This chapter introduces the foundational concepts and frameworks of learning-based sports analytics, offering a comprehensive overview of the field. It begins by exploring key concepts such as data analytics, data acquisition, and data visualization, which are essential for understanding how data drives insights in sports analytics. The chapter then focuses on the critical role of data in sports, discussing the various types of data utilized, the methods and techniques for data collection, and the challenges associated with data acquisition and management. Following this, the chapter outlines the general background of modeling techniques, including the machine learning algorithms that are central to learning-based sports analytics and their practical applications in sports contexts. Lastly, it provides guidance on accessing and contributing to research in the field, addressing the complexities of navigating publication venues and offering practical opinions on disseminating and discovering impactful research. The foundation laid in this chapter prepares the way for a deeper exploration of advanced modeling techniques and the practical applications of learning-based sports analytics in the following chapters.