Learning to Rank Sports Teams on a Graph
Shi, Jian1,2; Tian, Xin-Yu2,3
AbstractTo 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.
Keywordsports ranking graph model prediction basketball
Indexed BySCI
WOS Research AreaChemistry ; Engineering ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000570141600001
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Document Type期刊论文
Corresponding AuthorTian, Xin-Yu
Affiliation1.Huaqiao Univ, Sch Stat, Xiamen 361021, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
Recommended Citation
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.
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