  We analyse football (soccer) player performance data with mixed type
variables from the 2014-15 season of eight European major leagues. We cluster
these data based on a tailor-made dissimilarity measure.
  In order to decide between the many available clustering methods and to
choose an appropriate number of clusters, we use the approach by Akhanli and
Hennig (2020). This is based on several validation criteria that refer to
different desirable characteristics of a clustering. These characteristics are
chosen based on the aim of clustering, and this allows to define a suitable
validation index as weighted average of calibrated individual indexes measuring
the desirable features.
  We derive two different clusterings. The first one is a partition of the data
set into major groups of essentially different players, which can be used for
the analysis of a team's composition. The second one divides the data set into
many small clusters (with 10 players on average), which can be used for finding
players with a very similar profile to a given player. It is discussed in depth
what characteristics are desirable for these clusterings. Weighting the
criteria for the second clustering is informed by a survey of football experts.
