There has been rapid growth in the collection of player tracking data in recent years. These data, providing spatiotemporal locations of players and ball at high resolution, have spurred methodological developments in a range of sports. There have been impacts in the development of player performance measurement (e.g., distance traveled) and in the attribution of value to specific plays (e.g., expected points from a given position) or even specific actions within a play. This review highlights key methodological contributions via statistical and machine learning approaches. The studies and outcomes discussed show how sports can be a playground for extending analytical techniques in a range of areas. The review also describes the ongoing methodological challenges associated with the use of tracking data.