Browsing by Author "Morgans, Ryland"
Now showing 1 - 10 of 11
Results Per Page
Sort Options
- Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component ApproachPublication . Teixeira, José Eduardo; Branquinho, Luís; Ferraz, Ricardo; Morgans, Ryland; Encarnação, Samuel; Ribeiro, Joana; Afonso, Pedro; Ruzmetov, Nemat; Barbosa, Tiago M.; Monteiro, A.M.; Forte, PedroUtilizing 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.
- Classification of recovery states in U15, U17, and U19 sub-elite football players: a machine learning approachPublication . Teixeira, José Eduardo; Encarnação, Samuel; Branquinho, Luís; Ferraz, Ricardo; Portella, Daniel Leite; Monteiro, Diogo; Morgans, Ryland; Barbosa, Tiago M.; Monteiro, A.M.; Forte, PedroA 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.
- Data Mining Paths for Standard Weekly Training Load in Sub-Elite Young Football Players: A Machine Learning ApproachPublication . Teixeira, José Eduardo; Encarnação, Samuel; Branquinho, Luís; Morgans, Ryland; Afonso, Pedro; Rocha, João Pedro da Silva; Graça, Francisco M.; Barbosa, Tiago M.; Monteiro, A.M.; Ferraz, Ricardo; Forte, PedroThe 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 (xpredicted = 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 (xpredicted = 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.
- Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic reviewPublication . Teixeira, José Eduardo; Maio, Eduardo; Afonso, Pedro; Encarnação, Samuel; Machado, Guilherme; Morgans, Ryland; Barbosa, Tiago M.; Monteiro, António M.; Forte, Pedro; Ferraz, Ricardo; Branquinho, LuísFootball, as a dynamic and complex sport, demands an understanding of tactical behaviors to excel in training and competition. Artificial intelligence (AI) has evolutionized the tactical performance analysis in football, offering unprecedented data analytics insights for players, coaches, and analysts. This systematic review aims to examine and map out the current state of research on AI-based tactical behavior, collective dynamics, and movement patterns in football. A total of 2,548 articles were identified following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the Population-Intervention-Comparators-Outcomes framework. By synthesizing findings from 32 studies, this review elucidates the available AI-based techniques to analyze tactical behavior and identify the collective dynamic based on artificial neural networks, deep learning, machine learning, and timeseries techniques. Concretely, the tactical behavior was expressed by spatiotemporal tracking data using convolutional neural networks, recurrent neural networks, variational recurrent neural networks, and variational autoencoders, Delaunay method, player rank, hierarchical clustering, logistic regression, XGBoost, random forest classifier, repeated incremental pruning produce error reduction, principal component analysis, and T-distributed stochastic neighbor embedding. Furthermore, collective dynamics and patterns were mapped by graph metrics such as betweenness centrality, eccentricity, efficiency, vulnerability, clustering coefficient, and page rank, expected possession value, pitch control map classifier, computer vision techniques, expected goals, 3D ball trajectories, dangerousity assessment, pass probability model, and total passes attempted. The performance of technicaltactical key indicators was expressed by team possession, team formation, team strategy, team-space control efficiency, determining team formations, coordination patterns, analyzing player interactions, ball trajectories, and pass effectiveness. In conclusion, the AI-based models can effectively reshape the landscape of spatiotemporal tracking data into training and practice routines with real-time decision-making support, performance prediction, match management, tactical-strategic thinking, and training task design. Nevertheless, there are still challenges for the real practical application of AI-based techniques, as well as ethical regulation and the formation of professional profiles that combine sports science, data analytics, computer science, and coaching expertise.
- Match-to-Match Variation on High-Intensity Demands in a Portuguese Professional Football TeamPublication . Teixeira, José Eduardo; Branquinho, Luís; Leal, Miguel; Morgans, Ryland; Sortwell, Andrew; Barbosa, Tiago M.; Monteiro, A.M.; Afonso, Pedro; Machado, Guilherme; Encarnação, Samuel; Ferraz, Ricardo; Forte, PedroThe 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. Twentythree 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-tomatch 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.
- Player Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning ApproachPublication . Teixeira, José Eduardo; Afonso, Pedro; Schneider, André; Branquinho, Luís; Maio, Eduardo; Ferraz, Ricardo; Nascimento, Rafael; Morgans, Ryland; Barbosa, Tiago M.; Monteiro, António M.; Forte, PedroOptimizing recovery is crucial for maintaining performance and reducing fatigue and injury risk in youth football players. This study applied machine learning (ML) models to classify mental fatigue in U15, U17, and U19 male players using wearable signals, tracking data, and psychophysiological features. Over six weeks, training loads were monitored via GPS, psychophysiological scales, and heart rate sensors, analyzing variables such as total distance, high-speed running, recovery state, and perceived exertion. The data preparation process involved managing absent values, applying normalization techniques, and selecting relevant features. A total of five ML models were evaluated: K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). XGBoost, RF, and DT achieved high accuracy, while KNN underperformed. Using a correlation matrix, average speed (AvS) was the only variable significantly correlated with the rating of perceived exertion (RPE) (r = 0.142; p = 0.010). After dimensionality reduction, ML models were re-evaluated, with RF and DT performing best, followed by XGBoost and SVM. These findings confirm that tracking and wearable-derived data are effectively useful for predicting RPE, providing valuable insights for workload management and personalized recovery strategies. Future research should integrate psychological and interpersonal factors to enhance predictive modeling in the individual long-term health and performance of young football players.
- Quantification of training load across two competitive seasons in elite senior and youth male soccer players from an English Premiership clubPublication . Morgans, Ryland; Rhodes, Dave; Teixeira, José Eduardo; Modric, Toni; Versic, Sime; Oliveira, Rafael Franco SoaresThis study aimed to compare the daily training load (TL) in first-team and U-18 soccer players from an English Premiership club. 36 first-team (age 23.2 ± 5.9 years, weight 75.2 ± 8.1 kg, height 1.83 ± 0.06 m), and 22 U-18 players (age 17.5 ± 1.1 years, weight 71.1 ± 8.2 kg, height 1.78 ± 0.08 m) participated. GPS metrics were measured during all pitch training sessions throughout the 2020–21 and 2021–22 seasons. Linear mixed-effect model analyses revealed that, irrespective of training day, U-18 players covered greater total and explosive distance than first-team players, and performed a higher number of accelerations and decelerations, whereas first-team players covered greater sprint distance. Irrespective of the team, all examined variables were greater at match-day (MD)-3, while the number of accelerations and decelerations were higher at MD-4. Significant team-by-training day interactions revealed that U-18 players covered greater total and high-intensity distances than first-team players at MD-4, MD-2, and MD-1, whereas first-team players covered greater total and high-intensity distances at MD-3. Sprint distance was greater for first-team players at MD-3 and MD-4, while explosive distance was greater for U-18 players at MD-2. Also, U-18 players performed a higher number of accelerations than first-team players at MD-3 and MD-2, and a higher number of decelerations at MD-4. The present results provide novel information on TL patterns in English Premiership soccer and contribute to understanding how training methods to physically develop players are implemented in different countries and leagues.
- Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approachPublication . Teixeira, José Eduardo; Forte, Pedro; Ferraz, Ricardo; Branquinho, Luís; Morgans, Ryland; Silva, A.J.; Monteiro, A.M.; Barbosa, Tiago M.Applying data-reduction techniques to extract meaningful information from electronic performance and tracking systems (EPTS) has become a hot topic in football training load (TL) monitoring. The aim of this study was to reduce the dimensionality of the internal and external load measures, by a principal component approach, to describe and explain the resultant equations for TL monitoring during a standard in-season microcycle in sub-elite youth football. Additionally, it is intended to identify the most representative measure for each principal component. A principal component analysis (PCA) was conducted with a Monte Carlo parallel analysis and VariMax rotation to extract baseline characteristics, external TL, heart rate (HR)-based measures and perceived exertion. Training data were collected from sixty sub-elite young football players during a 6-week training period using 18 Hz global positioning system (GPS) with inertial sensors, 1 Hz short-range telemetry system, total quality recovery (TQR) and rating of perceived exertion (RPE). Five principal components accounted for 68.7% of the total variance explained in the training data. Resultant equations from PCA was subdivided into: (1) explosiveness, accelerations and impacts (27.4%); (2) high-speed running (16.2%); (3) HR-based measures (10.0%); (4) baseline characteristics (8.3%); and (5) average running velocity (6.7%). Considering the highest factor in each principal component, decelerations (PCA 1), sprint distance (PCA 2), average HR (PCA 3), chronological age (PCA 4) and maximal speed (PCA 5) are the conditional dimension to be considered in TL monitoring during a standard microcycle in sub-elite youth football players. Current research provides the first composite equations to extract the most representative components during a standard in-season microcycle in sub-elite youth football players. Futures research should expand the resultant equations within training days, by considering other well-being measures, technical-tactical skills and match-related contextual factors.
- The impact of injury on match running performance following the return to competitive match-play over two consecutive seasons in elite European soccer playersPublication . Morgans, Ryland; Rhodes, David; Bezuglov, Eduard; Etemad, O.; Di Michele, Rocco; Teixeira, José Eduardo; Modrić, Toni; Versic, Sime; Oliveira, Rafael Franco SoaresBased on the assessment and diagnosis, the rest period following a moderate/severe injury may lead to deconditioning for the injured player and therefore an association with a prolonged rehabilitation, re-conditioning and return to sport is observed post-injury. The aim of the present study was to assess the impact of all injuries on match running performance following the return to competitive match-play over two consecutive seasons in elite European soccer players. A retrospective analysis was conducted utilizing data related to a player’s injury and match running performance. A club physiotherapist consistently recorded availability and injury data in a standardized format. Linear mixed modelling analysis revealed no difference between PRE and POST1, POST2, and POST3 for total distance, running distance, high-intensity distance, and sprint distance (all p >0.05). Although, maximum speed was significantly (p<0.05) lower in POST1 and POST2 when compared to PRE, in both cases with a large (ES = 1.88) effect. No significant difference was observed for maximum speed between PRE and POST3 (p=0.07). There were very low correlations between the number of days absent and changes in maximum speed between POST1 and PRE (r = 0.09, 95% CI -0.42 to 0.56), and POST2 and PRE (r = 0.10, 95% CI -0.42 to 0.57), respectively. In conclusion, no variation in distance variables were found regardless of one, two or three matches post-injury compared to pre-injury status. Moreover, maximum speed was lower during the first three matches post-injury, although the mean value was slightly lower. Finally, a low correlation between absent days and maximum speed loss between pre-injury and following one and two matches were found.
- The relationship between ambient temperature and match running performance of elite soccer playersPublication . Morgans, Ryland; Bezuglov, Eduard; Rhodes, Dave; Teixeira, José Eduardo; Modric, Toni; Versic, Sime; Di Michele, Rocco; Oliveira, Rafael Franco SoaresThe influence of environmental factors on key physical parameters of soccer players during competitive match-play have been widely investigated in the literature, although little is known on the effects of sub-zero ambient temperatures on the performance of adult elite soccer players during competitive matches. The aim of this study was to assess how the teams' match running performance indicators are related to low ambient temperature during competitive matches in the Russian Premier League. A total of 1142 matches played during the 2016/2017 to 2020/2021 seasons were examined. Linear mixed models were used to assess the relationships between changes in ambient temperature at the start of the match and changes in selected team physical performance variables, including total, running (4.0 to 5.5 m/s), high-speed running (5.5 to 7.0 m/s) and sprint (> 7.0 m/s) distances covered. The total, running and high-speed running distances showed no significant differences across temperatures up to 10 & DEG;C, while these showed small to large decreases at 11 to 20 & DEG;C and especially in the >20 & DEG;C ranges. On the contrary, sprint distance was significantly lower at temperature of -5 & DEG;C or less compared to higher temperature ranges. At sub-zero temperatures, every 1 & DEG;C lower reduced team sprint distance by 19.2 m (about 1.6%). The present findings show that a low ambient temperature is negatively related to physical match performance behavior of elite soccer players, notably associated with a reduced total sprint distance.