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시간과 공간적 특성에 따른 축구 패스 성공률 분석: 2018 러시아 월드컵 대회 자료를 중심으로

Influences on Time and Spatial Characteristics of Soccer Pass Success Rate: A Case Study of the 2018 World Cup in Russia

  • 이승훈 (한국스포츠정책과학원 스포츠과학연구실) ;
  • 김영훈 (맥재활의학과의원)
  • 투고 : 2020.11.30
  • 심사 : 2021.01.20
  • 발행 : 2021.01.28

초록

이 연구는 2018 FIFA 러시아 월드컵 영상자료에서 수집한 2차 가공 데이터와 공식기록을 비교 및 활용하여 패스 정확도의 시간적, 공간적 특성을 규명 하는데 목적이 있었다. 이를 위해 총 128경기를 대상으로 경기결과, 패스 시간, 패스 위치에 따른 패스성공률을 반복측정 이원변량분석을 활용해 검증했다. 연구결과 승패 집단 간 패스성공률의 차이는 나타나지 않았으며, 패스시간 및 위치에 대한 상호작용효과도 발견되지 않았다. 패스시간에 따른 패스성공률은 전반전이 후반전에 비해 높게나왔으며, 15~30분 지점인 전반 중반(79.2%)과 60~75분 지점인 후반 중반(77.9%)에서 가장 높은 성공률을 보였다. 패스지역에 따른 패스성공률은 수비-미드필드지역(83.9%), 미드필드-공격지역(81.7%), 수비지역(70.6%), 공격지역(61.1%)순으로 나타났다. 결론적으로 월드컵 경기의 상대적 경쟁의 강도가 높은 특성에 따라 승패 팀의 패스성공률의 차이가 나타나지 않았다고 판단되며, 향후 다양한 매개변수를 적용해 승패 요소 보다는 경기내용 자체를 분석하기 위한 후속 연구가 필요하다.

The purpose of this study is to identify the temporal and spatial characteristics of pass accuracy by utilizing the second processing data and official records collected from the 2018 FIFA World Cup Russia video data. For a total of 128 games, the success rate of passes based on the results of the game, passing time, and passing position was two-way ANOVA with repeated measure. The results showed no difference between winning and losing groups, and no interaction effects were found for passing time and location. The difference in passing time was high in the first half, with the highest success rate in the middle of the first half (79.2%) and the middle of the second half (77.9%) in the 15~30 minutes and the 60~75 minutes. Pass success rates were in the order of defense-midfield area (83.9%), midfield-attack area (81.7%), defense area (70.6%) and attack area (61.1%). In conclusion, there was no difference in the passing success rate of the winning and losing teams depending on the characteristics of the relative competitive strength of the World Cup games, and it is believed that follow-up research is needed to analyze the game contents rather than the factors of the winning and losing in the future.

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참고문헌

  1. Gudmundsson, J., & Horton, M. (2017). Spatio-temporal analysis of team sports. ACM Computing Surveys (CSUR), 50(2), 1-34. DOI : 10.1145/3054132
  2. Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernandez, J., & Medina, D. (2018). Effective injury forecasting in soccer with GPS training data and machine learning. PloS one, 13(7), e0201264. DOI : 10.1371/journal.pone.0201264
  3. Pappalardo et al. (2019). A public data set of spatio-temporal match events in soccer competitions. Scientific data, 6(1), 1-15. DOI : 10.6084/m9.figshare.9711164
  4. Pappalardo, L., Cintia, P., Ferragina, P., Massucco, E., Pedreschi, D., & Giannotti, F. (2019a). PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5), 1-27. DOI :10.1145/3343172
  5. Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2019). Actions speak louder than goals: Valuing player actions in soccer. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1851-1861). DOI : 10.1145/3292500.3330758
  6. Min, D. K., Lee, Y. S., & Kim, Y. R. (2015). Performances analysis of football matches. Journal of the Korean Data and Information Science Society, 26(1), 187-196. DOI : 10.7465/jkdi.2015.26.1.187
  7. Redwood-Brown, A. J., O'Donoghue, P. G., Nevill, A. M., Saward, C., & Sunderland, C. (2019). Effects of playing position, pitch location, opposition ability and team ability on the technical performance of elite soccer players in different score line states. PloS one, 14(2), e0211707. DOI : 10.1371/journal.pone.0211707
  8. Choi, H. J., & Hyun, J. W. (2018). The clusters of performances based on the official data for the soccer Worldcup. The Korean Journal of Measurement and Evaluation in Physical Education and Sport Science. 20(4), 165-174. DOI : 10.21797/ksme.2018.20.4.012
  9. Choi, H. J., & Lee, Y. S. (2019). The prediction of game outcomes based on match data within soccer world cup. Korean Journal of Sports Science. 28(1), 1317-1325. DOI : 10.35159/kjss.2019.02.28.1.1317
  10. Park, Y. S., Kim, J. G., Hwang, B. K., & Joo, C. H. (2019). Characteristics of goal-scoring in major soccer tournaments(FIFA World Cup, UEFA Euro, AFC Asian Cup). Journal of Coaching Development. 21(2), 113-122. DOI : 10.47684/jcd.2019.06.21.2.113
  11. Choi, H. J. (2016). The visualization of the official data for soccer world cup. The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science 18(1), 83-92. DOI : 10.21797/ksme.2016.18.1.007
  12. Choi, Y. H. (2019). A study on the importance of the determinants of korean football game. The Korean Journal of Sport, 17(1), 675-683.
  13. Pappalardo et al. (2019b). A public data set of spatio-temporal match events in soccer competitions. Scientific data, 6(1), 1-15. DOI : 10.6084/m9.figshare.9711164
  14. Fortes, L. S., Nascimento-Junior, J. R., Mortatti, A. L., Lima-Junior, D. R. A. A. D., & Ferreira, M. E. (2018). Effect of dehydration on passing decision making in soccer athletes. Research quarterly for exercise and sport, 89(3), 332-339. DOI : 10.1080/02701367.2018.1488026
  15. Liu, H., Gomez, M. A., Goncalves, B., & Sampaio, J. (2016). Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences, 34: 509-518. DOI : 10.1080/02640414.2015.1117121
  16. Russell, M., Rees, G., & Kingsley, M. I. C. (2013). Technical demands of soccer match play in the English championship. Journal of Strength and Conditioning Research, 27, 2869-2873. DOI : 10.1519/JSC.0b013e318280cc13
  17. Bradley et al. (2011). The effect of playing formation on high-intensity running and technical profiles in English FA Premier League soccer matches. Journal of Sports Sciences, 29, 821-830. DOI : 10.1080/02640414.2011.561868
  18. Oh, I. Y., & Choo, J. H. (2013). Comparison analysis on pass pattern, ball possession ratio of the strong Spain national soccer team of europe soccer : Following 2010 FIFA World Cup South Africa. Journal of Coaching Development. 15(1), 55-61. UCI : G704-001507.2013.15.1.004
  19. Yoo, G. W., An, J. S. (2010). A study on enhancing match performance through analyzing the pass type of the KOR National football team and the champion of the 2010 FIFA World Cup. Korean Journal of Sports Science, 19(4), 733-744. UCI : G704-001369.2010.19.4.117
  20. Park, J. H., Kang, S. J., & Kim, H. J., (2008). Assessment system of performance using computerized pass-notation for soccer. The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science, 10(3), 51-63. DOI : 10.21797/ksme.2008.10.3.003
  21. Kim, S. D., Seong, T. Y., Lee, D. M., & Lee, M. H. (2016). Analysis of network for asian cup soccer final based on social network theory: based on centrality indexes. The Journal of the Korea Contents Association, 16(5), 205-216. DOI : 10.5392/JKCA.2016.16.05.205
  22. Lee, H. H., Kim, J. E., & Park, J. C. (2017). A study on the pass analysis of football game using social networking analysis. Journal of Digital Convergence, 15(7), 479-487. DOI : 10.14400/JDC.2017.15.7.479
  23. Lee, Y. S., & Kim, Y. R. (2018). Comparative analysis of athletic performance of Korea national football team by position and by 15-minute unit at the Asian qualifier of the 2018 Russia World Cup. Korean Journal of Sports Science, 27(1), 825-839. DOI : 10.35159/kjss.2018.02.27.1.825
  24. Oh, I. Y. (2014). Comparative analysis on game contents of Korea national soccer team of 2014 FIFA World Cup Asia qualifiers and 2012 London Olympic games. Korea Coaching Development Center, 16(3), 53-62. UCI : G704-001507.2014.16.3.014
  25. Gudmundsson, J., & Horton, M. (2017). Spatio-temporal analysis of team sports. ACM Computing Surveys (CSUR), 50(2), 1-34. DOI : 10.1145/3054132
  26. Kempe, M., Goes, F. R., & Lemmink, K. A. (2018, October). Smart data scouting in professional soccer: Evaluating passing performance based on position tracking data. In 2018 IEEE 14th International Conference on e-Science, (pp. 409-410). IEEE. DOI : 10.1109/eScience.2018.00126
  27. Oh, I. Y., Lee, G. W., Kim, S. G., & Choo, J. H. (2011). Analysis on pass success rate, ball possession rate and attack routes of the Korean national soccer team following preliminary match result of 2010 FIFA World Cup South Africa. Journal of Coaching Development, 13(1), 133-140. UCI(KEPA) : I410-ECN-0101-2012-692-004503270
  28. Lago-Penas, C., Lago-Ballesteros, J., & Rey, E. (2011). Differences in performance indicators between winning and losing teams in the UEFA Champions League. Journal of human kinetics, 27(2011), 135-146. DOI: 10.2478/v10078-011-0011-3
  29. Evangelos, B., Aristotelis, G., Ioannis, G., Stergios, K., & Foteini, A. (2014). Winners and losers in top level soccer. How do they differ?. Journal of Physical Education and Sport, 14(3), 398. DOI:10.7752/jpes.2014.03061
  30. Choi, K. S.,& Lee, H. J. (2012). The comparison of the type of pass and the pass success rate in the 2010 South Africa World Cup winning team. Journal of Korean Association of Physical Education and Sport for Girls and Women, 26(4), 509-514. UCI(KEPA) : I410-ECN-0101-2015-690-002780408
  31. Scoulding, A., James, N., & Taylor, J. (2004). Passing in the Soccer World Cup 2002. International Journal of Performance Analysis in Sport, 4(2), 36-41. DOI:10.1080/24748668.2004.11868302
  32. Winter, C., & Pfeiffer, M. (2016). Tactical metrics that discriminate winning, drawing and losing teams in UEFA Euro 2012®. Journal of sports sciences, 34(6), 486-492. https://doi.org/10.1080/02640414.2015.1099714
  33. Hwang, J. W., Kim, J. H., & Hong, S. J. (2013). An analysis of comparison on performances in soccer attacking-third. Korean Journal of Sport Science, 24(4), 653-661. DOI : 10.24985/kjss.2013.24.4.653
  34. Smith, M. R., Fransen, J., Deprez, D., Lenoir, M., & Coutts, A. J. (2017). Impact of mental fatigue on speed and accuracy components of soccer-specific skills. Science and medicine in football, 1(1), 48-52. DOI : 10.1080/02640414.2016.1252850
  35. Perl, J., & Memmert, D. (2016). Soccer analyses by means of artificial neural networks, automatic pass recognition and Voronoi-cells: An approach of measuring tactical success. In Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS) (pp. 77-84). Springer, Cham. DOI: 10.1007/978-3-319-24560-7_10
  36. Hong, S. J., (2017). The relative importance of football skill factors based on the position. The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science, 19(4), 89-98. UCI(KEPA) : I410-ECN-0101-2019-692-000992844 https://doi.org/10.21797/KSME.2017.19.4.008
  37. Rein, R., Raabe, D., & Memmert, D. (2017). "Which pass is better?" Novel approaches to assess passing effectiveness in elite soccer. Human movement science, 55, 172-181. https://doi.org/10.1016/j.humov.2017.07.010