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Article: Billiards Sports Analytics: Datasets and Tasks

TitleBilliards Sports Analytics: Datasets and Tasks
Authors
Keywordsbilliards layout generation
billiards layout prediction
billiards layout retrieval
billiards sports analytics
Issue Date14-Oct-2024
PublisherAssociation for Computing Machinery (ACM)
Citation
ACM Transactions on Knowledge Discovery from Data, 2024, v. 18, n. 9 How to Cite?
AbstractNowadays, it becomes a common practice to capture some data of sports games with devices such as GPS sensors and cameras and then use the data to perform various analyses on sports games, including tactics discovery, similar game retrieval, performance study, and so forth. While this practice has been conducted to many sports such as basketball and soccer, it remains largely unexplored on the billiards sports, which is mainly due to the lack of publicly available datasets. Motivated by this, we collect a dataset of billiards sports, which includes the layouts (i.e., locations) of billiards balls after performing break shots, called break shot layouts, the traces of the balls as a result of strikes (in the form of trajectories), and detailed statistics and performance indicators. We then study and develop techniques for three tasks on the collected dataset, including (1) prediction and (2) generation on the layouts data, and (3) similar billiards layout retrieval on the layouts data, which can serve different users such as coaches, players and fans. We conduct extensive experiments on the collected dataset and the results show that our methods perform effectively and efficiently.
Persistent Identifierhttp://hdl.handle.net/10722/366338
ISSN
2023 Impact Factor: 4.0
2023 SCImago Journal Rankings: 1.303

 

DC FieldValueLanguage
dc.contributor.authorZhang, Qianru-
dc.contributor.authorWang, Zheng-
dc.contributor.authorLong, Cheng-
dc.contributor.authorYiu, Siu Ming-
dc.date.accessioned2025-11-25T04:18:50Z-
dc.date.available2025-11-25T04:18:50Z-
dc.date.issued2024-10-14-
dc.identifier.citationACM Transactions on Knowledge Discovery from Data, 2024, v. 18, n. 9-
dc.identifier.issn1556-4681-
dc.identifier.urihttp://hdl.handle.net/10722/366338-
dc.description.abstractNowadays, it becomes a common practice to capture some data of sports games with devices such as GPS sensors and cameras and then use the data to perform various analyses on sports games, including tactics discovery, similar game retrieval, performance study, and so forth. While this practice has been conducted to many sports such as basketball and soccer, it remains largely unexplored on the billiards sports, which is mainly due to the lack of publicly available datasets. Motivated by this, we collect a dataset of billiards sports, which includes the layouts (i.e., locations) of billiards balls after performing break shots, called break shot layouts, the traces of the balls as a result of strikes (in the form of trajectories), and detailed statistics and performance indicators. We then study and develop techniques for three tasks on the collected dataset, including (1) prediction and (2) generation on the layouts data, and (3) similar billiards layout retrieval on the layouts data, which can serve different users such as coaches, players and fans. We conduct extensive experiments on the collected dataset and the results show that our methods perform effectively and efficiently.-
dc.languageeng-
dc.publisherAssociation for Computing Machinery (ACM)-
dc.relation.ispartofACM Transactions on Knowledge Discovery from Data-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectbilliards layout generation-
dc.subjectbilliards layout prediction-
dc.subjectbilliards layout retrieval-
dc.subjectbilliards sports analytics-
dc.titleBilliards Sports Analytics: Datasets and Tasks-
dc.typeArticle-
dc.identifier.doi10.1145/3686804-
dc.identifier.scopuseid_2-s2.0-85205033386-
dc.identifier.volume18-
dc.identifier.issue9-
dc.identifier.eissn1556-472X-
dc.identifier.issnl1556-4681-

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