This work uses a statistical framework to present and evaluate the ranking algorithm that has been used by F\'ed\'eration Internationale de Volleyball (FIVB) since 2020. The salient feature of the FIVB ranking is the use of the probabilistic model, which explicitly calculates the probabilities of the games to come. This explicit modeling is new in the context of official ranking, and we study the optimality of its parameters as well as its relationship with the ranking algorithm as such. The analysis is carried out using both analytical and numerical methods. We conclude that, from the modeling perspective, the use of the home-field advantage (HFA) would be beneficial and that the weighting of the game results is counterproductive. Regarding the algorithm itself, we explain the rationale beyond the approximations currently used and explain how to find new parameters which improve the performance. Finally, we propose a new model that drastically simplifies both the implementation and interpretation of the resulting algorithm.
