Efficient Computation of Rankings from Pairwise Comparisons
Abstract
We study the ranking of individuals, teams, or objects, based on pairwise comparisons between them, using the Bradley-Terry model. Estimates of rankings within this model are commonly made using a simple iterative algorithm first introduced by Zermelo almost a century ago. Here we describe an alternative and similarly simple iteration that provably returns identical results but does so much faster—over a hundred times faster in some cases. We demonstrate this algorithm with applications to a range of example data sets and derive a number of results regarding its convergence.
Cite
Text
Newman. "Efficient Computation of Rankings from Pairwise Comparisons." Journal of Machine Learning Research, 2023.Markdown
[Newman. "Efficient Computation of Rankings from Pairwise Comparisons." Journal of Machine Learning Research, 2023.](https://mlanthology.org/jmlr/2023/newman2023jmlr-efficient/)BibTeX
@article{newman2023jmlr-efficient,
title = {{Efficient Computation of Rankings from Pairwise Comparisons}},
author = {Newman, M. E. J.},
journal = {Journal of Machine Learning Research},
year = {2023},
pages = {1-25},
volume = {24},
url = {https://mlanthology.org/jmlr/2023/newman2023jmlr-efficient/}
}