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在篮球中建模防守空当的形成:队友运球切入时的切入。

Modeling the formation of defensive gaps in basketball: Cutting on a teammate's drive.

机构信息

Department of Sport Science, University of Vienna, Vienna, Austria.

Software Competence Center Hagenberg GmbH, Hagenberg, Austria.

出版信息

PLoS One. 2023 Feb 7;18(2):e0281467. doi: 10.1371/journal.pone.0281467. eCollection 2023.

Abstract

Basketball is a game of simultaneous actions, and inter-player coordination is key for offensive success. One of the most challenging aspects in this regard is basket cutting on a teammate's drive. The ability to make these cuts is considered to be an artistic skill, mastered by only a handful of players. This skill is also hard to assess, as there is no method to measure the players' capability with respect to this quality-especially not automatically. Using SportVU data from the NBA, we created a mathematical model that identifies the openings in the defense which allow to perform a cut. Our model succeeds to generalize, as it detects these openings on average 139ms earlier than the actual cuts start and has an overall (balanced) accuracy of 0.818 on the test set. Having a tree-based gradient boosting classifier, we received a clear hierarchy of feature importance and were able to inspect the interactions between these attributes during action. This way, the model gives insights about the kind of defensive movements needed for a player to allow enough space to cut while in practical usage the analysis of the output can also help the coaching staff in designing play options and assessing player abilities. By paying more attention to the possible off ball movements during drives, offensive plays can become more versatile-benefiting the participants and the spectators alike.

摘要

篮球是一项同时进行多项动作的运动,球员之间的配合对于进攻的成功至关重要。在这方面最具挑战性的方面之一是在队友运球时进行篮下切入。能够进行这些切入被认为是一种艺术技能,只有少数球员能够掌握。由于没有方法可以衡量球员在这方面的能力,因此很难评估这种技能——尤其是无法自动评估。我们使用 NBA 的 SportVU 数据创建了一个数学模型,该模型可以识别出防守中的空隙,从而可以进行切入。我们的模型成功地进行了概括,因为它可以平均提前 139 毫秒检测到这些空隙,并且在测试集上的整体(平衡)准确性达到 0.818。使用基于树的梯度提升分类器,我们获得了特征重要性的明确层次结构,并能够检查动作过程中这些属性之间的相互作用。这样,该模型可以深入了解球员在切入时需要进行哪种防守动作才能留出足够的空间,而在实际使用中,对输出的分析也可以帮助教练组设计比赛选项并评估球员的能力。通过在驱动器期间更多地关注可能的无球动作,进攻战术可以变得更加多样化——使参与者和观众都受益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f594/9904462/7e686cd7341e/pone.0281467.g001.jpg

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