TY - GEN
T1 - HistoryTracker
T2 - 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019
AU - Ono, Jorge Piazentin
AU - Gjoka, Arvi
AU - Salamon, Justin
AU - Dietrich, Carlos
AU - Silva, Claudio T.
N1 - Funding Information:
We would like to thank the CHI 2019 reviewers for their thoughtful comments and efforts towards improving our manuscript. We thank Neel Dey, Jean-Daniel Fekete, Raoni Lourenco, Oded Nov, and the members of the NYU Sports Analytics team for help and support throughout this project, and the AVIZ research team, INRIA Saclay, who hosted C. T. Silva during this work. MLB Advanced Media provided partial funding for the project and access to data. This research was partially supported by Labex DigiCosme (project ANR-11LABEX-0045-DIGICOSME) operated by ANR as part of the program “Investissement d’Avenir” Idex Paris-Saclay (ANR-11-IDEX-0003-02); NSF awards CNS-1229185, CCF-1533564, CNS-1544753, CNS-1730396, and CNS-1828576; the Moore-Sloan Data Science Environment at NYU; J. P. Ono and C. T. Silva are partially supported by the DARPA D3M program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA. This work was performed while J. P. Ono and C. T. Silva were NYU Provost’s Global Research Institute (GRI) fellows at NYU Paris.
Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/5/2
Y1 - 2019/5/2
N2 - 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.
AB - 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.
KW - Baseball
KW - Hand annota
KW - Sports analytics
KW - Sports tracking
UR - http://www.scopus.com/inward/record.url?scp=85067617471&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067617471&partnerID=8YFLogxK
U2 - 10.1145/3290605.3300293
DO - 10.1145/3290605.3300293
M3 - Conference contribution
AN - SCOPUS:85067617471
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 4 May 2019 through 9 May 2019
ER -