We revisit the problem of inferring the overall ranking among entities in the
framework of Bradley-Terry-Luce (BTL) model, based on available empirical data
on pairwise preferences. By a simple transformation, we can cast the problem as
that of solving a noisy linear system, for which a ready algorithm is available
in the form of the randomized Kaczmarz method. This scheme is provably
convergent, has excellent empirical performance, and is amenable to on-line,
distributed and asynchronous variants. Convergence, convergence rate, and error
analysis of the proposed algorithm are presented and several numerical
experiments are conducted whose results validate our theoretical findings
