Team Play in Football: How Science Supports F. C. Barcelona’s Training Strategy

doi:10.4236/psych.2013.49A2002

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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

Email: [email protected]

Received June 30th, 2013; revised August 1st, 2013; accepted August 29th, 2013

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

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

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

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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