Capturing the holistic profile of high performance Olympic weightlifting development
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- Capturing the holistic profile
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Recent expertise development studies have used retrospective recall methods
to explore developmental biographies and/or practice histories of current
or past athletes. This methodological approach limits the generalizability
and trustworthiness of findings. As such, a gap exists for research exploring
key multidisciplinary features in athlete development using prospective
longitudinal research designs. The present research aimed to holistically model
the development of talent in Olympic Weightlifting using such a design. We
observed the holistic profiles of 29 junior weightlifting athletes longitudinally
over a 10-month period, and subsequently classified six of the 23 athletes as
high performing based on their performances in competitions up to 12 months
following the study. This holistic profile was based on a framework of expertise
development themes: (1) demographics and family sport participation, (2)
anthropometrics and physiological factors, (3) psychosocial profiling, (4) sport
participation history, and (5) weightlifting specific practice activities. A summary
model was produced which selected a critical set of nine features that classified
group membership with 91% average accuracy. Odds ratio calculations
uncovered discriminating features in the holistic profiles of performance
groups, from which empirically derived logical statements could inform the
description of high-performance attainment.
to explore developmental biographies and/or practice histories of current
or past athletes. This methodological approach limits the generalizability
and trustworthiness of findings. As such, a gap exists for research exploring
key multidisciplinary features in athlete development using prospective
longitudinal research designs. The present research aimed to holistically model
the development of talent in Olympic Weightlifting using such a design. We
observed the holistic profiles of 29 junior weightlifting athletes longitudinally
over a 10-month period, and subsequently classified six of the 23 athletes as
high performing based on their performances in competitions up to 12 months
following the study. This holistic profile was based on a framework of expertise
development themes: (1) demographics and family sport participation, (2)
anthropometrics and physiological factors, (3) psychosocial profiling, (4) sport
participation history, and (5) weightlifting specific practice activities. A summary
model was produced which selected a critical set of nine features that classified
group membership with 91% average accuracy. Odds ratio calculations
uncovered discriminating features in the holistic profiles of performance
groups, from which empirically derived logical statements could inform the
description of high-performance attainment.
Keywords
- Talent development, Expertise, Coaching, Performance, Machine learning
Original language | English |
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Article number | 986134 |
Number of pages | 17 |
Journal | Frontiers |
Volume | 4 |
Early online date | 30 Sept 2022 |
DOIs | |
Publication status | Published - 30 Sept 2022 |
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