The role of elite coaches’ expertise in identifying key constraints on long jump performance: how practice task designs can enhance athlete self-regulation in competition

MCCOSKER, C, RENSHAW, I, RUSSELL, S, POLMAN, R and DAVIDS, Keith (2019). The role of elite coaches’ expertise in identifying key constraints on long jump performance: how practice task designs can enhance athlete self-regulation in competition. Qualitative Research in Sport, Exercise and Health, 1-17. [Article]

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Abstract
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Understanding performance behaviours provides useful information for practitioners that can assist with the design of tasks to enhance the specificity of practice. In this study, the experiential knowledge of six elite long jump coaches was investigated using a constructivist grounded theory approach, with the aim of furthering our understanding of the competitive behaviours of elite long jump athletes and how they adapt actions to the emotional and physical demands of performance environments. Findings offer a coaches’ perspective on three performance contexts which shape athlete performance–perform, respond and manage–towards two common performance intentions (maximum jump and sub-maximal jump). We contend that these findings reflect how coaches perceive performance as a series of connected events (jumps), during which athlete intentionality facilitates self-regulatory strategies in the face of unique interactions between individual, task and environmental constraints across a competition. These findings highlight how individuals must continually co-adapt with constraints in performance environments supporting how athletes self-regulate using intentionality, emotions and cognitions. Practice task designs should, therefore, provide greater opportunities for athletes to learn to self-regulate in performance contexts, with opportunities to perform, respond and manage. Interpreting the coaches’ insights, we suggest that these major performance contexts of perform, respond and manage could, therefore, be strategically used to frame representative learning designs, providing a framework for better organisation of training tasks.
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