PREDICTION OF ATHLETIC ABILITY AND TALENT DETECTION OF RURAL AND TRIBAL CHILDREN USING THE K-MEAN CLUSTERING ALGORITHM
Background - Talent Identification and Development (TID) in sports is a continuum and multidimensional process of identifying, selecting, and developing potentialathletes (at an early age) in specific sports based on their present performance or abilities. Government-bagged schemes are proven effect...
Gespeichert in:
Veröffentlicht in: | NeuroQuantology 2022-01, Vol.20 (1), p.1094 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 1 |
container_start_page | 1094 |
container_title | NeuroQuantology |
container_volume | 20 |
creator | Chaware, Utsav Das, Anindita |
description | Background - Talent Identification and Development (TID) in sports is a continuum and multidimensional process of identifying, selecting, and developing potentialathletes (at an early age) in specific sports based on their present performance or abilities. Government-bagged schemes are proven effective in the TI process but studies claim the rural and tribal athletes fail to claim due to the operational limitations. Therefore Talent Detection (TD) plays a vital role, however, research suggests with limited experts, coaches, and selectors and a lack of robust mechanism the selection is not data-driven and could be biased. Henceforth, the use of machine learning could be the solution to detect talent. Aim- The study aimed to develop a screening and classification model based on the anthropometric and physical parameters of ruraland tribal children using K-means clustering, an unsupervised clustering method. Material and Methods- 240 tribal and rural children aged between 15-17 years from tribal districts of Madhya Pradesh with no formal training were assessed for demographic, anthropometric and physical variables based on the ‘Khelo India’physical fitness and talent identification scheme. Data was analysed and clustering was done using a K-mean unsupervised algorithm in Python. Result and Discussion- Results of PCA show 4 physical attributes explaining a total variability of 70.18%. Athletes were classified into 3 clusters; sports fit(27.7%), athletic in at least one ability(23.7%), and non-atheltic(46.6 %). Further, ANOVA between the groups reveals significant differences (p0.05). However, the results were apparent between non-athletic and other groups. Conclusion- The present study provides a data-bagged classification system that clusters the low performers (non-athletic group) from another group, unloading the burden of mass scrutiny and data analysis, particularly for rural and tribal children as most of them have no formal skill training in any sports. |
doi_str_mv | 10.48047/NQ.2022.20.1.NQ22393 |
format | Article |
fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2901673097</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2901673097</sourcerecordid><originalsourceid>FETCH-proquest_journals_29016730973</originalsourceid><addsrcrecordid>eNqNisFOAjEURRsSE1D5BJOXuJ7xtWUYZ1k6D9pYOlLeLFgRF7ggBJQRv98xGtdu7r255whxJzGfPOKkfIirXKFSfeQyjyuldKUHYiQ16qyQBQ7FddftEYsSq-lIfD4nqr1l30Ro5mDYBWJvwcx88LwBE2tgEygy1MT0J6Y2mfBDk5_10zof6kQR2rWPC2BH8JQtyUSwoV0zpe_XhEWTPLvlrbh6fTl0u_Fv34j7ObF12dv59H7ZdR_b_elyPvZoqyqU01JjVer_WV86EEai</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2901673097</pqid></control><display><type>article</type><title>PREDICTION OF ATHLETIC ABILITY AND TALENT DETECTION OF RURAL AND TRIBAL CHILDREN USING THE K-MEAN CLUSTERING ALGORITHM</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Chaware, Utsav ; Das, Anindita</creator><creatorcontrib>Chaware, Utsav ; Das, Anindita</creatorcontrib><description>Background - Talent Identification and Development (TID) in sports is a continuum and multidimensional process of identifying, selecting, and developing potentialathletes (at an early age) in specific sports based on their present performance or abilities. Government-bagged schemes are proven effective in the TI process but studies claim the rural and tribal athletes fail to claim due to the operational limitations. Therefore Talent Detection (TD) plays a vital role, however, research suggests with limited experts, coaches, and selectors and a lack of robust mechanism the selection is not data-driven and could be biased. Henceforth, the use of machine learning could be the solution to detect talent. Aim- The study aimed to develop a screening and classification model based on the anthropometric and physical parameters of ruraland tribal children using K-means clustering, an unsupervised clustering method. Material and Methods- 240 tribal and rural children aged between 15-17 years from tribal districts of Madhya Pradesh with no formal training were assessed for demographic, anthropometric and physical variables based on the ‘Khelo India’physical fitness and talent identification scheme. Data was analysed and clustering was done using a K-mean unsupervised algorithm in Python. Result and Discussion- Results of PCA show 4 physical attributes explaining a total variability of 70.18%. Athletes were classified into 3 clusters; sports fit(27.7%), athletic in at least one ability(23.7%), and non-atheltic(46.6 %). Further, ANOVA between the groups reveals significant differences (p<0.01) in all the 3 classifications other than endurance between sports fit and athletic in at least one ability(p>0.05). However, the results were apparent between non-athletic and other groups. Conclusion- The present study provides a data-bagged classification system that clusters the low performers (non-athletic group) from another group, unloading the burden of mass scrutiny and data analysis, particularly for rural and tribal children as most of them have no formal skill training in any sports.</description><identifier>EISSN: 1303-5150</identifier><identifier>DOI: 10.48047/NQ.2022.20.1.NQ22393</identifier><language>eng</language><publisher>Bornova Izmir: NeuroQuantology</publisher><subject>Algorithms ; Anthropometry ; Athletes ; Classification ; Cluster analysis ; Clustering ; Data analysis ; Demographic variables ; Machine learning ; Physical fitness ; Physical properties ; Professional development ; Selectors ; Vector quantization</subject><ispartof>NeuroQuantology, 2022-01, Vol.20 (1), p.1094</ispartof><rights>Copyright NeuroQuantology 2022</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Chaware, Utsav</creatorcontrib><creatorcontrib>Das, Anindita</creatorcontrib><title>PREDICTION OF ATHLETIC ABILITY AND TALENT DETECTION OF RURAL AND TRIBAL CHILDREN USING THE K-MEAN CLUSTERING ALGORITHM</title><title>NeuroQuantology</title><description>Background - Talent Identification and Development (TID) in sports is a continuum and multidimensional process of identifying, selecting, and developing potentialathletes (at an early age) in specific sports based on their present performance or abilities. Government-bagged schemes are proven effective in the TI process but studies claim the rural and tribal athletes fail to claim due to the operational limitations. Therefore Talent Detection (TD) plays a vital role, however, research suggests with limited experts, coaches, and selectors and a lack of robust mechanism the selection is not data-driven and could be biased. Henceforth, the use of machine learning could be the solution to detect talent. Aim- The study aimed to develop a screening and classification model based on the anthropometric and physical parameters of ruraland tribal children using K-means clustering, an unsupervised clustering method. Material and Methods- 240 tribal and rural children aged between 15-17 years from tribal districts of Madhya Pradesh with no formal training were assessed for demographic, anthropometric and physical variables based on the ‘Khelo India’physical fitness and talent identification scheme. Data was analysed and clustering was done using a K-mean unsupervised algorithm in Python. Result and Discussion- Results of PCA show 4 physical attributes explaining a total variability of 70.18%. Athletes were classified into 3 clusters; sports fit(27.7%), athletic in at least one ability(23.7%), and non-atheltic(46.6 %). Further, ANOVA between the groups reveals significant differences (p<0.01) in all the 3 classifications other than endurance between sports fit and athletic in at least one ability(p>0.05). However, the results were apparent between non-athletic and other groups. Conclusion- The present study provides a data-bagged classification system that clusters the low performers (non-athletic group) from another group, unloading the burden of mass scrutiny and data analysis, particularly for rural and tribal children as most of them have no formal skill training in any sports.</description><subject>Algorithms</subject><subject>Anthropometry</subject><subject>Athletes</subject><subject>Classification</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Data analysis</subject><subject>Demographic variables</subject><subject>Machine learning</subject><subject>Physical fitness</subject><subject>Physical properties</subject><subject>Professional development</subject><subject>Selectors</subject><subject>Vector quantization</subject><issn>1303-5150</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqNisFOAjEURRsSE1D5BJOXuJ7xtWUYZ1k6D9pYOlLeLFgRF7ggBJQRv98xGtdu7r255whxJzGfPOKkfIirXKFSfeQyjyuldKUHYiQ16qyQBQ7FddftEYsSq-lIfD4nqr1l30Ro5mDYBWJvwcx88LwBE2tgEygy1MT0J6Y2mfBDk5_10zof6kQR2rWPC2BH8JQtyUSwoV0zpe_XhEWTPLvlrbh6fTl0u_Fv34j7ObF12dv59H7ZdR_b_elyPvZoqyqU01JjVer_WV86EEai</recordid><startdate>20220101</startdate><enddate>20220101</enddate><creator>Chaware, Utsav</creator><creator>Das, Anindita</creator><general>NeuroQuantology</general><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88G</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0S</scope><scope>M2M</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>Q9U</scope></search><sort><creationdate>20220101</creationdate><title>PREDICTION OF ATHLETIC ABILITY AND TALENT DETECTION OF RURAL AND TRIBAL CHILDREN USING THE K-MEAN CLUSTERING ALGORITHM</title><author>Chaware, Utsav ; Das, Anindita</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_29016730973</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Anthropometry</topic><topic>Athletes</topic><topic>Classification</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Data analysis</topic><topic>Demographic variables</topic><topic>Machine learning</topic><topic>Physical fitness</topic><topic>Physical properties</topic><topic>Professional development</topic><topic>Selectors</topic><topic>Vector quantization</topic><toplevel>online_resources</toplevel><creatorcontrib>Chaware, Utsav</creatorcontrib><creatorcontrib>Das, Anindita</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest Psychology</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><jtitle>NeuroQuantology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chaware, Utsav</au><au>Das, Anindita</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PREDICTION OF ATHLETIC ABILITY AND TALENT DETECTION OF RURAL AND TRIBAL CHILDREN USING THE K-MEAN CLUSTERING ALGORITHM</atitle><jtitle>NeuroQuantology</jtitle><date>2022-01-01</date><risdate>2022</risdate><volume>20</volume><issue>1</issue><spage>1094</spage><pages>1094-</pages><eissn>1303-5150</eissn><abstract>Background - Talent Identification and Development (TID) in sports is a continuum and multidimensional process of identifying, selecting, and developing potentialathletes (at an early age) in specific sports based on their present performance or abilities. Government-bagged schemes are proven effective in the TI process but studies claim the rural and tribal athletes fail to claim due to the operational limitations. Therefore Talent Detection (TD) plays a vital role, however, research suggests with limited experts, coaches, and selectors and a lack of robust mechanism the selection is not data-driven and could be biased. Henceforth, the use of machine learning could be the solution to detect talent. Aim- The study aimed to develop a screening and classification model based on the anthropometric and physical parameters of ruraland tribal children using K-means clustering, an unsupervised clustering method. Material and Methods- 240 tribal and rural children aged between 15-17 years from tribal districts of Madhya Pradesh with no formal training were assessed for demographic, anthropometric and physical variables based on the ‘Khelo India’physical fitness and talent identification scheme. Data was analysed and clustering was done using a K-mean unsupervised algorithm in Python. Result and Discussion- Results of PCA show 4 physical attributes explaining a total variability of 70.18%. Athletes were classified into 3 clusters; sports fit(27.7%), athletic in at least one ability(23.7%), and non-atheltic(46.6 %). Further, ANOVA between the groups reveals significant differences (p<0.01) in all the 3 classifications other than endurance between sports fit and athletic in at least one ability(p>0.05). However, the results were apparent between non-athletic and other groups. Conclusion- The present study provides a data-bagged classification system that clusters the low performers (non-athletic group) from another group, unloading the burden of mass scrutiny and data analysis, particularly for rural and tribal children as most of them have no formal skill training in any sports.</abstract><cop>Bornova Izmir</cop><pub>NeuroQuantology</pub><doi>10.48047/NQ.2022.20.1.NQ22393</doi></addata></record> |
fulltext | fulltext |
identifier | EISSN: 1303-5150 |
ispartof | NeuroQuantology, 2022-01, Vol.20 (1), p.1094 |
issn | 1303-5150 |
language | eng |
recordid | cdi_proquest_journals_2901673097 |
source | EZB-FREE-00999 freely available EZB journals |
subjects | Algorithms Anthropometry Athletes Classification Cluster analysis Clustering Data analysis Demographic variables Machine learning Physical fitness Physical properties Professional development Selectors Vector quantization |
title | PREDICTION OF ATHLETIC ABILITY AND TALENT DETECTION OF RURAL AND TRIBAL CHILDREN USING THE K-MEAN CLUSTERING ALGORITHM |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T11%3A08%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=PREDICTION%20OF%20ATHLETIC%20ABILITY%20AND%20TALENT%20DETECTION%20OF%20RURAL%20AND%20TRIBAL%20CHILDREN%20USING%20THE%20K-MEAN%20CLUSTERING%20ALGORITHM&rft.jtitle=NeuroQuantology&rft.au=Chaware,%20Utsav&rft.date=2022-01-01&rft.volume=20&rft.issue=1&rft.spage=1094&rft.pages=1094-&rft.eissn=1303-5150&rft_id=info:doi/10.48047/NQ.2022.20.1.NQ22393&rft_dat=%3Cproquest%3E2901673097%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2901673097&rft_id=info:pmid/&rfr_iscdi=true |