External load profile during different sport-specific activities in semi-professional soccer players

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

doi:10.1186/s13102-023-00633-3

Cited by 5 publications

(2 citation statements)

References 68 publications

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“…This approach goes beyond intensity and focuses on recreating the precise percentages of MP and EDI developed during actual game situations. Another non-negligible point of this research concerns the possibility of interpreting the data of MP and EDI as indicators of a more muscular effort or less oriented towards neuromuscular aspects; all this could implement recent studies with ML on fatigue and recovery to the specificity of the external load, helping to optimize training and customize loads and recovery also in team sports ( Mandorino et al, 2022 ; Pillitteri et al, 2023c ; Pillitteri et al, 2023d ). By considering physiological and kinematic parameters, our study highlights the need for a holistic approach to evaluating exercise intensity during different activities (Reference).…”

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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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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“…For example, consider that a team plays 3 games in 12 days, taking into account that soccer players can cover from 9 to 14 km during a match and perform a distance of 0.7-3.9 km of fast running (>20-25 km/h), 0.2-0.6 km sprinting (>25 km/h), and approximately 600 accelerations and decelerations. Recovery times may be too short during crowded professional soccer matches when it comes to maintaining physical fitness and preventing injuries [33][34][35]. This was shown in a study [36] considering the number of matches per week (one versus two), where the injury rate was significantly higher when players played two matches per week versus one match per week (25.6 versus 4.1 injuries per 1000 h of exposure; p < 0.001).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of a Step Load Unilateral and Bilateral Resistance Training Program on the Strength and Power of the Lower Limbs in Soccer Players

Drozd,

Kędra,

Motowidło

et al. 2024

Applied Sciences

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The purpose of the investigation was to determine the influence of a four-week unilateral (UNI) and bilateral (BIL) resistance training program on peak torque and peak power of the lower limbs in soccer players. Background: We evaluated the effects of a 3:1 step load training program using UNI and BIL forms of exercises on the level of peak torque and peak power of the knee joint extensors and flexors. Methods: The study included 16 division I soccer players having the highest number of matches played in the first round of the season. The motor tests included isokinetic evaluation of peak torque and peak power of the extensors and flexors of the knee joint. Results: The results showed that both types of training sessions were equally effective. Only in terms of power during knee flexion, unilateral training contributed to improvement, whereas bilateral training did not. Conclusions: The use of periodization using a step load progression based on an extended eccentric phase of the movement during the preseason period in combination with UNI training may increase peak torque and peak power of knee flexors and extensors in soccer players.

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Translating player monitoring into training prescriptions: Real world soccer scenario and practical proposals

Pillitteri,

Clemente,

Sarmento

et al. 2024

International Journal of Sports Science & Coaching

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Data-driven training prescription based on previous training or match data is thought to be associated with better training outcome, compared to prescription without considering any monitoring data. Understanding the complex relationship between training load, physical performance, fitness status, fatigue and injury risk represents a challenge for health and performance practitioners and researchers. Although studies have revealed a positive correlation between training load and injury risk, this cause-effect relation cannot be determined given the multifactorial nature of injuries. Additionally, conflicting findings have been published explaining the relationship between training load and injuries, underlining the importance of training load management, prescription, and communication within the multidisciplinary team to improve physical performance and reduce injury risk. In this sense, practitioners may benefit from practical examples based on training load data to make informed decisions for prescribing training. This narrative review provides real-world examples of training decisions based on training load data in soccer, including training prescription, drill design and multidisciplinary team communication. Finally, a framework was provided to make informed training prescription from a physiological standpoint and elucidate the relationship between training load and injury risk.

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External load profile during different sport-specific activities in semi-professional soccer players

Cited by 5 publications

References 68 publications

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

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

A Comparison of a Step Load Unilateral and Bilateral Resistance Training Program on the Strength and Power of the Lower Limbs in Soccer Players

Translating player monitoring into training prescriptions: Real world soccer scenario and practical proposals

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