THE ANALYSIS OF FOOTBALL-SPECIFIC PHYSICAL TRAINING UNDER THE BACKPROPAGATION ALGORITHM

This study aims to explore the improvement of football-specific physical training methods by introducing the backpropagation (BP) algorithm to enhance training effectiveness and personalization. Traditional football training methods face many challenges, including difficulties in quantitatively evaluating training effects, a lack of personalized design, and slow updates. These problems restrict the exertion of athletes’ potential and affect the overall competitiveness of the team. To address these problems, this study proposes a data mining physical training guidance system based on the BP neural network (BPNN). First, the physical fitness status of young football players is analyzed. Second, a BPNN-based data mining physical fitness training guidance system is established. Finally, the data pressure, transmission distance, and fault tolerance of the physical training guidance system are tested. It can be found that this system better meets the individual needs of athletes. The results show that the physical training of young football players is developing in a diversified direction and has achieved good results. Meanwhile, the physical training method indicates that the system’s packet loss rate is less than 0.08. The data wireless transmission distance can reach 200[Formula: see text]m, with 100% data integrity. The proposed system’s response time is controlled within 1.5[Formula: see text]s, showcasing remarkable performance. This demonstrates that this method can utilize athletes’ data to analyze and formulate customized training plans, making the training more accurate and efficient. The proposed method provides an innovative theoretical insight for football-specific physical training while offering a more beneficial reference for meeting athletes’ needs.