Short Survey in Machine Learning for Soccer Analytics

We investigate soccer analytics from supervised learning, unsupervised learning, and reinforcement learning perspectives. With the increasing availability of player tracking data and event logs, machine learning techniques have become essential for uncovering patterns in player and team performance. In this paper, we examine how supervised learning models are applied to tasks such as match outcome prediction and player rating systems, while unsupervised learning is utilized for player clustering, tactical analysis, and the discovery of hidden patterns in game data. Reinforcement learning, on the other hand, plays a key role in optimizing decision-making during matches by learning optimal strategies and tactics through trial and error. By providing a comprehensive overview of these approaches, we aim to highlight the transformative potential of machine learning in modern soccer analytics and how it continues to shape the sport. We also provide summary of other soccer analytics research in this work.