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HistoryTracker: Minimizing Human Interactions in Baseball Game Annotation

Published: 02 May 2019 Publication History

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-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.

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References

[1]
Alexander Klaser. 2010. LEAR - Image Annotation Tool. https: //lear.inrialpes.fr/people/klaeser/software_image_annotation
[2]
R. Arthur. 2016. MLB's Hit-Tracking Tool Misses A Lot Of Hits. https://fivethirtyeight.com/features/ mlbs-hit-tracking-tool-misses-a-lot-of-hits/
[3]
Major League Baseball. 2016. Official baseball rules. http://mlb.mlb. com/mlb/official_info/official_rules/official_rules.jsp
[4]
A. Bialkowski, P. Lucey, P. Carr, Y. Yue, and I. Matthews. 2014. Win at Home and Draw Away: Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors. MIT Sloan Sports Analytics Conference 28.
[5]
S. Bock and G. Widmer. 2013. Maximum Filter Vibrato Suppression for Onset Detection. In Proc. of the 16th Int. Conf. on Digital Audio Effects (DAFx). Maynooth, Ireland (Sept 2013). 7.
[6]
G. C. Bogdanis, V. Ziagos, M. Anastasiadis, and M. Maridaki. 2007. Effects of two different short-term training programs on the physical and technical abilities of adolescent basketball players. Journal of Science and Medicine in Sport 10, 2 (April 2007), 79--88.
[7]
D. Cervone, A. D'Amour, L. Bornn, and K. Goldsberry. 2014. POINTWISE: Predicting points and valuing decisions in real time with NBA Optical Tracking Data. MIT Sloan Sports Analytics Conference 28.
[8]
D. H. Chung, P. A. Legg, M. L. Parry, R. Bown, I. W. Griffiths, R. S. Laramee, and M. Chen. 2015. Glyph sorting: Interactive visualization for multi-dimensional data. Information Visualization 14, 1 (2015), 76--90.
[9]
D. H. S. Chung, M. L. Parry, I. W. Griffiths, R. S. Laramee, R. Bown, P. A. Legg, and M. Chen. 2016. Knowledge-assisted ranking: A visual analytic application for sports event data. IEEE Computer Graphics and Applications 36, 3 (2016), 72--82.
[10]
ChyronHego. 2016. TRACAB Optical Tracking. http://chyronhego. com/sports-data/tracab
[11]
J. Cross and D. Sylvan. 2015. Modeling spatial batting ability using a known covariance matrix. Journal of Quantitative Analysis in Sports 11, 3 (2015), 155--167.
[12]
C. Dietrich, D. Koop, H. T. Vo, and C. T. Silva. 2014. Baseball4D: A tool for baseball game reconstruction amp; visualization. In 2014 IEEE Conference on Visual Analytics Science and Technology (VAST). 23--32.
[13]
ESPN. 2012. Player tracking transforming NBA analytics. http: //www.espn.com/blog/playbook/tech/post/_/id/492/492
[14]
M. Fleischman and D. Roy. 2007. Unsupervised Content-based Indexing of Sports Video. In Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval (MIR '07). ACM, New York, NY, USA, 87--94.
[15]
K. Goldsberry. 2012. Courtvision: New visual and spatial analytics for the NBA. In MIT Sloan Sports Analytics Conference.
[16]
P. R. Kamble, A. G. Keskar, and K. M. Bhurchandi. 2017. Ball tracking in sports: a survey. Artificial Intelligence Review (Oct. 2017).
[17]
M. Lage, J. P. Ono, D. Cervone, J. Chiang, C. Dietrich, and C. T. Silva. 2016. StatCast Dashboard: Exploration of Spatiotemporal Baseball Data. IEEE Computer Graphics and Applications 36, 5 (Sept. 2016), 28--37.
[18]
H. M. Le, P. Carr, Y. Yue, and P. Lucey. 2017. Data-Driven Ghosting using Deep Imitation Learning. MIT Sloan Sports Analytics Conference, 15.
[19]
B. McFee, C. Raffel, D. Liang, D. Ellis, M. McVicar, E. Battenberg, and O. Nieto. 2015. librosa: Audio and Music Signal Analysis in Python. In Proceedings of the 14th python in science conference. 8.
[20]
A. McIntyre, J. Brooks, J. Guttag, and J. Wiens. 2016. Recognizing and Analyzing Ball Screen Defense in the NBA. MIT Sloan Sports Analytics Conference, 10.
[21]
P. E. Meltzer and R. Marazzi. 2013. So You Think You Know Baseball? A Fan's Guide to the Official Rules (1 edition ed.). W. W. Norton & Company, New York, NY.
[22]
K. P. Murphy. 2012. Machine learning: a probabilistic perspective. MIT Press, Cambridge, MA.
[23]
NFL. 2018. Glossary | NFL Next Gen Stats. https://nextgenstats.nfl. com/glossary
[24]
S. Nylander, J. Tholander, F. Mueller, and J. Marshall. 2014. HCI and Sports. In CHI '14 Extended Abstracts on Human Factors in Computing Systems (CHI EA '14). ACM, New York, NY, USA, 115--118.
[25]
J. P. Ono, C. Dietrich, and C. T. Silva. 2018. Baseball Timeline: Summarizing Baseball Plays Into a Static Visualization. Computer Graphics Forum 37, 3 (June 2018), 491--501.
[26]
T. Page. 2015. Applications of Wearable Technology in Elite Sports. Journal on Mobile Applications and Technologies 2, 1 (April 2015), 1--15.
[27]
C. Perin, R. Vuillemot, and J. D. Fekete. 2013. Real-Time Crowdsourcing of Detailed Soccer Data. In What's the score? The 1st Workshop on Sports Data Visualization.
[28]
C. Perin, R. Vuillemot, and J. D. Fekete. 2013. SoccerStories: A Kick-off for Visual Soccer Analysis. IEEE Transactions on Visualization and Computer Graphics 19, 12 (Dec. 2013), 2506--2515.
[29]
C. Perin, R. Vuillemot, C. D. Stolper, J. T. Stasko, J. Wood, and S. Carpendale. 2018. State of the Art of Sports Data Visualization. Computer Graphics Forum 37, 3 (June 2018), 663--686.
[30]
H. Pileggi, C. D. Stolper, J. M. Boyle, and J. T. Stasko. 2012. SnapShot: Visualization to Propel Ice Hockey Analytics. IEEE Transactions on Visualization and Computer Graphics 18, 12 (Dec. 2012), 2819--2828.
[31]
G. Pingali, A. Opalach, Y. Jean, and I. Carlbom. 2001. Visualization of Sports Using Motion Trajectories: Providing Insights into Performance, Style, and Strategy. In Proceedings of the Conference on Visualization '01 (VIS '01). IEEE Computer Society, Washington, DC, USA, 75--82.
[32]
C. B. Santiago, A. Sousa, M. L. Estriga, L. P. Reis, and M. Lames. 2010. Survey on team tracking techniques applied to sports. In 2010 International Conference on Autonomous and Intelligent Systems, AIS 2010. 1--6.
[33]
T. Seidl, A. Cherukumudi, A. Hartnett, P. Carr, and P. Lucey. 2018. Bhostgusters: Realtime Interactive Play Sketching with Synthesized NBA Defenses. MIT Sloan Sports Analytics Conference, 13.
[34]
L. Sha, P. Lucey, Y. Yue, X. Wei, J. Hobbs, C. Rohlf, and S. Sridharan. 2018. Interactive Sports Analytics: An Intelligent Interface for Utilizing Trajectories for Interactive Sports Play Retrieval and Analytics. ACM Transactions on Computer-Human Interaction 25, 2 (April 2018), 1--32.
[35]
M. Spencer, C. Rechichi, S. Lawrence, B. Dawson, D. Bishop, and C. Goodman. 2005. Time-motion analysis of elite field hockey during several games in succession: a tournament scenario. Journal of Science and Medicine in Sport (2005), 10.
[36]
Sportvision. 2016. PITCHf/x. http://www.sportvision.com/baseball/ pitchfx
[37]
STATS. 2016. SportVU Player Tracking | STATS SportVU Tracking Cameras. http://www.stats.com/sportvu/sportvu-basketball-media/
[38]
M. Stein, H. Janetzko, T. Breitkreutz, D. Seebacher, T. Schreck, M. Grossniklaus, I. D. Couzin, and D. A. Keim. 2016. Director's Cut: Analysis and Annotation of Soccer Matches. IEEE Computer Graphics and Applications 36, 5 (Sept. 2016), 50--60.
[39]
M. Stein, H. Janetzko, A. Lamprecht, T. Breitkreutz, P. Zimmermann, B. Goldlucke, T. Schreck, G. Andrienko, M. Grossniklaus, and D. A. Keim. 2018. Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2018), 13--22.
[40]
A. Tang and S. Boring. 2012. # EpicPlay: Crowd-sourcing sports video highlights. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1569--1572.
[41]
R. Theron and L. Casares. 2010. Visual Analysis of Time-Motion in Basketball Games. In International Symposium on Smart Graphics, Vol. 6133. 196--207.
[42]
USAToday. 2015. Data deluge: MLB rolls out Statcast analytics on Tuesday. https://www.usatoday.com/story/sports/mlb/2015/04/20/ data-deluge-mlb-rolls-out-statcast-analytics-on-tuesday/26097841/
[43]
G. Van Oorschot, M. Van Erp, and C. Dijkshoorn. 2012. Automatic extraction of soccer game events from twitter. Proceedings of the Workhop on Detection, Representation, and Exploitation of Events in the Semantic Web (DeRiVE 2012) 902 (2012), 21--30.
[44]
C. Vondrick, D. Ramanan, and D. Patterson. 2010. Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces. In European Conference on Computer Vision. Springer, 610--623.
[45]
W. Zhou, A. Vellaikal, and C. Kuo. 2000. Rule-based Video Classification System for Basketball Video Indexing. In Proceedings of the 2000 ACM Workshops on Multimedia (MULTIMEDIA '00). ACM, New York, NY, USA, 213--216.

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cover image ACM Conferences
CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
May 2019
9077 pages
ISBN:9781450359702
DOI:10.1145/3290605
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 02 May 2019

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  1. baseball
  2. hand annotation
  3. sports analytics
  4. sports tracking

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CHI '19 Paper Acceptance Rate 703 of 2,958 submissions, 24%;
Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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