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.

翻译：预测防守覆盖阵型是美式橄榄球进攻方至关重要却极具挑战的任务。由于防守队员的职责在开球前被刻意隐藏，实时识别这些部署极为困难。为应对这一挑战，我们开发了一个基于球员追踪数据的统计框架，整合了监督学习与无监督学习方法。本研究目标是通过弹性网络逻辑回归和梯度提升决策树，结合渐进式提取的特征，预测防守覆盖阵型——人盯人或区域防守。我们首先利用运动前态势的特征，随后以简单方式纳入球员运动过程中的轨迹数据，最终引入基于隐马尔可夫模型（HMM）构建的特征。该非齐次HMM根据球员移动数据推断运动过程中攻防球员间潜在的防守职责分配，并将解码后的状态序列转化为监督模型的有效特征。这些基于HMM的特征显著提升了预测性能，且与防守覆盖结果存在显著关联。此外，估计的随机效应为不同防守体系及位置如何调整其覆盖职责提供了可解释的洞见。