Authors:
Pavlina Kröckel | University of Erlangen-Nuremberg | Germany
Alexander Piazza | University of Erlangen-Nuremberg | Germany
The European Championship in 2016 was highlighted by the unexpected success of relatively unpopular teams like Iceland and Wales. In this paper, we focus on two games of the Iceland’s team. There are a couple of aspects that make this team interesting to analyze. First, this was the first appearance of Iceland in the 60 years of history of the European Championship. Second, Iceland does not have professional football clubs and its national team players are not playing at high professional level as the players of other more popular teams. Third, Iceland demonstrated that a tactic considered outdated by most modern managers, the 4-4-2, should not be overlooked. Iceland was eliminated at the quarter finals stage by France which is a strong competitor. Nevertheless, the team managed a draw against Portugal and a win against England. Therefore, we chose Iceland for our analysis.
In this paper, we derive the networks of the passing sequences of Iceland’s team against the teams of Portugal and England. The potential of social network analysis techniques on football data has been previously discussed in the literature (Clemente, Couceiro, Martins, & Mendes, 2015; Clemente, Lourenço, Kalamaras, Wong, & Mendes, 2015; Clemente, Martins, & Mendes, 2015; Cotta, Mora, Merelo, & Merelo-Molina, 2013; Loughead et al., 2016; Lusher, Robins, & Kremer, 2010; Yamamoto & Yokoyama, 2011). In our study, we use event data provided by Opta Sports, and analyze both games by using metrics at player, team and sub-group level of analysis. We first calculate and interpret well established network metrics such as density, betweenness and closeness centrality, clustering coefficient, PageRank, network diameter. This gives an overview of the static networks of each team for the whole game. In a second step, we integrate the time dimension by adding the timestamp attribute of each pass. We split the data in several time segments and analyze the metrics’ evolution over time. Finally, we interpret the metrics results in relation to the teams’ formation and the outcome of the match.