Play-by-Play Volleyball Win Probability Model

This paper introduces a volleyball point-by-point win probability model that updates the probability of winning a set after each play in the set. The covariate informed product partition model (PPMx) is well suited to flexibly include in-set team performance information when making predictions. However, making predictions in real time would be too expensive computationally as it would require refitting the PPMx for each prediction. Instead, we develop a predictive procedure based on a single training of the PPMx that predicts in real-time. We deploy this procedure using data from the 2018 Men’s World Volleyball Championship. The procedure first trains a PPMx model using end-of-set team performance statistics from the round robin stage of the tournament. Then based on the PPMx predictive distribution, we predict the win probability after every play of every match in the knockout stages. Finally, we show how the prediction procedure can be enhanced by including pre-set information towards the beginning of the set and set score towards the end.