政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/96376

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/96376

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113451/144438 (79%)
Visitors : 51281401 Online Users : 864

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Informatics > Department of Computer Science > Theses > 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.
Reference:	[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/.
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:	[Department of Computer Science ] Theses

Files in This Item:

File	Size	Format
index.html	0Kb	HTML2	463	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback