What is the concept behind the categorical-encoding used in the CatBoost benchmark problems?

I’m working through CatBoost quality benchmark problems (here). I’m particularly intrigued by the methodology adopted to convert categorical features to numerical values as described in the comparison_description.pdf (here). What is the reasoning behind this approach? Is this methodology model agnostic? What are the advantages and disadvantages?