External demands differences between roles in semi-professional soccer players during different physical activity tasks

Pillitteri, Guglielmo; Giustino, Valerio; Petrucci, Marco; Rossi, Alessio; Bellafiore, Marianna; Thomas, Ewan; Bianco, Antonino; Palma, Antonio; Battaglia, Giuseppe

doi:10.21203/rs.3.rs-1666944/v1

Cited by 1 publication

(1 citation statement)

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“…Additionally, GPS technology can measure equivalent distance (ED) as another metric in soccer, which quantifies the distance a player covers while adjusting for changes in speed and direction. It could provide a more accurate measure of the physical demands of a match or training session than simply measuring the total distance covered (Pillitteri et al, 2023a). ED considers that different movements have varying metabolic demands and thus contribute differently to a player's overall physical workload (Osgnach et al, 2010;Caldeira et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Exploring the interplay between metabolic power and equivalent distance in training games and official matches in soccer: a machine learning approach

Manzi,

Savoia,

Padua

et al. 2023

Front. Physiol.

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Introduction: This study aimed to explore the interplay between metabolic power (MP) and equivalent distance (ED) and their respective roles in training games (TGs) and official soccer matches. Furthermore, the secondary objective was to investigate the connection between external training load (ETL), determined by the interplay of metabolic power and equivalent distance, and internal training load (ITL) assessed through HR-based methods, serving as a measure of criterion validity.Methods: Twenty-one elite professional male soccer players participated in the study. Players were monitored during 11 months of full training and overall official matches. The study used a dataset of 4269 training games and 380 official matches split into training and test sets. In terms of machine learning methods, the study applied several techniques, including K-Nearest Neighbors, Decision Tree, Random Forest, and Support-Vector Machine classifiers. The dataset was divided into two subsets: a training set used for model training and a test set used for evaluation.Results: Based on metabolic power and equivalent distance, the study successfully employed four machine learning methods to accurately distinguish between the two types of soccer activities: TGs and official matches. The area under the curve (AUC) values ranged from 0.90 to 0.96, demonstrating high discriminatory power, with accuracy levels ranging from 0.89 to 0.98. Furthermore, the significant correlations observed between Edwards’ training load (TL) and TL calculated from metabolic power metrics confirm the validity of these variables in assessing external training load in soccer. The correlation coefficients (r values) ranged from 0.59 to 0.87, all reaching statistical significance at p < 0.001.Discussion: These results underscore the critical importance of investigating the interaction between metabolic power and equivalent distance in soccer. While the overall intensity may appear similar between TGs and official matches, it is evident that underlying factors contributing to this intensity differ significantly. This highlights the necessity for more comprehensive analyses of the specific elements influencing physical effort during these activities. By addressing this fundamental aspect, this study contributes valuable insights to the field of sports science, aiding in the development of tailored training programs and strategies that can optimize player performance and reduce the risk of injuries in elite soccer.

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Section: Introductionmentioning

confidence: 99%