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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/96376
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/96376


    Title: 團隊表現績效預測:以NBA籃球運動為例
    Team Performance Prediction in Cooperative Game: Using NBA as an Example
    Authors: 邱楚翔
    Chiu, Chu Hsiang
    Contributors: 沈錳坤
    Shan, Man Kwan
    邱楚翔
    Chiu, Chu Hsiang
    Keywords: 資料探勘
    團隊表現預測
    籃球
    Data mining
    Team performance prediction
    Basketball
    Date: 2014
    Issue Date: 2016-05-11
    Abstract: 團隊表現分析及預測是近年來資料探勘的研究熱門的領域之一,分析類型的研究主要在探討由多名成員形成的團隊,造成團隊表現優劣的原因;預測類型的研究則是透過過去的團隊表現進行學習,以預測未來團隊可能的表現。本研究目標在於探討具有團隊合作、雙方對抗的運動、遊戲項目中的團隊表現,並且進一步嘗試去預測團隊對抗的勝負。籃球運動是運動領域中,必須透過團隊合作與另一方團隊進行對抗的一種運動,同時也是世界上最流行的運動項目之一,因此本研究採用籃球運動作為團隊表現預測的目標,並以NBA聯盟作為研究對象。早期對於籃球領域的資料探勘(Data mining)主要以統計學習(statistical learning)的方式進行。雖然歷史悠久的美國NBA籃球比賽擁有豐富的數據紀錄帶給學者們很多的研究機會,然而甚少研究有關於球員之間彼此合作互動關聯性的深入探討。本篇論文使用了統計學習的技術,並加入以社會網絡的理論去分析團隊成員的合作關係,以NBA籃球聯盟為例,對聯盟中的團隊表現進行研究,利用團隊成員的個人能力、團隊的風格、以及團隊中球員彼此的合作關係作為依據,進行三種不同層面的籃球運動團隊表現預測,分別是團隊排名預測、任意對戰組合的團隊勝負預測、以及特定成員組合的表現預測。此研究的實驗證實了結合統計學習與網絡分析能夠具有更好的預測效果,並且我們也與過去類似的研究進行比較,本研究在預測表現上具有較良好的準確度。本研究以NBA籃球聯盟作為例子,除了建立起精確的預測模型之外,我們更期望能夠從研究過程中發掘更多潛藏在比賽數據之外的資訊,諸如球星之間的合作關係等等。
    Recently much work has been done on team formation in social network mining. Little attention has been paid to the team performance prediction problem. Given the game logs along with the performance information for each team, the task of team performance prediction is to predict the performance for a specified set of team members. A classification-based approach is proposed in this thesis. Three types of features are considered, namely the team strength feature, the team style feature and the team cooperation feature. The experiments which take the National Basketball Association as an example show that the proposed approach has good prediction accuracy and is superior to existing approach.
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    Description: 碩士
    國立政治大學
    資訊科學學系
    101753026
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0101753026
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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