Biomechanical spatio-temporal data analysis of football based on machine learning
Abstract
With the advent of the era of big data, how to analyze the massive data of players’ passing, shooting and position in football matches more effectively has become a new topic for the development of football. Machine learning algorithm has been widely used in various fields in recent years, relying on its strong data processing ability. Football match data analysis based on machine learning can effectively mine the effective characteristics of football data and better assist coaches’ tactical arrangement, personnel arrangement and player evaluation. In this paper, machine learning algorithms such as clustering algorithm, classification algorithm, Markov chain model and kernel density estimation algorithm are used to analyze the spatio-temporal data of players’ passing, shooting and position in football match. Compared with the traditional data analysis methods based on simple statistics, the method in this paper has more intuitive visualizations and deeper data insights. This approach is instrumental in guiding tactical planning and personnel strategies in football. Additionally, by integrating biomechanics into the analysis, we enhance our understanding of player performance. Biomechanical factors such as movement patterns, force application, and body mechanics play a critical role in a player's effectiveness on the field. Incorporating physiological data, including players’ heart rate and movement intensity, allows for a more holistic perspective on performance, addressing potential fatigue and injury risks. By analyzing how biomechanics influence spatio-temporal data—such as optimal angles for passing, shooting mechanics, and body positioning during play—we can provide actionable insights into player training and development. This comprehensive approach not only improves tactical decision-making but also fosters player longevity and performance optimization.
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