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Article

Assessing External Peak Physical Demands in Under-19 Years and Professional Male Football

by
Jaime Rebollo Mejía
1,
Juan Ángel Piñero Madrona
1,
Enrique Alonso-Pérez-Chao
1,2,
Manuel Barba-Ruíz
1,
Diego Muriarte Solana
1,3 and
Adrián Martín-Castellanos
1,3,*
1
Faculty of Biomedicine and Health Sciences, Universidad Alfonso X el Sabio, 28691 Madrid, Spain
2
Department of Real Madrid Graduate School, Faculty of Medicine, Health and Sports, Universidad Europea de Madrid, 28670 Madrid, Spain
3
Departamento de Deportes, Facultad de Ciencias de la Actividad Física y el Deporte, Universidad Politécnica de Madrid, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(13), 7135; https://doi.org/10.3390/app15137135
Submission received: 12 March 2025 / Revised: 13 June 2025 / Accepted: 14 June 2025 / Published: 25 June 2025
(This article belongs to the Special Issue The Impact of Sport and Exercise on Physical Health)
Browse Figures
Versions Notes

Abstract

This study aimed to compare the external peak physical demands (PDs) of under-19-year-old (U19) and professional male football players according to playing position. Positional data derived from Global Positioning System (GPS) tracking during 15 matches in the 2023/24 season for both groups were analyzed. The following variables were measured: total distance, high-intensity running distance, sprint distance, acceleration count, and high-intensity actions. A linear mixed-effects model was employed, with category and playing position included as fixed effects to compare these metrics at the player level. The results revealed only a few significant differences in physical demands between the U19 and professional players. Notably, central defenders and central midfielders exhibited lower performance in HSR distance compared to other positions, with the professional players registering higher values than their U19 counterparts. However, no significant differences were observed for total and relative sprint distances, the number of accelerations, high intensity and relative sprint running efforts. These findings highlight the overall similarity in physical demands between U19 players and professional players, suggesting that elite youth athletes may be adequately prepared to meet the physical challenges of professional competition, with the exception of HSR distance. These conclusions have practical implications for coaches and performance staff, supporting the development of position-specific training programs, optimizing workload management through GPS monitoring, improving microcycle planning, and enhancing injury prevention strategies.

1. Introduction

Football is one of the most technically and physically demanding sports [1,2]. Players are required to cover large distances while performing decelerations, accelerations, and continuous changes of direction [3]. Over the past decade, the physical profile of the modern game has evolved considerably, with a growing emphasis on high-intensity efforts [4]. These actions are increasingly decisive in key phases of play and have led to a notable rise in the volume and frequency of high-intensity activities per match. However, these efforts are not entirely linear or continuous; instead, they are distributed in short bursts or mini-matches throughout the game [5].
Traditional analyses based on average match values often underestimate the intensity of these critical moments, potentially obscuring the actual physical demands placed on players. To address this limitation, recent research has adopted time-based analysis methods using moving time windows such as 1 min and 5 min epochs [6]. Studies in this area consistently show that as the analyzed time interval increases, the relative intensity of all variables and positions tends to decrease, though this reduction does not always follow a linear pattern [7]. To identify these external peak demands (PDs), it is useful to determine the most intense activity experienced by players for a selected variable across a specified time window of interest [8]. Different time windows have been used to measure these PDs, typically ranging from 1 to 10 min, showing that intensity decreases when the window exceeds 5 min due to accumulated fatigue [9]. In fact, some studies analyzing physical and technical performance following these PDs periods report a decline in total distance covered and high-intensity efforts, as well as in technical actions that are closely linked to physical exertion (e.g., running with the ball) [10].
Monitoring these PDs and understanding the match context in which they occur, as well as identifying the most affected positions and their specific demands, can significantly improve the interpretation and contextualization of the data. This, in turn, facilitates more personalized and informed decision-making in each situation. For instance, during these periods, central midfielders tend to cover more overall distance but spend less time at sprinting speeds, whereas forwards cover more sprint distance but less overall distance [11]. Moreover, previous studies have highlighted clear positional differences in worst-case scenarios across professional football, in both male and female cohorts [12]. These differences are especially notable in sprint-related metrics, where central midfielders consistently register lower values [12], while wide midfielders and full-backs typically record higher total distances and greater volumes of high-intensity running [12,13]. Such insights further reinforce the importance of tailoring training loads according to positional demands. This allows physical trainers to monitor whether these intensity levels are reached, and, if not, to carry out specific adjustments to player training and recovery [14] to better prepare athletes in specific positions for these external PDs on match day [15].
While several studies are available, most have focused on professional male players [16], female players [12,17], and elite U19 players [7]. However, comparisons between professional and youth players remain scarce. Understanding the physical differences can help identify the gaps young or nonprofessional players need to bridge to reach the professional level. In addition, this comparison can reveal which physical attributes are most important for success at the professional level, aiding scouts and teams in identifying promising players who exhibit those characteristics. This study aims to analyze these external PDs in U19 players from the top national division of this category, with an emphasis on potential differences in physical variables compared to their counterparts on a professional team. To align with the study objectives, we hypothesized that professional players would present higher external peak physical demands compared to U19 players, since the physical requirements of professional-level competition are expected to be greater.

2. Materials and Methods

2.1. Participants

A non-probabilistic convenience sampling method was employed for this study. Performance data were collected from players belonging to a single professional football club. A total of 30 match observations were recorded during the 2023/2024 season, including 15 matches from the Spanish U19 First Division (the highest national league for this age group) and 15 matches from the Spanish Second Professional Division. Both squads belonged to the same club and ranked within the top five positions of their respective competitions. To avoid potential overlapping and ensure data independence, no players were shared between the two teams during the analyzed matches.
Each team was composed of 22 players. The U19 team had a mean age of 17.6 ± 1.0 years, while the professional team averaged 26.9 ± 4.1 years. Players were classified into five positional groups: central defenders, full-backs, central midfielders, wide midfielders, and forwards [11]. Figure 1 provides details on the number of players included, the tactical formation employed, and the total number of match records per position.
As exclusion criteria, matches with weak satellite tracking signals resulting in incomplete data were excluded, along with those where errors occurred during data collection. Players who participated for fewer than 45 min or switched positions during the match were also excluded (i.e., if a player started as a central defender, and later moved to full-back, they were omitted from the study). Additionally, goalkeepers were excluded due to their low high-speed running (HSR) demands [18]. Matches in which either team finished with a numerical advantage were also removed. These criteria align with previous studies, as match intensity and physical performance values could be affected [19,20].

2.2. Procedure

The data collection was carried out by certified strength and conditioning coaches who were part of the technical staff of both the U19 and professional teams. These professionals were trained and experienced in the placement and handling of GPS tracking devices, ensuring standardized and reliable data acquisition throughout the study. The WIMU PRO Global Positioning System (GPS) (RealTrack Systems, Almería, Spain), validated by Bastida et al. [21], was utilized for data collection. It was calibrated according to the manufacturer’s instructions.
Based on the methodology used in previous studies [7], players were provided with clear instructions regarding the proper use and placement of the GPS tracking devices. Each unit was securely positioned in a designated pocket on the upper back of a GPS vest and correctly oriented to minimize recording errors. This procedure was applied consistently during weekly training sessions to ensure that players became fully familiar with the device and its placement, as these measurements are routinely taken as part of their regular monitoring protocols [22]. To avoid variability in external load outputs across different devices, each player used the same assigned unit throughout the entire data collection period [23].
To determine the external peak demand (PD) for each match observation, raw data were collected in 1 s intervals for each player and exported into a custom-designed Microsoft Excel spreadsheet (version 16.0; Microsoft Corporation, Redmond, WA, USA) for further analysis. A rolling average method was applied over 1 min intervals to identify the highest value for each variable. The rolling window began at kickoff, paused during halftime, resumed at the start of the second half, and ended with the final whistle. The PD was defined as the highest value recorded for each metric within the specified 1 min window. This approach is frequently employed when assessing PDs and has been previously used in several studies [8,9,24].

2.3. Variables

The decision to record data in 1 min windows was informed by evidence suggesting that fatigue can potentially impact performance within a 3 min timeframe, as evidenced in Riboli et al. [24]. Additionally, prior studies have shown that 1 min rolling averages yield the highest peak values across various physical performance variables while also presenting the lowest coefficient of variation [12].
In this way, football is an intermittent sport where stoppages in play are common. As a result, 3, 5, and 10 min windows are more likely to be affected by interruptions that influence the intensity of the analyzed variables. Goals, fouls, throw-ins, offside calls, substitutions, or medical stoppages can significantly impact these time frames. By contrast, such interruptions are less frequent in 1 min windows, which is why they tend to reflect higher peak values [25]. For each duration, we recorded the maximum values of the following variables: total distance, high-speed running distance (>21 km/h, HSR), the number of HSR actions, relative sprint distance (defined as >80% of the player’s maximum speed) the number of relative sprint efforts, and the number of accelerations >3 m·s−2.

2.4. Statistical Analysis

We set statistical significance for all analyses at p < 0.05. To verify the assumption of normality, the Kolmogorov–Smirnov test was applied to each variable. The results confirmed that all key dependent variables followed a normal distribution. A linear mixed-effects model was conducted using JASP software (version 0.19.1.0) to examine the influence of competitive category and position on the performance variables collected for players. To account for individual differences, a random intercept for each player was included in the model. The Satterthwaite approximation was used to estimate degrees of freedom and assess the significance of fixed effects.
An a priori power analysis was conducted using G*Power (version 3.1.9.7) to estimate the required sample size for detecting medium effects in a mixed model framework. The calculation assumed an effect size of f = 0.25, an alpha level of 0.05, power of 0.95, 10 numerator degrees of freedom, 6 groups, and 1 covariate. The analysis yielded a required total sample size of 400, with a critical F value of 1.85 and 393 degrees of freedom for the denominator. The observed power was 0.9509. Given that our dataset included 420 observations, the sample size was deemed sufficient to detect medium-sized effects with high statistical power.

3. Results

Descriptive statistics for the comparisons are presented in Table 1. For total distance, the model indicated that neither category (F(1, 28.70) = 2.45; p = 0.128), position (F(4, 28.56) = 0.68, p = 0.609), nor their interaction (F(4, 28.56) = 1.11, p = 0.368) yielded significant effects. However, the random effect of player accounted for substantial variance (σ² = 161.18). Model fit indices were log likelihood = −2157.57, AIC = 4339.15, and BIC = 4387.63. Similarly, for accelerations, no significant main effects were found for category (F(1, 29.11) = 1.08; p = 0.307), position (F(4, 28.92) = 1.21, p = 0.326), or their interaction (F(4, 28.92) = 0.31, p = 0.864). The random effect of player continued to exhibit substantial variance (σ² = 18.21). Model fit indices were log likelihood = −2428.89, AIC = 2452.89, and BIC = 2501.37.
By contrast, for HSR distance, significant main effects were found for both category (F(1, 29.74) = 5.83; p = 0.021) and position (F(4, 29.55) = 4.00, p = 0.010), while their interaction was not significant (F(4,29.55) = 0.59, p = 0.672); the random effect of player was also notable (σ² = 296.66; log likelihood = −1789.39, AIC = 3602.78, BIC = 3651.26). Specifically, central defender (β = −6.621, p = 0.049) and central midfielder (β = −7.051, p = 0.009) were associated with significantly lower values, whereas professional players were linked to higher values (β = 3.724, p = 0.021), which are displayed in Figure 2C. No significant interaction between position and category was observed (all p > 0.05), indicating that the influence of position remained consistent across categories, and conversely, the effect of category was stable across positions. In the case of HSR effort count, no significant main effects were identified for category (F(1, 27.91) = 0.58; p = 0.450), position (F(4, 27.73) = 2.58, p = 0.059), or their interaction (F(4, 27.73) = 0.78, p = 0.544), although player variance remained substantial (σ² = 18.02; log likelihood = −1214.84, AIC = 2453.68, BIC = 2502.17).
Finally, analyses of RS distance and RS effort revealed no significant main effects of category (F(1, 30.33) = 0.01; p = 0.899 and F(1, 28.71) = 0.23; p = 0.634, respectively), position (F(4, 30.17) = 1.57, p = 0.206 and F(4, 28.56) = 1.05, p = 0.399, respectively), or their interactions (F(4, 30.17) = 0.54, p = 0.702 and F(4, 28.56) = 0.46, p = 0.758, respectively). Nonetheless, the random effect of player continued to account for notable variance in both models (RS distance: σ² = 161.54; log likelihood = −1672.62, AIC = 3369.25, BIC = 3417.73; RS effort: σ² = 7.97; log likelihood = −1056.01, AIC = 2136.01, BIC = 2184.50), highlighting individual differences across participants.

4. Discussion

This research aims to compare the external PDs between U19 and professional football teams, considering player position. The findings of this study generally showed no notable differences in the physical variables related to PDs across different positions. However, central defenders as well as central midfielders exhibited lower HSR distance values compared to other positions. Notably, HSR distance was the only variable influenced by category, with professional players demonstrating higher values than their counterparts.
Surprisingly, and contrary to previous research—which has consistently demonstrated physical performance differences across playing positions during full matches at both professional [26,27] and youth levels [28]—positional differences in the present study emerged solely for HSR distance. This finding also contrasts with earlier studies focused on PDs [15,29], where more pronounced positional effects have typically been reported. One potential explanation for this inconsistency lies in the absence of standardized methodological frameworks across investigations. Specifically, differences in the temporal duration used to define most demanding passages (e.g., 1 min vs. 30 s or up to 10 min), along with varying approaches to controlling for intra-player variability, may undermine the comparability of our findings. In fact, as highlighted by Riboli et al. [24], high-intensity actions such as accelerations and decelerations increase significantly when analyzed over a 1 min window compared to a full 90 min match analysis in elite footballers. This also can be observed for previous research, where positional differences become more apparent when longer time windows are employed [29].
Also, our findings differ from those reported by Mandorino and Lacome [30], who compared PDs for total distance and HSR distances between professional and U19 players. Their results indicated that U19 players recorded higher peak values for high-speed running distance than professional players. However, no comparisons were made between the positions of the two categories (professional vs. U19), nor were any distinctions drawn between the roles of midfielders. This lack of positional analysis makes it difficult to identify which positions may exhibit significative differences, particularly since wide midfielders tend to outperform central midfielders in HSR [31]. Regarding the positions that did show significant differences in this comparison, both central defenders and central midfielders have also been identified in previous studies as roles where notable disparities in physical demands commonly occur [12,13].When interpreting this positional analysis, it is also important to consider the criteria used to define PDs. As highlighted in the existing literature [13,32], the number of actions classified as PDs may vary significantly depending on the player’s position and the thresholds applied. For example, PDs are often determined using a relative percentage of each player’s individual maximum, which can create discrepancies in how peak efforts are identified across different roles. These methodological differences may influence comparative analyses by position, potentially masking or exaggerating actual performance differences depending on how PDs are defined. This represents one of the potential challenges when working with worst-case scenario analyses in team sports [33].
On the other hand, previous studies comparing average physical demands between U17, U18, and U19 teams [34,35], without considering the peak values, did not record any significant differences in terms of position when compared to professional players. At least in terms of match-related physical demands, differences may arise in specific tests such as the Yo-Yo Intermittent Recovery Test (Level 1) [36]. This may be attributed to the evolving demands of the sport [4] and the enhanced physical preparation of players, who often train alongside first-team squads at these ages within professional club structures.
However, some studies have compared the weekly distribution of external load [37]. Although this load may increase based on intensity and exhibit greater variability in professional football, these comparisons did not take into account either the players’ positions or the team’s ranking situation, despite both factors potentially having a significant influence on training dynamics. In fact, a great deal of controversy can be observed when analyzing the results of these types of studies based on the training of professional teams compared to youth academies [38]. For example, in Morgans et al. [39], it was found that U18 players performed a higher number of accelerations and decelerations, and covered greater distances. When conducting this type of analysis, it is essential to remember that each team has its own specific circumstances, which must be considered both by the coach and the staff. These factors can shape both performance and playing models, potentially influencing these physical demands [40,41].
Understanding how the demands of the game vary by age and competition level is essential for the progression and development of players in youth or pre-elite categories. Since no significant differences were found between youth and professional players in their respective positions, it could be suggested that U19 players are prepared to handle the most intense periods of play at a professional level, even within 1 min windows. This physical development may be influenced by talent selection biases at earlier stages, influenced by early maturation characteristics, including the relative age effect [42]. Such factors could have a meaningful impact on the early progression toward a professional debut for these players [43].
Finally, within the limitations of this study, it is important to acknowledge that playing surfaces in youth football, even at the highest level, differ considerably from those in professional football. While natural grass is mandatory in professional competitions, many youth matches are played on artificial turf. Although no significant differences in peak demands (PDs) have been reported based on playing surface, it has been noted that fewer tackles typically occur during matches and training sessions on artificial turf compared to natural grass [7]. This may not directly affect physical performance metrics, but it could potentially influence running technique or movement efficiency [44,45]. Furthermore, the sample size of this study was relatively limited, as only one team per category was included, which makes the generalization of the findings somewhat challenging. Therefore, these results should be interpreted with caution. Although only players with a minimum of 45 min of participation were included, we acknowledge that analyzing only full-match players could offer more consistent exposure across all cases. Future research should aim to incorporate multiple teams from each category and consider stricter playing time criteria to assess whether the observed patterns are consistent and generalizable across a broader population.

5. Conclusions

In conclusion, the results suggest that although the external physical demands (PDs) of professional and U19 players are largely similar across most positions, central defenders and central midfielders appear to display lower values compared to other positions. However, it is important to consider that in professional contexts, PDs typically involve greater high-speed running (HSR) demands than in youth categories. Based on these findings, it could be suggested that elite youth players may be approaching the PDs of the competitive professional context, at least in terms of 1 min peak periods, with the exception of HSR distance.

Author Contributions

Conceptualization, J.R.M., D.M.S. and A.M.-C.; data curation, J.R.M. and J.Á.P.M.; formal analysis, A.M.-C.; investigation, J.R.M., J.Á.P.M., D.M.S. and A.M.-C.; methodology, J.R.M., E.A.-P.-C., M.B.-R. and A.M.-C.; project administration, J.R.M., J.Á.P.M. and E.A.-P.-C.; resources, J.Á.P.M., M.B.-R. and D.M.S.; software, J.R.M. and E.A.-P.-C.; supervision, E.A.-P.-C. and A.M.-C.; visualization, J.Á.P.M.; writing—original draft, J.R.M.; writing—review and editing, E.A.-P.-C., M.B.-R. and A.M.-C. All authors have read and agreed to the published version of the manuscript.

Funding

The article processing charge (APC) was funded by Fundación Alfonso X el Sabio.

Institutional Review Board Statement

Ethics committee approval was not required, as players were routinely monitored during the entire competitive season [22].

Informed Consent Statement

The participants in this study agreed to the release of data for the purpose of this research.

Data Availability Statement

The data release should be requested by sending an e-mail to the corresponding author of this article.

Acknowledgments

We would like to express our gratitude to the players, the club, and the members of the staff for their willingness to carry out the measurements and for granting access to the facilities.

Conflicts of Interest

The authors report no conflicts of interest.

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Applsci 15 07135 g001
Figure 1. Summary of the sample collected for this research.
Figure 1. Summary of the sample collected for this research.
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Figure 2. Distribution of dependent variables across position and category. (A) = Total distance (m); (B) = Accelerations (count); (C) = High-speed running distance (m); (D) = High-speed running efforts (count); (E) = Relative sprint distance (m); (F) = Relative sprint efforts (count).
Figure 2. Distribution of dependent variables across position and category. (A) = Total distance (m); (B) = Accelerations (count); (C) = High-speed running distance (m); (D) = High-speed running efforts (count); (E) = Relative sprint distance (m); (F) = Relative sprint efforts (count).
Applsci 15 07135 g002
Table 1. Performance variables in most demanding passages compared by position and categories.
Table 1. Performance variables in most demanding passages compared by position and categories.
Central Defenders Full-Backs Central Midfielders Wide MidfieldersForwards
ProfessionalsU19ProfessionalsU19ProfessionalsU19ProfessionalsU19ProfessionalsU19
EMIC-L (95%)IC-U (95%)EMIC-L (95%)IC-U (95%)EMIC-L (95%)IC-U (95%)EMIC-L (95%)IC-U (95%)EMIC-L (95%)IC-U (95%)EMIC-L (95%)IC-U (95%)EMIC-L (95%)IC-U (95%)EMIC-L (95%)IC-U (95%)EMIC-L (95%)IC-U (95%)EMIC-L (95%)IC-U (95%)
Total distance (m)184.242162.898205.586186.632165.928207.337210.644189.220232.069187.388168.836205.939197.497182.305212.690200.306185.270215.342209.121189.158229.084184.035165.231202.839195.974174.071217.878190.685164.957216.414
Accelerations (count)7.0634.6749.4527.0574.7259.3888.2415.84410.6387.1345.0739.1956.2934.5947.9916.5834.9198.2476.6864.4828.8895.5573.4587.6556.1073.6648.5494.4741.6547.294
HSR distance (m)44.95634.28255.62937.31926.85647.78259.88349.17870.58944.37435.22953.51943.18035.59650.76338.23430.86145.60858.11148.36467.85749.57240.21758.92851.28240.40062.16450.67438.29863.050
HSR efforts (count)8.5935.99111.1949.7627.21312.31011.1918.58113.8009.3007.07011.5319.1197.27110.9678.0466.2489.8459.4017.02311.77910.3328.05212.6137.4944.84110.1465.4912.4698.513
RS distance (m)17.6977.35928.03522.22612.00932.44418.2017.84228.56023.04614.36531.72719.20811.87626.54018.46311.45325.47329.37220.12438.62026.64417.62535.66422.11211.64332.58014.3292.85825.799
RS efforts (count)3.7031.3906.0154.3552.0706.6415.2272.9107.5445.7723.8317.7124.8613.2226.5014.7893.2216.3565.9173.8507.9855.3023.2857.3194.9642.6237.3052.8720.3095.435
Note: HSR = high-speed running; RS = relative sprint; EM = estimated mean; IC-L (95%) = Inferior Confidence Limit; IC – U (95%) = Upper Confidence Limit.
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MDPI and ACS Style

Rebollo Mejía, J.; Piñero Madrona, J.Á.; Alonso-Pérez-Chao, E.; Barba-Ruíz, M.; Solana, D.M.; Martín-Castellanos, A. Assessing External Peak Physical Demands in Under-19 Years and Professional Male Football. Appl. Sci. 2025, 15, 7135. https://doi.org/10.3390/app15137135

AMA Style

Rebollo Mejía J, Piñero Madrona JÁ, Alonso-Pérez-Chao E, Barba-Ruíz M, Solana DM, Martín-Castellanos A. Assessing External Peak Physical Demands in Under-19 Years and Professional Male Football. Applied Sciences. 2025; 15(13):7135. https://doi.org/10.3390/app15137135

Chicago/Turabian Style

Rebollo Mejía, Jaime, Juan Ángel Piñero Madrona, Enrique Alonso-Pérez-Chao, Manuel Barba-Ruíz, Diego Muriarte Solana, and Adrián Martín-Castellanos. 2025. "Assessing External Peak Physical Demands in Under-19 Years and Professional Male Football" Applied Sciences 15, no. 13: 7135. https://doi.org/10.3390/app15137135

APA Style

Rebollo Mejía, J., Piñero Madrona, J. Á., Alonso-Pérez-Chao, E., Barba-Ruíz, M., Solana, D. M., & Martín-Castellanos, A. (2025). Assessing External Peak Physical Demands in Under-19 Years and Professional Male Football. Applied Sciences, 15(13), 7135. https://doi.org/10.3390/app15137135

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