## 1 Introduction
GNSS networks such as GPS have been shown to be valid indicators of distances of 40 m completed during different movement patterns but may not be a valid measure of shorter distances (less than 20 m) completed during high speed running, sprinting, and change of direction [20]. Higher sampling frequencies (5-10 Hz) have been shown to improve the accuracy of GPS [20,21], although some evidence suggests that increasing sampling frequency to 15 Hz does not improve accuracy when assessing distance completed during unstructured movements [22]. Assessing player position using GNSS networks may be influenced dramatically by thenumber and separation of satellites that are connected to the receiver. GNSS enables receivers to acquire signals from multiple satellite networks (e.g., GPS, GLONASS, Galileo, and BeiDou), increasing the number of available satellites. Combined satellite systems improve satellite geometry and resulting precision [23]. Dilution of Precision (DOP) is a description of satellite geometry. DOP is composed of two elements: horizontal dilution of precision (HDOP) and vertical dilution of precision (VDOP) (for more detail about DOP, see [23]). HDOP is one indicator of GNSS accuracy and is influenced by the separation of the satellites. HDOP values range from 0 to 50, with a value of less than 1 considered the ideal distribution of satellites. HDOP is low and precision is excellent when substantial distance exists between satellites, while HDOP is high and precision is poor when satellites are in close proximity. Additionally, indoor fields, stadiums with high walls or roofs, and cloudy weather are factors that can reduce the quality of GNSS data [4]. Recently, receivers capable of acquiring signals from multiple GNSS networks simultaneously (e.g., GPS, GLONASS, Galileo, and BeiDou) have enhanced the availability and signal strength of surrounding satellites [24]. [PERSON] et al. [25] suggested that using multiple GNSS networks could explain the smaller bias (2.3 \(\pm\) 1.1%) when measuring total distance during a sport-specific movement protocol [25] compared to the author's previous research that used only GPS to detect total distance in a shuttle run over 5-20 m (2.53 \(\pm\) 6.03%) [26]. Future research should compare the quality of data from single and multiple satellite systems in sport. To date, several investigations have assessed the relationship between accelerometry and GNSS derived measures. A study by [PERSON] et al. [27] found large to very large correlations between Player Load and total distance accumulated during men's hockey practice (r = 0.742; \(p<0.00001\)) and competition (r = 0.868; \(p<0.00001\)) [27]. Additionally, a strong correlation was found between Player Load and total distance completed in men's soccer training (r = 0.70; \(p<0.01\)) [28], and a nearly perfect correlation (r = 0.95; \(p<0.001\)) was found between Impulse Load and total distance in women's soccer matches [9]. However, to our knowledge, no study has investigated the relationship between different accelerometry based metrics and GNSS with a known distance. Therefore, this study aimed to assess and compare the ability of four different accelerometry derived metrics and a triple GNSS to predict known distance completed under different movement constraints.