  We provide mechanisms and new metric distortion bounds for line-up elections.
In such elections, a set of $n$ voters, $k$ candidates, and $\ell$ positions
are all located in a metric space. The goal is to choose a set of candidates
and assign them to different positions, so as to minimize the total cost of the
voters. The cost of each voter consists of the distances from itself to the
chosen candidates (measuring how much the voter likes the chosen candidates, or
how similar it is to them), as well as the distances from the candidates to the
positions they are assigned to (measuring the fitness of the candidates for
their positions). Our mechanisms, however, do not know the exact distances, and
instead produce good outcomes while only using a smaller amount of information,
resulting in small distortion.
  We consider several different types of information: ordinal voter
preferences, ordinal position preferences, and knowing the exact locations of
candidates and positions, but not those of voters. In each of these cases, we
provide constant distortion bounds, thus showing that only a small amount of
information is enough to form outcomes close to optimum in line-up elections.
