職業籃球的致勝關鍵因素-以NBA為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：68

、訪客IP：3.17.16.39

姓名

周迺鈺(Nai-Yu Chou) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

職業籃球的致勝關鍵因素-以NBA為例

相關論文

★ 以第四方物流經營模式分析博連資訊科技股份有限公司	★ 探討虛擬環境下隱性協調在新產品導入之作用--以電子組裝業為例
★ 動態能力機會擷取機制之研究－以A公司為例	★ 探討以價值驅動之商業模式創新－以D公司為例
★ 物聯網行動支付之探討-以Apple Pay與支付寶錢包為例	★ 企業資訊方案行銷歷程之探討-以MES為例
★ B2C網路黏著度之探討-以博客來為例	★ 組織機制與吸收能力關係之研究－以新產品開發專案為例
★ Revisit the Concept of Exploration and Exploitation	★ 臺灣遠距醫療照護系統之發展及營運模式探討
★ 資訊系統與人力資訊科技資源對供應鏈績效影響之研究-買方依賴性的干擾效果	★ 資訊科技對知識創造影響之研究-探討社會鑲嵌的中介效果
★ 資訊科技對公司吸收能力影響之研究－以新產品開發專案為例	★ 探討買賣雙方鑲嵌關係影響交易績效之機制 ─新產品開發專案為例
★ 資訊技術運用與協調能力和即興能力對新產品開發績效之影響	★ 團隊組成多元性影響任務衝突機制之研究─ 以新產品開發專案團隊為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

近年，運動博奕事業盛行，高風險高報酬成為多數人的獲利方式，尤其是 NBA 聯盟的觀眾人數擴及全球、事業版圖跨越國界，對全世界運動有深深的影響，因此成為賭徒的主要目標。除此之外，也有許多研究者試圖預測結果和勝隊，因此有許多詳細比賽數據在網路上公開取用。而本篇論文為瞭解 NBA 主場球隊隊比賽內容並找尋規則，以 NBA 2012~2018 六年、30 隊的每場比賽詳細數據做分析，並以兩種演算法:決策樹 ( Decision Tree )，找到主場球隊比賽的勝場和敗場規則，並以隨機森林 (RandomForest)驗證能夠影響主場球隊勝場的變數之重要程度。以此預測比賽勝負並觀察勝負隊的贏球模式和輸球原因，從中找出贏球方程式，能在職業球隊選秀、比賽戰術運用、球員交易等不同面向，或者在一般觀賞或者運動彩券等不同領域有所應用。

摘要(英)

In the recent year, sport betting industry is more popular nowadays, and the way of high risk with high return become a main way investment for more and more people. Especially NBA, it is the main target for professional gambler because it attracts audience from all the world and its business territory has been all over the world. So, there are uncountable researchers trying to analyze and predict the winning team for personal or business. Moreover, there are a lot of data of detailed contents of games on websites for people to research and analyze. And, the dataset in this paper is from www.kaggle.com.tw. In this paper, I collect data of NBA 30-team-games from 2012 to 2018, analyzing by 2 algorithms. As the result, I found out the rule of winning of home team by decision tree and test the level of importance by random forest, trying to figure out the formula of winning and the reason of losing, also, predicting the result of games. In sum, the result of research could be used in a variety of fields, like draft, games tactics, players trading, watching games, and betting.

關鍵字(中)

★ NBA
★ 比賽預測
★ 決策樹
★ 隨機森林

關鍵字(英)

★ NBA
★ Game Prediction
★ Decision Tree
★ Ramdom Forest

論文目次

中文摘要 I
ABSTRACT II
目錄 III
圖目錄 V
表目錄 VI
壹、緒論 1
一、研究動機 1
二、研究目的 2
三、研究流程 2
貳、文獻回顧探討 4
一、研究內容簡介 4
二、數據於球賽應用之探討 4
參、研究方法 6
一、原始資料 6
二、資料清理 6
（一）扣除和比賽無相關數據 6
（二）扣除得分相關數據 6
（三）扣除公式統計數據 8
（四）以主場對戰客場為分析單位 9
（五）對戰數據前後相減 9
三、資料探勘 10
（一）決策樹 10
（二）隨機森林 10
（三）以混淆矩陣、指標檢測模型 10
肆、研究結果分析 11
一、決策樹結果 11
二、混淆矩陣-決策樹 13
三、模型檢測－決策樹 14
四、實際比賽紀錄和建議 15
五、隨機森林結果 18
（一）分析結果 18
（二）混淆矩陣－隨機森林 20
（三）隨機森林模型檢測 21
伍、研究限制與結論 22
一、研究限制 22
二、研究結果 23
參考文獻 25

參考文獻

[1] Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An Introduction to Decision Tree Modeling. Journal of Chemometrics: A Journal of the Chemometrics Society, 18(6), 275-285.
[2] Willoughby, K. A., & Kostuk, K. J. (2005). An Analysis of A Strategic Decision in The Sport of Curling. Decision Analysis, 2(1), 58-63.
[3] Vistro, D. M., Rasheed, F., & David, L. G. (2019). The Cricket Winner Prediction with Application of Machine Learning And Data Analytics. International Journal of Scientific & Technology Research, 8(09).
[4] Huang, J., & Hsu, H. J. (2020). Approximating Strike Zone Size And Shape for Baseball Umpires under Different Conditions. International Journal of Performance Analysis in Sport, 20(2), 133-149.
[5] Hsu, P. H., Galsanbadam, S., Yang, J. S., & Yang, C. Y. (2018). Evaluating Machine Learning Varieties for NBA Players′ Winning Contribution. In 2018 International Conference on System Science and Engineering (ICSSE) (pp. 1-6). IEEE.
[6] Oughali, M. S., Bahloul, M., & El Rahman, S. A. (2019). Analysis of NBA Players And Shot Prediction Using Random Forest and XGBoost Models. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1-5). IEEE.
[7] Entine, O. A., & Small, D. S. (2008). The Role of Rest in The NBA Home-court Advantage. Journal of Quantitative Analysis in Sports, 4(2).
[8] Zak, T. A., Huang, C. J., & Siegfried, J. J. (1979). Production Efficiency: The Case of Professional Basketball. Journal of Business, 379-392.
[9] Thabtah, F., Zhang, L., & Abdelhamid, N. (2019). NBA Game Result Prediction Using Feature Analysis And Machine Learning. Annals of Data Science, 6(1), 103-116.
[10] Wu, W. (2020). Injury Analysis Based on Machine Learning in NBA Data. Journal of Data Analysis and Information Processing, 8(4), 295-308.
[11] Soliman, G., Misbah, A., & Eldawlatly, S. (2017). Predicting All Star Player in The National Basketball Association Using Random Forest. In 2017 Intelligent Systems Conference (IntelliSys) (pp. 706-713). IEEE.
[12] Lakew, N. A., Zenebe, A., & Kirk Williams, I. I. (2020). Applying Machine Learning to Classify Players Importance in the NBA. National Association of Business, Economics and Technology, 147.
[13] Mueller, S. Q. (2020). Pre- And Within-Season Attendance Forecasting in Major League Baseball: a random forest approach. Applied Economics, 52(41), 4512-4528.
[14] Papadaki, I., & Tsagris, M. (2020). Are NBA Players Getting Paid According To Their Performance on Court?. arXiv preprint arXiv:2007.14694.
[15] Migliorati, M. (2020). Detecting Drivers of Basketball Successful Games: An Exploratory Study with Machine Learning Algorithms. Electronic Journal of Applied Statistical Analysis, 13(2), 454-473.
[16] Kohli, I. S. (2016). Finding Common Characteristics among NBA Playoff and Championship Teams: A Machine Learning Approach. arXiv preprint arXiv:1604.05266.
[17] Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. Hoboken,
[18] D.W. Hosmer & S. Lemeshow(2000). Applied Logistic Regression, Second Edition. Retrieved from https://reurl.cc/LbWmG4

指導教授

陳炫碩(Ken Chen)

審核日期

2021-7-29

推文