  Sports game data is becoming increasingly complex, often consisting of
multivariate data such as player performance stats, historical team records,
and athletes' positional tracking information. While numerous visual analytics
systems have been developed for sports analysts to derive insights, few tools
target fans to improve their understanding and engagement of sports data during
live games. By presenting extra data in the actual game views, embedded
visualization has the potential to enhance fans' game-viewing experience.
However, little is known about how to design such kinds of visualizations
embedded into live games. In this work, we present a user-centered design study
of developing interactive embedded visualizations for basketball fans to
improve their live game-watching experiences. We first conducted a formative
study to characterize basketball fans' in-game analysis behaviors and tasks.
Based on our findings, we propose a design framework to inform the design of
embedded visualizations based on specific data-seeking contexts. Following the
design framework, we present five novel embedded visualization designs
targeting five representative contexts identified by the fans, including
shooting, offense, defense, player evaluation, and team comparison. We then
developed Omnioculars, an interactive basketball game-viewing prototype that
features the proposed embedded visualizations for fans' in-game data analysis.
We evaluated Omnioculars in a simulated basketball game with basketball fans.
The study results suggest that our design supports personalized in-game data
analysis and enhances game understanding and engagement.
