## 1 Introduction
While certain studies have assessed the value of a pass solely using event data [19; 20], integrating manually tagged event data with automatically collected positional data allows for a more detailed analysis of the pass's value. Numerous studies have approached this quantification of pass value in various ways, typically evaluating how a successful pass would enhance the probability of scoring [21; 22; 23]. Even though manually gathered event data provides valuable insights into individual players during specific ball actions, recent developments in computer vision have enabled accurate tracking of all 22 players and the ball throughout the match; commonly known as tracking or positional data, with this technology, notable improvements have been achieved. [8; 24]. Analytics have increasingly focused on the integration of events and tracking data to gain deeper insights into player performance and team tactics. For instance, a fine-grained framework for evaluating the instantaneous expected value of possessions (EPV) [25] has been proposed, revealing that even subtle spatial shifts such as receiving a pass a few meters closer to the center or in a less congested area can significantly change the expected outcome. Building on this foundation, different authors revisited EPV modeling using deep learning approaches and introduced a novel evaluation benchmark that incorporates the reward and risk of individual passes [26]. This demonstrates that pass events are highly sensitive to both timing and location, reinforcing the need to accurately pinpoint these moments to distinguish between high-risk, high reward passes and safer alternatives. However, existing tracking systems rely on different methods of data capturing, leading to a significant challenge in achieving accurate synchronization between event data and positional data. This spatio-temporal synchronization of positional and event data presents crucial improvement for football analysis, and more specifically of passes. There are authors interested in the problems regarding this synchronization [27]. They emphasized the importance of the synchronization step and referenced existing methodologies, extending the approach used for shot events [9] to the synchronization of passes [8]. However, specific details or evaluations of their implementation were not extensively discussed. This study explores a new methodology to synchronize event data with positional data, to adjust the precise moments of pass occurrence, using the ball positional data. Regarding this, we propose a custom algorithm that integrates events and positional data gathered from football matches, aiming to synchronize the moment of passing actions, with the event data from that match being presented. To evaluate the results, a dataset was prepared based on the manual identification of all passing actions from the same football match compared with the data from the proposed algorithm and the existing event data. To address the gap found in the literature, the objective of this study was to develop and validate a simplified and automated synchronization method for aligning positional data with event data. Specifically, the study aimed to (1) reduce the complexity and time required for data synchronization, (2) improve reproducibility and accuracy compared to other methods, and (3) provide a scalable solution adaptable to various data sources and sport contexts.