eprintid: 53 rev_number: 8 eprint_status: archive userid: 2 importid: 0 dir: disk0/00/00/00/53 datestamp: 2021-05-31 14:17:14 lastmod: 2022-03-03 23:55:05 status_changed: 2021-05-31 14:17:14 type: article succeeds: 0 commentary: 0 metadata_visibility: show item_issues_count: 0 sword_depositor: 0 creators_name: Casamichana Gomez, David creators_name: Castellano, Julen creators_name: Gómez Díaz, Antonio creators_name: Martín-García, Andrés creators_id: david.casamichana@uneatlantico.es creators_id: creators_id: creators_id: title: Looking for Complementary Intensity Variables in Different Training Games in Football ispublished: pub subjects: uneat_dp divisions: uneatlantico_produccion_cientifica full_text_status: none keywords: Small-sided games; Match analysis; GPS; Training load. abstract: The main aim of this study was to identify which combination of external intensity training load (iTL) metrics capture similar or unique information for different training game (TG) formats and official matches (OMs) in football using principal component (PC) analysis. Ten metrics of iTL were collected from 24 professional male football players using global positioning technology. A total of 348, 383, 120, 127, 148, and 207 individual files for small-sided possession games, medium-sided possession games, small-sided games, medium-sided games, large-sided games, and OMs, respectively, were studied. Principal component analysis was conducted on each game format. Extraction criteria were set at an eigenvalue of greater than one. Varimax rotation mode was used to extract more than one PC. Intensity training load metrics with PC “loadings” above 0.7 were deemed to possess well-defined relationships with the extracted PC. In each TG and OM, 3 PCs were identified. For the first PC, eigenvalues for each game format ranged from 3.89 to 4.45, which explained 39–44% of the information (i.e., variance) provided by the 10 iTL metrics. For the second PC, eigenvalues ranged from 2.17 to 2.47, explaining 22–26% of iTL information. For the third PC, eigenvalues ranged from 1.41 to 1.98, explaining 14–20% of iTL information. This would suggest that TG and OM have multidimensional demands; so, the use of only a single iTL could potentially lead to an underestimation of the physical demands. Consequently, a combination of 3 iTL metrics is required during professional football game formats. date: 2019 date_type: published publication: Journal of Strength and Conditioning Research volume: Publis pages: 0 id_number: doi:10.1519/jsc.0000000000003025 refereed: TRUE issn: 1064-8011 official_url: http://doi.org/10.1519/jsc.0000000000003025 num_pieces: 0 gscholar_impact: 0 gscholar_datestamp: 0000-00-00 00:00:00 access: close language: en citation: Artículo Materias > Educación física y el deporte Universidad Europea del Atlántico > Investigación > Producción Científica Cerrado Inglés The main aim of this study was to identify which combination of external intensity training load (iTL) metrics capture similar or unique information for different training game (TG) formats and official matches (OMs) in football using principal component (PC) analysis. Ten metrics of iTL were collected from 24 professional male football players using global positioning technology. A total of 348, 383, 120, 127, 148, and 207 individual files for small-sided possession games, medium-sided possession games, small-sided games, medium-sided games, large-sided games, and OMs, respectively, were studied. Principal component analysis was conducted on each game format. Extraction criteria were set at an eigenvalue of greater than one. Varimax rotation mode was used to extract more than one PC. Intensity training load metrics with PC “loadings” above 0.7 were deemed to possess well-defined relationships with the extracted PC. In each TG and OM, 3 PCs were identified. For the first PC, eigenvalues for each game format ranged from 3.89 to 4.45, which explained 39–44% of the information (i.e., variance) provided by the 10 iTL metrics. For the second PC, eigenvalues ranged from 2.17 to 2.47, explaining 22–26% of iTL information. For the third PC, eigenvalues ranged from 1.41 to 1.98, explaining 14–20% of iTL information. This would suggest that TG and OM have multidimensional demands; so, the use of only a single iTL could potentially lead to an underestimation of the physical demands. Consequently, a combination of 3 iTL metrics is required during professional football game formats. metadata Casamichana Gomez, David; Castellano, Julen; Gómez Díaz, Antonio y Martín-García, Andrés mail david.casamichana@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2019) Looking for Complementary Intensity Variables in Different Training Games in Football. Journal of Strength and Conditioning Research, Publis. ISSN 1064-8011