  We modeled the dynamics of a soccer match based on a network representation
where players are nodes discretely clustered into homogeneous groups. Players
were grouped by physical proximity, supported by the intuitive notion that
competing and same-team players use relative position as a key tactical tool to
contribute to the team's objectives. The model was applied to a set of matches
from a major European national football league, with players' coordinates
sampled at 10Hz, resulting in approx. 60,000 network samples per match. We took
an information theoretic approach to measuring distance between samples and
used it as a proxy for the game dynamics. Significant correlations were found
between measurements and key match events that are empirically known to result
in players jostling for position, such as when striving to get unmarked or to
mark. These events increase the information distance, while breaks in game play
have the opposite effect. By analyzing the frequency spectrum of players'
cluster transitions and their corresponding information distance, it is
possible to build a comprehensive view of player's interactions, useful for
training and strategy development. This analysis can be drilled down to the
level of individual players by quantifying their contribution to cluster
breakup and emergence, building an overall multi-level map that provides
insights into the game dynamics, from the individual player, to the clusters of
interacting players, all the way to the teams and their matches.

Social and Information Networks;Physics and Society