  The Bradley-Terry model is widely used for pairwise comparison data analysis.
In this paper, we analyze the asymptotic behavior of the maximum likelihood
estimator of the Bradley-Terry model in its logistic parameterization, under a
general class of linear identifiability constraints. We show that the
constraint requiring the Bradley-Terry scores for all compared objects to sum
to zero minimizes the sum of the variances of the estimated scores, and
recommend using this constraint in practice.
