  Pairwise comparison models have been widely used for utility evaluation and
ranking across various fields. The increasing scale of problems today
underscores the need to understand statistical inference in these models when
the number of subjects diverges, a topic currently lacking in the literature
except in a few special instances. To partially address this gap, this paper
establishes a near-optimal asymptotic normality result for the maximum
likelihood estimator in a broad class of pairwise comparison models, as well as
a non-asymptotic convergence rate for each individual subject under comparison.
The key idea lies in identifying the Fisher information matrix as a weighted
graph Laplacian, which can be studied via a meticulous spectral analysis. Our
findings provide a unified theory for performing statistical inference in a
wide range of pairwise comparison models beyond the Bradley--Terry model,
benefiting practitioners with theoretical guarantees for their use. Simulations
utilizing synthetic data are conducted to validate the asymptotic normality
result, followed by a hypothesis test using a tennis competition dataset.
