Understanding team collapse via probabilistic graphical models
This work addresses team collapse, a problem for organizations and sports teams, but appears incremental as it applies existing graphical modeling techniques to this domain.
The authors tackled the problem of understanding team collapse by developing a probabilistic graphical model to capture team dynamics, using simulations and real-world experiments to identify main causes and provide principles for building resilient teams, with concrete analysis applied to NBA teams.
In this work, we develop a graphical model to capture team dynamics. We analyze the model and show how to learn its parameters from data. Using our model we study the phenomenon of team collapse from a computational perspective. We use simulations and real-world experiments to find the main causes of team collapse. We also provide the principles of building resilient teams, i.e., teams that avoid collapsing. Finally, we use our model to analyze the structure of NBA teams and dive deeper into games of interest.