Bayesian multilevel step-and-turn models for evaluating player movement in American football

TL;DR

This paper introduces a Bayesian multilevel modeling framework for analyzing and evaluating player movement in American football, enabling comparison of observed actions against simulated alternatives to assess performance.

Contribution

The paper presents a novel generative Bayesian multilevel model for frame-level player movement, specifically modeling step length and turn angle in NFL player tracking data.

Findings

Developed a framework for generating hypothetical player movements.

Applied the model to NFL data to derive new performance metrics.

Enabled comparison of observed and simulated player trajectories.

Abstract

In sports analytics, player tracking data have driven significant advancements in the task of player evaluation. We present a novel generative framework for evaluating the observed frame-by-frame player positioning against a distribution of hypothetical alternatives. We illustrate our approach by modeling the within-play movement of an individual ball carrier in the National Football League (NFL). Specifically, we develop Bayesian multilevel models for frame-level player movement based on two components: step length (distance between successive locations) and turn angle (change in direction between successive steps). Using the step-and-turn models, we perform posterior predictive simulation to generate hypothetical ball carrier steps at each frame during a play. This enables comparison of the observed player movement with a distribution of simulated alternatives using common valuation measures in American football. We apply our framework to tracking data from the first nine weeks of the 2022 NFL season and derive novel player performance metrics based on hypothetical evaluation.