iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations

iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations

Zhutian Chen, Qisen Yang, Jiarui Shan, Tica Lin, Johanna Beyer, Haijun Xia, and Hanspeter Pfister.

ACM: Proceedings of the CHI Conference on Human Factors in Computing Systems (ACM CHI), 2023.

We present iBall, a basketball video-watching system that leverages gaze-moderated embedded visualizations to facilitate game understanding and engagement of casual fans. Video broadcasting and online video platforms make watching basketball games increasingly accessible. Yet, for new or casual fans, watching basketball videos is often confusing due to their limited basketball knowledge and the lack of accessible, on-demand information to resolve their confusion. To assist casual fans in watching basketball videos, we compared the game-watching behaviors of casual and die-hard fans in a formative study and developed iBall based on the findings. iBall embeds visualizations into basketball videos using a computer vision pipeline, and automatically adapts the visualizations based on the game context and users’ gaze, helping casual fans appreciate.


The authors wish to thank Salma Abdel Magid for her beautiful voice and help on the video narration. This research is supported in part by the NSF award III-2107328, NSF award IIS-1901030, NIH award R01HD104969, and the Harvard Physical Sciences and Engineering Accelerator Award.