Sports performance prediction based on support vector machine

To deal with the problem of low accuracy of sports performance prediction, and to obtain ideal sports performance prediction results, this paper proposes a sports performance prediction model based on the selection of influencing factors and support vector machine. In this model, particle swarm optimization is introduced to determine the most relevant influencing factors related to the change characteristics of sports performance, which reduces the number of input vectors of sports performance prediction model and speeds up the modelling speed of sports performance. Then, the support vector machine is used to learn the historical data of sports performance, which overcomes the defects of traditional models such as artificial neural network and improves the prediction accuracy of sports performance. Experimental results are provided to verify the advantage of the proposed algorithm with respect to the traditional methods.