Anthropometric Clusters of Competitive Cyclists and Their Sprint and Endurance Performance

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, trueV˙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.

Quadriceps and Patellar Tendon Thickness and Stiffness in Elite Track Cyclists: An Ultrasonographic and Myotonometric Evaluation

“…Both ultrasound and myotonometry showed good to excellent relative reliability. These findings are interpreted in relation to the anthropometrics characteristics of sprint track cyclists (Van Der Zwaard et al, 2019) and competition characteristics (Craig and Norton, 2001) as well as with respect to the risk of getting injury. Qt Thick , quadriceps tendon thickness; Pt Thick , patellar tendon thickness; PT Stiff , patellar tendon stiffness.…”

“…Sprint and endurance track cyclists have different anthropometric characteristics, muscle's mechanical properties, and fiber type composition. For instance, sprinters exhibit greater thigh girths but shorter thigh lengths (Van Der Zwaard et al, 2019 ) and their quadriceps muscle contains predominantly fast-twitch fibers (Loturco et al, 2015 ). Cyclists are more exposed to tendon injuries (i.e., patellar quadriceps tendinopathies) than muscle sprains (Wanich et al, 2007 ).…”

Putative Role of MCT1 rs1049434 Polymorphism in High-Intensity Endurance Performance: Concept and Basis to Understand Possible Individualization Stimulus

“…The second aim of this study was to determine if there was a statistical association between the MCT1 A1470T polymorphism and the high-intensity performance in nonpower-trained athletes. The PP and MP output during a WAnT indicated that participants of our study showed excellent fitness (see results in Table 2 ) according to available data from endurance-trained cyclists/triathletes [ 34 , 35 ]. Although the sample size could limit the detection of small genetic effects of this SNP on high-intensity endurance, no significant differences were found between the MCT1 A1470T polymorphism and the measured physical variables.…”

Morphological Asymmetries Profile and the Difference between Low- and High-Performing Road Cyclists Using 3D Scanning

“…Morphological research has been widely used in road cycling [ 11 , 12 , 13 , 14 , 15 , 16 ]. As one of the sports with repetitive movements, cycling can cause muscle force and/or flexibility asymmetries [ 17 , 18 ], leading to morphological asymmetries [ 14 ].…”