Sport Practitioners as Sport Ecology Designers: How Ecological Dynamics Has Progressively Changed Perceptions of Skill “Acquisition” in the Sporting Habitat

Abstract

Over two decades ago, Davids et al. (1994) and Handford et al. (1997) raised theoretical concerns associated with traditional, reductionist, and mechanistic perspectives of movement coordination and skill acquisition for sport scientists interested in practical applications for training designs. These seminal papers advocated an emerging consciousness grounded in an ecological approach, signaling the need for sports practitioners to appreciate the constraints-led, deeply entangled, and non-linear reciprocity between the organism (performer), task, and environment subsystems. Over two decades later, the areas of skill acquisition, practice and training design, performance analysis and preparation, and talent development in sport science have never been so vibrant in terms of theoretical modeling, knowledge generation and innovation, and technological deployment. Viewed at an ecological level of analysis, the work of sports practitioners has progressively transitioned toward the facilitation of an evolving relationship between an organism (athlete and team) and its environment (sports competition). This commentary sets out to explore how these original ideas from Davids et al. (1994) and Handford et al. (1997) have been advanced through the theoretical lens of ecological dynamics. Concurrently, we provide case study exemplars, from applied practice in high-performance sports organizations, to illustrate how these contemporary perspectives are shaping the work of sports practitioners (sport ecology designers) in practice and in performance preparation.

Full text

fpsyg-11-00654 April 24, 2020 Time: 15:56 # 1
HYPOTHESIS AND THEORY
published: 24 April 2020
doi: 10.3389/fpsyg.2020.00654
Edited by:
John Komar,
Nanyang Technological University,
Singapore
Reviewed by:
Carlota Torrents,
National Institute of Physical
Education of Catalonia (INEFC), Spain
Bruno Travassos,
University of Beira Interior, Portugal
*Correspondence:
Carl T. Woods
carl.woods@vu.edu.au
Specialty section:
This article was submitted to
Movement Science and Sport
Psychology,
a section of the journal
Frontiers in Psychology
Received: 13 January 2020
Accepted: 18 March 2020
Published: 24 April 2020
Citation:
Woods CT, McKeown I,
Rothwell M, Araújo D, Robertson S
and Davids K (2020) Sport
Practitioners as Sport Ecology
Designers: How Ecological Dynamics
Has Progressively Changed
Perceptions of Skill “Acquisition”
in the Sporting Habitat.
Front. Psychol. 11:654.
doi: 10.3389/fpsyg.2020.00654
Sport Practitioners as Sport Ecology
Designers: How Ecological Dynamics
Has Progressively Changed
Perceptions of Skill “Acquisition” in
the Sporting Habitat
Carl T. Woods1,2,3*, Ian McKeown2, Martyn Rothwell4, Duarte Araújo5, Sam Robertson1
and Keith Davids4
1Institute for Health and Sport, Victoria University, Melbourne, VIC, Australia, 2Port Adelaide Football Club, Football
Department, Adelaide, SA, Australia, 3Department of Sport and Exercise Science, James Cook University, Townsville, QLD,
Australia, 4Centre for Sport and Human Performance, Sheffield Hallam University, Sheffield, United Kingdom, 5CIPER,
Faculdade de Motricidade Humana, University de Lisboa, Lisbon, Portugal
Over two decades ago, Davids et al. (1994) and Handford et al. (1997) raised theoretical
concerns associated with traditional, reductionist, and mechanistic perspectives of
movement coordination and skill acquisition for sport scientists interested in practical
applications for training designs. These seminal papers advocated an emerging
consciousness grounded in an ecological approach, signaling the need for sports
practitioners to appreciate the constraints-led, deeply entangled, and non-linear
reciprocity between the organism (performer), task, and environment subsystems.
Over two decades later, the areas of skill acquisition, practice and training design,
performance analysis and preparation, and talent development in sport science
have never been so vibrant in terms of theoretical modeling, knowledge generation
and innovation, and technological deployment. Viewed at an ecological level of
analysis, the work of sports practitioners has progressively transitioned toward the
facilitation of an evolving relationship between an organism (athlete and team)
and its environment (sports competition). This commentary sets out to explore
how these original ideas from Davids et al. (1994) and Handford et al. (1997) have
been advanced through the theoretical lens of ecological dynamics. Concurrently,
we provide case study exemplars, from applied practice in high-performance
sports organizations, to illustrate how these contemporary perspectives are shaping
the work of sports practitioners (sport ecology designers) in practice and in
performance preparation.
Keywords: constraints-led approach, ecological dynamics, self-learning and preparation for performance,
practice designs, skill adaptability
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INTRODUCTION
The gardener cannot actually “grow” tomatoes, squash, or
beans – she can only foster an environment in which the
plants do so.
– Stanley McChrystal
This is an exciting era for sports practitioners and applied
scientists interested in understanding how to help athletes
“grow and flourish” in complex performance surroundings. In
a high-performance sport environment, the significant aims of
coaches, sport scientists, and performance analysts are to develop
“athletes of the future” and prepare “athletes of the present”
for competitive performance. To foster successful interactions of
athletes and teams with competitive performance and practice
environments, the areas of skill acquisition, practice and training
design, performance analysis, and talent development have never
been so vibrant in terms of theoretical modeling, knowledge
generation, technological deployment, and the application
of innovative ideas in practice, training, and performance
preparation. Viewed at an ecological level of analysis, the work
of sports practitioners is to facilitate a productive, evolving
relationship between an organism (athlete and team) and its
environment (sports competition).
Indeed these ideas were originally promoted in sport science
over two decades ago in theoretical concerns raised with
traditional, mechanistic perspectives of movement coordination
(Davids et al., 1994) and skill acquisition (Handford et al.,
1997). Those position papers advocated the potential of
an ecological approach to sport scientists and scrutinized
reductionist information-processing perspectives on human
performance dominant at that time. An important insight
was that skill “acquisition” was conceptualized to emerge
from an evolving practice ecology, which necessitated sports
practitioners to appreciate the complex, deeply integrated, and
non-linear reciprocity of the organism (performer), task, and
environment subsystems (Newell, 1986). Such a theoretical
conceptualization challenged the traditional perspectives of skill
acquisition, having profound implications for understanding
the performer–environment relationship and for how sports
practitioners viewed their role in the preparation of athletes
for performance. Here we seek to examine the progress made
on complementing that emergent consciousness through the
contemporary theoretical lens of ecological dynamics, exploring
how the original ideas have been advanced in the intervening
decades. We also examine case studies showing how the
key concepts are currently shaping the work of some sports
practitioners in practice and in performance preparation.
An ecological dynamics rationale, integrating ecological
psychology, dynamical systems theory, the complexity sciences,
and evolutionary science, views skilled behavior as the emergence
of functionally adaptable performance solutions (i.e., actions,
for a detailed review, see Araújo et al., 2020). In this framework,
behavior is a self-organizing phenomenon that emerges
from the continuously dynamic interplay of an organism’s
characteristics and the affordances (possibilities for action:
Gibson, 1979) offered in a specific competitive performance
environment (Araújo et al., 2006). Thus, skilled behavior evolves
over timescales of performance, learning, and development
(Button et al., 2020). These theoretical propositions are
grounded in James Gibson’s (1979) theory of direct perception
in ecological psychology and in Scott Kelso’s seminal work on
coordination dynamics (e.g., Kelso, 1981a,b, 1984). Specifically,
Gibson (1979) proposed how detection of information
regulated action (and vice versa) and how the realization
of affordances underpinned functional behaviors in dynamic
performance environments. In a series of laboratory experiments,
Kelso observed inherent, spontaneous self-organization
tendencies in human movement systems and sudden phase
transitions between states of coordination as the participants
interacted with informational constraints of the environment
(Kelso, 1981b, 1984).
In this commentary, we discuss how the role of a
sports practitioner has shifted through the application in
sport science of these key ideas in ecological psychology,
behavioral neuroscience, and human movement science. Sports
practitioners have moved on from an instrumental role of
ensuring compliance of performers with “operational standards”
or “technical performance templates” defined in coaching
and performance manuals toward the designer of a learning
ecosystem, working in multidisciplinary teams, to promote
emergent, self-organized athlete–environment interactions. We
highlight how this role perspective focuses more attention on
the adaptability of athletes in performance, predicated on being
excellent learners. The aims of this commentary are to: (1)
provide an appreciation of advances in key concepts in ecological
dynamics made in the past two decades and (2) provide (brief)
practical insights from case studies in high-performance sport
describing how this ongoing conceptualization is facilitating the
implementation of practice designs inviting effective behaviors.
PART 1: SKILL ACQUISITION AS AN
EVOLVING PRACTICE ECOLOGY – AN
UPDATE
A Progression Toward Ecological
Dynamics
A critical theoretical tenet of the ecological approach to skill
acquisition, highlighted by Handford et al. (1997), is the
appreciation of the performer–environment mutuality. From an
ecological perspective, the “environment” refers to an animal’s
surroundings within which it can perceive and act, changing
the environment and their deeply entwined relationship with
it (Gibson, 1979). These relationships can be changed across
different timescales (in sport, evolving along the macro-scale
of talent development and changing within the micro-structure
of practice; see Davids et al., 2017;Balagué et al., 2019). Thus,
actions and behaviors should be understood as the result of
specialized relationships that emerge between an organism and
its environment (Handford et al., 1997). More directly, behaviors
and actions do not appear in a vacuum. An athlete’s behaviors
cannot be understood without sustained reference to the specific
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environmental context in which they emerge (Renshaw et al.,
2009). Specifically, the ecological dynamics approach focuses
less on the putative control mechanisms of organisms, like
internalized representations and knowledge structures stored
in memory, and more on the reciprocal nature of perception
and action which supports performance functionality. This was
captured elegantly by Beek and Meijer (1988, p. 160) as the
appreciation of “phenomena within the organism–environment
synergy rather than within the organism per se.” This more
biophysically oriented theoretical conceptualization subsequently
rejects the more mechanistic traditions of mental information-
processing theories of skill acquisition. Such theories historically
view movements as idealized, internalized templates for actions
that originate from the mind and which are optimized with
practice, rather like a computer programmer “debugs” a piece
of software (for an original overview of implications for sports
science; see Davids et al., 1994).
Organismic Asymmetry in Human
Behavior
This inordinate emphasis on internalized representations
somehow acquired in the mind of the athlete is another example
in science of a dualism, in this case mind–body, proposed in
explanation of natural physical phenomena (Turvey and Shaw,
1995). A prominent example is the confected “nature vs. nurture
debate” to discuss exclusive influences on human behaviors such
as learning, intelligence, propensity to disease, and expertise.
The manifestation of this organism–environment dualism was
recognized by Dunwoody (2007) who criticized the inherent
bias caused by “organismic asymmetry” in the study of human
behavior. Dunwoody (2007) identified one such organismic
asymmetry as neglecting the foundational person–environment
relationship as an interrelated basis for explaining human
behavior, in favor of a biased preference for organismic-centered
mechanisms such as internal mental models of the world.
Brunswik (1955) indicated that, in organism–environment
interactions, it was considered that both equally contribute to the
organization of behavior. Brunswik (1955) noted a bias in most
psychologists for attributing achievement to the internal process
of humans, rather neglecting the influence of the environment
in co-shaping human behaviors. Typically, much cognitive
psychology remains focused on conscious mental life, with little
reference to the role of the environment in shaping behavior
(Davids and Araújo, 2010).
In 2011, Araújo and Davids highlighted the relevance of
organismic asymmetry to sport scientists seeking to understand
how athletes self-organized during practice and performance.
This theoretical re-positioning offered significant implications
for how sports practitioners could learn to rely less on traditional
approaches to athlete development and preparation for
performance, which emphasized verbal instructions and
corrections, constant repetitions to “optimize a movement
pattern,” and the internalization of rehearsed behavioral
reactions and responses in training. Indeed this theoretical
re-positioning was in agreement with the empirical work
conducted by Schöllhorn and colleagues, who demonstrated
both inter-individual (Schöllhorn and Bauer, 1998) and intra-
individual (Schöllhorn et al., 2002) variability and differences
with regards to movement patterning, highlighting the fallibility
of sport pedagogies grounded in the (attempted) acquisition
and reproduction of “optimal” movement patterns. To further
exemplify, an organismic asymmetry can be detected in some
current notions of the concept of self-regulation in human
behavior. Traditionally, self-regulation has been defined from a
cognitive orientation referring to all the “self-generated thoughts,
feelings, and actions that are planned and cyclically adapted to
the attainment of personal goals (Zimmerman, 2000, p. 14).
The bias toward the internalized regulation of behavior through
planned goal achievement is apparent. From an ecological
dynamics rationale, self-regulation can be conceptualized in
a broader behavioral framework, emphasizing an individual’s
emergent interactions with the environment rather than
referring to behaviors that are guided by internalized plans
and goals with little reference to environmental interactions.
In ecological dynamics, individuals can learn to self-regulate
by developing and exploiting a deeply intertwined relationship
between their actions, perceptions, intentions, and emotions
to continuously support these emergent interactions. By
harnessing this functional relationship with a performance
environment, athletes learn to self-regulate by adapting stable
action–perception couplings developed in rich and varied
practice environments.
Variability and Performance
In their position statement, Handford et al. (1997) suggested that
there was an over-emphasis on the measures of performance
outcome variability (such as standard deviations and coefficients
of variation) in sport and movement science research, which was
focused on the magnitude of variability in task outcomes. This
is only part of the picture and biased to the view that variability
was often equated with “noise” or error in humans, considered
as information-processing channels. This conceptualization was
due to the linear movement models that were popular in
motor behavior theories in the 1960s to the 1970s and that
somewhat still prevail in current practice. Contemporary models
of movement, such as ecological dynamics, advocate that humans
and groups are complex adaptive systems with inherent non-
linear properties. Variability in such systems needs to be much
more carefully interpreted in a nuanced way, which is the
challenge for sports practitioners interested in enhancing athlete
and team performance.
Complex systems with many degrees of freedom can be
seen as a “curse” (of organization, coordination, and control)
or “blessing” (adaptability, re-organization, and functionality)
as was discussed by Handford et al. (1997). The blessing is
that athletes can continuously be encouraged to exploit self-
organizing tendencies in their movement systems to form
synergies (coordination patterns). This is where the variability
can be functional. However, it is important to recognize that
variability in movement patterns can be detrimental. Variability
does not just exist within coordination and can manifest at
different levels within an individual’s kinematic profile. One level
consists of fluctuations in individual elements such as joints
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and segments, usually seen in novices and considered less than
desirable. Another perhaps is whole system variability, where
several coordinated elements combine to produce an overall
movement pattern, which manifests itself in system degeneracy
(Edelman and Gally, 2001). At the first level, there is consistent
evidence that variability decreases as skill level increases (Button
et al., 2003;Bradshaw et al., 2009;Fleisig et al., 2009;Betzler et al.,
2012;Hiley et al., 2013). It could be hypothesized that higher
levels of movement variability in the lower skilled athletes at
this level are reflective of them searching for effective movement
patterns in line with the degrees of freedom and U-shaped
curve hypotheses. However, the consistent decrease in individual
variability as skill levels increase may be evidence of the need to
constrain element variability to facilitate functional coordination
and allow multi-element coordination variability to emerge
(Button et al., 2020). Understanding the change in variability
profile at each level, in particular during any interaction with
motor learning and/or adaptation, could provide insight into how
any functional role of variability emerges.
One of the functions attributed to movement variability
is facilitation of the adaptation of an organism to changing
environmental and task constraints (Davids et al., 2003;Glazier
and Davids, 2009). In discrete movements, this type of variability
is different to the undesired variability in the endpoint or the
outcome of the movement (e.g., the number of targets accurately
hit). Functional movement variability is now considered to be a
characteristic of highly skilled movers (Button et al., 2003;Wilson
et al., 2008), and an individual’s variability profile is thought to
change during task learning. For example, a U-shaped curve has
been hypothesized to characterize coordination variability across
skills, where the highest and lowest skilled display increased
variability while those in intermediate stages have their variance
constrained (Wilson et al., 2008).
In summary, the challenge for sports practitioners is to sort
what is “good” (functional) variability from “bad” (dysfunctional)
variability in an individual athlete’s performance [see Scholz and
Schöner (1999) and Latash et al. (2010) on the Uncontrolled
Manifold Hypothesis]. At this stage, it is worth drawing attention
to the influential theoretical insights and experimental data of
Bernstein’s (1967) which highlighted the need for psychologists,
movement scientists, and sport scientists to re-consider how
measures of movement variability should be conceptualized for
human performance. Movement pattern variability can support
the skill adaptations needed as the influence of task constraints on
athlete behaviors emerges during practice and performance. The
implications for practice and performance were captured in the
phrase of “repetition without repetition,” indicating how practice
designs for trainers and coaches should provide opportunities for
athletes to solve performance problems in different ways using a
variety of behaviors.
Skill Adaptation
This re-conceptualization of self-regulation and functional
variability has important implications for the translation into
practice in sports performance preparation, suggesting that the
commonly used term skill “acquisition” does not actually involve
the acquisition of a physically reproducible motor memory
stored in the brain. Rather, a more relevant description of
the learning process in sport may be considered as “skill
adaptation” (Araújo and Davids, 2011). What is developed is a
highly functional relationship that evolves between an athlete
and a competitive performance environment over extended
timescales: a flourishing relationship that is supported by
learning, experience, growth, and development (Seifert et al.,
2013). Interestingly, this conceptualization of skill acquisition,
predicated on continuously growing athlete functionality, was
foreshadowed by Bernstein’s (1967, p. 134) notion of dexterity,
which he defined as the “the ability to find a motor solution for any
external situation, that is, to adequately solve any emerging motor
problem correctly (i.e., adequately and accurately), quickly (with
respect to both decision making and achieving a correct result),
rationally (i.e., expediently and economically), and resourcefully
(i.e., quick-wittedly and initiatively)” (italics in the original).
Furthermore, according to Bernstein, the “demand for dexterity
is not in the movements themselves but in (adapting to) the
surrounding conditions” (Bernstein, 1996, p. 23). In this respect,
Bernstein’s’s (1967) insights foreshadowed how dexterity could
provide a foundation for skill adaptation, with his definition
of dexterous behavior showing the deeply intertwined links
between cognition, action, and perception, the interaction of
which is continually used to negotiate a dynamic performance
environment. His ideas clarified how movement variability and
skill adaptation are founded on the self-organization tendencies
that can be exploited in dynamic performance contexts
(Chow et al., 2011).
These theoretical insights on athlete performance illustrate
the fundamental importance of many natural phenomena
in the environments studied by ecologists, exemplified by
the inherent self-organizing tendencies observed in complex
systems formed by shoaling fish, flocking birds, synchronization
of insect emission of sound and light as information, and
the exploration of growing conditions by plants or mosses
(Passos et al., 2013). Self-organization tendencies are ubiquitous
in nature. Based on the key principle of “information–
action coupling,” these tendencies have even been observed
in single-cell organisms without a nervous system (Boisseau
et al., 2016). The dynamics of self-organization have drawn
attention to the fundamentality of the organism–environment
relationship, predicated on actions regulated by surrounding
information, emphasizing the ecological systems at the heart
of these links. It is important to note that the self-organizing
tendencies in ecology are rarely expressed in isolation of a
context (i.e., what is happening in the environment). For
example, the organizing principle in a self-organizing system
like a shoal, with each fish functionally co-adapting with
each other, concerns their emergent co-movements (remaining
within one “fish” length of each other) relative to those
of an approaching predator or food source (informational
constraints). The emergence of these rich and sophisticated
global behavioral patterns in complex neurobiological systems
is not pre-programmed within a knowledge structure shared
between each single fish in the shoal nor pre-orchestrated by
a piscatorial “leader” (acting as a collective system “coach”).
Rather, they emerge from the information created by the
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movements of each complex system component, continually co-
adapting to each other.
Implications of These Ideas for Sports
Practitioners: Representative Design
Through an ecologist’s perspective, an important part of a sports
practitioner’s role is to identify the critical informational sources
or, more technically, the affordances (defined as opportunities
or invitations for action; Gibson, 1979;Withagen et al., 2012)
of a training setting that are likely to impact an athlete’s or
a teams’ behaviors (similar to an ecologist being cognizant of
how the presence of a predator or food source shapes the time–
space relations underlying the emergent patterns of behavior
of each fish within the shoal as a collective). Understanding
the relevant affordances used to regulate performance behaviors
allows groups of practitioners to carefully coordinate the design
of learning activities that represent, or closely simulate, the
demands of competitive performance contexts. While Handford
et al. (1997) addressed the issue of specificity of practice,
later work in ecological dynamics precisely located the key
issues for sport practitioners as ensuring representative design
after insights of Brunswik (1955) (see Araújo et al., 2005,
2006, 2007). The ensuing work of Pinder et al. (2011a,b)
drew the attention of sport scientists and sport practitioners
to the relevance of this concept for ensuring that the
task constraints of learning sessions, especially informational
constraints, represented (that is faithfully simulated) those
of competitive performance environments. It is through the
prolonged exposure to representative practice tasks that a
performer learns to attune to (or “detect”) the information
sources that specify the relevant properties of the affordances of
their environment using a variety of modalities such as haptic,
visual, and auditory sensory systems [i.e., a surfer progressively
learning to detect the motion of a wave (using haptic and visual
sensory systems) to inform a “cutting” manoeuver used to score
points in competition] (Withagen et al., 2017). The ongoing
process of attunement to performance opportunities helps
athletes and teams to develop a more functional and adaptable
relationship with a particular competitive environment. More
specifically, if we consider a performance environment as a rich
landscape of affordances (Rietveld and Kiverstein, 2014), some
of them designed by the coach when presenting practice tasks,
then such practice tasks are directing or guiding the search of the
performers. Moreover, some affordances can attract or invite the
athletes to act upon them, especially if they precisely match the
current capacities, abilities, and skills [termed “effectivities” by
Gibson (1979)] of the athlete and the task constraints channel the
athlete toward them (Araújo et al., 2019). From this perspective,
affordances have both body-scaled (e.g., limb lengths) and action-
scaled (e.g., strength output) properties that are perceived relative
to the performer’s current action capabilities (Fajen et al., 2009).
This idea is most important to consider in athlete development
programs in high-performance sport.
The current thinking on the affordance landscape notion for
practice design suggests that, with experience, skill, and quality of
practitioner support, athletes can become increasingly competent
at perceiving and utilizing the most soliciting of affordances.
This process is predicated on strong coupling tendencies
between the presented affordance landscape and the skill of
the athletes’ perception and action in specific environmental
designs (Withagen et al., 2017). Thus, through the landscape
design, the practitioner can “nudge” or guide the athlete to
use specific affordances while ignoring other less relevant ones.
This ecologist’s perspective leads to another important tenet
of ecological dynamics for sports practitioners, that of synergy
formation and self-organization under constraints.
Synergy Formation in Athletes and
Sports Teams Exploits Self-Organization
To assist with the understanding and subsequent explanation
of synergy formation, it is important to, firstly, appreciate
the theoretical roots of ecological dynamics. Ecological
dynamics is grounded in theoretical approaches, such as
direct perception in ecological psychology, explaining how
(detection of) information regulates actions and actions are
coupled to perception of affordances (Gibson, 1979). At its
core, it provides scientists with a framework for describing the
emergence of complex, non-linear, and self-organized behaviors
shaped by task, organismic and environmental constraints
(Newell, 1986), and the order parameter–control parameter
relations underpinning the dynamics of coordination in nature
(Kelso, 1981a,b, 1984). Newell (1986) modeled how nested,
interacting task and organismic and environmental constraints
shaped coordination development, later applied to coordination
behaviors and their acquisition in sport performance (Davids
et al., 1994;Handford et al., 1997;Renshaw and Davids, 2004).
Kelso (1981a,b, 1984, 1995) produced data showing how the
coordination dynamics of brain and behavior shaped perceptions,
intentions, and actions, during performance and learning, not
as separated entities stored in the brain but as self-organizing
patterns of behavior formed through the interaction of system
components (order parameters) and the critical informational
constraints of the environment (control parameters) (Kelso,
1995). In the central nervous system, the functioning of “system
components” is observed at a macroscopic level, such as
the stimulation of neurons simultaneously firing. In human
movement, muscles of different limb segments synergistically
interact to form multi-articular actions (Kelso, 1992, 1995). The
interaction of system components with critical informational
or environmental constraints results in the emergence of
coordinated, self-organized behaviors (Kelso, 1981b;Kugler and
Turvey, 1987). Ecological dynamics, therefore, fundamentally
blends key concepts and insights specific to ecological psychology
and dynamical systems theory in the explanation of synergy
formation and coordination of action in complex neurobiological
systems (for further insights, see Araújo et al., 2006;
Warren, 2006).
The initial implications of these theoretical ideas for sport
practitioners were raised by Handford et al. (1997) in a discussion
of coordination and its acquisition. Gradually over the years,
several lines of research began to reveal how these applied
scientific insights had radical implications for the work of
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sport practitioners interested in how athletes coordinated their
actions in sport collectives at a mesoscopic level, for example,
in synchronized swimming and diving, cycling in a group,
and especially in team sports (e.g., Passos et al., 2009;Duarte
et al., 2012, 2013;Vilar et al., 2012;Silva et al., 2014;Passos
and Davids, 2015;Ric et al., 2016). Over the following two
decades, key insights on processes of co-adaptation were raised
for understanding the functioning of 1v1 dyadic systems in team
sports like basketball (Bourbousson et al., 2010a,b), association
football, rugby union, and small sub-groups of athletes in sub-
phases of play (e.g., 4v2 in rugby union, 6v6 in association
football, 5v5 in futsal) (for empirical examples, see Araújo et al.,
2006; Araujo et al., 2014;Passos et al., 2009). These insights
now theoretically guide applied scientific work in the fields
of performance analytics and biomechanics, sports pedagogy,
tactical behaviors in team sports, physiology, skill acquisition,
and practice design (Travassos et al., 2013;Araújo and Davids,
2016;Ribeiro et al., 2019a). From an ecological dynamics
perspective, the processes of performance and functionality in
sport can clearly draw inspiration from biological systems which
function in a symbiotic way to flourish together in a specific
environment. In sport and other ecological systems, function is
predicated on information that reciprocally shapes the ongoing
evolution of co-habiting organisms in a particular environment,
with each organism shaping the environment while being shaped
by its surrounds.
The Coach as the “Designer”
One of the key issues raised by Handford et al. (1997) was
that the learner needed to be placed at the center of the
learning process, with less of an emphasis of the coach being
at the center of the instructional process. Over the past two
decades, the ecological dynamics framework has emphasized
how the role of the sports practitioner has evolved from an
autocratic instructor who leads every sequential step of athlete
progression through continuous provision of verbal information
and corrective feedback to one of a “learning designer” whose
role it is to work with athletes to identify and manipulate
the key constraints of practice environments (Davids, 2012,
2015). This co-designing learning activity places the athlete and
his/her needs at the heart of the development and performance
preparation process. This is likely to augment the design of
representative practice tasks as the coach and the athlete work
together to co-design critical affordances that the athlete attunes
to, thus guiding their behaviors. Traditionally, for example,
the role of the coach has been conceived in a hierarchical way,
sometimes even autocratically, preparing athletes and teams
for performance through a strong emphasis on global-to-local
synergy formation processes to externally regulate dynamics in
performance and learning (Ribeiro et al., 2019b). In team sports,
this can be typically exemplified through an external agent (i.e.,
coach, instructor, and trainer) prescribing strategic patterns of
behavior in specific phases of a game. Conversely, an ecological
dynamics framework advocates local-to-global synergistic
tendencies, in which a system’s synergy formation tendencies
can be exploited in self-organization through interactions with
the performance environment designed into representative
practice tasks (Ribeiro et al., 2019a,b). Buekers et al. (2019) have
re-iterated this point by arguing that the tactical performance
of players in sports teams can be understood with respect to
the ecological laws governing the perception of information
in surrounding energy arrays during performance (aligned
with the local-to-global synergy formation tendencies in sports
teams). Team sports strategizing, on the other hand, is focused
on the adherence to a performance plan prepared in advance
(global-to-local synergy formation emphasized, often being
led by a coach as Ribeiro et al., 2019b, noted). More recently,
this distinction between different pedagogical approaches has
been focused on the differences between the more traditional,
command-driven practices of “hard education” and eliciting
of learning opportunities in practices of “soft education”
(van der Kamp et al., 2019).
So, How Does a Sports Practitioner Design a Learning Environment
That Places the Athlete at Its Center and Appreciates the Bi-
directional Nature of Synergy Formation to Enable the Rich
Behavioral Patterns That Self-Organize at Both Intra-individual
(Within an Athlete) and Inter-individual (Between Athletes) Levels?
In early recognition of the above question, Handford
et al. (1997) paid particular attention to the manipulation
of task constraints for sports practitioners, suggesting
that it implied a more “hands-off” approach to sport
pedagogy. Rather like an ecologist, the practitioner can
create conditions for an athlete to exploit and flourish during
the development and learning process. The implication is that
a practitioner did not need to intervene and “nourish” an
athlete continually but instead can work with the individual
organism to adapt to the surrounding environment and
flourish by getting everything needed from interactions with
environmental constraints.
While this descriptor of hands-off coaching to prevent
hyperactive verbal interference from coaches has been well
understood and heeded over the past decades, there have been
some indications that the new role, aligned with an ecologist, has
been mis-conceptualized by some in a literal sense. To clarify,
hands-off coaching signals a shift to a deep understanding of task,
personal, and environmental constraints on individual learners
and finding ways to co-design learning environments replete
with affordances to guide each learner toward active exploration
of a range of performance solutions. The role of practitioners,
therefore, has become more important than ever, evolving from
a prescriptive instructor with complete control over the whole
process (hands-on) to a learning designer deeply integrated
as a member of a team of sports practitioners focused on
athlete performance and development at all stages. An important
point to highlight in the hands-off approach is that, instead of
offering their pre-programmed task solutions (according to the
personal view of the coach), coaches need to work with the
athlete to find individualized creative solutions for a performance
problem. In this way, coaches are guiding the athletes to find
solutions to the unknown problems that they may face in future
competitions, not just repeating solutions for the training task
problems (Araújo et al., 2009). For example, both tactical and
strategical work in contemporary methods for preparation for
team sports performance are now predicated on “Big Data”
and technology implemented by teams of sports practitioners
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within the framework of an ecological dynamics rationale for
learning designs in practice programs (Woods et al., 2019b;
Browne et al., 2019).
A Department of Methodology: A
Platform for Integrative Sport Science
and Coaching
Although a theoretical and applied move toward practitioners
as learning designers is welcome, some practitioners may
be locked into traditions of practice and performance that
advocate deterministic models of human behavior (e.g., Chow
and Knudson, 2011), leading to coach-centric and hands-on
approaches (resulting in rather over-dominating performance
preparation). Practitioners who are guided by historic traditions
of supporting athlete performance and development (a type of
“path dependency” or acculturation process) can be subjected
to “system capture.” System capture occurs when the work of
a sport practitioner is not guided by a theoretical framework
of athlete development and performance but rather is captured
by “operational standards” defined in coach education manuals
that promote “optimal” performance templates (Rothwell et al.,
2020). System capture of this nature can inhibit the development
of innovative methods of athlete support and also disrupt
multidisciplinary sport science teams when collaborating to
design learning environments. The result is that practice and
performance dissonance amongst practitioners could lead to
“silo” working (Springham et al., 2018) and disjointed athlete
preparation practices. One way for practitioners to avoid system
capture and operate effectively as learning designers is to
work collaboratively in a department of methodology (DoM)
(Rothwell et al., 2020).
A DoM in an applied sport habitat should be composed
of a group of practitioners and applied scientists who share
integrative tendencies based on a rich mix of empirical and
experiential knowledge. The aim of a DoM would be for
group members to work within a unified theoretical framework
(i.e., ecological dynamics) to: (i) coordinate activity through
shared principles and language to avoid working in “silos,”
(ii) provide an integrative platform to communicate coherent
ideas, (iii) collaboratively design practice landscapes rich in
information (i.e., visual, acoustic, proprioceptive and haptic),
and (iv) guide the emergence of multi-dimensional behaviors
in athlete performance (Chow et al., 2011). In addition, as
foreshadowed by Davids et al. (1994) a DoM can support
practitioners and applied scientists to bridge the gap between
theory and practice to enable the design of highly integrated
and representative learning tasks. Since Newell’s (1986) model
focused on the integrated interacting constraints related to
the individual, task, and environment, the nested relationship
between them advocates the need for practitioners to collaborate
together in a DoM to prevent sport practitioners from treating
each constraint in isolation (Rothwell et al., 2020). As recently
discussed by Woods et al. (2019b), the contemporary practice
design of this nature requires an effective multidisciplinary
approach, where a team of practitioners such as performance
analysts, coaches, sport psychologists, sport scientists, and
skill acquisition specialists, can work collaboratively in a
DoM to analyze, sample, integrate, and manipulate nested
practice task constraints on each individual athlete based
on evidence from large sets of competitive performance
data. This contemporary multidisciplinary approach would
likely resolve behaviors that are perceived to be desirable
for team and/or athlete success (product) in addition to
the resolution of the interacting constraints that shape their
emergence (process). Such information creates the basis for
representative learning designs in practice and training. Further,
this approach would likely lead to innovation in practice design
as sport practitioners would not simply follow sequential steps
advocated in coaching manuals as a result of path dependency.
Rather, sports practitioners would identify critical sources
of information within a competitive environment perceived
to impact an individual athlete’s performance behaviors and
create an ecosystem that augments an athlete’s perceptual
attunement (i.e., detection) to relevant affordances in the
landscape. In this respect, practitioners and applied sport
scientists should focus the learning and practice design on a
deeply intertwined relationship between value (affordances) and
meaning (information) to support the development of highly
attuned athletes. Affordances immediately (directly) indicate
their value of use in an environment where structured patterns
of (visual, acoustic, haptic, and proprioceptive) information
(energy) reveal what objects and surfaces are (i.e., their meaning;
Withagen et al., 2012). Accordingly, from an ecological dynamics’
perspective, an athlete would not “acquire” an idealized skill.
Rather, over time, he/she would develop a deeply functional
and adaptive relationship with the performance environment
(Araújo and Davids, 2011).
In the remainder of this evaluation of the research progress
made since the appearance of the paper of Handford et al.
(1997) depicting how coaching of athletes at all levels of
performance could advance, we will refer to two case studies as
examples of the practical application of the conceptualization
of ecological dynamics in modern professional sport, namely,
Australian football (AF). Importantly, these case studies do not
intend to offer a comprehensive empirical examination into the
application of ecological dynamics. They provide readers with
an initial “how to” perspective when attempting to integrate
aspects of its theoretical propositions as discussed in the first
part of this paper. We encourage other “practitioner-scientists”
to continue to provide rich exemplars of its integration for
performance preparation in the continued support of sport
practitioners interested in how to apply its key concepts within
their ecosystems.
PART 2: IMPLICATIONS FOR THE WORK
OF SPORT PRACTITIONERS
Practitioners as Learning Environment
Designers
This section offers two case studies of ongoing practice to
exemplify how sporting practitioners have integrated the key
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components of ecological dynamics in their preparation for
performance in elite AF. These examples should provide the
reader with thought provocation, affording the opportunity
to adapt the practice designs presented to suit the need of
their ecosystem. Central to these examples, however, is the
philosophical shift in how a sports practitioner perceives his
role in preparation for performance, viewing themselves as
learning environment designers rather than as prescribers of
pre-programmed “optimal” movement solutions. It is hoped
that these examples will demonstrate that viewing sporting
practitioners as sporting ecology designers is not as provocative of
a thought as perhaps initially perceived.
To instantiate these examples, we will (briefly) discuss
the ontological shift that is required for sports practitioners
evolving toward learning environment designers. For example,
the integration of a “contemporary” approach to preparation
for performance may challenge socio-cultural norms that have
been engrained from generational traditions (Hodges and Baron,
1992). It is these socio-cultural norms that can subsequently
constrain the emergence of new epistemologies (Hodges and
Baron, 1992). Accordingly, practitioners are encouraged to
theoretically anchor values or principles that shape their
practice ecology, which may require a deep introspection of
their role in preparation for performance. In these presented
examples, sports practitioners were challenged to conceptualize
themselves as the designer of an ecosystem that provides
a rich landscape of affordances in the achievement of a
task goal. In this broad ecosystem, the athletes were free to
explore and inhabit certain regions of their landscape. The
central tenet of the ecosystem, however, was predication on
the notion of representative learning design (Pinder et al.,
2011a). Put simply, the practice designs were to consist of a
clear task goal predicated on informational constraints sampled
from the competitive performance environment. The sporting
practitioners subsequently built these informational sources into
the ecosystem (“hands-on”) and then observed (“hands-off ”)
the emergent interactions that unfolded between the athlete
and their environment. It was globally acknowledged that,
through this interaction, athletes progressively attuned to the
informational sources within their workspace, developing fine-
grained relationships with their performance environment –
described as developing knowledge of their environment rather
than knowledge about their environment (Gibson, 1966;Araújo
et al., 2009;Silva et al., 2013).
CASE STUDY 1: INFORMATIONAL
CONSTRAINT MANIPULATION SHAPES
BALL PASSING INTERACTIONS
BETWEEN PLAYERS
Introduction
Match-play within AF is contested between two teams of 18
(fielded) players, with the primary intention being to have
outscored their opponents at the conclusion of the match. Thus,
“match score” could be considered as a critical performance
indicator (environmental constraint) that guides the players’
perceptions, intentions, and actions as they attempt to “manage a
game” (i.e., either maintain or obtain the lead over the opposition
team). The aim of this example was to demonstrate how the
manipulation of key informational constraints (score) within a
player’s performance environment can result in the emergence
of self-organized behaviors as they exploit their environment
to achieve a task goal. It is through careful practice design
that players can develop a deeply integrated relationship with
their performance environment, learning how to co-adapt to
and direct the self-organization of their behaviors in response to
emergent problems (thus, developing their knowledge of the AF
performance environment).
Methodology
Procedures
In this example, data were collected from seven match
simulations performed during a preseason training phase within
an elite Australian Football League (AFL) team. Each match
simulation was performed in accordance with the regulation
rulings imposed by the AFL (premier AF competition) and
officiated by registered umpires. The two competing teams of 18
players were quasi-randomized across each of the seven match
simulations, ensuring that neither team had a playing experience
bias. Each match simulation was performed for a minimum of
20 min on separate training days across a 4-week period.
All match simulations were scored as in competitive AFL
games (six points awarded for a “goal” and one point awarded
for a “behind”). Prior to each match simulation, all players
were instructed to play for their team to win. To enhance
competitiveness, the players were informed of a penalty for
the losing team. the players were informed that with ˜3 min
left to play within the match simulation, the scores would be
manipulated to place one team in front by less than six points (a
goal) irrespective of the current score. The scoreboard was visible
to the players at all times throughout the match simulation. In
addition to this information, the players in the separate teams
were given 60 s prior to the start of each match simulation to
postulate tactical actions that they perceived could exploit the
constraint manipulation to achieve the task goal (winning the
match simulation) pending the score (either being in front or
behind by less than six points). The practitioners facilitated this
process via the use of questioning (Chow et al., 2007), which
directed the attention of the players to key affordances enabling
possible solutions to the impending constraint manipulation
(defined as the tactical problem). The important point to note
here is that questioning from an ecological dynamics rationale
does not involve the athletes providing verbalized reasoning
and responses, which would emphasize the acquisition of what
Gibson (1979) terms “knowledge about” the environment, framed
by verbal descriptions. Rather, the aim of questioning is to direct
the athletes’ attention to relevant affordances of the performance
landscape so that they can respond to verbalized questions with
“knowledge of ” the performance environment (Gibson, 1966)
expressed through actions, perceptions, and skilled intentionality
(Button et al., 2020).
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Data Collection
To observe the emergent responses to the informational
constraint manipulation, a multidisciplinary approach was used,
which consisted of a team of sports practitioners with expertise in
different sub-disciplines of sport science. Each match simulation
was filmed using three two-dimensional cameras positioned
from behind the goals (frontal/posterior) and broadcast (sagittal)
perspective. The augmented visual information was subsequently
stacked such that each perspective was concurrently observable
during video analysis, with the periods of the match simulations
in which the informational constraint manipulation occurred
being time-stamped to the vision.
To study emergent ball passing tendencies between players
following the score manipulation, notational analysis was
performed on all disposal types (kicks and handballs). In
accordance with the constraint-led framework (Davids et al.,
2008), performer, environmental, and task constraints were
heuristically selected, being informed by prior work in AF
(Woods et al., 2019a) and recommendations from an expert
AF practitioner (defined by holding a senior coaching position
within the AFL for more than 5 years) and skill acquisition
specialist. These constraints are presented elsewhere (Woods
et al., 2019a,b), but a brief description is provided here and
in Table 1: possession time (task constraint) was defined as
the time between the player first obtaining ball possession
to the time of ball disposal. This was then split into two
components – a possession in general play and a possession
from a mark or stoppage (e.g., free kick) – and then into four
temporal epochs. The environmental constraints were defined
by the number of opposition players within a 3-m radius of
the ball carrier at the point of ball disposal (carrier density)
and the intended receiver of the passed ball at ball reception
(receiver density). Performer constraints were defined relative
to the locomotive characteristics of the player at the point of
ball disposal – stationary (standing still or walking) or dynamic
(jogging or running). The same performance analyst quantified
these constraints across each of the seven match simulations
using specific notational software (Sportscode version 11.2.18,
Sportstec Inc., Warriewood, NSW, Australia).
Descriptive Analysis
All data were transformed to represent a percent of total disposals
performed within each constraint class. The data were split
into two categories: “pre-informational constraint manipulation”
(i.e., before the score-imposed change) and “post-informational
constraints manipulation” (i.e., after the score-imposed change),
with descriptive statistics (mean) being calculated for each
condition. A radar plot was used to visualize the distribution of
the disposal percentage within each constraint category across
both conditions (Woods et al., 2019a). This analytical approach
was chosen as it afforded a relatively simple yet informative
means of quantifying the emergent co-adaptability that ensued
from the informational constraint manipulation.
Results
As shown in Figure 1A, the team that was in front following
the constraint manipulation possessed: (i) considerably fewer
disposals performed within the 0–1 temporal epoch across both
general play and stoppage constraint categories, (ii) a greater
percent of total disposals performed from a stoppage in the >3-
s temporal epoch, (iii) a greater percent of total disposals to
uncontested and superiorly numbered targets, and iv) fewer
total disposals performed to inferiorly numbered targets relative
to conditions prior to the score manipulation. Interestingly,
this was in contrast to the team who was behind at the point
of constraint manipulation (Figure 1B), exhibiting (i) fewer
disposals to uncontested targets, (ii) fewer disposals performed
with <1 opponent within a 3-m radius, (iii) greater disposals
TABLE 1 | The constraint matrix used within this example.
Constraint class Constraint Description Sub-category label
Task Possession time (general play) Time between a player obtaining and disposing
of the ball while in general play (i.e., not from a
“mark” or “free kick”)
0–1 s
1–2 s
2–3 s
>3 s
Possession time (stoppage) Time between a player obtaining and disposing
the ball from a stoppage in play (“mark” or “free
kick”)
0–1 s
1–2 s
2–3 s
>3 s
Environmental Target density Number of opposition players within a 3-m
radius of the intended disposal target
Uncontested
Even (e.g., 1 vs. 1)
Superior (e.g., 2 vs. 1)
Inferior (e.g., 1 vs. 2)
Ball carrier density Number of opposition players within a 3 m
radius of the ball carrier at ball disposal
<1 opposition player (unpressured)
1 opposition player
2 opposition players
3 opposition players
>3 opposition players
Individual Disposal movement Locomotive state at point of ball disposal Stationary (e.g., walking)
Dynamic (e.g., running)
s, seconds; m, meters.
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FIGURE 1 | Radar plots demonstrating the mean differences between “pre” and “post” informational constraint manipulation for the team in front (A) and behind (B)
following constraint manipulation.
FIGURE 2 | Practice design for two activities that are designed to offer deceptive action opportunities – note the representative values that have been calculated
using the methodology described by Farrow and Robertson (2017) and applied by Woods et al. (2019b); *A successful deceptive action was defined as one that
coerced an opponent into a movement pattern that was exploited. The dots denote players.
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performed with >3 opponents within a 3-m radius, (iv) greater
disposals in the 0–1 temporal epoch in general play, and (v) a
greater percent of total disposals performed while running.
Discussion
Collectively, the results of this case study indicated that
the informational constraint manipulation (i.e., induced score
change) led to the emergence of two distinct passing strategies
utilized by players on either team: (i) one in which the players
searched their workspaces for opportunities to slow their ball
speed down and take lesser-risk disposal options when passing
the ball to a teammate (1A) and (ii) another in which players
searched their workspaces for opportunities to speed up their
ball movement at the expense of seeming to take riskier disposal
options when passing the ball to a teammate (1B). Specifically,
the strategy demonstrated in 1A appeared to reflect a team who
was “resting with the ball” in a somewhat conservative attempt
to preserve their lead following the informational constraint
manipulation. Conversely, the strategy demonstrated in 1B
appeared to be one in which the players “threw caution to the
wind” in an attempt to optimize their perceived likelihood to
score. To further these insights, practitioners could consider the
use of more advanced machine learning techniques such as rule
association (Browne et al., 2019). Such an approach extends the
descriptive analysis described here through the appreciation of
the interaction between nested task constraints, offering greater
insight into the combination of constraints that are likely to shape
the disposal characteristics in response to an emergent “tactical
problem” experienced within the competition.
Beyond these nuanced findings, this example demonstrates
the utility of a practice design conceptualized through ecological
dynamics. Specifically, this practice design afforded opportunities
for players to build deeper relationships with their competitive
environment, exhibiting skilled intentionality (Rietveld and
Kiverstein, 2014) through the collective co-adaptability shown
in their passing strategy relative to the informational constraint
manipulation. This observation echoes our sentiment discussed
earlier in this commentary that “behaviors” do not occur in a
vacuum but, rather, through the ecological dynamics lens; “skilled
behaviors” are functionally adaptable performance solutions
that arise from the continuous interactions that an organism
shares with their environment (referred to as skill adaptability;
Araújo and Davids, 2011).
CASE STUDY 2: INVITING DECEPTIVE
BEHAVIOR THROUGH INFORMATIONAL
CONSTRAINT MANIPULATION
Introduction
An important design feature of practice tasks in AF is
the presentation of affordances where time and space are
manipulated to channel successful ball disposal actions
between teammates (Robertson et al., 2016). Thus, providing
opportunities for players to explore behaviors that could
successfully deceive their opponents in the search for
time and space should be included within preparation for
performance models. The intention of this second case study
is to offer the reader insights into how sports practitioners
may design a practice activity that solicits deceptive behaviors.
Specifically, this example presents a practice task that intends
to provide a rich landscape that promotes the exploration of
deceptive behavior in AF.
Methodology
Procedures
The same population as described in the first case study was
used here. The two practice tasks designed to invite deceptive
behaviors are presented in Figure 2. Both practice tasks were
performed once (14 min in duration) during a pre-season phase
of performance preparation. First, the subtle scoring system used
within both games is worth noting (Figure 2). Given that the task
goal of both games was to outscore their opposition, the points
awarded for a successful deceptive action immediately led to the
emergence of a landscape where deceptive actions were afforded
and solicited. Further, it is important to note the environmental
constraint manipulation in the second game. Specifically, team
association was convoluted through the use of the same colored
bibs for both teams, with the players being distinguishable
only through the use of a colored wristband. This constraints
manipulation methodology was used to encourage the players to
explore unique ways to achieve the task goal relative to the first
game. Additionally, the utility of such a constraint manipulation
was informed from prior work describing the development
of expertise in soccer, where team convolution was discussed
as a common strategy that promoted scanning and deceptive
behaviors (Uehara et al., 2018). To direct the attention to key
informational sources of the task for the exploration of deceptive
behaviors, the players discussed for 60 s prior to the start of
each game about task configurations and possible behaviors that
they perceived could be performed to deceive their opponent.
As was done in the first case study, the practitioners facilitated
this process via the use of questioning to elicit knowledge of the
performance environment (Chow et al., 2007).
TABLE 2 | Deception categories and subsequent descriptions.
Deception category Description
Faked disposal An action of ball disposal that led an opponent to
move in a different direction to where the ball was
subsequently disposed
Creative disposal An “unconventional” means of ball disposal that
successfully reached its intended target (e.g.,
handballing between the legs of one’s direct
opponent)
Calling for the ball in
defense
An act of calling for and receiving the ball from an
opponent while in defense
Teammate blocking an
opponent
An act of physically blocking an opponent from a
teammate who is in possession of the ball
Other Any emergent deceptive action that was undefined
in the above
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FIGURE 3 | The percentage of total deceptive actions observed in both games 1 and 2.
Data Collection
As was done in the first example, a multidisciplinary approach
was used to observe the emergent deceptive behaviors. Both
games were filmed using three two-dimensional cameras
positioned from a behind the goals (frontal/posterior)
and broadcast (sagittal) perspective. The augmented visual
information was subsequently stacked such that each perspective
was concurrently observable during video analysis. To quantify
emergent deceptive actions, notational analysis was used
(Sportscode version 11.2.18, Sportstec Inc., Warriewood, NSW,
Australia). Specifically, “successful deceptions” were coded
and categorized into one of five categories, with a description
of each category being provided in Table 2. These deception
categories were chosen and defined in accordance with the sports
practitioner’s experiential knowledge.
Descriptive Analysis
All data were transformed to represent a percent of total deceptive
behaviors performed within each category, enabling a simple
comparison relative to the constraint manipulation. Following
this, a bar graph was used to visualize the distribution of the
deceptive behaviors across both games.
Results
The most commonly observed deceptive behavior in the first
game was the “faked disposal,” followed by the “creative disposal”
(Figure 3). This observation indicates that the most common
solicitations for deceptive actions afforded in the first game
involved movement adaptability relative to an opponent for the
player in possession of the ball. Interestingly, however, while
both “faked disposals” and “creative disposals” still remained
as primary deceptive behaviors in the second game, “calling
for the ball in defense” emerged as a prominent strategy for
deceptive actions relative to the first game (Figure 3). It was likely
that the augmentation for this was the additional environmental
constraint manipulation that convoluted team association.
Specifically, the players appeared to exploit this environmental
constraint while in defense by hiding their wristband and calling
for the ball from their opponent. This indicates that the additional
environmental constraint manipulation invited more exploratory
deceptive actions for the players when they were not in possession
of the ball, relative to the first game.
Discussion
Collectively, this example demonstrates the utility of practice
design framed through ecological dynamics where the sports
practitioner designs a rich landscape that affords opportunities
for a specific action to emerge. In this rich affordance landscape,
the players were free to accept or reject invitations for action.
From this perspective, the use of constraint manipulations
directed, or guided, the players’ attention toward the exploration
and the exploitation of performance invitations (affordances)
within their environment relative to their current action
capabilities. For example, given that a specific performance
solution was not prescribed in this practice task, the players were
free to undertake any form of deceptive manoeuver that they felt
could exploit their opponent based on the constraints designed
in (e.g., score system and team convolution). It is presumed
that through this design, the players would progressively learn
to couple their movements to the opportunities presented and
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detected within their environment, progressively “acquiring” a
deeper knowledge of (Gibson, 1966) their environment through
the development of their perception–action coupling. Thus,
in this case study, “hands-on coaching” occurred through the
practice design rather than through the provision of prescriptive
instructions of how to deceive an immediate opponent (i.e., how
to perform a “football action”).
This more ecological perspective of practice task design
draws a stark contrast to the more traditional, linear approach.
Specifically, framed through a more traditional perspective, it
is likely that the target football action (in this case, a deceptive
movement) would have been practised in a de-contextualized
manner in isolated, unopposed practice based around the
reproduction of a putative gold-standard movement template.
Contrastingly, the practice task design framed through ecological
dynamics offers the practitioners with a different perspective
of skill “acquisition,” being the development or “acquisition”
of the performers’ functionally adaptable relationship to their
performance environment, which can be fostered through
targeted and careful constraint manipulation, not the repetition
of an uncoupled and physically reproducible “technique.”
CONCLUDING REMARKS
As poignantly highlighted by McChrystal et al. (2015) in the
opening quotation, gardeners do not actually grow a plant;
rather, they facilitate an environment to which vegetation adapts
and in which plant growth emerges. This commentary and set
of case studies sought to foster reflection in readers on the
alignment of key ideas in this framework and the fundamentals
of preparation for performance models in sport. Pertinently,
this practice ecology was originally discussed over two decades
ago by Davids et al. (1994) and Handford et al. (1997),
who proposed the notion of an ecological approach to skill
“acquisition.” In their propositions, sports practitioners were
urged to appreciate the complex and deeply integrated reciprocity
of the organism (performer), task, and environment subsystems,
which signaled a change in how their role was conceptualized in
preparation for sport performance. Over two decades later, we
have seen the continued evolution of this rationale through the
contemporary theoretical lens of ecological dynamics. Through
this theoretical rationale, sports practitioners are now afforded a
guiding framework that fosters many areas of sport science, such
as skill “acquisition,” practice and training design, performance
analysis and preparation, and talent development. We proposed
how the framework of ecological dynamics could support the
integrated work of an extensive group of sport practitioners in
a DoM in sports organizations dedicated to athlete development
and preparation for performance.
Indeed this is an exciting era for sports practitioners and
applied scientists interested in augmenting athlete performance.
We now find ourselves on the cusp of the next “frontier”
of ecological dynamics, one which sees the offering of
rich exemplars as to how teams of sports practitioners
have successfully integrated its propositions into preparation
for performance models. To continue to aid this progress,
we propose that sports practitioners should conceptualize
themselves through a different light, one which sees them
appreciating the non-linearities of human behavior, and design
ecosystems that have the athlete–environment interaction at its
core. It is perhaps through this conceptualization that sporting
practitioners may actually see that viewing themselves as sport
ecology designers is not as farfetched as initially thought.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by James Cook University Human Ethics Committee.
The organization provided their written informed consent to
participate in this study.
AUTHOR CONTRIBUTIONS
CW and KD conceptualized the paper, while CW and IM
established the case studies. CW, KD, SR, MR, and DA each
contributed to and drafted the first section of the paper, while
CW, KD, SR, DA, and IM wrote and drafted the second section
of the paper. All authors contributed to the manuscript revisions
based on a reviewer’s commentary.
FUNDING
DA was partly supported by the Fundação para a Ciência
e Tecnologia, under Grant UIDB/00447/2020 to CIPER –
Centro Interdisciplinar para o Estudo da Performance
Humana (unit 447).
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Woods, McKeown, Rothwell, Araújo, Robertson and Davids.
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