  In this study, a temporal graph model is designed to model the behavior of
collective sports teams based on the networks of player interactions. The main
motivation for the model is to integrate the temporal dimension into the
analysis of players' passing networks in order to gain deeper insights into the
dynamics of system behavior, particularly how a system exploits the degeneracy
property to self-regulate. First, the temporal graph model and the entropy
measures used to assess the complexity of the dynamics of the network structure
are introduced and illustrated. Second, an experiment using basketball data is
conducted to investigate the relationship between the complexity level and team
performance. This is accomplished by examining the correlations between the
entropy measures in a team's behavior and the team's final performance, as well
as the link between the relative score compared to that of the opponent and the
entropy in the team's behavior. Results indicate positive correlations between
entropy measures and final team performance, and threshold values of relative
score associated with changes in team behavior -- thereby revealing common and
unique team signatures. From a complexity science perspective, the model proves
useful for identifying key performance factors in team sports and for studying
the effects of given constraints on the exploitation of degeneracy to organize
team behavior through various network structures. Future research can easily
extend the model and apply it to other types of social networks.
