## 2 Materials and Methods  
To create a heuristic methodology for identifying goalkeeper movement initiation, this study relies on broadcast footage of all penalty kicks attempted during the 2022 FIFA World Cup. Using this footage, the following framework was utilized:  
1. Collect a dataset of penalty kicks from the broadcast footage. 2. Annotate the dataset with ground truth labels for goalkeeper movement and save outcomes. 3. Train and validate a pose estimation model using the annotated dataset. 4. Evaluate the accuracy of the pose estimation model for detecting goalkeeper movement during penalty kicks. The primary data source for this study was broadcast video footage of all penalty shootouts from the 2022 FIFA World Cup. Typical soccer matches can end in a draw [24]. However, elimination games, such as the post-group stage matches at the World Cup, require a winner [24]. Penalty shootouts, which consist of a series of penalty kicks by both teams, occur when the match remains tied after regulation and extra time periods have expired [24]. We identified a total of 41 penalty kicks and collected broadcast footage of each kick from publicly available sources. All footage in the dataset has a resolution of 1080 p and a frame rate of 50 frames per second. We cut the footage from each match to isolate the footage of each penalty kick attempt, starting from the kicker's run-up and ending with the kick outcome. For each penalty kick attempt, we identified two frames of interest: the frame of movement initiation and the frame of flight. These frames will be referenced as f0 and f1, respectively. Due to the interplay of goalkeeper and kicker strategies, there is no single definition of movement initiation. Movement initiation, in the context of this problem, is defined as the frame in which the goalkeeper initiates their save attempt, disregarding any extraneous motion prior to committing to the dive side. For example, if a goalkeeper jumps or shuffles on the line prior to the dive attempt, this is ignored when labeling. This serves to validate and understand the accuracy of automated movement detection. We marked f0 using [PERSON] et al. 's subjective methodology of manually marking the frame and based our subjective observation on [PERSON] et al. 's findings that goalkeepers initiate a dive by pushing off with their contralateral leg, or the leg opposite of the dive side [5; 20]. [PERSON] et al. described three strategies that goalkeepers use to start their dives: \"(1) Exerting horizontal forces for horizontal displacement towards the ball, (2) Exerting vertical forces for a pre-push-off jump and (3) Exerting vertical forces with the contralateral leg for stepping sideward with the ipsilateral leg towards the ball\" [5]. [PERSON] et al. identified \"pushing off\" using ground reaction force data collected from force plates [5]. This approach provided precise kinetic and kinematic data that allowed for an objective determination of dive initiation. However, given the constraints of our study, specifically the use of broadcast footage without access to ground reaction force data, we adapted this methodology for visual observation. To mark f0 accurately, the first author visually observed the contralateral knee and ankle movement of the goalkeeper. This observation aligns with [PERSON] et al. 's findings, as the contralateral leg's motion is a critical indicator of the initiation of the dive. Specifically, the first author observed contralateral knee abduction and adduction, as well as contralateral ankle inversion and eversion in the mediolateral plane. By focusing on the contralateral knee and ankle, one can identify the moment when the goalkeeper committed to the dive. This method, while subjective, provides a practical solution for the limitations posed by the use of single-camera pose estimation applied to broadcast footage. All data processing and analysis was conducted using Python version 3.9.