“Play by play”: A dataset of handball and basketball game situations in a standardized space

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES Data in Brief Pub Date : 2025-02-01 Epub Date: 2024-12-27 DOI:10.1016/j.dib.2024.111265
Bruno Cabado , Bertha Guijarro-Berdiñas , Emilio J. Padrón
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Abstract

This paper presents a synthetic dataset of labeled game situations in recordings of federated handball and basketball matches played in Galicia, Spain. The dataset consists of synthetic data generated from real video frames, including 308,805 labeled handball frames and 56,578 labeled basketball frames extracted from 2105 handball and 383 basketball 5-s video clips.
Experts manually labeled the video clips based on the respective sports, while the individual frames were automatically labeled using computer vision and machine learning techniques. The dataset encompasses seven classes of game situations: left attack, left counterattack, left penalty, right attack, right counterattack, right penalty, and timeout. In basketball, the penalty class refers to the free throws attempted by players after they have been fouled by an opposing player.
Each frame in the dataset is assigned to one of these classes, considering the game situation and specific context. Importantly, the dataset does not contain actual video frames; instead, it provides a synthetic, normalized representation of each frame in JSON format. This tabular data includes player, referee, and ball positions on a normalized field, player and referee velocities, and key regions on the court. Positions of players, referees, and the ball were automatically inferred in each frame by an object detector, followed by a tracking step to detect object positions across frames and compute the velocity vectors. Finally, the obtained coordinates underwent normalization through a perspective transformation, ensuring that the data remained unaffected by variations in camera configurations across different arenas and camera setups. We refer to this standardized coordinate space as the 'unified space'.
The dataset holds significant potential for reuse in various domains related to sports analytics and machine learning research. It can serve as a valuable resource for researchers, coaches, and sports enthusiasts, contributing to improvements in player performance, game strategies, match retransmissions, and sports-related technologies.

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“逐场比赛”:一个标准化空间中手球和篮球比赛情况的数据集。
本文介绍了在西班牙加利西亚进行的联邦手球和篮球比赛录音中标记比赛情况的合成数据集。该数据集由真实视频帧生成的合成数据组成,包括308,805个标记手球帧和56,578个标记篮球帧,分别从2105个手球和383个5秒篮球视频片段中提取。专家们根据各自的运动手动标记视频片段,而使用计算机视觉和机器学习技术自动标记单个帧。该数据集包含七种比赛情况:左进攻、左反击、左点球、右进攻、右反击、右点球和暂停。在篮球比赛中,罚球是指球员在被对方球员犯规后尝试罚球。考虑到游戏情境和特定背景,数据集中的每一帧都被分配到这些类中的一个。重要的是,数据集不包含实际的视频帧;相反,它以JSON格式提供了每个帧的合成的规范化表示。此表格数据包括规范化场地上的球员、裁判和球的位置、球员和裁判的速度以及球场上的关键区域。目标检测器在每一帧中自动推断球员、裁判和球的位置,然后通过跟踪步骤检测跨帧的目标位置并计算速度矢量。最后,通过透视变换对获得的坐标进行归一化处理,确保数据不受不同场地和摄像机设置的摄像机配置变化的影响。我们把这个标准化的坐标空间称为“统一空间”。该数据集具有在与体育分析和机器学习研究相关的各个领域重用的巨大潜力。它可以作为研究人员、教练和体育爱好者的宝贵资源,有助于提高球员的表现、比赛策略、比赛转播和体育相关技术。
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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