Neural Network of Vector Quantization as a Tool for Predicting Sports Achievements in Individual and Team Sports

Abstract

Planning and forecasting in sport is an integral element of the sphere and system of physical culture and sport functioning, and many related areas of activity. The development of information technologies, methods and means of artificial intelligence gives wide opportunities for their application in the field of sports forecasting. The article describes the features of design and application of the system that predicts sports results in team and individual sports.The functional structure of the system is given, the basic principles of its operation are considered. A feature of the structure is the introduction of a data clustering module based on a neural network of vector quantization of signals. The algorithm of this module functioning and the possibility of its expansion for detailing the formed forecast is described. The recommendations to users on the formation and detailing of the overall sample, evaluation of the generated sample effectiveness and analysis of the results are defined. The results of experiments on synthetic samples and real methods are presented and analyzed. Convenient and intuitive interface allows the system to be used by specialists of different profiles in the field of physical culture and sports, starting with the athletes themselves and ending with the heads of sports clubs, organizations and federations.

Formation of a Fragmented Training Sample in an Specialized Sports Event Forecasting System

“…Отдельные из параметров («столбцов») могут оказывать наибольшее влияние на результат или промежуточный результат (итоговый параметр в одном из фрагментов) прогноза. Такие параметры являются прогнозоформирующими предикторами, в работах [10][11][12] описан алгоритм определения прогнозофорирующих предикторов.…”

“…Для реализации прогноза спроектирован каскад нейросетевых модулей. Каскад состоит из двух обобщенно-регрессионных нейронных сетей во входном (первом) слое и одной LVQ сети в заключительном модуле выходного (второго) слоя каскада [12]. Непосредственно результаты прогнозирования приведены в таблице.…”