디지털융복합연구 (Journal of Digital Convergence)

  • 제16권9호
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  • Pages.437-442
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  • 2018
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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사회연결망 이론을 통한 여자필드하키 중심성분석

A Study on Women's Field Hockey Centrality Analysis using Social Network Theory

  • 김지응 (상명대학교 체육학과) ;
  • 이소미 (상명대학교 체육학과) ;
  • 박종철 (한국스포츠정책과학원 스포츠과학연구실) ;
  • 이희화 (상명대학교 스포츠산업학과)
  • Kim, JI-Eung (Department of Physical Education, Sangmyung University) ;
  • LEE, So-Mi (Department of Physical Education, Sangmyung University) ;
  • Park, Jong-Chul (Department of Sport Science, Korea Institute of Sport Science) ;
  • Lee, Hee-Hwa (Department of Sports Industry, Sangmyung University)
  • 투고 : 2018.07.11
  • 심사 : 2018.09.20
  • 발행 : 2018.09.28
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초록

본 연구는 리우올림픽에 참가한 대한민국과 상위4개국을 대상으로 슈팅서클 진입 시 마지막 5번의 패스를 통하여 핵심선수 파악하는 것이 목적이다. 1차적으로 스포츠코드를 활용하여 분석코드를 만들어 총33경기 중 대한민국과 상위4개국이 포함된 29경기를 대상으로 분석하였으며, 다음으로 Ucinet6를 활용하여 국가별 근접중심성 분석을 하였다. 연구결과는 대한민국은 FW인 13번, 뉴질랜드는 MF인 1번, 독일은 DF인 5번, 네덜란드는 MF인 9번, 영국은 MF인 8번 선수가 서클로 진입하는 패스연결망에서 핵심선수로 나타났다. 특히 결승전에 올라간 두 팀은 근접중심성 지수가 평균 60이 넘어갔음을 알 수 있었다. 이러한 결과를 바탕으로 여자필드하키 경기분석에서 핵심선수를 파악하는 하나의 수단으로 활용되기를 기대해 본다.

The study aims to identify key players through the last five passes when entering shooting circles in Korea and top four countries participated in the Rio Olympics. First, the analysis code was created using the Sports code to analyze the 29 games including Korea and the top 4 countries among 33 games. Second, Ucinet 6 has been used to analyze the Closeness Centrality of each country. The results of the study show that Korea is a key player in No.13 FW, New Zealand in No.1 MF, Germany in No.5 DF, Netherlands in No.9 MF and U.K in No.8 MF. In particular, the two teams that advanced to the finals saw their proximity center index average over 60. Based on these results, it is expected that the analysis of women's field hockey matches will serve as a tool to identify key players.

키워드

참고문헌

  1. S. L. Kim. (2018). An Evaluation Model of IT Investment Effect. Journal of Digital Convergence, 16(2), 27-36. https://doi.org/10.14400/JDC.2018.16.2.027
  2. W. Y. Ku. (2002). A Plan for Acivation of Sports through Infomation Technology. Journal of Dong-Eui university institute of sport science, 1, 25-32.
  3. J. K. Park. (2001). Information Society and Sport : Roles and Prospects. Korean journal of physical education, 40(1), 87-102.
  4. Y. H. Kim. (2016). Social Network Analysis. Hakjisa.
  5. S. B. Jeon. (2016). Association between coaches' social network and their performance outcome : Korea national soccer team. Master dissertation. Yonsei university. Seoul.
  6. S. H. Kim & R. S. Chang. (2010). The Study on the Research Trend of Social Network Analysis and the its Applicability to Information Science. 27(4), 71-87. https://doi.org/10.3743/KOSIM.2010.27.4.071
  7. B. K. Kim. (2014). A Study in Product Strategy of On-line Shopping Mall based in Social Network Analysis. Doctoral dissertation. Kyungil University. Gyeongsan.
  8. J. A. Seo. (2016). Analyzing the Destination Image of Daegu from Onine Content through Social Network Anlaysis. Doctoral dissertation. Keimyung university, Daegu.
  9. T. K. An. (2009). Network structure between advertising agency and Event promotion agency using social network analysis. Doctoral dissertation. Dongkuk university. Seoul.
  10. S. C. Song, S. H. Park & G. T. Yeo. (2018). SNA Approach for Analyzing the Research Trend of China's Logistics. Journal of Digital Convergence, 16(5), 55-63. https://doi.org/10.14400/JDC.2018.16.5.055
  11. H. J. Kim. (2007). Notational Analysis of Sports using Social Network Analysis" The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science, 9(1), 99-112.
  12. Y. H. Choi. (2012). The Application of Social Network theory on a Soccer Game Visualization System. Master dissertation. Ajou University. Suwon.
  13. J. E. Kim, H. H. Lee & J. C. Park. (2017). A Study on the Pass Analysis of Football Game using Social Networking Analysis. Journal of Digital Convergence, 15(7), 479-487. https://doi.org/10.14400/JDC.2017.15.7.479
  14. B. U. Kang, M. K. Huh & S. B. Choi. (2015). Performance analysis of volleyball games using the social network and text mining techniques. Journal of the Korean data & information science society, 26(3), 619-630. https://doi.org/10.7465/jkdi.2015.26.3.619
  15. J. W. Lim. (2009). Indices of analysis for performance evaluation in field-hockey. Doctoral dissertation. Korea National Sport University. Seoul.