  In common law jurisdictions, legal practitioners rely on precedents to
construct arguments, in line with the doctrine of \emph{stare decisis}. As the
number of cases grow over the years, prior case retrieval (PCR) has garnered
significant attention. Besides lacking real-world scale, existing PCR datasets
do not simulate a realistic setting, because their queries use complete case
documents while only masking references to prior cases. The query is thereby
exposed to legal reasoning not yet available when constructing an argument for
an undecided case as well as spurious patterns left behind by citation masks,
potentially short-circuiting a comprehensive understanding of case facts and
legal principles. To address these limitations, we introduce a PCR dataset
based on judgements from the European Court of Human Rights (ECtHR), which
explicitly separate facts from arguments and exhibit precedential practices,
aiding us to develop this PCR dataset to foster systems' comprehensive
understanding. We benchmark different lexical and dense retrieval approaches
with various negative sampling strategies, adapting them to deal with long text
sequences using hierarchical variants. We found that difficulty-based negative
sampling strategies were not effective for the PCR task, highlighting the need
for investigation into domain-specific difficulty criteria. Furthermore, we
observe performance of the dense models degrade with time and calls for further
research into temporal adaptation of retrieval models. Additionally, we assess
the influence of different views , Halsbury's and Goodhart's, in practice in
ECtHR jurisdiction using PCR task.
