Predicting Basketball Shot Outcome From Visuomotor Control Data Using Explainable Machine Learning.
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 o...
| Publicado en: | Journal of Sport & Exercise Psychology Vol. 46; no. 5; pp. 293 - 301 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Human Kinetics Publishers, Inc.
Oct2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=179968129&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 179968129 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 08952779 SEG jtl: Journal of Sport & Exercise Psychology issn: 08952779 maglogo: N pubinfo: dt: Oct2024 vid: 46 iid: 5 pid: 553 pub: Human Kinetics Publishers, Inc. artinfo: ui: 179968129 10.1123/jsep.2024-0063 ppf: 293 ppct: 8 formats: tig: atl: Predicting Basketball Shot Outcome From Visuomotor Control Data Using Explainable Machine Learning. aug: au: Aitcheson-Huehn, Nikki MacPherson, Ryan Panchuk, Derek Kiefer, Adam W. affil: 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 Chiron Performance, Charnwood, ACT, Australia su: Visuomotor coordination Random forest algorithms Eye tracking Decision trees Machine learning sug: subj: Visuomotor coordination Random forest algorithms Eye tracking Decision trees Machine learning keyword: basketball shooting decision trees eye tracking gaze quiet eye skill development basketball shooting decision trees eye tracking gaze quiet eye skill development ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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