Prevalence of interactions and influence of performance constraints on kick outcomes across Australian Football tiers: Implications for representative practice designs

Abstract

Introduction: Representative learning design is a key feature of the theory of ecological dynamics, conceptualising how task constraints can be manipulated in training designs to help athletes selfregulate during their interactions with information-rich performance environments. Implementation of analytical methodologies can support representative designs of practice environments by practitioners recording how interacting constraints influence events, that emerge under performance conditions. To determine key task constraints on kicking skill performance, the extent to which interactions of constraints differ in prevalence and influence on kicking skills was investigated across competition tiers in Australian Football (AF).Method: A data sample of kicks (n = 29,153) was collected during junior, state-level and national league matches. Key task constraints were recorded for each kick, with performance outcome recorded as effective or ineffective. Rules were based on frequency and strength of associations between constraints and kick outcomes, generated using the Apriori algorithm.Results: Univariate analysis revealed that low kicking effectiveness was associated with physical pressure (37%), whereas high efficiency emerged when kicking to an open target (70%). Betweencompetition comparisons showed differences in constraint interactions through seven unique rules and differences in confidence levels in shared rules.Discussion: Results showed how understanding of key constraints interactions, and prevalence during competitive performance, can be used to inform representative learning designs in athlete training programmes. Findings can be used to specify how the competitive performance environment differs between competition tiers, supporting the specification of information in training designs, representative of different performance levels.

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

“…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.…”

“…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 within the framework of an ecological dynamics rationale for learning designs in practice programs (Woods et al, 2019b;Browne et al, 2019).…”

Theory to Practice: Performance Preparation Models in Contemporary High-Level Sport Guided by an Ecological Dynamics Framework

“…Clearly, greater depth of, and diversity in key constraints and their interaction sampled from both competition and practice landscapes, would enable deeper insight into the representativeness of training tasks. One way to achieve this could be through the use of more advanced machine learning techniques, such as rule induction (for detailed methodological insight, see [25]).…”

“…transiting from junior to senior competition, sustaining high-performance participation and prolonged success). Currently, targeted research is guiding the work of professionals in the practical integration of relevant propositions within specific sporting environments (for some notable examples, see [10,13,[22][23][24][25][26]). Continued examples of implementing an ecological dynamics framework by sporting practitioners could support those who seek to avoid reverting to more traditional models of performance preparation grounded in 'operational standards' or 'technical performance templates' prescribed in coaching manuals.…”

Applications of a working framework for the measurement of representative learning design in Australian football

“…Whilst constraints can be collected from training and competition environments, such approaches often overlook constraint interaction and are unable to capture then analyse the complexity of systems in full [8,9]. Recently, the interaction among constraints was examined via machine learning techniques in Australian football (AF) [6,10]. The application of a rules-based approach enables the complexity of RLD to be measured, through the identification of key constraint interactions based on both their frequency and their displayed influence on behaviours.…”