Integrating Unsupervised and Supervised Learning for the Prediction of Defensive Schemes in American football

TL;DR

This paper presents a novel statistical framework combining supervised and unsupervised learning to predict defensive schemes in American football using player tracking data, improving real-time recognition accuracy.

Contribution

It introduces an integrated approach using HMM-derived features with elastic net and gradient boosting models for better coverage scheme prediction.

Findings

HMM-based features improve predictive accuracy

Significant association between features and coverage outcomes

Provides interpretable insights into defensive adjustments

Abstract

Anticipating defensive coverage schemes is a crucial yet challenging task for offenses in American football. Because defenders' assignments are intentionally disguised before the snap, they remain difficult to recognize in real time. To address this challenge, we develop a statistical framework that integrates supervised and unsupervised learning using player tracking data. Our goal is to forecast the defensive coverage scheme -- man or zone -- through elastic net logistic regression and gradient-boosted decision trees with incrementally derived features. We first use features from the pre-motion situation, then incorporate players' trajectories during motion in a naive way, and finally include features derived from a hidden Markov model (HMM). Based on player movements, the non-homogeneous HMM infers latent defensive assignments between offensive and defensive players during motion and transforms decoded state sequences into informative features for the supervised models. These HMM-based features enhance predictive performance and are significantly associated with coverage outcomes. Moreover, estimated random effects offer interpretable insights into how different defenses and positions adjust their coverage responsibilities.