  Augmented sports videos, which combine visualizations and video effects to
present data in actual scenes, can communicate insights engagingly and thus
have been increasingly popular for sports enthusiasts around the world. Yet,
creating augmented sports videos remains a challenging task, requiring
considerable time and video editing skills. On the other hand, sports insights
are often communicated using natural language, such as in commentaries, oral
presentations, and articles, but usually lack visual cues. Thus, this work aims
to facilitate the creation of augmented sports videos by enabling analysts to
directly create visualizations embedded in videos using insights expressed in
natural language. To achieve this goal, we propose a three-step approach - 1)
detecting visualizable entities in the text, 2) mapping these entities into
visualizations, and 3) scheduling these visualizations to play with the video -
and analyzed 155 sports video clips and the accompanying commentaries for
accomplishing these steps. Informed by our analysis, we have designed and
implemented Sporthesia, a proof-of-concept system that takes racket-based
sports videos and textual commentaries as the input and outputs augmented
videos. We demonstrate Sporthesia's applicability in two exemplar scenarios,
i.e., authoring augmented sports videos using text and augmenting historical
sports videos based on auditory comments. A technical evaluation shows that
Sporthesia achieves high accuracy (F1-score of 0.9) in detecting visualizable
entities in the text. An expert evaluation with eight sports analysts suggests
high utility, effectiveness, and satisfaction with our language-driven
authoring method and provides insights for future improvement and
opportunities.
