Application study of Grey GM (1, 1) model on the prediction of world elite athletes' long jump performance

Author(s): Jinzhu Li

It uses document literature method and mathematical statistics method, analyzes the annual best performance in the world long jump from2000 to 2013. By using GM (1, 1) model, GM (2, 1) model and GM (1, 1) model group, it conducts comparative analysis on the results of the three gray modeling, and in particular carries through a detailed study on the application of the three in athletic performance prediction. The results show that: for the forecasting problem of sports performance whose time series do not swing strongly, the GM (2, 1) prediction model is not applicable. GM (1, 1) model is more suitable for the prediction problem application that the athletic performance’s time series have stronger exponent law. By comparison study, for the prediction issues with a relatively large number of statistical data,GM(1, 1)model groups aremore conducive to improving the prediction accuracy of the athletic performance in this paper, so itmakes the graymodelmore flexible in practical application.

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