Prevalence of interactions and influence of performance constraints on kick outcomes across Australian Football tiers: Implications for representative practice designs |
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| Affiliation: | 1. Institute for Health and Sport (IHES), Victoria University, Victoria, Australia;2. Western Bulldogs, Victoria, Australia;3. Centre for Sports Engineering Research (CSER), Sheffield Hallam University, Sheffield, UK;1. School of Human Sciences (Exercise and Sports Science), University of Western Australia, Australia;2. Auckland University of Technology, Sports Performance Research Institute New Zealand (SPRINZ), New Zealand;1. Sport & Exercise Discipline Group, Faculty of Health, University of Technology Sydney (UTS), Australia;2. Sydney Swans Football Club, Australia;1. School of Exercise Science, Australian Catholic University, Australia;2. School of Human Movement Studies, University of Queensland, Australia;1. School of Exercise and Health Sciences, Edith Cowan University, Australia;2. School of Health Sciences, Notre Dame University, Australia;3. Western Bulldogs Football Club, Australia;4. Institute for Sport, Exercise & Active Living, Victoria University, Australia |
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| Abstract: | 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 self-regulate 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).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.Univariate analysis revealed that low kicking effectiveness was associated with physical pressure (37%), whereas high efficiency emerged when kicking to an open target (70%). Between-competition comparisons showed differences in constraint interactions through seven unique rules and differences in confidence levels in shared rules.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. |
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| Keywords: | Representative learning design Machine learning Practice task design Performance analysis Skill acquisition Kicking |
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