Spatio-temporal data, i.e. data about moving objects, is omnipresent and its use grows strongly in various areas. Especially in the highly competitive sports sector, new insights can be gained from positional information of players - tracked by camera or sensor-based systems during a game. This can have a major impact on the training and tactics of a team. The capabilities of columnar in-memory databases and optimized algorithms enable the analysis of moving objects in real-time. Our interactive tactic board presents a novel approach to find recurring patterns in video recordings by analyzing the positional information of soccer players. By leveraging machine-learning techniques, the system automatically identifies recurring patterns before or after a certain event and empowers users to gain tactical insights in an intuitive, efficient, and fast way.