  Expressive Range Analysis (ERA), an approach for visualising the output of
Procedural Content Generation (PCG) systems, is widely used within PCG research
to evaluate and compare generators, often to make comparative statements about
their relative performance in terms of output diversity and search space
exploration. Producing a standard ERA visualisation requires the selection of
two metrics which can be calculated for all generated artefacts to be
visualised. However, to our knowledge there are no methodologies or heuristics
for justifying the selection of a specific metric pair over alternatives. Prior
work has typically either made a selection based on established but unjustified
norms, designer intuition, or has produced multiple visualisations across all
possible pairs. This work aims to contribute to this area by identifying
valuable characteristics of metric pairings, and by demonstrating that pairings
that have these characteristics have an increased probability of producing an
informative ERA projection of the underlying generator. We introduce and
investigate three quantifiable selection criteria for assessing metric pairs,
and demonstrate how these criteria can be operationalized to rank those
available. Though this is an early exploration of the concept of quantifying
the utility of ERA metric pairs, we argue that the approach explored in this
paper can make ERA more useful and usable for both researchers and game
designers.
