Geodesic
Application of the Maximum Likelihood Sampling mathematical model to reduce costs in psycho-functional diagnostics of athletes' conditions in youth sports
Journal of computational and engineering mathematics, Tome 11 (2024) no. 3, pp. 44-51.

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Acyclic non-standard sports (including hockey) are characterized by the multifactorial nature of competitive activities with a lack of decision-making time; the complex biomechanical structure of movements distinguished by many dynamic stereotypes; the variability of their motor component; and high demands on the reserves of all energy supply systems of the body. This paper discusses the applicability of an algorithm for completing the missing indicators of the psycho-functional state based on young hockey players using adjustment factors found by the maximum similarity sampling method. The use of the algorithm allows the calculation of missing indicators without their direct diagnosis will significantly refine the need to change scheduled training and competitive activities. The study into the psycho-functional state of athletes can result in the timely adjustment of training and competitive plans and the individualization of educational and training, which is essential in sports training.
Keywords: cost reduction, youth sports, mathematical modeling, psycho-functional diagnostics, maximum similarity sampling. 
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     title = {Application of the {Maximum} {Likelihood} {Sampling} mathematical model to reduce costs in psycho-functional diagnostics of athletes' conditions in youth sports},
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     language = {en},
     url = {http://geodesic.mathdoc.fr/item/JCEM_2024_11_3_a3/}
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T. S. Demyanenko; E. F. Surina-Maryisheva; L. M. Semenenko; A. O. Zarayskaya. Application of the Maximum Likelihood Sampling mathematical model to reduce costs in psycho-functional diagnostics of athletes' conditions in youth sports. Journal of computational and engineering mathematics, Tome 11 (2024) no. 3, pp. 44-51. http://geodesic.mathdoc.fr/item/JCEM_2024_11_3_a3/