## 4 Discussion  
Recent advances in data integration approaches have addressed a long-standing issue: data source independence. As has been pointed out, ensuring consistency and accuracy during the integration of different data is extremely difficult [12]. To improve the integration of different data sources, this method leverages ball speed and event data accuracy to enhance the harmonization. By incorporating precise measurements of ball speed and meticulous event data, this method ensures that inconsistencies are identified and corrected. This approach not only enhances data consistency and accuracy but also allows a more refined and reliable integration process, effectively mitigating the issues previously encountered. Additionally, this approach presents a validation within the synchronization process to address the concerns regarding the reliability of synchronized data [14]. This mechanism involves a validation framework that utilizes ball speed metrics and event accuracy data to cross-reference synchronized data against notational dataset to verify its integrity. One notable constraint of this methodology is its reliance on the accuracy of positional data. Despite advancements in tracking technology over the past decade, the precision of ball tracking remains a topic lacking comprehensive validation within existing literature [8]. The synchronization of spatio-temporal data between positional and event datasets, often collected through distinct systems, is essential for enhancing the analysis of passes. This study addresses this challenge by combining the intrinsic value of the positional data, using this to understand the moment where the ball increases speed, and the accuracy of the event data, through the identification of the player and the approximation of the time of the action. While the temporal enhancement discussed in this paper offers valuable benefits, the advancement in spatial accuracy remains of paramount importance. Frequently, analyses rely on positional locations from event data, [PERSON] and colleagues [19] constructed a model that predicts shot opportunities based in pass origins and destinations, while [PERSON] and [PERSON] [20] proposed to value each pass by computing the difference between the values of the possession sequence before and after the pass, and [16] through a supervised learning approach. The study proposes a methodology to estimate the risk and reward of all passes, with the intention of enhancing the understanding and analysis of the game of football. Recently, several authors have developed models to evaluate possessions where the moment and location of the pass influence greatly the results, revealing that these models are highly sensitive to subtle spatial displacements [25,26]. Hence, improving the precision of spatial data significantly contributes to the robustness and reliability of analytical findings and modeling outcomes in football research. A previous study discussed the development of a novel approach to pass synchronization [9], building upon the foundation laid by their previous work [8], although the framework requires further clarification. This model aims to bridge the gap between theoretical development and practical implementation. This could enhance accessibility and facilitate replication/validation by other researchers but also fosters collaboration and advancement within the field of football analytics. Moreover, by improving the synchronization of passes, this model contributes to the refinement and accuracy of analytical insights derived from tracking data, thus enhancing the overall understanding of football dynamics. An important methodological limitation of this study lies in the algorithm's reliance on ball velocity derived from positional tracking data. Since the detection of pass events is triggered by sharp changes in ball speed, any inaccuracy in ball position (whether due to tracking noise, interpolation artifacts, or system latency) can directly affect velocity calculations.