Assessment of Football Cooperation Performance Based on Evaluation Model: a Case Study of the Everton Team


Assessment of Football Cooperation Performance Based on Evaluation Model: a Case Study of the Everton Team


Boying Lv1*, Yongxin Chen2, Jiatao Li1, Yishuai Tian1

1College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China.
2College of Science, China Three Gorges University, Yichang, 443002, China.


To comprehensively measure the effect of football team cooperation, this paper established a football team performance evaluation model and takes the performance of Everton F.C. in 2017 to 2018 season as an example. We selected 12 indicators from three aspects and use hierarchical clustering to divide the performance into four levels: very successful, relatively successful, unsuccessful and very failed. Then, we evaluated the performance of the team’s changes and focus on the opponent’s strategy indicators to analyze its impact. It is found that the reason for most of the failed games is that the team is affected by the away effect, the intra-team cooperation is not dominant and the opponent’s ability is strong, resulting in low CPI scores. At the end of the season, the influence of the opponent strategy on team performance becomes stronger and stronger.


Keywords: Football team performance; Everton F.C.; Entropy weight method; Coefficient of variation method

Free Full-text PDF


How to cite this article:
Boying Lv, Yongxin Chen, Jiatao Li, Yishuai Tian. Assessment of Football Cooperation Performance Based on Evaluation Model: a Ca-se Study of the Everton Team. Ad-\vances in Research and Reviews, 2020; 1:6. DOI: 10.28933/arr-2020-05-1605


References:

1. Strategies for effective collaborative manuscript development in interdisciplinary science teams [J]. Ecosphere, 2018, 9.
2. Wuchty S, Jones B F, Uzzi B. The Increasing Dominance of Teams in Production of Knowledge[J]. ence, 2007, 316(5827): p.1036-1039.
3. Hughes M, Franks I. Analysis of passing sequences, shots and goals in soccer[J]. Journal of Sports Sciences, 2005, 23(5):509-514.
4. Jinshan Xu, Analysis of Patterns In Football passing skills[J].
5. J. M. Buldú, Busquets J, Echegoyen I, et al. Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona[J]. entific Reports, 2019, 9(1).
6. Cintia P, Pappalardo L, Pedreschi D, et al. The harsh rule of the goals: Data-driven performance indicators for football teams[C]// IEEE International Conference on Data Science & Advanced Analytics. IEEE, 2015.
7. Jordi Duch, Waitzman Joshua-S, Amaral Luis-A-Nunes. Quantifying the Performance of Individual Players in a Team Activity[J]. PLOS ONE, 2010, 5(e109376).
8. Hui Tong, an analysis of the goal characteristics of Real Madrid in the Champions League from 2016 to 2017[J]. Contemporary Sports Science and Technology, (25).
9. Home. 2020. https://www.optasports.com/
10. V Armatas, Yiannakos A, Sileloglou P. Relationship between time and goal scoring in soccer games: Analysis of three World Cups[J]. International Journal of Performance Analysis in Sport, 2007, 7(2): 48-58.
11. Tao Wen, Deng Yong. The vulnerability of communities in complex network: An entropy approach[J].
12. Faber D S, Korn H. Applicability of the coefficient of variation method for analyzing synaptic plasticity[J]. Biophysical Journal, 1991, 60(5):1288-1294.
13. G Karypis, Han Eui-Hong, Kumar V. Chameleon: hierarchical clustering using dynamic model-ing[J]. Computer, 32(8): 68-75.
14. Conor C. ‘Ireland’s Second Capital’? Irish Footballers’ Migration to Liverpool, the Growth of Support and the Organisation of Liverpool and Everton Football Clubs’ Matches in Dublin: An Historical Assessment[J]. Immigrants & Minorities, 2018:1-29.