Literature DB >> 31533541

Assessment of elite junior tennis serve and return practice: A cross-sectional observation.

Lyndon Mark Krause1,2, Tim Buszard1,2, Machar Reid1,2, Ross Pinder3, Damian Farrow1.   

Abstract

Practice tasks that more closely represent the demands of competition are thought to augment skill learning and transfer. This study observed the serve and return performances of junior grand slam tennis and used this benchmark to evaluate the representativeness of serve and return practice among elite junior tennis players. The serve and return behaviour of 26 junior tennis players competing in junior Australian Open grand slam matches were observed and compared with the serve and return practice behaviours of 12 elite junior tennis players over an 8-week period. The variables measured included the number of serves/returns landing in, serve/return type, serve direction and the variability of practised skills. Serve and return practice contributed to <13% of total practice time, with each skill predominately practised in isolation. Compared to the matchplay benchmark, players typically had less success (i.e., fewer serves/returns landing in the court), were less variable in shot selection and hit fewer serves to the extremities of the service box. As task representativeness increased fewer differences between practice and matchplay were observed. Tennis serve and return practice could be improved by better simulating specific competition affordances, providing greater opportunities to practice serve/return tactics and/or increasing the variability of practised skills.

Entities:  

Keywords:  Coaching; ecological dynamics; practice; representative learning design; skill

Mesh:

Year:  2019        PMID: 31533541     DOI: 10.1080/02640414.2019.1665245

Source DB:  PubMed          Journal:  J Sports Sci        ISSN: 0264-0414            Impact factor:   3.337


  1 in total

1.  Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition.

Authors:  Yuxuan Wang; Xiaoming Yang; Lili Wang; Zheng Hong; Wenjun Zou
Journal:  Front Neurorobot       Date:  2022-04-28       Impact factor: 3.493

  1 in total

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