Sports Big Data: Management, Analysis, Applications, and Challenges

被引:31
作者
Bai, Zhongbo [1 ]
Bai, Xiaomei [2 ]
机构
[1] Anshan Normal Univ, Sch Sports Sci, Anshan, Peoples R China
[2] Anshan Normal Univ, Ctr Comp, Anshan, Peoples R China
关键词
SOCIAL NETWORK ANALYSIS; PARTICIPATION; TAXONOMY; TEAM; IOT;
D O I
10.1155/2021/6676297
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first introduce the background of sports big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes' performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions.
引用
收藏
页数:11
相关论文
共 86 条
[1]   Efficient Machine Learning for Big Data: A Review [J].
Al-Jarrah, Omar Y. ;
Yoo, Paul D. ;
Muhaidat, Sami ;
Karagiannidis, George K. ;
Taha, Kamal .
BIG DATA RESEARCH, 2015, 2 (03) :87-93
[2]  
[Anonymous], 2015, DeepDive: A data management system for automatic knowledge base construction
[3]  
Baert S, 2019, PLOS ONE, V14
[4]   The validity of small-sided games in predicting 11-vs-11 soccer game performance [J].
Bergkamp, Tom L. G. ;
den Hartigh, Ruud J. R. ;
Frencken, Wouter G. P. ;
Niessen, A. Susan M. ;
Meijer, Rob R. .
PLOS ONE, 2020, 15 (09)
[5]   Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights [J].
Brooks, Joel ;
Kerr, Matthew ;
Guttag, John .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :49-55
[6]  
Bunker RP, 2019, Applied Computing and Informatics, V15, P27, DOI DOI 10.1016/J.ACI.2017.09.005
[7]   XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794
[8]  
Cheriet M, 2010, P INT C INT NETW INT, P418
[9]   Towards smart-data: Improving predictive accuracy in long-term football team performance [J].
Constantinou, Anthony ;
Fenton, Norman .
KNOWLEDGE-BASED SYSTEMS, 2017, 124 :93-104
[10]   Techniques and applications for soccer video analysis: A survey [J].
Cuevas, Carlos ;
Quilon, Daniel ;
Garcia, Narciso .
MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (39-40) :29685-29721