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Application Research of Basketball Physical Training Based on Artificial Intelligence

Published: 22 November 2021 Publication History

Abstract

The purpose of athletes playing basketball is to shoot and score. Basketball requires athletes to have the ability to quickly change offense and defense, and have strong physical fitness. Basketball is a combination of skills and physical fitness. Athletes who want to use superb skills flexibly in the game must have corresponding physical fitness. Physical fitness is the basic condition for athletes to play for a long time. With the continuous development of basketball, the confrontation intensity in the basketball game is becoming more and more fierce, and the physical fitness requirements of basketball players are getting higher and higher. This article first analyzes the current research status of basketball physical training, then studies the training process and specific characteristics of basketball physical training, and finally designs the experiment. Taking 4 basketball teams from 4 major colleges and universities in a certain city in China as the research object, through the four teams were randomly selected into a control group and an experimental group. The control group used the previous training method, and the experimental group used the new training method. After the experiment, the teams in the experimental group and the control group were tested on physical fitness indicators. The test results showed that the improvement of the experimental group's performance on all indicators was greater than that of the control group.

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ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided Education
September 2021
2972 pages
ISBN:9781450390255
DOI:10.1145/3482632
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2021

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