PEP: a tackle value measuring the prevention of expected points

TL;DR

This paper introduces PEP, a novel metric that quantifies the value of tackles in American Football by estimating prevented expected points using tracking data and machine learning, providing a more context-aware assessment of defensive performance.

Contribution

The paper develops a new method to measure tackle value in American Football based on prevented expected points, integrating tracking data, random forests, and mixed-effect models for nuanced analysis.

Findings

PEP effectively quantifies tackle value in terms of prevented expected points.

The method reveals differences in tackling effectiveness among players.

The approach enhances understanding of defensive performance in football.

Abstract

Traditional assessments of tackling in American Football often only consider the number of tackles made, without adequately accounting for their context and importance for the game. Aiming for improvement, we develop a metric that quantifies the value of a tackle in terms of the prevented expected points (PEP). Specifically, we compare the real end-of-play yard line of tackles with the predicted yard line given the hypothetical situation that the tackle had been missed. For this, we use high-resolution tracking data, that capture the position and velocity of players, and a random forest to account for uncertainty and multi-modality in yard-line prediction. Moreover, we acknowledge the difference in the importance of tackles by assigning an expected points value to each individual tree prediction of the random forest. Finally, to relate the value of tackles to a player's ability to tackle, we fit a suitable mixed-effect model to the PEP values. Our approach contributes to a deeper understanding of defensive performances in American football and offers valuable insights for coaches and analysts.