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Predictive modelling of the physical demands during training and competition in professional soccer players
Affiliation:1. Department of Exercise Physiology, Faculty of Sport Sciences, University of Isfahan, Isfahan, 81746-73441, Iran;2. Sports Scientist, Sepahan Football Club, Isfahan, Iran;3. Department of Sports, Center of Physical Education and Sports, Federal University of Espírito Santo, Vitória, Brazil;4. Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal;5. Department of Exercise Physiology, Faculty of Sport Sciences, University of Guilan, Rasht, Iran;6. HEME Research Group, Faculty of Sport Sciences, University of Extremadura, Cáceres, Spain;1. Faculty of Physical Activity and Sports Sciences, Technical University of Madrid, Madrid, Spain;2. Faculty of Science and Technology, Middlesex University, London Sports Institute, London, United Kingdom;3. CIDESD, Research Center in Sports Sciences, Health Sciences and Human Development, Department of Sport Sciences, University of Beira Interior, Covilhã, Portugal
Abstract:ObjectivesThe present study aimed to predict the cut-off point-values that best differentiate the physical demands of training and competition tasks including friendly matches (FM), small sided games (SSG), large sided games (LSG), mini-goal games (MG) and ball circuit-training (CT) in professional soccer players.DesignExperimental randomized controlled trial.MethodsFourteen professional players participated in all tasks with the CT, SSG and MG consisting of 8 repetitions of 4-min game play, interspersed by 2-min of active recovery. The training data were compared to the first 32-min of the LSG and two competitive FM per player. All movement patterns from walking to sprint running were recorded using 10 Hz GPS devices while player perception of exertion was recorded via a visual analogue scale, post-task. Decision tree induction was applied to the dataset to assess the cut-off point-values from four training drills (SSG, LSG, MG, and CT) and FM for every parameter combination.ResultsDistance covered during jogging (2.3–3.3 m/s; >436 m), number of decelerations (≤730.5) and accelerations (≤663), and maximum velocity reached (>5.48 m/s) characterized the physical demands during competition (FM) with great variability amongst training drills.ConclusionThe use of these novel, cut-off points may aid coaches in the design and use of training drills to accurately prepare athletes for soccer competition.
Keywords:GPS technology  Artificial intelligence  Decision tree  Performance assessment  Football
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