ArticlePloS one2025
Predicting the sport level in elite male motorcycle speedway riders based on physical profile and experience.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The main objective of this research was to analyze the physical profile and experience of elite male motorcycle speedway riders, in order to develop a mathematical model to predict their level of sport performance. One hundred and eleven male professional motorcycle speedway riders in the top Polish motorcycle speedway league participated in this study (age: 25.04 ± 6.03 years, height: 172.28 ± 4.76 cm, weight: 65.60 ± 4.66 kg). The riders were divided by the k-means clustering method into three groups of sport level: high (n1 = 31), medium (n2 = 30), low (n3 = 50). To define sport level, several parameters were normalized and aggregated into a new conceptual metric of sport level called 'Indicator of Sport Level' (ISL). Body composition assessment, Wingate Anaerobic Test on a cycloergometer, and analysis of acute cardiorespiratory and biochemical responses were performed. One-way ANOVA tests were performed for independent samples, and Pearson's r linear correlation was calculated between clusters of riders. Multiple linear regression (MLR) was performed to predict the ISL. The elite male motorcycle speedway athletes were characterized by average age (25.10 ± 5.80 years) and years of experience (9.16 ± 5.62 years), which made them different from other clusters (p < 0.05). Also, they were 1.99% taller (173.02 ± 4.91 cm) than low sport level riders (p = 0.003). The strongest predictive variable of sport level in the regression model (R2 = 0.28) was body height (p = 0.043) and sport experience (p < 0.001). The results of this study present the optimal range of physical attributes, body height, sports experience, age, and body surface area of the best motorcycle speedway riders and indicate that body height and experience are the most predictive parameters in the model to estimate the sport level of riders.
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