mlsanityData to 7 Oct 2026

FIVB ranking: Misstep in the right direction

arXiv 2408.01603 ↗First posted 2 Aug 2024cs.LGcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 39 references that are in the map. First posted on arXiv 2 Aug 2024.

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8 of 39 references.

References · 8

  1. 2019Optimizing Millions of Hyperparameters by Implicit Differentiationcited by 303
  2. 2021Efficient and Modular Implicit Differentiationcited by 199
  3. 2017On Optimal Generalizability in Parametric Learningcited by 12
  4. 2020G-Elo: Generalization of the Elo algorithm by modelling the discretized margin of victorycited by 6
  5. 2021Simplified Kalman filter for online rating: one-fits-all approachcited by 4
  6. 2022Stochastic analysis of the Elo rating algorithm in round-robin tournamentscited by 3
  7. 2020Optimizing Approximate Leave-one-out Cross-validation to Tune Hyperparameterscited by 2
  8. 2021FIFA ranking: Evaluation and path forwardcited by 2

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