Match Load Physical Demands in U-19 Professional Soccer Players Assessed by a Wearable Inertial Sensor

Pillitteri, Guglielmo; Giustino, Valerio; Petrucci, Marco; Rossi, Alessio; Leale, Ignazio; Bellafiore, Marianna; Thomas, Ewan; Iovane, Angelo; Palma, Antonio; Battaglia, Giuseppe

doi:10.3390/jfmk8010022

Cited by 3 publications

(1 citation statement)

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“…In contrast, no significant correlation was found between MP and EDI in both TGs and official soccer matches. Overall, the correlation between external and internal load is a crucial concept for sports science and exercise physiology, and the use of MP and EDI as indicators of external load in soccer can provide valuable insights for coaches and trainers in monitoring and managing training loads ( Rossi et al, 2019 ; Pillitteri et al, 2023b ; Impellizzeri et al, 2023 ). It is worth noting that previous studies ( Di Mascio et al, 2015 ; Gaudino et al, 2015 ; Akenhead and Nassis, 2016 ) used slightly different external load metrics to correlate with Edwards’ internal TL, including total distance, high-intensity distance, sprinting frequency, high-speed distance, and the number of accelerations and decelerations derived from GPS data.…”

Section: Discussionmentioning

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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