## 7 Discussion  
The primary goal of this paper was to examine the pivotal role that continuously emerging technologies such as AI, VR, AR, and DV play in improving sports performance analysis. These technologies have undergone extensive development over the last three decades, particularly in the fields of computer science techniques, miniaturization, and processing power [4; 43]. Technological advancements have impacted sports performance analysis in various ways, including data acquisition, processing, and reporting [4; 43]. Additionally, due to the availability of new measurement devices (e.g., higher-resolution cameras and wearable sensors) [47; 48; 49; 50; 51; 57], the volume, variety, and velocity of data have also increased [5]. The era of Big Data presents exciting challenges for sports analysts, with one of the major hurdles being the processing of vast multimedia data, and translating them into practical, applied, and valuable information [5; 174]. Ultimately, it is this information that will be communicated to various stakeholders, including athletes, coaches, delegates, and the media. Quality information can lead to valuable insights, and valuable insights can contribute to achieving goals. These goals can primarily be addressed through training and improving performance, necessitating a continuous cycle of data measurement and processing [4; 43]. Therefore, as technological boundaries continue to expand, it is paramount to understand how these individual tools support coaches and enhance athletic performance. When used synergistically, they create unparalleled potential for optimizing performance and decision-making. Getting coaches and athletes to use the innovations may be the most difficult challenge facing performance analysts. The interrelation of AI, VR, AR, and DV creates a comprehensive platform for sports performance analysis. AI serves as the backbone for data collection, advanced data analysis, enhanced video, and notational, time-motion, and wearable data analysis efficiency [2; 25; 61; 98; 105; 106]. Powerful AI tools can be used for injury prediction and prevention, empowering analysts' work by increasing processing speed, and offering predictive analytics and real-time feedback. VR and AR, on the other hand, provide immersive coaching and training experiences, as well as real-time data superimposition [32; 33]. Used correctly, VR and AR can enhance skills and strategies, deliver tactical insights, facilitate data-driven decisions, and support rehabilitation and mental training. Lastly, DV techniques simplify data, leading to insights and tactical analysis. They enhance narrative power, which is crucial when translating data into information for real-time decisions and historical predictions. While these technologies individually contribute to the realm of sports performance analysis, their true power emerges when they are used synergistically and integrated. In fact, AI, VR, AR, and DV naturally interplay and form an intrinsic integration, which can be translated into an explicit framework. We propose that AI assumes the central role, serving as the hub for comprehensive data management, encompassing collection, processing, modelling, and storage. Ultimately, AI models and systems can handle various tasks within the proposed sports performance model. The capability to address a specific task will depend on the available tools and models for analysis. However, at least theoretically, almost any task can be implemented given sufficient time and resources. The encouraging news is that one does not need to master all the techniques; APIs can be employed to manage different data types. Companies can specialize in different tasks and sports modalities, offering APIs as a service, which benefits analysts and sports institutions. We would like to emphasize that AI has the potential to enhance any kind of video analysis. Historically, video analysis has been a massive and time-consuming task.