The purpose is to evaluate the possibility of a successful performance of the Russian national rhythmic gymnastics team at the XXXII Olympic Games in Tokyo based on the development of model indicators of age, technical skills and sportsmanship of the world's leading gymnasts.
The article presents a retrospective analysis of the age of achievement of the highest result by gymnasts who competed in the individual all-around at the Olympic Games from 1984 to 2016. The analysis of the dynamics of changes in the performance criteria of 7 leading world-class gymnasts at the official FIG competitions from 2018 to 2021 was carried out. Model indicators of technical skills and sportsmanship of the winner of the upcoming Olympic Games 2021 have been developed using linear extrapolation and least squares methods. The model indicators were predictive performance criteria: representativeness, reliability, stability, and growth rates of technical complexity. It was found that the performance indicators of Dina Averina are closest to the model of the champion of the XXXII Olympic Games in Tokyo. Averina A. (RUS), Ashram L. (ISR) and 4 Kaleyn B. (BGR) can also compete for the Olympic podium.
- Zakharieva NN, Yashkina EN, Konyaev I. [Mathematical modeling of the success of competitive activity of female gymnasts-artists of high qualification] Theory and practice of physical culture. – 2017. No. 6. pp. 48-50. (in Russian)
- Kuramshin, Yu.F. [Acmeology of sports achievements: Theoretical and applied aspects]: abstract of thesis. ... doctors of pedagogical sciences: 13.00.04 / St. Petersburg. P. F. Lesgaft National State University of Physical Culture, Sports and Health. – St. Petersburg, 2002 . 80 p. (in Russian).
- Methodological recommendations for the analysis of sports results and the system for predicting the success of the performance of Moscow athletes in preparation for the Olympic Games in Sochi 2014. – Moscow, 2012 . 64 p. (in Russian).
- Platonov, VN [The system of training athletes in the Olympic sport. General theory and its practical applications: textbook [for trainers]: in 2 kn. – K. : Olympus. lit., 2015. Book. 1. 2015 . 680 p. (in Russian).
- Terekhina, RN, Kryuchek ES, Medvedeva EN, Viner-Usmanova I.А. [The balance of forces in the world rhythmic gymnastics at the beginning of the new Olympic cycle] Scientific Notes of the University of Lesgaft. 2017. No. 3 (145). рр. 220-223. (in Russian).
- Bunker R., Susnjak T. The application of machine learning techniques for predicting results in team sport: a review //arXiv preprint arXiv:1912.11762. – 2019.
- Chen D., Yang Y., Liu X. The Development Research of Computer-aided Rhythmic Gymnastics Selection System //2015 International Conference on Education Technology, Management and Humanities Science (ETMHS 2015). – Atlantis Press, 2015. – С. 208-212.
- Grycmann P, Maszczyk A, Socha T, et al. Modelling analysis and prediction of women javelin throw results in the years 1946 – 2013. Biol Sport. 2015; 32(4), С. 345-350. https://doi:10.5604/20831862.1189201
- Horvat T., Job J. The use of machine learning in sport outcome prediction: A review //Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. – 2020. – Т. 10. – №. 5. – С. e1380.
- Knechtle B. et al. Performance trends in master freestyle swimmers aged 25–89 years at the FINA World Championships from 1986 to 2014 //Age. – 2016. – Т. 38. – №. 1. – С. 18.
- Kozhanova, O., Nesterova, T., Gnutova, N., & Gnutov, E. (2015). Application of methodological approach to selection of sportswomen to calisthenics teams for group exercises, considering compatibility factor. Pedagogics, Psychology, Medical-Biological Problems of Physical Training and Sports, 19(4), 27-32. https://doi.org/10.15561/18189172.2015.0405;
- Kyriakides, George & Talattinis, Kyriacos & Stephanides, George. (2017). A Hybrid Approach to Predicting Sports Results and an AccuRATE Rating System. International Journal of Applied and Computational Mathematics. 3. 10.1007/s40819-015-0103-1.
- Maszczyk A. et al. Application of neural and regression models in sports results prediction //Procedia-Social and Behavioral Sciences. – 2014. – Т. 117. – С. 482-487.
- Pion J. et al. Predictive models reduce talent development costs in female gymnastics //Journal of sports sciences. – 2017. – Т. 35. – №. 8. – С. 806-811. Chen D., Yang Y., Liu X. The Development Research of Computer-aided Rhythmic Gymnastics Selection System //2015 International Conference on Education Technology, Management and Humanities Science (ETMHS 2015). – Atlantis Press, 2015. – С. 208-212.
- Prasetio D. et al. Predicting football match results with logistic regression //2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA). – IEEE, 2016. – С. 1-5.
- Rudrapal D. et al. A Deep Learning Approach to Predict Football Match Result //Computational Intelligence in Data Mining. – Springer, Singapore, 2020. – С. 93-99.
- Thabtah F., Zhang L., Abdelhamid N. NBA game result prediction using feature analysis and machine learning //Annals of Data Science. – 2019. – Т. 6. – №. 1. – С. 103-116.
- Wilk R. et al. Predicting competitive swimming performance //Central European Journal of Sport Sciences and Medicine. – 2015. – Т. 9. – №. 1. – С. 105-112.
- FIG: https://www.gymnastics.sport/site/events/searchresults.php (date of access: 20.05.2021).