NAME: ELLIOTT GRIFFIN
UNIVERSITY NUMBER: ST07002774
CARDIFF SCHOOL OF SPORT
UNIVERSITY OF WALES INSTITUTE
CARDIFF
THE INCIDENCE AND OUTCOME OF
ATTEMPTS TO CREATE PERTURBATIONS
IN SOCCER
Table of Contents
Page Number
Acknowledgements
i
Abstract
ii
CHAPTER I - INTRODUCTION
1.0
Introduction
1
1.1
Scope of the Study
1
1.2
Research Expectations
2
CHAPTER II – LITERATURE REVIEW
2.0
Literature Review
3
2.1
Dynamical Systems
3
2.2
Perturbations in Racket Sports
4
2.3
Perturbations in Team Sports
4
2.4
Utilising Perturbation Information
6
2.5
Purpose of the Study
8
2.6
Aims of the Study
9
CHAPTER III - METHODOLOGY
3.0
Methodology
10
3.1
Introduction
10
3.2
Equipment and Data Population
11
3.3
Pilot Study
11
3.4
System Design
12
3.5
Procedure
14
3.6
Reliability
15
3.7
Data Processing
17
3.8
Limitations and Delimitations
17
CHAPTER IV - RESULTS
4.0
Results
19
4.1
Team 1’s Data
19
4.1.1
Perturbation Attempts
19
4.1.2
Success of Attempted Perturbations
20
4.1.3
Pitch Location
21
4.1.4
Perturbation Variables
21
4.1.5
Match Situation
23
4.1.6
Players Creating Perturbations
23
4.1.7
Outcome of Successful Perturbations
25
4.2
Opposition’s Data
26
4.2.1
Perturbation Attempts
26
4.2.2
Success of Attempted Perturbations
27
4.2.3
Pitch Location
28
4.2.4
Perturbation Variables
29
4.2.5
Match Situation
30
4.2.6
Players Creating Perturbations
30
4.2.7
Outcome of Successful Perturbations
31
4.3
Comparison of Team 1’s and their Opposition’s Data
32
4.3.1
Perturbation Attempts
32
4.3.2
Success of Attempted Perturbations
33
4.3.3
Pitch Location
34
4.3.4
Perturbation Variables
34
4.3.5
Match Situation
34
4.3.6
Players Creating Perturbations
35
4.3.7
Outcome of Successful Perturbations
35
CHAPTER V - DISCUSSION
5.0
Discussion
37
5.1
Perturbation frequency
37
5.2
Success Rate
38
5.3
Match Situation
39
5.4
Perturbation Location
40
5.5
Perturbation Variable
41
5.6
Players Attempting to Create Perturbations
42
5.7
Perturbation Outcome
42
5.8
Practical Implications
43
5.9
Limitations
44
CHAPTER VI - CONCLUSION
6.0
Conclusion
45
6.1
Directions for Future Research
45
REFERENCES
APPENDICES
Appendix A – Notation Sheets
Appendix B – Percentage Error Calculations
Appendix C – Notated Data
46
List of Tables
Page Number
Table 1
Operational definitions of perturbation variables
13
Table 2
Operational definitions of the possible outcomes of
13
attempted perturbations
Table 3
The frequency and percentage error of perturbation
16
data between different trials
Table 4
Perturbations attempted by Team 1 during different
23
match situations
Table 5
The amount of perturbations attempted by each of
23
Team 1’s players
Table 6
The outcomes of Team 1’s successful perturbations
25
Table 7
Perturbations attempted by opposition during different
30
match situations
Table 8
The amount of perturbations attempted by each of the
30
opposition’s players
Table 9
The outcomes of the opposition’s successful perturbations 31
Table 10
The average frequencies of Team 1’s and their opposition’s 32
perturbation attempts and non perturbation attempts, and
their calculated differences using a Wilcoxon Signed
Ranks Test
Table 11
The average frequencies of the skill variables used by
Team 1 and their opponents in an attempt to create
perturbations
34
List of Figures
Page Number
Figure 1
Pitch grids used to record information regarding the
12
attempt to create perturbation category
Figure 2
Key used to identify perturbation variables
14
Figure 3
The frequency of perturbation attempts and passes and
19
dribbles not deemed to be perturbation attempts for
Team 1 during each match
Figure 4
The percentage of Team 1’s successful and unsuccessful
20
perturbations during each match
Figure 5
The distribution of Team 1’s perturbation attempts in
21
relation to pitch location
Figure 6
The outcome of Team 1’s perturbation attempts in relation 22
to pitch location
Figure 7
The frequency and outcomes of Team 1’s perturbation
22
variables
Figure 8
Perturbation attempts by Team 1’s left midfielder during
24
match four
Figure 9
The successful perturbations that led to Team 1’s goals
25
Figure 10
The frequency of perturbation attempts and passes and
26
dribbles not deemed to be perturbation attempts for the
opponents during each match
Figure 11
The percentage of the opposition’s successful and
27
unsuccessful perturbations during each match
Figure 12
The distribution of the opposition’s perturbation attempts
28
in relation to pitch location
Figure 13
The outcome of the opposition’s perturbation attempts in
29
relation to pitch location
Figure 14
The frequency and outcomes of the opposition’s
29
perturbation variables
Figure 15
Perturbation attempts by the opposition’s central
midfielders during match eight
31
Figure 16
The successful perturbations that led to the opposition’s
32
goals
Figure 17
The perturbation success rates of Team 1 and their
33
opponents during each match
Figure 18
Team 1 and their opponents’ perturbation attempts per
35
minute during different match situations.
Figure 19
Outcomes of successful perturbations for Team 1 and the
opponents
36
Acknowledgments
I would like to take this opportunity to thank Dr Nic James, whose expertise and
guidance has been invaluable throughout the completion of this dissertation.
i
Abstract
Perturbations in soccer have been reliably identified (Hughes et al., 1997, 1998) as the
skill elements that disrupt the regular stability of a match so that an advantageous
situation may be achieved. Consequently perturbation analysis identifies the phases
of a match that are most relevant to its outcome. The aims of this study were firstly to
determine the frequency at which teams attempt to create perturbations when in
possession and to establish the success rate of teams in converting their perturbation
attempts into goal scoring opportunities, and then to identify how match situation can
affect a team’s desire to create perturbations. An original hand notation system was
designed specifically for this study which allowed for the identification of several
related variables (pitch location, skill variable, player attempting perturbation and
outcome of successful perturbations). Eight games involving a specific Coca-Cola
League One team from the 2007/2008 season were analysed. Results indicated that
teams attempted to turn on average 14.76% of their possession into perturbations. It
was found that success rate of perturbations attempts was an indicator of successful
performance, though significantly more perturbation attempts were unsuccessful than
successful (Z = 2.371, P < 0.05). Results regarding match situation were somewhat
inconclusive, although once a team had established a lead both the winning and losing
team increased the rate at which they attempted to create perturbations.
By
identifying the related perturbation variables it was possible to give teams specific
attacking profiles, which could be utilised by coaches to predict how future opponents
may play and to maximise successful performance within their own team.
ii
CHAPTER I
INTRODUCTION
1.0
Introduction
The primary objective of notational analysis is to support coaches and players in the
decision making process by providing relevant information regarding performance
(O’Donoghue, 2006). For analysis to be utilised to its full potential it is necessary to
go beyond descriptions of performance and to move towards the prediction of future
performances (Grehaigne et al., 2001). A way in which to achieve this could be
through dynamical analysis which is a relatively new area of research that is
recognised as an original way to analyse data in the field of performance analysis
(Reed & Hughes, 2006). A characteristic of a dynamic system which seems to occur
naturally in sport is brief periods of instability where performance perturbs from its
original state. Such instances are referred to as perturbations and subsequently much
work has been undertaken in analysing perturbations in sports such as squash
(McGarry et al., 1999) tennis (Palut and Zanone, 2005) and soccer (Hughes et al.,
1996, 1997). One of the major benefits of perturbation analysis is that it allows for
the identification of the most critical incidents within a match rather than
concentrating on instances that are somewhat irrelevant to its outcome.
Perturbations in soccer are of particular interest due to its unpredictability and
irregular patterns of play making the detection of perturbations more complex as there
are various factors which can contribute to their creation. Research has confirmed the
existence of perturbations in soccer (Hughes et al., 1997) and perturbation data has
consequently been used to generate profiles of individual teams in an attempt to
predict future performances (Hughes and Reed, 2005). However research has failed
to identify unsuccessful perturbation attempts in accordance with successful
perturbations to determine which related variables are most likely to lead to an
advantage and those which are predominantly ineffective.
1.1
Scope of the Study
This study will examine perturbations in soccer through analysing performances of an
individual team. Several related variables will be assessed including; the rate at
which teams attempt to create perturbations when in possession, success rate, match
situation, perturbation variables, pitch location, player attempting to create
perturbation and outcome of successful perturbations. Pitch Diagrams will be used to
record data, which is an original method of perturbation analysis in soccer. Results
1
will be divided between the data recorded for the team analysed and the data recorded
for their opponents, after which comparisons will be made. Each action which was
deemed to be an attempt to create instability and therefore to create an advantage was
recorded whether successful or unsuccessful in an attempt to ascertain how a team can
maximise successful performance.
1.2
Research Expectations
As this is an individual study concentrating on the performances of a particular team it
is expected that common trends will emerge from the data which will give an
indication of the playing style adopted by the team analysed. The frequency at which
teams attempt to create perturbations when in possession should give an indication of
how teams utilize possession which will also help to identify playing styles. A team’s
success rate of turning perturbation attempts into advantageous situations is expected
to be an indicator of successful performance. The majority of perturbation attempts
are predicted to come from midfield players and as found in previous research
(Hughes et al., 1996, 1997) passing is expected to be the skill by which most
perturbations are attempted.
2
CHAPTER II
LITERATURE REVIEW
2.0
Literature Review
The review of literature will begin with an examination of the way in which sport has
been explored as a dynamical system with particular interest in perturbations. Focus
will then turn to the work of McGarry and other researchers who have identified the
existence of perturbations in racket sports, before reviewing perturbations in team
sports, in particular soccer. Soccer will remain at the focal point throughout when
examining how analysis of perturbations have been utilised in sport. Through such
thorough examination of perturbation literature potential gaps in the current research
will be unearthed which will form the basis of this study.
2.1
Dynamical Systems
Within sport the dynamical systems theory is recognised where a performance
deviates through a series of states before returning to its original stable state. This
approach was initially used in mathematical and physical sciences to explain the
fluctuating nature of systems which changed over time (Clark, 1995). It was also
recognised by Clark (1995) that such dynamical systems consist of four essential
concepts; constraint, self-organisation, patterns and stability. This is concurrent with
the work of Kelso (1995) who refers to a dynamical system as a structure which is
self-organising and generates a pattern of stability for a specific set of circumstances.
It is acknowledged that these patterns of stability are not rigid, rather they consist of
variability which cause a system to briefly perturb from its original state (McGarry,
2006). These distinctive losses of stability in dynamical systems are brought about
through changes in a systems control parameters (McGarry, 2005). Such disturbances
are referred to as perturbations and are fully expected as they act as a critical property
of the system (Clark, 1995). Sport performance consists of these stability disrupting
perturbations, therefore demonstrating one characteristic of a dynamical system in
particular (Reed and Hughes, 2006). In regards to this the prospect of dynamical
analysis that leads to the identification of perturbations in sport can be attractive to
both analysts and coaches as it can allow them to focus on the most important aspects
of a match which are most relevant to its outcome as opposed to scrutinizing through
a wealth of data that make up a match (Hughes et al., 1996).
3
2.2
Perturbations in Racket Sports
Work by McGarry has provided strong evidence for the existence of perturbations in
squash as good agreement between observers in identifying the individual shots that
cause a perturbation has been demonstrated (see McGarry and Franks, 1995; McGarry
et al., 1999). Their findings indicated that squash performance consists of dynamical
systems which are characterized by the onsets and offsets of perturbations, which
were identified as weak or strong shots giving one player a distinct advantage over
their opponent and therefore disrupting the systems stability.
Subsequently, the
provision of theoretical support for the existence of perturbations in squash (McGarry
et al., 1999) encouraged further research into racket sports as dynamical systems.
Thus, Palut and Zanone (2005) used the baseline movements of players to analyse
tennis as a self-organised dynamical system. Their findings gave recognition to
perturbations in tennis and acknowledged tennis as a complex system where
behaviour is governed by the dynamics of stability and instability. Therefore the
work of Palut and Zanone (2005) and particularly McGarry et al., (1999) suggest that
a more dynamic and critical method of analysis can be achieved through the
identification of perturbations in sport, which can consequently offer coaches more
effective support regarding performance (Reed and Hughes, 2006).
2.3
Perturbations in Team Sports
Such dual sports as squash and tennis have regimented structures and are generally
competed between two players (with the exception of doubles) where the actions of
one player directly influences the behaviour of their opponent (Murray et al., 2009).
Therefore due to these definitive passages of play which end in specific outcomes (i.e.
points) and their rhythmic nature many dual sports, such as squash, are potentially
ideal for dynamic analysis and furthermore analysis of perturbations (Davies et al.,
2009). Team sports on the other hand tend to have more intermittent patterns of play
where periods of possession are unlimited making the detection of perturbations more
difficult (Hughes and Reed, 2005). Grehaigne et al. (1997) explored the phenomenon
of analysing team sports as dynamical systems through collective actions in soccer.
They found transitions between the different states of the system which exist in
soccer, therefore lending itself to the identification of perturbations in team sports.
Work by Hughes was first to confirm that the identification of perturbations in soccer
could be verified reliably through analysing events leading to shots on goal. In their
4
study a perturbation was defined as an element of skill that causes an imbalance in the
rhythmic flow of attack and defence in soccer.
They acknowledged that such
imbalances could be brought about through good attacking skill or defensive errors.
Their findings also identified the skill variables creating the perturbations, allowing
the researchers to establish winning and losing performance traits. The researchers
also concluded that the identification of perturbations in soccer can prove extremely
advantageous when planning team strategies and tactics for future performances (see
Hughes et al., 1996, 1997).
However the initial work of Hughes et al. (1996, 1997) only analysed perturbations
which lead to critical incidents, shots on goal, ignoring perturbations which did not
lead to critical incidents due to good defensive skill or poor attacking skill, allowing
play to revert to its normal stability. Consequently Hughes et al. (1998) furthered this
research by ascertaining the skill variables which smoothed out perturbations in
soccer, allowing play to return to its normal rhythm. Their findings indicated that
only one in four perturbations were actually successful in leading to a shot at goal,
demonstrating that not all perturbations result in critical incidents and that the skill
variables that smooth out such perturbations could be reliably identified. Through the
detection of these defensive and attacking skill variables that prevent perturbations
being converted into critical incidents Hughes et al. (1998) recognised the possibility
of further identifying aspects of successful and unsuccessful performance related to
both defence and attack, which could potentially help to predict how an opposing side
will play.
The work by McGarry and Hughes established that sports performance could be
analysed dynamically through the identification of perturbations in both racket sports
(i.e. squash), which consist of rallies that have somewhat easily identified rhythms
and team sports (i.e. soccer), which have more irregular patterns of play.
Consequently various sports have since been analysed as dynamical systems,
including tennis (Palut and Zanone, 2005), cricket (Glazier et al., 2003) and in regards
to perturbations rugby league (Eaves and Evers, 2007). Although rugby league and
soccer may possess many similar structural characteristics, rugby league is arguably a
more complex sport (Reed and Hughes, 2006) which was indeed recognised by Eaves
and Evers (2007) who hypothesised that in rugby league it is of greater relevance to
5
identify the playing pattern preceding the perturbation than the actual perturbation
itself.
It was their contention that through identification of the factors which
contribute to the creation of perturbations it is possible to develop playing strategies
that increase the regularity of perturbations and critical incidents during a match and
in doing so lead to more successful performances.
2.4
Utilising Perturbation Information
It is evident that analysis and identification of perturbations in sport can be beneficial
in terms of enhancing performance if information is utilised effectively by coaches.
However perturbations have also proved extremely valuable when exploring other
avenues of sports performance, such as momentum (Davies et al., 2009) and
performance profiling (Hughes and Reed, 2005; Murray et al., 2009). Davies et al.
(2009) investigated the role of momentum in squash by using perturbations to create
momentum profiles of elite squash players. Within sport an individuals or teams
momentum is regularly mentioned in regards to the advantage that an individual or
team has over their opponents at that given time (Reed and Hughes, 2006), though in
its definition momentum refers to ‘the quantity of motion that an object has’ (Davies
et al., 2009, p. 118). In their study a player’s momentum was calculated using
perturbations, if a player hit a perturbation then their momentum increased, but if their
opponent hit a perturbation then their momentum either decreased (interactive
momentum) or stayed the same (individual momentum). The momentum graphs
created illustrated a player’s use of perturbations during match play and therefore the
frequency at which they were trying to put their opponents under pressure. The
momentum profiles also gave an indication of the players’ attacking and defensive
strategies, and their specific styles of play. Thus, it is suggested that the analysis of
squash can be furthered through the assessment of momentum using perturbations
(Davies et al., 2009), which can again provide coaches with more effective support.
Perturbations in squash have also been used to build performance profiles of elite
players (Murray et al., 2009). In regards to sports performance, a performance profile
is fundamentally a representation of an analysed individual’s or team’s pattern of
performance (Hughes et al., 2001). Traditionally, in squash, performance profiles are
usually generated using an individual’s information regarding their execution of
winners and errors during performance, conversely Murray et al. (2009) created their
6
performance profiles using perturbations which were then compared to those profiles
created using more traditional methods.
After comparison results displayed
considerable disparity between the perturbation profile and the winner and error
profile, suggesting that more comprehensive analysis of squash can be provided
through perturbations.
Performance profiles have also been created using perturbations in soccer (Hughes
and Reed, 2005).
However in contrast to individual sports, such as squash,
performance profiling in soccer can prove more complex as teams performances are
rarely consistent as moments of extreme skill can often decide the course of a game
and teams often apply different strategies and tactics depending on the match situation
(Hughes and Reed, 2005). Despite this Hughes and Reed (2005) recognised that the
creation of such profiles could identify a specific opponent’s strengths and
weaknesses that would help to predict their pattern of play, which could provide
coaches with tactical information that could be used to their advantage. Consequently
in their study it was attempted to create a normative performance profile for a specific
professional soccer team using perturbation data, which was achieved by recording
perturbations alongside other related variables including perturbation location and
time of perturbation. Although attempting to create a performance profile to predict
perturbations in future matches the researchers and previous researchers (Hughes et
al., 1997) did concede that it may not be possible to achieve a success level any
higher than 60% when formulating predictions using perturbations in soccer due to
the sports irregular nature.
However results in this study only demonstrated a
prediction accuracy of 41.7% when compared to actual perturbations during matches,
nevertheless the researchers concluded that to achieve predictions of perturbations
which are more successful than the ones achieved in their study would be extremely
difficult due to the unpredictability of elite soccer teams and also the numerous
factors (e.g. match situation, injuries, suspensions etc) which may influence a teams
tactics.
7
2.6
Purpose of the Study
The existence and definition of perturbations has been comprehensively confirmed
and the potential benefits of identifying perturbations in soccer have been thoroughly
explored (Hughes et al., 1997, 1998; Hughes and Reed, 2005).
Therefore
perturbations in soccer have been analysed in relation to a range of variables
including actions that nullify perturbations, the pitch location of perturbations, the
player creating the perturbation and the match time at which perturbations occur
during performance. However research has failed to identify how successful teams
are at actually creating perturbations in soccer. During soccer performance it could be
considered that the primary objective of the team in possession as to gain a clear
advantage over their opposition (to enable a goal scoring opportunity) by creating a
perturbation. However a teams attempt to create a perturbation will not always be
successful and play will therefore remain in a stable state. Identification of such
failed perturbation attempts, in addition to successful perturbation attempts, will allow
us to determine the success rate of teams when attempting to create perturbations.
Additionally research has failed to identify a team’s desire to create perturbations
when in possession. Not all actions are attempts at creating a perturbation as it is
often the situation that no opportunity is seen by the player in possession of the ball.
In this situation the decision to pass to a team mate who is unmarked or dribble the
ball to a more advanced position is often made. These actions are not an attempt to
gain a significant advantage rather it maintains the equilibrium but allows for another
opportunity to seek out the possibility of creating a perturbation. By identifying such
actions in accordance with actions that are attempts to create perturbations would
allow for the rate at which teams make perturbation attempts when in possession to be
calculated.
Hughes and Reed (2005) reported that the team used in their study to generate
performance profiles adopted different tactics in accordance to different match
situations. Therefore match situation, winning, losing or drawing, may affect a team’s
desire to create perturbations, which has also yet to be explored in previous research.
Utilising this knowledge it then seems necessary to therefore identify perturbation
information in relation to match situation in order to ascertain how the score line (in
terms of winning, losing or drawing) affects team tactics in regards to perturbations.
8
Through the identification of other related variables, such as pitch location,
perturbation variable, outcome of successful perturbations and player attempting to
create perturbation, in accordance with perturbation success rate, a team’s desire to
create perturbations and match situation it will also be possible to identify other
significant factors, for instance where and how success in regards to perturbations is
most likely to arise.
2.6
Aims of the Study
The objectives of this particular study are: 1. To determine the rate in which teams are successful at generating
advantageous situations from their perturbation attempts.
2. To establish the frequency in which teams attempt to create perturbations
when in possession.
3. To identify a relationship between frequency of perturbation attempts and
match situation (winning, losing or drawing).
9
CHAPTER III
METHODOLOGY
3.0
3.1
Methodology
Introduction
A perturbation can be defined as an action that causes one side to have an advantage
over the other. For example a 2 v 1 is a situation where the numerical advantage of
players on one side results in a clear advantage to that side. McGarry et al. (1999)
refer to such a situation as an unstable system state. They also refer to the converse of
an unstable state being a stable state. In their terminology the perturbation is the
action which causes the transition between stable and unstable states. Using this
definition of a perturbation also results in the acknowledgement that a perturbation
can also occur when an action causes the team which had the advantage to cease
having that advantage. In the 2 v 1 scenario this could occur if the lone player
intercepted a pass between the two players or the pass went astray. In soccer, the
perturbation that occurs during an unstable state (i.e. when one team has a clear
advantage) could result in either a stable state (i.e. no advantage to either side) or a
new unstable state where the team that has the clear advantage has changed from the
previous situation.
Using the term perturbation in soccer allows us to reconsider the objectives of both
the team in possession of the ball and the defending team. When in possession of the
ball the objective is to score a goal and to do this it is usual that some advantage over
the opposition is gained through creating a perturbation. Using this logic it can be
stated that football is characterised by the team in possession trying to create a
perturbation. In a similar vein it makes sense that the defending team’s objective is to
either prevent the team in possession from creating a perturbation that would
advantage the team in possession or alternatively to create a perturbation in favour of
the defending team. Going back to the 2 v 1 example if the pass was intercepted by
the lone player then the perturbation would have been prevented and the stable
situation (no advantage for either team) maintained as long as the team now in
possession did not have a clear advantage over the opposition. If such an advantage
was evident then an unstable situation would have arisen although in favour of the
defending team as opposed to the team originally in possession.
In terms of designing a system for analysing soccer matches for perturbations using
the above criteria allows for the calculation of a teams desire to create perturbations as
10
well as the success rate of perturbations.
Further analysis can then be used to
determine how teams create perturbations and also concede perturbations.
3.2
Equipment and Data Population
The soccer matches analysed in this study were recorded onto Sony DVD-R’s which
were played back for the recording of data either using a E-System EI 3103 laptop or
a Samsung E235D DVD player. Printed hand notation sheets were used to code
relevant perturbation information and different coloured pens were used to distinguish
between the perturbation variables when recording data.
Microsoft Excel
spreadsheets were used to collect the perturbation data of each match.
Eight
professional soccer matches were analysed during this study, all involving a specific
professional Coca-Cola League One team from the 2007/2008 season. All matches
were competitive in nature against opponents of a similar standard.
3.3
Pilot Study
A random Premiership game was analysed to determine the skills commonly used in
an attempt to create perturbations and also the possible outcomes of successful
perturbations. A table was created and used for analysis, from which perturbation
variables that did not yield significant data during a match were either discarded or
combined with another variable.
During completion of the pilot study an alternative system of analysis was devised.
Initially a table was used to record data concerning attempted perturbations during the
pilot study which required entering a substantial amount of data into the necessary
sections.
Therefore a more relevant format of data collection was developed.
ProZone, a match analysis system used by many elite soccer teams, records and
represents specific events using pitch diagrams (Bradley et al., 2007). Thus the use of
pitch grids was seen as a more effective way of representing and recording
perturbation information, and therefore a more comprehensive system design.
11
3.4
System Design
Previous research in soccer has used hand notation designs to reliably identify and
record perturbations and other related information (Hughes et al., 1996, 1997, 1998;
Hughes and Reed, 2005), consequently it seemed appropriate to use a hand notation
system to analysis perturbation information in this study. A system was designed
which was comprised of two main categories (attempt to create perturbation and no
attempt to create perturbation) under which operated various sub-categories. In the no
attempt to create perturbation category there were two possible variables, pass and
dribble, which were recorded in order to determine how often teams attempted to
create perturbations when in possession. Results for this category were collected
using printed notation sheets (Appendix A). This category became active each time a
player made a pass or dribble in an attempt to maintain stability rather than to gain an
advantage.
In the attempt to create perturbation category more comprehensive information was
required, including the perturbation variables (Table 1), the outcome of the attempted
perturbation (Table 2), the player creating the perturbation, the perturbation area and
if the perturbation was successful the outcome of the advantage gained (i.e. goal). For
this reason pitch grids were used as the notation sheet to record information, which
divided the pitch into ten sections (Figure 1). Three separate pitch grids were devised
for each of the possible outcomes of the attempted perturbations, advantage for the
team in possession, no advantage for either team and advantage for the defending
team (Table 2).
5
2
1
3
4
8
6
9
7
10
Figure 1. Pitch grids used to record information regarding the attempt to create
perturbation category.
12
Table 1. Operational definitions of perturbation variables.
Perturbation Variables
Pass
The ball is accurately transferred from one player to
another eluding the defenders and putting the defence in
a vulnerable position which subsequently puts the team in
possession at an advantage.
Dribble
Using a combination of pace and skill the player in
possession creates an advantage by going past the
defender or defenders whilst maintaining possession of
the ball.
Set Piece
An advantage is gained from a dead ball situation
(corner, free-kick or throwing – penalty considered as
defensive mistake rather than attacking team attempting
to create perturbation). The advantage could come
directly from the set piece (i.e. shot at goal from a freekick) or from the delivery of the set piece (i.e. cross into
the penalty box from a corner).
Tackle
Player without the ball dispossess’ their opponent with
possession and therefore obtains possession in an
advantageous situation.
Table 2. Operational definitions of the possible outcomes of attempted perturbations.
Outcomes of Attempted Perturbations
Advantage for team in possession
The team in possession gains a clear
advantage over their opponents and
therefore the attempt to create a
perturbation is considered successful.
No advantage for either team
There is not an advantage achieved from
the attempted perturbation and play
remains in a stable state, therefore neither
team is at an advantage.
Advantage for defending team The team in possession are unsuccessful in
their attempt to create a perturbation and
the defending gain possession in which they
use to create an immediate advantage of
their own which is a consequence of their
opponents trying to create a perturbation.
13
When a team attempted to create a perturbation the outcome of this attempt was
acknowledged. The respective pitch grid which represented the outcome was then
selected and the perturbation area was then identified by drawing an arrow from the
initial area of the attempted perturbation to the final area. In order to increase the
accuracy of identifying the perturbation pitch divisions were based upon existing
pitch markings as much as possible. To identify the perturbation variable specific
arrows and symbols were used to represent specific variables, which were recognised
through the use of a key (Figure 2).
The player who attempted to create the
perturbation was simply identified by notating the abbreviation of the player’s
position at the beginning of the arrow or symbol (i.e. CM represented central
midfielder). If the perturbation attempt was successful and an advantage was gained
it was also necessary to record the outcome of the advantage, this was done by
notating the abbreviation of the outcome at the end of the arrow or symbol (i.e. SS
represented shot saved).
Pass Set Piece Dribble Tackle - X
Figure 2. Key used to identify perturbation variables.
In accordance with perturbation information, match situation (winning, losing or
drawing) was also ascertained during analysis as done in previous soccer match
analysis research (Taylor et al., 2008).
This allowed identification of specific
perturbation information in relation to the match situation.
3.5
Procedure
Analysis took place post event in lapse time as live analysis was not realistic due to
the speed of the game and the amount of information which needed to be recorded
during analysis.
Each action was analysed to establish whether the player in
possession was attempting to create a perturbation or just trying to maintain
possession and therefore maintain equilibrium. Passes or dribbles not deemed to be
an attempt to create a perturbation were simply noted in the no attempt to create
perturbation category using tallies.
However, when an attempted perturbation
14
occurred the outcome of the attempt (Table 2) was firstly established. At the point in
which the outcome of the attempted perturbation had been identified and the relevant
pitch grid had been selected the footage was rewound to the beginning of the team’s
possession. From observing the passage of play again and using the pause function
when necessary the perturbation areas, the player creating the perturbation, the
perturbation variable and when successful the outcome of the perturbation were all
identified and notated onto the pitch grids.
Each match was analysed twice in its entirety in order to gather perturbation
information in regards to both teams. To record perturbation information in relation
to match situation a new notation sheet, specific to the match situation at that given
time, was used each time the match situation changed. When analysis was complete
the data for all perturbation information was transferred onto spreadsheets using
Microsoft Excel as numerical data, in preparation for data analysis.
3.6
Reliability
Reliability in terms of data is seen as a measure of its consistency after that data has
been assessed more than once under similar circumstances (Vincent, 1999).
In
regards to performance analysis reliability determines the accuracy at which events
have been coded (James et al., 2007). Thus, in the current study intra observer
reliability was performed using the test-retest method to ensure that the researcher
was correctly identifying perturbations and other related variables. One game was
randomly selected and analysed on three separate occasions with sufficient time
between each analysis in order to prevent memory effects. The percentage error
equation (Hughes et al., 2002) was employed to assess the reliability for the intra
operator observations, which is as follows: % error = (Σ (mod (V1-V2))/VTOTmean) x 100
Where ‘mod’ is the modulus, Σ the sum of (the difference between variables collected
in the different tests) and VTOTmean the mean of the total variables measured. For this
study a percentage error of 10% (90% level of confidence) was deemed acceptable.
The three separate sets of data of the selected game used for reliability testing were
grouped together to produce sufficient frequencies for analysis (Table 3).
15
Table 3. The frequency and percentage error of perturbation data between different
trials.
T1-T2
T1-T3
T2-T3
Perturbation Attempt
Successful
No Advantage
Advantage for DT
Total
Σ (mod (V1-V2)
VTOTmean
% error
24 24
28 28
0 0
52 52
0
52
0%
24 23
28 28
0 0
52 51
1
51.5
1.9%
24 23
28 28
0 0
52 51
1
51.5
1.9%
No Perturbation Attempt
Pass
Dribble
Total
Σ (mod (V1-V2)
VTOTmean
% error
407 411
42 47
449 458
9
453.5
1.9%
407 416
42 45
449 461
12
455
2.6%
411 416
47 45
458 461
7
459.5
1.5%
Perturbation Variable
Pass
Dribble
Set Piece
Tackle
Total
Σ (mod (V1-V2)
VTOTmean
% error
33 35
14 13
5 4
0 0
52 52
4
52
7.7%
33 33
14 14
5 4
0 0
52 51
1
51.5
1.9%
35
13
4
0
52
Pitch Location
1
2
3
4
5
6
7
8
9
10
Total
Σ (mod (V1-V2)
VTOTmean
% error
0 0
2 2
1 1
4 4
10 10
10 9
5 5
6 5
4 5
10 11
52 52
4
52
7.7%
0 0
2 2
1 1
4 4
10 10
10 10
5 4
6 5
4 4
10 11
52 51
3
51.5
5.8%
0
2
1
4
10
9
5
5
5
11
52
33
14
4
0
51
3
51.5
5.8%
0
2
1
4
10
10
4
5
4
11
51
3
51.5
5.8%
The categories central to this study, attempt to create perturbation, no attempt to
create perturbation, perturbation variable and pitch location were all assessed using
intra-operator percentage error (Table 3). The percentage difference of each of these
categories, between each of the trials, was consistently at an appropriate level, below
the 10% error limit initially set. The attempt to create perturbation category in
particular showed good reliability with the first two trials showing a 0% percentage
16
difference and a relatively low percentage difference (= 1.9%) was shown between
trials one and three, and trials two and three respectively. The categories which
revealed the highest levels of percentage error was that of perturbation variable (T1T2=7.7%, T1-T3=1.9%, T2-T3=5.8%) and pitch location (T1-T2=7.7%, T1T3=5.8%, T2-T3=5.8%). However several variables within these categories yielded
little or no data (i.e. tackle) in comparison to other variables which may account for
their higher percentage error. Intra-operator percentage error was also used to assess
the reliability of the outcome of successful perturbations, the player creating the
perturbations and perturbations in relation to match situation (additional categories
analysed during this study) where percentage difference was again persistently at a
level that was deemed reliable (<10% error) (Appendix B). Consequently the results
of this intra-operator reliability test suggest that this study is concurrent with previous
research (Hughes et al., 1997; Hughes et al., 1998; Hughes and Reed, 2005) in
demonstrating that perturbations can be reliably identified in soccer.
3.7
Data Processing
All raw data was entered into Microsoft Excel, where means and percentages were
calculated for the frequency of perturbation attempts and non perturbation attempts,
the success of perturbation attempts, the pitch locations of perturbation attempts, the
type of skills used to attempt to create perturbations, the players most likely to attempt
to create a perturbation and the outcomes of successful perturbations. The analysis
also included whether teams attempted to create more or less perturbations when they
were winning, losing or drawing. The data gathered was non-parametric so specific
data was transferred into SPSS where Wilcoxon Signed Rank statistical analyses were
used to determine differences between the perturbation data recorded for the specific
team who were involved in all analysed matches and their opponents.
3.8
Limitations and Delimitations
The operational definitions applied in this study are primarily subjective and may not
be universally agreed or accepted. Also intra operator reliability, which was used in
this study, does not demonstrate a system to be objective, it merely shows that the
system can be used consistently by the operator (O’Donoghue, 2007). However as
this study was an independent project an intra operator reliability test was most
practicable. The researcher also recognised that the pitch diagrams used to record
17
perturbation information in this study were not exactly to scale with actual soccer
pitch dimensions, nevertheless the pitch diagrams were deemed sufficient enough to
record pitch location accurately.
18
CHAPTER IV
RESULTS
4.0
Results
The results are summarised into three sections. Firstly qualitative data which could be
utilised by coaches to identify successful and unsuccessful performance traits
concerning the specific team involved in all analysed matches (who for ethical
reasons will be referred to as Team 1) will be presented, followed by qualitative data
concerning their opponents. The final section will then involve a more quantitative
analysis comparing Team 1 and their opponents to establish any similarities or
differences between their results.
4.1
Team 1’s Data
4.1.1
Perturbation Attempts
The frequency at which Team 1 attempted to create perturbations ranged from 30 to
as high as 70 in a single match.
Actions which were not attempts to create
perturbations (passes and dribbles) accounted for on average 88.22% of all Team 1’s
actions when in possession. More comprehensive frequencies of Team 1’s attempts
and no attempts to create perturbations in regards to each match are illustrated in
Figure 3.
500
450
Frequency
400
350
300
250
Perturbations
200
Dribble
150
Pass
100
50
0
1
2
3
4
5
6
7
8
Match
Figure 3. The frequency of perturbation attempts and passes and dribbles not
deemed to be perturbation attempts for Team 1 during each match.
19
Team 1 made more passes which were not attempts to create perturbations (mean =
331.75) in comparison to dribbles (mean = 43.25) and perturbation attempts (mean =
48.13). Through comparison of the attempt to create perturbation category and the no
attempt to create perturbation category it was possible to calculate the rate at which
teams attempted to create perturbations when in possession, and on average Team 1
attempted to turn 11.88% of all possessions into perturbations.
4.1.2
Success of Attempted Perturbations
Throughout analysis the average success rate of Team 1 in converting perturbations
into advantageous situations was 40%, therefore 60% of their perturbation attempts
were deemed unsuccessful.
90%
80%
70%
60%
50%
40%
Successful
30%
No Advantage
20%
10%
0%
1
2
3
4
5
6
7
8
Match
Figure 4. The percentage of Team 1’s successful and unsuccessful perturbations
during each match.
The majority of perturbations attempted in each match by Team 1 resulted in no
advantage with the exception of match two where 50% of their attempted
perturbations were successful and led to advantages over their opponents (Figure 4).
It was shown using a Wilcoxon Signed Ranks Test (Z = 2.371, P < 0.05) that there
were significantly more perturbations which were unsuccessful with no advantage
gained than successful perturbations created by Team 1. Match three saw Team 1’s
lowest success rate (17%) in regards to perturbation attempts, which coincides with
their only defeat during analysis.
20
4.1.3
Pitch Location
The initial area and final area of perturbation attempts were recorded during analysis.
The final location of perturbation attempts were essentially directed into the middle
offensive area, which accounted for 84.16% of all Team 1’s perturbation attempts.
The initial areas of Team 1’s perturbation attempts however were more dispersed
throughout numerous pitch locations (Figure 5).
Figure 5. The distribution of Team 1’s perturbation attempts in relation to pitch
location.
The largest proportion of perturbation attempts from an individual pitch area for Team
1 came equally from their middle pre-offensive area and their right offensive area
(Figure 5). The defensive area only accounted for 1% of the perturbations attempted
by Team 1 and perturbation attempts from the pre-defensive area was also relatively
low, accounting for only 10% of Team 1’s perturbation attempts. The majority of
their perturbation attempts (48%) came from their offensive area. Figure 6 highlights
the outcome of Team 1’s perturbation attempts (successful, no advantage gained and
advantage for defending team) in each pitch area. There were very few incidences
during analysis where a perturbation attempt resulted in an advantage for the
defending team, in fact there were no such incidences recorded for Team 1. The most
successful pitch area for Team 1 was their middle offensive area with 61% of their
perturbation attempts from this area being successful and resulting in an advantageous
situation (Figure 6).
21
Figure 6.
location.
4.1.4
The outcome of Team 1’s perturbation attempts in relation to pitch
Perturbation Variables
The most common skill used by Team 1 to attempt to create perturbations was passing
(244), of which 36.07% led to advantageous situations. Of the 72 perturbations
attempted by set pieces 31.94% were successful and 58.21% of the dribble
perturbation attempts (67) were successful (Figure 7).
Only two of Team 1’s
perturbation attempts were tackles which were both unsuccessful in creating an
advantage. Therefore Team 1’s most effective skill variable in creating perturbations
was dribbling with the ball.
250
200
150
No Advantage
Total
Successful
100
50
0
Pass
Dribble
Set Piece
Tackle
Perturbation Variable
Figure 7. The frequency and outcomes of Team 1’s perturbation variables.
22
4.1.5
Match Situation
The total time Team 1 spent in each situation was recorded to calculate the amount of
perturbations attempted per minute when winning, losing or drawing (Table 4).
Table 4. Perturbations attempted by Team 1 during different match situations.
Winning
Losing
Drawing
Total Minutes
285
55
380
Perturbations
Attempted
137
25
221
Perts/min
2.08
2.20
1.72
Team 1 attempted to create the majority of their perturbations when drawing and their
minority when losing (Table 4). When perturbation attempts are related to the amount
of minutes spent in each match situation Team 1 attempted to create a perturbation
every 2.08 minutes when winning, every 2.20 minutes when losing and every 1.72
minutes when drawing.
Therefore Team 1 tried to create an unstable situation
through a perturbation attempt most frequently when drawing and least frequently
when losing.
4.1.6
Players Creating Perturbations
During the eight games analysed Team 1 attempted to create a total of 385
perturbations, which each player contributed towards (Table 5).
Table 5. The amount of perturbations attempted by each of Team 1’s players.
Perturbations
Attempted
GK
RB
CD
LB
CDM
CM
RM
LM
ST
3
49
7
26
13
85
70
99
33
Midfield players accounted for 69.35% of Team 1’s perturbation attempts. A total of
105 perturbation attempts came from players from central positions, 119 perturbation
attempts came from right-sided players and 125 perturbation attempts came from leftsided players. The player who attempted to create the most perturbations was the left
midfielder (Table 5), an example of their perturbation attempts are illustrated in
23
Figure 8. In the example shown 50% of the left midfielders perturbation attempts
were successful in creating an advantage over their opponents.
Successful Perturbations
No Advantage Gained/Unsuccessful Perturbations
G – Goal
SS – Shot Saved
SOT – Shot Off-Target
Pass Set Piece Dribble -
Figure 8. Perturbation attempts by Team 1’s left midfielder during match four.
24
4.1.7
Outcome of Successful Perturbations
Of the 385 perturbations attempted by Team 1 150 were successful and the outcome
of these advantages were recorded (Table 6).
Table 6. The outcomes of Team 1’s successful perturbations.
Frequency
Goal
Shot
Saved
Shot Off
Target
Shot
Blocked
Smoothed
Over
Fouled
14
29
45
18
40
4
Shot off-target was Team 1’s most frequent outcome when in advantageous positions
followed by the advantage being smoothed over and allowing play to return to its
stable state. Team 1 being fouled after successful perturbations was the least frequent
outcome and fourteen successful perturbations resulted in goals (Table 6), therefore
Team 1 had a score rate of 1.75 goals per game. The perturbations that led to these
goals are illustrated in Figure 9.
5
2
8
LM
LM
CM
ST
LM
1
6
3
RB
9
CM
ST
CM
7
4
RM
RM
10 ST
RM
RM
Pass Set Piece Dribble Figure 9. The successful perturbations that led to Team 1’s goals.
25
4.2
Opposition’s Data
4.2.1
Perturbation Attempts
The frequency of the oppositions perturbation attempts ranged from 28 (match 5) to
50 (match 4) (Figure 10). In regards to actions which were not attempts to create
perturbations, 82.46% of all opposition actions when in possession were passes or
dribbles which were attempts to maintain possession and uphold the equilibrium of
attack and defence, as opposed to perturbation attempts.
300
Frequency
250
200
150
Petrurbations
Dribble
100
Pass
50
0
1
2
3
4
5
6
7
8
Match
Figure 10. The frequency of perturbation attempts and passes and dribbles not
deemed to be perturbation attempts for the opponents during each match.
The opponents dominant action when in possession of the ball was passing with no
intent of creating a perturbation (mean = 176.5) compared to dribbles (mean = 21) and
attempts to create perturbations (mean = 39.63) (Figure 10). When comparing data
concerning perturbation attempts and non-perturbation attempts the calculated amount
of possession in which the opposition tried to convert into perturbations was 17.64%.
26
4.2.2
Success of Attempted Perturbations
The average success rate of the opponents throughout analysis was relatively low at
28%, consequently there were significantly more (Z = 2.524, P < 0.05) perturbation
attempts which were unsuccessful in creating an advantage than successful.
90.00%
80.00%
70.00%
60.00%
50.00%
Successful
40.00%
No Advantage
30.00%
Advantage for DT
20.00%
10.00%
0.00%
1
2
3
4
5
6
7
8
Match
Figure 11. The percentage of the opposition’s successful and unsuccessful
perturbations during each match.
An extremely low proportion of the opposition’s perturbation attempts resulted in an
advantage for the defending team, only three such outcomes were recorded during the
entire analysis, whereas a large majority of the opposition’s perturbation attempts
(65% - 82%) during each match were unsuccessful in creating advantages (Figure 11).
In regards to successful perturbation attempts the opposition’s success rate varied
throughout analysis with their highest success rate (34%) being recorded during
matches three and seven and their lowest success rate (17%) being recorded during
match five.
27
4.2.3
Pitch Location
A total of 90.22% of the opposition’s perturbation attempts were directed into their
middle offensive area, however the initial location of their perturbation attempts were
spread out amongst various pitch locations (Figure 12).
Figure 12. The distribution of the opposition’s perturbation attempts in relation to
pitch location.
The predominant individual area of the opposition’s perturbation attempts was their
middle pre-offensive area and the area which yielded least perturbation attempts was
their defensive area (Figure 12). The pitch area that saw the most activity regarding
opposition perturbation attempts was their pre-offensive area, accounting for 49% of
all their perturbation attempts. The oppositions most successful pitch location was
their middle offensive area with 50% of all their perturbation attempts from this area
leading to advantageous situations (Figure 13).
The opposition were however
extremely unsuccessful in creating perturbations from their right pre-defensive area
and their left pre-defensive area with no advantage being gained from 100% of
perturbation attempts from these areas (Figure 13).
28
Figure 13. The outcome of the opposition’s perturbation attempts in relation to pitch
location.
4.2.4
Perturbation Variables
The oppositions most frequent perturbation variable was passing accounting for
56.15% of their perturbation attempts, with 36.91% of their attempts coming from set
pieces, 6.31% coming from dribbles and only 0.63% of their attempts were tackles.
Consequently insufficient data was collected for the tackle variable as only two
perturbation attempts made by the opposition were tackles, therefore results for this
variable would prove inconclusive. Although the opposition made only 20 attempts
to create perturbations by dribbles 60.00% of the attempts were successful, whereas
34.19% of their set piece perturbation attempts were successful and only 20.79% of
their passing perturbation attempts were successful (Figure 14).
180
160
140
120
100
Frequency
Advantage for DT
80
No Advantage
60
Successful
40
20
0
Pass
Dribble
Set Piece
Tackle
Perturbation Variable
Figure 14. The frequency and outcomes of the opposition’s perturbation variables.
29
4.2.5
Match Situation
Despite drawing being the most frequent match situation during analysis the
opposition made the majority of their perturbation attempts (152) whilst losing. The
opposition’s perturbation attempt per minute rate was also most frequent when losing,
attempting a perturbation every 1.88 minutes compared to an attempt every 2.59
minutes when drawing and only one attempt every 3.06 minutes when winning (Table
7).
Table 7. Perturbations attempted by opposition during different match situations.
Winning
Losing
Drawing
Total Minutes
55
285
380
Perturbations
Attempted
18
152
147
Perts/min
3.06
1.88
2.59
4.2.6
Players Creating Perturbations
All perturbations attempted by the opposition (317) came from players in a variety of
positions (Table 8).
Table 8. The amount of perturbations attempted by each of the opposition’s players.
Perturbations
Attempted
GK
RB
CD
LB
CDM
CM
RM
LM
ST
6
44
27
44
16
87
40
23
30
Players from midfield positions made 52.37% of the opposition’s perturbation
attempts. The opposition’s central midfield players were responsible for the majority
of these perturbation attempts (Table 8), an example of which are shown in Figure 15.
After the central midfielders it was the opposition’s defensive right and left backs that
were responsible for the most perturbation attempts (Table 8).
30
Successful Perturbations
No Advantage Gained/Unsuccessful Perturbations
SO – Smoothed Over
SB – Shot Blocked
F – Fouled
Pass Set Piece Dribble -
Figure 15. Perturbation attempts by the opposition’s central midfielders during match
eight.
4.2.7
Outcome of Successful Perturbations
In total during the games analysed the opposition created 91 successful perturbations,
the outcomes of which varied (Table 9).
Table 9. The outcomes of the opposition’s successful perturbations.
Frequency
Goal
Shot
Saved
Shot Off
Target
Shot
Blocked
Smoothed
Over
Fouled
6
15
25
19
24
2
31
The most common end product of the opposition’s successful perturbations was to
shoot off target, followed closely by the advantage being smoothed over (Table 9).
Of the successful perturbations created by the opposition 6.59% resulted in goals
which are illustrated in Figure 16.
5
2
8
CM
6
3
1
CM
CM
CM
CD
ST
9
ST
4
7
4
CM
10
Pass Set Piece Dribble Figure 16. The successful perturbations that led to the opposition’s goals.
4.3
Comparison of Team 1’s and their Opposition’s Data
4.3.1
Perturbation Attempts
A Wilcoxon Signed Ranks Test (Z = 2.380, P < 0.05) showed that Team 1 attempted
significantly more perturbations than their opponents (Table 10).
Table 10. The average frequencies of Team 1’s and their opposition’s perturbation
attempts and non perturbation attempts, and their calculated differences using a
Wilcoxon Signed Ranks Test.
Attempt to
Create Perturbation
No Attempt to
Create Perturbation
Pass
Dribble
Perturbation %
Team A
48.13
331.75
43.25
11.88%
Opponents
39.63
176.5
21
17.64%
Z Score
2.380
2.380
2.524
2.386
P Value
.017
.017
.012
.017
32
Team 1 also made significantly more (P < 0.05) passes and dribbles than their
opponents in the no attempt to create perturbation category. However the rate at
which the opposition (mean = 17.64%) attempted to create perturbations when in
possession was significantly more (P < 0.05) than Team 1 (mean = 11.88%), despite
Team 1 attempting more perturbations and completing more passes and dribbles than
their opponents (Table 10).
4.3.2
Success of Attempted Perturbations
Team 1 frequently had a higher success rate than their opponents with the exception
of match three, which coincidently was the only match that Team 1 lost during
analysis (Figure 17). The outcomes of the other matches were five wins (matches 2,
4, 5, 6 and 7) and two draws (matches 1 and 8). The success rate in relation to
perturbations in the matches which were drawn showed little difference between the
two teams (4 – 7%) in comparison to matches which resulted in wins (12 – 27%).
Despite these differences the success rate of perturbation attempts did not differ
significantly (P = 0.093) when comparing successful perturbations between Team 1
and their opponents.
60%
Success Rate
50%
40%
Team 1
30%
Opponents
20%
10%
0%
1
2
3
4
5
6
7
8
Match
Figure 17. The perturbation success rates of Team 1 and their opponents during each
match.
33
4.3.3
Pitch Location
Team 1 and their opponents directed the majority of their perturbation attempts into
the middle offensive area of the pitch, accounting for 87% of all perturbation attempts
during analysis. The initial pitch location which Team 1 made the majority of their
perturbation attempts (48%) from was their offensive area whereas the opposition
attempted the majority of their perturbations (49%) from their pre-offensive area. The
middle offensive area was the most successful pitch location for Team 1 (61%) and
their opponents (50%) in relation to perturbation attempts which resulted in
advantageous situations.
4.3.4
Perturbation Variables
Team 1 and their opponents used different skills in attempts to create perturbations
(Table 11).
Table 11. The average frequencies of the skill variables used by Team 1 and their
opponents in an attempt to create perturbations.
Pass
Dribble
Set Piece
Tackle
Team 1
30.50
8.38
9.00
0.25
Opponents
22.25
2.50
14.63
0.25
Team 1 on average attempted to create perturbations more frequent than their
opponents using passes and significantly more (Z = 2.371, P < 0.05) through dribbles.
However the opponents on average attempted perturbations significantly more (Z =
2.371, P < 0.05) than Team 1 through set pieces.
The tackle variable proved
somewhat irrelevant as Team1 and their opponents rarely attempted to create
perturbations by tackling (Table 11).
4.3.5
Match Situation
During winning and drawing situations Team 1 on average attempted more
perturbations per minute than their opponents (Figure 18). However during losing
situations Team 1’s frequency of perturbation attempts per minute decreased whilst
their opponents increased, therefore on average when losing the opposition attempted
perturbations more frequently than Team 1.
34
3.5
3
Minutes
2.5
2
Team 1
Opponents
1.5
1
0.5
0
Drawing
Winning
Losing
Match Situation
Figure 18. Team 1 and their opponents’ perturbation attempts per minute during
different match situations.
4.3.6
Players Creating Perturbations
Midfield players were responsible for the majority of perturbation attempts for Team
1 and their opponents. The individual player who attempted the most perturbations
for Team 1 was their left midfielder and for their opponents was their central
midfielder, responsible for 25% and 27% of their teams’ perturbation attempts
respectively.
4.3.7
Outcome of Successful Perturbations
The most important statistic in regards to successful perturbations is goals, and Team
1 converted 9.33% of their successful perturbations into goals compared to their
opponents 6.59%. The most frequent outcome for Team 1 and their opponents when
creating successful perturbations was to shoot off-target, and Team 1 and their
opposition had an equal percentage (26%) of their successful perturbations smoothed
over (Figure 19). When advantages were gained through perturbations the least
frequent outcome for Team 1 and their opponents was being fouled. The main
difference between Team 1’s and the opposition’s outcomes was that of shot-blocked,
with the opposition having 21% of their successful perturbations blocked compared to
12% of Team 1’s successful perturbations (Figure 19).
35
35%
30%
25%
20%
Team 1
15%
Opponents
10%
5%
0%
Goal
Shot Saved
Shot OffTarget
Shot
Blocked
Smoothed
Over
Fouled
Outcome of Successful Perturbations
Figure 19. Outcomes of successful perturbations for Team 1 and the opponents.
36
CHAPTER V
DISCUSSION
5.0
5.1
Discussion
Perturbation Frequency
From the eight matches analysed in this study a total of 702 perturbation attempts
were observed, of which 54.84% came from Team 1.
Team 1 also completed
significantly more passes and dribbles which were not perturbation attempts than their
opponents which implies that Team 1 generally had more frequent possessions and
longer possessions in comparison to their opponents. Therefore more perturbation
attempts from Team 1 may be expected as longer periods of possession have been
found to result in higher frequencies of goal scoring opportunities (Hook and Hughes,
2001), which also means higher frequencies of perturbations. Consequently Team 1’s
superior perturbation attempt count may possibly be explained by them having
possession for longer periods than their opponents, which would present them with
more opportunities to seek out perturbations.
Despite Team 1’s somewhat dominance in these areas the opposition attempted to
turn significantly more of their possession (17.64%) into perturbations than Team 1
(11.88%). When compared to the probable amount of possession each team had these
results suggest that teams adopted alternative attacking styles. The fact that Team 1
had less of a desire to create perturbations yet seemed to have more possession
suggests that they had a patient playing style in which they maintained possession by
using short, close controlled passing patterns, waiting for the opportunity to make a
perturbation attempt to arise. The opposition on the other hand seemed to have a
more direct playing style as they had less possession but attempted to turn more of
their possession into perturbations.
This suggests that when in possession the
opposition were more impatient and tried to force perturbation opportunities, possibly
through long balls, rather than waiting for such opportunities to arise. Such direct
methods of play have been suggested to be more favourable and effective than
possession football (Bate, 1988), however contrary to these suggestions the results of
this study indicate that Team 1, who seemed to have most possession, were generally
more successful than their opponents, who appeared to have a more direct approach.
37
5.2
Success Rate
In each match analysed the winning team always had a better success rate in
converting perturbation attempts into advantageous situations than their opponents.
Although the team with the highest success rate did sometimes draw they were always
the team who created the most goal scoring opportunities during the match,
suggesting that perturbation success rate can be used as a performance indicator in
soccer. In soccer a common performance indicator is possession with various studies
indicating that possession duration is related to successful performance (Hook and
Hughes, 2001; Jones et al., 2004), perhaps in regards to match outcome perturbation
success rate may prove a more valid indicator of successful performance as successful
perturbations are directly linked to goal scoring opportunities and goals are the
ultimate determinant of successful performance. Therefore if a team has a good
perturbation success rate then this may increase their winning opportunity. However
there was no significant difference found between Team 1’s perturbation success rate
and their opposition’s perturbation success rate. This may be explained by match
three (Appendix C) in which the opposition won and had a higher perturbation
success rate than Team 1, which disrupts Team 1’s overall success rate during
analysis as they achieved a higher success rate than their opponents in all other
matches.
Although the analysis revealed that the rate at which teams produce successful
perturbations can be an indicator of successful performance, it was also found that the
analysed teams in this study produced significantly more unsuccessful perturbations
than successful perturbations, with 65.67% of all perturbation attempts in this study
being unsuccessful. Hughes et al. (1998) also found that the majority of perturbations
in their study were unsuccessful but with a higher figure of approximately 75%.
However in their study a perturbation was only considered successful if it resulted in a
shot at goal, a smoothed out perturbation was consequently considered unsuccessful.
The current study though recognised that for a perturbation to be smoothed out there
must have initially been an advantage gained and the attempt was therefore successful
in causing instability, a perturbation was only deemed unsuccessful if there was no
advantage achieved from the perturbation attempt. A smoothed out perturbation was
therefore deemed successful, which may account for the lower percentage of
unsuccessful perturbations in this study.
38
5.3
Match Situation
A further aim of the current study was to examine how match situation affected a
teams perturbation attempts. Results were contrasting with Team 1 attempting most
perturbations per minute when drawing whereas the opposition attempted most of
their perturbations per minute whilst losing. One of the observations from Hughes
and Reed’s (2005) study was that teams had almost twice as many shots when losing
compared to when winning, suggesting that teams also create more perturbations
when losing.
This proved true with the opposition who seemed to attempt
perturbations at an increased rate when losing in an effort to exert greater pressure on
the defence and create more goal scoring opportunities which supports the assertions
of Gray and Drewett, (1999) who suggest that when teams are chasing the game they
create more scoring opportunities.
Although Team 1 when winning seemed to be put under increased pressure by their
opponents, their own perturbation attempt rate per minute was also high when a lead
had been established, which contradicts Hughes and Reed’s (2005) notion that teams
are reluctant to attack when winning. Rather Team 1 when winning seemed to be
more inclined to attack than when losing. This may be explained by Team 1’s
momentum during different match situations. In squash increases in momentum have
been associated with players hitting winners (Hughes et al., 2006), therefore in soccer
a team’s momentum could be associated with scoring goals. Consequently Team 1
taking the lead may have allowed them to gain momentum, conversely when Team 1
were losing momentum may have been lost by conceding a goal. Hence Team 1’s
high perturbation attempt rate when winning could be due to them having momentum
and their low attempt rate when losing could be due to a lack of momentum.
However during this study it was observed that once Team 1 had taken the lead they
tended to continue attempting perturbations at a high rate as well as their opponents
who increased the rate in which they attempted perturbations. This suggests that by
Team 1 taking the lead the game became more open and expansive which allowed
both teams to attempt to create more perturbations.
39
5.4
Perturbation Location
Work in this study and that by previous researchers (Hughes and Reed, 2005)
revealed that teams had a strong preference of directing their perturbations into central
offensive areas (in and around their opponents penalty box). Such findings are not
surprising considering it is these areas which cause most threat to their opponents
goal, which is supported by the fact that 80% of the goals scored during analysis in
this study came from inside the central offensive area. The area where Team 1’s
perturbation attempts most regularly originated from was their offensive area which
provides further evidence for their patient build up play, trying not to risk a loss of
possession through a perturbation attempt until in an offensive position. The initial
location of the majority of the opposition’s perturbations attempts, their pre-offensive
area, suggests they attempted to create perturbations from deeper positions which
again provides further evidence of their direct attacking style. These alternative
preferences of pitch location therefore indicate different attacking profiles which
reflect the strengths and weaknesses of these particular teams, hence the system of
analysis used in this study should be applied when analysing individual teams rather
than a random sample of matches were results can be generalised.
Contrary to Hughes and Reed’s (2005) findings that attacks primarily come from
central positions only 40.5% of perturbation attempts in this study came from central
positions with 59.5% of attacks coming from wide positions. Although these findings
are conflicting both this study and Hughes and Reed’s (2005) study analysed matches
involving a specific team and results may just reflect the different attacking styles
adopted by these different teams. It should also be noted that these two studies used
matches from alternative leagues to form the basis of their analysis and therefore
teams used were of a different standard which may account for these contrasting
findings concerning attacks from central and wide positions. Results of this study
also indicate that success from perturbations is most likely to come from offensive
areas. This finding may be due to the fact that when a team are in possession in
offensive areas there are naturally more supporting team mates also in offensive areas,
consequently when a perturbation is attempted there are more players committed to
the attack increasing the likelihood of success.
These findings could also be a
contributing factor towards Team 1 generally being more successful than their
opponents as they attempted more perturbations from offensive areas whereas the
40
opposition attempted to create the majority of their perturbations from areas where
success was less likely.
5.5
Perturbation Variables
The work of Hughes incorporated considerably more perturbation variables than the
four measured in this study. However it was the researcher’s perspective that a
defensive mistake was not a perturbation until taken advantage of by the attacking
team, for example if a defender miss tackles which puts the attacking team at an
advantage it was the work of the attacking player who drew the defending to the
tackle in the first place that made the advantage possible, therefore the perturbation
should be attributed to the quality of the actions of the attacking player. Also as
perturbations attempts whether successful or unsuccessful were recorded in this study
it seemed irrelevant to record defensive perturbations as no team purposely attempts
to make a defensive mistake which places their opponents at an advantage,
consequently defensive perturbation variables were eliminated from this study.
Furthermore some perturbation variables were merged together, such as the run off
the ball and the pass variable, as a run off the ball is not a perturbation until
complimented with an appropriate pass.
Therefore this study allowed for better
clarity in accurately identifying perturbation variables.
Of the perturbation variables measured pass was the most common skill used by
teams when attempting to create perturbations and was also the variable that led to
most goals (55%) which compares favourably to previous research (Hughes et al.,
1996, 1997; Hughes and Reed, 2005). However similar to the findings by Hughes et
al. (1996) teams differed significantly in the ways in which they attempted to produce
their perturbations. This may again suggest alternative attacking patterns employed
by Team 1 and their opponents, with Team 1 attempting more perturbations from
open play, specifically through dribbles, and the opposition attempting more
perturbations from set pieces. Dribble was the variable with the highest success rate
though there were noticeably less perturbations attempts through dribbling compared
to passes and set pieces, which could be due to the difficulty of identifying a failed
dribble as a perturbations attempt. For example a player may have the intention of
disrupting an opposing team’s defence via a dribble but is tackled immediately,
making it difficult to recognise as an unsuccessful perturbation attempt as there is no
41
clear indication of an attempt to create instability. Consequently it could be argued
that for a dribble to be identified as a perturbation attempt some degree of success
must be achieved in order to identify the action as a dribble which is an attempt to
disrupt the opposing team’s defence. Few perturbation attempts were made through
the tackle variable in this study, which was also found by Hughes et al. (1997). This
finding may be due to teams exerting little pressure on the ball in their opponents half
of the field, which is a style of play that is often seen by elite teams as they allow their
opponents to play in front of them, which lessens their probability of potentially
making a tackle in an offensive area where there is an immediate advantage to be
gained.
5.6
Players Attempting to Create Perturbations
As predicted midfield players were responsible for the majority of perturbation
attempts (61.68%). Midfield players are predominantly the link between defence and
attack and are therefore often responsible for initiating attacks, and when combined
with the findings of Taylor et al. (2004) where midfield players were found to
perform most dribbles and be largely responsible for set pieces the fact that
midfielders attempted to create most perturbations is not surprising.
After the
midfield players the full backs attempted to create the most perturbations which given
their wide positioning it is probable that the majority of them came from crossing
positions, which once again is supported by the assertions of Taylor et al. (2004) who
suggests that full backs primarily attempt the most crosses.
5.7
Perturbation Outcome
The desired outcome of all perturbation attempts is to ultimately score a goal. Of the
total 241 successful perturbations recorded in this study 20 resulted in goals,
accordingly there was a successful perturbation to goal ratio of 12:1. This does not
compare favourably to Hughes and Reed’s (2005) study where a much lower
perturbation to goal ratio of 6:1 was found. The fact that the matches analysed in this
study involved League One teams and the matches analysed by Hughes and Reed
involved Premiership teams playing at a higher standard may explain these
contrasting results, suggesting that teams of a higher quality are more prolific in
converting advantageous situations into goals.
Also this study allowed for the
identification of more successful perturbations as all advantages gained were recorded
42
irrespective of whether it resulted in a shot at goal or not which was not recognised in
Hughes and Reed’s study.
It was clear that successful teams produced more shots which would be expected to
relate to more goals. Team 1 had a shot to goal ratio of 7.5:1 compared to a ratio of
10.8:1 for their opponents, which supports the findings or previous research (Hughes
et al., 1988; Hughes and Reed, 2005) that successful teams are more effective at
converting shots into goals.
Outcomes of successful perturbations such as shot
blocked and smoothed over may give a vague representation of defensive strengths.
As more of the oppositions successful perturbations resulted in such outcomes (47%)
compared to Team 1 (39%) it may indicate that defensively Team 1 were generally
more alert when reacting to situations where their opponents had gained an advantage
and therefore more successful at regaining stability.
This could be another
contributing factor towards Team 1 generally achieving more success than their
opponents.
5.8
Practical Implications
Firstly, as was originally established by Hughes et al. (1996) the identification of
perturbations allows coach and analyst to focus on the most relevant aspects of a
match in regards to its outcome. Specifically, analysing perturbations using the
system adopted in this study can help a coach to plan and predict future performances.
It allows for the identification of both an opponents and a teams own strengths and
weaknesses. When analysed the areas in which teams are most likely to attack from
and how teams are most likely to attack can be identified along with the players who
pose the biggest threat of causing a disruption in the defence.
Conversely,
identification of weak links in a team can be identified through establishing areas in
which teams are most likely to concede a perturbation and how perturbations are
generally conceded, which could prove just as beneficial. A coach may find that a
team regularly concede perturbations from crosses in offensive wide positions
suggesting that success can be achieved by isolating and attacking their full backs.
Through all the perturbation related variables that are measured in this system it is
possible to give coaches some indication of a team’s attacking style. Information
generated by this system can then be used by coaches to predict how a future
opponent may play and plan team strategies and tactics accordingly, or to identify
43
how goal scoring opportunities are being created and conceded by their own team and
then react effectively to minimise or maximise specific situations in future matches.
Finally perturbation information could also be used to predict how an opposing team
will play during different match situations.
5.9
Limitations
Although the current study has provided a more detailed analysis of perturbations in
soccer there are several limitations to consider. The first limitation concerns the
amount of matches analysed. Ideally more games would have been analysed to
produce a greater data sample as some variables produced little data which affects the
accuracy of their interpretations. For example during the entire analysis Team 1 were
only winning and their opposition were only losing for a total of 55 minutes, therefore
its difficult to establish a true reflection of how these specific match situations effects
their attempts to create perturbations with such a small amount of data. Also as all
games analysed involved a specific team (Team 1) the data only represents a general
trend of opposition play as a range of opposition teams were involved in the analysis.
Opposition data may therefore be used to reflect Team 1’s defensive play rather than
opposition attacking play. Furthermore, the results can only truly represent Team 1 as
there was not a multitude of teams analysed and therefore results cannot be
generalised in regards to all soccer teams. The study also assumed that extraneous
factors such as team selection, formation and players being sent off did not impact the
creation of perturbations.
44
CHAPTER VI
CONCLUSION
6.0
Conclusion
The most common skill variable used in an attempt to create a perturbation was
passing but the most effective perturbation of causing instability was dribbling.
Midfield players were found to be the most creative players in regards to perturbation
attempts. Success rate of perturbation attempts proved to be an indicator of successful
performance as the teams who won consistently had a higher success rate than their
opponents, although only one in three perturbation attempts were successful in
leading to an advantage.
The frequency at which teams attempted to create
perturbations when in possession seemed to give some indication of a teams attacking
style, with Team 1 displaying a patient attacking style and the opposition displaying a
more direct attacking style. No conclusive results were really established regarding
match situation as teams seemed to react differently from one another when in certain
match situations, for example Team 1’s perturbation attempt rate per minute
decreased when losing whereas the opposition’s rate increased. Similar findings were
found in relation to pitch location with different teams favouring different pitch areas
in which to attempt perturbations. Therefore such contrasting results indicate that this
system should be used to generate profiles of individual teams and not to analysis a
sample of teams in an attempt to generalise results.
The fact that there were
extremely few incidences where a perturbation attempt resulted in an advantage for
the defending team suggests that there is a low level of risk associated with
perturbation attempts. Consequently teams should be encouraged to attempt to create
perturbations as findings in this study demonstrate that it is extremely unlikely to
result in an advantage for the opposing team, therefore the worse case scenario is a
loss of possession whilst the best case scenario is scoring a goal.
6.1
Directions for Future Research
The system used in this study was hand notated and analysis was done in lapse time
due to the amount of related variables measured. Consequently data could only be
presented to coaches for interpretation post match. However if the same data was
available to a coach during the actual match, tactics and strategies could be altered, if
necessary, in an attempt to impact performance positively. Future research could
therefore develop a similar system where such analysis would be possible in real time.
As results concerning match situation were somewhat unclear further investigation
could also be given to the impact match status (winning, losing or drawing) has on the
45
creation of perturbations. The techniques used in this study to analyse perturbations
could also be applied to other sports, and more accurate profiles of individual teams
could be achieved by enlarging the data sample.
46
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50
APPENDICES
APPENDIX A
NOTATION SHEETS
Match: ____________________________
Team: _________________________
Success/advantage for team in possession
2
1
No advantage for either team
5
6
3
4
Match Situation: ________________________
7
8
2
9
3
1
10
4
5
8
6
9
7
10
Success/advantage for defending team
No attempt to create perturbation
2
1
3
4
5
8
Pass
6
9
7
10
Total
Dribble
Totals
Match: __________________________________________
No Attempt to Create
Perturbation
Attempt to Create Perturbation
PS
PNA
Team: _________________________________
PDT
Pass
Player Attempting to Create Perturbation
GK
RB
CD
LB
CDM
CM
CAM
LM
RM
ST
Dribble
Total
Total
Successful Perturbations (PS)
Variable
Outcome
Perturbation Area
Initial Area
Pass
Dribble
Set Piece
Tackle
G
SS SOT SB SO
F
1
2
3
4
5
6
7
Final Area
8
9 10 1
Total
No Advantage for either team (PNA)
Variable
Perturbation Area
Initial Area
Pass
Total
Dribble
Set Piece
Tackle
1
2
3
4
5
6
7
Final Area
8
9 10 1
2
3
4
5
6
ST
7
8
9 10
2
3
4
5
6
7
8
9 10
Advantage for defending team (PDT)
Variable
Perturbation Area
Initial Area
Pass
Dribble
Set Piece
Tackle
1
2
3
4
5
6
7
Final Area
8
9 10 1
2
3
4
5
6
7
8
9 10
Total
Match Situation
Drawing
PS
Total
PNA
Winning
PDT
PS
PNA
Losing
PDT
PS
PNA
PDT
APPENDIX B
PERCENTAGE ERROR
CALCULATIONS
T1-T2
T1-T3
T2-T3
Match Situation
Drawing
Winning
Losing
Total
Σ (mod (V1-V2)
VTOTmean
% error
14 14
38 38
0 0
52 52
0
52
0%
14
38
0
52
14
38
0
52
Perturbation Variable
Goal
Shot Saved
Shot Off Target
Shot Blocked
Smoothed Over
Foul
Total
Σ (mod (V1-V2)
VTOTmean
% error
2 2
5 5
7 7
3 3
6 6
1 1
24 24
0
24
0%
2 2
5 5
7 7
3 3
6 5
1 1
24 23
1
23.5
4.3%
2
5
7
3
6
1
24
0
8
0
4
5
6
9
16
4
52
0
7
0
4
5
6
10
17
3
52
Player Attempting Perturbation
GK
0 0
RB
8 7
CD
0 0
LB
4 4
CDM
5 5
CM
6 6
RM
9 10
LM
16 17
ST
4 3
Total
52 52
Σ (mod (V1-V2)
4
VTOTmean
52
% error
7.7%
14
37
0
51
1
51.5
1.9%
0
7
1
4
5
6
9
16
3
51
3
51.5
5.8%
14
37
0
51
1
51.5
1.9%
2
5
7
3
6
1
23
1
23.5
4.3%
0
7
1
4
5
6
9
16
3
51
3
51.5
5.8%
APPENDIX C
NOTATED DATA
MATCH 2
MATCH 2
MATCH 2
MATCH 2
MATCH 2
MATCH 2
MATCH 2
MATCH 2
MATCH 2
MATCH 2
MATCH 3
MATCH 3
MATCH 3
MATCH 3
MATCH 3
MATCH 3
MATCH 3
MATCH 3
MATCH 3
MATCH 3
MATCH 4
MATCH 4
MATCH 4
MATCH 4
MATCH 4
MATCH 4
MATCH 4
MATCH 4