ABSTRACT
This chapter provides a rationale for applying a variety of machine learning techniques in sports. The chapter introduces the wider area of machine learning, distinguishing between supervised and unsupervised learning. The approaches covered include clustering techniques, survival analysis, artificial intelligence, rule-based approaches, graph-based approaches and inductive logic programming. The chapter then describes various types of neural networks and deep learning, identifying general purpose toolkits that can be used. The importance of data reduction prior to machine learning application is also discussed. The machine learning techniques are discussed in the contexts of data mining and analytic approaches in sports.
