Conference Agenda

Choosing between man and zone coverage is one of the most critical strategic decisions a defensive coordinator must make before each offensive play in American football. In simple terms, in man coverage each defender is assigned to guard a specific offensive player, while zone coverage requires defenders to protect designated areas of the field. This choice fundamentally shapes how the defense reacts to offensive formations and movements. Traditionally, experienced offensive coordinators and quarterbacks rely on visual cues, such as defenders’ alignment or pre-snap motion, to infer these defensive schemes. However, with the increasing availability of high-resolution player tracking data, statistical models can now uncover such tactical patterns quantitatively rather than relying solely on expert intuition.

In this project, we first employ an elastic net and an XGBoost classifier to predict whether a defense is in man or zone coverage based on all players’ positions once both teams are set before the snap. The models thus captures spatial configurations that often reveal underlying defensive intentions. In a second step, we incorporate dynamic information from pre-snap player movements. Finally, in a third step, we employ features derived from a hidden Markov model (HMM). Specifically, we use an HMM to represent defenders’ movement trajectories over time. The hidden states correspond to potential offensive players being covered by each defender. From the decoded state sequences, we extract summary statistics, such as the number of state (defender) switches. Including these HMM-based features in the aforementioned models significantly enhances the model’s predictive accuracies. Beyond the pure classification performance, our approach also enables deeper tactical analyses. For instance, it allows us to explore how pre-snap motion helps offenses identify defensive coverages more effectively. Comparing these pre- and post-motion probabilities provides insight into how well offensive movements reveal defensive strategies.

Overall, this framework demonstrates how modern machine learning techniques in combination with a statistical model can provide quantitative insights into complex team sports tactics. While developed within an American football context, the methodology may generalize to other sports where spatial positioning and interaction dynamics play similarly crucial roles.