Rankings play a crucial role in various contexts but often exhibit incompleteness, top-weightedness, and indefiniteness. Comparing rankings can reveal underlying similarities, yet traditional correlation coefficients like Kendall's tau do not adequately address these complexities. Rank-Biased Overlap (RBO) addresses these challenges by accommodating differences in rank length, appropriately weighting ranks, and minimizing data assumptions. This paper investigates the average Rank-Biased Overlap (RBO) between independent rankings, addressing the need for clearly indicated reference values similar to those of correlation coefficients. Our study explores how the expected RBO changes with varying p-parameters, prefix lengths, and degrees of conjointness between domains.
To facilitate this analysis, an algorithm is developed that performs extensive simulations across different values of p, list and domain sizes. By analyzing the simulation results, trends are provided in the average RBO between independent rankings based on these varying parameters and establish relevant reference values. This study focuses on scenarios where prefixes are of the same length and there are no ties in the rankings.