## 1 Introduction  
Over the years, sport analytics have contributed to a significant shift in the way performance analysis is conducted. While traditional approaches have relied on manual annotation and subjective assessments, advancements in analytics and computing have revolutionized the field [1]. Spectators and elite clubs now have access to a wide range of tools and technologies that facilitate real-time tracking and match analysis [2]. The possibility of collecting and processing an enormous amount of data during matches has driven a transformation in sports science, enabling more sophisticated and data-driven approaches to evaluate performance [3]. This evolution aligns with broader big-data principles, where the volume, variety, and velocity of information can be harnessed to extract meaningful patterns and insights, ultimately enhancing tactical and performance analysis in football [4; 5]. As big data continues to expand in volume and complexity, its principles are increasingly used to transform raw data through advanced analytics and machine learning models, supporting performance optimization and control [6; 7]. This influx of data has created new opportunities for the development of advanced metrics and analytical tools with potential to transform the way the game is understood, techniques like expected passes or goals, Voronoi diagrams, or pitch control [8; 9; 10; 11]. However, for these techniques to be applied effectively, it is crucial to ensure maximum reliability in data. The use of advanced tracking technologies, standardization of data collection protocols, data validation and filtering processes, integration of multiple data sources, and the application of machine learning and statistical methods all contribute to this goal. One of the key issues in this field of research is the integration of data from different systems and sources, such as technical event data and positional data. Independent data sources pose a significant challenge in maintaining consistency and accuracy while integrating data [12]. Several challenges impact the accuracy and reliability of synchronizing positional and event data in sports. Previous studies have shown that ensuring precise alignment between these datasets is crucial for meaningful insights [13]. However, issues such as those regarding the collection of both types of data can lead to discrepancies in data alignment [8]. Furthermore, the lack of validation in the synchronization process raises concerns about the reliability of synchronized data [14]. These types of challenges can directly impact the quality and performance of an analysis, even more so for training models on this data [15]. In football, determining the precise moment and location of key events such as passes is crucial for evaluating players and teams' performance. In the modern era of football, where data-driven insights are of extreme importance, pass analysis provides a lens through which teams can fine-tune their strategies and optimize their performance. By analyzing passing activities, teams can identify patterns of play, positional tendencies, and areas of improvement. The significance of passes in football has been explored through two main approaches, notational and experimental studies. More recently, the proliferation of football data has opened new paths for pass analysis, such as the risks and benefits of passes [16], the classification of pass quality [17], and the evaluation of passing effectiveness along with player involvement in creating scoring opportunities [18]. Inspired by these works, meticulous records of all on-the-ball actions such as shots, passes, and tackles became commonly collected across most professional football leagues. Utilizing these events data, numerous studies have conducted pass analyses on a much larger scale than was previously possible with experimental studies.