title: Analysis of football game-related statistics using multivariate techniques.
abstract: Abstract The purpose of this study was to explore football game-related statistics during a competition, using principal component and cluster analyses to determine if it is possible to distinguish the winning teams from the drawing and losing ones. We collected the game-related statistics of the group phase matches of the 2006 World Cup and organised them into a matrix. The principal components of the covariance matrix were calculated. The scores of the first and second components were used to represent the new data, and cluster analysis was applied to separate the elements in two groups (G1 and G2). To analyse the degree of separation between the groups, we calculated the Silhouette Coefficient for each group. Finally, we checked if the winning teams were classified into the same group. The Silhouette Coefficients found for G1 and G2 were 0.54 and 0.55, respectively. Results showed that 70.3% of the winning teams were classified into the same group (G1). Similarly, 67.8% of the drawing and losing teams were classified in G2. This study presented a different way to analyse game-related statistics that allowed the multivariate differences to be shown between successful and unsuccessful teams.
authors.affiliation: a Sport Sciences Department , Laboratory of Applied Biomechanics, State University of Londrina , Londrina , Brazil.
authors.forename: Felipe Arruda
authors.initials: FA
authors.lastname: Moura
authors.forename: Luiz Eduardo Barreto
authors.initials: LE
authors.lastname: Martins
authors.forename: Sergio Augusto
authors.initials: SA
authors.lastname: Cunha
keywords: cluster analysis; match performance; notational analysis; principal components