NFL Ghosts: A framework for evaluating defender positioning with conditional density estimation

TL;DR

This paper introduces a novel framework for evaluating defender positioning in American football using conditional density estimation on player tracking data, providing new metrics for performance analysis.

Contribution

It presents the first public framework to evaluate player positioning relative to ghost defenders using flexible random forests for conditional density estimation.

Findings

Effective modeling of defender positioning and receiver yards gained.

New metrics for assessing player and team performance based on tracking data.

Demonstrated the framework's utility in analyzing catch scenarios.

Abstract

Player attribution in American football remains an open problem due to the complex nature of twenty-two players interacting on the field, but the granularity of player tracking data provides ample opportunity for novel approaches. In this work, we introduce the first public framework to evaluate spatial and trajectory tracking data of players relative to a baseline distribution of "ghost" defenders. We demonstrate our framework in the context of modeling the nearest defender positioning at the moment of catch. In particular, we provide estimates of how much better or worse their observed positioning and trajectory compared to the expected play value of ghost defenders. Our framework leverages multi-dimensional tracking data features through flexible random forests for conditional density estimation in two ways: (1) to model the distribution of receiver yards gained enabling the estimation of within-play expected value, and (2) to model the 2D spatial distribution of baseline ghost defenders. We present novel metrics for measuring player and team performance based on tracking data, and discuss challenges that remain in extending our framework to other aspects of American football.