  Judging scholarly posters creates a challenge to assign the judges
efficiently. If there are many posters and few reviews per judge, the commonly
used Balanced Incomplete Block Design is not a feasible option. An additional
challenge is an unknown number of judges before the event. We propose two
connected near-balanced incomplete block designs that both satisfy the
requirements of our setting: one that generates a connected assignment and
balances the treatments and another one that further balances pairs of
treatments. We describe both fixed and random effects models to estimate the
population marginal means of the poster scores and rationalize the use of the
random effects model. We evaluate the estimation accuracy and efficiency,
especially the winning chance of the truly best posters, of the two designs in
comparison with a random assignment via simulation studies. The two proposed
designs both demonstrate accuracy and efficiency gains over the random
assignment.
