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The Q-Pass Index: A Multifactorial IMUs-Based Tool to Assess Passing Skills in Basketball
Despite being a key sport-specific characteristic in performance, there is no practical tool to assess the quality of the pass in basketball. The aim of this study is to develop a tool (the quality-pass index or Q-Pass) able to deliver a quantitative, practical measure of passing skills quality base...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271510/ https://www.ncbi.nlm.nih.gov/pubmed/34283154 http://dx.doi.org/10.3390/s21134601 |
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author | Quílez-Maimón, Arturo Rojas-Ruiz, Francisco Javier Delgado-García, Gabriel Courel-Ibáñez, Javier |
author_facet | Quílez-Maimón, Arturo Rojas-Ruiz, Francisco Javier Delgado-García, Gabriel Courel-Ibáñez, Javier |
author_sort | Quílez-Maimón, Arturo |
collection | PubMed |
description | Despite being a key sport-specific characteristic in performance, there is no practical tool to assess the quality of the pass in basketball. The aim of this study is to develop a tool (the quality-pass index or Q-Pass) able to deliver a quantitative, practical measure of passing skills quality based on a combination of accuracy, execution time and pass pattern variability. Temporal, kinematics and performance parameters were analysed in five different types of passes (chest, bounce, crossover, between-the-leg and behind-the-back) using a field-based test, video cameras and body-worn inertial sensors (IMUs). Data from pass accuracy, time and angular velocity were collected and processed in a custom-built excel spreadsheet. The Q-pass index (0–100 score) resulted from the sum of the three factors. Data were collected from 16 young basketball players (age: 16 ± 2 years) with high (experienced) and low (novice) level of expertise. Reliability analyses found the Q-pass index as a reliable tool in both novice (CV from 4.3 to 9.3%) and experienced players (CV from 2.8 to 10.2%). Besides, important differences in the Q-pass index were found between players’ level (p < 0.05), with the experienced showing better scores in all passing situations: behind-the-back (ES = 1.91), bounce (ES = 0.82), between-the-legs (ES = 1.11), crossover (ES = 0.58) and chest (ES = 0.94). According to these findings, the Q-pass index was sensitive enough to identify the differences in passing skills between young players with different levels of expertise, providing a numbering score for each pass executed. |
format | Online Article Text |
id | pubmed-8271510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82715102021-07-11 The Q-Pass Index: A Multifactorial IMUs-Based Tool to Assess Passing Skills in Basketball Quílez-Maimón, Arturo Rojas-Ruiz, Francisco Javier Delgado-García, Gabriel Courel-Ibáñez, Javier Sensors (Basel) Article Despite being a key sport-specific characteristic in performance, there is no practical tool to assess the quality of the pass in basketball. The aim of this study is to develop a tool (the quality-pass index or Q-Pass) able to deliver a quantitative, practical measure of passing skills quality based on a combination of accuracy, execution time and pass pattern variability. Temporal, kinematics and performance parameters were analysed in five different types of passes (chest, bounce, crossover, between-the-leg and behind-the-back) using a field-based test, video cameras and body-worn inertial sensors (IMUs). Data from pass accuracy, time and angular velocity were collected and processed in a custom-built excel spreadsheet. The Q-pass index (0–100 score) resulted from the sum of the three factors. Data were collected from 16 young basketball players (age: 16 ± 2 years) with high (experienced) and low (novice) level of expertise. Reliability analyses found the Q-pass index as a reliable tool in both novice (CV from 4.3 to 9.3%) and experienced players (CV from 2.8 to 10.2%). Besides, important differences in the Q-pass index were found between players’ level (p < 0.05), with the experienced showing better scores in all passing situations: behind-the-back (ES = 1.91), bounce (ES = 0.82), between-the-legs (ES = 1.11), crossover (ES = 0.58) and chest (ES = 0.94). According to these findings, the Q-pass index was sensitive enough to identify the differences in passing skills between young players with different levels of expertise, providing a numbering score for each pass executed. MDPI 2021-07-05 /pmc/articles/PMC8271510/ /pubmed/34283154 http://dx.doi.org/10.3390/s21134601 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Quílez-Maimón, Arturo Rojas-Ruiz, Francisco Javier Delgado-García, Gabriel Courel-Ibáñez, Javier The Q-Pass Index: A Multifactorial IMUs-Based Tool to Assess Passing Skills in Basketball |
title | The Q-Pass Index: A Multifactorial IMUs-Based Tool to Assess Passing Skills in Basketball |
title_full | The Q-Pass Index: A Multifactorial IMUs-Based Tool to Assess Passing Skills in Basketball |
title_fullStr | The Q-Pass Index: A Multifactorial IMUs-Based Tool to Assess Passing Skills in Basketball |
title_full_unstemmed | The Q-Pass Index: A Multifactorial IMUs-Based Tool to Assess Passing Skills in Basketball |
title_short | The Q-Pass Index: A Multifactorial IMUs-Based Tool to Assess Passing Skills in Basketball |
title_sort | q-pass index: a multifactorial imus-based tool to assess passing skills in basketball |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271510/ https://www.ncbi.nlm.nih.gov/pubmed/34283154 http://dx.doi.org/10.3390/s21134601 |
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