REDUCING HUMAN INTERACTIONS IN GAME ANNOTATION

The sport data tracking systems available today are based on specialized hardware to detect and track targets on the field. While effective, implementing and maintaining these systems pose a number of challenges, including high cost and need for close human monitoring. On the other hand, the sports analytics community has been exploring human computation and crowdsourcing in order to produce tracking data that is trustworthy, cheaper and more accessible. However, state-of-the-art methods require a large number of users to perform the annotation, or put too much burden into a single user. Example methods, systems and user interfaces that facilitate the creation of tracking data sequences of events (e.g., plays of baseball games) by warm-starting a manual annotation process using a vast collection of historical data are described.