A Temporal Graph Model to Study the Dynamics of Collective Behavior and Performance in Team Sports: An Application to Basketball
<|reference_start|>A Temporal Graph Model to Study the Dynamics of Collective Behavior and Performance in Team Sports: An Application to Basketball: 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.<|reference_end|>
@article{bourgeais2024a,
    title={A Temporal Graph Model to Study the Dynamics of Collective Behavior and
  Performance in Team Sports: An Application to Basketball},
    author={Quentin Bourgeais (CETAPS), Eric Sanlaville (LITIS), Rodolphe Charrier
  (LITIS), Ludovic Seifert (CETAPS)},
    journal={arXiv preprint arXiv:2404.01909},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01909},
    primaryClass={cs.DM}
}