# FIVB ranking: Misstep in the right direction

arXiv: https://arxiv.org/abs/2408.01603 · HTML: https://mlsanity.com/paper/2408.01603/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [Optimizing Millions of Hyperparameters by Implicit Differentiation](https://mlsanity.com/paper/1911.02590/) (2019)
- [Efficient and Modular Implicit Differentiation](https://mlsanity.com/paper/2105.15183/) (2021)
- [On Optimal Generalizability in Parametric Learning](https://mlsanity.com/paper/1711.05323/) (2017)
- [G-Elo: Generalization of the Elo algorithm by modelling the discretized margin of victory](https://mlsanity.com/paper/2010.11187/) (2020)
- [Simplified Kalman filter for online rating: one-fits-all approach](https://mlsanity.com/paper/2104.14012/) (2021)
- [Stochastic analysis of the Elo rating algorithm in round-robin tournaments](https://mlsanity.com/paper/2212.12015/) (2022)
- [Optimizing Approximate Leave-one-out Cross-validation to Tune Hyperparameters](https://mlsanity.com/paper/2011.10218/) (2020)
- [FIFA ranking: Evaluation and path forward](https://mlsanity.com/paper/2201.00691/) (2021)

## Built on by (most cited first, 1 of 1)

- [New insights into Elo algorithm for practitioners and statisticians](https://mlsanity.com/paper/2604.03840/) (2026)

## Abstract (arXiv)

This work presents and evaluates the ranking algorithm that has been used by Federation Internationale de Volleyball (FIVB) since 2020. The prominent feature of the FIVB ranking is the use of the probabilistic model, which explicitly calculates the probabilities of the future matches results using the estimated teams' strengths. Such explicit modeling is new in the context of official sport rankings, especially for multi-level outcomes, and we study the optimality of its parameters using both analytical and numerical methods. We conclude that from the modeling perspective, the current thresholds fit well the data but adding the home-field advantage (HFA) would be beneficial. Regarding the algorithm itself, we explain the rationale behind the approximations currently used and show a simple method to find new parameters (numerical score) which improve the performance. We also show that the weighting of the match results is counterproductive.