Abstract
The research based on data science can provide more effective and personalized methods for the training of teen-age sprint events. This article analyzed the correlation between the consequent improvement in scores with gender, training methods, intensity, and cycles by collecting the key data of 400-meter running events. A multiple linear regression model was established to predict the running performance of teen-age athletes under different dependent variables, in order to provide references for the optimization of a teen-age training plan.
Description
This article analyzes training methods for teen-age 400-meter runners using data science.