Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players
| dc.contributor.author | Giménez de Galanes, Jesús Vicente | |
| dc.contributor.author | Jiménez Linares, Luis | |
| dc.contributor.author | García Unanue, Jorge Fernando | |
| dc.contributor.author | Sánchez Sánchez, Javier | |
| dc.contributor.author | Gallardo Guerrero, Leonor | |
| dc.contributor.author | Felipe Hernández, José Luis | |
| dc.date.accessioned | 2020-11-19T15:01:07Z | |
| dc.date.available | 2020-11-19T15:01:07Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | The aim of this study was to analyse different success models and split time on cut-off point values on physical demands to keep category in semi-professional football players. An ad hoc observational controlled study was carried out with a total of ten (840 match data) outfield main players (25.2 ± 6.3 years, 1.79 ± 0.75 m, 74.9 ± 5.8 kg and 16.5 ± 6 years of football experience) and monitored using 15 Hz GPS devices. During 14 official matches from the Spanish division B in the 2016/2017 season, match data were coded considering the situational variable (score) and classified by match results (winning, losing or drawing). The results show significant differences between high-intensity attributes criteria that considered split time in velocity zones of 0–15 min (p = 0.043, ηp2 = 0.065, medium), 30–45 min (p = 0.010, ηp2 = 0.094, medium) and 60–75 min (p = 0.015, ηp2 = 0.086, medium), as well as sprint 60–75 min (p = 0.042, ηp2 = 0.066, medium) and 75–90 min (p = 0.002, ηp2 = 0.129, medium). Decision tree induction was applied to reduce the disparity range of data according to six 15-min intervals and to determine the cut-off point values for every parameter combination. It was possible to establish multivariate models for the main high-intensity actions criteria, allowing the establishment of all rules with their attributes and enabling the detection and visualisation of relationships and the pattern sets of variables for determining success. | spa |
| dc.description.filiation | UEM | spa |
| dc.description.impact | 2.679 JCR (2020) Q2, 73/160 Physics, Applied | spa |
| dc.description.impact | 0.435 SJR (2020) Q2, 366/2196 Computer Science Applications | spa |
| dc.description.impact | No data IDR 2019 | spa |
| dc.description.sponsorship | Sin financiación | spa |
| dc.identifier.citation | Giménez, J. V., Jiménez-Linares, L., García-Unanue, J., Sánchez-Sánchez, J., Gallardo, L., & Felipe, J. L. (2020). Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players. Applied Sciences, 10(15), 5299. https://doi.org/10.3390/app10155299 | spa |
| dc.identifier.doi | 10.3390/app10155299 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.uri | http://hdl.handle.net/11268/9491 | |
| dc.language.iso | eng | spa |
| dc.peerreviewed | Si | spa |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.rights.accessRights | open access | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject.uem | Fútbol | spa |
| dc.subject.uem | Sistema de Posicionamiento Global | spa |
| dc.subject.uem | Muestreo (Estadística) | spa |
| dc.subject.unesco | Deporte | spa |
| dc.subject.unesco | Tecnología electrónica | spa |
| dc.subject.unesco | Análisis de datos | spa |
| dc.title | Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players | spa |
| dc.type | journal article | spa |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 8b785fb1-fee8-4d8a-8bcc-4e975d464da4 | |
| relation.isAuthorOfPublication | 04f97791-47fa-4516-b12f-83c10de74eea | |
| relation.isAuthorOfPublication | 67acdf55-2cbb-4304-bd5e-8064bdb4bcc3 | |
| relation.isAuthorOfPublication.latestForDiscovery | 8b785fb1-fee8-4d8a-8bcc-4e975d464da4 |
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