1. Introduction
’ movements
The popularity of competitive sports, particularly football, has grown in tandem with cultural and economic advancements [This paragraph compiles several studies examining the effects of functional training on the strength and performance of football players. A study focused on the high-intensity tactical training program employed by the United States Marine Corps, which shares similarities with high-intensity functional training, and its growing popularity among military personnel. The authors also assessed the benefits and drawbacks of high-intensity functional training for the military [Despite the available research, there is a dearth of studies investigating the specific relationship between functional training and football players. Machine learning algorithms have demonstrated proficiency in recognizing human and athletic movements. While functional training has shown the potential to enhance athletes’ strength, there is a limited body of evidence specifically focused on the impact of functional training on the strength of football players. Furthermore, the imbalanced nature of sample data for feature extraction during changes in athletes’ actions presents a challenge in the recognition task. Further investigation is warranted in this area to provide a comprehensive understanding of the effects of functional training on the strength and performance of football players.In this work, the movements of football players are identified using machine learning techniques to investigate the effectiveness of strength training in football. A total of 116 young participants aged 8 to 13, engaged in football team training, were randomly selected. Both groups undergo 24 training sessions, with the experimental group receiving an additional 15–20 minutes of functional strength training after each session. Machine learning algorithms are employed to recognize the distinct postures adopted by football players during training. Specifically, a BPNN is utilized to analyze the kicking motion of the players. By comparing images of the players’ movements using the BPNN, incorporating factors such as speed, sensitivity, and strength as input vectors, the output result measures the similarity between the players’ kicking action and the standard action. This approach aims to enhance training efficiency. Subsequently, the experimental scores of the kicking motions for both the experimental and control groups are compared against the standard movements. The findings of this work offer a valuable theoretical foundation for the development of training programs for football players, with the potential to enhance training efficiency and overall performance.