NBA賽事結果預測：結合球隊數據、球員薪資、與社群媒體情緒

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：14

、訪客IP：18.118.193.123

姓名

鍾佑偉(Yu-Wei Chung) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

NBA賽事結果預測：結合球隊數據、球員薪資、與社群媒體情緒
(NBA Game Prediction based on Team-centric Data, Player Salary, and Social Media Sentiment)

相關論文

★ 創造力與創業精神-探索創業動機與創業環境之調節	★ 工作壓力源、人格特質與工作滿意度之關係—以在職進修者為例
★ Shopping Motivation on Purchase intention: Can Loyalty Program and Corporate Image Enhance Such Relationship?	★ 企業社會責任對求職者應徵意圖之影響：以組織人才吸引力與企業聲望為中介
★ 人力資源彈性策略和組織創新相關性之研究：論適應型文化調節適應能力及知識整合之中介效果	★ 個人靈性與人生滿意度-探討人格特質之中介
★ 購買動機與便利商店集點行為之研究	★ 五大與九型人格對工作績效、組織公民行為及工作滿意度的影響
★ 體育賽事觀賽體驗與球隊吉祥物品牌個性對職業球隊品牌權益之關聯－以MLB為例	★ 學習成效、組織承諾對留職意願之影響：
★ 消費者特徵與購物意願：探討網站與商品特性之中介	★ 資訊來源之信任與品質對於購買傾向之影響
★ 享樂購物動機與衝動購買：探討購後後悔之影響與因應行為	★ 產品包裝設計與化妝品之購買意圖：探討生活型態與消費者認知之中介
★ 信任與價值觀一致性對轉型領導與適應性績效關係之中介	★ 品牌信任受價值知覺與服務品質之影響：以旅館顧客滿意度為中介

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]

至系統瀏覽論文 (2026-7-29以後開放)

摘要(中)

預測運動賽事結果主要是使用球隊比賽數據，較少考量其他向度。本研究預測NBA賽事結果，除了使用球隊數據，也加入該球季當場比賽被登錄球員的薪資，作為個別球員表現反映其職業運動的專業程度與貢獻，以及社群媒體情緒分數，作為大眾對於整體球隊評價、表現讚賞與認同的程度。社群媒體情緒是透過Python撰寫爬蟲程式抓取使用者在Twitter上的推文，使用自然語言處理技術將其轉換成情緒分數。預測方法採用機器學習中的邏輯斯迴歸、支持向量機、決策樹、隨機森林、以及梯度上升決策樹。結果發現若只用球隊數據，最佳為邏輯斯迴歸，有67.6%的準確率。在加入球員薪資與社群媒體情緒分數後，最佳為梯度上升決策樹模型，可達 69.4%的準確率。依研究結果提出討論、管理意涵、研究限制與未來發展方向可供後續研究參考。

摘要(英)

To predict an athletic game outcome, most are based on game statistics, and seldom consider other dimensions. Besides game data, player salary that reflects the individual’s contribution to the professional sport, as well as social media sentiment scores that reflects the general public evaluation, approval, and identification with the team, are proposed to predict an NBA game outcome. Postings called tweets on Twitter were first scraped using a Python crawler, followed by natural language processing to convert data into sentiment scores. The prediction methods used in machine learning includes logistic regression, support vector machine, decision tree, random forest, and gradient boosting decision tree. Results show that to use game data only, the highest is logistic regression, with 67.6% accuracy. After adding player salary and social media sentiment scores, the highest becomes gradient boosting decision tree, with accuracy increased to 69.4%. Results based discussion, management implications, limitations and directions for future research are included for future research reference.

關鍵字(中)

★ NBA
★ 運動賽事結果預測
★ 球員薪資
★ 情緒分數
★ 機器學習

關鍵字(英)

★ NBA
★ game prediction
★ player salary
★ sentiment score
★ machine learning

論文目次

摘要 VIII
ABSTRACT IX
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 3
1-3 研究流程 3
第二章文獻探討 5
2-1 運動賽事結果預測 5
2-2 球隊數據 6
2-3 球員薪資 9
2-4 社群媒體情緒 12
2-4-1 Twitter 13
2-4-2 自然語言處理 15
2-4-3 情緒分析 18
2-5 機器學習 20
2-5-1 邏輯斯回歸 21
2-5-2 支持向量機 22
2-5-3 決策樹 24
2-5-4 隨機森林 25
2-5-5梯度上升決策樹 26
第三章研究方法 27
3-1 資料蒐集與來源 27
3-2 球隊數據 29
3-2-1傳統數據 29
3-2-2進階數據 31
3-3 球員薪資 35
3-4 社群媒體情緒 37
3-5 研究流程 39
第四章結果 41
4-1 資料預處理 41
4-2 訓練與測試 43
4-3 評估方法 44
4-4 研究結果 45
4-4-1單一預測 45
4-4-2 整合預測 50
第五章討論 55
5-1 研究討論 55
5-1-1 球隊數據 55
5-1-2 球員薪資 56
5-1-3 社群媒體情緒 56
5-2 管理意涵 57
5-2-1 球隊數據 57
5-2-2 球員薪資 57
5-2-3 社群媒體情緒 57
5-3 研究限制與未來建議 58
5-3-1 研究限制 58
5-3-2 未來建議 58
參考文獻 60

參考文獻

Balahur, A., Mohammad, S., Hoste, V., & Klinger, R. (2018, October). Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis.
Ben-Hur, A., Horn, D., Siegelmann, H. T., & Vapnik, V. (2001). Support vector clustering. Journal of Machine Learning Research, 2, 125-137.
Bifet, A., & Frank, E. (2010, October). Sentiment knowledge discovery in twitter streaming data. International conference on discovery science (pp. 1-15). Springer. https://doi.org/10.1007/978-3-642-16184-1_1
Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: Analyzing text with the natural language toolkit. O′Reilly Media, Inc.
Breslow, J. F. (2017). Salary Inequality in the NBA: Changing Returns to Skill or Wider Skill Distributions? CMC Senior Theses. 1645.
Cao, C. (2012). Sports data mining technology used in basketball outcome prediction. Unpublished masters dissertation, Technological University Dublin.
Cavnar, W. B., & Trenkle, J. M. (1994, April). N-gram-based text categorization. Proceedings of SDAIR-94, 3rd annual symposium on document analysis and information retrieval, 161175.
Cheng, G., Zhang, Z., Kyebambe, M. N., & Kimbugwe, N. (2016). Predicting the outcome of NBA playoffs based on the maximum entropy principle. Entropy, 18(12), 450. https://doi.org/10.3390/e18120450
Chowdhury, G. G. (2003). Natural language processing. Annual review of information science and technology, 37(1), 51-89. https://doi.org/10.1002/aris.1440370103
Cramer, J. S. (2002). The origins of logistic regression. Tinbergen Institute Working Paper No. 2002-119/4. http://dx.doi.org/10.2139/ssrn.360300
Csataljay, G., O’Donoghue, P., Hughes, M., & Dancs, H. (2009). Performance indicators that distinguish winning and losing teams in basketball. International Journal of Performance Analysis in Sport, 9(1), 60-66. https://doi.org/10.1080/24748668.2009.11868464
Dunning, T. (1994). Statistical identification of language (pp. 94-273). New Mexico State University.
Ertug, G., & Castellucci, F. (2013). Getting what you need: How reputation and status affect team performance, hiring, and salaries in the NBA. Academy of Management Journal, 56(2), 407-431. https://doi.org/10.5465/amj.2010.1084
Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. https://doi.org/10.1145/2436256.2436274
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451
Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N project report. Stanford University.
Groothuis, P. A., & Hill, J. R. (2004). Exit discrimination in the NBA: A duration analysis of career length. Economic Inquiry, 42(2), 341-349. https://doi.org/10.1093/ei/cbh065
Haghighat, M., Rastegari, H., Nourafza, N., Branch, N., & Esfahan, I. (2013). A review of data mining techniques for result prediction in sports. Advances in Computer Science: An International Journal, 2(5), 7-12.
Hill, J. R., & Jolly, N. A. (2012). Salary distribution and collective bargaining agreements: A case study of the NBA. Industrial Relations: A Journal of Economy and Society, 51(2), 342-363. https://doi.org/10.1111/j.1468-232X.2012.00680.x
Ho, T. K. (1995, August). Random decision forests. Proceedings of 3rd international conference on document analysis and recognition, 278-282. IEEE. https://doi.org/10.1109/ICDAR.1995.598994
Hoffer, A. J., & Freidel, R. (2014). Does salary discrimination persist for foreign athletes in the NBA? Applied Economics Letters, 21(1), 1-5. https://doi.org/10.1080/13504851.2013.829183
Horowitz, I. (2018). Competitive balance in the NBA playoffs. The American Economist, 63(2), 215-227. https://doi.org/10.1177/0569434517747250
Hsu, Y.-C. (2020). Using machine learning and candlestick patterns to predict the outcomes of American football games. Applied Sciences, 10(13), 4484. https://doi.org/10.3390/app10134484
Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8, 216-225.
Kamiński, B., Jakubczyk, M., & Szufel, P. (2018). A framework for sensitivity analysis of decision trees. Central European Journal of Operations Research, 26(1), 135-159. https://doi.org/10.1007/s10100-017-0479-6
Kelly, T. (2017). Effects of TV Contracts on NBA Salaries. Colgate Economics.
Loeffelholz, B., Bednar, E., & Bauer, K. W. (2009). Predicting NBA games using neural networks. Journal of Quantitative Analysis in Sports, 5(1). https://doi.org/10.2202/1559-0410.1156
McCabe, A., & Trevathan, J. (2008, April). Artificial intelligence in sports prediction. Fifth International Conference on Information Technology: New Generations, 1194-1197. IEEE. https://doi.org/10.1109/ITNG.2008.203
Mikołajec, K., Maszczyk, A., & Zając, T. (2013). Game indicators determining sports performance in the NBA. Journal of Human Kinetics, 37, 145. https://doi.org/10.2478/hukin-2013-0035
Miljković, D., Gajić, L., Kovačević, A., & Konjović, Z. (2010, September). The use of data mining for basketball matches outcomes prediction. IEEE 8th international symposium on intelligent systems and informatics, 309-312. IEEE. https://doi.org/10.1109/SISY.2010.5647440
Min, B., Kim, J., Choe, C., Eom, H., & McKay, R. (2008). A compound framework for sports prediction: The case study of football. Knowledge-Based Systems, 21(7), 551-562. https://doi.org/10.1016/j.knosys.2008.03.016
Oliver, D. (2004). Basketball on paper: Rules and tools for performance analysis. Potomac Books, Inc.
Powers, D. M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 16061. http://doi.org/10.9735/2229-3981
Quinlan, J. R. (1987). Simplifying decision trees. International Journal of Man-Machine Studies, 27(3), 221-234. https://doi.org/10.1016/S0020-7373(87)80053-6
Radovanović, S., Radojičić, M., Jeremić, V., & Savić, G. (2013). A novel approach in evaluating efficiency of basketball players. Management, 67, 37-45. https://doi.org/10.7595/management.fon.2013.0012
Ramaswamy, S., Rastogi, R., & Shim, K. (2000, May). Efficient algorithms for mining outliers from large data sets. Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 427-438. https://doi.org/10.1145/342009.335437
Raschka, S., & Mirjalili, V. (2017). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and tensorflow.(2nd ed.), Packt.
Sinha, S., Dyer, C., Gimpel, K., & Smith, N. A. (2013). Predicting the NFL using Twitter. In A. Zimmermann & J. Davis (eds), MLSA13–Proceedings of Machine Learning and Data Mining for Sports Analytics.
Smith, L., Lipscomb, B., & Simkins, A. (2007). Data mining in sports: Predicting cy young award winners. Journal of Computing Sciences in Colleges, 22(4), 115-121. https://dl.acm.org/doi/10.5555/1229637.1229658
Somberg, A. K., & Sommers, P. M. (2012). Payrolls and playoff probabilities in major league baseball. Atlantic Economic Journal, 40(3), 347-348. https://doi.org/10.1007/s11293-012-9317-3
Stanek, T. (2016). Player performance and team revenues: NBA player salary analysis. CMC Senior Theses. Paper 1257, Claremont McKenna College. http://scholarship.claremont.edu/cmc_theses/1257
Stiroh, K. J. (2007). Playing for Keeps: Pay and Performance in the NBA. Economic Inquiry, 45(1), 145-161. https://doi.org/10.1111/j.1465-7295.2006.00004.x
Strobl, C., Boulesteix, A.-L., & Augustin, T. (2007). Unbiased split selection for classification trees based on the Gini index. Computational Statistics & Data Analysis, 52(1), 483-501. https://doi.org/10.1016/j.csda.2006.12.030
Trawiński, K. (2010, July). A fuzzy classification system for prediction of the results of the basketball games. International conference on fuzzy systems, 1-7. IEEE. https://doi.org/10.1109/FUZZY.2010.5584399
Van Rossum, G., & Drake Jr, F. L. (1995). Python reference manual. Centrum voor Wiskunde en Informatica.
Weissbock, J. (2014). Forecasting success in the National Hockey League using in-game statistics and textual data. Unpublished doctoral dissertation, University of Ottawa. http://dx.doi.org/10.20381/ruor-6351
Yang, Y. S. (2015). Predicting regular season results of NBA teams based on regression analysis of common basketball statistics. Unbublished honors thesis, University of California at Berkeley.
Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919-926. https://doi.org/10.1016/j.dss.2012.12.028
Zdravevski, E., & Kulakov, A. (2009, September). System for prediction of the winner in a sports game. International Conference on ICT Innovations, 55-63. Springer. https://doi.org/10.1007/978-3-642-10781-8_7

指導教授

杜秉叡(Ben-Roy Do)

審核日期

2021-7-29

推文