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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/97125


    Title: NBA球員表現與薪資關聯性之分析
    An Analysis of the Relationship between Performance and Salary of NBA Players
    Authors: 朱炫光
    Contributors: 徐國偉
    朱炫光
    Keywords: NBA(國家籃球協會)
    資料探勘
    特徵選取
    NBA(National Baseball Association)
    data mining
    feature selection
    Date: 2016
    Issue Date: 2016-06-01 13:57:58 (UTC+8)
    Abstract: 本研究目標在於探討以資訊科技的角度,將NBA(國家籃球協會)球員的數據依統計與資料探勘方法進行分析,以進一步探討球員表現及薪資變化之影響。NBA在世界各國有廣大的球迷,進而吸引各國球員擠身投入,希望能提升自身球技水平與此聯盟球員一同競技,另一方面,也促進了球員本身及其相關周邊的消費市場,造就球團和球員商業價值不斷提升。由於NBA有薪資上限和豪華稅等相關規定,球團在與球員簽訂合約時,除了薪資金額外,仍會進一步考量球員本身球技、相關位置、年齡、功能性及過往績效,此時,如何進行相關的數據分析,進而以經濟實惠的方式進行球員的尋找和合約簽訂,會是一個很重要的關鍵。在熱門的NBA運動中,每場比賽產生出大量的數據,並且有豐富的歷史資料,可激勵我們去發掘出隱含的知識,使用資料探勘進行個人表現與薪資合約相關分析,是較為少見的,所以,我們希望運用此一技術,經過特徵選取,找出有比較直接相關的特徵,再利用決策樹、支援向量機和貝式分類器,對資料進行分類,期望能夠從研究過程中,利用球員各項不同的統計數據指標,進一步發現球員表現和薪資之間的相關性。
    Reference: [1] http://www.basketball-reference.com/
    [2] 王浚宇,"NBA 外籍球員薪資與效率衡量之關聯性研究."政治大學會計研究所學位論文 (2006): 1-51.
    [3] 邱咏平,"球員在合約年及非合約年績效—以 NBA 為例."政治大學會計研究所學位論文 (2010): 1-59.
    [4] 王彥智,"以 B-Spline 方法預測 NBA 冠軍". 政治大學統計研究所學位論文 (2012): 1-32.
    [5] 邱楚翔,"團隊表現績效預測: 以 NBA 籃球運動為例." 政治大學資訊科學學系學位論文 (2012): 1-80.
    [6] Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. "From data mining to knowledge discovery in databases." AI Magazine, 17.3 (1996): 37.
    [7] https://en.wikipedia.org/wiki/Statistical_significance
    [8] https://en.wikipedia.org/wiki/John_Hollinger
    [9] https://en.wikipedia.org/wiki/Data_mining
    [10] Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques: concepts and techniques. Elsevier, 2011.
    [11] Kohavi, Ron, and George H. John. "Wrappers for feature subset selection."Artificial Intelligence,97.1 (1997): 273-324.
    [12] Guyon, Isabelle, and André Elisseeff. "An introduction to variable and feature selection." The Journal of Machine Learning Research,3 (2003): 1157-1182.
    [13] Kira, Kenji, and Larry A. Rendell. "A practical approach to feature selection." Proceedings of the Ninth International Workshop on Machine Learning. 1992.
    [14] Yang, Yiming, and Jan O. Pedersen. "A comparative study on feature selection in text categorization." International Conference on Machine Learning. Vol. 97. 1997.
    [15] Jain, Anil, and Douglas Zongker. "Feature selection: Evaluation, application, and small sample performance." IEEE Transactions on Pattern Analysis and Machine Intelligence,19.2 (1997): 153-158.
    [16] Dash, Manoranjan, and Huan Liu. "Feature selection for classification." Intelligent Data Analysis, 1.1 (1997): 131-156.
    [17] J.Weston, S.Mukherjee, O.Chapelle, M.Pontil, T.Poggio, V.Vapnik. "Feature selection for SVMs." NIPS. Vol. 12. 2000.
    [18] Liu, Huan, et al. "Feature Selection: An Ever Evolving Frontier in Data Mining."FSDM, 10 (2010): 4-13.
    [19] 朱啟源,資料前處理之研究: 以基因演算法為例; Feature and Instance Selection Using Genetic Algorithms: An Empirical Study. 中央大學資訊管理學系學位論文 (2011): 1-62.
    [20] Haupt, Randy L., and Sue Ellen Haupt. Practical genetic algorithms. John Wiley & Sons, 2004.
    [21] Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. Data Mining and Knowledge Discovery. Springer US, 1998.
    [22] Michael, J. A., and S. Linoff Gordon. "Data mining technique: For marketing, sales and customer support." New York: John Wiley&Sons Inc. 445 (1997)
    [23] Cabena, Peter, et al. Discovering data mining: from concept to implementation. Prentice-Hall, Inc., 1998.
    [24] Safavian, S. Rasoul, and David Landgrebe. "A survey of decision tree classifier methodology." IEEE transactions on systems, man, and cybernetics, 21.3 (1991): 660-674.
    [25] 蔡佳玲, 洪新原, and 袁繼銓. "以決策樹模型探討未開立慢性病連續處方之影響因子." 資訊管理學報, 18.4 (2011): 139-164.
    [26] Fawcett, Tom. "An introduction to ROC analysis." Pattern recognition letters, 27.8 (2006): 861-874.
    [27] Ramaswamy, Sridhar, Rajeev Rastogi, and Kyuseok Shim. "Efficient algorithms for mining outliers from large data sets." ACM SIGMOD Record. Vol. 29. No. 2. ACM, 2000.
    [28] Na Wei, "Predicting the outcome of NBA playoffs using the naïve bayes algorithms." Department of Biomedical Engineering, College of Engineering, University of South Florida, Tampa, FL 33620, USA (2011).
    [29] Loeffelholz, Bernard, Earl Bednar, and Kenneth W. Bauer. "Predicting NBA games using neural networks." Journal of Quantitative Analysis in Sports, 5.1 (2009).
    [30] https://en.wikipedia.org/wiki/Ordinary_least_squares
    Description: 碩士
    國立政治大學
    資訊科學系碩士在職專班
    102971001
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0102971001
    Data Type: thesis
    Appears in Collections:[資訊科學系碩士在職專班] 學位論文

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