## 1 Introduction  
Markerless human pose estimation has the potential to revolutionize sports analytics by providing detailed insights into athlete movement. Pose estimation provides biomechanical data on athlete movement by accurately detecting and tracking the spatial positions of key body joints in video frames [1]. This serves as a valuable tool for training, performance optimization, injury prevention, and rehabilitation [1, 2, 3]. Coaches can analyze an athlete's posture and movement in real-time or recorded footage, identify areas for improvement in technique, and tailor training strategies to individual athletes [2, 4]. Currently, marker-based motion capture and force plate analysis are the most common methods used for athlete movement analysis [5, 6, 7]. While these conventional methods provide valuable data on athlete movement and biomechanics, they require specialized equipment and controlled environments, making them impractical for use during live game play. This creates a gap in information between what occurs in training sessions and what happens in live game play, where conditions are dynamic and unpredictable, resulting in low ecological validity. Pose estimation offers a practical alternative for the capture and analysis of player movement in live game play [8]. In recent years, markerless motion capture systems using pose estimation have gained popularity in professional sports due to their ability to accurately track and analyze player movement without the need for physical markers placed on the body [9]. For example, during the 2022 FIFA World Cup, Hawk-Eye Innovations unveiled a new Video Assistant Referee (VAR) system to assist referees in making accurate offsides calls [10]. Pose estimation has also found applications among MLB teams for pitcher and hitter biomechanical analyses [9] and among NBA teams for the automation of goaltending and out-of-bounds calls [11]. Despite its benefits, the widespread adoption of markerless motion capture is limited by its high costs. Estimates for Hawk-Eye markerless motion capture systems range from EUR 7000-EUR 8000 per match for Gaelic football [12], USD 60,000 for tennis courts, and GBP 250,000 for soccer stadiums [13]. In addition, these systems do not allow for the retroactive collection of data [14]. As pose estimation becomes more widespread, there has been growing interest in exploring more affordable and less invasive alternatives to motion capture systems [9]. Any sports organization or individual with access to footage, a computer for executing code, and open-source libraries such as MediaPipe and YOLO can utilize and benefit from markerless pose estimation technology [15]. In this study, we apply markerless human pose estimation to 2022 FIFA World Cup broadcast footage to identify the initiation of goalkeeper movement during penalty shootout kicks. Goalkeepers play a critical role in determining match outcomes, and the timing of dive initiation is a critical component of goalkeeper performance [16]. However, accurately identifying the initiation of goalkeeper movement during live game play remains a challenging task. This study aims to advance sports analytics by leveraging markerless human pose estimation technology to provide valuable insights into athlete movement, particularly the initiation of goalkeeper movement during penalty shootout kicks in live soccer matches. Despite substantial research on goalkeeper performance during penalty kicks, several significant gaps exist. First, existing research predominantly focuses on the spatial and perceptual aspects of goalkeeping, leaving the temporal aspects of goalkeeper movement relatively understudied [16]. Second, most studies overlook the environmental factors present in professional matches, as they typically occur in controlled settings [5; 6; 17]. Third, frequently cited studies suffer from inappropriate reaction criteria (i.e., keyboard responses) [18], the use of stationary targets [5; 6], or irrelevant stimulus presentation (i.e., light flashes) [5; 19].