Analyse Success Model of Split Time and Cut-Off Point Values of Physical Demands to Keep Category in Semi-Professional Football Players

Applied Sciences

Jorge Garcia-Unanue¹  Leonor Gallardo¹  Luis Jimenez-Linares²  Javier Sanchez-Sanchez³  JesusVicente Gimenez³  JoseLuis Felipe³ 

[1] IGOID Research Group, Department of Physical Activity and Sport Sciences, University of Castilla-La Mancha, Avenida Carlos III, s/n, Campus Tecnológico Fábrica de Armas, Laboratorio Gestión Deportiva, 45071 Toledo, Spain;School of Computer Science, Department of Information Technologies and Systems, University of Castilla-La Mancha, 13017 Ciudad Real, Spain;School of Sport Sciences, Universidad Europea de Madrid, Edificio Juan Mayorga, C/Tajo, s/n, Villaviciosa de Odón, 28670 Madrid, Spain;

关键词: competition analysis;    GPS technology;    decision tree;    fuzzy set;    football;   

DOI  :  10.3390/app10155299

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown