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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/158541


    Title: 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
    Authors: 李承翰
    Lee, Cheng-Han
    Contributors: 吳文傑
    Wu, Wen-Chieh
    李承翰
    Lee, Cheng-Han
    Keywords: 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
    Date: 2025
    Issue Date: 2025-08-04 14:17:25 (UTC+8)
    Abstract: 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.
    Reference: 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
    Description: 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
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112266004
    Data Type: thesis
    Appears in Collections:[應用經濟與社會發展英語碩士學位學程 (IMES)] 學位論文

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