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基于可解释机器学习的视动控制数据预测篮球投篮结果。

Predicting Basketball Shot Outcome From Visuomotor Control Data Using Explainable Machine Learning.

机构信息

Human Movement Science Curriculum, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Department of Exercise and Sport Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

出版信息

J Sport Exerc Psychol. 2024 Sep 6;46(5):293-300. doi: 10.1123/jsep.2024-0063. Print 2024 Oct 1.

DOI:10.1123/jsep.2024-0063
PMID:39244200
Abstract

Quiet eye (QE), the visual fixation on a target before initiation of a critical action, is associated with improved performance. While QE is trainable, it is unclear whether QE can directly predict performance, which has implications for training interventions. This study predicted basketball shot outcome (make or miss) from visuomotor control variables using a decision tree classification approach. Twelve basketball athletes completed 200 shots from six on-court locations while wearing mobile eye-tracking glasses. Training and testing data sets were used for modeling eight predictors (shot location, arm extension time, and absolute and relative QE onset, offset, and duration) via standard and conditional inference decision trees and random forests. On average, the trees predicted over 66% of makes and over 50% of misses. The main predictor, relative QE duration, indicated success for durations over 18.4% (range: 14.5%-22.0%). Training to prolong QE duration beyond 18% may enhance shot success.

摘要

静视(QE),即在开始关键动作前对目标的视觉注视,与表现的提升有关。QE 可训练,但尚不清楚它是否能直接预测表现,这对训练干预措施有影响。本研究采用决策树分类方法,从运动视觉控制变量预测篮球投篮结果(命中或未命中)。12 名篮球运动员在佩戴移动眼动追踪眼镜的情况下,从六个球场位置完成 200 次投篮。通过标准和条件推理决策树和随机森林,利用训练和测试数据集对 8 个预测因子(投篮位置、手臂伸展时间以及绝对和相对 QE 的起始、结束和持续时间)进行建模。平均而言,树预测了超过 66%的命中和超过 50%的未命中。主要预测因子,即相对 QE 持续时间,表明持续时间超过 18.4%(范围:14.5%-22.0%)时成功。训练将 QE 持续时间延长到 18%以上可能会提高投篮成功率。

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