{"title":"欧洲球员和非欧洲球员有何不同:欧洲篮球联赛的多元统计分析证据","authors":"Erhan Çene, Fırat Özdalyan, Coşkun Parim, Egemen Mancı, Tuğbay İnan","doi":"10.1177/17543371241242835","DOIUrl":null,"url":null,"abstract":"This study has multiple purposes and these are (i) to divide EuroLeague players into clusters according to their statistics and positions, (ii) to examine the clusters according to the players’ characteristics and their continents of origin, and (iii) to compare statistics of European and non-European EuroLeague basketball players according to their positions. Dataset is based on the 2020–2021 EuroLeague season. Similarities and differences between players are explained by using the t-test/Mann-Whitney U test, effect sizes, Cluster Analysis (CA), and Multi-Dimensional Scaling (MDS). Six different MDS maps have been introduced to separate guards, forwards, and centers in terms of performance indicators (including both raw and per 40 min statistics). Moreover, the vast majority of MDS maps revealed for all positions are visualized with six clusters. MDS Results show that players playing in similar positions and exhibiting similar performances in the EuroLeague are on the same maps. Also, the results prove that European and non-European basketball players have different playing styles and certain clusters are dominated by either European or non-European players. The information to be obtained from this study may benefit on basketball players, coaches, and managers regarding various issues (player development plan and player transfer policy).","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":"75 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How do European and non-European players differ: Evidence from EuroLeague basketball with multivariate statistical analysis\",\"authors\":\"Erhan Çene, Fırat Özdalyan, Coşkun Parim, Egemen Mancı, Tuğbay İnan\",\"doi\":\"10.1177/17543371241242835\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study has multiple purposes and these are (i) to divide EuroLeague players into clusters according to their statistics and positions, (ii) to examine the clusters according to the players’ characteristics and their continents of origin, and (iii) to compare statistics of European and non-European EuroLeague basketball players according to their positions. Dataset is based on the 2020–2021 EuroLeague season. Similarities and differences between players are explained by using the t-test/Mann-Whitney U test, effect sizes, Cluster Analysis (CA), and Multi-Dimensional Scaling (MDS). Six different MDS maps have been introduced to separate guards, forwards, and centers in terms of performance indicators (including both raw and per 40 min statistics). Moreover, the vast majority of MDS maps revealed for all positions are visualized with six clusters. MDS Results show that players playing in similar positions and exhibiting similar performances in the EuroLeague are on the same maps. Also, the results prove that European and non-European basketball players have different playing styles and certain clusters are dominated by either European or non-European players. The information to be obtained from this study may benefit on basketball players, coaches, and managers regarding various issues (player development plan and player transfer policy).\",\"PeriodicalId\":20674,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology\",\"volume\":\"75 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/17543371241242835\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/17543371241242835","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
摘要
本研究有多个目的,包括:(i) 根据欧洲联赛球员的统计数据和位置将其划分为若干群组;(ii) 根据球员的特点和原籍大洲研究这些群组;(iii) 根据球员的位置比较欧洲和非欧洲联赛篮球运动员的统计数据。数据集基于 2020-2021 赛季的欧洲联赛。使用 t 检验/曼-惠特尼 U 检验、效应大小、聚类分析(CA)和多维尺度(MDS)来解释球员之间的相似性和差异性。我们引入了六种不同的 MDS 地图,以区分后卫、前锋和中锋的表现指标(包括原始数据和每 40 分钟统计数据)。此外,针对所有位置揭示的绝大多数 MDS 地图都有六个群组。MDS 结果表明,在欧洲联赛中,位置相似、表现相似的球员在相同的地图上。此外,结果还证明,欧洲和非欧洲篮球运动员具有不同的比赛风格,某些聚类由欧洲或非欧洲球员主导。从这项研究中获得的信息可能会对篮球运动员、教练员和管理者在各种问题(球员发展计划和球员转会政策)上有所帮助。
How do European and non-European players differ: Evidence from EuroLeague basketball with multivariate statistical analysis
This study has multiple purposes and these are (i) to divide EuroLeague players into clusters according to their statistics and positions, (ii) to examine the clusters according to the players’ characteristics and their continents of origin, and (iii) to compare statistics of European and non-European EuroLeague basketball players according to their positions. Dataset is based on the 2020–2021 EuroLeague season. Similarities and differences between players are explained by using the t-test/Mann-Whitney U test, effect sizes, Cluster Analysis (CA), and Multi-Dimensional Scaling (MDS). Six different MDS maps have been introduced to separate guards, forwards, and centers in terms of performance indicators (including both raw and per 40 min statistics). Moreover, the vast majority of MDS maps revealed for all positions are visualized with six clusters. MDS Results show that players playing in similar positions and exhibiting similar performances in the EuroLeague are on the same maps. Also, the results prove that European and non-European basketball players have different playing styles and certain clusters are dominated by either European or non-European players. The information to be obtained from this study may benefit on basketball players, coaches, and managers regarding various issues (player development plan and player transfer policy).
期刊介绍:
The Journal of Sports Engineering and Technology covers the development of novel sports apparel, footwear, and equipment; and the materials, instrumentation, and processes that make advances in sports possible.