  We propose a time-varying generalization of the Bradley-Terry model that
allows for nonparametric modeling of dynamic global rankings of distinct teams.
We develop a novel estimator that relies on kernel smoothing to pre-process the
pairwise comparisons over time and is applicable in sparse settings where the
Bradley-Terry may not be fit. We obtain necessary and sufficient conditions for
the existence and uniqueness of our estimator. We also derive time-varying
oracle bounds for both the estimation error and the excess risk in the
model-agnostic setting where the Bradley-Terry model is not necessarily the
true data generating process. We thoroughly test the practical effectiveness of
our model using both simulated and real world data and suggest an efficient
data-driven approach for bandwidth tuning.
