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OBTracker:篮球无球移动的视觉分析

OBTracker: Visual Analytics of Off-ball Movements in Basketball.

作者信息

Wu Yihong, Deng Dazhen, Xie Xiao, He Moqi, Xu Jie, Zhang Hongzeng, Zhang Hui, Wu Yingcai

出版信息

IEEE Trans Vis Comput Graph. 2023 Jan;29(1):929-939. doi: 10.1109/TVCG.2022.3209373. Epub 2022 Dec 16.

Abstract

In a basketball play, players who are not in possession of the ball (i.e., off-ball players) can still effectively contribute to the team's offense, such as making a sudden move to create scoring opportunities. Analyzing the movements of off-ball players can thus facilitate the development of effective strategies for coaches. However, common basketball statistics (e.g., points and assists) primarily focus on what happens around the ball and are mostly result-oriented, making it challenging to objectively assess and fully understand the contributions of off-ball movements. To address these challenges, we collaborate closely with domain experts and summarize the multi-level requirements for off-ball movement analysis in basketball. We first establish an assessment model to quantitatively evaluate the offensive contribution of an off-ball movement considering both the position of players and the team cooperation. Based on the model, we design and develop a visual analytics system called OBTracker to support the multifaceted analysis of off-ball movements. OBTracker enables users to identify the frequency and effectiveness of off-ball movement patterns and learn the performance of different off-ball players. A tailored visualization based on the Voronoi diagram is proposed to help users interpret the contribution of off-ball movements from a temporal perspective. We conduct two case studies based on the tracking data from NBA games and demonstrate the effectiveness and usability of OBTracker through expert feedback.

摘要

在篮球比赛中,无球球员(即不控球的球员)仍能有效地为球队进攻做出贡献,比如突然移动以创造得分机会。因此,分析无球球员的动作有助于教练制定有效的战术。然而,常见的篮球统计数据(如得分和助攻)主要关注球周围发生的情况,且大多以结果为导向,这使得客观评估和全面理解无球动作的贡献具有挑战性。为应对这些挑战,我们与领域专家密切合作,总结了篮球无球动作分析的多层次要求。我们首先建立一个评估模型,从球员位置和团队协作两方面定量评估无球动作的进攻贡献。基于该模型,我们设计并开发了一个名为OBTracker的可视化分析系统,以支持对无球动作的多方面分析。OBTracker能让用户识别无球动作模式的频率和有效性,并了解不同无球球员的表现。我们提出了一种基于Voronoi图的定制可视化方法,帮助用户从时间角度解读无球动作的贡献。我们基于NBA比赛的追踪数据进行了两个案例研究,并通过专家反馈证明了OBTracker的有效性和实用性。

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