Bayesian Hierarchical Models for the Prediction of Volleyball Results

TL;DR

This paper introduces a Bayesian hierarchical model to predict volleyball team rankings and match outcomes, filling a research gap in volleyball analytics with validation on Italian Serie A1 data.

Contribution

The paper develops and compares two Bayesian hierarchical models specifically designed for volleyball team ranking and match outcome prediction, a novel approach in this sport.

Findings

Models accurately predict team rankings and match results.

Validation shows the models perform well on Italian Serie A1 data.

Hierarchical structure captures team-specific effects effectively.

Abstract

Statistical modelling of sports data has become more and more popular in the recent years and different types of models have been proposed to achieve a variety of objectives: from identifying the key characteristics which lead a team to win or lose to predicting the outcome of a game or the team rankings in national leagues. Although not as popular as football or basketball, volleyball is a team sport with both national and international level competitions in almost every country. However, there is almost no study investigating the prediction of volleyball game outcomes and team rankings in national leagues. We propose a Bayesian hierarchical model for the prediction of the rankings of volleyball national teams, which also allows to estimate the results of each match in the league. We consider two alternative model specifications of different complexity which are validated using data from the women's volleyball Italian Serie A1 2017-2018 season.