Artificial Intelligence and Machine Learning-Based Data Analytics for Sports: General Overview and NBA Case Study

Akemi Gálvez (), Vei S. Chan (), Sara Pérez-Carabaza () and Andrés Iglesias ()
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Akemi Gálvez: University of Cantabria
Vei S. Chan: University of Technology Malaysia
Sara Pérez-Carabaza: University of Cantabria
Andrés Iglesias: University of Cantabria

A chapter in Artificial Intelligence, Optimization, and Data Sciences in Sports, 2025, pp 149-194 from Springer

Abstract: Abstract Artificial intelligence and machine learning are two of the most groundbreaking and disruptive computing technologies in today’s digital world. Recent developments in these fields, such as deep learning, reinforcement learning, and others, are having a profound impact in many domains, including sports. On the other hand, data analytics represents a new paradigm for processing and analyzing the massive amount of data on sports that can be obtained today through cameras, sensors, GPS, wearables, and other devices. Combining these three technologies is revolutionizing the way we approach sports, from analyzing player performance to developing new training methods, preventing injuries, optimizing game strategies, engaging spectators, and many others. The objective of this chapter is twofold: on the one hand, it provides a general overview about the historical evolution, uses, tools, and applications of artificial intelligence and machine learning-based data analytics to the world of sports in a comprehensive way; on the other hand, it presents a more detailed analysis of a case study—the world of NBA professional basketball. Several illustrative examples about the use of these technologies in sports are also presented. As these technologies continue to evolve, we can expect to see even more innovative applications of these fields in sports for the years to come.

Keywords: Artificial intelligence; Machine learning; Data analytics; Sports analytics; Game strategy Optimization; NBA gameplay optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-76047-1_5

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