Abstract
This paper presents a probabilistic model for predicting the score of a volleyball set that considers the players’ initial positions on court, and the position rotations required by the rules of the game. Each clockwise rotation results in a different team formation that encompasses different probabilistic capabilities. While other models for volleyball games assume a single probability for each team throughout the entire game, the proposed model considers different probabilities as a function of the formation of each rotation. We establish an ergodic Markov chain where each state represents a specific rotation formation; then, the expected final score of a set is estimated. The model is validated using information observed from actual games in several countries (the Australian and Israeli national volleyball teams) in various international competitions. The results show a high correlation between the actual scores and the model’s estimated scores. Sensitivity and error analyses show high robustness to inaccurate estimates of probabilities and to changes in players’ performances during a set. The model can be used as a support-decision tool to assist coaches and managers in selecting the optimal team formation and game tactics, leading to better utilization of resources and an improved success rate.
Disclosure statement
No potential conflict of interest was reported by the authors.