## 4 Discussion and Implication
Three different smoothing techniques were used for this study, specifically a Butterworth filter, single exponential smoothing, and moving average, as these are commonly applied to athlete tracking system acceleration data [7] and have distinct characteristics that balance noise reduction and signal preservation in dynamic acceleration data (see Introduction for more in-depth information) [15; 17]. However, other smoothing techniques,median filters, polynomial smoothing, and weighted Gaussian filters, have been reported in the literature to be used on athlete tracking system acceleration data [7]. These specific smoothing techniques were not chosen for this study as they could have introduced distortions, computational complexity, or overfitting that could have been detrimental to the quality and responsiveness of the smoothed data [15; 17]. Furthermore, other smoothing techniques (e.g., wavelet smoothing) exist that might result in more valid acceleration data. Further research is needed to investigate the commonly applied smoothing techniques to GNSS acceleration data. The authors suggest that researchers and practitioners using raw GNSS data (instead of the manufacturer smoothed/processed data) apply a variety of smoothing techniques on their data and investigate the influence of the smoothing on the data of interest. For example, if interested in acceleration efforts, it is recommended that the smoothing techniques are applied to the raw speed data before deriving the acceleration and investigating the influence it has on the acceleration efforts. While this research has provided valuable insights into the acceleration data of GNSS devices, the limitations need to be acknowledged. The use of 10 Hz GNSS devices to measure time-series acceleration data has been widely adopted in the sports science community [1; 39]. It is also widely accepted, based on validation studies, that 10 Hz GNSS devices are sufficient to measure high-intensity acceleration-based movements accurately [19; 40; 41]. However, there are some limitations to take into consideration when evaluating validation studies. There are currently no universal standards for acceptable errors in athlete tracking technology validity research. To improve consistency and comparability, future research should establish these standards, aiding in cross-study comparisons and providing universally interpretable results. Since this research aimed at improving the accuracy of acceleration data from a widely used and accepted 10 Hz GNSS device through various smoothing techniques, rather than assessing its suitability for measuring acceleration, no acceptable error thresholds were set. The results of this research showed that the manufacturer smoothed acceleration data of the GNSS device (which are the data widely used and accepted in the sports science community) had a larger deviation from the 3D motion analysis data compared to the most accurate smoothing technique. Thus, the most accurate smoothing technique used in this study is more accurate than those currently used and accepted in the sports science community. Research including this study have used location data from gold standard 3D motion analysis systems, such as VICON, as a criterion measure to validate the acceleration data of 10 Hz GNSS devices. Data from these systems are often reduced to 10 Hz to be able to compare and validate datasets [20]. Furthermore, the displacement data of 3D motion analysis systems need to be double differentiated to calculate acceleration and is subject to smoothing processes, which was shown by this study and previous research to affect the data output [42; 43]. This data processing and downsampling may impose a bias when measuring acceleration, and it is currently unknown as to whether 3D motion analysis systems accurately represent acceleration data. Only one type of GNSS device was used (Catapult S7), so the findings might not apply to other GNSS devices.