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| Title: | 籃球數據致勝：技術統計對勝率的實證分析
Winning by the Numbers: A Data-Driven Analysis of Box Score Metrics |
| Authors: | 林政語
Lin, Cheng-Yu |
| Contributors: | 吳文傑
林政語
Lin, Cheng-Yu |
| Keywords: | 邏輯迴歸
邊際效應
技術統計
P. LEAGUE+
勝率分析
籃球數據分析
Basketball analytics
Win probability
P. LEAGUE+
Logistic regression
Marginal effects |
| Date: | 2025 |
| Issue Date: | 2025-08-04 12:53:58 (UTC+8) |
| Abstract: | 本研究旨在探討台灣 P. LEAGUE+ 職業籃球聯盟中，球隊的技術統計與勝率之間的關聯性。透過蒐集近兩個賽季的比賽數據，並以邏輯迴歸模型分析，包括進攻與防守端的多項統計指標如何影響球隊勝負。本研究特別強調邊際效應分析，以量化每一項統計變數對勝率的實質影響，例如增加一次防守籃板是否會提升勝率，或額外的一次失誤是否會降低勝率。
實證結果顯示，防守籃板對勝率的影響最大，對勝率有正向且顯著的影響；而進攻籃板與失誤則未如預期般對勝率產生顯著的負面關係。另一方面，二分球與三分球命中數皆顯著提升勝率，突顯出得分效率在現代籃球中的重要性。此外，對手的防守籃板與失誤數也展現出可觀的影響力，雖然其中部分變數未達統計顯著水準。
本研究不僅驗證了部分傳統籃球觀念，也挑戰了一些常見假設，如助攻數未必正向關聯勝率。此研究提供教練團具體的數據依據，作為訓練設計、戰術部署與賽前準備的參考，並可應用於系列賽對戰分析及球季成效追蹤。
This thesis investigates the relationship between box score statistics and team win probability in Taiwan's professional basketball league, P. LEAGUE+, using game data from two full seasons. Focusing on a single team’s performance, the study applies logistic regression and marginal effects analysis to evaluate how offensive and defensive metrics contribute to winning outcomes. The results reveal that defensive rebounds, three-point field goals, and two-point field goals are the most significant predictors of victory. Surprisingly, offensive rebounds and turnovers did not show a statistically significant effect, and assists were negatively associated with winning. These findings challenge conventional coaching assumptions and offer practical implications for strategy and training. By emphasizing high-efficiency scoring and possession control—especially through defensive rebounding—teams can meaningfully increase their chances of success. The thesis also proposes a data-informed feedback loop for continuous performance monitoring and outlines how these methods can be extended for playoff preparation and future opponent-specific analysis. This work contributes to the growing field of basketball analytics by adapting proven methods to a regional league context and translating insights into actionable coaching decisions. |
| Reference: | Baumer, B. S., Matthews, G. J., & O’Neil, D. (2017). The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball and Other Sports. University of Pennsylvania Press.
Csapo, P., & Raabe, D. (2019). Performance indicators in basketball: A systematic review. International Journal of Performance Analysis in Sport, 19(2), 185–202. https://doi.org/10.1080/24748668.2019.1579751
Goldman, M., & Rao, J. M. (2012). Effort vs. concentration: The asymmetric impact of pressure on NBA performance. Journal of Economic Behavior & Organization, 83(3), 602–617.
Kubatko, J., Oliver, D., Pelton, K., & Rosenbaum, D. T. (2007). A starting point for analyzing basketball statistics. Journal of Quantitative Analysis in Sports, 3(3), 1–22.
Miller, S. J. (2018). Sports Analytics and Data Science: Winning the Game with Methods and Models. FT Press.
Oliver, D. (2004). Basketball on Paper: Rules and Tools for Performance Analysis. Potomac Books.
Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.
P. LEAGUE+. Official box score data and game logs. |
| Description: | 碩士
國立政治大學
國際經營管理英語碩士學位學程(IMBA)
110933009 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110933009 |
| Data Type: | thesis |
| Appears in Collections: | [國際經營管理英語碩士學程IMBA] 學位論文
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| 300901.pdf | | 1178Kb | Adobe PDF | 0 | View/Open |
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