Construction of Women's All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms.

ABSTRACT:

Introduction

Accurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women's all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance.

Objective

The present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women's all-around speed skating event and provide effective training and competition strategies.

Methods

The data, consisting of 16 seasons of world-class women's all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the

SUBMITTER: Liu M 

PROVIDER: S-EPMC9326501 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature