Journal of Siberian Federal University. Humanities & Social Sciences / Artificial Intelligence Usage in Prediction of the Sports Results of Athletes Competing in Greco-Roman Wrestling

Full text (.pdf)
Issue
Journal of Siberian Federal University. Humanities & Social Sciences. 2024 17 (2)
Authors
Osipov, Aleksander Yu.; Nagovitsyn, Roman S.; Ratmanskaya, Tatyana I.; Vapaeva, Anna V.; Kudryavtsev, Mikhail D.
Contact information
Osipov, Aleksander Yu. : Siberian Federal University Krasnoyarsk, Russian Federation; Prof. V.F. Voino-Yasenetsky Krasnoyarsk State Medical University Krasnoyarsk, Russian Federation; Siberian Law Institute of the Ministry of Internal Affairs of the Russian Federation Krasnoyarsk, Russian Federation; ORCID: 0000-0002-2277-4467; Nagovitsyn, Roman S.: Glazov State Pedagogical Institute named after V. G. Korolenko Glazov, Russian Federation; Kazan State Institute of Culture Kazan, Russian Federation; ORCID: 0000-0003-4471-0875; Ratmanskaya, Tatyana I. : Siberian Federal University Krasnoyarsk, Russian Federation; ORCID: 0000-0001-9544-1674; Vapaeva, Anna V. : Siberian Federal University Krasnoyarsk, Russian Federation; ORCID: 0000- 0002-8081-8974; Kudryavtsev, Mikhail D. : Siberian Federal University Krasnoyarsk, Russian Federation; Siberian Law Institute of the Ministry of Internal Affairs of the Russian Federation Krasnoyarsk, Russian Federation; Siberian State University of Science and Technology Krasnoyarsk, Russian Federation; , ; ORCID: 0000-0002-2432-1699
Keywords
combat sports; analysis and forecasting; competition performance; combat athletes; artificial intelligence
Abstract

Today, the use of machine learning algorithms and neural networks to increase the effectiveness of sports selection at the early stages of the athletes’ training process is becoming particularly relevant. The aim of this scientific work: to develop a program for predicting the athletic performance of young athletes, who competing in Greco-Roman wrestling, based on artificial intelligence technology. Collection and processing of individual data of 18–25 years old athletes (n=67) on 21 comparison criteria, ranked into categories in two directions, were implemented: sports space and individual achievements. Two forecasting categories were determined: participants who have obtained a sports title or the highest category (n=16), and participants who have not reached this level (n=17). The control testing of the created program showed only a 14 % probability of error in predicting the participants competition performance. According to the functionality of the program in the field of classification of signs by categories, the author’s intellectual development with 100 % probability on the basis of experimental approbation revealed key categories of signs that reliably affect the results of the athletes future sports performance

Pages
278–286
EDN
SRIMHW
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/152598

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