INNOVATIVE APPROACHES AND PROSPECTS FOR ANALYZING TACTICAL STRATEGIES IN FOOTBALL

Abstract

The conducted analysis revealed that a high level of technical and tactical preparation is one of the key factors for gaining an advantage over the opponent and achieving victory. To select an effective match strategy, several crucial factors of the team must be considered: players’ anthropometric data, squad balance, player versatility, and levels of physical and technical preparation. Equally important are the opponent’s characteristics, such as their anthropometric features, playing style, and levels of technical and physical readiness. Additionally, the venue of the match-whether it is a home or away game-play s a significant role. In modern football, where player skill levels are often nearly equal, the decisive factor in determining the outcome is a well-chosen tactical formation and player positioning on the field. This study consolidates scientific research regarding the impact of tactical strategies on game efficiency and explores the role of innovative technologies (big data, video analysis, machine learning) in the process of tactical analysis. The authors propose a potential solution for constructing models and integrating various data sources using big data technologies. Big data is characterized by three key elements: volume, variety, and velocity. Regarding tactical analytics in football, these aspects can be interpreted as follows: volume pertains to the size of football data sets; variety refers to the different formats and sources of data; velocity describes the speed at which new data is generated. In football, velocity varies significantly between real-time streams of physiological and positional data and delayed data from traditional analysis conducted during training and competitions. Big data technologies are specifically designed to process and store high-velocity data. The adaptation of big data technologies in football research opens new possibilities for addressing key issues in tactical analysis at the elite sports level. Modern data processing methods enable the development of deeper theoretical models that reflect the complexity of tactical decisions and team play. However, this requires an interdisciplinary approach that combines the expertise of sports analysts, coaches, biomechanists, and computer scientists for comprehensive data analysis and interpretation.