HistoryTracker

Best Paper Honorable Mention at CHI 2019

Paper: https://dl.acm.org/doi/abs/10.1145/3290605.3300293

Github: https://github.com/jorgehpo/WeatherTaxiExploration

Abstract: 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-start 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 system. A: Users can create a description of the play based on simple questions. B) A video of the play to be annotated. C) Trajectories are recommended based on the play description provided by the user. D) Events can be used to create a more fine grained query of the play.