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Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs

Daniel J. Cunningham, David A. Shearer, Neil Carter, Scott Drawer, Ben Pollard, Mark Bennett, Robin Eager, Christian J. Cook, John Farrell, Mark Russell, Liam Kilduff Orcid Logo

PLOS ONE, Volume: 13, Issue: 4, Start page: e0195197

Swansea University Author: Liam Kilduff Orcid Logo

Abstract

The assessment of competitive movement demands in team sports has traditionally relied upon global positioning system (GPS) analyses presented as fixed-time epochs (e.g., 5–40 min). More recently, presenting game data as a rolling average has become prevalent due to concerns over a loss of sampling...

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Published in: PLOS ONE
ISSN: 1932-6203
Published: 2018
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2018-05-14T14:29:07.5700044</datestamp><bib-version>v2</bib-version><id>39115</id><entry>2018-03-21</entry><title>Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs</title><swanseaauthors><author><sid>972ed9a1dda7a0de20581a0f8350be98</sid><ORCID>0000-0001-9449-2293</ORCID><firstname>Liam</firstname><surname>Kilduff</surname><name>Liam Kilduff</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2018-03-21</date><deptcode>STSC</deptcode><abstract>The assessment of competitive movement demands in team sports has traditionally relied upon global positioning system (GPS) analyses presented as fixed-time epochs (e.g., 5&#x2013;40 min). More recently, presenting game data as a rolling average has become prevalent due to concerns over a loss of sampling resolution associated with the windowing of data over fixed periods. Accordingly, this study compared rolling average (ROLL) and fixed-time (FIXED) epochs for quantifying the peak movement demands of international rugby union match-play as a function of playing position. Elite players from three different squads (n = 119) were monitored using 10 Hz GPS during 36 matches played in the 2014&#x2013;2017 seasons. Players categorised broadly as forwards and backs, and then by positional sub-group (FR: front row, SR: second row, BR: back row, HB: half back, MF: midfield, B3: back three) were monitored during match-play for peak values of high-speed running (&gt;5 m&#xB7;s-1; HSR) and relative distance covered (m&#xB7;min-1) over 60&#x2013;300 s using two types of sample-epoch (ROLL, FIXED). Irrespective of the method used, as the epoch length increased, values for the intensity of running actions decreased (e.g., For the backs using the ROLL method, distance covered decreased from 177.4 &#xB1; 20.6 m&#xB7;min-1 in the 60 s epoch to 107.5 &#xB1; 13.3 m&#xB7;min-1 for the 300 s epoch). For the team as a whole, and irrespective of position, estimates of fixed effects indicated significant between-method differences across all time-points for both relative distance covered and HSR. Movement demands were underestimated consistently by FIXED versus ROLL with differences being most pronounced using 60 s epochs (95% CI HSR: -6.05 to -4.70 m&#xB7;min-1, 95% CI distance: -18.45 to -16.43 m&#xB7;min-1). For all HSR time epochs except one, all backs groups increased more (p &lt; 0.01) from FIXED to ROLL than the forward groups. Linear mixed modelling of ROLL data highlighted that for HSR (except 60 s epoch), SR was the only group not significantly different to FR. For relative distance covered all other position groups were greater than the FR (p &lt; 0.05). The FIXED method underestimated both relative distance (~11%) and HSR values (up to ~20%) compared to the ROLL method. These differences were exaggerated for the HSR variable in the backs position who covered the greatest HSR distance; highlighting important consideration for those implementing the FIXED method of analysis. The data provides coaches with a worst-case scenario reference on the running demands required for periods of 60&#x2013;300 s in length. This information offers novel insight into game demands and can be used to inform the design of training games to increase specificity of preparation for the most demanding phases of matches.</abstract><type>Journal Article</type><journal>PLOS ONE</journal><volume>13</volume><journalNumber>4</journalNumber><paginationStart>e0195197</paginationStart><publisher/><issnElectronic>1932-6203</issnElectronic><keywords/><publishedDay>5</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2018</publishedYear><publishedDate>2018-04-05</publishedDate><doi>10.1371/journal.pone.0195197</doi><url/><notes/><college>COLLEGE NANME</college><department>Sport and Exercise Sciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>STSC</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2018-05-14T14:29:07.5700044</lastEdited><Created>2018-03-21T10:27:36.4224736</Created><path><level id="1">College of Engineering</level><level id="2">Sports Science</level></path><authors><author><firstname>Daniel J.</firstname><surname>Cunningham</surname><order>1</order></author><author><firstname>David A.</firstname><surname>Shearer</surname><order>2</order></author><author><firstname>Neil</firstname><surname>Carter</surname><order>3</order></author><author><firstname>Scott</firstname><surname>Drawer</surname><order>4</order></author><author><firstname>Ben</firstname><surname>Pollard</surname><order>5</order></author><author><firstname>Mark</firstname><surname>Bennett</surname><order>6</order></author><author><firstname>Robin</firstname><surname>Eager</surname><order>7</order></author><author><firstname>Christian J.</firstname><surname>Cook</surname><order>8</order></author><author><firstname>John</firstname><surname>Farrell</surname><order>9</order></author><author><firstname>Mark</firstname><surname>Russell</surname><order>10</order></author><author><firstname>Liam</firstname><surname>Kilduff</surname><orcid>0000-0001-9449-2293</orcid><order>11</order></author></authors><documents><document><filename>0039115-17042018154806.pdf</filename><originalFilename>cunningham2018(2).pdf</originalFilename><uploaded>2018-04-17T15:48:06.6430000</uploaded><type>Output</type><contentLength>1008255</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><embargoDate>2018-04-17T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2018-05-14T14:29:07.5700044 v2 39115 2018-03-21 Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs 972ed9a1dda7a0de20581a0f8350be98 0000-0001-9449-2293 Liam Kilduff Liam Kilduff true false 2018-03-21 STSC The assessment of competitive movement demands in team sports has traditionally relied upon global positioning system (GPS) analyses presented as fixed-time epochs (e.g., 5–40 min). More recently, presenting game data as a rolling average has become prevalent due to concerns over a loss of sampling resolution associated with the windowing of data over fixed periods. Accordingly, this study compared rolling average (ROLL) and fixed-time (FIXED) epochs for quantifying the peak movement demands of international rugby union match-play as a function of playing position. Elite players from three different squads (n = 119) were monitored using 10 Hz GPS during 36 matches played in the 2014–2017 seasons. Players categorised broadly as forwards and backs, and then by positional sub-group (FR: front row, SR: second row, BR: back row, HB: half back, MF: midfield, B3: back three) were monitored during match-play for peak values of high-speed running (>5 m·s-1; HSR) and relative distance covered (m·min-1) over 60–300 s using two types of sample-epoch (ROLL, FIXED). Irrespective of the method used, as the epoch length increased, values for the intensity of running actions decreased (e.g., For the backs using the ROLL method, distance covered decreased from 177.4 ± 20.6 m·min-1 in the 60 s epoch to 107.5 ± 13.3 m·min-1 for the 300 s epoch). For the team as a whole, and irrespective of position, estimates of fixed effects indicated significant between-method differences across all time-points for both relative distance covered and HSR. Movement demands were underestimated consistently by FIXED versus ROLL with differences being most pronounced using 60 s epochs (95% CI HSR: -6.05 to -4.70 m·min-1, 95% CI distance: -18.45 to -16.43 m·min-1). For all HSR time epochs except one, all backs groups increased more (p < 0.01) from FIXED to ROLL than the forward groups. Linear mixed modelling of ROLL data highlighted that for HSR (except 60 s epoch), SR was the only group not significantly different to FR. For relative distance covered all other position groups were greater than the FR (p < 0.05). The FIXED method underestimated both relative distance (~11%) and HSR values (up to ~20%) compared to the ROLL method. These differences were exaggerated for the HSR variable in the backs position who covered the greatest HSR distance; highlighting important consideration for those implementing the FIXED method of analysis. The data provides coaches with a worst-case scenario reference on the running demands required for periods of 60–300 s in length. This information offers novel insight into game demands and can be used to inform the design of training games to increase specificity of preparation for the most demanding phases of matches. Journal Article PLOS ONE 13 4 e0195197 1932-6203 5 4 2018 2018-04-05 10.1371/journal.pone.0195197 COLLEGE NANME Sport and Exercise Sciences COLLEGE CODE STSC Swansea University 2018-05-14T14:29:07.5700044 2018-03-21T10:27:36.4224736 College of Engineering Sports Science Daniel J. Cunningham 1 David A. Shearer 2 Neil Carter 3 Scott Drawer 4 Ben Pollard 5 Mark Bennett 6 Robin Eager 7 Christian J. Cook 8 John Farrell 9 Mark Russell 10 Liam Kilduff 0000-0001-9449-2293 11 0039115-17042018154806.pdf cunningham2018(2).pdf 2018-04-17T15:48:06.6430000 Output 1008255 application/pdf Version of Record true 2018-04-17T00:00:00.0000000 true eng
title Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs
spellingShingle Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs
Liam Kilduff
title_short Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs
title_full Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs
title_fullStr Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs
title_full_unstemmed Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs
title_sort Assessing worst case scenarios in movement demands derived from global positioning systems during international rugby union matches: Rolling averages versus fixed length epochs
author_id_str_mv 972ed9a1dda7a0de20581a0f8350be98
author_id_fullname_str_mv 972ed9a1dda7a0de20581a0f8350be98_***_Liam Kilduff
author Liam Kilduff
author2 Daniel J. Cunningham
David A. Shearer
Neil Carter
Scott Drawer
Ben Pollard
Mark Bennett
Robin Eager
Christian J. Cook
John Farrell
Mark Russell
Liam Kilduff
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department_str Sports Science{{{_:::_}}}College of Engineering{{{_:::_}}}Sports Science
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description The assessment of competitive movement demands in team sports has traditionally relied upon global positioning system (GPS) analyses presented as fixed-time epochs (e.g., 5–40 min). More recently, presenting game data as a rolling average has become prevalent due to concerns over a loss of sampling resolution associated with the windowing of data over fixed periods. Accordingly, this study compared rolling average (ROLL) and fixed-time (FIXED) epochs for quantifying the peak movement demands of international rugby union match-play as a function of playing position. Elite players from three different squads (n = 119) were monitored using 10 Hz GPS during 36 matches played in the 2014–2017 seasons. Players categorised broadly as forwards and backs, and then by positional sub-group (FR: front row, SR: second row, BR: back row, HB: half back, MF: midfield, B3: back three) were monitored during match-play for peak values of high-speed running (>5 m·s-1; HSR) and relative distance covered (m·min-1) over 60–300 s using two types of sample-epoch (ROLL, FIXED). Irrespective of the method used, as the epoch length increased, values for the intensity of running actions decreased (e.g., For the backs using the ROLL method, distance covered decreased from 177.4 ± 20.6 m·min-1 in the 60 s epoch to 107.5 ± 13.3 m·min-1 for the 300 s epoch). For the team as a whole, and irrespective of position, estimates of fixed effects indicated significant between-method differences across all time-points for both relative distance covered and HSR. Movement demands were underestimated consistently by FIXED versus ROLL with differences being most pronounced using 60 s epochs (95% CI HSR: -6.05 to -4.70 m·min-1, 95% CI distance: -18.45 to -16.43 m·min-1). For all HSR time epochs except one, all backs groups increased more (p < 0.01) from FIXED to ROLL than the forward groups. Linear mixed modelling of ROLL data highlighted that for HSR (except 60 s epoch), SR was the only group not significantly different to FR. For relative distance covered all other position groups were greater than the FR (p < 0.05). The FIXED method underestimated both relative distance (~11%) and HSR values (up to ~20%) compared to the ROLL method. These differences were exaggerated for the HSR variable in the backs position who covered the greatest HSR distance; highlighting important consideration for those implementing the FIXED method of analysis. The data provides coaches with a worst-case scenario reference on the running demands required for periods of 60–300 s in length. This information offers novel insight into game demands and can be used to inform the design of training games to increase specificity of preparation for the most demanding phases of matches.
published_date 2018-04-05T03:53:08Z
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