Machine learningMachine Learning (ML) is becoming an important part of modern sports, supporting athlete performanceAthlete performance, tactical decision-making, and fan engagementFan engagement. Applying machine learningMachine Learning (ML) in sports can also present challenges related to data acquisition, physiological variability, and interdisciplinary collaboration. This chapter explores these complexities, beginning with an overview of data sources. Afterwards it examines they shape machine learningMachine Learning (ML) applications. We discuss important sports domains such as physiological modeling, movement analysis, tactical optimization, and personalized fan experiences, analyzing both current capabilities and limitations. The chapter also addresses broader issues of privacy and resource disparities. Finally, it looks ahead to short-term innovations such as sensor fusion, explainable AIExplainable AI, and digital twins and future possible applications.