{"title":"台球运动分析:数据集和任务","authors":"Qianru Zhang, Zheng Wang, Cheng Long, Siu-Ming Yiu","doi":"arxiv-2407.19686","DOIUrl":null,"url":null,"abstract":"Nowadays, it becomes a common practice to capture some data of sports games\nwith devices such as GPS sensors and cameras and then use the data to perform\nvarious analyses on sports games, including tactics discovery, similar game\nretrieval, performance study, etc. While this practice has been conducted to\nmany sports such as basketball and soccer, it remains largely unexplored on the\nbilliards sports, which is mainly due to the lack of publicly available\ndatasets. Motivated by this, we collect a dataset of billiards sports, which\nincludes the layouts (i.e., locations) of billiards balls after performing\nbreak shots, called break shot layouts, the traces of the balls as a result of\nstrikes (in the form of trajectories), and detailed statistics and performance\nindicators. We then study and develop techniques for three tasks on the\ncollected dataset, including (1) prediction and (2) generation on the layouts\ndata, and (3) similar billiards layout retrieval on the layouts data, which can\nserve different users such as coaches, players and fans. We conduct extensive\nexperiments on the collected dataset and the results show that our methods\nperform effectively and efficiently.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Billiards Sports Analytics: Datasets and Tasks\",\"authors\":\"Qianru Zhang, Zheng Wang, Cheng Long, Siu-Ming Yiu\",\"doi\":\"arxiv-2407.19686\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, it becomes a common practice to capture some data of sports games\\nwith devices such as GPS sensors and cameras and then use the data to perform\\nvarious analyses on sports games, including tactics discovery, similar game\\nretrieval, performance study, etc. While this practice has been conducted to\\nmany sports such as basketball and soccer, it remains largely unexplored on the\\nbilliards sports, which is mainly due to the lack of publicly available\\ndatasets. Motivated by this, we collect a dataset of billiards sports, which\\nincludes the layouts (i.e., locations) of billiards balls after performing\\nbreak shots, called break shot layouts, the traces of the balls as a result of\\nstrikes (in the form of trajectories), and detailed statistics and performance\\nindicators. We then study and develop techniques for three tasks on the\\ncollected dataset, including (1) prediction and (2) generation on the layouts\\ndata, and (3) similar billiards layout retrieval on the layouts data, which can\\nserve different users such as coaches, players and fans. We conduct extensive\\nexperiments on the collected dataset and the results show that our methods\\nperform effectively and efficiently.\",\"PeriodicalId\":501309,\"journal\":{\"name\":\"arXiv - CS - Computational Engineering, Finance, and Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Computational Engineering, Finance, and Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2407.19686\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computational Engineering, Finance, and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.19686","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nowadays, 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, etc. 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.