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A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach

The talent development processes in youth football are both complex and multidimensional. The purpose of this two-fold study was to apply a multidisciplinary, machine learning approach to examine: (a) the developmental characteristics of under-9 to under-16 academy players (n = 98; Study 1), and (b)...

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Autores principales: Kelly, Adam L., Williams, Craig A., Cook, Rob, Sáiz, Sergio Lorenzo Jiménez, Wilson, Mark R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611883/
https://www.ncbi.nlm.nih.gov/pubmed/36287772
http://dx.doi.org/10.3390/sports10100159
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author Kelly, Adam L.
Williams, Craig A.
Cook, Rob
Sáiz, Sergio Lorenzo Jiménez
Wilson, Mark R.
author_facet Kelly, Adam L.
Williams, Craig A.
Cook, Rob
Sáiz, Sergio Lorenzo Jiménez
Wilson, Mark R.
author_sort Kelly, Adam L.
collection PubMed
description The talent development processes in youth football are both complex and multidimensional. The purpose of this two-fold study was to apply a multidisciplinary, machine learning approach to examine: (a) the developmental characteristics of under-9 to under-16 academy players (n = 98; Study 1), and (b) the characteristics of selected and deselected under-18 academy players (n = 18; Study 2). A combined total of 53 factors cumulated from eight data collection methods across two seasons were analysed. A cross-validated Lasso regression was implemented, using the glmnet package in R, to analyse the factors that contributed to: (a) player review ratings (Study 1), and (b) achieving a professional contract (Study 2). Results showed non-zero coefficients for improvement in subjective performance in 15 out of the 53 analysed features, with key findings revealing advanced percentage of predicted adult height (0.196), greater lob pass (0.160) and average dribble completion percentage (0.124), more total match-play hours (0.145), and an older relative age (BQ1 vs. BQ2: −0.133; BQ1 vs. BQ4: −0.060) were the most important features that contributed towards player review ratings. Moreover, PCDEQ Factor 3 and an ability to organise and engage in quality practice (PCDEQ Factor 4) were important contributing factors towards achieving a professional contract. Overall, it appears the key factors associated with positive developmental outcomes are not always technical and tactical in nature, where coaches often have their expertise. Indeed, the relative importance of these factors is likely to change over time, and with age, although psychological attributes appear to be key to reaching potential across the academy journey. The methodological techniques used here also serve as an impetus for researchers to adopt a machine learning approach when analysing multidimensional databases.
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spelling pubmed-96118832022-10-28 A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach Kelly, Adam L. Williams, Craig A. Cook, Rob Sáiz, Sergio Lorenzo Jiménez Wilson, Mark R. Sports (Basel) Article The talent development processes in youth football are both complex and multidimensional. The purpose of this two-fold study was to apply a multidisciplinary, machine learning approach to examine: (a) the developmental characteristics of under-9 to under-16 academy players (n = 98; Study 1), and (b) the characteristics of selected and deselected under-18 academy players (n = 18; Study 2). A combined total of 53 factors cumulated from eight data collection methods across two seasons were analysed. A cross-validated Lasso regression was implemented, using the glmnet package in R, to analyse the factors that contributed to: (a) player review ratings (Study 1), and (b) achieving a professional contract (Study 2). Results showed non-zero coefficients for improvement in subjective performance in 15 out of the 53 analysed features, with key findings revealing advanced percentage of predicted adult height (0.196), greater lob pass (0.160) and average dribble completion percentage (0.124), more total match-play hours (0.145), and an older relative age (BQ1 vs. BQ2: −0.133; BQ1 vs. BQ4: −0.060) were the most important features that contributed towards player review ratings. Moreover, PCDEQ Factor 3 and an ability to organise and engage in quality practice (PCDEQ Factor 4) were important contributing factors towards achieving a professional contract. Overall, it appears the key factors associated with positive developmental outcomes are not always technical and tactical in nature, where coaches often have their expertise. Indeed, the relative importance of these factors is likely to change over time, and with age, although psychological attributes appear to be key to reaching potential across the academy journey. The methodological techniques used here also serve as an impetus for researchers to adopt a machine learning approach when analysing multidimensional databases. MDPI 2022-10-19 /pmc/articles/PMC9611883/ /pubmed/36287772 http://dx.doi.org/10.3390/sports10100159 Text en © 2022 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
Kelly, Adam L.
Williams, Craig A.
Cook, Rob
Sáiz, Sergio Lorenzo Jiménez
Wilson, Mark R.
A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach
title A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach
title_full A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach
title_fullStr A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach
title_full_unstemmed A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach
title_short A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach
title_sort multidisciplinary investigation into the talent development processes at an english football academy: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611883/
https://www.ncbi.nlm.nih.gov/pubmed/36287772
http://dx.doi.org/10.3390/sports10100159
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