Physical Determinants of Velocity and Agility in High School Football Players: Differences Between Position Groups
PURPOSE: The purpose of this study was to develop prediction equations for speed and agility within different position groupings of High School football players using age, height, body mass, and vertical jump as predictor variables. METHODS: A total of 987 athletes (14-18y) completed testing at a re...
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description | PURPOSE: The purpose of this study was to develop prediction equations for speed and agility within different position groupings of High School football players using age, height, body mass, and vertical jump as predictor variables. METHODS: A total of 987 athletes (14-18y) completed testing at a regional high school football combine. Athletes were divided into three groups according to their playing position: Linemen (LM; n = 354; 16.3 ± 0.8y; 179.4 ± 5.6cm; 105.1 ± 18.3kg), Big Skill Players (BSP; n = 189; 16.2 ± 0.9y; 175.5cm ± 5.2cm; 85.7 ± 11.6kg), and Skill Players (SP; n = 444; 16.4 ± 09y; 175.8 ± 5.9cm; 75.5 ± 8.9kg). The LM included offensive tackles, offensive guards, centers, defensive tackles, and defensive ends. BSP included linebackers, running backs, and tight ends. SP included wide receivers, corner backs, safeties, and quarterbacks. In sequential order, height (HT), body mass (BM), 40 yard sprint (40Y), 5-10-5 shuttle (SH), and countermovement vertical jump height (VJ) were determined on each player. HT and BM were measured using a stadiometer and an electronic scale, respectively. 40Y and SH were measured using hand-held stop watches; the highest of two trials was used for analysis. Two vertical jumps were completed on an electronic pressure mat; vertical jump height was calculated using flight time. For each position grouping, correlations (Pearson's r) were calculated between the four predictor variables (Age, HT, BM, and VJ) and 40Y and SH performance. Combined multivariate models were derived using backward elimination; p < 0.05 was set as the criterion for inclusion. Non-significant variables were eliminated from the final model. RESULTS: Final multivariate models are provided in Table 1. Within LM, 40Y was significantly correlated to AGE (r = -0.22), BM (r = 0.60), and VJ (r = -0.84); SH was correlated to AGE (r = -0.22), BM (r = 0.46), and VJ (r = -0.67). Within the BSP group, 40Y was significantly correlated to AGE (r = -0.20), BM (r = 0.30), and VJ (r = -0.77); SH was correlated to AGE (r = -0.27) and VJ (r = -0.47). Within the SP group, 40Y was significantly correlated to AGE (r = -0.31) and VJ (r = -0.61); SH was correlated with AGE (r = -0.26) and VJ (r = -0.37). CONCLUSIONS: These data suggest that VJ and Age are a strong predictor of 40y and SH regardless of position. BM is a strong indicator of speed within the LM, but is less important to other positions. The combined regression models are more accurate for 40Y than SH ind |
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fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1284358643</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2884309751</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1363-6f6ed9c144760e955ca084fd7e4d9dbb68a6c5205c3ff33187518c7495441bd63</originalsourceid><addsrcrecordid>eNpFkF9PwjAAxBejiYh-h0afN9v1zzreEAQ0JJKgvjZd10LJaLEdMfv2DjHxXu4eLnfJL0nuEcwQLItHiLLX9SSDvXBJGaYZw3leZkRdJANEMU5JzovLPkNGUg4Ruk5uYtxBmFNK8SAJq20XrZINmOpWh7110rUReAM-deOVbTsgXQ3GG9ucsnVgYTdbsFZb7xsw876tZNOAVSM7HeIITK0xOmindARPuv3W2oGVj7a13oF58MdDvE2ujGyivvvzYfIxe36fLNLl2_xlMl6mCmGGU2aYrkuFCCkY1CWlSkJOTF1oUpd1VTEumaI5pAobgzHiBUVcFaSkhKCqZniYPJx3D8F_HXVsxc4fg-svBco5wZQzgvvW6NxSwccYtBGHYPcydAJBcWIsIBI9Y_HPWPwyFkThH31OcKk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1284358643</pqid></control><display><type>article</type><title>Physical Determinants of Velocity and Agility in High School Football Players: Differences Between Position Groups</title><source>Journals@Ovid Complete</source><creator>Xu, J ; Tufano, J ; Brown, D ; Amonette, W E ; Coleman, A E ; Dupler, T ; Wenzel, T ; Spiering, B A</creator><creatorcontrib>Xu, J ; Tufano, J ; Brown, D ; Amonette, W E ; Coleman, A E ; Dupler, T ; Wenzel, T ; Spiering, B A</creatorcontrib><description>PURPOSE: The purpose of this study was to develop prediction equations for speed and agility within different position groupings of High School football players using age, height, body mass, and vertical jump as predictor variables. METHODS: A total of 987 athletes (14-18y) completed testing at a regional high school football combine. Athletes were divided into three groups according to their playing position: Linemen (LM; n = 354; 16.3 ± 0.8y; 179.4 ± 5.6cm; 105.1 ± 18.3kg), Big Skill Players (BSP; n = 189; 16.2 ± 0.9y; 175.5cm ± 5.2cm; 85.7 ± 11.6kg), and Skill Players (SP; n = 444; 16.4 ± 09y; 175.8 ± 5.9cm; 75.5 ± 8.9kg). The LM included offensive tackles, offensive guards, centers, defensive tackles, and defensive ends. BSP included linebackers, running backs, and tight ends. SP included wide receivers, corner backs, safeties, and quarterbacks. In sequential order, height (HT), body mass (BM), 40 yard sprint (40Y), 5-10-5 shuttle (SH), and countermovement vertical jump height (VJ) were determined on each player. HT and BM were measured using a stadiometer and an electronic scale, respectively. 40Y and SH were measured using hand-held stop watches; the highest of two trials was used for analysis. Two vertical jumps were completed on an electronic pressure mat; vertical jump height was calculated using flight time. For each position grouping, correlations (Pearson's r) were calculated between the four predictor variables (Age, HT, BM, and VJ) and 40Y and SH performance. Combined multivariate models were derived using backward elimination; p < 0.05 was set as the criterion for inclusion. Non-significant variables were eliminated from the final model. RESULTS: Final multivariate models are provided in Table 1. Within LM, 40Y was significantly correlated to AGE (r = -0.22), BM (r = 0.60), and VJ (r = -0.84); SH was correlated to AGE (r = -0.22), BM (r = 0.46), and VJ (r = -0.67). Within the BSP group, 40Y was significantly correlated to AGE (r = -0.20), BM (r = 0.30), and VJ (r = -0.77); SH was correlated to AGE (r = -0.27) and VJ (r = -0.47). Within the SP group, 40Y was significantly correlated to AGE (r = -0.31) and VJ (r = -0.61); SH was correlated with AGE (r = -0.26) and VJ (r = -0.37). CONCLUSIONS: These data suggest that VJ and Age are a strong predictor of 40y and SH regardless of position. BM is a strong indicator of speed within the LM, but is less important to other positions. The combined regression models are more accurate for 40Y than SH indicating that physical characteristics not measured in the study could contribute to SH performance. PRACTICAL APPLIACTIONS: Because VJ is a strong predictor of speed regardless of position, football players desiring to increase 40Y should incorporate exercises that improve vertical ground reaction forces. Reduction in body mass is important for improving speed in LM, but this may not be advantageous for success at the position. [PUBLICATION ABSTRACT]</description><identifier>ISSN: 1064-8011</identifier><identifier>EISSN: 1533-4287</identifier><identifier>DOI: 10.1097/01.JSC.0000395635.63229.4c</identifier><language>eng</language><publisher>Champaign: Lippincott Williams & Wilkins Ovid Technologies</publisher><subject>Age ; Athletes ; High school football ; Secondary schools</subject><ispartof>Journal of strength and conditioning research, 2011-03, Vol.25, p.S36-S37</ispartof><rights>Copyright Lippincott Williams & Wilkins Mar 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1363-6f6ed9c144760e955ca084fd7e4d9dbb68a6c5205c3ff33187518c7495441bd63</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Xu, J</creatorcontrib><creatorcontrib>Tufano, J</creatorcontrib><creatorcontrib>Brown, D</creatorcontrib><creatorcontrib>Amonette, W E</creatorcontrib><creatorcontrib>Coleman, A E</creatorcontrib><creatorcontrib>Dupler, T</creatorcontrib><creatorcontrib>Wenzel, T</creatorcontrib><creatorcontrib>Spiering, B A</creatorcontrib><title>Physical Determinants of Velocity and Agility in High School Football Players: Differences Between Position Groups</title><title>Journal of strength and conditioning research</title><description>PURPOSE: The purpose of this study was to develop prediction equations for speed and agility within different position groupings of High School football players using age, height, body mass, and vertical jump as predictor variables. METHODS: A total of 987 athletes (14-18y) completed testing at a regional high school football combine. Athletes were divided into three groups according to their playing position: Linemen (LM; n = 354; 16.3 ± 0.8y; 179.4 ± 5.6cm; 105.1 ± 18.3kg), Big Skill Players (BSP; n = 189; 16.2 ± 0.9y; 175.5cm ± 5.2cm; 85.7 ± 11.6kg), and Skill Players (SP; n = 444; 16.4 ± 09y; 175.8 ± 5.9cm; 75.5 ± 8.9kg). The LM included offensive tackles, offensive guards, centers, defensive tackles, and defensive ends. BSP included linebackers, running backs, and tight ends. SP included wide receivers, corner backs, safeties, and quarterbacks. In sequential order, height (HT), body mass (BM), 40 yard sprint (40Y), 5-10-5 shuttle (SH), and countermovement vertical jump height (VJ) were determined on each player. HT and BM were measured using a stadiometer and an electronic scale, respectively. 40Y and SH were measured using hand-held stop watches; the highest of two trials was used for analysis. Two vertical jumps were completed on an electronic pressure mat; vertical jump height was calculated using flight time. For each position grouping, correlations (Pearson's r) were calculated between the four predictor variables (Age, HT, BM, and VJ) and 40Y and SH performance. Combined multivariate models were derived using backward elimination; p < 0.05 was set as the criterion for inclusion. Non-significant variables were eliminated from the final model. RESULTS: Final multivariate models are provided in Table 1. Within LM, 40Y was significantly correlated to AGE (r = -0.22), BM (r = 0.60), and VJ (r = -0.84); SH was correlated to AGE (r = -0.22), BM (r = 0.46), and VJ (r = -0.67). Within the BSP group, 40Y was significantly correlated to AGE (r = -0.20), BM (r = 0.30), and VJ (r = -0.77); SH was correlated to AGE (r = -0.27) and VJ (r = -0.47). Within the SP group, 40Y was significantly correlated to AGE (r = -0.31) and VJ (r = -0.61); SH was correlated with AGE (r = -0.26) and VJ (r = -0.37). CONCLUSIONS: These data suggest that VJ and Age are a strong predictor of 40y and SH regardless of position. BM is a strong indicator of speed within the LM, but is less important to other positions. The combined regression models are more accurate for 40Y than SH indicating that physical characteristics not measured in the study could contribute to SH performance. PRACTICAL APPLIACTIONS: Because VJ is a strong predictor of speed regardless of position, football players desiring to increase 40Y should incorporate exercises that improve vertical ground reaction forces. Reduction in body mass is important for improving speed in LM, but this may not be advantageous for success at the position. [PUBLICATION ABSTRACT]</description><subject>Age</subject><subject>Athletes</subject><subject>High school football</subject><subject>Secondary schools</subject><issn>1064-8011</issn><issn>1533-4287</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpFkF9PwjAAxBejiYh-h0afN9v1zzreEAQ0JJKgvjZd10LJaLEdMfv2DjHxXu4eLnfJL0nuEcwQLItHiLLX9SSDvXBJGaYZw3leZkRdJANEMU5JzovLPkNGUg4Ruk5uYtxBmFNK8SAJq20XrZINmOpWh7110rUReAM-deOVbTsgXQ3GG9ucsnVgYTdbsFZb7xsw876tZNOAVSM7HeIITK0xOmindARPuv3W2oGVj7a13oF58MdDvE2ujGyivvvzYfIxe36fLNLl2_xlMl6mCmGGU2aYrkuFCCkY1CWlSkJOTF1oUpd1VTEumaI5pAobgzHiBUVcFaSkhKCqZniYPJx3D8F_HXVsxc4fg-svBco5wZQzgvvW6NxSwccYtBGHYPcydAJBcWIsIBI9Y_HPWPwyFkThH31OcKk</recordid><startdate>201103</startdate><enddate>201103</enddate><creator>Xu, J</creator><creator>Tufano, J</creator><creator>Brown, D</creator><creator>Amonette, W E</creator><creator>Coleman, A E</creator><creator>Dupler, T</creator><creator>Wenzel, T</creator><creator>Spiering, B A</creator><general>Lippincott Williams & Wilkins Ovid Technologies</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>201103</creationdate><title>Physical Determinants of Velocity and Agility in High School Football Players: Differences Between Position Groups</title><author>Xu, J ; Tufano, J ; Brown, D ; Amonette, W E ; Coleman, A E ; Dupler, T ; Wenzel, T ; Spiering, B A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1363-6f6ed9c144760e955ca084fd7e4d9dbb68a6c5205c3ff33187518c7495441bd63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Age</topic><topic>Athletes</topic><topic>High school football</topic><topic>Secondary schools</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, J</creatorcontrib><creatorcontrib>Tufano, J</creatorcontrib><creatorcontrib>Brown, D</creatorcontrib><creatorcontrib>Amonette, W E</creatorcontrib><creatorcontrib>Coleman, A E</creatorcontrib><creatorcontrib>Dupler, T</creatorcontrib><creatorcontrib>Wenzel, T</creatorcontrib><creatorcontrib>Spiering, B A</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science 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>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science 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>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</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 Central China</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of strength and conditioning research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, J</au><au>Tufano, J</au><au>Brown, D</au><au>Amonette, W E</au><au>Coleman, A E</au><au>Dupler, T</au><au>Wenzel, T</au><au>Spiering, B A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Physical Determinants of Velocity and Agility in High School Football Players: Differences Between Position Groups</atitle><jtitle>Journal of strength and conditioning research</jtitle><date>2011-03</date><risdate>2011</risdate><volume>25</volume><spage>S36</spage><epage>S37</epage><pages>S36-S37</pages><issn>1064-8011</issn><eissn>1533-4287</eissn><abstract>PURPOSE: The purpose of this study was to develop prediction equations for speed and agility within different position groupings of High School football players using age, height, body mass, and vertical jump as predictor variables. METHODS: A total of 987 athletes (14-18y) completed testing at a regional high school football combine. Athletes were divided into three groups according to their playing position: Linemen (LM; n = 354; 16.3 ± 0.8y; 179.4 ± 5.6cm; 105.1 ± 18.3kg), Big Skill Players (BSP; n = 189; 16.2 ± 0.9y; 175.5cm ± 5.2cm; 85.7 ± 11.6kg), and Skill Players (SP; n = 444; 16.4 ± 09y; 175.8 ± 5.9cm; 75.5 ± 8.9kg). The LM included offensive tackles, offensive guards, centers, defensive tackles, and defensive ends. BSP included linebackers, running backs, and tight ends. SP included wide receivers, corner backs, safeties, and quarterbacks. In sequential order, height (HT), body mass (BM), 40 yard sprint (40Y), 5-10-5 shuttle (SH), and countermovement vertical jump height (VJ) were determined on each player. HT and BM were measured using a stadiometer and an electronic scale, respectively. 40Y and SH were measured using hand-held stop watches; the highest of two trials was used for analysis. Two vertical jumps were completed on an electronic pressure mat; vertical jump height was calculated using flight time. For each position grouping, correlations (Pearson's r) were calculated between the four predictor variables (Age, HT, BM, and VJ) and 40Y and SH performance. Combined multivariate models were derived using backward elimination; p < 0.05 was set as the criterion for inclusion. Non-significant variables were eliminated from the final model. RESULTS: Final multivariate models are provided in Table 1. Within LM, 40Y was significantly correlated to AGE (r = -0.22), BM (r = 0.60), and VJ (r = -0.84); SH was correlated to AGE (r = -0.22), BM (r = 0.46), and VJ (r = -0.67). Within the BSP group, 40Y was significantly correlated to AGE (r = -0.20), BM (r = 0.30), and VJ (r = -0.77); SH was correlated to AGE (r = -0.27) and VJ (r = -0.47). Within the SP group, 40Y was significantly correlated to AGE (r = -0.31) and VJ (r = -0.61); SH was correlated with AGE (r = -0.26) and VJ (r = -0.37). CONCLUSIONS: These data suggest that VJ and Age are a strong predictor of 40y and SH regardless of position. BM is a strong indicator of speed within the LM, but is less important to other positions. The combined regression models are more accurate for 40Y than SH indicating that physical characteristics not measured in the study could contribute to SH performance. PRACTICAL APPLIACTIONS: Because VJ is a strong predictor of speed regardless of position, football players desiring to increase 40Y should incorporate exercises that improve vertical ground reaction forces. Reduction in body mass is important for improving speed in LM, but this may not be advantageous for success at the position. [PUBLICATION ABSTRACT]</abstract><cop>Champaign</cop><pub>Lippincott Williams & Wilkins Ovid Technologies</pub><doi>10.1097/01.JSC.0000395635.63229.4c</doi></addata></record> |
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subjects | Age Athletes High school football Secondary schools |
title | Physical Determinants of Velocity and Agility in High School Football Players: Differences Between Position Groups |
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