Understanding team collapse via probabilistic graphical models
Iasonas Nikolaou Konstantinos Pelechrinis Evimaria Terzi
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.