Title: Statistical ranking with dynamic covariates

Abstract: The Plackett–Luce (PL) model has long been used for rank aggregation in sports analytics and social choice. In this talk, we introduce a covariate-assisted ranking model within the PL framework that incorporates dynamic covariates. This added flexibility enables individualized and dynamic rankings and improves model fit, but it also introduces challenges for analysis. We address these challenges in the context of maximum likelihood estimation (MLE) under a general network topology. Specifically, we establish conditions for model identifiability and the unique existence of the MLE, propose an alternating maximization algorithm for computing the MLE, and prove a uniform consistency result. We illustrate the proposed model through applications to ATP tennis data spanning the past 40+ years and to a horse racing dataset.