Influence of Different Passing Methods of Physical Fitness in Football Using Deep Learning

Abstract

Deep learning is a new direction in the field of machine learning, which learns the inherent laws and levels of data sample representation. The information gained during learning plays an important role in interpreting data such as text, images, and speech. This paper aims to study how to analyze and study the physical energy consumption of passers and receivers in different passing methods in football based on deep learning. This paper proposes the problem of physical energy consumption, which is based on deep learning, then elaborates on the concept of deep learning and related algorithms, and designs and analyzes the case of physical energy consumption of athletes. The experimental results showed that the average heart rhythm (184.35) of the subjects in the first and third experiments was more than twenty points higher than the average heart rhythm (159.85) of the kickers in the second and fourth experiments. Different passing styles have significantly different effects on the physical energy expenditure of players and defensive receivers.