<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Fischer-Sonderegger, Karin</dc:creator>
  <dc:creator>Taube, Wolfgang</dc:creator>
  <dc:creator>Rumo, Martin</dc:creator>
  <dc:creator>Tschopp, Markus</dc:creator>
  <dc:date>2019-04-11</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">To investigate the strengths and limitations of different indicators to measure physical  load. Furthermore, indicators were evaluated for discrimination between performance  levels and playing positions. Methods: Ninety positional match files from 70 elite  players and 91 match files from 69 subelite players were collected during 14 official  under-18 matches using a local position measurement system. Indicators are  calculated from speed, absolute acceleration (acc-abs), or percentage acceleration  (acc-%). The acc-% describes the level of acceleration depending on the maximal  voluntary acceleration (amax) for each initial running speed. Effect sizes (ES) were  used to determine discriminative ability. Results: The number of high accelerations  largely depended on the method (absolute threshold [&gt;3 m·s−2 and &gt;4 m·s−2] 120  and 59 efforts; high percentage threshold [&gt;75% amax] 84 efforts). Only a small  number of highly accelerated efforts reached speeds considered high-speed running  (&gt;19.8 km·h−1: 32.6%). More high acc-% exists from initial running speed &gt;2 m·s−1  (23.0) compared with acc-abs (&gt;3 m·s−2 14.4, &gt;4 m·s−2 5.9). Elite players achieve  higher values in most performance indicators, with ES being highest for the number of  high acc-% (ES = 0.91) and high acc-abs (&gt;3 m·s−2 ES = 0.86, &gt;4 m·s−2 ES = 0.87),  as well as for covered distance in jogging (ES = 0.94). Conclusions: Estimated  physical load, discriminative ability of physical indicators, and positional requirements  largely depend on the applied method. A combination of speed-based and acc-%  methods is recommended to get a comprehensive view.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://folia.unifr.ch/global/documents/307762</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/307762/files/tau_mpl.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1123/ijspp.2017-0768</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:source>International Journal of Sports Physiology and Performance. - 2019, vol. 14, no. 5, p. 627–634</dc:source>
  <dc:subject>info:eu-repo/classification/udc/796</dc:subject>
  <dc:title xmlns:ns1="xml" ns1:lang="en">Measuring physical load in soccer: strengths and limitations of 3 different methods</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
