Abstract
Spatiotemporal soccer data enables in-depth analysis of a soccer game. However, the amount and the nature of the data makes it challenging for analysts to easily uncover insights from the data. In this article, we introduce an interactive visualization tool that uses novel data mining and machine learning methods to enable coaches and analysts to work on large amounts of data by moving most of the complicated models to the backend and presenting interactive visualization that can be manipulated in real-time. A unique interactive replay and modification feature enables creation of what-if scenarios on existing game data to explore alternative situations, such as a defensive player taking a different position or an offensive player choosing another pass, while making the experience seamless to the users.• Information systems ➝Information system applications • Applied computing ➝ Computers in other domains.
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