VREN: Volleyball Rally Dataset with Expression Notation Language

TL;DR

This work introduces VREN, a Volleyball Rally Expression Notation, and a large, manually annotated indoor volleyball rally dataset spanning NCAA Division I and national teams. By defining a 26-zone court grid and rally-centric variables, VREN enables automated analysis of rally outcomes, set/hit types, and attacking tactics, bridging volleyball with computer science. The authors implement and compare multiple models for Rally Result Prediction, showing Transformer-based approaches yield the strongest performance, and extend similar methods to predict setting and hitting types, with college and professional level results revealing differences in predictability. They also propose Volleyball Tactics and Attacking Zone Statistics to deliver granular, real-time style of play insights, and demonstrate practical use with in-depth statistics from a professional match. The work lays a foundation for data-driven coaching and tactical development, with future plans to expand data, enhance modeling, and integrate computer vision for automated, scalable labeling.

Abstract

This research is intended to accomplish two goals: The first goal is to curate a large and information rich dataset that contains crucial and succinct summaries on the players' actions and positions and the back-and-forth travel patterns of the volleyball in professional and NCAA Div-I indoor volleyball games. While several prior studies have aimed to create similar datasets for other sports (e.g. badminton and soccer), creating such a dataset for indoor volleyball is not yet realized. The second goal is to introduce a volleyball descriptive language to fully describe the rally processes in the games and apply the language to our dataset. Based on the curated dataset and our descriptive sports language, we introduce three tasks for automated volleyball action and tactic analysis using our dataset: (1) Volleyball Rally Prediction, aimed at predicting the outcome of a rally and helping players and coaches improve decision-making in practice, (2) Setting Type and Hitting Type Prediction, to help coaches and players prepare more effectively for the game, and (3) Volleyball Tactics and Attacking Zone Statistics, to provide advanced volleyball statistics and help coaches understand the game and opponent's tactics better. We conducted case studies to show how experimental results can provide insights to the volleyball analysis community. Furthermore, experimental evaluation based on real-world data establishes a baseline for future studies and applications of our dataset and language. This study bridges the gap between the indoor volleyball field and computer science. The dataset is available at: https://github.com/haotianxia/VREN.

Figures (6)

• Figure 1: The grid system we propose to represent positions on the volleyball court. The thick solid red line represents the net. The court is symmetrically and uniformly divided into 26 areas on both sides of the red line (the net).
• Figure 2: Video screenshots showing how we identify our key variables to format individual ball rounds in the VREN language representation. The green dash represent the trajectory and direction of the ball. Based on the ball trajectory and our location information and volleyball terms, we came up with VREN.
• Figure 3: Serve location breakdowns for float serves and jump serves. Notice how jump serves mostly land in the back of the court in areas 2, 3 and 4 while float serves mostly land in the middle of the court in areas 7, 8 and 9.
• Figure 4: The Rally Winning Probability Breakdown by Round
• Figure 5: Winning probability of the rally breakdown by round