Use of GPS to measure external load and estimate the incidence of muscle injuries in men's football: A novel descriptive study

Characterization of acute effects of football competition on hamstring muscles by muscle functional MRI techniques

Muscle functional MRI identifies changes in metabolic activity in each muscle and provides a quantitative index of muscle activation and damage. No previous studies have analyzed the hamstrings activation over a football match. This study aimed at detecting different patterns of hamstring muscles activation after a football game, and to examine inter- and intramuscular differences (proximal-middle-distal) in hamstring muscles activation using transverse relaxation time (T2)-weighted magnetic resonance images. Eleven healthy football players were recruited for this study. T2 relaxation time mapping-MRI was performed before (2 hours) and immediately after a match (on average 13 min). The T2 values of each hamstring muscle at the distal, middle, and proximal portions were measured. The primary outcome measure was the increase in T2 relaxation time value after a match. Linear mixed models were used to detect differences pre and postmatch. MRI examination showed that there was no obvious abnormality in the shape and the conventional T2 weighted signal of the hamstring muscles after a match. On the other hand, muscle functional MRI T2 analysis revealed that T2 relaxation time significantly increased at distal and middle portions of the semitendinosus muscle (p = 0.0003 in both cases). By employing T2 relaxation time mapping, we have identified alterations within the hamstring muscles being the semitendinosus as the most engaged muscle, particularly within its middle and distal thirds. This investigation underscores the utility of T2 relaxation time mapping in evaluating muscle activation patterns during football matches, facilitating the detection of anomalous activation patterns that may warrant injury reduction interventions.

Physical Performance Is Affected by Players' Position, Game Location, and Substitutions During Official Competitions in Professional Championship English Football

ABSTRACT

Chaize, C, Allen, M, and Beato, M. Physical performance is affected by players' position, game location, and substitutions during official competitions in professional Championship English football. J Strength Cond Res XX(X): 000-000, 2024-This study aimed to verify, first, if physical parameters were different between positions during official matches in the English Football League Championship. Second, whether game location (home vs. away games) and playing status (full match vs. substitute) affected players' physical performance. Twenty-six, male, professional, football players of the same club were included in this data analysis during the 2023-24 season. STATSports 10 Hz global navigation satellite systems Apex units (Northern Ireland, United Kingdom) were used to monitor official matches (21 games). The metrics recorded were distance covered (m·min-1), high-speed running distance (>19.8 km·h-1), sprint distance (>25.2 km·h-1), the number of accelerations (>3 m·s-2), decelerations (<-3 m·s-2), and high-metabolic-load distance (HMLD) measured in meters (>25.5 w·kg-1). Significant differences were found between positions and game location for distance covered and high-speed running (HSR; p < 0.05) as well as between game location (p = 0.020) for sprinting distance. Differences were found between game location (p = 0.034) for decelerations. Differences were found between positions and game location for HMLD (p < 0.05). Significant difference between full-match players vs. substitutes were found for distance covered (p < 0.001), HSR (p = 0.002), accelerations (p = 0.017), decelerations (p = 0.023), and HMLD (p = 0.008). In conclusion, this study found that physical performance was influenced by players' positions and player status, whereas it found that game location affected a minor number of physical metrics. Therefore, practitioners should be aware that training should be tailored based on the physical demands of the players' positions. Moreover, practitioners and managers need to be aware of the positive impact of substitutions on match intensity.

Predicting Injuries in Elite Female Football Players With Global-Positioning-System and Multiomics Data

PURPOSE

Injury prevention is a crucial aspect of sports, particularly in high-performance settings such as elite female football. This study aimed to develop an injury prediction model that incorporates clinical, Global-Positioning-System (GPS), and multiomics (genomics and metabolomics) data to better understand the factors associated with injury in elite female football players.

METHODS

We designed a prospective cohort study over 2 seasons (2019-20 and 2021-22) of noncontact injuries in 24 elite female players in the Spanish Premiership competition. We used GPS data to determine external workload, genomic data to capture genetic susceptibility, and metabolomic data to measure internal workload.

RESULTS

Forty noncontact injuries were recorded, the most frequent of which were muscle (63%) and ligament (20%) injuries. The baseline risk model included fat mass and the random effect of the player. Six genetic polymorphisms located at the DCN, ADAMTS5, ESRRB, VEGFA, and MMP1 genes were associated with injuries after adjusting for player load (P < .05). The genetic score created with these 6 variants determined groups of players with different profile risks (P = 3.1 × 10-4). Three metabolites (alanine, serotonin, and 5-hydroxy-tryptophan) correlated with injuries. The model comprising baseline variables, genetic score, and player load showed the best prediction capacity (C-index: .74).

CONCLUSIONS

Our model could allow efficient, personalized interventions based on an athlete's vulnerability. However, we emphasize the necessity for further research in female athletes with an emphasis on validation studies involving other teams and individuals. By expanding the scope of our research and incorporating diverse populations, we can bolster the generalizability and robustness of our proposed model.

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Introduction

In competitive sports, teams are increasingly relying on advanced systems for improved performance and results. This study reviews the literature on the role of artificial intelligence (AI) in managing these complexities and encouraging a system thinking shift. It found various AI applications, including performance enhancement, healthcare, technical and tactical support, talent identification, game prediction, business growth, and AI testing innovations. The main goal of the study was to assess research supporting performance and healthcare.

Methods

Systematic searches were conducted on databases such as Pubmed, Web of Sciences, and Scopus to find articles using AI to understand or improve sports team performance. Thirty-two studies were selected for review.

Results

The analysis shows that, of the thirty-two articles reviewed, fifteen focused on performance and seventeen on healthcare. Football (Soccer) was the most researched sport, making up 67% of studies. The revised studies comprised 2,823 professional athletes, with a gender split of 65.36% male and 34.64% female. Identified AI and non-AI methods mainly included Tree-based techniques (36%), Ada/XGBoost (19%), Neural Networks (9%), K-Nearest Neighbours (9%), Classical Regression Techniques (9%), and Support Vector Machines (6%).

Conclusions

This study highlights the increasing use of AI in managing sports-related healthcare and performance complexities. These findings aim to assist researchers, practitioners, and policymakers in developing practical applications and exploring future complex systems dynamics.

Analyzing the impact of non-participation in the FIFA World Cup Qatar 2022 on LaLiga players' physical performance

Background

Monitoring external load demands in soccer is crucial for optimizing performance and reducing injury risk. However, events like the FIFA World Cup Qatar 2022 and unexpected interruptions can disrupt load management strategies. Understanding the impact of such events on player performance is essential for effective training and recovery strategies.

Objective

This study retrospectively assessed the impact of the FIFA World Cup Qatar 2022 on the physical performance of LaLiga elite soccer players who were not part of the tournament. The aim was to analyze various external load parameters and determine the direction of their changes post-tournament.

Methods

Data from 239 LaLiga players who were not selected for the World Cup were analyzed. External load parameters from 8 matches before and after the tournament were compared. Statistical analyses, including repeated measures ANOVA, were conducted to evaluate changes in performance metrics.

Results

Minutes played and total distance covered showed no significant changes post-tournament. However, maximal speed decreased significantly (p < 0.001; η2p = 0.117). High-speed running parameters improved significantly (p < 0.05), except for HSRRelCount (p = 0.074; η2p = 0.013). Sprint-related variables demonstrated significant enhancements, except for SprintAbsAvgDuration, SprintMaxAvgDuration, and Sprints >85% Vel Max. Acceleration metrics showed significant improvements in Accel_HighIntensityAccAbsCount (p = 0.024; η2p = 0.021), while Accel_Accelerations showed no significant changes. Deceleration metrics remained unchanged, but Accel_HighIntensityDecAbsCount and Accel_HighIntensityDecAbsDistance increased significantly post-tournament (p = 0.002; η2p = 0.040, p = 0.001; η2p = 0.044, respectively).

Conclusion

Non-participant LaLiga players demonstrated enhanced performance in most external load metrics after the FIFA World Cup Qatar 2022. These findings highlight the importance of effective load management during periods of competition interruption and suggest strategies to optimize performance and reduce injury risk. Further research should consider holistic performance metrics and internal load parameters to provide comprehensive insights into player response to mid-season tournaments.

Validity and Reliability of the Acceleration-Speed Profile for Assessing Running Kinematics' Variables Derived From the Force-Velocity Profile in Professional Soccer Players

ABSTRACT

Alonso-Callejo, A, García-Unanue, J, Guitart-Trench, M, Majano, C, Gallardo, L, and Felipe, J. Validity and reliability of the acceleration-speed profile for assessing running kinematics' variables derived from the force-velocity profile in professional soccer players. J Strength Cond Res 38(3): 563-570, 2024-The aim of this research is to assess the validity and reliability of the acceleration-speed profile (ASP) for measuring the mechanical variables of running kinematics when compared with the force-velocity profile (FVP) obtained by reference systems. The ASP and FVP of 14 male players of an elite football club were assessed during a competitive microcycle. Three ASPs were tested according to the number and type of sessions included in its plotting (ASP1: 5 training sessions and competitive match; ASP2: 5 training sessions; ASP3: competitive match). Force-velocity profile was tested 4 days before match (MD-4) with a 30-m linear sprint using 3 previously validated devices (encoder, mobile App, and global positioning system). Level of significance was p < 0.05. Acceptable reliability (intraclass correlation coefficient > 0.5) was found between the ASP1 and the encoder for all variables (F 0 -A 0 , V 0 -S 0 , and V max ). The more reliable ASP method was the ASP1 showing a lower bias than the ASP2 and ASP3 methods for almost all variables and reference systems. For ASP1, lower mean absolute error (MAE: 0.3-0.5) and higher correlation (P-M corr: 0.57-0.92) were found on variables related to the velocity in comparison with variables related to the early acceleration phase (F 0 -A 0 ; MAE: 0.49-0.63; P-M corr: 0.13-0.41). Acceleration-speed profile, when computed with data from a complete competitive week, is a reliable method for analyzing variables derived from velocity and acceleration kinematics. From these results, practitioners could implement ASP and the applications of the FVP previously studied, such as resistance training prescription, performance assessment, and return-to-play management.

Weekly External Load Performance Effects on Sports Injuries of Male Professional Football Players

One of the most challenging issues professional football players face throughout their careers is injuries. Those injuries often result from suboptimal training programs that were not designed according to the players' individual needs. This prospective study aimed to examine in detail the effects of sports injuries on professional football players' weekly external load performances. Thirty-three male professional football players were monitored using 10-Hz Global Positioning System (GPS) units (Apex pro series, StatSports) during an entire season. The variables considered in the analysis were total distance (TD), high-speed running (HSR), accelerations (ACC), and decelerations (DEC). The comparisons were made between the four-week block before injury (-4T), four-week block after return (+4T), and players' season averages (S). Players displayed significantly higher values of TD, HSR, ACC, and DEC in the -4T, compared to the other two moments (+4T and S). Furthermore, the comparison between the +4T and S showed no significant variations in the GPS metrics. It was shown that a significant increase in players' weekly external load performance over a four-week period may have a negative effect on the occurrence of injuries from a professional football standpoint. Future research should consider the effects of injury severity on players' external load variations.

Effect of playing position and microcycle days on the acceleration speed profile of elite football players

The aim of this study was to analyse the differences in the A-S profile of elite football players induced by playing position and the microcycle day. Players belonged to a second division club in the Spanish La Liga competition. They were classified into five playing positions: central defenders (CD), full backs (FB), midfielders (MF), wide midfielders (WMF) and forwards (FW). Microcycle days were categorised according to the days until matchday (MD, MD-1, MD-2, MD-3, MD-4 and MD-5). Data was collected along six microcycles, including one match per microcycle. The variables analysed were: maximal theoretical acceleration (A₀), maximal theoretical speed (S₀), maximal acceleration (ACC_max), maximal speed (S_max) and A-S slope (AS_slope). Significant differences were found within positions and microcycle day for all variables (p < 0.05). Match day (MD) showed greater values than the training sessions in A₀, ACC_max and S_max (p < 0.05). The highest values for variables associated with acceleration capabilities were found in CD on MD, whereas speed variables were higher in WMF. MD-2 showed the lowest values in all variables except for AS_slope. Maximal acceleration and sprint abilities are therefore affected by playing position. Wide positions showed the highest speed capacity, and CD presented a likely acceleration profile. Higher values for all variables concerning the microcycle day, were achieved on MD, and were not reproduced during training with the consequent injury risk and performance decrease it takes.

A targeted metabolic analysis of football players and its association to player load: Comparison between women and men profiles

Professional athletes undertake a variety of training programs to enhance their physical performance, technical-tactical skills, while protecting their health and well-being. Regular exercise induces widespread changes in the whole body in an extremely complex network of signaling, and evidence indicates that phenotypical sex differences influence the physiological adaptations to player load of professional athletes. Despite that there remains an underrepresentation of women in clinical studies in sports, including football. The objectives of this study were twofold: to study the association between the external load (EPTS) and urinary metabolites as a surrogate of the adaptation to training, and to assess the effect of sex on the physiological adaptations to player load in professional football players. Targeted metabolic analysis of aminoacids, and tryptophan and phenylalanine metabolites detected progressive changes in the urinary metabolome associated with the external training load in men and women’s football teams. Overrepresentation analysis and multivariate analysis of metabolic data showed significant differences of the effect of training on the metabolic profiles in the men and women teams analyzed. Collectively, our results demonstrate that the development of metabolic models of adaptation in professional football players can benefit from the separate analysis of women and men teams, providing more accurate insights into how adaptation to the external load is related to changes in the metabolic phenotypes. Furthermore, results support the use of metabolomics to understand changes in specific metabolic pathways provoked by the training process.

Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes

Background

In professional sports, injuries resulting in loss of playing time have serious implications for both the athlete and the organization. Efforts to quantify injury probability utilizing machine learning have been met with renewed interest, and the development of effective models has the potential to supplement the decision-making process of team physicians.

Purpose/Hypothesis

The purpose of this study was to (1) characterize the epidemiology of time-loss lower extremity muscle strains (LEMSs) in the National Basketball Association (NBA) from 1999 to 2019 and (2) determine the validity of a machine-learning model in predicting injury risk. It was hypothesized that time-loss LEMSs would be infrequent in this cohort and that a machine-learning model would outperform conventional methods in the prediction of injury risk.

Study Design

Case-control study; Level of evidence, 3.

Methods

Performance data and rates of the 4 major muscle strain injury types (hamstring, quadriceps, calf, and groin) were compiled from the 1999 to 2019 NBA seasons. Injuries included all publicly reported injuries that resulted in lost playing time. Models to predict the occurrence of a LEMS were generated using random forest, extreme gradient boosting (XGBoost), neural network, support vector machines, elastic net penalized logistic regression, and generalized logistic regression. Performance was compared utilizing discrimination, calibration, decision curve analysis, and the Brier score.

Results

A total of 736 LEMSs resulting in lost playing time occurred among 2103 athletes. Important variables for predicting LEMS included previous number of lower extremity injuries; age; recent history of injuries to the ankle, hamstring, or groin; and recent history of concussion as well as 3-point attempt rate and free throw attempt rate. The XGBoost machine achieved the best performance based on discrimination assessed via internal validation (area under the receiver operating characteristic curve, 0.840), calibration, and decision curve analysis.

Conclusion

Machine learning algorithms such as XGBoost outperformed logistic regression in the prediction of a LEMS that will result in lost time. Several variables increased the risk of LEMS, including a history of various lower extremity injuries, recent concussion, and total number of previous injuries.

Nutritional Intake and Training Load of Professional Female Football Players during a Mid-Season Microcycle

Football (soccer) is a high-intensity intermittent sport with large energy demands. In a repeated-measures design, we analysed the nutritional intake and training load of fourteen female football players (22.50 ± 4.38 y; 57.23 ± 8.61 kg; 164 ± 6.00 cm; 18.33 ± 2.48% of fat mass and 23.71 ± 2.51 kg of muscle mass) competing in the highest female Football Portuguese League across a typical mid-season microcycle. The microcycle had one match day (MD), one recovery session (two days after the MD, MD+2), three training sessions (MD-3, MD-2, MD-1) and two rest days (MD+1). Energy intake and CHO (g.kg.BW−1) intake were lower on the days before the competition (MD+2, MD-3, MD-2 and MD-1 vs. MD; p < 0.05; ES: 0.60−1.30). Total distance, distance covered at high-speed running (HSRD) and the high metabolic distance load (HMLD) were lower on MD+2, MD-3 and MD-1 compared with MD (p < 0.05; ES: <0.2−5.70). The internal training load was lower in all training sessions before the competition (MD+2, MD-3, MD-2 and MD-1 vs. MD; p ≤ 0.01; ES: 1.28−5.47). Despite the small sample size and a single assessment in time, the results suggest that caloric and CHO intake were below the recommendations and were not structured based on the physical requirements for training sessions or match days.