Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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A Study of Influencing Factors on World Handball Win-Loss using the Decision Tree Analysis

의사결정나무 분석을 통한 세계핸드볼 승패결정요인 분석

  • Received : 2021.04.07
  • Accepted : 2021.05.20
  • Published : 2021.05.28
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Abstract

The purpose of this study is to collect official records of the 2019 Men's and Women's Handball World Championships to identify important shooting variables that determine the team's record of winning or losing. After collecting 192 games of men's and women's national teams from 24 countries and verifying the difference in competition records according to the winning and losing groups, the decision tree method, one of the data mining techniques, is analyzed. According to the analysis, the 9m shooting success rate and Near shooting success rate were the most important factors for both men and women. Men win 83.3% if the 9m shooting success rate is 32.5% or higher and the Near shooting success rate is 67.5%, and women win 75% if the 9m shooting success rate is 75% or more and the Near shooting success rate is 51%. Also, the women's yellow cards are considered important variables that determine victory or defeat. In conclusion, both men and women were able to identify the factors of winning and losing decision shooting, but follow-up studies are needed considering the relativity of various record variables and performance in future handball.

이 연구는 2019년 남녀 핸드볼 세계선수권대회에 참가하는 모든 국가를 대상으로 국제핸드볼연맹이 제공한 공식 기록을 수집해 팀 경기 기록의 승패를 가리는 중요한 슈팅 변수를 규명하는 데 목적이 있다. 이 연구의 목적을 달성하기 위해 2019 핸드볼 세계선수권대회에 참가한 24개국 남녀대표팀의 총 192경기를 수집해 승패 그룹에 따른 대회 기록의 차이를 검증한 이후 8가지 슈팅 변수에 따라 승패요인을 분류하기 위해 데이터마이닝 기법의 하나인 의사결정 트리 방식(CART 알고리즘)을 적용해 분석했다. 분석 결과 9m 슈팅성공률과 Near 슈팅성공률이 남녀 모두에게 가장 중요한 요인으로 평가됐다. 남자는 9m 슈팅성공률이 32.5% 이상, Near 슈팅성공률이 67.5% 이상이면 83.3% 승리하며, 여자는 9m 슈팅성공률이 75%이상, Near 슈팅성공률이 51% 이상이면 75%를 승리한다. 또한, 여자팀은 페널티 요인 중 옐로카드는 승패를 결정짓는 중요 변수로 판단된다. 결론적으로 본 연구를 통하여 국제핸드볼 경기에서 승리와 패배 팀의 기록 특성 차이와 승패를 구분하는 중요 슈팅 변수를 남녀 모두에서 확인 할 수 있었다.

Keywords

References

  1. Hatzimanouil, D. (2020). Goalkeeper's Efficiency in Relation with Throws from Different Attacking Court Areas in Team Handball. Journal of Physical Education, 7(1), 11-18. DOI: 10.15640/jpesm.v7n1a2
  2. H. J. Kim, J. R. Park, J. H. Park, E. H. Cho. (2013). Evaluation of Handball Performance Based on Quantitative Index. The Korean Journal of Measurement and Evaluation in Physical Education and Sport Science, 15(1), 1-12. DOI : 10.21797/ksme.2013.15.1.001
  3. Volossovitch, A. (2005). Analysis of the performance in handball: perspectives and tendencies. Technical Handball Magazine, 3, 16-20.
  4. Meletakos, P., Vagenas, G., & Bayios, I. (2011). A multivariate assessment of offensive performance indicators in Men's Handball: Trends and differences in the World Championships. International Journal of Performance Analysis in Sport, 11(2), 285-295. DOI : 10.1080/24748668.2011.11868548
  5. Ferrari, W.R., Valente Dos Santos, J., & Simoes Vaz, V.P. (2014). Offensive process analysis in handball: Identification of game actions that differentiate winning from losing teams. American Journal of Sports Science, 2(4), 92-96. DOI : 10.11648/j.ajss.20140204.14
  6. C. H. Choi, J. W. Yoon. (2017). Cycling winner prediction model by using match information : application of decision tree analysis based on data mining, The Korean Journal of Measurement and Evaluation in Physical Education and Sport Science, 19(4), 15-26. DOI : 10.21797/ksme.2017.19.4.002
  7. A Gomez, M., Lago-Penas, C., Viano, J., & Gonzalez-Garcia, I. (2014). Effects of game location, team quality and final outcome on game-related statistics in professional handball close games. Kinesiology: International journal of fundamental and applied kinesiology, 46(2), 249-257. DOI : 796.322:796.092.298
  8. S. H. Kim (2012). Estimating the Determinants of Victory and Defeat through Analyzing Records of Korea Handball Game. Korean Journal of Sport Science, 23(2), 244-253. DOI : 10.24985/kjss.2012.23.2.244
  9. J. S. Lee, K. K. Kim, S. K. Lee, D. J. Kim, Y. K. Kim. (2007). A Analysis of Aggression and Competitive Anxiety as Antecedent Variable of the Outcome in Handball Game. Journal of Sport and Leisure Studies. 0(31). 1201-1210. DOI : G704-000763.2007..31.099
  10. de Paula, L. V., Costa, F. E., Ferreira, R. M., Menezes, R. P., Werneck, F. Z., Coelho, E. F., & Greco, P. J. (2020). Analysis of Discriminatory Game Variables Between Winners and Losers in Women's Handball World Championships from 2007 to 2017. Kinesiology, 52(1), 54-63. DOI : 10.26582/k.52.1.6
  11. Ferrari, W., Vaz, V., Sousa, T., Couceiro, M., & Dias, G. (2018). Comparative analysis of the performance of the winning teams of the handball world championship: senior and junior levels. International Journal of Sports Science, 8(2), 43-49. DOI: 10.5923/j.sports.20180802.01
  12. Daza, G., Andres, A., & Tarrago, R. (2017). Match Statistics as Predictors of Team's Performance in Elite competitive Handball.[Estadistica del partido como predictor del rendimiento de equipo en el balonmano de elite]. RICYDE. Revista Internacional de Ciencias del Deporte. 13(48), 149-161. DOI: 10.5232/ricyde,
  13. Vuleta, D., Rogulj, N., & Milanovic, D. (2017, January). Differences between winning and defeated handball teams in competition performance indicators. In 8th International Scientific Conference on Kinesiology.
  14. J. H. Kim, H. J. Choi. (2015). Reinterpretation of Winning and Losing Performances based on Different Data Ranges. The Korean Journal of Measurement and Evaluation in Physical Education and Sport Science, 17(1), 1-12. DOI : 10.21797/ksme.2015.17.1.001
  15. S. H. Oh. (2005). A Historical Study on the Introduction and Development In Korea Handball. Master degree. Pukyong National University, Busan.
  16. H, K. Chung, S. S. Baek. (2017). Analysis of Handball Strategies of World Top Class Team to Prepare Rio 2016 Olympic Games: Focusing on Women's Team. Sport Science. 34(2), 175-180.
  17. Utgoff, P. E. (1989). Incremental induction of decision trees. Machine learning, 4(2), 161-186. https://doi.org/10.1023/A:1022699900025
  18. J. H. Hong, J. H. Park. (2016). Analysis Athletic Performance Assessment Factors and Importance Based on Handball Players" Position. The Korea Journal of Sports Science, 25(4), 1443-1454. DOI : G704-001369.2016.25.4.081
  19. H, K. Chung. (2006). Analysis of Games for Performance Evaluation in Men's Handball. Journal of coaching development, 8(1), 125-132. DOI : G704-001507.2006.8.1.015
  20. S. H. Kim, H. J. Kim, J. H. Park. (2011). Development of Model to Evaluate Handball Shooting Ability: Weight Elicitation of Shooting Positions. The Korean Journal of Measurement and Evaluation in Physical Education and Sport Science., 13(3), 77-87. DOI : 10.21797/ksme.2011.13.3.007
  21. H. J. Choi. (2016). The Visualization of the Official Data for Soccer World Cup. The Korean Journal of Measurement and Evaluation in Physical Education and Sport Science. 18(1), 83-92. DOI : 10.21797/ksme.2016.18.1.007
  22. B. R. Oh. (2002). A Study on Scoring Types of Handball Games. Master degree. Wonkwang University,