Rankings are more present in our daily lives than most people realize. Whether you are browsing Netflix and getting movies or shows based on your previous likes or dislikes, or you want to compare search engine results. To use rankings in the field of Computer Science a rank similarity is needed. Rank-Biased Overlap is one of those. It is top-weighted, can be used on uneven rankings, and when only a part of the ranking is known. A well-known problem in rank similarity measures is ties. There have been some ways of dealing with ties proposed since RBO was introduced. These ways have been shown to be promising but they only relate to the seen part. The unseen part of rankings is still a new concept with little research done about it. This paper aims to change that a bit. First, a full explanation is given of the three variations of dealing with ties. Then using these variants we show how the assumption that no ties exist in the unseen part affects these variants. Also, the current extrapolation method is researched as there is also a big influence of the above-mentioned assumption. We then use simulated data to give a clear data visualization to show how the theory relates to practice. We have tried to be clear and concise with our explanations and data visualizations so future researchers can use this paper to improve and progress RBO in the world of rank similarity measures.