As the earliest competitive sport, wrestling has become a world-class event through continuous development and improvement. Practitioners of competitive sports combine modern science such as life science, nutrition, statistics and so on to study the techniques and methods of improving wrestling performance. In this paper, a quantitative prediction model of wrestling performance is established by improving RBF neural network based on dropout theory. By analyzing the quantitative factors such as hemoglobin, blood urea and the technical factors such as reaction strength, speed and endurance of athletes, data were collected and network training was conducted. The predicted results have a high fitting degree with the real results, which can be used to predict the achievements of wrestlers and provide important reference for relevant practitioners in making training plans, selecting competitors and improving the competitive level.
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