Participation trends, dynamics of results and forecasting of finishing times of athletes specializing in 100 km ultra-marathon
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Published:2024-02-17
Issue:2(174)
Volume:
Page:150-156
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ISSN:2311-2220
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Container-title:Scientific Journal of National Pedagogical Dragomanov University. Series 15. Scientific and pedagogical problems of physical culture (physical culture and sports)
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language:
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Short-container-title:PCS
Abstract
In recent years, there has been an increase in popularity and results in road ultramarathon (running events which includes distances over 42 195 m). Attention to them from athletes, coaches and scientists is increasing. The search for scientifically based approaches to the construction of the training process is relevant. The study of statistical data on the performance of leading athletes at competitions is a source of important information for making recommendations for improving the training process. The purpose of the study was to determine the age and competitive characteristics of qualified ultramarathoners at a distance of 100 km and to develop methodological recommendations for building a training process and predicting the competitive result. The article analyzes statistical data on the performances of qualified ultramarathoners at the 100 km World Championships in 2022 and 2018, and related competition. The obtained results indicate a tendency to improve the finishing time of athletes of various qualifications. Age has a weak correlation with outcome. Qualified ultramarathoners compete in 2 or 3 main competitions during the year, with a period of 12-16 weeks between them. 100 km runners tend to slow down over the distance. More skilled athletes show less reduction in speed. The 100 km performance has a strong correlation with the 50 km performance. You can predict the competitive result at a distance of 100 km using the formula y=788.96 + 2.16x, where y is the result of running 100 km, x is the result of running 50 km in seconds. It is advisable to plan the training process of ultramarathoners based on a two- cycle or three-cycle periodization model. The derived regression equation makes it possible to adjust the training process and plan a rational running pace during the competition. Further research is needed to determine the optimal model of load distribution in the process of training ultramarathon runners.
Publisher
National Pedagogical Dragomanov University
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