{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:09:24Z","timestamp":1784138964784,"version":"3.55.0"},"reference-count":75,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Modern load monitoring in soccer increasingly employs relative individualized acceleration intensity thresholds derived from individual maximal acceleration\u2013initial running speed (a\n                    <jats:sub>max<\/jats:sub>\n                    \u2013v\n                    <jats:sub>init<\/jats:sub>\n                    ) regression lines. This study examined the reliability and validity of soccer players\u2019 individual a\n                    <jats:sub>max<\/jats:sub>\n                    \u2013v\n                    <jats:sub>init<\/jats:sub>\n                    regression lines determined solely from game locomotion data. Using a GPS-based tracking system, we prospectively collected game locomotion data from 159 male youth elite soccer players over one season and repeatedly determined their a\n                    <jats:sub>max<\/jats:sub>\n                    \u2013v\n                    <jats:sub>init<\/jats:sub>\n                    regression lines using a recently introduced game data\u2013based method. To examine the influence of data volume, regression lines were determined using different numbers of consecutive games. As a criterion measure, the a\n                    <jats:sub>max<\/jats:sub>\n                    \u2013v\n                    <jats:sub>init<\/jats:sub>\n                    regression line was determined using an acceleration test in 55 athletes. Reliability was assessed via the typical error (TE) and the intraclass correlation coefficient (ICC), and validity via the typical error of the estimate (TEE) and the Pearson correlation coefficient. The magnitude of errors and correlation coefficients was assessed using standardization and published thresholds, respectively. Reliability improved gradually with increasing data volume, resulting in TEs of 3.4% (\n                    <jats:italic>a<\/jats:italic>\n                    <jats:sub>max<\/jats:sub>\n                    intercept), 7.3% (\n                    <jats:italic>v<\/jats:italic>\n                    <jats:sub>init<\/jats:sub>\n                    intercept), and 10.3% (slope). Nonetheless, all TEs were large in magnitude, and ICCs very low to low. Validity was unaffected by data volume, with TEEs consistently ~7% (\n                    <jats:italic>a<\/jats:italic>\n                    <jats:sub>max<\/jats:sub>\n                    intercept),\u2009~\u200912% (\n                    <jats:italic>v<\/jats:italic>\n                    <jats:sub>init<\/jats:sub>\n                    intercept), and ~17% (slope). All TEEs were assessed as extremely large, and Pearson correlations as impractical. These findings indicate that the game data\u2013based method currently lacks the precision required for individual-level applications, and this limitation cannot be resolved simply by including more games. Therefore, establishing relative individualized acceleration intensity thresholds within a team still requires a test-based approach. Moreover, the results suggest that properties inherent in GPS-based game locomotion data, rather than data volume, underlie the observed imprecision. We propose that substantial future improvements will depend on more precise tracking systems.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0353385","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:38:04Z","timestamp":1784137084000},"page":"e0353385","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reliability and validity of the individual GPS game data\u2013based maximal acceleration\u2013initial running speed regression line in youth elite soccer players"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0812-8899","authenticated-orcid":true,"given":"Pascal","family":"Andrey","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karin","family":"Fischer-Sonderegger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Taube","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3446-2907","authenticated-orcid":true,"given":"Markus","family":"Tschopp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"issue":"5","key":"pone.0353385.ref001","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1123\/ijspp.2015-0331","article-title":"Training Load and Player Monitoring in High-Level Football: Current Practice and Perceptions","volume":"11","author":"R Akenhead","year":"2016","journal-title":"Int J Sports Physiol Perform"},{"issue":"3","key":"pone.0353385.ref002","doi-asserted-by":"crossref","first-page":"377","DOI":"10.5114\/biolsport.2021.99328","article-title":"Contemporary practices of strength and conditioning coaches in professional soccer","volume":"38","author":"A Weldon","year":"2021","journal-title":"Biol Sport"},{"issue":"1","key":"pone.0353385.ref003","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s40798-021-00332-8","article-title":"The Quantification of Acceleration Events in Elite Team Sport: a Systematic Review","volume":"7","author":"RIM Delves","year":"2021","journal-title":"Sports Med Open"},{"issue":"3","key":"pone.0353385.ref004","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1080\/15438627.2019.1686703","article-title":"Methods to collect and interpret external training load using microtechnology incorporating GPS in professional football: a systematic review","volume":"28","author":"V Rago","year":"2020","journal-title":"Res Sports Med"},{"key":"pone.0353385.ref005","doi-asserted-by":"crossref","first-page":"715122","DOI":"10.3389\/fspor.2021.715122","article-title":"Load Monitoring Practice in Elite Women Association Football","volume":"3","author":"LS Luteberget","year":"2021","journal-title":"Front Sports Act Living"},{"issue":"1","key":"pone.0353385.ref006","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40798-022-00408-z","article-title":"Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis","volume":"8","author":"L Torres-Ronda","year":"2022","journal-title":"Sports Med Open"},{"issue":"3","key":"pone.0353385.ref007","doi-asserted-by":"crossref","first-page":"919","DOI":"10.5114\/biolsport.2023.122480","article-title":"Arbitrary absolute vs. individualized running speed thresholds in team sports: A scoping review with evidence gap map","volume":"40","author":"FM Clemente","year":"2023","journal-title":"Biol Sport"},{"key":"pone.0353385.ref008","doi-asserted-by":"crossref","first-page":"1116293","DOI":"10.3389\/fspor.2023.1116293","article-title":"High-speed running and sprinting in professional adult soccer: Current thresholds definition, match demands and training strategies. 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