## 1 Introduction
A promising feature of this method is the use of machine learning to train a markerless video-based pose estimation (PosEst) system to recognise a ski jumpers movement and replace the process of manual annotation. This type of technology has evolved considerably in recent years and will reduce the post-processing time substantially [23]. Hence, this method may help to increase the sample size and generalizability of such studies [9]. To date, this type of technology has only rarely been used in ski jumping [24] and other winter sports such as alpine skiing [25]. Given the applicability, resources and effort needed for video and force measurements, these are often limited to the take-off and early flight phase. Therefore, in recent years, wearable sensors that allow coverage of the entire process from start of the in-run to the landing have been used. Most commonly, inertial measurement units (IMUs) have been used to measure both kinematics and kinetics with promising results [4; 26; 27; 28; 29; 30; 31]. The shortcoming with IMU measures is that accuracy can be questionable when estimating position integrated from acceleration measures [8]. In addition, interpretation of the position data in a global coordinate system is difficult without support from an additional technology [32]. A method that is better suited to providing global position accuracy is differential global navigation satellite system (dGNSS). While this has only rarely been used in ski jumping [33], dGNSS has been extensively used and validated in alpine skiing [34; 35; 36; 37; 38]. The use of a dGNSS unit allows measurement of the flight path with \(\pm\)0.05 m position accuracy [39]. Once the ambiguities of the differential carrier phase solution are addressed [39], position accuracy also allows the derivation of velocity, the total braking force during the in-run and the aerodynamic forces during the flight phase, by calculation of these as time-positionderivatives. Using this system, point mass kinematics and kinetics can be described for the entire period from the start of the in-run to the landing with a single, wearable system. GNSS signal reception through the GNSS antenna requires a direct line of sight to the satellites and is therefore constrained to being mounted on the back or head of the ski jumper, as shown in Figure 2. The antenna mounting point can be considered a reasonable representation of the athlete as a point mass in the stable flight phase and in-run, but not in the take-off, where athletes change body extension and orientation within a short period of time. Thus, an insufficiently accurate representation of the centre of mass position (CoM) occurs during this phase. The stable flight phase is a commonly used term in ski jumping literature, referring to the aerial phase where the ski jumper has reached a fairly constant flight position, and which lasts until the landing starts. It is worth noting that the term'stable' is misleading since the state of stability is irrelevant. From a physics point of view, this phase should be described as 'isometric-static'; i.e., acceleration of some key parameter equals zero (some forces seem in balance), and so is the velocity of these parameters. The use of the term'stable' implies that other phases are 'unstable', meaning that some perturbation would lead to disastrous changes of position. In the same sense, the term 'flight' is not entirely appropriate; physically, ski-jumping resembles more a state between 'falling' and 'gliding'. However, since the results from this paper will be compared to studies referring to this as the stable flight phase, the word 'flight phase' will be used to avoid misunderstanding. A better understanding of technique in ski jumping is needed to enhance performance. The above description of the ski jumping methodologies shows that one method alone cannot capture enough relevant data to describe performance and the underlying parameters.