  Sports analytics has been an up-and-coming field of research among
professional sporting organizations and academic institutions alike. With the
insurgence and collection of athlete data, the primary goal of such analysis is
to improve athletes' performance in a measurable and quantifiable manner. This
work is aimed at alleviating some of the challenges encountered in the
collection of adequate swimming data. Past works on this subject have shown
that the detection and tracking of swimmers is feasible, but not without
challenges. Among these challenges are pool localization and determining the
relative positions of the swimmers relative to the pool. This work presents two
contributions towards solving these challenges. First, we present a pool model
with invariant key-points relevant for swimming analytics. Second, we study the
detectability of such key-points in images with partial pool view, which are
challenging but also quite common in swimming race videos.
