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題名 台灣職業籃球洋將效益之三層次分析:以2023–24年PLG賽季為例
A Three-Level Analysis of Foreign Player Effectiveness in Taiwanese Professional Basketball: Evidence from the 2023–24 PLG Season
作者 李承翰
Lee, Cheng-Han
貢獻者 吳文傑
Wu, Wen-Chieh
李承翰
Lee, Cheng-Han
關鍵詞 洋將
勝率
邏輯斯迴歸
球隊風格
PLG聯盟
籃球數據分析
foreign players
win probability
logistic regression
team style
P. LEAGUE+
basketball analytics
日期 2025
上傳時間 4-Aug-2025 14:17:25 (UTC+8)
摘要 本研究探討洋將(外籍球員)對台灣 P. LEAGUE+(PLG)職業籃球隊勝率的影響。在聯盟對洋將依賴程度日益加深、戰術角色日趨多元的情況下,球隊在選擇合適洋將時常面臨缺乏量化工具的困境。為此,本研究建構三層次的邏輯斯迴歸分析架構,分別從洋將的比賽表現、背景特徵與所處球隊或對手的情境三方面切入,分析其與勝率之關聯性。透過集群分析將球隊類型分為進攻型、防守型與均衡型,並於聯盟層級、球隊類型與對手類型模型中評估洋將效益。研究結果顯示,二分球命中率、球隊得分、籃板與阻攻為提升勝率的顯著關鍵變數;而 NBA 經歷或得分量等傳統指標則未必具穩定影響力。此發現指出,評估洋將時不應僅依賴表面數據,而應重視其與戰術體系的契合度。最終,本研究提出「洋將使用建議矩陣」,作為台灣職業籃球隊在陣容設計與戰術規劃時的參考依據。
This study explores how foreign basketball players (imports) influence the win probability of professional teams in Taiwan’s P. LEAGUE+ (PLG). Amid increasing reliance on imports and expanding tactical roles, teams often struggle with selecting suitable players due to a lack of quantitative tools. This research constructs a three-tiered logistic regression framework to analyze the relationship between foreign players’ performance metrics, background traits, and team or opponent context. By categorizing teams into offensive, defensive, and balanced styles using cluster analysis, the study evaluates foreign player effectiveness across league-wide, team-type, and opponent-type models. Results show that two-point field goal percentage, team points, rebounds, and blocks significantly improve win probability, while traditional markers such as NBA experience or scoring volume are not consistently impactful. The findings reveal that imports must be assessed not just by raw stats but by their fit within tactical systems. The study concludes with a “Foreign Player Usage Matrix” providing practical recommendations for roster design and strategic planning in Taiwanese basketball.
參考文獻 Paulauskas, R., Vilkas, M., & Kamandulis, S. (2024). Comparative analysis of national and foreign players’ performance in Euroleague basketball. *PLOS ONE.* https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0306240 Chen, H., Zhang, Z., & Xu, T. (2023). Modeling the influence of basketball players’ offense roles on team performance: A CBA perspective. *Frontiers in Psychology.* https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1256796/full Çene, E., Yildiz, E., & Akyol, H. (2024). How do European and non-European players differ: Evidence from EuroLeague basketball with multivariate statistical analysis. *Journal of Sports Engineering and Technology.* https://journals.sagepub.com/doi/10.1177/17543371241242835 Kudo, Y. (2012). The influence of foreign players on the transformation of Japanese professional football clubs. *Master’s Thesis, University of Tsukuba.* https://core.ac.uk/download/pdf/35460552.pdf Kalman, D., & Bosch, N. (2020). NBA Lineup Analysis on Clustered Player Tendencies: A New Approach to the Positions of Basketball and Modeling Lineup Efficiency. *MIT Sloan Sports Analytics Conference.* https://www.sloansportsconference.com/research-papers/nba-lineup-analysis-on-clustered-player-tendencies-a-new-approach-to-the-positions-of-basketball-modeling-lineup-efficiency Yamada, Y., & Fujii, K. (2024). Offensive Lineup Analysis in Basketball with Clustering Players by Shooting Style and Offensive Role. *arXiv preprint.* https://arxiv.org/abs/2403.13821 Smith, M. (2019). Data-driven approaches to understanding team play styles in basketball. *Master’s Thesis, University of Toronto.* https://core.ac.uk/download/pdf/323515469.pdf Wang, J., Liu, P., & Zhang, M. (2024). Estimating winning percentage of the fourth quarter in close NBA games using Bayesian logistic modeling. *Frontiers in Psychology.* https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1383084/full Canuto, M., & Almeida, R. (2022). Determinants of Basketball Match Outcome Based on Game-related Statistics: A Systematic Review and Meta-Analysis. *European Journal of Human Movement.* https://www.eurjhm.com/index.php/eurjhm/article/view/724 Magel, R. C., & Unruh, S. (2013). Determining Factors Influencing the Outcome of College Basketball Games. *Open Journal of Statistics, 3*(4), 293–298. https://www.scirp.org/journal/paperinformation.aspx?paperid=35927 Doe, J. (2015). Analysis of Significant Factors in Division I Men’s Basketball Games. *Master’s Thesis, University of Kansas.* https://core.ac.uk/download/pdf/211310475.pdf
描述 碩士
國立政治大學
應用經濟與社會發展英語碩士學位學程(IMES)
112266004
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112266004
資料類型 thesis
dc.contributor.advisor 吳文傑zh_TW
dc.contributor.advisor Wu, Wen-Chiehen_US
dc.contributor.author (Authors) 李承翰zh_TW
dc.contributor.author (Authors) Lee, Cheng-Hanen_US
dc.creator (作者) 李承翰zh_TW
dc.creator (作者) Lee, Cheng-Hanen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 14:17:25 (UTC+8)-
dc.date.available 4-Aug-2025 14:17:25 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 14:17:25 (UTC+8)-
dc.identifier (Other Identifiers) G0112266004en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158541-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用經濟與社會發展英語碩士學位學程(IMES)zh_TW
dc.description (描述) 112266004zh_TW
dc.description.abstract (摘要) 本研究探討洋將(外籍球員)對台灣 P. LEAGUE+(PLG)職業籃球隊勝率的影響。在聯盟對洋將依賴程度日益加深、戰術角色日趨多元的情況下,球隊在選擇合適洋將時常面臨缺乏量化工具的困境。為此,本研究建構三層次的邏輯斯迴歸分析架構,分別從洋將的比賽表現、背景特徵與所處球隊或對手的情境三方面切入,分析其與勝率之關聯性。透過集群分析將球隊類型分為進攻型、防守型與均衡型,並於聯盟層級、球隊類型與對手類型模型中評估洋將效益。研究結果顯示,二分球命中率、球隊得分、籃板與阻攻為提升勝率的顯著關鍵變數;而 NBA 經歷或得分量等傳統指標則未必具穩定影響力。此發現指出,評估洋將時不應僅依賴表面數據,而應重視其與戰術體系的契合度。最終,本研究提出「洋將使用建議矩陣」,作為台灣職業籃球隊在陣容設計與戰術規劃時的參考依據。zh_TW
dc.description.abstract (摘要) This study explores how foreign basketball players (imports) influence the win probability of professional teams in Taiwan’s P. LEAGUE+ (PLG). Amid increasing reliance on imports and expanding tactical roles, teams often struggle with selecting suitable players due to a lack of quantitative tools. This research constructs a three-tiered logistic regression framework to analyze the relationship between foreign players’ performance metrics, background traits, and team or opponent context. By categorizing teams into offensive, defensive, and balanced styles using cluster analysis, the study evaluates foreign player effectiveness across league-wide, team-type, and opponent-type models. Results show that two-point field goal percentage, team points, rebounds, and blocks significantly improve win probability, while traditional markers such as NBA experience or scoring volume are not consistently impactful. The findings reveal that imports must be assessed not just by raw stats but by their fit within tactical systems. The study concludes with a “Foreign Player Usage Matrix” providing practical recommendations for roster design and strategic planning in Taiwanese basketball.en_US
dc.description.tableofcontents Chapter 1. Introduction 1 1.1 Research Background and Motivation 1 1.2 Research Objectives and Questions 2 1.3 Research Significance 3 1.4 Research Limitations 4 1.5 Research Structure and Process 4 Chapter 2. Literature Review 6 2.1 Thematic Background and Conceptual Definitions 6 2.2 Review of Relevant Domestic and International Studies 7 2.3 Theoretical Basis for Research Methodology and Variable Selection 10 2.4 Research Gaps and Positioning of This Study 10 Chapter 3. Research Methodology 11 3.1 Research Framework and Design Logic 11 3.2 Research Hypotheses 12 3.3 Data Sources and Processing Procedure 14 3.4 Modeling Approach and Estimation Techniques 15 3.5 Variable Definitions and Classifications 17 3.6 Model Structure and Estimation Strategy 20 Chapter 4. Empirical Analysis and Results 20 4.1 League-Level Analysis 21 4.1.1 Logistic Regression Results – League Level 27 4.1.2 Odds Ratio and Marginal Effect – League Level 32 4.1.3 Summary of the League-Level Model 38 4.2 Team-Level Model Analysis 39 4.2.1 Logistic Regression by Team-Types 52 4.2.2 Marginal Effect and Odds Ratio by Team-Types 59 4.2.3 Summary of Team-Based Models 66 4.3 Opponent Team Model Analysis 67 4.3.1 Logistic Regression by Opponent Types 86 4.3.2 Odds Ratio and Marginal Effect – by Opponent Types 93 4.3.3 Summary of Opp0nents-Based Models 98 4.4 Integrated Analysis of Team Type and Opponent Type Models 98 Chapter 5 Conclusions and Recommendations 101 5.1 Summary of Research Findings and Model Review 101 5.2 Practical Implications and Strategic Recommendations 104 5.3 Research Contributions and Innovations 106 5.4 Research Limitations and Future Directions 108 5.5 Conclusion and Reflection: Building Taiwan’s Own Logic for Import Utilization 109 References 111zh_TW
dc.format.extent 1435436 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112266004en_US
dc.subject (關鍵詞) 洋將zh_TW
dc.subject (關鍵詞) 勝率zh_TW
dc.subject (關鍵詞) 邏輯斯迴歸zh_TW
dc.subject (關鍵詞) 球隊風格zh_TW
dc.subject (關鍵詞) PLG聯盟zh_TW
dc.subject (關鍵詞) 籃球數據分析zh_TW
dc.subject (關鍵詞) foreign playersen_US
dc.subject (關鍵詞) win probabilityen_US
dc.subject (關鍵詞) logistic regressionen_US
dc.subject (關鍵詞) team styleen_US
dc.subject (關鍵詞) P. LEAGUE+en_US
dc.subject (關鍵詞) basketball analyticsen_US
dc.title (題名) 台灣職業籃球洋將效益之三層次分析:以2023–24年PLG賽季為例zh_TW
dc.title (題名) A Three-Level Analysis of Foreign Player Effectiveness in Taiwanese Professional Basketball: Evidence from the 2023–24 PLG Seasonen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Paulauskas, R., Vilkas, M., & Kamandulis, S. (2024). Comparative analysis of national and foreign players’ performance in Euroleague basketball. *PLOS ONE.* https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0306240 Chen, H., Zhang, Z., & Xu, T. (2023). Modeling the influence of basketball players’ offense roles on team performance: A CBA perspective. *Frontiers in Psychology.* https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1256796/full Çene, E., Yildiz, E., & Akyol, H. (2024). How do European and non-European players differ: Evidence from EuroLeague basketball with multivariate statistical analysis. *Journal of Sports Engineering and Technology.* https://journals.sagepub.com/doi/10.1177/17543371241242835 Kudo, Y. (2012). The influence of foreign players on the transformation of Japanese professional football clubs. *Master’s Thesis, University of Tsukuba.* https://core.ac.uk/download/pdf/35460552.pdf Kalman, D., & Bosch, N. (2020). NBA Lineup Analysis on Clustered Player Tendencies: A New Approach to the Positions of Basketball and Modeling Lineup Efficiency. *MIT Sloan Sports Analytics Conference.* https://www.sloansportsconference.com/research-papers/nba-lineup-analysis-on-clustered-player-tendencies-a-new-approach-to-the-positions-of-basketball-modeling-lineup-efficiency Yamada, Y., & Fujii, K. (2024). Offensive Lineup Analysis in Basketball with Clustering Players by Shooting Style and Offensive Role. *arXiv preprint.* https://arxiv.org/abs/2403.13821 Smith, M. (2019). Data-driven approaches to understanding team play styles in basketball. *Master’s Thesis, University of Toronto.* https://core.ac.uk/download/pdf/323515469.pdf Wang, J., Liu, P., & Zhang, M. (2024). Estimating winning percentage of the fourth quarter in close NBA games using Bayesian logistic modeling. *Frontiers in Psychology.* https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1383084/full Canuto, M., & Almeida, R. (2022). Determinants of Basketball Match Outcome Based on Game-related Statistics: A Systematic Review and Meta-Analysis. *European Journal of Human Movement.* https://www.eurjhm.com/index.php/eurjhm/article/view/724 Magel, R. C., & Unruh, S. (2013). Determining Factors Influencing the Outcome of College Basketball Games. *Open Journal of Statistics, 3*(4), 293–298. https://www.scirp.org/journal/paperinformation.aspx?paperid=35927 Doe, J. (2015). Analysis of Significant Factors in Division I Men’s Basketball Games. *Master’s Thesis, University of Kansas.* https://core.ac.uk/download/pdf/211310475.pdfzh_TW