2026-09-20T09:24:22Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1568052024-09-17T03:40:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Hosoi, Anette
Billings, Jordan A.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-09-16T13:50:18Z
2024-09-16T13:50:18Z
2024-05
2024-07-11T14:37:03.665Z
https://hdl.handle.net/1721.1/156805
Event data- compiled time-stamped list of important events during football matchesare key sources of analysis for teams, analysts, and fans of the sport. A recent undertaking by the MIT Sports Lab and FIFA has resulted in the creation of an algorithm to generate event data from player tracking data, removing the need for manual compilation. The algorithm performs well, but possesses edge cases for which it cannot distinguish events due to algorithmic or data limitations. We propose and test a learning-based approach to classifying set pieces from tracking data, aiming to use differences in ball and/or player motion to inform us of which set piece corresponds to a dead-ball interval. The model shows promise in distinguishing corners, free kicks, and throw-ins. While far from ready to be utilized in a real game scenario, it shows the potential viability in distinguishing a certain class of events without relying on noisy ball data.
MNG
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
Using Machine Learning to Differentiate Set Pieces in Football via Tracking Data
Thesis
application/pdf
Master
Publication
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unknown
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<Abstract>Event data- compiled time-stamped list of important events during football matchesare key sources of analysis for teams, analysts, and fans of the sport. A recent undertaking by the MIT Sports Lab and FIFA has resulted in the creation of an algorithm to generate event data from player tracking data, removing the need for manual compilation. The algorithm performs well, but possesses edge cases for which it cannot distinguish events due to algorithmic or data limitations. We propose and test a learning-based approach to classifying set pieces from tracking data, aiming to use differences in ball and/or player motion to inform us of which set piece corresponds to a dead-ball interval. The model shows promise in distinguishing corners, free kicks, and throw-ins. While far from ready to be utilized in a real game scenario, it shows the potential viability in distinguishing a certain class of events without relying on noisy ball data.</Abstract>
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