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<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
				<PublisherName>Kharazmi University</PublisherName>
				<JournalTitle>Research in Sports Management and Movement Behavior</JournalTitle>
				<Issn>2255-0716</Issn>
				<Volume></Volume>
				<Issue>Articles in Press</Issue>
				<PubDate PubStatus="epublish">
					<Year>2020</Year>
					<Month>10</Month>
					<Day>22</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Investigation of social network variables in different positions of professional basketball game</ArticleTitle>
<VernacularTitle>Investigation of social network variables in different positions of professional basketball game</VernacularTitle>
			<FirstPage></FirstPage>
			<LastPage></LastPage>
			<ELocationID EIdType="pii">7001</ELocationID>
			
			
			<Language>FA</Language>
<AuthorList>
<Author>
					<FirstName>Mohammad Mehdi</FirstName>
					<LastName>Kheirkhiz</LastName>
<Affiliation>Shahid Beheshti University, Tehran</Affiliation>

</Author>
<Author>
					<FirstName>Behrouz</FirstName>
					<LastName>Abdoli</LastName>
<Affiliation>Shahid Beheshti University, Tehran</Affiliation>
<Identifier Source="ORCID">0000-0001-6646-9854</Identifier>

</Author>
<Author>
					<FirstName>Lorenzo</FirstName>
					<LastName>Laporta</LastName>
<Affiliation>, Universidade Federal de Santa Maria (Santa Maria, Brazil).</Affiliation>

</Author>
<Author>
					<FirstName>Alireza</FirstName>
					<LastName>Farsi</LastName>
<Affiliation>Shahid Beheshti University, Tehran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2022</Year>
					<Month>11</Month>
					<Day>16</Day>
				</PubDate>
			</History>
		    <Abstract>The present study aims to investigate the variables of social networks in different positions in basketball. These variables were applied in two levels of analysis: micro (individual) and macro (global interaction of the team). 24 official Chemidoor Club competitions in the 2020 men&#039;s Iranian Premier League were selected by available sampling. This research analyzed the network properties of Degree, Betweenness, Closeness, Eigenvector, and Density centrality across teams and positions. The one-way ANOVA for the factor position in the micro-level found statistical differences between the game positions in the dependent variables of Dc: (F(4,15)= 61/29, p= 0/000), Bc: (F(4,15)= 210/11, p= 0/000), Cc: (F(4,15)= 78/55, p= 0/000). However, no significant difference was observed in the Eig: (F (4, 15) = 1/58, p= 0/184). Results of post hoc test indices were significantly different between position 1 (point guard) and other positions. Macro-level team density analysis showed a significant difference between performance results in successful and unsuccessful. The guard player role was observed as the situation that establishes the most interactions with teammates during the competition. Therefore, players with higher degrees were not the ones assisting the most shots. The other players with higher degrees were not the ones assisting the most shots. These results may be used as a tool for coaching to improve their teams’ strategies in concrete, measurable ways.</Abstract>
			<OtherAbstract Language="FA">The present study aims to investigate the variables of social networks in different positions in basketball. These variables were applied in two levels of analysis: micro (individual) and macro (global interaction of the team). 24 official Chemidoor Club competitions in the 2020 men&#039;s Iranian Premier League were selected by available sampling. This research analyzed the network properties of Degree, Betweenness, Closeness, Eigenvector, and Density centrality across teams and positions. The one-way ANOVA for the factor position in the micro-level found statistical differences between the game positions in the dependent variables of Dc: (F(4,15)= 61/29, p= 0/000), Bc: (F(4,15)= 210/11, p= 0/000), Cc: (F(4,15)= 78/55, p= 0/000). However, no significant difference was observed in the Eig: (F (4, 15) = 1/58, p= 0/184). Results of post hoc test indices were significantly different between position 1 (point guard) and other positions. Macro-level team density analysis showed a significant difference between performance results in successful and unsuccessful. The guard player role was observed as the situation that establishes the most interactions with teammates during the competition. Therefore, players with higher degrees were not the ones assisting the most shots. The other players with higher degrees were not the ones assisting the most shots. These results may be used as a tool for coaching to improve their teams’ strategies in concrete, measurable ways.</OtherAbstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Professional Basketball</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Social Network Analysis</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Mach Analysis</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">performance</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Team Sports</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jrsm.khu.ac.ir/article_7001_5e9d17e41f784ae361ada1d0817186f6.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
