Molecular, Physical, and Technical Performance Response After a Competitive Match in Male Professional Soccer Players

BACKGROUND

Male professional soccer players frequently compete in multiple matches weekly, and each match significantly impacts their homeostasis, health, and performance. This study evaluates players response at 48 h post-match by combining biological and GPS data. Investigating biochemical and performance metrics offers insights into the physical demands of high-intensity exercise, essential for optimizing performance, recovery, and overall athlete health.

METHODS

The study involved an Italian "Serie A" team, and we assessed players' effort during a single match using GPS data and compared it to "Serie A" averages. Additionally, we evaluated oxidative stress and metabolism 48 h after the match.

RESULTS

At 48 h post-match, there were no signs of oxidative stress and changes in salivary IgA levels, but total antioxidant potential was significantly low. Moreover, increased plasma metabolites linked to energy production were also observed.

CONCLUSIONS

The results indicate that 48 h after a match in "Serie A", well-trained athletes showed no oxidative stress, to the detriment of the antioxidant potential, along with increased metabolites crucial for energy production. Combining GPS and metabolic analysis enhances player performance, informs tactical decisions, and supports team success, fostering data-driven approaches in soccer.

A comparative analysis of weekly internal and external workloads between starting and non-starting professional soccer players: A retrospective 21-week examination

OBJECTIVES

Aims of the present investigation encompassed: (i) the quantification of training and match loads experienced by starters and non-starters within a professional soccer team; (ii) the identification of variations in these loads across different training modalities, namely, Preparation Training (PT), Match and Match Compensation Training (M&MCT), and their cumulative sum (Total Load; TL); and (iii) the formulation of guidelines aimed at harmonizing the weekly workloads between the groups.

METHODS

Internal and external load of training sessions (n = 97), competitive matches (n = 21) and running based sessions (n = 4) were recorded for 21 weeks to investigate possible heterogeneity between starters and non-starters across PT, M&MCT and TL.

RESULTS

During PT, time spent in heart rate zone 5 (HRZ5) was increased for non-starters (+46.1%). During M&MCT, lower loads for non-starters were found in the rate of perceived exertion (sRPE) (-45.6%), HRZ4 (-54%) and HRZ5 (-77.8%), total distance (TD) (-37%), number of sprints (-58.1%), distance in speed zone 1 (-51.1%), zone 3 (-61.5%) and zone 4 (-59.8%) (SZ1, SZ3&4) and all acceleration and deceleration zones (Acc1-4; Dec1-4) (Acc1: -53.1%; Acc2: -56.3%; Acc3: -59.2%; Acc4: 57.8%) (Dec1: -45.9%; Dec2: -55.2%; Dec3: -63.2%; Dec4: -67.7%). Regarding TL, the non-starters' loads remain lower compared to starters for sRPE (-19.2%), HRZ4 (-21.6%) and HRZ5 (-41.4%), number of sprints (-26.7%), SZ3 (-34.2%), Acc3 (-24.4%), Acc4 (-26.1%), Dec2 (-18.7%), Dec3 (-24%) and Dec4 (-31.2%).

CONCLUSIONS

By implementing a running-based regimen on matchday and MCT the day after (MD+1), TD, distances in SZ1, SZ2, SZ4, SZ5, and counts of accelerations in Acc1&2, as well as Dec1, were effectively replicated for non-starters. All other variables remained unaligned for the non-starters. Given the prevalent emphasis on Small-Sided Games (SSGs) during MCT at MD+1, the incorporation of an additional running-based session for non-starters on MD is advised to address gaps in TD, sprint counts, and high-intensity load variables, such as HRZ4&5 and SZ 3 to 5.

Practitioner Usage, Applications, and Understanding of Wearable GPS and Accelerometer Technology in Team Sports

ABSTRACT

Dawson, L, McErlain-Naylor, SA, Devereux, G, and Beato, M. Practitioner usage, applications, and understanding of wearable GPS and accelerometer technology in team sports. J Strength Cond Res 38(7): e373-e382, 2024-Monitoring training load is essential for optimizing the performance of athletes, allowing practitioners to assess training programs, monitor athlete progress, and minimize the risk of injury and overtraining. However, there is no universal method for training load monitoring, and the adoption of wearable global positioning system (GPS) and accelerometer technology in team sports has increased the volume of data and, therefore, the number of possible approaches. This survey investigated the usage, applications, and understanding of this technology by team sports practitioners. Seventy-two practitioners involved in team and athlete performance monitoring using GPS and accelerometer technology completed the survey. All respondents reported supporting the use of GPS technology in their sport, with 70.8% feeling that GPS technology is important for success. Results showed that 87.5% of respondents use data from wearable technology to inform training prescription, although only 50% use the data to influence decisions in competition. In addition, results showed that GPS metrics are used more than accelerometer-derived metrics; however, both are used regularly. Discrepancies in accelerometer usage highlighted concerns about practitioners' understanding of accelerometer-derived metrics. This survey gained insight into usage, application, understanding, practitioner needs, and concerns and criticisms surrounding the use of GPS and accelerometer metrics for athlete load monitoring. Such information can be used to improve the implementation of this technology in team sport monitoring and highlight gaps in the literature that will help to design future studies to support practitioner needs.

Building Individual Player Performance Profiles According to Pre-Game Expectations and Goal Difference in Soccer

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players' energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder-Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87±61.42 and root mean squared error (RMSE) of 520.69±88.66 achieved by our model, as opposed to the B1 MAE of 429.04±84.87 and RMSE of 581.34±185.84, and B2 MAE of 421.57±95.96 and RMSE of 613.47±300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players' responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics.

Training Volume and High-Speed Loads Vary Within Microcycle in Elite North American Soccer Players

ABSTRACT

Ishida, A, Draper, G, Wright, M, Emerson, J, and Stone, MH. Training volume and high-speed loads vary within microcycle in elite North American soccer players. J Strength Cond Res 37(11): 2229-2234, 2023-The purposes of this study were to reduce dimensionality of external training load variables and examine how the selected variables varied within microcycle in elite North American soccer players. Data were collected from 18 players during 2018-2020 in-seasons. Microcycle was categorized as 1, 2, 3, 4, 5 days before match day (MD-1, MD-2, MD-3, MD-4, and MD-5, respectively). Training load variables included total distance, average speed, maximum velocity, high-speed running distance (HSR), average HSR, HSR efforts, average HSR efforts, sprint distance, average sprint distance, sprint efforts, average sprint efforts, total PlayerLoad, and average PlayerLoad. The first principal component (PC) can explain 66.0% of the variances and be represented by "high-speed load" (e.g., HSR and sprint-related variables) with the second PC relating to "volume" (e.g., total distance and PlayerLoad) accounting for 17.9% of the variance. Average sprint distance and total distance were selected for further analysis. Average sprint distance was significantly higher at MD-3 than at MD-2 (p = 0.01, mean difference = 0.36 m•minute-1, 95% confidence intervals [CIs] = 0.07-0.65 m•minute-1) and MD-4 (p = 0.012, mean difference = 0.26 m•minute-1, 95% CIs = 0.10-0.41 m•minute-1). Total distance was significantly higher at MD-3 than at MD-1 (p < 0.001, mean difference = 1,465 m, 95% CIs = 1,003-1926 m), and MD-2 (p < 0.001, mean difference = 941 m, 95% CIs = 523-1,360 m). Principal component analysis may simplify reporting process of external training loads. Practitioners may need to choose "volume" and "high-speed load" variables. Elite North American Soccer players may accumulate higher average sprint distance at MD-3 than at other training days.

Performance Adaptations to Intensified Training in Top-Level Football

Because physical demands are surging in football (soccer, USA), clubs are more and more seeking players who have a high capacity to perform repeated intense exercise. High-intensity interval training (HIIT), comprising exercise performed at intensities near or exceeding the capacity of aerobic energy systems, effectively enhances the physical conditioning of players. But given that HIIT imposes high loads, it increases the risk of overload-associated match performance decline and injury. This makes some coaches inclined to conduct HIIT in the weeks leading up to the season and during the season. Therefore, the challenge is how to optimize and dose HIIT during these phases, as they can be decisive. Studies have highlighted the utility of conducting periods of intensified training to overcome the risk of overload while at the same time enhancing performance. During intensified training periods of typically a few weeks, intensity is increased by enlarging the amount of HIIT, for example, aerobic high-intensity training or speed endurance training, while volume at low-to-moderate intensity is significantly reduced. The outcome depends on training composition and prescription-most notably, intensity and duration of bouts and recovery. When work intervals are prescribed for a few minutes at intensities > 90% heart rate max (i.e., aerobic high-intensity training), then beneficial adaptations pertaining to aerobic power and capacity are apparent. But when work intervals are conducted at much higher intensities, as all-out efforts or sprinting of typically 10- to 40-s duration with longer recovery periods (i.e., speed endurance training), beneficial adaptations pertaining to anaerobic energy systems, ion handling, and fatigue resilience are commonly observed. In this review, we discuss the utility of conducting intensified training periods to enhance performance in elite football players during the late preparation phase and competitive season.

Extended Energy-Expenditure Model in Soccer: Evaluating Player Performance in the Context of the Game

Every soccer game influences each player's performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is to use data derived from GPS wearable devices to present a new framework for performance analysis. A player's energy expenditure is analyzed using data analytics and K-means clustering of low-, middle-, and high-intensity periods distributed in 1 min segments. Our framework exhibits a higher explanatory power compared to usual game metrics (e.g., high-speed running and sprinting), explaining 45.91% of the coefficient of variation vs. 21.32% for high-, 30.66% vs. 16.82% for middle-, and 24.41% vs. 19.12% for low-intensity periods. The proposed methods enable deeper game analysis, which can help strength and conditioning coaches and managers in gaining better insights into the players' responses to various game situations.

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.

High metabolic load distance in professional soccer according to competitive level and playing positions

Background

High metabolic load distance provides global information about the soccer players' total high-intensity activities. Thus, this study aimed to examine the Spanish professional soccer players' high metabolic load distance profile, comparing competitive level and playing positions.

Methods

A total of 18,131 individual match observations were collected from outfield players competing during the 2018/2019 and 2019/20 seasons in the First and Second Spanish Professional Soccer Leagues (LaLiga™). High Metabolic Load Distance (HMLD; distance covered with a power consumption above 25.5 W·kg^-1 and accelerations or decelerations (e.g., accelerating from 2 to 4 m·s^-2 for 1 s) were included), and HMLD per minute (HMLD_min) were analyzed by the ChryonHego^® video-tracking system. Players were classified according to their playing position as follows: Central Backs (CB), Full Backs (FB), Center Midfields (CM), Wide Midfields (WM), and Forwards (FW).

Results

No differences between competitive levels were found in any variable when all players were analyzed conjointly except for HMLD_min overall and during the second half. However, when playing positions were considered, differences between competitive levels were observed in all positions, mainly in HMLD and HMLD during the first-half variables. In addition, several differences between playing positions were observed, with CB presenting the lowest values in all variables compared to their counterparts in both competitive levels, whereas CM in First Division and WM in Second Division showed the highest values in the HMLD variables.

Discussion

The findings are of interest to analyze the HMLD in professional soccer players, enabling the adaptation and individualization of training in this population according to the competitive level and specific playing position of each player.

Accuracy of Tracking Devices' Ability to Assess Exercise Energy Expenditure in Professional Female Soccer Players: Implications for Quantifying Energy Availability

The purpose of the study was to assess the accuracy of commonly used GPS/accelerometer-based tracking devices in the estimation of exercise energy expenditure (EEE) during high-intensity intermittent exercise. A total of 13 female soccer players competing at the highest level in Norway (age 20.5 ± 4.3 years; height 168.4 ± 5.1 cm; weight 64.1 ± 5.3 kg; fat free mass 49.7 ± 4.2 kg) completed a single visit test protocol on an artificial grass surface. The test course consisted of walking, jogging, high-speed running, and sprinting, mimicking the physical requirements in soccer. Three commonly used tracking devices were compared against indirect calorimetry as the criterion measure to determine their accuracy in estimating the total energy expenditure. The anaerobic energy consumption (i.e., excess post-exercise oxygen consumption, EPOC) and resting time were examined as adjustment factors possibly improving accuracy. All three devices significantly underestimated the total energy consumption, as compared to the criterion measure (p = 0.022, p = 0.002, p = 0.017; absolute ICC = 0.39, 0.24 and 0.30, respectively), and showed a systematic pattern with increasing underestimation for higher energy consumption. Excluding EPOC from EEE reduced the bias substantially (all p's becoming non-significant; absolute ICC = 0.49, 0.54 and 0.49, respectively); however, bias was still present for all tracking devices. All GPS trackers were biased by showing a general tendency to underestimate the exercise energy consumption during high intensity intermittent exercising, which in addition showed a systematic pattern by over- or underestimation during lower or higher exercising intensity. Adjusting for EPOC reduced the bias and provided a more acceptable accuracy. For a more correct EEE estimation further calibration of these devices by the manufacturers is strongly advised by possibly addressing biases caused by EPOC.

The dose-response relationship between training-load measures and aerobic fitness in elite academy soccer players

The aim of the current study is to examine the dose-response relationships between training load (TL) measures and the consequent changes in aerobic fitness. Data were collected over the 6-week pre-season period in elite youth soccer players. Participants completed a lactate threshold test to identify changes in treadmill speed at 2 mmol · l^-1 (S2) and 4 mmol · l^-1 (S4). Internal TL was quantified with the following training impulse (TRIMP) methods: Banister TRIMP, Edwards TRIMP, Lucia TRIMP, individual TRIMP (iTRIMP) and rate of perceived exertion was also collected. External TL measures were total distance, PlayerLoad, high speed running (14.4-19.8 km · h^-1), very high-speed running (19.8-25.2 km · h^-1) and maximal sprint distance (>25.2 km · h^-1). Individual high-speed distance was derived from each participants treadmill speed at S4. Different Bayesian regression models were run with different likelihood functions. The best-fitting models with both the lowest out-of-sample prediction error and the highest variance explained (R²) were used. iTRIMP had the strongest relationships with changes in S2 (r = 0.93, R² = 0.90) and S4 (r = 0.88, R² = 0.82). Explained variance ranged from 10%-69% and 11%-38% for all other internal TL measures and external measures, respectively. In summary, the iTRIMP method demonstrates a dose-response relationship with changes in aerobic fitness in elite youth soccer players.

The Prediction of Running Velocity during the 30-15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach

Measuring exercise variables is one of the most important points to consider to maximize physiological adaptations. High-intensity interval training (HIIT) is a useful method to improve both cardiovascular and neuromuscular performance. The 30–15_IFT is a field test reflecting the effort elicited by HIIT, and the final velocity reached in the test is used to set the intensity of HIIT during the training session. In order to have a valid measure of the velocity during training, devices such as GPS can be used. However, in several situations (e.g., indoor setting), such devices do not provide reliable measures. The aim of the study was to predict exact running velocity during the 30–15_IFT using accelerometry-derived metrics (i.e., Player Load and Average Net Force) and heart rate (HR) through a machine learning (ML) approach (i.e., Support Vector Machine) with a leave-one-subject-out cross-validation. The SVM approach showed the highest performance to predict running velocity (r = 0.91) when compared to univariate approaches using PL (r = 0.62), AvNetForce (r = 0.73) and HR only (r = 0.87). In conclusion, the presented multivariate ML approach is able to predict running velocity better than univariate ones, and the model is generalizable across subjects.

Win, Draw, or Lose? Global Positioning System-Based Variables' Effect on the Match Outcome: A Full-Season Study on an Iranian Professional Soccer Team

The aim of the study was to determine the between-match and between-halves match variability of various Global Positioning System (GPS) variables and metabolic power average (MPA) in competitions, based on the match results obtained by professional soccer players over a full season. Observations on individual match performance measures were undertaken on thirteen outfield players competing in the Iranian Premier League. The measures selected for analysis included total duration, accelerations in zones (AccZ1, 2, and 3), decelerations in zones (DecZ1, 2, and 3), and MPA collected by the Wearable Inertial Measurement Unit (WIMU). The GPS manufacturer set the thresholds for the variables analyzed as follows: AccZ1 (<2 m·s⁻²); AccZ2 (2 to 4 m·s⁻²); AccZ3 (>4 m·s⁻²); DecZ1 (<−2 m·s⁻²); DecZ2 (−2 to −4 m·s⁻²); DecZ3 (>−4 m·s⁻²). The results revealed significant differences between wins and draws for the duration of the match and draws compared to wins for the first- half duration (p ≤ 0.05; ES = 0.36 [−0.43, 1.12]), (p ≤ 0.05; ES = −7.0 [−8.78, −4.78], respectively. There were significant differences on AccZ1 during the first-half between draws and defeats (p ≤ 0.05; ES = −0.43 [−1.32, 0.46]), for AccZ3 in the second-half between draws and defeats (p ≤ 0.05; ES = 1.37 [0.48, 2.25]). In addition, there were significant differences between wins and draws (p ≤ 0.05; ES = 0.22 [−0.62, 1.10]), and wins and defeats for MPA in the first- half (p ≤ 0.05; ES = 0.34 [−0.65, 1.22]). MPA showed further differences between draws and defeats in the second- half (p ≤ 0.05; ES = 0.57 [−0.22, 1.35]). Descriptive analysis revealed differences between the first and second half for wins in AccZ2 (p = 0.005), DecZ2 (p = 0.029), and MPA (p = 0.048). In addition, draws showed significant differences between the first and second half in duration, AccZ1, AccZ2, and DecZ2 (p = 0.008), (p = 0.017), (p = 0.040), and (p = 0.037) respectively. Defeats showed differences between the first and second half in AccZ1, AccZ3, and MPA (p = 0.001), (p = 0.018), and (p = 0.003) respectively. In summary, the study reveals large variations between the match duration, accelerometer variables, and MPA both within and between matches. Regardless of the match outcome, the first half seems to produce greater outputs. The results should be considered when performing a half-time re-warm-up, as this may be an additional factor influencing the drop in the intensity markers in the second half in conjunction with factors such as fatigue, pacing strategies, and other contextual variables that may influence the results.

Comparison of Official and Friendly Matches through Acceleration, Deceleration and Metabolic Power Measures: A Full-Season Study in Professional Soccer Players

Soccer is a popular team sport and highly demanding activity that requires high effort and long-term training plans. The goals of this study were to compare the accelerations, decelerations and metabolic power between official and friendly full matches, between the first and second halves of the matches, and between both halves of official and friendly matches. Twelve professional soccer players (age, 28.6 ± 2.7 years; height, 182.1 ± 8.6 cm; body mass, 75.3 ± 8.2 kg; BMI, 22.6 ± 0.7 kg/m²) participated in this study. A total of 33 official and 10 friendly matches were analyzed from the Iranian Premier League. All matches were monitored using GPSPORTS systems Pty Ltd. The following variables were selected: total duration of the matches, metabolic power, accelerations Zone1 (<2 m·s^-2) (AccZ1), accelerations Zone2 (2 to 4 m·s^-2) (AccZ2), accelerations Zone3 (>4 m·s^-2) (AccZ3), decelerations Zone1 (<-2 m·s^-2) (DecZ1), decelerations Zone2 (-2 to -4 m·s^-2) (DecZ2) and decelerations Zone3 (>-4 m·s^-2) (DecZ3). The major finding was shown in metabolic power, where higher values occurred in friendly matches (p < 0.05 with small effect size). Furthermore, total duration, AccZ3, DecZ1, DecZ2, and DecZ3 were revealed to be higher in official matches, while AccZ1 and AccZ2 were higher in friendly matches. The second half of the official matches revealed higher values for total duration compared to friendly matches (p < 0.05, moderate effect size). In conclusion, this study observed higher values of metabolic power in friendly matches compared to official matches. AccZ3, DecZ1, DecZ2, and DecZ3 were higher in official matches, while AccZ1 and AccZ2 were higher in friendly matches.

Load Measures in Training/Match Monitoring in Soccer: A Systematic Review

In soccer, the assessment of the load imposed by training and a match is recognized as a fundamental task at any competitive level. The objective of this study is to carry out a systematic review on internal and external load monitoring during training and/or a match, identifying the measures used. In addition, we wish to make recommendations that make it possible to standardize the classification and use of the different measures. The systematic review was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search was conducted through the electronic database Web of Science, using the keywords "soccer" and "football", each one with the terms "internal load", "external load", and "workload". Of the 1223 studies initially identified, 82 were thoroughly analyzed and are part of this systematic review. Of these, 25 articles only report internal load data, 20 report only external load data, and 37 studies report both internal and external load measures. There is a huge number of load measures, which requires that soccer coaches select and focus their attention on the most useful and specific measures. Standardizing the classification of the different measures is vital in the organization of this task, as well as when it is intended to compare the results obtained in different investigations.

Effect of Playing Position, Match Half, and Match Day on the Trunk Inclination, G-Forces, and Locomotor Efficiency Experienced by Elite Soccer Players in Match Play

The rapid growth of wearable sensors has allowed the analysis of trunk kinematics during the match, which is necessary for having a better understanding of the postural demands of soccer players. However, some contextual variables may have an impact on the physical demands of the players. This study aimed to analyze the effect of three contextual variables (playing position, match half, and match day) on the sagittal trunk inclination, G-forces, and locomotor efficiency experienced by soccer players in match play. Then, wearable sensors were used to collect the trunk kinematics during 13 matches. Firstly, positional differences were found on the trunk inclination (p = 0.01) and the G-forces experienced by the players (p < 0.001). For example, the greatest and lowest trunk inclination was found for FW (~34.01°) and FB (~28.85°) while the greatest and lowest G-forces were found for WMF (1.16 G) and CD (1.12 G), respectively. However, there were no positional differences in the locomotor efficiency (p = 0.10). Secondly, the match half had a significant effect on the trunk inclination (p = 0.01) and the G-forces experienced by the players (p < 0.001) with significantly lower values observed during the second half. No differences between halves were found on the locomotor efficiency for any playing position (p = 0.41). Finally, no significant effect of match day on any variable was observed. This investigation is one of the first steps towards enhancing the understanding of trunk kinematics from elite soccer players. The positional differences found on the trunk inclination and G-forces imply that the development of position-specific training drills considering the postural demands is necessary to prepare the players not only for the physical demands but also for successful performance in the field of regard. The resistance to fatigue needs to be trained given the differences between halves.

[Monitoring and load management in team handball]

BACKGROUND

 The monitoring of athletes has become a focus in elite-level team sports and research in recent years. The goal is to optimise load management for maximising performance. Due to the considerable amount of data gathered during monitoring in team sports, the trend is towards integrating different monitoring methods in a software-based tool. In addition, decision-support systems are used in order to enable coaches to make quick decisions to control further burdens. This article is intended to give an overview of the large number of monitoring methods used in contact team sports, to consider their test quality criteria and to check a useful application of monitoring methods for team handball, taking into account the sports-specific requirements.

METHODS

 PubMed-based review of the literature. Narrative review focusing on current data from the years 2010 to 2019.

RESULTS

 Demands on athletes in elite team handball vary depending on the playing position. Therefore, individualised load control is necessary. Monitoring methods with proven validity and reliability should ideally be used in a multivariate approach to represent the various aspects of external and internal load, recovery and fatigue, well-being and readiness. It is important to know the meaningful change for each athlete while using a monitoring method. This circumstance necessitates an individualised approach for each parameter. There are not many studies on the use of monitoring methods in team handball, neither in the amateur field nor in an elite sports environment. Nevertheless, based on evidence from other contact team sports, a recommendation for a combined use of monitoring methods can be given. Burden, time and cost/benefit should always be considered.

CONCLUSION

 There is a considerable need for research regarding the use of athlete monitoring in team handball. The combination of monitoring methods proposed in this article can be used as a framework for studies with handball players in an elite sports environment in order to implement an increasingly handball-specific monitoring concept in the future.

A Survey to Assess the Quality of the Data Obtained by Radio-Frequency Technologies and Microelectromechanical Systems to Measure External Workload and Collective Behavior Variables in Team Sports

Electronic performance and tracking systems (EPTS) and microelectromechanical systems (MEMS) allow the measurement of training load (TL) and collective behavior in team sports so that match performance can be optimized. Despite the frequent use of radio-frequency (RF) technology (i.e., global positioning navigation systems (GNSS/global positioning systems (GPS)) and, local position systems (LPS)) and MEMS in sports research, there is no protocol that must be followed, nor are there any set guidelines for evaluating the quality of the data collection process in studies. Thus, this study aims to suggest a survey based on previously used protocols to evaluate the quality of data recorded by RF technology and MEMS in team sports. A quality check sheet was proposed considering 13 general criteria items. Four additional items for GNSS/GPS, eight additional items for LPS, and five items for MEMS were suggested. This information for evaluating the quality of the data collection process should be reported in the methods sections of future studies.

A comparison of a GPS device and a multi-camera video technology during official soccer matches: Agreement between systems

The aim of this study was to compare the agreement of the movement demands data during a soccer match (total distance, distance per minute, average speed, maximum speed and distance covered in different speed sectors) between an optical tracking system (Mediacoach System) and a GPS device (Wimu Pro). Participants were twenty-six male professional soccer players (age: 21.65 ± 2.03 years; height: 180.00 ± 7.47 cm; weight: 73.81 ± 5.65 kg) from FC Barcelona B, of whom were recorded a total of 759 measurements during 38 official matches in the Spanish second division. The Mediacoach System and the Wimu Pro were compared using the standardized mean bias, standard error of estimate, intraclass correlation coefficients (ICC), coefficient of variation (%), and the regression equation to estimate data for each variable. In terms of agreement between systems, the magnitude of the ICC was almost perfect (> 0.90-1.00) for all variables analyzed. The coefficient of the variations between devices was close to zero (< 5%) for total distance, distance per minute, average speed, maximum speed, and walking and jogging, and between 9% and 15% for running, intense running, and sprinting at low and at high intensities. It can be observed that, compared to Wimu Pro the Mediacoach System slightly overestimated all the variables analyzed except for average speed, maximum speed, and walking variables. In conclusion, both systems can be used, and the information they provide in the analyzed variables can be interchanged, with the benefits implied for practitioners and researchers.