Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach DOI Creative Commons
José E. Teixeira, Samuel Encarnação, Luís Branquinho

и другие.

Frontiers in Psychology, Год журнала: 2024, Номер 15

Опубликована: Окт. 29, 2024

A promising approach to optimizing recovery in youth football has been the use of machine learning (ML) models predict states and prevent mental fatigue. This research investigates application ML classifying male young players aged under (U)15, U17, U19 according their state. Weekly training load data were systematically monitored across three age groups throughout initial month 2019-2020 competitive season, covering 18 sessions 120 observation instances. Outfield tracked using portable 18-Hz global positioning system (GPS) devices, while heart rate (HR) was measured 1 Hz telemetry HR bands. The rating perceived exertion (RPE 6-20) total quality (TQR scores employed evaluate exertion, internal load, state, respectively. Data preprocessing involved handling missing values, normalization, feature selection correlation coefficients a random forest (RF) classifier. Five algorithms [K-nearest neighbors (KNN), extreme gradient boosting (XGBoost), support vector (SVM), RF, decision tree (DT)] assessed for classification performance. K-fold method cross-validate outputs.

Язык: Английский

Editorial: Training load in sport: current challenges and future perspectives DOI Creative Commons
Luís Branquinho, Pedro Forte, Elias de França

и другие.

Frontiers in Sports and Active Living, Год журнала: 2025, Номер 7

Опубликована: Фев. 21, 2025

The concept of training load is not merely a measure the amount work performed, it complex interplay factors that can significantly influence an athlete's performance trajectory (3). Understanding how to optimize essential maximizing athletic while minimizing risks excessive fatigue, injury, and overtraining, which negatively impact ability compete train effectively, as well overall health (1) Recent research has demonstrated clear relationship between increasing loads incidence injuries, particularly in high-impact sports where risk cumulative trauma increased (4,5). Therefore, comprehensive understanding dynamics crucial for coaches athletes enable balance be found thresholds injury risk.Recent advances technology data analytics have revolutionized way are monitored managed. integration wearable devices software applications allows realtime tracking physiological responses training, providing valuable insights into their recovery needs readiness (6). This type data-driven approach facilitates creation individualized programs consider physical, physiological, psychological profiles, consequently promote satisfaction reduce (7). Furthermore, emphasis on aligned with contemporary philosophies advocate athlete-centered methodologies, contribution experiences optimization process (8) To clarify further explore these issues, this Research Topic, Training Load Sport: Current Challenges Future Perspectives, presents collection studies explored current perspective knowledge challenges associated effects careful manipulation management across different competitive levels.Throughout topic, there were numerous contributions investigate state future perspectives relation sport. Tilp et al. (9) investigated systemic local muscle breaking points single-leg cycling, finding strong correlations but significant individual variability. Similarly, Kårström (10) revealed discrepancies internal external assessments biathlon, suggesting multimodal necessary accurate monitoring. Masur (11) infrared thermography noninvasive tool track burden, although inconsistencies its traditional markers indicate validation needed. Meanwhile, methods, such those by Wei (12) Quan (13), showed small-sided games (SS) high-intensity interval (HITT) generate varied benefits, especially lower physical conditioning. Sheykhlouvand Gharaatin (14) turn, analysed adaptations cardiorespiratory fitness biomotor skills soccer players trained short sprint (sSIT) SSG. sSIT promoted more homogeneous ventilatory thresholds, stroke volume, maximal power, SSG proportions responders oxygen uptake, anaerobic greater effectiveness consistent adaptations. Talsnes (15) splitting moderate-intensity two shorter sessions reduces stress maintaining adaptations.Physiological go beyond outcomes, influencing vascular function, muscular strategies. Sugawara (16) observed football matches induced transient reductions arterial wave reflection without stiffness, adaptive repeated exposure matches. Yu (17) recommended periodized HIIT, sprint, threshold sedentary youth maximize cardiovascular benefits avoiding overload. Studies strength power development also provided stimuli. Cui (18) identified specific velocity loss enhance post-activation potentiation boxers. Naczk al (19) inertial offers small advantages over resistance knee extensor strength. Singer (20) pointed out rest intervals longer than 60 seconds may provide additional hypertrophic 90 seconds. Ma (21) blood flow restriction viable alternative conventional offering similar improvements thickness.Injury prevention strategies components effective management. Huang (22) examined whole-body cryotherapy (WBC) elite rowers, concluding WBC accelerates lactate clearance, does improve recovery. Xie (23) HIIT continuous improving post-exercise clearance. In context prevention, Iwasaki (24) established link contact rugby players, emphasizing importance monitoring acute chronic workload ratios. Reverte-Pagola (25) LaLiga who did participate FIFA World Cup, optimized during tournament break led improved acceleration performance. (26) load-adjusted improves punching capacity energy efficiency female boxers effectively methods.

Язык: Английский

Процитировано

0

Impact of congested match schedules during the COVID-19 pandemic on technical and physical performance in the Chinese Super League DOI
Xudong Yang,

Zhigang Liu,

Zhou Xu

и другие.

International Journal of Performance Analysis in Sport, Год журнала: 2025, Номер unknown, С. 1 - 13

Опубликована: Март 13, 2025

Язык: Английский

Процитировано

0

Match-to-Match Variation on High-Intensity Demands in a Portuguese Professional Football Team DOI Creative Commons
José E. Teixeira, Luís Branquinho, Miguel Leal

и другие.

Journal of Functional Morphology and Kinesiology, Год журнала: 2024, Номер 9(3), С. 120 - 120

Опубликована: Июль 4, 2024

The aim of this study was to analyze the match-to-match variation in high-intensity demands from one Portuguese professional football team according playing positions. Twenty-three male outfield players were observed during eighteen matches Second League. Time–motion data collected using Global Positioning System (GPS) technology. Match running performance analyzed based on following three positions: defenders (DF), midfielders (MF), and forwards (FW). Repeated measures ANOVA utilized compare match within each position role, seasonal variation. Practical differences assessed smallest worthwhile change (SWC), coefficient (CV), twice (2CV). Significant found among positions total distance covered (F = 15.45, p < 0.001, η2 0.33), average speed 12.79, 0.29), high-speed 16.93, 0.36), sprinting 13.49, 0.31), accelerations 4.69, 0.132), decelerations 12.21, 0.284). encompassed TD (6.59%), AvS (8.67%), HSRr (37.83%), SPR (34.82%), ACC (26.92%), DEC (27.85%). CV values for ranged 4.87–6.82%, with exhibiting greatest variation, respectively. Midfielders demonstrated highest all other variables (8.12–69.17%). All showed significant high-demanding (26.94–37.83%). This presents initial analysis a team. Thus, position’s specificity context can provide helpful strategy evaluating performance, recommending individualized training exercises peak player’s role game.

Язык: Английский

Процитировано

2

Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component Approach DOI Creative Commons
José E. Teixeira, Luís Branquinho, Ricardo Ferraz

и другие.

Sports, Год журнала: 2024, Номер 12(7), С. 194 - 194

Опубликована: Июль 16, 2024

Utilizing techniques for reducing multivariate data is essential comprehensively understanding the variations and relationships within both biomechanical physiological datasets in context of youth football training. Therefore, objective this study was to identify primary factors influencing training sessions a standard microcycle among young sub-elite players. A total 60 male Portuguese footballers (15.19 ± 1.75 years) were continuous monitored across six weeks during 2019-2020 in-season, comprising days from match day minus (MD-) 3, MD-2, MD-1. The weekly load collected by an 18 Hz global positioning system (GPS), 1 heart rate (HR) monitors, perceived exertion (RPE) quality recovery (TQR). principal component approach (PCA) coupled with Monte Carlo parallel analysis applied datasets. condensed into three five components, explaining between 37.0% 83.5% explained variance (proportion cumulative) according (

Язык: Английский

Процитировано

0

Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approach DOI Creative Commons
José E. Teixeira, Samuel Encarnação, Luís Branquinho

и другие.

Frontiers in Psychology, Год журнала: 2024, Номер 15

Опубликована: Окт. 29, 2024

A promising approach to optimizing recovery in youth football has been the use of machine learning (ML) models predict states and prevent mental fatigue. This research investigates application ML classifying male young players aged under (U)15, U17, U19 according their state. Weekly training load data were systematically monitored across three age groups throughout initial month 2019-2020 competitive season, covering 18 sessions 120 observation instances. Outfield tracked using portable 18-Hz global positioning system (GPS) devices, while heart rate (HR) was measured 1 Hz telemetry HR bands. The rating perceived exertion (RPE 6-20) total quality (TQR scores employed evaluate exertion, internal load, state, respectively. Data preprocessing involved handling missing values, normalization, feature selection correlation coefficients a random forest (RF) classifier. Five algorithms [K-nearest neighbors (KNN), extreme gradient boosting (XGBoost), support vector (SVM), RF, decision tree (DT)] assessed for classification performance. K-fold method cross-validate outputs.

Язык: Английский

Процитировано

0