Sports performance prediction for college students through ensemble learning algorithm

抄録

High-precision sports performance prediction of college students is significant in formulating scientific training mechanisms and reasonable physical fitness courses. Traditional sports performance prediction primarily relies on subjective experience, which suffers from limitations such as individual variations and low reliability. To overcome these shortcomings, an ensemble learning algorithm is proposed in this study. Firstly, a historical dataset for college students is established, including physical characteristics (such as age, height, weight, and lung capacity) and sports performance (such as the 50-meter dash, 1000-meter run, and standing long jump). Then, three forecasting engines including support vector regression, extreme learning machine and decision tree are employed for preliminary prediction based on the preprocessed physical characteristics. Sequentially, three preliminary predictions are combined by the Gaussian process regression in a nonlinear manner to achieve a final probabilistic prediction. By using the data collected from college students, the feasibility of the established ensemble model is evaluated. Practical confirms that the proposed model can fix the performance gap of the individual forecasting engine, effectively improving the prediction accuracy. In addition, the proposed method not only provides point predictions but also generates confidence interval information, which greatly enhances the prediction reliability.