Psychology
2013. Vol.4, No.9A2, 7-12
Published Online September 2013 in SciRes (http://www.scirp.org/journal/psych) http://dx.doi.org/10.4236/psych.2013.49A2002
Team Play in Football: How Science Supports F. C. Barcelona’s
Training Strategy
Philippe Chassy
Department of Psychology, Liverpool Hope University, Liverpool, UK
Received June 30th, 2013; revised August 1st, 2013; accepted August 29th, 2013
Copyright © 2013 Philippe Chassy. This is an open access article distributed under the Creative Commons At-
tribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
The paper applies the theory of self-organising systems to sport. The central hypothesis is that team play,
implemented as passing in football, is the central factor generating performance. Data from the 2013
European Champions League were used to show that passing speed and precision predict domination
(possession), shooting opportunities and performance. By using principal component analysis, the article
offers a formula to estimate the team play of any team considering its passing speed, passing precision
and ability to score. The compound measure accounts for about 42% of the variance in performance. The
impact of this research on the training of football players is discussed.
Keywords: Passing; Expertise; Football; Team Play; Self-Organisation
Introduction
The present paper proposes to examine team performance in
sports through the lens of the theory of self-organised systems.
Self-organisation is concerned with how dynamics at the local
level determine coordination and cohesion at the system level.
Primarily used in physics (Prigogine & Nicolis, 1977), the the-
ory of self-organisation has developed ramifications in numer-
ous disciplines such as biology (Camazine et al., 2003), artifi-
cial intelligence (Fan & Yen, 2004), and sociology (Radzicki,
1990). Self-organisation in football has been used to analyse the
dynamical characteristics of the actions leading to a goal (Gre-
haigne, Bouthier, & David, 1997; McGarry, Anderson, Wallace,
Hughes, & Franks, 2002). In spite of this interesting research,
the theory of self-organising systems has received compara-
tively little attention in sports. Recently, the ongoing research
on the sources of performance has sparked a controversy on the
role of possession in performance; some authors believe that
mere possession ensures good performance while others believe
that possession is not a sufficient factor (Collet, 2013; Jones,
James, & Mellalieu, 2004; Lago-Ballesteros, Lago-Peñas, &
Rey, 2012; Lago & Martin, 2007). The present paper will use
the self organising theory to show that researchers have over-
looked the central role of passing. We further show that the
application of the theory of self-organising systems to sport has
some implications on identifying the cognitive factors that un-
derpin performance. The theoretical section introduces the con-
cept of self-organisation and how it applies to sport psychology.
In the second section, evidence from professional games is used
to show how self-organisation reveals the factors underlying
team play in world-class football. Finally, the contribution of
the present approach in developing new training areas in foot-
ball is discussed.
Theory
Self-organisation in biological systems (i.e., made of living
organisms) emerges whenever the capabilities of an individual
to comply with the task are not sufficient. In such case, the
coordinated effort of many individuals is required. An illustra-
tive case of self-organising systems is societies of insects
(Bonabeau, Theraulaz, Deneubourg, Aron, & Camazine, 1997).
By noticing the apparent synchrony at the system level, a naïve
observer might conclude that the animals follow a general plan.
For example, in spite of being mostly blind, ants can coordinate
the efforts of hundreds of thousands to explore a region around
the nest and organise the collection of food along well-defined
paths (Deneubourg & Goss, 1989). Research has revealed that
the perception of units being coordinated by a general working
plan is an illusion; the animals actually respond to local patterns
(Karsai & Theraulaz, 1995). At the system level, the illusion
stems from the fact that each animal responds as promptly as
possible to the incentives and stimulations that are relevant to
its function. In several circumstances animals can cooperate in
small groups and coordinate actions between groups. Yet, each
animals carries out part of the work without knowledge of what
the others are doing. What is referred to as collective intelli-
gence is thus the efficiency that emerges at the group level from
distributed organisation. The representation of the task is, also,
distributed among agents; no one animal has a representation of
the big picture. More than the actual knowledge of each animal,
it is the speeded coordination of their work that generates effi-
ciency. The behavioural pattern of the colony emerges as a
result of rules applied at the local level. Self-organisation is
thus concerned with how individual skills and abilities create a
group behaviour which has characteristics of its own.
The present paper will use football to apply self-organisation
in sports. Previous studies have demonstrated that teams can be

P. CHASSY
8
conceptualised as dynamical systems (Grehaigne, Bouthier, &
David, 1997; McGarry, Anderson, Wallace, Hughes, & Franks,
2002). In this perspective, the dynamics of opposing teams of
similar strength usually generate a stable state. In football, a
stable state indicates that the speed and coordination of the
attackers is balanced by the speed and coordination of the de-
fenders. Sporadically though, the equilibrium is altered by a
perturbation of the system, consider for example a penetrating
pass. Should the perturbation be sufficient to break out the
equilibrium then the dynamics of the attacker dominates the
one of the defender, potentially creating a shooting opportunity.
The theory of self-organised systems has enabled researchers to
integrate several variables at different levels into a mathemati-
cal description. Since we consider living organisms, the dy-
namics are ultimately underpinned by cognitive abilities. The
present paper will complete the research in the field by speci-
fying the nature of the cognitive factors operating at both the
team and individual levels.
The situation at the individual level is determined by the
constraints that competitive, professional football imposes on
cognitive processes. Carling (2010) recorded the behavioural
performance of 28 professional players during 30 first-division
league games taking place over two seasons. Averaged across
possessions, a player covers about 4 meters at a speed of 12.9
km/h during which he touches the football 2.1 times. The dura-
tion of the possession is 1.1 second in average. Such a time
window limits drastically the options that a player can explore
before making a decision. The 4 meters of progress when in
possession of the ball seem negligible (4.4%) when compared
to the length of a pitch (120 m). It is worth noting that 4 meters
is also the average distance of the closest opponent when re-
ceiving the ball. These huge constraints stem from the fact that
opposed to the multitude, a single individual cannot win. The
solution lies in an exact coordination of actions that will over-
come the sum of the individual skills of the opponents.
Coordination of action within the framework of self-organi-
sation translates as team play in sports. Anecdotal evidence
supports the view that team play can overcome the sum of the
individual skills for having the best player in one’s team does
not necessarily ensure victory. A perfect indicator of team play
is passing for it entails all the skills that a player needs to re-
spond appropriately to the proximal configuration of teammates
and opponents. Furthermore, for a pass to be complete, the
player should know where the teammate is, know how to tech-
nically make the pass and properly execute motor program.
Hence, the number of passes constitutes an elementary, yet very
accurate, measure of the degree of coordination of the players.
In line with the theory of self-organising systems, professional
teams (i.e., experts) must demonstrate better dynamics (i.e.,
coordination) and thus better passing skills than non-profes-
sional teams. Russell (2010) recorded the performance of 10
recreational players and 10 professional players in a wide range
of physical and technical skills. They found that professional
players were 14% faster and 17% more precise when passing
than recreational players. The authors showed in parallel that
the mean dribbling speed, that is the individual ability to pro-
gress with the ball, did not differ between the two levels of
expertise. That skills relating to team play are on average more
developed than individual skills supports the self-organising
systems as a valid theoretical framework.
Possession of the ball has been considered to be a significant
factor in generating performance (Jones et al., 2004; Lago-
Ballesteros et al., 2012; Lago & Martin, 2007). Yet, by ana-
lysing games played at the international level over a three-year
period, Collet (2013) has showed that possession actually cor-
relates negatively with performance when other factors influ-
encing play are accounted for (i.e., home advantage). In line
with Collet’s findings, it could be argued that to speed up the
process of bringing the ball close to the opponents’ goal the
minimum number of passes would be required. This view
would also be consistent with the idea that minimizing the
number of passes should reduce the opponent’s time to respond.
Empirical data has demonstrated otherwise, Hughes (2005) has
analysed the relationship between the number of passes per
possession and the frequency of shots and found a positive
correlation meaning that the more passes per possession the
more shots a team gets. Another study informative of the rela-
tionship between possession and performance was conducted
by Redwood-Brown (2008). The author showed that the fre-
quency of passes actually increases before the goal. Consider-
ing self-organising systems as the framework these results sug-
gest that it is not possession that matters as much as the ability
of the team to self-organise when it is in possession of the ball;
an ability that is reflected by the speed at which the players pass
the ball to one another. Hence, possession is not the factor that
is to be correlated with performance: It is the frequency and
precision of the passes.
The core principle underlying self-organised systems is that
organisation at the group level emerges from local interactions.
In football, the ability to move as a group is underpinned by the
passing abilities of individual players. Should self-organisation
be efficient, then the acquisition of the ball, by any player of the
team, should lead to the ball being passed from one player to
the next until the ball is passed to the player best positioned for
shooting. From this logic, we distinguish two factors in the
formalisation of a team’s performance. The first factor relates
to the ability of the team to pass the ball while changing its
spatial configuration to progress on the pitch. At the player
level, the first factor refers to the ability of player to pass accu-
rately when in possession and to occupy spaces when a team-
mate is in possession. The more teammates create space at
proximity of the possessor of the ball, the more options a player
has to make a pass, the more likely the player will make a pass
that makes the team progress as a group towards the goal. At
the team level it is thus essential to keep changing the spatial
configuration of the players so as to limit the capacity of the
defender to adapt. Passing in an ever-changing spatial configu-
ration of players requires perfect coordination. The ability to
pass efficiently thus reflects how quickly a team can change its
configuration, and how efficient it is at moving as a whole. If
the defenders have less team play than the attackers their or-
ganisation will collapse and spaces will be opened for the at-
tackers. The second factor is the ability of the shooter to score a
goal. Since it is rare that goals are scored by the sole action of
one player, the ability of a team to score a goal also depends on
passing abilities. It is commonly measured by the ability of the
shooter to convert a try (shoot) into a goal.
Within the framework of self organising systems, the ability
of a team to make frequent and accurate passes reflects its level
of self-organisation which should predict its ability to keep the
ball and its ability to generate shooting opportunities. Then, by
integrating the ability to convert a shoot into a goal, the vari-
ables should predict performance (as indicated by the number
of goals). In the framework presented in Figure 1, the speed

P. CHASSY
and precision of the passes, as well was the ability to shoot,
reflect the capability of the team to act as a whole. By consid-
ering these three factors as mere indicators of self-organisation
we suggest that not only they correlate with performance but
also that they can offer a mean to actually measure self-or-
ganisation. Such measure would on its own account for a sig-
nificant amount of performance.
Methods
Raw statistical data from the UEFA champions’ league 2013
were downloaded from the official website (www.uefa.com) on
May 26th. The set was constituted of six parameters for each of
the 32 teams: total number of passes, total number of passes
completed, mean time of possession per game (in minutes),
number of games played, total number of shots, and total num-
ber of goals. From these raw data, we computed five parameters
of interest (See Table 1). Pass density is the frequency of
passes per minute of possession of the ball. It reflects how
quickly a team in average makes passes. The precision of the
passes was assessed by dividing the number of completed
passes by the number of total passes. The ability to score was
computed by dividing the number of goals per the number of
shots; it was termed hit ratio. These three parameters constitute
the factors reflecting self-organisation. Hit ratio translates the
capacity of the shooter to convert a shot into a goal and as such
it is an indicator of performance at the individual level. Yet,
when considering performance at the team level, the shooter
represents only one factor and as such it is integrated with other
factors (e.g., passing) to model performance. Two parameters
of interest reflecting performance were computed. The first
parameter was shot density which is the number of shots per
minute of possession of the ball. This factor enters the defini-
tion of performance since it provides a measurable indicator of
what the team produces per unit of time. The second parameter
Figure 1.
The combination of factors underlying team play.
Table 1.
Parameters of interest.
Parameter of interest Calculation
Pass density Number of passes per minute of possession
Pass precision Number of completed passes/total passes
Hit ratio Number of goals/shots
Shot density Number of shots per minute of possession
Performance Number of goals per minute of possession
is the actual performance of the team defined as the number of
goals per minute of possession.
With the quantifiable indicators of self-organisation in mind
(see Table 1), it is possible to derive a set of hypotheses.
Hypothesis 1. Pass density and precision correlate positively
with possession;
Hypothesis 2. Pass density and precision correlate positively
with shooting opportunities;
Hypothesis 3. Pass density, pass precision and hit ratio cor-
relate positively with performance (i.e., goals per minute of
possession);
Hypothesis 4. A principal component analysis (PCA) con-
ducted on pass density, pass precision, and hit ratio, will yield a
unique indicator of self-organisation. The indicator correlates
positively with performance.
Results
Table 2 reports the descriptive statistics of the raw data and
of the parameters of interest.
Table 2 shows the means for the parameters of interests av-
eraged across the 32 teams. A regression of pass density and
pass precision on possession has been carried out. The results
support hypothesis 1; F(2,30) = 9856.97, p < .01, MSE = 1.35.
Equation 1 accounts for 99.85% of the variance: Pass density
and precision are thus crucial to generate domination. The same
two parameters also correlate strongly with shot density (F(2,30)
= 280.55, p < .01, MSE < .01). Equation 2 predicts 94.92% of
the variance. Passing density and precision correlate very strongly
with shooting opportunities (hypothesis 2). Finally, there is a
significant correlation between the three parameters (i.e., den-
sity & precision & hit ratio) and performance, F(3,29) = 156.00,
p < .01, MSE < .01. Equation (3) accounts for 94.16% of the
variance thus supporting hypothesis 3. Interestingly, and in line
with previous findings from Collet (2013), possession time in
minutes per game was not predictive of performance (F(1,30) =
4.02, p = .05, r2 = .13).
Equation 1: Possession = −.71 * Density + 59.32 * Precision,
respective standardized coefficients: β = −.44 and β = 1.44.
Equation 2: Shot = (.022 * Density) + (−.219 * Precision),
respective standardized coefficients: β = 1.57 and β = −0.60.
Equation 3: Goal Density = (.003 * Density) + (−.071 * Pre-
cision) + (.173 * Scoring Power), respective standardized coef-
ficients: β = 1.22, β = −0.95, and β = 0.71.
To provide a unique indicator of the ability of a team to self-
organise, we used principal component analysis. The technique
estimated the relative weight of pass density, pass precision and
hit ratio to yield a unique measure of self-organisation. Fol-
lowing Stevens (1996) we retained the factors for which eigen-
values were superior to 1 and which commonalities were supe-
rior to .70.
Two factors met these criteria. Table 3 shows that Factor 1 is
heavily loaded with pass density and precision and negatively
load with hit ratio. It explains 42.75% of the total variance. We
term this factor self-organisation capability since it reflects the
ability of the team to pass quickly and with precision. Factor 2
is made of efficiency and density and accounts for 35.94%.
This factor shows the importance of scoring at each shooting
opportunity. We term this factor offensive power. The self-
organisation capability and offensive power combine to form
the team play index which accounts for as much as 78.69% of
the variance (see Figure 1).

P. CHASSY
10
Team play as the combination of self organisation and offen-
sive power was computed for each team on standardized values.
A regression of team play on efficiency reveals a linear trend
(see Figure 2) where team play explains a significant 42.54%
of the variance, F(1,30) = 22.21, MSE < .01, p < .01.
Discussion
The paper explored the idea that a football team can be for-
malised as a self-organising system. By applying the definition
of self organisation to football we came to the conclusion that
team play constitutes the core of performance. Considering
passing as the hallmark of team-play, we derived four hypothe-
ses. The first hypothesis was that passing density and passing
precision predict possession. This hypothesis has been sup-
ported (see Equation 1: r2 = .99). The second hypothesis was
that passing density and passing precision predict shooting
opportunities. Here too, the data have supported the hypothesis
(see Equation 2: r2 = .95). The third hypothesis was that passing
and shooting abilities predict performance. The third hypothesis
has been confirmed by statistical analysis (see Equation 3: r2
= .94). The fourth hypothesis was that team play, formalised as
a compound of self-organisation capability and offensive power,
explains a significant amount of variance in performance. This
hypothesis has been confirmed and a mathematical model of
self organisation has been put forward (See factors Table 3, r2
= .43). Furthermore, and in line with previous findings from
Collet (2013), there was no significant relationship between
possession and performance.
Table 2.
Average performance across the 32 teams for the European Champions
Leagues 2013.
Variable Mean Standard
deviation Unit
Games 7.813 2.242 Games played
Possession 28.688 3.137 Minutes/game
Total passes 4131.250 1616.475 Passes for all games played
Completed passes 2930.719 1323.929 Passes for all games played
Shots 56.313 27.815 Shots for all games played
Goals 11.500 6.782 Goals for all games played
Pass density 18.149 1.898 Passes per minute
of possession
Pass precision 0.698 0.054 Ratio of completed passes
Hit ratio 0.203 0.069 Ratio of goals
Shot density 0.245 0.071 Shots per minute of possession
Performance 0.049 0.020 Goals per minute of possession
Table 3.
Component for the two factors.
Component Factor 1 Factor 2
Pass density .793 .335
Pass precision .808 −.286
Hit ratio −.037 .940
Figure 2.
Relationship between team play (compound of pass density, pass preci-
sion, and hit ratio) and performance (goals per minute of possession).
The results have revealed that passing density and precision
are very good predictors (99.85%) of the ability of a team to
keep possession of the ball and to have shooting opportunities
(94.92%). We also showed that possession itself was not pre-
dictive of shooting opportunities. That pass density and preci-
sion play a more central role than possession does support the
idea that passing rather than possession is crucial to generate
play. Such an important finding is perfectly supported by some
studies examining the relationship between spatial patterns of
passes and performance. By using the theory of free-scale net-
works, Yamamoto (2009) has demonstrated that the path of the
ball is not random. This study is in resonance with Hirano and
Tsumoto (2004) who showed that some pass patterns are more
efficient than others, implicitly underlining the centrality of
some players in passing to all other players. Such central play-
ers act as passing hubs. In addition, since passing patterns vary
with the spatial distribution of players, we would expect differ-
ent tactics to generate different passing patterns. Indeed, the
spatial distribution of players (i.e., tactics) affects the speed at
which the players cooperate (Bradley et al., 2011). These find-
ings are easy to interpret in the framework of self-organising
systems, some positions (i.e., offensive midfielder) provide
players with a more central position and as such makes them
closer to a greater amount of team mates. Changing the tactics
changes the passing hubs thus setting new passing paths. Even
if the spatial patterns are changed, the key factor remains the
speed at which the passes are made. Passing quickly and accu-
rately is the essence of keeping the ball and the source of scor-
ing opportunities.
Adding hit ratio to pass density and precision enables pre-
dicting performance with a level of high accuracy (94.16%).
That the opportunities to shoot are generated by pass density
and precision sheds a new light on the research on shooting.
Much of this research is focusing on technique (Alcock, Gil-
leard, Hunter, Baker, & Brown, 2012; Hennig, Althoff, &
Hoemme, 2009; Lees, Asai, Andersen, Nunome, & Sterzing,
2010) but does not necessarily relate the skills necessary to

P. CHASSY
shoot with the position played on the field. The self-organising
approach suggests that the position of the shooter (angle and
distance to goal) will critically depend on the tactics. It is thus
likely that the shooter will be in similar positions across at-
tempts. The technique he should be trained with in terms of
controlling the ball and shooting should then focus on those
useful in the determined type of spatial configuration (e.g.,
4-4-2).
We now turn our attention to the impact on the cognitive as-
pects of playing football. That passing plays a central role
raises the question of which knowledge is useful to the player.
The mean passing density in our sample was M = 18.15
passes/min (SD = 1.90 passes/min) which provides 3.31seconds
on average for a player to control the ball, keep the ball away
from the opponent’s reach, make a few meters of progress and
eventually make a decision about to whom he should pass the
ball. What psychological mechanism is able to provide an indi-
vidual with a potentially correct answer to a complex, dynami-
cal problem in a short time frame? Psychological theories of
decision making describe the decider as an agent who frames
the situation according to a reference point, then he or she ana-
lyses separately each option by evaluating the possible evolu-
tion of events, and finally optimizes the decision by selecting
the most rewarding option (Kahneman & Tversky, 1979; Schmidt
& Zank, 2009). It is clear that professional players do not have
the luxury of analysing all potential courses of action and select
the most appropriate option. The speed required to comply with
the task calls for psychological mechanisms that are automatic
and yet will offer a satisfactory level of performance. Pattern
recognition is not only a common mechanism in human percep-
tion (Gobet, Chassy, & Bilalic, 2011) but it also has proved to
be a central component of expert performance; for example in
reducing perceptual complexity (Chassy & Gobet, 2013) or in
orienting strategic thinking (Chassy, 2013). The power of pat-
tern recognition lies in the knowledge that is immediately acti-
vated upon recognition of domain-specific patterns. Since each
pattern identifies specific features of the problem situation, it
does activate a restricted set of potential solutions that enable
the experts to perform well even under drastic time constrains
(Burns, 2004). Given the huge time constrains in professional
football; pattern recognition is thus the psychological mecha-
nisms operating at the agent level in sports.
Considering pattern recognition as the psychological mecha-
nisms underlying performance in football implies that the
knowledge that is automatically activated includes both motor
programs (how to make the pass) and spatial information (to
whom it is best to make the pass). The technical aspects of
modern football have been the focus of much research (Bruce,
Farrow, Raynor, & Mann, 2012; Lees et al., 2010; Miranda,
Antunes, Pauli, Puggina, & da Silva, 2013; Savelsbergh, Haans,
Kooijman, & van Kampen, 2010). On the other hand, how spa-
tial knowledge is encapsulated in the understanding of the
player has received comparatively little attention. Spatial
knowledge is crucial since it is the format in which the player
will understand the dynamics linked to its tactics. If the motor
abilities that have been trained do not correspond exactly to the
spatial dynamics of the team then the player will have to cogni-
tively compensate for the discrepancy between spatial dynam-
ics and technical limits. Great passers are thus those players
who can make a pass that is in adequacy with the tactics and
who can predict the dynamical evolution of play. How much
does it take to have good pass makers? The developmental
trajectories of great passers inform us that at least five years are
necessary. For example Xavi and Iniesta, the passing hubs of
the F. C. Barcelona, joined the club at 11 and 12 years old re-
spectively. They have been trained playing the FC Barcelona
style since and they have started their professional career both
at 18. These figures are in line with expertise in other domains
suggesting that practice should start early (around 10 - 12 years
old) and be exercised for several years (Campitelli & Gobet,
2008).
A few caveats have to be born in mind when interpreting the
results of the present study. The first caveat is that the present
study presents averages for teams and as such erases individual
differences. It is clear that the passing requirement of midfield-
ers surpasses the one of the centre forward or of the support
striker. Yet, on the basis of self-organising system the team will
only be as strong as its weakest player. That is, if one player
has very poor passing abilities then all the passing paths that in-
volve this player will see the probability to fail raise dramati-
cally. Then, the estimate of team play will be biased by one
player. The influence of local poor play over the overall per-
formance should be the focus of further research. Another limit
to bear in mind is that the theory presented in this paper applies
to team sports wherein the cooperation among team-mates is
part and parcel of success. Team games that do not involve
cooperation with the ball such as baseball do not fall within the
scope of the present theory. Finally, since the frequencies of-
fered by the UEFA were aggregated over all games we could
not control for home advantage (see Collet, 2013). Further re-
search should address the importance of this factor in modulat-
ing team play.
The principal component analysis has revealed that team play,
as a compound between self-organising capability and offen-
sive power, accounts for 42.54% of the variance of a team’s
performance. Our index of team play provides a very good
measure of the ability of a team to self-organise its play. That
self-organisation plays a huge role in performance lends cre-
dence to theories of football that placed passing at the centre of
their philosophy. Johan Cruyff has contributed to develop a
passing game in Barcelona first as a player and then as a coach.
The passing paradigm has now reached its peak. The approach
has also been used by the Spanish national team. The results
over the past decade have been tremendous for the national
team and for F. C. Barcelona. The efficiency of passing has
been acknowledged by the opponents of such teams. Sir Alex
Ferguson, coach of Manchester United, has labelled F. C. Bar-
celona style a passing carousel that makes you dizzy. The pre-
sent results, and the index of team play calculated on pass den-
sity pass precision and hit ratio, support the training approach
that is championed at F. C. Barcelona.
The article has brought convincing evidence that the theory
of self-organising systems is a correct framework to understand
team play in football. The results extend previous research done
with the theory of self-organising systems to the analysis of
dynamical factors enabling the team to self-organise. It is
demonstrated that it is not possession but speed and precision
of passes that generate play. The psychological interpretation of
these results is that training should focus on the technical skills
that enhance passing abilities and tactical understanding. These
cognitive factors also emphasise the fact that good passing
players are trained over years and that the understanding of
tactics might be the actual crucial factor in developing new
talents. At the individual level expertise in spatial cognition and

P. CHASSY
12
passing skills are the two factors that ensure good team play.
Thus, in the perspective of self organizing systems, it is this
specific pattern of skills that underpins the potential to create
disturbance in the system’s dynamics, the key factor for a win.
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