## 3 Results
These improvements, while relatively small in magnitude, contribute to greater confidence in the temporal accuracy of the data and support the use of the proposed method. Additionally, as these previous results demonstrated an improvement when compared to the \"gold standard\" for the optimize method, the same procedure was applied to analyze the OSD data in comparison to the RSD with a mean difference of 42.8 ms (95% CI = [37.5, 48.2]). The results showed small differences between the two datasets with 47.08 ms of _RMSE_ and 33.81 m/s of _MAE_. The same method was applied without considering outliers (9 outliers were removed with 127.44 \(\pm\) 156.12 ms), with results of 41.58 ms of _RMSE_ and 31.97 ms of _MAE_. An essential component of the analysis involved examining ball speed over time and identifying the precise moment when a pass occurred. Figure 3 illustrates the ball speed plot during 30 s of a game, highlighting fluctuations and where six passes occur during this window. After the results scored in the previous tests, where an improvement by the OSD was observed, it was important to comprehend the pass moments in both datasets (OSD and RSD). A ball speed plot was created, and the specific point where the pass happened was marked for both synchronized datasets. The presented Figure 3 showed that the novel technique appears with a more coherent definition of the moment of the pass in OSD when comparing to RSD. It was marked frequently in the moment when the ball reaches the most speed in that sequence, where the RSD had fluctuations in the position where it's marked. After comparing the benefits of the algorithm regarding time then it was important to compare the data regarding their locations in meters (m). The spatial accuracy of pass events was also examined by comparing pass locations recorded in the OSD against those from RSD and raw event data. In this context, three different reference points were used to evaluate pass location. First, the OSD refers to the moment identified by the algorithm based on a sharp increase in ball velocity, typically interpreted as the likely initiation of a pass. Second, the RSD marks the location at which the algorithm officially classifies an  
Figure 3: Ball Speed with the specific moment where the pass occurs in OSD and RSD. action as a pass, integrating both ball movement characteristics and temporal alignment with event data. Finally, the raw event data corresponds to the timestamp provided by the provider dataset, representing the annotated pass location without necessarily capturing the precise physical initiation of the action. Figure 4 presents a football field visualization of pass locations across datasets, illustrating areas of convergence and divergence. Figure 4: Pass Locations Comparison (**a**) OSD (yellow dots) compared to RSD (red crosses); (**b**) OSD (yellow dots) compared to Event Data (blue crosses). Quantitative results indicate a mean positional deviation of 0.41 \(\pm\) 0.75 m (95% CI = [0.36, 0.46], \(p\) < 0.001) (Figure 4a) between OSD and RSD data (\(RMSE\) = 0.861 m, \(MAE\) = 0.410 m) with most differences in the range of 0 to 1 m, but with 108 passes within the range of 1 to 2 m. Additionally, between OSD and event data (\(RMSE\) = 1.785 m, \(MAE\) = 1.586 m) there is a mean positional deviation of 1.59 \(\pm\) 0.82 m (95% CI = [1.53, 1.64], \(p\) < 0.001) (Figure 4b), with most differences within the range of 1 to 2 m (475). The larger discrepancy in event data likely results from lower spatial resolution and manual annotation errors inherent in event-based tracking.