team game tactics are "the management (positioning and displacement/movement) of the playing space by players and teams" (Teoldo Guilherme, & Garganta, 2022: 22). These actions tend to be repetitive in specific situations such that it seemed fitting to compare sport teams to the superorganisms studied by sociobiologists (Duarte, Araújo, Correia, & Davids, 2012). ...
We study the replication of organizational routines through key employee mobility in the context of major football championships. While discussed in the literature of evolutionary economics and in some management studies, this kind of routine replication lacks systematic empirical evidence. The empirical analysis exploits two related samples assembled from several web sources. Employing a combination of descriptive and econometric approaches we show that: 1) when a coach moves from one team to another, there is no significant difference between the routines he/she employs in the latter compared to the routines he/she employed in the former, and 2) when one team changes its coach, there is a significant change in the team routines.
... As individuals, Key players are viewed as important network nodes in team coordinative structures, acting as a mediator or 'bridge' with the actions of pass out and pass in [23,24]. As groups, the core group of players is more than the sum of individual aggregated performances but through interpersonal interactions among players to shape playing styles [25]. Throughout the season, Beijing Guoan team scored 64 goals, creating the highest single-season scoring record in the history of team. ...
The current study proposes the use of Network Science as a complementary tool to analyse how specific and unique the playing style of Chinese football teams is. Departing from all passes made by a team during a whole season, we construct the pitch passing network of each match, where nodes are the different areas of the pitch, and the links account for the number of passes between any two areas. In this way, we obtain a network containing information about how a team moves the ball during the offensive phase of a match. For each match, we construct the pitch passing networks at different scales by using partitions of the pitch of different sizes. Next, we compare how consistent are the pitch-passing networks during a whole season and how the spatial scale affects the quantification of this consistency. Importantly, we also compare the networks of each team with the rest of the teams in the league, which allow us to obtain an identifiability parameter, which accounts for how particular the networks of a team are. Finally, we repeat the analysis during 5 consecutive seasons and detect what teams maintain their particular playing style during the years.
... Collectively, the results found in this study, combined with other previously published investigations, allow us concluding that the adoption of a favorable position for the continuation of the offensive play by reducing the geodesic distance between nodes (players) of the network (team) does not depend on body size. This evidence suggests that the influence of morphofunctional constraints on the central role of midfielders appears to be quite limited, given the inherent complexity of the game (Duarte et al., 2012). ...
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This study verified the effects of body size and game position on interactions performed by young soccer players in small-sided games (SSG). The sample consisted of 81 Brazilian soccer players (14.4 ± 1.1 years of age). Height, body mass, and trunk-cephalic height were measured. SSG was applied in the GK + 3v3 + GK format, and Social Network Analyses were carried out through filming the games to obtain the following prominence indicators: degree centrality, closeness centrality, degree prestige, and proximity prestige, in addition to network intensity and number of goals scored. Factorial ANCOVA (bone age as covariate) was used to test the effects of game position, body size, and respective interaction on centrality measurements (p < 0.05). Similarity between game positions in body size indicators (p > 0.05) was observed. The game position affected degree centrality (p = 0.01, η 2 = 0.16), closeness centrality (p = 0.01, η 2 = 0.11), and network intensity (p = 0.02, η 2 = 0.09), in which midfielders presented the highest network prominence values when compared to defenders and forwards. In conclusion, midfielders are players with high interaction patterns in the main offensive plays, which behavior is independent of body size.
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Desde que el minivoleibol fue introducido en la década de los años 1960, no han dejado de aparecer fórmulas competitivas y procesos metodológicos con la finalidad de adecuar este deporte a las características de niños y niñas menores de 12 años. Este trabajo reúne de forma analítica y crítica tanto el legado histórico como los fundamentos teóricos que vienen inspirando este fenómeno. Desde distintos ángulos científicos se encuentran argumentos para configurar un modelo que evoluciona recorriendo dos ejes. Uno, basado en la relación velocidad – precisión, el cual pretende maximizar la posibilidad de acción de las habilidades motoras específicas. Otro basado en la evolución precisión-control, donde desde un compromiso con la precisión de la ejecución, expone la toma decisiones a problemas progresivamente más complejos. En esta propuesta, el juego se mantiene en el centro neurálgico del modelo; sin embargo, se justifica el abordaje de aprendizaje específico fuera del juego en sí y la inclusión necesaria de contenidos genéricos comprometidos con los modelos de iniciación a largo plazo y la alfabetización física. Por tanto, se ha sintetizado la experiencia práctica y la tradición de la iniciación al voleibol a nivel mundial, las teorías, las perspectivas científicas e investigaciones de carácter general, referentes de la iniciación deportiva, principios de aprendizaje y propuestas pedagógicas previas. Su resultado son 20 aplicaciones prácticas para un modelo que resulta original en lo específico y comprometido con las corrientes del desarrollo deportivo a largo plazo en lo genérico.
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Predicting the winner of a basketball game is a difficult task, due to the inherent complexity of team sports. All 10 players on the court interact with each other and this intricate web of relationships makes the prediction task difficult, especially if the prediction model aims to account for how different players amplify or inhibit other players. Building our approach on complex systems and prototype heuristics, we identify player types through clustering and use cluster memberships to train prediction models. We achieve a prediction accuracy of ∼76% over a period of five NBA seasons and a prediction accuracy of ∼71% over a season not used for model training. Our best models outperform human experts on prediction accuracy. Our research contributes to the literature by showing that player stereotypes extracted from individual statistics are a valid approach to predict game winners.
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While undesirable, unexpected disruptions offer unique opportunities to enact adaptive expertise. For adaptive expertise to flourish, individuals and teams must embrace both efficiency and adaptation. While some industries do it readily, others continue to struggle with the tension between efficiency and adaptation, particularly when otherwise stable situations are unexpectedly disrupted. For instance, in healthcare settings, the efficiency mandate for strict compliance with scopes of practice can deter teams from using the adaptive strategy of making their members interchangeable. Yet, interchangeability has been hinted as a key capacity of today’ teams that are required to navigate fluid team structures. Because interchangeability – as an adaptive strategy – can generate antagonistic reactions, it has not been well studied in fluid teams. Thus, in this exploratory qualitative study we sought to gain insights into how interchangeability manifests when fluid teams from five different contexts (healthcare, emergency services, orchestras, military, and business) deal with disruptive events. According to our participants, successful interchangeability was possible when people knew how to work within one’s role while being aware of their teammates’ roles. However, interchangeability included more than just role switching. Interchangeability took various forms and was most successful when teams capitalized on the procedural, emotional, and social dimensions of their work. To reflect this added complexity, we refer to interchangeability in fluid teams as Ecological Interchangeability. We suggest that ecological interchangeability may become a desired feature in the training of adaptive expertise in teams, if its underlying properties and enabling mechanisms are more fully understood.
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O presente estudo objetivou analisar a realização das ações de approach, subida à rede e voleio, em um cenário crítico durante situação de treino. Dezessete atletas infantojuvenis foram analisados durante jogos tie-break de até 10 pontos, totalizando 696 trocas de bola, das quais 137 possibilitaram o approach. Foram avaliadas quatro variáveis relacionadas ao ajuste técnico, uma à tomada de decisão e uma à eficácia do voleio por meio do Game Performance Assessment Instrument. Nossos resultados evidenciam que os jovens tenistas analisados usaram o approach em 9,5% das situações favoráveis, conseguindo em 53,8% dos casos efetivar a ação tática. Sugere-se que os treinadores enfatizem cenários críticos em seus treinos para estimular a leitura da situação de jogo de seus atletas.
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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.