A Supervised Learning Approach to Rankability

TL;DR

This paper reviews existing rankability measures for data represented as graphs, proposes new efficient methods to assess rankability, and compares these measures using synthetic and real sports data.

Contribution

It introduces new methods for evaluating data rankability that are computationally efficient and provides a comparative analysis with existing measures.

Findings

New methods effectively assess rankability

Existing measures are compared on synthetic data

Application to real sports data demonstrates practical utility

Abstract

The rankability of data is a recently proposed problem that considers the ability of a dataset, represented as a graph, to produce a meaningful ranking of the items it contains. To study this concept, a number of rankability measures have recently been proposed, based on comparisons to a complete dominance graph via combinatorial and linear algebraic methods. In this paper, we review these measures and highlight some questions to which they give rise before going on to propose new methods to assess rankability, which are amenable to efficient estimation. Finally, we compare these measures by applying them to both synthetic and real-life sports data.