Learning to Rank Sports Teams on a Graph

doi:10.3390/app10175833

	Learning to Rank Sports Teams on a Graph
	Shi, Jian 2,3; Tian, Xin-Yu 1,3
刊名	APPLIED SCIENCES-BASEL
	2020-09-01
卷号	10 期号:17 页码:10
关键词	sports ranking graph model prediction basketball
DOI	10.3390/app10175833
英文摘要	To improve the prediction ability of ranking models in sports, a generalized PageRank model is introduced. In the model, a game graph is constructed from the perspective of Bayesian correction with game results. In the graph, nodes represent teams, and a link function is used to synthesize the information of each game to calculate the weight on the graph's edge. The parameters of the model are estimated by minimizing the loss function, which measures the gap between the predicted rank obtained by the model and the actual rank. The application to the National Basketball Association (NBA) data shows that the proposed model can achieve better prediction performance than the existing ranking models.
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000570141600001
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/52187]
专题	中国科学院数学与系统科学研究院
通讯作者	Tian, Xin-Yu
作者单位	1.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 2.Huaqiao Univ, Sch Stat, Xiamen 361021, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shi, Jian,Tian, Xin-Yu. Learning to Rank Sports Teams on a Graph[J]. APPLIED SCIENCES-BASEL,2020,10(17):10.
APA	Shi, Jian,&Tian, Xin-Yu.(2020).Learning to Rank Sports Teams on a Graph.APPLIED SCIENCES-BASEL,10(17),10.
MLA	Shi, Jian,et al."Learning to Rank Sports Teams on a Graph".APPLIED SCIENCES-BASEL 10.17(2020):10.