Search

Introduction: A promising approach to optimizing recovery in youth football has been the use of machine learning (ML) models to predict recovery states and prevent mental fatigue. This research investigates the application of ML models in classifying male young football players aged under (U)15, U17, and U19 according to their recovery state. Weekly training load data were systematically monitored across three age groups throughout the initial month of the 2019–2020 competitive season, covering 18 training sessions and 120 observation instances. Outfield players were tracked using portable 18-Hz global positioning system (GPS) devices, while heart rate (HR) was measured using 1 Hz telemetry HR bands. The rating of perceived exertion (RPE 6–20) and total quality recovery (TQR 6–20) scores were employed to evaluate perceived exertion, internal training load, and recovery state, respectively. Data preprocessing involved handling missing values, normalization, and feature selection using correlation coefficients and a random forest (RF) classifier. Five ML algorithms [K-nearest neighbors (KNN), extreme gradient boosting (XGBoost), support vector machine (SVM), RF, and decision tree (DT)] were assessed for classification performance. The K-fold method was employed to cross-validate the ML outputs. Results: A high accuracy for this ML classification model (73–100%) was verified. The feature selection highlighted critical variables, and we implemented the ML algorithms considering a panel of 9 variables (U15, U19, body mass, accelerations, decelerations, training weeks, sprint distance, and RPE). These features were included according to their percentage of importance (3–18%). The results were cross-validated with good accuracy across 5-fold (79%). Conclusion: The five ML models, in combination with weekly data, demonstrated the efficacy of wearable device-collected features as an efficient combination in predicting football players' recovery states. [ABSTRACT FROM AUTHOR]

Background/Objectives: This study examined the effects of an intervention on anthropometrics, body composition, physical fitness, and sleep quality in aged individuals, comparing a control group (N = 11) and an experimental group (N = 13) across two measurement points. Methods: A multicomponent training program of 8 months was adopted as the intervention group. A bioimpedance balance, functional fitness test, and Pittsburgh Sleep Quality Index measured body composition, functional fitness, and sleep quality. Results: Both groups showed minimal changes in body mass and hand grip strength. However, the experimental group experienced significant improvements in physical fitness, including a 26% increase in arm curl repetitions, an 18% reduction in 5 times sit-to-stand (5TSTS) completion time, and a 29% rise in 2-min step test (2MST) steps, indicating enhanced muscle endurance and cardiovascular fitness. Flexibility decreased significantly in the experimental group, while body fat percentage was reduced by 10%. Sleep quality improved by 47% in the experimental group but declined by 14% in the control group. Correlational analysis revealed that better sleep quality was linked to improved fitness performance and reduced body fat in the experimental group, with post-intervention results further confirming the connection between sleep and fat reduction. In the control group, improved sleep quality was associated with higher metabolic rates after 8 months. Conclusions: These findings suggest that the intervention positively impacted physical fitness and sleep quality, with potential benefits for overall health. [ABSTRACT FROM AUTHOR]

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

The aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data were collected from the target variables of accelerations (ACCs), decelerations (DECs), and dynamic stress load (DSL) using an 18 Hz global positioning system (GPS). Additionally, we monitored the perceived exertion and biological characteristics using total quality recovery (TQR), rating of perceived exertion (RPE), session RPE (sRPE), chronological age, maturation offset (MO), and age at peak height velocity (APHV). The ML model was computed by a feature selection process with a linear regression forecast and bootstrap method. The predictive analysis revealed that the players' MO demonstrated varying degrees of effectiveness in predicting their DEC and ACC across different ranges of IQR. After predictive analysis, the following performance values were observed: DEC ( x ¯ predicted = 41, β = 3.24, intercept = 37.0), lower IQR (IQRpredicted = 36.6, β = 3.24, intercept = 37.0), and upper IQR (IQRpredicted = 46 decelerations, β = 3.24, intercept = 37.0). The player's MO also demonstrated the ability to predict their upper IQR (IQRpredicted = 51, β = 3.8, intercept = 40.62), lower IQR (IQRpredicted = 40, β = 3.8, intercept = 40.62), and ACC ( x ¯ predicted = 46 accelerations, β = 3.8, intercept = 40.62). The ML model showed poor performance in predicting the players' ACC and DEC using MO (MSE = 2.47–4.76; RMSE = 1.57–2.18: R2 = −0.78–0.02). Maturational concerns are prevalent in football performance and should be regularly checked, as the current ML model treated MO as the sole variable for ACC, DEC, and DSL. Applying ML models to assess automated tracking data can be an effective strategy, particularly in the context of forecasting peak ACC, DEC, and bodily effects in sub-elite youth football training. [ABSTRACT FROM AUTHOR]

Utilizing techniques for reducing multivariate data is essential for comprehensively understanding the variations and relationships within both biomechanical and physiological datasets in the context of youth football training. Therefore, the objective of this study was to identify the primary factors influencing training sessions within a standard microcycle among young sub-elite football players. A total of 60 male Portuguese youth sub-elite footballers (15.19 ± 1.75 years) were continuous monitored across six weeks during the 2019–2020 in-season, comprising the training days from match day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel analysis was applied to the training datasets. The training datasets were condensed into three to five principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and cumulative) according to the training day (p < 0.001). Notably, the eigenvalue for this study ranged from 1.20% to 5.21% within the overall training data. The PCA analysis of the standard microcycle in youth sub-elite football identified that, across MD-3, MD-2, and MD-1, the first was dominated by the covered distances and sprinting variables, while the second component focused on HR measures and training impulse (TRIMP). For the weekly microcycle, the first component continued to emphasize distance and intensity variables, with the ACC and DEC being particularly influential, whereas the second and subsequent components included HR measures and perceived exertion. On the three training days analyzed, the first component primarily consisted of variables related to the distance covered, running speed, high metabolic load, sprinting, dynamic stress load, accelerations, and decelerations. The high intensity demands have a high relative weight throughout the standard microcycle, which means that the training load needs to be carefully monitored and managed. [ABSTRACT FROM AUTHOR]

In cycling, drag is the force that opposes the cyclist's motion and is caused by the cyclist's and their equipment's interaction with the air. The surface area of the cyclist and their equipment, such as the bike, helmet, and body postures, substantially impact how much drag they encounter. This study compared the energy cost (Ec) of an able-bodied and shoulder amputee cyclist through numerical simulations using computer fluid dynamics (CFD). According to the hypothesis, an able-bodied cyclist may use more energy at a given speed than an ablebodied cyclist. For this study, a professional male cyclist who weighs 65 kg and is 1.72 m tall took part. The estimated Ec was lower for a shoulder amputee in comparison to an ablebodied cyclist. Significant statistical differences and relationships were found between the cyclists for the 11 selected speeds. Altogether, this study allows us to conclude that, for the same conditions, an able-bodied cyclist delivers less Ec in comparison to a shoulder amputee. Such knowledge contributes to understanding cycling performance and may inform training, equipment design, and energy optimisation strategies for diverse cyclist populations. [ABSTRACT FROM AUTHOR]

Drag crisis has been described as a drop in the drag coefficient (Cd). This procedure was lately assessed in able-bodied cyclists in time trial position by computational fluid dynamics (CFD). Therefore, the aim of this study was to assess the phenomenon of the drag crisis by computer fluid dynamics in transradial and transtibial amputee cyclists in the upright position. A male elite-level cyclist was recruited for this research. The subject was scanned (3D geometry with a 3D Scan) with his competition bicycle, helmet, suit, and shoes. Consequently, transradial and transtibial 3D models were created editing the able-bodied 3D model. To perform the CFD analyses, a domain measuring 7 x 2.5 x 2.5 m (length, width and height, respectively) was created around each bicycle-cyclist model. The domains were meshed with more than 42 million elements. Numerical simulations by CFD were performed in fluent numerical for speeds between 1 m/s and 13 m/s, with increments of 1 m/s. The total Cd decreased with speed. It ranged from 0.84 to 0.55 in the able-bodied, 0.86 to 0.64 in the transradial and 0.90 to 0.61 in the transtibial. The drag crisis phenomenon was observed between 1m/s and 4m/s in all models. [ABSTRACT FROM AUTHOR]