Trends of tactical performance analysis in team sports: bridging the

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Trends of tactical performance analysis in team sports: bridging the
4. revista:miolo 8/6/09 12:13 Page 81
Trends of tactical performance analysis in team sports:
bridging the gap between research, training and competition
Júlio Garganta
Centre of Research, Education, Innovation
and Intervention in Sport (CIFI2D)
Faculty of Sports
University of Porto
Performance in Team Sports is carried out through a long term
and methodical training process planned to improve skills and
competence required to deal with competitive demands.
Despite that tactical constraint play a major role in Team
Sports performance the history of its scientific analysis has
been driven by physiological and biomechanical approach, paying little attention to the tactical behaviour of the players and
team organisation. For coaches and researchers, tactical analyses can be helpful, since they offer the opportunity to identify
match regularities and random features of game events. The
information about performance is crucial to achieve individual
and team efficacy, also because it constitutes a basic criterion
for training process. Once tactical major features are identified,
they can inform training and performance enhancement programs. Regardless the technological progress, the analysis of
tactical performance in Team Sports remains an under-theorised field, since there was no significant amount of research
undertaken to identify the most important factors underpinning performance. Thus, it seems relevant to find out concepts
and methods allowing to assemble and to organise knowledge
about game complexity and dynamic interaction properties of
the teams. The main purpose of this paper is to point out that
conceptual frame about tactical indicators in Team Sports
should be a major orientation to bridge the gap between
research, training and competition.
Tendências da análise do desempenho táctico nos jogos desportivos:
em busca da harmonia entre investigação, treino e competição
Key-words: team sports, tactics, performance analysis
A performance nos jogos desportivos colectivos é viabilizada, em grande
parte, pelo recurso a processos de treino metódicos e planeados a longo
prazo para desenvolver habilidades e competências que permitam lidar de
modo eficaz com as exigências das competições. Apesar de, reconhecidamente, os constrangimentos tácticos desempenharem um papel nuclear nos jogos
desportivos colectivos, a investigação tem sido predominantemente orientada para as abordagens fisiológicas e biomecânicas, em detrimento da atenção devotada ao comportamento táctico dos jogadores e das equipas.
A análise da performance táctica pode ser profícua para treinadores e
investigadores, na medida em que possibilita a identificação de regularidades e contingências, com base na observação do modo como jogadores e
equipas engendram e gerem os eventos de jogo. Assim sendo, a informação sobre o desempenho táctico torna-se crucial para perseguir a eficácia
individual e colectiva, também porque constitui um preceito fundamental
para dar coerência ao processo de treino, na relação com a competição que
o legitima. Uma vez identificadas as principais características e exigências
tácticas, a partir delas é possível tornar o treino mais específico e adequar
outros programas de aprimoramento do desempenho.
Deste modo, o défice de investigação empreendida para identificar os
constrangimentos mais relevantes que condicionam o rendimento nos
jogos desportivos colectivos, nomeadamente no que se reporta ao desempenho táctico, justifica a necessidade de agenciar conceitos e métodos
que permitam organizar o conhecimento sobre a complexidade do jogo e
as propriedades de interacção dinâmica das equipas.
O propósito principal deste artigo é aduzir argumentos que mostrem que a
procura e a identificação de indicadores tácticos relevantes em jogos desportivos colectivos constitui uma orientação fundamental para demandar a
harmonia entre pesquisa, treino e competição neste grupo de modalidades.
Palavras-chave: jogos desportivos colectivos, táctica, análise da performance
Rev Port Cien Desp 9(1) 81–89
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Júlio Garganta
The limits of human performance are continually
being pushed in keeping with the Olympic motto
output: ‘stronger, higher, faster’(60). In effect, sports
scientists, coaches, and athletes are continuously
looking for ways to provide a slight, legal advantage
in athletic performance (49).
Team Sports (TS) refer to games played between two
opposing teams. The players interact directly and
concurrently to achieve an objective that involves
team members facilitating the movement of a ball or
a similar item in accordance with a set of rules, in
order to score points and to prevent the opposition
from scoring(14,38,62). In these sport disciplines, the
performance is carried out through a long term and
methodical training process planned to improve
technical and tactical skills, as well as strategic competence, required to deal with match demands.
In TS, the activity of players and teams is developed
by altering conditions, with the preponderance of
tactical features depending on (14): 1) the sort of
opposition amongst opponents and the kind of cooperation involving team-mates; 2) the huge degrees of
freedom and variability; 3) the characteristics of
technical skills to act in specific conditions.
Gréhaigne(22) points out that TS brings in three main
categories of problems, related with: a) space and
time; b) information, and c) organization. Therefore,
the French author highlights tactical and strategic
facets of the game.
Taking into account the basic motion of players in its
different modalities (standing, walking, jogging,
moderate speed running, sprinting, …), it is possible
to state that the genuine reasons for its expression
must be constantly based upon on a tactical/strategic
purpose; the player stands or positions himself to
some place, with higher or lower intensity, at a certain moment, in relation to the game configuration.
Given that any action should have a tactical aim, the
analysis of indicators such as the distance covered
during the game, players´ heart-rate, or time motion,
can acquire a larger pertinence when related to the
game tactical requests, namely the style of play, the
offensive and defensive play methods, and the positional and functional status of the players(14, 15).
Thus, in TS setting, the Olympic slogan looks incomplete - “stronger, higher, faster” – because it lacks the
Rev Port Cien Desp 9(1) 81–89
word “smarter”. Smartness in TS refers to the capacity to deal with space, time and task constraints, not
only to react to the different game scenarios but also
acting in order to create them.
Despite tactical constraints plays a main role in TS,
only a few papers deal explicitly with scientific
approach on tactical setting. In fact, the history of
scientific analysis in TS has been driven by physiological and biomechanical approach, paying little
attention to the tactical behaviour of the players and
team organization.
The focal purpose of this paper is to argue that
research about tactical features, mainly in what concerns team’s organization, in different game phases
(offensive, defensive and transition play), should be
a major orientation to bridge the gap between
research, training and competition in TS.
For coaches and researchers, tactical modelling can
be helpful because it offers the opportunity to identify match regularities and random features of game
events according to the offensive and defensive play.
Obviously the information about performance is crucial to achieve individual and team efficacy, also
because it constitutes a basic criterion for the training process.
Several authors have been trying to outline significant tactical performance features in TS(7, 9, 14, 20, 21,
29, 38, 41, 48, 51, 56). In this range, game modelling has
been used to provide detection of patterns among
match play events, according to the characteristics
that afford players and team’s success or failure.
As stated by Lames & Hansen(37), it is important to
ask whether models contain the essential attributes
of the original game sport observed. That’s why,
recently, game sports research has become aware
that another aspect of the model building process
has perhaps not been enough attention: the purpose
of the model.
In order to achiever deeper insight into the TS tactical
game, it is necessary to record the substantial tactical
actions in a chronological, sequential order, so the
stream of tactical behaviour can be recognized(55).
In view of TS as the composite of complex interactions, systemic approach brings us to consider,
among others, two main organizational levels:
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Tactical analysis in team sports
“match” and “team”(25). A match1 constitutes a
complex system and the central notion of opposition
leads us to consider two teams as interacting organized systems(24).
The game may be thought of as living in the regions
of meta-stability (see Kelso(34), where individual
actions may serve to destabilize or (re)stabilize the
system. The facility with which an attacker or a
defender may destabilize or (re)stabilize the system
would be considered a hallmark of quality in sport
competition. In general terms, the ability of a team to
destabilize or (re)stabilize a system might be examined at critical junctures of a game, say on the occurrence of an unexpected change of ball possession.
Modelling a dynamic system means mapping not
only its components and input-output behaviour but
also in particular its components interaction(4, 14, 24,
41, 53). From this viewpoint, the information about
the interaction processes generated by the interactivity by teammates and opponents happens to show
an outstanding relevancy because observing how
interaction in a concurrent and competitive situation
occurs can facilitate the design of specific and advantageous preparation.
To date research does not progress significantly further than the original work of McGarry & Franks(43)
and Hughes et al.(31) to develop new and inclusive
methods of dynamic analysis of sports contests, and
particularly in TS. Nevertheless, dynamic systems
analyses may hold the key to unlocking the “hidden
logic” of sports performance and variability within(19). The potential of these models to concentrate
enormously complex behaviour into simple expressions has been confirmed(4, 30, 31, 38, 53, 55) and offers a
significant advantage over the labour intensive and
inefficient approach required within traditional notational analysis.
In order to describe and interpret game sequences in
different sports, Anguera et al.(2) suggest a noteworthy tool - the Observational Methodology. In this
scope some authors have been using sequential
analysis and polar-coordinates technique in their
works(1, 5, 11, 12, 40, 52, 56, 59).
Garganta(14) put forward an approach to game observation based on a double level analysis plan: i) the
creation of a theoretical map with relevant match
performance indicators regarding tactical organiza-
tion; ii) the observation of game sequences and
exploitation of data coming from both qualitative
and quantitative analysis of team’s and player’s
Such an intention is very challenging due the nature
and diversity of the constraints that compete for the
success in TS, namely: i) the complexity concerning
the plentiful relationships among the players(24,64):
ii) the fact that game events do not correspond to a
predictable sequence of actions(8,13); iii) the acute
sensitivity of team and player’s behaviours to the
initial conditions, taking into account the large
amount of variables and its interaction(14, 39). For
instance, in sports disciplines such as Soccer,
Basketball or Handball, the teams compete for possession of the ball, which must be passed through a
goal, while in Volleyball, the teams pass the ball in
an attempt to place it in contact with an area of the
opponents playing field.
The teams involved in a match behave similar to
self-organized systems searching for order and shape
in a macroscopic plan, according to the interactions
produced by the players(18). The individuality and
degrees of freedom of team’s performance are
dependent on a number of players and their possible
interactions in game(39). Each team aims to disturb
or to break the opponents’ balance, with the intention to generate disorder in its organization. On the
other hand, teams intend to assure their own stability and organization. This way, the actions performed
along the matches tend to assure space and time
advantage over the contender, which means that the
confrontation determines, usually, a winner and a
Because teams represent dynamical systems2 organised in accordance with principles and prescriptions,
players and team’s behaviour is generated from the
tension among regularities (14, 44, 57) and the production of novelty (14, 21). In this sense, teams proceed as
specialised systems strongly dominated by strategy
and heuristic competences(18).
Some years ago Leon Teodorescu(61) claims that it is
not advisable to reduce TS to any algorithm model,
because team action does not represent predictable
sequences. Gréhaigne(23) appeals for a type of heuristic reasoning and he reinforces this idea referring
that if the cascade of decisions will be restricted to
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Júlio Garganta
an algorithm of binary choice, an impoverishment
necessarily takes place, bringing about a limitation
in game analysis. Lames & Hansen(37) alleged that
the multi-causal structure of diagnosis in TS
demands an interpretative rather than algorithmic
The swot up of team’s and player’s tactical organization afford the possibility to identify game events,
namely the identification of some pattern expressing
preferential ways or forms of action, and the distinctive characters showing the variability of behaviours
and events (14, 17).
Lames & McGarry(38) asserts that what we see by
observing a sports game is a dynamical interaction
process in which measures and countermeasures are
taken in an attempt to overcome the opponent. This
implies that the behaviour produced is not primarily
the expression of stable properties of the individual
players. In this context, the decision-making behaviour is best considered at the level of the performerenvironment relationship and viewed as emerging
from the interactions of individuals with environmental constraints over time specific functional
Therefore, the difficulty is that an adequate interpretation of numerical and visual data has to consider
individual circumstances (tactics, strategy), but also
situational aspects like physical and cognitive
processes during the game, the quality of opponent
and the preparation level(37).
The key role of tactical performance indicators
The last few years have seen considerable research
on the performance analysis of sport competition(for a
review see 28). The introduction of computer technology
facilitated the detailed recording and analysis of
sports behaviours and took centre stage in the early
development of various notation systems. The
assumption implicit in many of these initial studies
was that the recorded variables were relevant to the
performance outcome. On this expectation, the
coach would seek out the critical performance features to change future behaviours on the basis of
information gathered from past performances(41).
Although we do not deny the importance of videotechnology, mathematical methods or software and
hardware improvement, the actual strategy must
Rev Port Cien Desp 9(1) 81–89
focus on effort to assemble indicators that would be
able to describe main game events, considering the
opposition and cooperation relationships among the
players and teams. Much more than figures, information elapses from the notation and interpretation
of the amount of tactical modelling of game play.
This implies to understand the game beyond the
analysis and notation systems. Match analysts must
be able to check the relevance and descriptive power
of performance indicators and to distinguish the core
features of the game.
According to Hughes & Bartlett(33), a performance
indicator is a selection, or combination, of action
variables that aims to define some or all aspects of a
performance. Clearly, to be useful, performance indicators should relate to successful performance or
outcome. Analysts and coaches use performance
indicators to assess the performance of an individual, a team or elements of a team.
Also Hughes & Bartlett(33) affirm that the selection
and use of performance indicators depend upon the
research questions being posed. Teams and players
are either ’actors’ or ’reactors’. Actors are more likely to initiate a perturbation and to destabilize the
balance, whereas reactors are more likely to respond
to a perturbation and to restore the balance to some
semblance of stability. In such the team that lead
phase relation (action) can take advantage over the
team with the lag phase relation (reaction), which
should materialize in a winning outcome.
The notion that a perturbation may lead to a disruption in sports behaviour has been analysed in soccer
(see 24, for a related consideration of the changing configurations).
Hughes et al.(31) defined a perturbation in soccer as
an incident that changes the rhythmic flow of attacking and defending, leading to a shooting opportunity.
For example, a perturbation could be identified from
a penetrating pass, a dribble, a change of pace or any
skill that creates a disruption in the defence and
allows an attacker a shooting opportunity. In some
cases, a perturbation of the defence may not result
in a shot, owing to defensive skills or a lack of skill
in attack. This reasoning supposes that the defending team looks to (re)stabilize the just destabilized
system, in effect dampening or ’smoothing out’ the
disruption caused by the perturbation. If a perturbation should result in a shooting opportunity, then
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Tactical analysis in team sports
this event is termed a ’critical incident’. Using this
definition, Hughes et al.(31) reported significant differences in the goal to perturbation ratios between
successful and unsuccessful teams in the 1996
European Championships. Such an analysis supposed that a critical incident (a shot on goal) must
be preceded by a perturbation - that is, some aspect
of skill that disrupted the normal rhythm of the
The collective behaviour of a complex system cannot
be explained from separate investigations of the
behaviour of its parts(45). Instead, the system must
be viewed in its entirety and then reduced to a minimum but universal set of principles, rather than to
the elemental properties(35).
It was recognised that some characteristics of
dynamic systems – namely transient periods of instability – were occurring naturally within observed
sports performance. McGarry et al.(41), therefore reasoned, and later confirmed(46) that a stability disrupting perturbation occurred when the usual stable
rhythm of play was disturbed by extreme elements
of high or low skill. It became clear that the analysis
of perturbations in sport offered a more critical and
dynamic method of investigation on “dynamical configuration of play”(24) and therefore a significant step
towards effective support to coaches and performance.
A team game is a global event made up of several
related micro-events. Individual members must harmonise into an effective unit in order to achieve the
desired result. In such contexts the assessment of
how well the team is playing and how much individuals contribute to team effort presents a challenge
both to the coach and to sport scientists(6).
Perl & Weber(54) held that the processes in sport can
be described as time series of patterns, which can as
well characterize situations (e.g. positions on the
playground) as activities (e.g., moving of players).
Tools such neural networks permit recognition and
classification of these patterns.
In TS setting, Schöllhorn(58) illustrates some holistic
team qualities for describing the behaviour of a team
in space and time as a whole, namely the time cours-
es of movements on the field, the area covered by
players, the team’s geometric shape in time, and the
movement of team geometric centre.
During the last years, some studies have attempted
to provide a theoretical basis to performance analysis research in terms of feature identification(10, 14, 51)
and essential variables which characterise game patterns in TS(30, 32, 51). However our understanding of
critical behaviours still remains in its infancy.
In a large part of several works, the authors gather
and characterise amounts of data and describe the
game variables behaviour, restricting their analysis
to the situations leading to score. Nevertheless, the
description of the offensive process and the evaluation of its effectiveness based only on the score
opportunities, only allow a very restricted understanding of the game dynamics and team performance(15, 27).
For researchers and coaches, it seems relevant to
focus not only on the scoring actions, but also on
other ones that permit to notice teams´ production,
in conformity with the cascade of purposes concerning the attack, defence and turnovers. In this way,
the holistic analyses that point out team organisation, through the identification of regularities and
random features of game actions, considering offensive and defensive efficacy, could be advantageous. It
justifies searching for vital indicators concerning
game events and so its required to scrutinize the
transitions and metamorphosis that show the
dynamical flow of player’s and team’s performance.
For example, Lago & Martin(36) made an empirical
research about the determinants of ball possession
as a performance indicator in soccer; and
Garganta(14) suggests that tactical performance indicators should reproduce the relative importance of
illustrative latent variables, e.g., time, space and
game playing tasks (Figure 1), as well as how players and teams exploit these aspects of performance.
These will be reflected in the ways that individuals
and teams attack and defend, how they use the
spaces in the playing surface and the variety of playing actions(14, 17, 42).
As such, the main subject of tactical analysis should
not be the player’s actions, taken disjointedly, but
the game play sequences resulting from the actions
that occur during the different phases of the match.
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Júlio Garganta
Figure 1. Example of latent variables and tactical
performance indicators in Soccer
[Adap. Garganta(15)].
Behaviours are significant if they brake the balance
attack/defence of the opponent, or because they
exhibit a certain permanence in the variability of
From this point of view, such a change implies the
construction of observational and notational systems
taking into account (Figure 2): the match organisation, starting from the features of sequential actions
(tactical units), performed by the teams; the characteristics of the sequences leading to different outcomes; and the situations in which, whether a score
occurs or not, there is a perturbation in the balance
While the vital challenge to players in TS is to generate and to manage interaction in order to organize
the own team and to brake the opponent’s balance,
tactical features must be understood as game “functional units”, containing the crucial information
about match play organization and its efficacy.
Hence, it is possible to use information about the
organization patterns revealed by a team along several games to come up to conclusions about the
effectiveness of players’ behaviour in other games.
Starting from an analysis of this type it seems perti-
Rev Port Cien Desp 9(1) 81–89
nent to design models that formalise team’s organization according to variations and regularities that
configure match play events, according to the game
phases, i.e., attack, defence and transition play.
Regardless the technological progress, tactical modelling remains an under-theorised field, since there
was no significant amount of research undertaken to
identify tactical features underpinning performance
in TS. Thus, it seems relevant to find out concepts
and methods allowing to assemble and to organise
knowledge about game complexity and dynamic
interaction properties of the teams. Once tactical
focal features and its pertinence are identified, they
can inform training and performance enhancement
programmes. So, it has to be realised relevant coupling of information from game observation and the
player’s and team’s training process(37).
The question is, as states Perl(53), how tactical modelling can help to analyse and understand the present state as well as predict the future behaviour of a
dynamic system, in order to update training and
competition. Because of their complex internal inter-
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Tactical analysis in team sports
Figure 2. Evolution of
match analysis in Team
Sports [Adap.
actions, the time-dependent behaviour of dynamic
systems cannot be predicted using static description
models only. Instead, models have to be developed
that reflect the system dynamics and help to simulate its behaviour.
Memmert & Perl(47) refers that to evaluate performance data from TS, normally qualitative and quantitative methods are used separately, and suggested
the combination of net-based qualitative analyses
and stochastic quantitative analyses to improve the
information output significantly.
Neville, Atkinson & Hughes(49) note that despite
many sort of research methods and techniques to
model performance in sport (i.e., empirical modelling, stochastic modelling, dynamic systems, neural
networks, and fuzzy logic), used singly or in combination, to date, results have been disappointing
In fact, during the last years the use of computers
and sophisticated software develops clearly faster
than the improvement of concepts and ideas about
how to observe and to learn from starting tactical
game setting and its dynamical properties.
However, and being essential to decide what information is important and whether it can be used to
improve performance(6), the decisions regarding
strategies for collecting data, processing information and presenting the results are connected with
the way of thinking(4).
For that reason, methods and tools to modelling performance in TS need not to be exclusive of each
other. A hybrid type of description (or model) may
be appropriate in the future. Thus, further research
on sports contests using various types of system
descriptions is warranted 49.
We do not dare to doubt the importance concerning
technological development in analysis of performance in TS. Nevertheless, we support that the technological sophistication is not sufficient to observe
and to note efficiently game features neither to
understand its configurations. Performance analysis
becomes useful whenever it corresponds to the progressive refinement and extension of the observational variables, in the sense of increasing its
descriptive and explanatory potential according to
the representative game events.
Consequently, the dynamic interactions expressed by
the balance and misbalance of team organization,
seems to be key-features to describe and shape performance in TS. Considering the complexity and
uncertainty of TS (14, 63), deterministic modelling
seems not appropriate to set up performance analysis. As states Balagué & Torrents 4 and Lames &
McGarry(37), behind the use of mathematical modelling, simulation techniques or computing techniques, it is imperative to include qualitative
research methods to arrive at the necessary inference
for sport practice.
Searching for identification and interpretation of
substantial game behaviour, it’s imperative to assemble information based on quantities of quality of game
playing. In this sense we must be aware of “game
flow” and its changes(16), developing concepts and
tools from the dynamic systems approach and computer science to cope with complexity(4).
First we must found (the accurate variables and indicators); then we have to search for its expression in
the match. In another words, the game can answer
to all our questions … if we know how and what to
As it seems pertinent to create and to improve
dynamics-sensitive tools to understand game’s logic
in TS, according tactical stream (see Gréhaigne,
Mahut & Fernandez(26), game analysts and match
observers should be team sport specialists prior to
technological experts. May be this is one of the keys
to bridge the gap towards a comprehensive link
between research, training and competition.
The author wishes to thank Marc Verlinden (Vrije
Universiteit Brussel) for his help, suggestions and
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Júlio Garganta
1 According to Lebed(39), “game” is as system of ordered information, a code of rules restricting and defining participants´
behaviours in specially constructed conditions of space, time
and means; “match” is a process of participating sides (systems) competing between them; “game playing” is a system,
which directly functions in sport competition conditions. A single player or a team of players can represent such a system.
2 According to McGarry & Franks(45), a dynamical system is a
type of complex system, one in which regularity self-organizes
from within as a result of information exchanges that occur
both inside and outside the system (i.e., among the parts that
comprise the system, and between the system and its surrounding constraints, respectively).
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