  The Mallows model is a popular distribution for ranked data. We empirically
and theoretically analyze how the properties of rankings sampled from the
Mallows model change when increasing the number of alternatives. We find that
real-world data behaves differently than the Mallows model, yet is in line with
its recent variant proposed by Boehmer et al. [2021]. As part of our study, we
issue several warnings about using the model.
