Abstract
Do athletes specialize toward sports disciplines that are well aligned with their anthropometry? Novel machine-learning algorithms now enable scientists to cluster athletes based on their individual anthropometry while integrating multiple anthropometric dimensions, which may provide new perspectives on anthropometry-dependent sports specialization. We aimed to identify clusters of competitive cyclists based on their individual anthropometry using multiple anthropometric measures, and to evaluate whether athletes with a similar anthropometry also competed in the same cycling discipline. Additionally, we assessed differences in sprint and endurance performance between the anthropometric clusters. Twenty-four nationally and internationally competitive male cyclists were included from sprint, pursuit, and road disciplines. Anthropometry was measured and k-means clustering was performed to divide cyclists into three anthropometric subgroups. Sprint performance (Wingate 1-s peak power, squat-jump mean power) and endurance performance (mean power during a 15 km time trial, O2peak) were obtained. K-means clustering assigned sprinters to a mesomorphic cluster (endo-, meso-, and ectomorphy were 2.8, 5.0, and 2.4; n = 6). Pursuit and road cyclists were distributed over a short meso-ectomorphic cluster (1.6, 3.8, and 3.9; n = 9) and tall meso-ectomorphic cluster (1.5, 3.6, and 4.0; n = 9), the former consisting of significantly lighter, shorter, and smaller cyclists (p < 0.05). The mesomorphic cluster demonstrated higher sprint performance (p < 0.05), whereas the meso-ectomorphic clusters established higher endurance performance (p < 0.001). Overall, endurance performance was associated with lean ectomorph cyclists with small girths and small frontal area (p < 0.05), and sprint performance related to cyclists with larger skinfolds, larger girths, and low frontal area per body mass (p < 0.05). Clustering optimization revealed a mesomorphic cluster of sprinters with high sprint performance and short and tall meso-ectomorphic clusters of pursuit and road cyclists with high endurance performance. Anthropometry-dependent specialization was partially confirmed, as the clustering algorithm distinguished short and tall endurance-type cyclists (matching the anthropometry of all-terrain and flat-terrain road cyclists) rather than pursuit and road cyclists. Machine-learning algorithms therefore provide new insights in how athletes match their sports discipline with their individual anthropometry.
Highlights
The athlete’s physique is important for success in many sports (Norton et al, 1996)
It is unknown whether a machinelearning approach – grouping athletes based on individual anthropometry using multiple anthropometric dimensions and independent of sports specialization – will reveal clusters of athletes that have a similar anthropometry and compete in the same sports discipline
Our findings demonstrate that it is difficult to distinguish pursuit and road cyclists based on their individual anthropometry, which corresponds to previous literature reporting similar anthropometric characteristics for pursuit and road cyclists (Garay et al, 1974; Foley et al, 1989)
Summary
The athlete’s physique is important for success in many sports (Norton et al, 1996). Even though there are many determinants that contribute to the performance of athletes, most sports require a specific range in body size and shape to compete at the top level (Norton and Olds, 2001). Physical comparisons of athletic champions support this anthropometry-dependent specialization, revealing large anthropometric differences between sports disciplines and a much more similar physique within sports disciplines, especially at higher levels of competition (Carter, 1970). It should be noted, that anthropometric measures are commonly reported for groups of a specific sports discipline (Carter, 1970; Norton and Olds, 2001), focusing on group averages and standard deviations (Norton and Olds, 2001) or distributions of a single anthropometric measure within these groups (Carter, 1970). What remains to be elucidated is whether grouping of athletes based on similarities in their individual anthropometry using multiple anthropometric dimensions, and subsequently evaluating their sports discipline, will provide new insights in anthropometrydependent specialization
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