## 1 Introduction
This makes it computationally efficient and suitable for capturing trends. Moving averages, on the other hand, smooth data by averaging values within a sliding window, applying equal weight to all points within this window. While effective at removing short-term fluctuations, moving averages are less responsive to rapid changes in the data [15]. Digital filtering, such as a low-pass Butterworth filter, applies a frequency-based approach to smoothing, filtering out high-frequency noise components while preserving lower-frequency signals that often represent meaningful trends [15]. This frequency-selective method allows for the precise control over the bandwidth of smoothed data, making digital filters particularly adaptable for applications requiring the exact noise reduction parameters. Insufficient smoothing will maintain high levels of noise, and over smoothing could eliminate important portions of the signal by smoothing the peaks. As such, bad choices of smoothing techniques can be detrimental by distorting the true component of the signal of interest (e.g., lowering the amplitude of a high acceleration so much that it is removed from the summary metrics). For example, many GNSS acceleration metrics are threshold-based, where time/counts/distance spent in a threshold (e.g., \(\geq\)3 m\(\cdot\)s\({}^{-2}\)) are analysed [2; 7; 18]. If a practitioner wants to know when their players have performed a high acceleration (\(\geq\)3 m\(\cdot\)s\({}^{-2}\)), applying bad smoothing could lower the acceleration values, resulting in the data not reaching the high threshold zone (although it was a high acceleration). Thus, determining the correct smoothing technique for a dataset is important, especially if summary metrics are of interest. The acceleration data of GNSS devices have typically been validated across the literature with the criterion measure of an infrared camera-based motion capture system [19; 20; 21]. The sampling frequencies of the validated GNSS systems vary from 1 to 18 Hz [1; 22]. The validity and reliability of GNSS data generally have smaller error margins for devices with increased sampling frequencies of \(\geq\)10 Hz [22; 23], however, 10 Hz GNSS devices are superior to 5 Hz GNSS devices that are interpolated to 15 Hz [24]. The validity of 1 and 5 Hz GNSS devices is impacted by high rates of change in speed, running from stationary or slow-moving starts and changes of direction [25; 26], where 1 Hz devicesare less valid in complex courses [27]. Devices with sampling frequencies of \(\geq\)10 Hz are not greatly affected by short straight-line movements, frequent changes of direction, or high-speed movements [19; 26; 28; 29]. It is recommended for practitioners to utilise a GNSS device with a sampling frequency of \(\geq\)10 Hz. Smoothing has shown to improve the validity of data [30; 31]. However, GNSS manufacturer smoothing techniques have shown to be detrimental to effort-based acceleration summary metrics [18; 20]. Currently, it is unknown as to how changes to the smoothing technique affect the acceleration of GNSS devices, which in turn affects the effort-based summary metrics. In research using athlete tracking technologies, the smoothing techniques used are often not reported, limiting the reproducibility of the research and the ability to compare results [7; 32]. Smoothing techniques are often not reported, as researchers and practitioners are unaware of the smoothing performed inside the manufacture's software, and there is no consensus on the selection of the most suitable smoothing technique for obtaining valid acceleration data. Therefore, this study aimed to examine how various smoothing techniques changed the acceleration data obtained from a GNSS device during accelerating and decelerating movements, resembling those commonly observed in team sports.