A Basketball Big Data Platform for Box Score and Play-by-Play Data.

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2024-04-12 DOI:10.1089/big.2023.0177

G. Vinué

{"title":"A Basketball Big Data Platform for Box Score and Play-by-Play Data.","authors":"G. Vinué","doi":"10.1089/big.2023.0177","DOIUrl":null,"url":null,"abstract":"This is the second part of a research diptych devoted to improving basketball data management in Spain. The Spanish ACB (Association of Basketball Clubs, acronym in Spanish) is the top European national competition. It attracts most of the best foreign players outside the NBA (National Basketball Association, in North America) and also accelerates the development of Spanish players who ultimately contribute to the success of the Spanish national team. However, this sporting excellence is not reciprocated by an advanced treatment of the data generated by teams and players, the so-called statistics. On the contrary, their use is still very rudimentary. An earlier article published in this journal in 2020 introduced the first open web application for interactive visualization of the box score data from three European competitions, including the ACB. Box score data refer to the data provided once the game is finished. Following the same inspiration, this new research aims to present the work carried out with more advanced data, namely, play-by-play data, which are provided as the game runs. This type of data allow us to gain greater insight into basketball performance, providing information that cannot be revealed with box score data. A new dashboard is developed to analyze play-by-play data from a number of different and novel perspectives. Furthermore, a comprehensive data platform encompassing the visualization of the ACB box score and play-by-play data is presented.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1089/big.2023.0177","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

Abstract

This is the second part of a research diptych devoted to improving basketball data management in Spain. The Spanish ACB (Association of Basketball Clubs, acronym in Spanish) is the top European national competition. It attracts most of the best foreign players outside the NBA (National Basketball Association, in North America) and also accelerates the development of Spanish players who ultimately contribute to the success of the Spanish national team. However, this sporting excellence is not reciprocated by an advanced treatment of the data generated by teams and players, the so-called statistics. On the contrary, their use is still very rudimentary. An earlier article published in this journal in 2020 introduced the first open web application for interactive visualization of the box score data from three European competitions, including the ACB. Box score data refer to the data provided once the game is finished. Following the same inspiration, this new research aims to present the work carried out with more advanced data, namely, play-by-play data, which are provided as the game runs. This type of data allow us to gain greater insight into basketball performance, providing information that cannot be revealed with box score data. A new dashboard is developed to analyze play-by-play data from a number of different and novel perspectives. Furthermore, a comprehensive data platform encompassing the visualization of the ACB box score and play-by-play data is presented.

查看原文本刊更多论文

篮球大数据平台，用于盒式比分和逐场比赛数据。

本文是致力于改进西班牙篮球数据管理的双联研究的第二部分。西班牙 ACB（篮球俱乐部协会，西班牙语缩写）是欧洲顶级国家级赛事。它吸引了 NBA（北美国家篮球协会）之外的大多数最优秀的外国球员，也加速了西班牙球员的发展，这些球员最终为西班牙国家队的成功做出了贡献。然而，体育界的这种卓越表现并没有得到球队和球员产生的数据（即所谓的统计数据）的先进处理。相反，对这些数据的使用还很初级。本刊早前于 2020 年发表的一篇文章介绍了首个开放式网络应用程序，用于交互式可视化包括 ACB 在内的三项欧洲赛事的盒式比分数据。盒式比分数据指的是比赛结束后提供的数据。基于同样的灵感，这项新的研究旨在介绍使用更先进数据开展的工作，即在比赛进行过程中提供的逐场比赛数据。这类数据能让我们更深入地了解篮球比赛的表现，提供盒装比分数据无法显示的信息。我们开发了一个新的仪表板，可以从多个不同的新角度分析逐场比赛数据。此外，还介绍了一个综合数据平台，其中包括 ACB 盒装比分和逐场比赛数据的可视化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.