  We propose using Network Science as a complementary tool to analyze player
and team behavior during a football match. Specifically, we introduce four
kinds of networks based on different ways of interaction between players. Our
approach's main novelty is to use tracking datasets to create football tracking
networks, instead of constructing and analyzing the traditional networks based
on events. In this way, we are able to capture player interactions that go
beyond passes and introduce the concepts of (a) Ball Flow Networks, (b) Marking
Networks, (c) Signed Proximity Networks and (d) Functional Coordination
Networks. After defining the methodology for creating each kind of network, we
show some examples using tracking datasets from four different matches of
LaLiga Santander. Finally, we discuss some of the applications, limitations,
and further improvements of football tracking networks.
