  We argue that current IR metrics, modeled on optimizing user experience,
measure too narrow a portion of the IR space. If IR systems are weak, these
metrics undersample or completely filter out the deeper documents that need
improvement. If IR systems are relatively strong, these metrics undersample
deeper relevant documents that could underpin even stronger IR systems, ones
that could present content from tens or hundreds of relevant documents in a
user-digestible hierarchy or text summary. We reanalyze over 70 TREC tracks
from the past 28 years, showing that roughly half undersample top ranked
documents and nearly all undersample tail documents. We show that in the 2020
Deep Learning tracks, neural systems were actually near-optimal at top-ranked
documents, compared to only modest gains over BM25 on tail documents. Our
analysis is based on a simple new systems-oriented metric, 'atomized search
length', which is capable of accurately and evenly measuring all relevant
documents at any depth.
