Simulating Defensive Trajectories in American Football for Predicting League Average Defensive Movements - Schmid, Marc; Blauberger, Patrick; Lames, Martin (Frontiers in Sports and Active Living, 2021)
This study proposes a data driven ghosting approach in American football. Deep neural networks are trained with a multi agent imitation learning approach, using players' tracking data of a whole NFL regular season. This approach introduces a method to evaluate defensive trajectories, by comparing the movements of individual plays to simulated league average behavior. To evaluate the quality of the predicted movements, a formation based pass completion probability model is introduced. With the implementation of a learnable order invariant model, based on insights of molecular dynamical machine learning, the accuracy of the model is increased to 81%. The trained pass completion probability model is used to evaluate the ghosted trajectories and serves as a metric to compare the true trajectory to the ghosted ones. Additionally, the study evaluates the ghosting approach with respect to different optimization methods and dataset augmentation. It is shown that a multi agent imitation learning approach, trained with a dataset aggregation method outperforms vanilla approaches on the dataset. This network and evaluation scheme presents a new method for teams, sports analysts and sport scientists to evaluate defensive plays in American football and lays the foundation for more sophisticated data driven simulation methods.