## 1 Introduction
Fourth, the lack of research focused on elite goalkeepers limits the generalizability of findings to elite populations, with many studies focusing on amateur-level subjects or subjects with no soccer experience [7; 18]. Traditional methods of determining goalkeeper movement initiation during penalties often rely on subjective observations. For example, in a study by [PERSON] et al., a soccer coach with 10 years of coaching experience was asked to review footage of 395 penalty kicks and identify the frame at which he believed the goalkeeper initiated the dive [20]. However, the coach was not instructed on how to identify dive initiation [20]. Though no criteria were provided for how to identify dive initiation, one author independently labeled dive initiation and found inter-rater reliability of labels to be satisfactory [20]. [PERSON] et al. developed an observational analysis system for penalty kicks based on a questionnaire focused on variables that were likely to distinguish the characteristics of successful or unsuccessful penalty kicks [21; 22]. The observational system was validated by experts, but the authors noted that observer perception could be influenced by the viewing angle and that camera angles behind either the penalty taker or goalkeeper were the most appropriate for assessing penalty kicks [22]. [PERSON] et al. analyzed 10 elite Dutch goalkeepers diving off force plates towards suspended balls in response to an LED flash [5]. The dive initiation time was determined using ground reaction forces from the force plate data [5]. Similarly, [PERSON] et al. placed 37 reflective markers on six elite goalkeepers diving off a force plate towards stationary balls in response to a life-sized image of an outfield player projected on a screen [6]. Dive initiation time was identified by an exponential increase in vertical ground reaction forces [6]. In a similar study, [PERSON] et al. studied 19 adolescent goalkeepers with 17 sensors performing dives in response to a whistle [7]. The dive initiation time was marked by the contralateral foot toe-off, identified through a custom Matlab script (v2022a, The MathWorks, Natick, MA, USA) and video footage review. The stimuli presented in each of these studies is not reflective of real game play, and the use of markers and force plates is impractical for live game play. Some studies have applied pose estimation to broadcast footage to evaluate goalkeeper strategies [21; 23]. However, these studies only analyze selected key frames, rather than analyzing a continuous video. For example, [PERSON] et al. utilized Open Pose to classify goalkeeper strategy, a component of their validated observational analysis system [21]. This study only looked at two key frames and found that orientation and movement between the two key frames could accurately classify goalkeeper strategy [21]. However, this study did not attempt to understand the timing component of anticipation, instead opting to use the distance moved as a measure of goalkeeper anticipation [21]. [PERSON] et al. assembled a dataset of 590 1v1 and penalty saves from broadcast footage, extracted a single frame at the moment of the kicker's contact with the ball, and used an unsupervised classifier to cluster goalkeeper pose data in the extracted frames [23]. However, it is important to note that this study assumed the goalkeeper was in a ready position and did not examine the development of goalkeeper positioning [23]. Markerless human pose estimation applied to broadcast footage presents a promising avenue to advance sports analytics, offering a non-invasive and cost-effective solution to capture and analyze athlete movements and bridging the gap between training and live game play. This study provides a robust, safer, and more standardized methodology for determining when a goalkeeper initiates a save attempt. The use of broadcast footage eliminates the need for markers and platforms, making the process of capturing dives less invasive, safer, and more affordable.