Extended OpenTT Games Dataset: A table tennis dataset for fine-grained shot type and point outcome

TL;DR

This paper introduces an extended, richly annotated version of the OpenTTGames dataset, enabling detailed analysis of table tennis strokes, player postures, and rally outcomes for improved training, analytics, and model development.

Contribution

The authors provide a new extension to the OpenTTGames dataset with detailed, frame-accurate annotations of stroke types, player postures, and rally outcomes, supporting fine-grained tactical understanding.

Findings

Enhanced dataset with detailed annotations for strokes and postures.

Supports development of models for tactical shot and outcome prediction.

Openly released under permissive license for community use.

Abstract

Automatically detecting and classifying strokes in table tennis video can streamline training workflows, enrich broadcast overlays, and enable fine-grained performance analytics. For this to be possible, annotated video data of table tennis is needed. We extend the public OpenTTGames dataset with highly detailed, frame-accurate shot type annotations (forehand, backhand with subtypes), player posture labels (body lean and leg stance), and rally outcome tags at point end. OpenTTGames is a set of recordings from the side of the table with official labels for bounces, when the ball is above the net, or hitting the net. The dataset already contains ball coordinates near events, which are either "bounce", "net", or "empty_event" in the original OpenTTGames dataset, and semantic masks (humans, table, scoreboard). Our extension adds the types of stroke to the events and a per-player taxonomy so models can move beyond event spotting toward tactical understanding (e.g., whether a stroke is likely to win the point or set up an advantage). We provide a compact coding scheme and code-assisted labeling procedure to support reproducible annotations and baselines for fine-grained stroke understanding in racket sports. This fills a practical gap in the community, where many prior video resources are either not publicly released or carry restrictive/unclear licenses that hinder reuse and benchmarking. Our annotations are released under the same CC BY-NC-SA 4.0 license as OpenTTGames, allowing free non-commercial use, modification, and redistribution, with appropriate attribution.