Interactive Sports Analytics: An Intelligent Interface for Utilizing Trajectories for Interactive Sports Play Retrieval and Analytics
Analytics in professional sports has experienced a dramatic growth in the last decade due to the wide deployment of player and ball tracking systems in team sports, such as basketball and soccer. With the massive amount of fine-grained data being generated, new data-points are being generated, which can shed light on player and team performance. However, due to the complexity of plays in continuous sports, these data-points often lack the specificity and context to enable meaningful retrieval and analytics. In this article, we present an intelligent human--computer interface that utilizes trajectories instead of words, which enables specific play retrieval in sports. Various techniques of alignment, templating, and hashing were utilized by our system and they are tailored to multi-agent scenario so that interactive speeds can be achieved. We conduct a user study to compare our method to the conventional keywords-based system and the results show that our method significantly improves the retrieval quality. We also show how our interface can be utilized for broadcast purposes, where a user can draw and interact with trajectories on a broadcast view using computer vision techniques. Additionally, we show that our method can also be used for interactive analytics of player performance, which enables the users to move players around and see how performance changes as a function of position and proximity to other players.
© 2018 ACM. Received January 2017; revised September 2017; accepted January 2018. The authors would like to thank Dr. P. Carr of Disney Research Pittsburgh for his guidance in the computer vision part of this work.