An Intelligent Physical Training System in Football Education
DOI:
https://doi.org/10.18063/lne.v3i3.816Keywords:
Machine learning, Football education, Sports, Data-driven coachingAbstract
With the advancement of intelligent technology, data-driven evaluation methods have gained increasing attention in physical education, particularly in the application of intelligent physical training systems in football education. These systems enable precise assessment of athletes’ training status and provide scientific support for personalized training, thereby enhancing training efficiency and game performance. This study employs Random Forest and Neural Network models to construct an intelligent evaluation system for predicting students’ overall performance in football training. Key performance indicators such as passing frequency, sprint speed, and shooting accuracy are collected and analyzed to determine their impact on comprehensive scores. Experimental results demonstrate that the Random Forest model excels in stability and interpretability, while the Neural Network achieves higher prediction accuracy in complex pattern recognition. The combination of both models enhances generalization ability and applicability. Additionally, feature importance analysis identifies sprint speed and shooting accuracy as the most critical factors influencing training performance. This study proposes data-driven training optimization strategies to help students improve their football training performance. The findings confirm that intelligent physical training systems can effectively support football education, promoting the development of personalized and refined training programs and providing strong technological support for modern sports education.
References
Kostiukevych V, Stasiuk V, 2017, Training Process Programming of Qualified Football Players in Higher Education Establishments. Physical Education, Sport and Health Culture in Modern Society, 2(38): 41–50.
Yang R, Lin H, 2022, Evaluation of the Quality of Football Teaching in Colleges and Universities Based on Artificial Neural Networks. Computational Intelligence and Neuroscience, 2022(1): 8001252.
Xing J, 2024, Practice of Artificial Intelligence Technology Based on Big Data in Football Training and Physical Education. Frontiers in Sport Research, 6(3).
Yin X, Vignesh CC, Vadivel T, 2022, Motion Capture and Evaluation System of Football Special Teaching in Colleges and Universities Based on Deep Learning. International Journal of System Assurance Engineering and Management, 13(6): 3092–3107.
Guan Y, Qiu Y, Tian C, 2022, Trajectory Planning in College Football Training Using Deep Learning and the Internet of Things. The Journal of Supercomputing, 78(17): 18616–18635.
Stoeve M, Schuldhaus D, Gamp A, et al., 2021, From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning. Sensors, 21(9): 3071.
Zhou D, Chen G, Xu F, 2022, Application of Deep Learning Technology in Strength Training of Football Players and Field Line Detection of Football Robots. Frontiers in Neurorobotics, 16: 867028.
Cao X, Zhao X, Tang H, et al., 2023, Football Players’ Strength Training Method Using Image Processing Based on Machine Learning. PLoS One, 18(6): e0287433.
Hollaus B, Reiter B, Volmer JC, 2023, Catch Recognition in Automated American Football Training Using Machine Learning. Sensors, 23(2): 840.
Rigatti SJ, 2017, Random Forest. Journal of Insurance Medicine, 47(1): 31–39.
Xu Y, 2021, A Sports Training Video Classification Model Based on Deep Learning. Scientific Programming, 2021(1): 7252896.
Wu YC, Feng JW, 2018, Development and Application of Artificial Neural Network. Wireless Personal Communications, 102: 1645–1656.
Fenil E, Manogaran G, Vivekananda GN, et al., 2019, Real Time Violence Detection Framework for Football Stadium Comprising of Big Data Analysis and Deep Learning Through Bidirectional LSTM. Computer Networks, 151: 191–200.
Barquero-Ruiz C, Morales-Belando MT, Arias-Estero JL, 2021, A Teaching Games for Understanding Program to Deal with Reasons for Dropout in under-11 Football. Research Quarterly for Exercise and Sport, 92(4): 618–629.
Liu B, Pu Z, Zhang T, et al., 2022, Learning to Play Football from Sports Domain Perspective: A Knowledge-Embedded Deep Reinforcement Learning Framework. IEEE Transactions on Games, 15(4): 648–657.
