AUTHOR=van der Zwaard Stephan , de Ruiter Cornelis J. , Jaspers Richard T. , de Koning Jos J. 

TITLE=Anthropometric Clusters of Competitive Cyclists and Their Sprint and Endurance Performance

JOURNAL=Frontiers in Physiology

VOLUME=10

YEAR=2019

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2019.01276

DOI=10.3389/fphys.2019.01276

ISSN=1664-042X

ABSTRACT=<p>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 <italic>k</italic>-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, <inline-formula><mml:math id="M1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover accent="true"><mml:mtext>V</mml:mtext><mml:mo>˙</mml:mo></mml:mover></mml:math></inline-formula>O<sub>2</sub><sub>peak</sub>) were obtained. <italic>K</italic>-means clustering assigned sprinters to a mesomorphic cluster (endo-, meso-, and ectomorphy were 2.8, 5.0, and 2.4; <italic>n</italic> = 6). Pursuit and road cyclists were distributed over a short meso-ectomorphic cluster (1.6, 3.8, and 3.9; <italic>n</italic> = 9) and tall meso-ectomorphic cluster (1.5, 3.6, and 4.0; <italic>n</italic> = 9), the former consisting of significantly lighter, shorter, and smaller cyclists (<italic>p</italic> &lt; 0.05). The mesomorphic cluster demonstrated higher sprint performance (<italic>p</italic> &lt; 0.05), whereas the meso-ectomorphic clusters established higher endurance performance (<italic>p</italic> &lt; 0.001). Overall, endurance performance was associated with lean ectomorph cyclists with small girths and small frontal area (<italic>p</italic> &lt; 0.05), and sprint performance related to cyclists with larger skinfolds, larger girths, and low frontal area per body mass (<italic>p</italic> &lt; 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.</p>