职业篮球运动员投篮命中率与投篮命中率的联合分析:贝叶斯非参数资源

Eliot Wong-Toi, Hou‐Cheng Yang, Weining Shen, Guanyu Hu
{"title":"职业篮球运动员投篮命中率与投篮命中率的联合分析:贝叶斯非参数资源","authors":"Eliot Wong-Toi, Hou‐Cheng Yang, Weining Shen, Guanyu Hu","doi":"10.6339/22-jds1062","DOIUrl":null,"url":null,"abstract":"Understanding shooting patterns among different players is a fundamental problem in basketball game analyses. In this paper, we quantify the shooting pattern via the field goal attempts and percentages over twelve non-overlapping regions around the front court. A joint Bayesian nonparametric mixture model is developed to find latent clusters of players based on their shooting patterns. We apply our proposed model to learn the heterogeneity among selected players from the National Basketball Association (NBA) games over the 2018–2019 regular season and 2019–2020 bubble season. Thirteen clusters are identified for 2018–2019 regular season and seven clusters are identified for 2019–2020 bubble season. We further examine the shooting patterns of players in these clusters and discuss their relation to players’ other available information. The results shed new insights on the effect of NBA COVID bubble and may provide useful guidance for player’s shot selection and team’s in-game and recruiting strategy planning.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Joint Analysis for Field Goal Attempts and Percentages of Professional Basketball Players: Bayesian Nonparametric Resource\",\"authors\":\"Eliot Wong-Toi, Hou‐Cheng Yang, Weining Shen, Guanyu Hu\",\"doi\":\"10.6339/22-jds1062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Understanding shooting patterns among different players is a fundamental problem in basketball game analyses. In this paper, we quantify the shooting pattern via the field goal attempts and percentages over twelve non-overlapping regions around the front court. A joint Bayesian nonparametric mixture model is developed to find latent clusters of players based on their shooting patterns. We apply our proposed model to learn the heterogeneity among selected players from the National Basketball Association (NBA) games over the 2018–2019 regular season and 2019–2020 bubble season. Thirteen clusters are identified for 2018–2019 regular season and seven clusters are identified for 2019–2020 bubble season. We further examine the shooting patterns of players in these clusters and discuss their relation to players’ other available information. The results shed new insights on the effect of NBA COVID bubble and may provide useful guidance for player’s shot selection and team’s in-game and recruiting strategy planning.\",\"PeriodicalId\":73699,\"journal\":{\"name\":\"Journal of data science : JDS\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of data science : JDS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.6339/22-jds1062\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of data science : JDS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.6339/22-jds1062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

了解不同球员的投篮模式是篮球比赛分析中的一个基本问题。在本文中,我们通过前场周围12个不重叠区域的投篮命中率和命中率来量化投篮模式。建立了一个联合贝叶斯非参数混合模型,根据球员的投篮模式寻找潜在的球员簇。我们将提出的模型应用于2018-2019赛季常规赛和2019-2020赛季NBA比赛中被选中的球员之间的异质性。2018-2019赛季确定了13个集群,2019-2020赛季确定了7个集群。我们进一步研究了这些集群中球员的投篮模式,并讨论了它们与球员其他可用信息的关系。研究结果为NBA COVID泡沫的影响提供了新的见解,并可能为球员的投篮选择和球队的比赛和招募策略规划提供有用的指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Joint Analysis for Field Goal Attempts and Percentages of Professional Basketball Players: Bayesian Nonparametric Resource
Understanding shooting patterns among different players is a fundamental problem in basketball game analyses. In this paper, we quantify the shooting pattern via the field goal attempts and percentages over twelve non-overlapping regions around the front court. A joint Bayesian nonparametric mixture model is developed to find latent clusters of players based on their shooting patterns. We apply our proposed model to learn the heterogeneity among selected players from the National Basketball Association (NBA) games over the 2018–2019 regular season and 2019–2020 bubble season. Thirteen clusters are identified for 2018–2019 regular season and seven clusters are identified for 2019–2020 bubble season. We further examine the shooting patterns of players in these clusters and discuss their relation to players’ other available information. The results shed new insights on the effect of NBA COVID bubble and may provide useful guidance for player’s shot selection and team’s in-game and recruiting strategy planning.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信