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題名 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作者 朱炫光 貢獻者 徐國偉
朱炫光關鍵詞 NBA(國家籃球協會)
資料探勘
特徵選取
NBA(National Baseball Association)
data mining
feature selection日期 2016 上傳時間 1-Jun-2016 13:57:58 (UTC+8) 摘要 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。 參考文獻 [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 描述 碩士
國立政治大學
資訊科學系碩士在職專班
102971001資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102971001 資料類型 thesis dc.contributor.advisor 徐國偉 zh_TW dc.contributor.author (Authors) 朱炫光 zh_TW dc.creator (作者) 朱炫光 zh_TW dc.date (日期) 2016 en_US dc.date.accessioned 1-Jun-2016 13:57:58 (UTC+8) - dc.date.available 1-Jun-2016 13:57:58 (UTC+8) - dc.date.issued (上傳時間) 1-Jun-2016 13:57:58 (UTC+8) - dc.identifier (Other Identifiers) G0102971001 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/97125 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系碩士在職專班 zh_TW dc.description (描述) 102971001 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.tableofcontents 目錄 i圖目錄 iii表目錄 iv第一章 緒論 11.1 研究背景 11.2 研究動機與目的 81.3 論文架構 9第二章 文獻探討 102.1 Data Mining起源和發展 102.2 NBA數據分析相關研究 12第三章 資料擷取與前置處理 163.1 工具說明 163.2 資料處理流程 173.3 資料統計分析 183.4 統計指標 213.2.1 Efficiency 213.2.2 GmSc 223.2.3 PER 233.2.4 ORtg 263.2.5 DRtg 293.2.6 AST_Per 313.2.7 STL_Per 313.2.8 BLK_Per 323.2.9 TOV_Per 323.2.10 TS_Per 333.2.11 FT_Per 333.2.12 Three_Per 333.2.13 DRB_Per 343.2.14 ORB_Per 343.2.15 TRB_Per 343.2.16 USG_Per 353.5 成績與薪資之關連性預測 35第四章 進階分析 434.1 薪資分級 434.2 特徵選取 444.3 分類 514.3.1 決策樹 524.3.2 支援向量機 524.3.3 貝式分類 534.3.4 隨機森林 534.3.5 交叉驗證 544.3.6 分類結果統計指標 544.4 實驗結果 57第五章 結論與未來展望 665.1 結論 665.2 未來展望 67附錄 參考文獻 69 zh_TW dc.format.extent 1783703 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102971001 en_US dc.subject (關鍵詞) NBA(國家籃球協會) zh_TW dc.subject (關鍵詞) 資料探勘 zh_TW dc.subject (關鍵詞) 特徵選取 zh_TW dc.subject (關鍵詞) NBA(National Baseball Association) en_US dc.subject (關鍵詞) data mining en_US dc.subject (關鍵詞) feature selection en_US dc.title (題名) NBA球員表現與薪資關聯性之分析 zh_TW dc.title (題名) An Analysis of the Relationship between Performance and Salary of NBA Players en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [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 zh_TW