The sport data tracking systems available today are based on specialized hardware (high-definition cameras, speed radars, RFID) 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. We propose HistoryTracker, a methodology that facilitates the creation of tracking data for baseball games by warm-starting the annotation process using a vast collection of historical data. We show that HistoryTracker helps users to produce tracking data in a fast and reliable way.
HistoryTracker: Minimizing Human Interactions in Baseball Game Annotation
J. P. Ono, A. Gjoka, J. Salamon, C. A. Dietrich, and C. T. Silva
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI'19), Glasgow, UK, May 2019.
[ACM][PDF][BibTeX]
The paper received a CHI 2019 Honorable Mention Award:
HistoryTracker: Minimizing Human Interactions in Baseball Game Annotation
J. P. Ono, A. Gjoka, J. Salamon, C. A. Dietrich, and C. T. Silva
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI'19), Glasgow, UK, May 2019.
[ACM][PDF][BibTeX]
The paper received a CHI 2019 Honorable Mention Award:
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Jorge's full presentation at CHI 2019:
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