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題名 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
作者 邱楚翔
Chiu, Chu Hsiang
貢獻者 沈錳坤
Shan, Man Kwan
邱楚翔
Chiu, Chu Hsiang
關鍵詞 資料探勘
團隊表現預測
籃球
Data mining
Team performance prediction
Basketball
日期 2014
上傳時間 2016-05-11
摘要 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.
參考文獻 [1] H. Abbot, “Bad use of statistics is killing Anderson Varejão,” True Hoop, November 2007.
     [2] M. D. Akers, S. Wolff, and T. Buttress, “An empirical examination of the factors affecting the success of NCAA Division I College Basketball teams,” Journal of Business and Economic Studies, 1, 57-71, 1991.
     [3] E. Ben-Naim, F. Vazquez. and S. Redner, “Parity and predictability of competitions,” Journal of Quantitative Analysis in Sports, 2(4):1, 2007.
     [4] I. S. Bhandari, E. Colet, J. Parker, Z. Pines, R. Pratap, and K. Ramanujam, “Advanced scout: Data mining and knowledge discovery in NBA data,” Data Mining and Knowledge Discovery, 1(1): 121- 125, 1997.
     [5] C.-C. Chang and C.-J. Lin. “LIBSVM: a library for support vector machines,” 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
     [6] J. N. Cummings, and R. Cross, “Structural properties of work groups and their consequences for performance,” Social Networks 25(3): 197-210, 2003.
     [7] L.C. Freeman, “Centrality in Social Networks Conceptual Clarification,” Social Networks, 1, 1979.
     [8] M. Girvan, and M. E. J. Newman, "Community structure in social and biological networks," Proceedings of the National Academy of Sciences, 2002.
     [9] Y. de Saá Guerra, J. M. González, S. S. Montesdeocaa, D. R. Ruiz,, N. Arjonilla-López, J. M. García-Manso, “Basketball scoring in NBA games: an example of complexity,” Journal of Systems Science and Complexity 26(1), 94–103, 2013.
     [10] Y. de Saá Guerra, J. M. González, S. S. Montesdeocaa, D. R. Ruiz, A. García-Rodríguez, and J.M. García-Manso, “A model for competitiveness level analysis in sports competitions: Application to basketball,” Physica A: Statistical Mechanics and its Applications 391(10):2997-3004, 2012.
     [11] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. “The WEKA data mining software: An update,” SIGKDD Explorations, 11, 2009.
     [12] J. Hollinger, “The Player Efficiency Rating,” 2009.
     [13] S. J. Ibañez, J. Sampaio, S. Feu, A. Lorenzo, M. A. Gomez, and E. Ortega,
     “Basketball game-related statistics that discriminate between teams` season-long success,” European Journal of Sport Science 8(6), 2008.
     [14] S. Ilardi, “Adjusted plus-minus: An idea whose time has come,“ 82game.,com, October 2007.
     [15] G. Kalna and D. J. Higham, "A clustering coefficient for weighted networks, with application to gene expression data," AI Communications, 20(4):263–271, 2007.
     [16] A. Karipidis, P. Fotinakis, K. Taxildaris, and J. Fatouros, “Factors characterising a successful performance in basketball,” Journal of Human Movement Studies, 41, 385-397, 2001,
     [17] B. Kozar, R. E. Vaughn, K. E. Whitfield, R. H. Lord, and B. Dye, “Importance of free-throws at various stages of basketball games,” Perceptual and Motor Skills, 78, 243-248, 1994.
     [18] J. Kubatko, D. Oliver, K. Pelton, and D. Rosenbaum, “A starting point for analyzing basketball statistics,” Journal of Quantitative Analysis in Sports, 3, 1-22, 2007.
     [19] J. Leskovec, D. Huttenlocher, and J. Kleinberg, “Signed Networks in Social Media,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2010.
     [20] B. Loeffelholz, E. Bednar, and K. W. Bauer, “Predicting NBA Games Using Neural Networks,” Journal of Quantitative Analysis in Sports, 5(1), 2009.
     [21] S. Milgram, “The small world problem,” Psychology Today, 1:60–67, 1967.
     [22] M. E. J. Newman, “Modularity and community structure in networks,” Proceedings of the National Academy of Sciences, 2006.
     [23] R. N. Onody, and P. A. de Castro, “Complex network study of brazilian soccer players,” Physical Review E, 70:037103, 2004.
     [24] R. Pastor-Satorras, A. Vazquez, and A. Vespignani, “Dynamical and correlation properties of the Internet,” Physical Review Letter, 87:258701, 2001.
     [25] K. Pelton, "Introducing SCHOENE: Our NBA Projection System," Basketball Prospectus, 2008.
     [26] J. Saramäki, M. Kivelä, J. Onnela, K. Kaski, and J. Kertész. “Generalizations of the clustering coefficient to weighted complex networks,” Physical Review E, 75: 027105, 2007.
     [27] C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal 27, 379-423, 1948.
     [28] K. J. Shim, R. Sharan, J. Srivastava, ”Player Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs),” 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD, 2010.
     [29] K. J. Shim, J. Srivastava, "Team Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs)," Proceedings of the 2010 IEEE Social Computing (SocialCom-10), 2010.
     [30] N. Silver, "Introducing PECOTA," Baseball Prospectus, 2003.
     [31] B. Skinner, “The price of anarchy in basketball,” Journal of Quantitative Analysis in Sports, 2010.
     [32] A. Smedlund, “Characteristics of routine, development and idea networks in teams,” Team Performance Management, 16(1/2), 95-117, 2010.
     [33] C. Spearman, “The Proof and Measurement of Association Between Two Things,” American Journal of Psychology, 15, 72-101, 1904.
     [34] P. O. S. Vaz de Melo, V. A. F. Almeida, A. A. F. Loureiro, “Can complex network metrics predict the behaviour of NBA teams?,” Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.695–703, 2008.
     [35] P. O. S. Vaz de Melo, V. A. F. Almeida, A. A. F. Loureiro, C. Faloutsos, “Forecasting in the NBA and Other Team Sports: Network Effects in Action,” ACM Transactions on Knowledge Discovery from Data, Vol. 6, No. 3, 2012.
     [36] A. Veloso, W. Meira, and M. J. Zaki, "Lazy associative classification," IEEE International Conference on Data Mining, 645–654, 2006.
     [37] D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature 393, 440–442, 1998.
     [38] L. You, “Understanding the Social Network in Cooperative Games”, 2012
     [39] The official site of the NBA, http://www.nba.com/.
     [40] The Basketball-reference website, http://www.basketball-reference.com/.
     [41] ESPN website, http://espn.go.com/.
描述 碩士
國立政治大學
資訊科學學系
101753026
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101753026
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Shan, Man Kwanen_US
dc.contributor.author (作者) 邱楚翔zh_TW
dc.contributor.author (作者) Chiu, Chu Hsiangen_US
dc.creator (作者) 邱楚翔zh_TW
dc.creator (作者) Chiu, Chu Hsiangen_US
dc.date (日期) 2014en_US
dc.date.accessioned 2016-05-11-
dc.date.available 2016-05-11-
dc.date.issued (上傳時間) 2016-05-11-
dc.identifier (其他 識別碼) G0101753026en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/96376-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 101753026zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 第一章 緒論 1
     1.1 研究背景 1
     1.2 研究動機與目的 4
     1.3 論文架構 7
     第二章 文獻探討 10
     2.1 團隊表現分析 10
     2.2 籃球團隊表現分析與預測 13
     第三章 團隊表現績效預測 19
     3.1 研究架構 20
     3.2 團隊實力特徵 22
     3.3 團隊風格特徵 23
     3.4 團隊合作特徵 24
     3.5 預測方法 28
     第四章 以NBA籃球為例的團隊表現績效預測 30
     4.1 NBA歷史簡介 30
     4.2 由團隊成員個人表現探勘團隊實力特徵 32
     4.3 探勘團隊風格特徵 39
     4.4 由團隊成員之間合作強弱探勘團隊合作特徵 43
     4.5 NBA團隊表現預測方法 52
     4.5.1對戰勝負預測 53
     4.5.2球隊排名預測 54
     第五章 實驗與評估 55
     5.1 資料與分析相關工具 55
     5.1.1 資料來源 55
     5.1.2 資料篩選 59
     5.1.3 相關工具 60
     5.2 實驗與結果 64
     5.2.1 參數的調整與設定 64
     5.2.2 不同特徵對預測結果的影響 65
     5.2.3 不同分類器的預測結果比較 67
     5.2.4 對戰勝負預測與團隊排名預測實驗結果 69
     5.2.5與其他預測模型的比較 71
     第六章 結論與討論 73
     參考文獻 75
     附錄一 NBA球隊 79		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101753026en_US
dc.subject (關鍵詞) 資料探勘zh_TW
dc.subject (關鍵詞) 團隊表現預測zh_TW
dc.subject (關鍵詞) 籃球zh_TW
dc.subject (關鍵詞) Data miningen_US
dc.subject (關鍵詞) Team performance predictionen_US
dc.subject (關鍵詞) Basketballen_US
dc.title (題名) 團隊表現績效預測：以NBA籃球運動為例zh_TW
dc.title (題名) Team Performance Prediction in Cooperative Game: Using NBA as an Exampleen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] H. Abbot, “Bad use of statistics is killing Anderson Varejão,” True Hoop, November 2007.
     [2] M. D. Akers, S. Wolff, and T. Buttress, “An empirical examination of the factors affecting the success of NCAA Division I College Basketball teams,” Journal of Business and Economic Studies, 1, 57-71, 1991.
     [3] E. Ben-Naim, F. Vazquez. and S. Redner, “Parity and predictability of competitions,” Journal of Quantitative Analysis in Sports, 2(4):1, 2007.
     [4] I. S. Bhandari, E. Colet, J. Parker, Z. Pines, R. Pratap, and K. Ramanujam, “Advanced scout: Data mining and knowledge discovery in NBA data,” Data Mining and Knowledge Discovery, 1(1): 121- 125, 1997.
     [5] C.-C. Chang and C.-J. Lin. “LIBSVM: a library for support vector machines,” 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
     [6] J. N. Cummings, and R. Cross, “Structural properties of work groups and their consequences for performance,” Social Networks 25(3): 197-210, 2003.
     [7] L.C. Freeman, “Centrality in Social Networks Conceptual Clarification,” Social Networks, 1, 1979.
     [8] M. Girvan, and M. E. J. Newman, "Community structure in social and biological networks," Proceedings of the National Academy of Sciences, 2002.
     [9] Y. de Saá Guerra, J. M. González, S. S. Montesdeocaa, D. R. Ruiz,, N. Arjonilla-López, J. M. García-Manso, “Basketball scoring in NBA games: an example of complexity,” Journal of Systems Science and Complexity 26(1), 94–103, 2013.
     [10] Y. de Saá Guerra, J. M. González, S. S. Montesdeocaa, D. R. Ruiz, A. García-Rodríguez, and J.M. García-Manso, “A model for competitiveness level analysis in sports competitions: Application to basketball,” Physica A: Statistical Mechanics and its Applications 391(10):2997-3004, 2012.
     [11] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. “The WEKA data mining software: An update,” SIGKDD Explorations, 11, 2009.
     [12] J. Hollinger, “The Player Efficiency Rating,” 2009.
     [13] S. J. Ibañez, J. Sampaio, S. Feu, A. Lorenzo, M. A. Gomez, and E. Ortega,
     “Basketball game-related statistics that discriminate between teams` season-long success,” European Journal of Sport Science 8(6), 2008.
     [14] S. Ilardi, “Adjusted plus-minus: An idea whose time has come,“ 82game.,com, October 2007.
     [15] G. Kalna and D. J. Higham, "A clustering coefficient for weighted networks, with application to gene expression data," AI Communications, 20(4):263–271, 2007.
     [16] A. Karipidis, P. Fotinakis, K. Taxildaris, and J. Fatouros, “Factors characterising a successful performance in basketball,” Journal of Human Movement Studies, 41, 385-397, 2001,
     [17] B. Kozar, R. E. Vaughn, K. E. Whitfield, R. H. Lord, and B. Dye, “Importance of free-throws at various stages of basketball games,” Perceptual and Motor Skills, 78, 243-248, 1994.
     [18] J. Kubatko, D. Oliver, K. Pelton, and D. Rosenbaum, “A starting point for analyzing basketball statistics,” Journal of Quantitative Analysis in Sports, 3, 1-22, 2007.
     [19] J. Leskovec, D. Huttenlocher, and J. Kleinberg, “Signed Networks in Social Media,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2010.
     [20] B. Loeffelholz, E. Bednar, and K. W. Bauer, “Predicting NBA Games Using Neural Networks,” Journal of Quantitative Analysis in Sports, 5(1), 2009.
     [21] S. Milgram, “The small world problem,” Psychology Today, 1:60–67, 1967.
     [22] M. E. J. Newman, “Modularity and community structure in networks,” Proceedings of the National Academy of Sciences, 2006.
     [23] R. N. Onody, and P. A. de Castro, “Complex network study of brazilian soccer players,” Physical Review E, 70:037103, 2004.
     [24] R. Pastor-Satorras, A. Vazquez, and A. Vespignani, “Dynamical and correlation properties of the Internet,” Physical Review Letter, 87:258701, 2001.
     [25] K. Pelton, "Introducing SCHOENE: Our NBA Projection System," Basketball Prospectus, 2008.
     [26] J. Saramäki, M. Kivelä, J. Onnela, K. Kaski, and J. Kertész. “Generalizations of the clustering coefficient to weighted complex networks,” Physical Review E, 75: 027105, 2007.
     [27] C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal 27, 379-423, 1948.
     [28] K. J. Shim, R. Sharan, J. Srivastava, ”Player Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs),” 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD, 2010.
     [29] K. J. Shim, J. Srivastava, "Team Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs)," Proceedings of the 2010 IEEE Social Computing (SocialCom-10), 2010.
     [30] N. Silver, "Introducing PECOTA," Baseball Prospectus, 2003.
     [31] B. Skinner, “The price of anarchy in basketball,” Journal of Quantitative Analysis in Sports, 2010.
     [32] A. Smedlund, “Characteristics of routine, development and idea networks in teams,” Team Performance Management, 16(1/2), 95-117, 2010.
     [33] C. Spearman, “The Proof and Measurement of Association Between Two Things,” American Journal of Psychology, 15, 72-101, 1904.
     [34] P. O. S. Vaz de Melo, V. A. F. Almeida, A. A. F. Loureiro, “Can complex network metrics predict the behaviour of NBA teams?,” Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.695–703, 2008.
     [35] P. O. S. Vaz de Melo, V. A. F. Almeida, A. A. F. Loureiro, C. Faloutsos, “Forecasting in the NBA and Other Team Sports: Network Effects in Action,” ACM Transactions on Knowledge Discovery from Data, Vol. 6, No. 3, 2012.
     [36] A. Veloso, W. Meira, and M. J. Zaki, "Lazy associative classification," IEEE International Conference on Data Mining, 645–654, 2006.
     [37] D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature 393, 440–442, 1998.
     [38] L. You, “Understanding the Social Network in Cooperative Games”, 2012
     [39] The official site of the NBA, http://www.nba.com/.
     [40] The Basketball-reference website, http://www.basketball-reference.com/.
     [41] ESPN website, http://espn.go.com/.		zh_TW