## 1 Introduction  
Wearable technologies have become popular tools used in team and individual sports. Tracking player activity using these microtechnologies is an essential component of load monitoring [1]. Accelerometers and Global Navigation Satellite System (GNSS) devices have become some of the dominant wearable technologies used to monitor training-load in sport [2]. These technologies can be integrated or used separately to provide an indicator of the external work performed by athletes; consequently, practitioners may be better able to manage fatigue and direct adaptation. GNSS primarily measures horizontal displacement, while accelerometers primarily measure acceleration in single or multiple axes. The role of accelerometers and GNSS in load monitoring has received increased attention across a number of sports in recent years [3,4]. Despite this, the relationship between both technologies to quantify the same load is not well established. Accelerometers are responsive motion sensors that measure the magnitude of acceleration in one or more axes. Accelerometers are valid and reliable instruments to measure training load in the field and in laboratory environments [5, 6, 7]. There is a growing body of literature that recognizes the ability of accelerometers to quantify the external demand of team and individual sports. For instance, the within and between device reliability of accelerometers has been established across a variety of movement demands in both laboratory and on-field conditions in Australian football [8]. [PERSON] et al. [9] found strong to nearly perfect correlations between accelerometry derived training load and session rating of perceived exertion (sRPE) (r = 0.84; \(p<0.001\)) and total distance measured using GPS (r = 0.95; \(p<0.001\)) among NCAA women's soccer players [9]. Accelerometers have also been used to illustrate the differences in the activity profile between single and double match play in tennis [10]. In rugby, accelerometers outperformed GPS in quantifying positional (backs vs. forward) and halves (1 st vs. 2 nd) differences in player maximum mean movement [11]. Moreover, accelerometry has also been shown to be a valid assessment of a test designed to simulate basketball play, suggesting that accelerometers can be used to quantify the external demand of basketball [12]. Many accelerometry derived metrics have been used in the literature to quantify training load, including Body Load [13], Player Load [8], Force Load [14], Dynamic Stress Load [15], and Impulse Load [9]. Although Player Load is the most commonly reported measure in the literature [16], its potential to monitor training load has been questioned [17]. Player Load is the sum of the square root of the sum of absolute differences of acceleration divided by the device sampling frequency [17]. Therefore, Player Load does not represent the sum of all accelerations, and of the available accelerometry derived measures, it may not best represent training load [17]. Additionally, training-load could be misrepresented due to the inclusion of non-locomotor activities in Player Load [14]. Interestingly, different equations and descriptions for Player Load have also been reported in the literature [8, 11, 18]. Player Load has also been described as Body Load [13] and Acceleration Load [19]. To our knowledge, no study has compared different accelerometry derived measures when assessing training load, indicating a need for further investigation of accelerometry based measures of training load. GNSS is an umbrella term that includes several different satellite networks including Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), Galileo, and BeiDou. In sport, GNSS networks are used to provide information about a player's position, velocity, and movement patterns on the field. Total distance and distance in speed zones are common variables used to monitor training loads.