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      현대 프로농구 선수 포지션 역할에 대한 새로운 접근 : 주성분 분석을 통한 NBA(미국프로농구) 선수 포지션 재정의 = A New Approach to Modern Professional Basketball Player Positions: Redefining National Basketball Association (NBA) Player Positions through Principal Component Analysis

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      https://www.riss.kr/link?id=A108955877

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      Purpose: This study aims to empirically reclassify the roles of National Basketball Association (NBA) players, moving beyond the traditional categories of point guard (PG), shooting guard (SG), small forward (SF), power forward (PF), and center (C).
      Method: We utilized the game statistics from three NBA seasons (2020-2023) to redefine players’ positional roles. We employed principal component analysis and regression analysis, utilizing Python and SPSS 26.0 software package for methodological assessment.
      Results: The empirical results of the principal component analysis showed that modern NBA players can be categorized into six distinct roles: Big Man, First Option, 3-and-Defense, Defensive Guard, Bench Ace, and Role Players. Regression analysis identified 155 players as Role Players, 145 as Big Man, 126 as First Options, 87 as 3-and-Defense players, and 75 as Defensive Guards.
      Conclusion: This study holds academic and practical importance by systematically collecting, preprocessing, and analyzing extensive NBA data. It redefines player roles based on modern basketball play styles and provides a comprehensive list of players corresponding to these new categories, offering a fresh perspective on player posions.
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      Purpose: This study aims to empirically reclassify the roles of National Basketball Association (NBA) players, moving beyond the traditional categories of point guard (PG), shooting guard (SG), small forward (SF), power forward (PF), and center (C). M...

      Purpose: This study aims to empirically reclassify the roles of National Basketball Association (NBA) players, moving beyond the traditional categories of point guard (PG), shooting guard (SG), small forward (SF), power forward (PF), and center (C).
      Method: We utilized the game statistics from three NBA seasons (2020-2023) to redefine players’ positional roles. We employed principal component analysis and regression analysis, utilizing Python and SPSS 26.0 software package for methodological assessment.
      Results: The empirical results of the principal component analysis showed that modern NBA players can be categorized into six distinct roles: Big Man, First Option, 3-and-Defense, Defensive Guard, Bench Ace, and Role Players. Regression analysis identified 155 players as Role Players, 145 as Big Man, 126 as First Options, 87 as 3-and-Defense players, and 75 as Defensive Guards.
      Conclusion: This study holds academic and practical importance by systematically collecting, preprocessing, and analyzing extensive NBA data. It redefines player roles based on modern basketball play styles and provides a comprehensive list of players corresponding to these new categories, offering a fresh perspective on player posions.

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      참고문헌 (Reference)

      1 박성배 ; 권태근 ; 전종환, "한국프로야구 선수들의 연봉 산정모델 개발" 29 (29): 520-533, 2018

      2 김필수 ; 김대권, "한국프로농구팀의 응집력과 경기성과간의 관계 : 감독역량과 감독경험의 조절효과를 중심으로" 62 : 105-117, 2015

      3 김필수, "프로야구감독의 선수지도경력, 경기지휘역량, 교체시기의 요인이 프로야구팀의 경기성과에 미치는 영향" 25 (25): 59-78, 2020

      4 정태성 ; 김필수 ; 이상현 ; 이상범, "프로스포츠 산업 조직구성원의 역량에 따른 관리자의 역할 : 미국프로농구(NBA)와한국프로농구(KBL)의 감독과 선수단 전력 수준에 관한실증연구 분석" 17 (17): 195-208, 2022

      5 김필수 ; 김필수 ; 이상현, "자원기반관점의 한국프로야구팀 정규리그승률 결정요인에 관한 연구" 27 (27): 16-37, 2022

      6 김필수 ; 이상현, "머신러닝을 활용한 한국프로농구 정규리그최종 순위 예측 : 스포츠 애널리틱스 관점" 25 (25): 103-115, 2023

      7 Jacheć, T., "Three-pointer! A 40-year NBA history" 15 : 196-206, 2021

      8 Gløersen, Ø., "Technique analysis in elite athletes using principal component analysis" 36 (36): 229-237, 2018

      9 Jayal, A., "Sports analytics: Analysis, visualisation and decision making in sports performance" Routledge 2018

      10 Sarlis, V., "Sports analytics-Evaluation of basketball players and team performance" 93 : 101562-, 2020

      1 박성배 ; 권태근 ; 전종환, "한국프로야구 선수들의 연봉 산정모델 개발" 29 (29): 520-533, 2018

      2 김필수 ; 김대권, "한국프로농구팀의 응집력과 경기성과간의 관계 : 감독역량과 감독경험의 조절효과를 중심으로" 62 : 105-117, 2015

      3 김필수, "프로야구감독의 선수지도경력, 경기지휘역량, 교체시기의 요인이 프로야구팀의 경기성과에 미치는 영향" 25 (25): 59-78, 2020

      4 정태성 ; 김필수 ; 이상현 ; 이상범, "프로스포츠 산업 조직구성원의 역량에 따른 관리자의 역할 : 미국프로농구(NBA)와한국프로농구(KBL)의 감독과 선수단 전력 수준에 관한실증연구 분석" 17 (17): 195-208, 2022

      5 김필수 ; 김필수 ; 이상현, "자원기반관점의 한국프로야구팀 정규리그승률 결정요인에 관한 연구" 27 (27): 16-37, 2022

      6 김필수 ; 이상현, "머신러닝을 활용한 한국프로농구 정규리그최종 순위 예측 : 스포츠 애널리틱스 관점" 25 (25): 103-115, 2023

      7 Jacheć, T., "Three-pointer! A 40-year NBA history" 15 : 196-206, 2021

      8 Gløersen, Ø., "Technique analysis in elite athletes using principal component analysis" 36 (36): 229-237, 2018

      9 Jayal, A., "Sports analytics: Analysis, visualisation and decision making in sports performance" Routledge 2018

      10 Sarlis, V., "Sports analytics-Evaluation of basketball players and team performance" 93 : 101562-, 2020

      11 Morgulev, E., "Sports analytics and the big-data era" 5 : 213-222, 2018

      12 Bianchi, F., "Role revolution : Towards a new meaning of positions in basketball" 10 (10): 712-734, 2017

      13 Jyad, A., "Redefining NBA player classifications using clustering: Using hierarchical clustering to define NBA players"

      14 Hedquist, A. L., "Redefining NBA basketball positions through visualization and mega-cluster analysis" Utah State University 2022

      15 Freeman, J., "Niche width and the dynamics of organizational populations" 88 (88): 1116-1145, 1983

      16 Butts, D., "NCAA adds three-point basket"

      17 Kalman, S., "NBA lineup analysis on clustered player tendencies: A new approach to the positions of basketball & modeling lineup efficiency of soft lineup aggregates" 2020

      18 Thabtah, F., "NBA game result prediction using feature analysis and machine learning" 6 (6): 103-116, 2019

      19 DiMaggio, P. J., "Interest and agency in institutional theory" Institutional Patterns and Organizations 3-21, 1988

      20 Meyer, J. W., "Institutionalized organizations : Formal structure as myth and ceremony" 83 (83): 340-363, 1977

      21 Bafna, P. B., "Identification of significant challenges in the sports domain using clustering and feature selection techniques" 1-5, 2019

      22 Kirchberg, C., "Hoop lore: A history of the National Basketball Association" McFarland 2007

      23 Lynch, J., "High school basketball draws line, adopts 3-point rule"

      24 Thompson, M., "Golden: The miraculous rise of Steph Curry" Simon and Schuster 2017

      25 Alagappan, M., "From 5 to 13: Redefining the positions in basketball" 2012

      26 Wedding, C., "Examining the evolution and classification of player position using performance indicators in the National Rugby League during the 2015-2019 seasons" 23 (23): 891-896, 2020

      27 Zhang, S., "Clustering performances in the NBA according to players’ anthropometric attributes and playing experience" 36 (36): 2511-2520, 2018

      28 Brill, R. S., "Algorithmic NBA player acquisition" 2023

      29 Baumann, A, "A multi-stage clustering algorithm to reevaluate basketball positions and performance analysis" National College of Ireland 2022

      30 Sarlis, V., "A data science approach analysing the impact of injuries on basketball player and team performance" 99 : 101750-, 2021

      31 Sanders, S., "22 will get you 3"

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