ArticleHeliyon2023
Association between internal load responses and recovery ability in U19 professional soccer players: A machine learning approach.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- An updated conceptual framework on physical performance and training in soccer: a narrative review.Sports medicine - open · 2026Review
- Machine learning applications in sport: a scoping review.Frontiers in psychology · 2026Review
- Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors.Preventive medicine reports · 2024Article
- Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach.Frontiers in psychology · 2024Article
- Exploring the interplay between metabolic power and equivalent distance in training games and official matches in soccer: a machine learning approach.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The objective of soccer training load (TL) is enhancing players' performance while minimizing the possible negative effects induced by fatigue. In this regard, monitoring workloads and recovery is necessary to avoid overload and injuries. Given the controversial results found in literature, this study aims to better understand the complex relationship between internal training load (IL) by using rating of perceived exertion (RPE), recovery, and availability (i.e., subjective players' readiness status). Methods: In this cross-sectional study, twenty-two-professional soccer players (age: 18.5 ± 0.4 years, height: 177 ± 6 cm, weight: 67 ± 6.7 kg) competing in the U19 Italian Championship were monitored using RPE scale to assess IL, and TreS scale to detect information about recovery and training/match availability during an entire season (2021-2022). Results: Autocorrelation analysis showed a repeated pattern with 7 days lag (weekly microcycle pattern) for all the variables considered (i.e., TL, recovery, and availability). For recovery (r = 0.64, p < 0.001) and availability (r = 0.63, p < 0.001) the best lag for both of them is 1 day. It indicates that recovery and availability are related to the past day value. Moreover, TL was found to be negatively affected by recovery and availability of the current day (lag = 0 day). Cross-correlation analysis indicates that TL is negatively affected by recovery (r = 0.46, p < 0.001) and availability (r = 0.42, p < 0.001) of the current day (lag = 0 day). In particular, lower recovery and availability will result in following lower TL. Furthermore, we found that TL negatively affects recovery (r = 0.52, p < 0.001) and availability (r = 0.39, p < 0.01) of the next day (lag = 1 day). In fact, the higher the TL in a current day is, the lower the recovery and availability in the next day will be. Conclusion: In conclusion, this study highlights that there is a relationship between TL and recovery and that these components influence each other both on the same day and on the next one. The use of RPE and TreS scale to evaluate TL and recovery/availability of players allows practitioners to better adjust and schedule training within the microcycle to enhance performance while reducing injury risk.
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