For mapping football (soccer) player information by using multidimensional scaling, and for clustering football players, we construct a distance measure based on players’ performance data. The variables are of mixed type, but the main focus of this paper is how count variables are treated when defining a proper distance measure between players (e.g., top and lower level variables). The distance construction involves four steps: 1) representation , 2) transformation, 3) standardisation, 4) variable weighting. Several distance measures are discussed in terms of how well they match the interpretation of distance and similarity in the application of interest, with a focus on comparing Aitchison and Manhattan distance for variables giving percentage compositions. Preliminary outcomes of multidimensional scaling and clustering are shown.