The proliferation of potential programs reflects encouraging technological advancements in the sports industry, yet it also introduces new challenges. The availability of adequate data is crucial for effective machine learning and artificial intelligence applications. Major players in the industry, including IBM, Google, Facebook, and Disney Research, have already undertaken significant sports research initiatives. The importance of big data sports analytics is evident, demonstrating a robust and positive correlation with optimizing a sports team's potential. Teams that fail to integrate themselves actively with big data analytics risk placing themselves at a considerable disadvantage in the competitive landscape.

Nowadays, technologies could provide coaches and teams with improved accuracy in analyzing common mistakes and improving plays at a faster rate than humans. Particularly, media outlets are increasingly focused on enhancing the spectator experience through technology, and AI is helping to shape the look and feel of the sports enthusiasts’ experience. Effective coaching is a skill that requires experience and is developed over time; it is also an imperfect science. The utilization of various sensors for bioinformatics data acquisition has become popular quickly in some recent years. Meanwhile, various research fields such as computer vision, sensing technology, wearable technology, machine learning and data-driven approaches recently have made huge advancements, and have massively impacted many aspects of sports. Moreover, the joint assessment of multiple modalities for sports data analytics offers appealing innovations to advance the field.

Data-driven machine learning technique plays a vital role in developing and improving sports in recent years. Coaches and athletes can utilize this data to make better decisions for developing their teams. For example, popular sports like football fuel the drive for technological advances in AI and machine learning. With the current technology, specific details and strategies can be extracted from the data to help coaches and players see the whole picture with clarity. By adding context to the collected data, coaches and analysts can allocate more time towards developing strategies.