A Supervised Learning Approach to Rankability
This work addresses the rankability problem for data analysis, particularly in domains like sports, but is incremental as it builds on existing measures.
The paper tackles the problem of rankability, which assesses a dataset's ability to produce meaningful rankings, by proposing new methods for efficient estimation and comparing them on synthetic and real-life sports data, showing practical applicability.
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.