Early promise versus late bloomers: a longitudinal and multidisciplinary analysis of relative age effects throughout an elite weightlifting pathway.
Research output: Contribution to journal › Article › peer-review
Standard Standard
In: Journal of Expertise, Vol. 4, No. 4, 4, 25.12.2021, p. 335-364.
Research output: Contribution to journal › Article › peer-review
HarvardHarvard
APA
CBE
MLA
VancouverVancouver
Author
RIS
TY - JOUR
T1 - Early promise versus late bloomers: a longitudinal and multidisciplinary analysis of relative age effects throughout an elite weightlifting pathway.
AU - Gottwald, Vicky
AU - Anderson, Dior
AU - Lawrence, Gavin
N1 - Accepted 19 Jan 2022 - issue date is December 2021
PY - 2021/12/25
Y1 - 2021/12/25
N2 - Over a series of three studies, we investigated the relative age effect (RAE) across an elite weightlifting pathway, in the context of individual, task, and environmental constraints. Study 1 investigated the influence of gender and bodyweight on RAE. Where previous literature has often assumed success based on selection alone, the current authors also adopted medal success as a more valid indication of attainment. While it might be expected that the presence of weight categories may negate RAE, significant chi2 effects were robust across developmental stages and weight categories, with some gender-related nuances. Furthermore, multiple logistic regressions revealed RAE to be less prevalent in male athletes who transitioned from non-medalist to medalist (p < 0.05). Findings suggest that Q1athletes, perhaps selected based on early promise as a result of their older status, may not follow through in terms of potential talent at later stages of the pathway and may in fact drop out once maturational biases are no longer in their favor. Study 2 tested this with a longitudinal design to investigate the influence of athlete birth month on progression through the pathway. Results revealed that a higher proportion of Q4 athletes were retained in the pathway. While Q1 athletes were more likely to show early promise, Q4 athletes were more likely to “bloom” and deliver talent later in the pathway. Finally, Study 3 investigated the role of psychological characteristics in accounting for these findings. Sophisticated machine learning techniques differentiated between Q1 and Q4 athletes with an accuracy of 76%, based on psychological determinants of expertise: mastery approach, concern over mistakes, emotional stability, and openness to experience. These findings have important implications for practitioners with regard to talent identification and athlete selection protocols.
AB - Over a series of three studies, we investigated the relative age effect (RAE) across an elite weightlifting pathway, in the context of individual, task, and environmental constraints. Study 1 investigated the influence of gender and bodyweight on RAE. Where previous literature has often assumed success based on selection alone, the current authors also adopted medal success as a more valid indication of attainment. While it might be expected that the presence of weight categories may negate RAE, significant chi2 effects were robust across developmental stages and weight categories, with some gender-related nuances. Furthermore, multiple logistic regressions revealed RAE to be less prevalent in male athletes who transitioned from non-medalist to medalist (p < 0.05). Findings suggest that Q1athletes, perhaps selected based on early promise as a result of their older status, may not follow through in terms of potential talent at later stages of the pathway and may in fact drop out once maturational biases are no longer in their favor. Study 2 tested this with a longitudinal design to investigate the influence of athlete birth month on progression through the pathway. Results revealed that a higher proportion of Q4 athletes were retained in the pathway. While Q1 athletes were more likely to show early promise, Q4 athletes were more likely to “bloom” and deliver talent later in the pathway. Finally, Study 3 investigated the role of psychological characteristics in accounting for these findings. Sophisticated machine learning techniques differentiated between Q1 and Q4 athletes with an accuracy of 76%, based on psychological determinants of expertise: mastery approach, concern over mistakes, emotional stability, and openness to experience. These findings have important implications for practitioners with regard to talent identification and athlete selection protocols.
KW - Relative age effect
KW - Psychology
KW - Olympic Weightlifting
KW - Drop-out
KW - Talent development
KW - Expertise
M3 - Article
VL - 4
SP - 335
EP - 364
JO - Journal of Expertise
JF - Journal of Expertise
SN - 2573-2773
IS - 4
M1 - 4
ER -