Analysis of the offensive process of AS Monaco professional soccer team: A mixed-method approach

• Published in: Chaos, solitons and fractals Vol. 133; p. 109676
• Main Authors: Sarmento, Hugo, Clemente, Filipe Manuel, Gonçalves, Eder, Harper, Liam D, Dias, Diogo, Figueiredo, António
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2020
• Subjects: Association football; Graphs theory; Match analysis; Qualitative analysis; Quantitative analysis; Social network analysis; Match analysis; Graphs theory; Qualitative analysis; Social network analysis; Association football; Quantitative analysis
• ISSN: 0960-0779; 1873-2887
• DOI: 10.1016/j.chaos.2020.109676

The purpose of this research was to analyze the offensive process of AS Monaco through the combination of network methods and semi-structured interviews of two coaches from the technical staff. The sample included 16 home matches of AS Monaco, resulting in 1569 passes analyzed and converted in a weighted adjacency matrix. Using that matrix, macro network measures and network centralities were calculated. Moreover, semi-structured interviews were carried out with two members of the technical staff (head coach and performance analyst). Data were analyzed using content analysis via Nvivo 11.0. There was a moderate degree of heterogeneity in the passing sequences, with the most prominent players identified as 10 (defensive midfielder), 11 (box-to-box midfielder) and 7 (central defender) that, interestingly, were nominated by the coaches as the main players in the attacking process. It was also revealed that the region of the pitch with greater centrality levels was the right pre-offensive zone. Through the content analysis we observed that coaches interpreted these results based on: (1) tactical-strategic aspects; (2) tactical-technical aspects, and; (3) the characteristics of the players on their team. Some important information about the specificities of the game style came from their analysis. This cooperation between scientists and technical staff is productive and should be used regularly in order to improve both scientific and training methods.