Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Computing | en_US |
| dc.creator | Liu, Zhen | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/6413 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Data mining in soccer match | en_US |
| dcterms.abstract | With the rapid development of techniques of video tracking and detecting object, we could get considerable data of sport games. In recent years, soccer match as an ideal research object has been applied to test these techniques; today, we could find several statistic data from Internet easily than ever before. However, how to discover more useful, meaningful and valued information from them has become a new question. This paper provides a new method to analyze soccer match and to mine the information stated above. Identifying key player and key group as two important parts in our paper, which are also the popular topics in social network analysis, so we decide to use SNA as basic theory approach to address them. However, after comparing and discussing the existing SNA methods, we find that they might not be suitable for soccer-based social network defined by us, for this reason, we explore a new algorithm based on information theory to help us to identify key player in soccer game. Difference from other criteria which are used to divide the whole network into small groups, we design a novel method that not only focuses on inner connectivity of key group but also takes account into others. A new concept proposed by us is key destroyer, this player as a player of opponent could affect on effect of passing the ball of our team. To solve it, we employ a classic data mining algorithm named KNN. In future works, we discuss some potential approaches which might be appropriate our research firstly, and whether our algorithm could be applied to analyze other sport games. | en_US |
| dcterms.extent | vii, 63 leaves : col. ill. ; 30 cm. | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2011 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Data mining. | en_US |
| dcterms.LCSH | Soccer -- Statistics. | en_US |
| dcterms.LCSH | Social networks. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b24735875.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.67 MB | Adobe PDF | View/Open |
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