## 4 Discussion
The focus of such technologies is sometimes to identify human actions in real time, but this study demonstrates a simple, descriptive solution for identifying when a specific action occurs and demonstrates that [PERSON]'s choice to classify actions over five-frame intervals can be extended to action timing without the use of clustering [32]. This study could be used in conjunction with clustering/classifying algorithms to enhance the accuracy of each method. Perhaps the most interesting application of this study is the possibility of evaluating affordance-based performance as outlined by [PERSON] et al. [16]. In their study, they created a theoretical framework for assessing the performance of goalkeeper dives within the context of their physical capabilities. One of the main components of this framework was the timing of the goalkeeper's dive relative to the timing of the ball kick. This study is a step forward in being able to make that framework actionable, as it allows for an absolute measurement of the dive time and thereby a comparison to the time of the ball kick. [PERSON] et al. 's framework is promising in its approach to benchmark a goalkeeper's dive time to their physical capabilities and further evaluate their decision making [16]. The two main limitations in this study are the sample size and the information that can be captured by the pose estimation framework. We limited our sample to the 2022 World Cup to ensure as consistent footage as possible. However, the small sample size of both attempts and goalkeepers likely limit the statistical takeaways from this study. While the dataset includes repeated instances of the same goalkeepers, this does not necessarily introduce bias. The diversity in direction, approach, and strategy within the sample ensures a wide variety of scenarios, as goalkeepers face different opponents and situations during each penalty kick. In this study, we used two-dimensional pose estimation and therefore could only observe motion in the frontal plane. Due to this limitation, we lose information in other planes. More robust coordinate mapping (i.e., 3D instead of 2D) could help provide more accurate pose estimation and enable the analysis of velocities and accelerations. Additionally, there is currently no feasible way to capture ground reaction forces from broadcast footage, potentially leading to a lag in the identification of goalkeeper dive initiation compared to methods that directly measure ground reaction forces. Further, the methodology used here relies on processed videos and qualitative labeling. The results cannot be applied to a continuous broadcast without prior annotation or labeling, and the criteria for labeling are loose. Although we describe movements of interest, the exact criteria are ambiguous and subject to human evaluation, potentially impacting the accuracy of a trained model. The research presented here only presents a methodology to detect when a goalkeeper initiates their movement but does not explore the context of this timing. This research provides a stepping stone to better understand timing outside of binary save outcomes. Future studies may consider analyzing the kinematics of the penalty taker as part of a more holistic evaluation of the penalty kick. It may be possible to use pose estimation data to create a probabilistic model based on kicker kinematics that attempts to assign probabilities of kick direction at each frame leading up to foot-to-ball contact and benchmarks goalkeeper timing and dive direction to the model's confidence in kick direction. Future studies may also look to combine pose estimation predictions of timing with force plate data to understand how goalkeeper strength and ability interplay with the timing of movement.