Redirigiendo al acceso original de articulo en 15 segundos...
Inicio  /  Applied Sciences  /  Vol: 13 Par: 22 (2023)  /  Artículo
ARTÍCULO
TITULO

Training to Compete: Are Basketball Training Loads Similar to Competition Achieved?

Sebastián Feu    
Juan M. García-Ceberino    
Pablo López-Sierra and Sergio J. Ibáñez    

Resumen

Basketball players should train at intensities similar to those recorded in competition, but are the intensities really similar? This study aimed to quantify and compare the internal and external intensities assimilated by professional basketball players, both in training and in competition, according to context and the specific player position. Players from the same team in the Spanish ACB competition were monitored for three weeks. The sample recorded intensities in 5 vs. 5 game situations in both training (n = 221) and competition (n = 32). The intensities, as dependent variables, were classified into kinematic external workload demands (distances, high-intensity displacements, accelerations, decelerations, the acceleration:deceleration ratio, jumps, and landings), neuromuscular external workload demands (impacts and player load), and internal workload demands (heart rate). They were measured using inertial measurement devices and pulsometers. The playing positions, as independent variables, were grouped into guard, forward, and center. According to the context, the results reported a significant mismatch of all training intensities, except jumps, with respect to competition; these intensities were lower in training. According to the playing position, inside players recorded more jumps and landings per minute than point guards and outside players in training. In turn, inside players recorded a higher average heart rate per minute than outside players in this same context. There were no significant differences in intensity according to the playing position in the competition. Considering the context?position interaction, no differences were observed in the intensities. Adjusting and optimizing training intensities to those recorded in competition is necessary.

 Artículos similares

       
 
Ryota Higashimoto, Soh Yoshida and Mitsuji Muneyasu    
This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning an... ver más
Revista: Applied Sciences

 
Hyeon-Kyu Noh and Hong-June Park    
A convolutional neural network (CNN) transducer decoder was proposed to reduce the decoding time of an end-to-end automatic speech recognition (ASR) system while maintaining accuracy. The CNN of 177 k parameters and a kernel size of 6 generates the proba... ver más
Revista: Applied Sciences

 
Seokjoon Kwon, Jae-Hyeon Park, Hee-Deok Jang, Hyunwoo Nam and Dong Eui Chang    
Deep learning algorithms are widely used for pattern recognition in electronic noses, which are sensor arrays for gas mixtures. One of the challenges of using electronic noses is sensor drift, which can degrade the accuracy of the system over time, even ... ver más
Revista: Applied Sciences

 
Hellena Hempe, Alexander Bigalke and Mattias Paul Heinrich    
Background: Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities enables proactive measures to mitigate the risk of severe back pain and disabilit... ver más
Revista: Information

 
Peranut Nimitsurachat and Peter Washington    
Emotion recognition models using audio input data can enable the development of interactive systems with applications in mental healthcare, marketing, gaming, and social media analysis. While the field of affective computing using audio data is rich, a m... ver más
Revista: AI