Literature DB >> 31326736

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

Peter R Browne1, Alice J Sweeting2, Keith Davids3, Sam Robertson2.   

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.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Apriori algorithm; Kicking; Machine learning; Performance analysis; Practice task design; Representative learning design; Skill acquisition

Year:  2019        PMID: 31326736     DOI: 10.1016/j.humov.2019.06.013

Source DB:  PubMed          Journal:  Hum Mov Sci        ISSN: 0167-9457            Impact factor:   2.161


  5 in total

Review 1.  Future Directions and Considerations for Talent Identification in Australian Football.

Authors:  Nathan Bonney; Paul Larkin; Kevin Ball
Journal:  Front Sports Act Living       Date:  2020-11-30

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

Authors:  Peter R Browne; Carl T Woods; Alice J Sweeting; Sam Robertson
Journal:  PLoS One       Date:  2020-11-30       Impact factor: 3.240

3.  Physical and technical demands of Australian football: an analysis of maximum ball in play periods.

Authors:  Christopher Wing; Nicolas H Hart; Fadi Ma'ayah; Kazunori Nosaka
Journal:  BMC Sports Sci Med Rehabil       Date:  2022-01-25

4.  Modelling the Influence of Task Constraints on Goal Kicking Performance in Australian Rules Football.

Authors:  Peter R Browne; Alice J Sweeting; Sam Robertson
Journal:  Sports Med Open       Date:  2022-01-24

5.  Physical and technical demands of offence, defence, and contested phases of play in Australian Football.

Authors:  Christopher Wing; Nicolas H Hart; Fadi Ma'ayah; Kazunori Nosaka
Journal:  BMC Sports Sci Med Rehabil       Date:  2022-03-01
  5 in total

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