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  1. NTU Theses and Dissertations Repository
  2. 生物資源暨農學院
  3. 農藝學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71167
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor蔡政安
dc.contributor.authorPin-Huai Huangen
dc.contributor.author黃品懷zh_TW
dc.date.accessioned2021-06-17T04:56:31Z-
dc.date.available2021-08-01
dc.date.copyright2018-08-01
dc.date.issued2018
dc.date.submitted2018-07-27
dc.identifier.citation[1] Bashuk, M. (2012). Using Cumulative Win Probability to Predict NCAA Basketball Performance. In Sloan Sports Analytics Conference, pp. 1–10.
[2] Berri, D. J., Brook, S. L., & Schmidt, M. B. (2007). Does one simply need to score to score? International Journal of Sport Finance, 2(4), 190-205.
[3] Berri, D., Schmidt, M., & Brook, S. (2006). The wages of wins: Taking measure of the many myths in modern sport. Stanford University Press.
[4] Deshpande, S. K., & Jensen, S. T. (2016). Estimating an NBA Player’s Impact on is Team’s Chances of Winning. Journal of Quantitative Analysis in Sports,12(2), 51-72
[5] Golliver, B. (2016) Draymond Green stakes his claim as the NBA’s best all-around player. Retrieved 15 December 2017, from https://www.si.com/nba/2016/05/02/draymond-green-golden-state-warriors-nba-playoffs-lebron-james
[6] Hastie, T., Tibshirani, R, & Friedman, J. (2009). The elements of statistical learning. New York: Springer series in statistics.
[7] Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
[8] Hollinger J. (2009). Projecting PER for the coming season. Retrieved 24 October 2017, from http://www.espn.com/nba/columns/story?columnist=hollinger_john&id=3055049
[9] Hollinger, J. (2005). Pro basketball forecast: 2005-2006. Dulles, VA: Potomac.
[10] Iradi, S. & Barzilai, A. (2008). Adjusted plus-minus ratings: new and improved for 2007-2008. Retrieved 23 July 2017, from http://www.82games.com/ilardi2.htm
[11] James, B., & Henzler, J. (2002). Win shares. Stats Inc. IL: Chicago.
[12] NBA win shares. Retrieved 15 January 2018, from https://www.basketball-reference.com/about/ws.html
[13] Kubatko, J., Oliver, D., Pelton, K., and Rosenbaum, D. T. (2007). A Starting Point for Analyzing Basketball Statistics. Journal of Quantitative Analysis in Sports, 3(3). doi: 10.2202/1559-0410.1070.
[14] O’Brien, D. (2015). Top candidates for the 2015 NBA most improved player so far. Retrieved 17 April 2018, from https://bleacherreport.com/articles/2347178-top-candidates-for-the-2015-nba-most-improved-player-so-far#slide0
[15] Oliver, D. (2004). Basketball on paper: rules and tools for performance analysis. Potomac Books, Inc..
[16] Omidiran, D. (2011). A new look at adjusted plus/minus for basketball analysis. In MIT Sloan Sports Analytics Conference.
[17] Rosenbaum, D. T. (2004). Measuring how NBA players help their teams win. Retrieved 18 January 2018, from http://www.82games.com/comm30.htm
[18] Saenz M. (2016). NBA: Seven 2015-16 most improved player of the year candidates. Retrieved 17 April 2018 from https://sircharlesincharge.com/2016/04/08/nba-seven-2015-16-comeback-player-of-the-year-candidates/
[19] Sill, J. (2010). Improved NBA adjusted +/- using regularization and out-of-sample testing. In Proceedings of the 2010 MIT Sloan Sports Analytics Conference.
[20] Stern, H. (1994). A Brownian Motion Model for the Progress of Sports Scores. Journal of the American Statistical Association 89, 1128–1134.
[21] Suchman, A. (2014). Explaining ESPN’s real plus-minus. Retrieved 10 July 2017, from https://cornerthreehoops.wordpress.com/2014/04/17/explaining-espns-real-plus-minus/
[22] Ilardi S. (2014). The Next Big Thing: Real Plus-Minus. Retrieved 10 July 2017, from http://www.espn.com/nba/story/_/id/10740818/introducing-real-plus-minus
[23] Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267-288.
[24] Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.
[25] Lunn, D., Spiegelhalter, D., Thomas, A., & Best, N. (2009). The BUGS project: evolution, critique and future directions. Statistics in medicine, 28(25), 3049-3067.
[26] NBA Dictionary. Retrieved 2 July 2018, from https://www.sportingcharts.com/nba/dictionary/
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71167-
dc.description.abstract由於籃球比賽相較於棒球是團隊合作成分較大的球賽類型,在評估球員個人的表現時會變得比較困難。使用正負值為基礎的方法可以將球員對球隊及隊友的各種可能影響考慮進去,而不是只有參考球員個人的表現。本篇文章使用貝氏的加權回歸線性迴歸模型(BWLR),找出個別球員以及個別球隊對每次球權結果的影響,包含了進攻以及防守兩部分,並且利用實證資料估計在不同比分及比賽時間之下,每次球權結果的變異,進而將”垃圾時間”效應加入我們的模型。交叉驗證的結果顯示貝氏加權線性迴歸模型在每次球權及每場比分差的均方根誤差均較未加權的模型小,而預測每場比賽比分差的相關係數以及比賽勝負的準確率也提高,代表此模型有抓到垃圾時間的分數變異。另外我們將BWLR與Lasso, Ridge, Elastic net的表現作比較,發現BWLR雖在每次球權及每場比分差的均方根誤差較大,但其預測最後比分差的相關係數以及最後比賽勝負的準確率較高。
同時我們也可以利用BWLR找出在球隊之中,哪個球員才是貢獻最大的,我們發現在2014’-15’賽季中,冠軍隊伍金州勇士隊中對球隊在攻守兩端幫助最大的是Draymond Green,而不是名聲較高的Stephen Curry。
zh_TW
dc.description.abstractComparing to baseball games, basketball games are more about team works. It makes evaluating the performance of an individual player or individual team more difficult. The plus-minus based models could take every possible influence of a player on his team or teammates into account, rather than only considering the player’s personal performance. In this thesis, we applied Bayesian Weighted Linear Regression (BWLR) to evaluate the player performance and team effect on the points per possession (PPP), including both offensive and defensive parts. In addition, we tried to consider the garbage-time effect into this model by estimating the variance of points per possession of empirical data under different points differential and game time. The cross-validation results showed that root-mean-squared-error (RMSE) of possession and game points of BWLR were smaller, and the correlation of predicted game points and accuracy of predicted winning results were higher than the unweighted model, which meant that the BWLR model did catch the variance of the points per possession in garbage time. We also compared BWLR to shrinkage methods including lasso, ridge, and elastic net. Although the RMSE of possession and game points of BWLR were larger, the correlation of predicted game points and accuracy of predicted winning results were higher than shrinkage methods.
We can use BWLR to find which player has the most important contribution in a team. We found that in 2014’-15’ season, the most contributed player in the champion team, Golden State Warriors, was not the most famous one, Stephen Curry, but Draymond Green, who performed well in both offensive and defensive ratings.
en
dc.description.provenanceMade available in DSpace on 2021-06-17T04:56:31Z (GMT). No. of bitstreams: 1
ntu-107-R03621206-1.pdf: 2559763 bytes, checksum: 502d2f5229d68bf1dd1b0ae4b6f38be1 (MD5)
Previous issue date: 2018
en
dc.description.tableofcontents誌謝 i
中文摘要 ii
Abstract iii
Content v
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
Background 1
Literature review 2
Objectives 7
Chapter 2 Materials and Methods 11
Dataset and Preprocess 11
Method 13
Shrinkage methods – glmnet 13
Bayesian hierarchical model & glmnet 15
BWLR 17
Chapter 3 Results 24
Chapter 4 Conclusion and Discussion 52
Reference 54
Appendix A 58
Appendix B 77
dc.language.isoen
dc.subject球員評估zh_TW
dc.subject貝氏加權線性迴歸zh_TW
dc.subject正規化調整正負值zh_TW
dc.subjectBayesian Weighted Linear Regression (BWLR)en
dc.subjectevaluate the performance of playersen
dc.subjectRegularized Adjusted Plus-Minusen
dc.title評估NBA球員表現的統計方法zh_TW
dc.titleA Statistical Method to Evaluate the Performance of NBA Playersen
dc.typeThesis
dc.date.schoolyear106-2
dc.description.degree碩士
dc.contributor.oralexamcommittee劉仁沛,連家瑩
dc.subject.keyword貝氏加權線性迴歸,球員評估,正規化調整正負值,zh_TW
dc.subject.keywordBayesian Weighted Linear Regression (BWLR),evaluate the performance of players,Regularized Adjusted Plus-Minus,en
dc.relation.page112
dc.identifier.doi10.6342/NTU201801631
dc.rights.note有償授權
dc.date.accepted2018-07-27
dc.contributor.author-college生物資源暨農學院zh_TW
dc.contributor.author-dept農藝學研究所zh_TW
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