Sage Journals: Discover world-class research

Abstract

This study compares the physical demands between two age categories (U18 and U21) during different types of sided games (small, medium, and large) based on relative pitch area (RPA). Thirty-seven elite youth soccer players from two late-stage academy teams participated in small-sided games (SSGs) over 9 weeks. Distances covered at different speed intensities (total distance, high-speed running distance, and sprinting distance) and the number of accelerations and decelerations were analyzed. Multilevel analyses revealed significant differences in physical performance between age categories. The U21 team covered significantly more high-speed running and sprinting distances, while the U18 team performed more accelerations and decelerations. Large-sided games resulted in the greatest total distance, high-speed running and sprinting, while small-sided games produced the most accelerations and decelerations. The findings suggest age-related differences in physical performance, particularly in medium- and large-sided games, with older players covering greater distances at higher speeds. These differences may be due to biological maturation and could also be influenced by tactical behavior. This study contributes to understanding the physical performance variations among elite late-stage academy soccer players, emphasizing the impact of age category and game format. Insights into manipulating RPA are crucial for coaches to optimize training sessions and facilitate the physical transition between late-stage academy teams.

Keywords

Association football biological maturation high-speed running relative pitch area tactical behaviour youth sport

Introduction

In the last two decades, small-sided games (SSGs) have gained widespread popularity as a training format.¹ SSGs are modified games of soccer, featuring shorter durations, adjusted rules, smaller pitch sizes, and fewer players compared to traditional soccer matches.^2,3 These modified games are known for their specificity and efficiency, as their specific formats enable the replication of multi-dimensional demands of a competitive soccer match (i.e. technical, tactical, and physical).⁴ Furthermore, previous studies have shown that similar fitness and performance gains can be achieved with SSGs as with traditional interval training methods.^5,6 For these reasons, SSGs are more time efficient than solely physical training, as technical skills and tactical awareness can also be improved simultaneously.^7,8 However, the primary objective of an SSG is not always physical, depending on the context, coaches may want to focus more on tactical or technical skills. So, it is important to highlight that the benefits of SSGs depend on the specific game design. One key contextual factor to consider when designing SSGs is the age of the players.⁹

Nowadays, physical performance plays a vital role in soccer as games require players to cover large distances across a range of speeds and intensities, as well as perform repeated bouts of high-intensity efforts. In this regard, high-speed running and sprinting distances in official matches have considerably increased over the last 15 years (∼29% increase and ∼50% increase, respectively), and now represent ∼7–11% and ∼1–3% of the total distance covered during a match, respectively.^10,11 To prepare players adequately, training must replicate these match demands, emphasizing high-speed running (>19.8 km/h), sprinting (>25 km/h) and the need for accelerations and decelerations.^2,12 In this regard, SSGs can be beneficial as they create these game-like situations that include physical challenges. However, their effectiveness in replicating match demands depends on how they are designed. Some formats may prioritize technical and tactical skills while resulting in lower physical demands. Therefore, the design of the SSG must be accurate to reach those physical stimuli.

Understanding how the physical demands on soccer players change with age is important in the development of elite soccer players.¹³ Especially, considering the players’ transition from youth to adulthood, as their physical capabilities undergo significant transformations.¹⁴ A recent systematic review on training intensity in youth players has emphasized the importance of recognizing the differences in required physical outputs between late-stage academy soccer players and senior-level soccer players.¹⁵ This understanding is vital for practitioners to design tailored methods for the physical development of elite academy players, which is essential for injury prevention and overall player readiness for match demands, and transition to the senior level. As stated earlier, an effective tool in managing these physical demands is the use of SSGs, which allows practitioners to manipulate external load. Rábano-Muñoz et al.¹⁶ conducted a study comparing U17, U19 and senior players in SSGs, revealing that all age categories have different physical demands. Notably, senior and U19 players covered greater distances than U17 players. Furthermore, it suggests that age influences high-speed efforts, with older players achieving greater levels of high-velocity movement, in accordance with the literature.¹³

The physical performance in SSGs is affected by a range of task constraints and contextual factors, which together shape the external demands of these games.¹⁷ Task constraints include variables such as the number of players and pitch size.¹² For example, larger pitch sizes or a decrease in the number of players tend to result in more total distance (TD), high-speed running (HSR) and sprinting covered among players.^18,19 Conversely, when the pitch size is smaller or the number of players is increased, players tend to have more ball touches, and the prevalence of locomotor activities is generally characterized by more accelerations and decelerations.^19,20 The same is true, when pitch dimensions are held constant and the number of players is altered. This altercation changes the amount of space per player, which in turn impacts game dynamics, like game speed.²¹ In the context of training interventions involving SSGs, a crucial variable to manipulate the constraint of individual playing area, known as “relative pitch area” (RPA), expressed as square meters per player (m² × player), has been proposed. This metric combines both the pitch size and the number of players participating.^2,22 Furthermore, it is used to divide sided games by small, medium, and large. In turn, one should address these formats as small-scaled games given their focus on the individual playing area.²¹ For consistency we will summarize all the aforementioned changes in game formats under the umbrella term SSG. Recent research has demonstrated a strong correlation between RPA and physical performance variables, such as TD, HSR, and sprinting. This highlights that a larger RPA leads to increased physical demands among elite youth soccer players.²³ By decreasing the number of players, a match-derived RPA (≈357 m²) can be obtained, accurately mimicking the physical demands of an official match compared to an SSG, which represents an optimal preparation for an official match.²⁰

In summary, there is a lack of comparative research examining the physical differences between late-stage academy players. Only one study investigated similar age categories on their physical performance in SSGs.¹⁶ However, the study by Rabano-Munoz et al.¹⁶ focused only on a conditioned SSG, thus lacking exploration of other game formats. Subsequently, the current study integrates different types of SSGs, categorized into small, medium, and large based on the RPA. Therefore, this study aims to bridge the knowledge gap by evaluating the physical disparities across late academy age categories (U18 and U21) within different types of SSGs. Based on existing literature, we hypothesize that the older players (U21) will cover greater distances in higher intensities compared to the younger players (U18), especially in larger formats. Finally, this research wants to enhance the training design and provide valuable insights into understanding the readiness of youth players for the demanding transition to elite senior-level soccer.

Method

Participants

A total of 37 elite youth soccer players (18.05 years ±1.48) were involved in this study, all playing at the same elite soccer youth academy. The players provided informed consent to participate in the study and the research received ethical approval. Players were selected from both the U18 (n = 17; 16.65 years ±0.66) and U21 (n = 20; 19.24 years ±0.79) teams. The U18 players had a mean height of 178 cm (±5.4) and a body mass of 71.4 kg (±6.7), while the U21 players had a mean height of 180.5 cm (±6.2) and a body mass of 75.9 kg (±6.8). The players were assigned one playing position by the head coach. Playing positions were divided into center back (CB), full back (FB), midfielder (MF) and attacker (AT), aligning with comparable studies.^24,25 Note that, wide midfielders are grouped into attackers based on the playing style of the teams.

Research procedure

The observational study was conducted following ethical principles outlined in the Helsinki Declaration. The study was carried out over 9 weeks during the season (August–November) to ensure the minimum number of training sessions required according to an a priori power analysis, consistent with durations used in previous research.²⁶ The participants undertook their traditional weekly training routine (see Appendix A). The sessions were performed on artificial grass, at a fixed training time, limiting circadian variation's effects.

Small-, medium-, or large-sided games were categorized as SSGs and specified by RPA. Specifically, small-sided games were defined as those with an area of less than 125 m² per player, medium-sided games with an area of 125 to 225 m² per player and large-sided games with an area of over 225 m² per player.^27,28 Following recent recommendations by the review of Rumpf et al.,²⁹ RPA was calculated by dividing the total pitch area (length × width) by the number of outfield players, excluding goalkeepers. Detailed descriptions of SSGs’ characteristics were reported and can be seen in Appendix A. The frequency of SSG formats used per training session is summarized in Table 1. The SSGs were performed under the supervision and motivation of several coaches to keep up a high work rate. For the same reason, a ball was always available for prompt replacement when it went out of play, also regarding standard situations. Only formats with regular goals and goalkeepers were included, without additional modifications (e.g. floaters or mini goals). Furthermore, the offside rule was not applied during the SSGs.

Type of data

The external load was measured using a 10 Hz Global Positioning System (GPS) unit (JOHAN Sports, Noordwijk, Netherlands) with an embedded 100-Hz triaxial inertial sensor (accelerometer, gyroscope, and magnetometer). The GPS sensor, measuring at 10 Hz, appears to be valid and reliable to record position and speed in sports settings.^30,31 The measurement error of this equipment has been reported as 2.5 ± 0.41% for the total distance covered.³² However, this study is based on the V4 version of JOHAN Sports, it is important to note that the V5 version (PACER system), the version in this research used, has undergone further development, and can be considered valid for our purposes based on unpublished research.

The tracking data from the units was uploaded post-experimentally to the JOHAN Sports online analysis platform. Here, the SSGs were labeled, and GPS data was manually checked for measurement errors. The GPS sensor measured the total distance (TD), high-speed running distance (HSRD), sprinting distance (SD), accelerations and decelerations. These variables were normalized as relative distance covered per minute (m/min).

Outcome measures

The outcome measures were the physical parameters relative to the time (per minute), such as total distance (TD/min), high-speed running distance (distance covered >20 km/h (HSRD/min)), sprinting distance (distance covered >25 km/h (SD/min)), accelerations (>2 m/s² (acc/min)) and decelerations (>2 m/s² (dec/min)). The systematic review of Oliveira et al.¹⁵ revealed that these physical parameters were used as primary outcome measures across multiple studies conducted with different age categories. In turn, these parameters are chosen to enable comparability of the results.

Statistical procedures

The statistical analysis for this study was performed using SPSS Statistics (version 28.0). Initially, a descriptive analysis of means and standard deviations was conducted for physical performance variables (TD/min, HSRD/min, SD/min, acc/min, dec/min) for both U18 and U21 teams, as well as for each SSG format (small-, medium-, large-sided game) and playing position (center back, full back, midfielder, and attacker).

Longitudinal changes in physical performance over time were analyzed with multilevel modeling by means of mixed effect models. Multilevel analyses were performed with the multilevel program MLwiN 3.04 (Centre for Multilevel Modelling, University of Bristol, Bristol, United Kingdom).³³ A 2-level multilevel structure was used; level 1 represented repeated measures within the players and level 2 represented the differences between individual players. For the physical performance parameters (TD/min, HSRD/min, SD/min, acc/min, dec/min) separate random intercept 2-level models were created. Hereafter, the age group was added to the multilevel model to indicate possible differences between the 18 and U21 teams, U21 was used as the reference group. In addition, the SSG format (small-, medium-, large-sided game) was added to the multilevel model. The large-sided game was used as the reference group. These possible predictors for the multilevel models were added to investigate their influence on the physical performance parameters. The predicted variables were entered separately into the initial model; during each step, goodness of fit was evaluated by comparing the −2*Log likelihood (IGLS deviance) of the previous model. A simpler model can be rejected with a decrease in deviance and a p-value of less than 0.05. The final model fit was evaluated by analyzing the explained variance R² of the final multilevel model including the predictors, compared to the empty 2-level model.³¹ R² represents the proportional reduction in prediction error at the player level. In addition, effect sizes ƒ² related to variance explained are calculated.³⁴ Guidelines for interpretation of ƒ² indicate that 0.02 is a small effect, 0.15 is a medium effect, and 0.35 is a large effect.³⁵

The significance level was set at p < 0.05. Data visualization methods were used to represent the statistical results visually. Finally, the findings were interpreted in the context of the research aims.

Results

Descriptive statistics of the physical performance of the two age categories (U18 and U21) during three different game formats (small-, medium- and large-sided games) are reported in Table 2. Furthermore, Figures 1 and 2 provide a comparison of physical performance variables for both age categories across the game formats.

Figure 1.

Comparison of physical demands (m/min) between age categories (U18 and U21) and different sided-game formats (small, medium, and large). The mean and 95% confidence interval of total distance (Panel A), high-speed running distance (Panel B), and sprinting distance (Panel C) are shown for each subgroup.

Figure 2.

Comparison of physical demands (x/min) between age categories (U18 and U21) and different sided-game formats (small, medium, and large). The mean and 95% confidence interval of the accelerations (Panel A), and decelerations (Panel B) are shown for each subgroup.

Table 1.

Frequency of SSGs formats played per training session.

Team	Type of Sided Game	Frequency
U21	Large	6
	Medium	7
	Small	7
U18	Large	6
	Medium	5
	Small	4

Notes: Large: RPA > 225 m², Medium: RPA = 125–225 m², Small: RPA < 125 m².

Table 2.

The descriptives (mean (SD)) of physical performance during different types of game formats and playing positions.

			Total Distance (TD/min)	High-Speed Running Distance (HSRD/min)	Sprinting Distance (SD/min)	Accelerations (acc/min)	Decelerations (dec/min)
U21	Large	AT (n = 28)	134.3 (9.6)	11.2 (4.7)	2.5 (2.1)	2.5 (0.8)	2.5 (0.6)
		FB (n = 12)	122.1 (15.1)	10.9 (6.0)	3.8 (3.1)	2.3 (0.4)	2.2 (0.5)
		CD (n = 16)	125.4 (8.1)	9.5 (4.5)	2.5 (3.0)	2.1 (0.7)	2.0 (0.6)
		MF (n = 23)	140.3 (9.4)	11.1 (4.6)	1.7 (1.4)	2.4 (0.8)	2.5 (0.7)
		TOT (n = 79)	132.4 (12.1)	10.8 (4.8)	2.5 (2.3)	2.4 (0.7)	2.4 (0.6)
	Medium	AT (n = 32)	125.8 (11.5)	6.6 (3.4)	1.0 (1.0)	2.9 (0.5)	2.8 (0.5)
		FB (n = 18)	117.7 (11.3)	6.0 (2.8)	1.2 (0.8)	2.3 (0.6)	2.1 (0.5)
		CD (n = 16)	114.2 (10.2)	4.5 (2.2)	0.9 (0.9)	2.5 (0.5)	2.4 (0.3)
		MF (n = 19)	128.8 (12.9)	6.4 (4.1)	0.7 (0.8)	3.0 (0.7)	2.9 (0.6)
		TOT (n = 85)	122.5 (12.6)	6.0 (3.3)	1.0 (0.9)	2.7 (0.6)	2.6 (0.6)
	Small	AT (n = 28)	105.1 (9.5)	1.8 (1.0)	0.1 (0.1)	3.8 (0.8)	3.5 (0.7)
		FB (n = 13)	101.4 (10.4)	1.8 (1.6)	0.2 (0.4)	3.0 (0.6)	2.9 (0.7)
		CD (n = 13)	102.7 (10.9)	2.0 (0.8)	0.1 (0.1)	3.6 (0.5)	3.2 (0.7)
		MF (n = 12)	111.1 (11.9)	2.0 (1.2)	0.1 (0.2)	3.9 (0.8)	3.9 (0.7)
		TOT (n = 66)	105.0 (10.7)	1.9 (1.1)	0.1 (0.2)	3.6 (0.8)	3.4 (0.8)
U18	Large	AT (n = 24)	130.5 (8.6)	9.3 (2.4)	1.4 (0.8)	2.6 (0.4)	2.5 (0.3)
		FB (n = 18)	123.4 (8.7)	7.5 (2.8)	1.4 (0.8)	2.3 (0.3)	2.3 (0.3)
		CD (n = 12)	123.8 (7.2)	4.7 (2.0)	0.6 (0.6)	2.2 (0.5)	2.3 (0.6)
		MF (n = 31)	1130.4 (8.5)	5.9 (1.7)	0.6 (0.4)	2.2 (0.4)	2.4 (0.3)
		TOT (n = 85)	128.0 (8.5)	7.1 (2.7)	1.0 (0.8)	2.4 (0.4)	2.4 (0.4)
	Medium	AT (n = 14)	125.2 (12.8)	4.6 (4.1)	0.4 (0.8)	3.9 (0.5)	3.5 (0.7)
		FB (n = 7)	119.3 (11.8)	2.6 (1.4)	0.2 (0.3)	4.4 (1.2)	4.0 (1.0)
		CD (n = 6)	127.7 (10.4)	2.4 (0.7)	0.1 (0.2)	4.6 (0.6)	4.2 (1.0)
		MF (n = 16)	123.6 (12.5)	3.8 (2.9)	0.2 (0.4)	4.1 (0.7)	4.0 (1.0)
		TOT (n = 43)	124.0 (12.1)	3.7 (3.1)	0.3 (0.5)	4.1 (0.8)	3.8 (0.9)
	Small	AT (n = 14)	117.6 (8.7)	3.1 (1.6)	0.2 (0.3)	4.1 (0.7)	3.8 (0.7)
		FB (n = 9)	108.8 (7.7)	1.8 (0.9)	0.1 (0.2)	3.4 (0.7)	3.2 (0.7)
		CD (n = 6)	112.6 (8.9)	1.5 (0.5)	0.1 (0.1)	3.7 (0.6)	3.9 (0.8)
		MF (n = 19)	114.0 (6.7)	2.1 (1.1)	0.1 (0.1)	3.5 (0.6)	3.5 (0.4)
		TOT (n = 48)	114.0 (8.1)	2.3 (1.3)	0.1 (0.2)	3.7 (0.7)	3.5 (0.6)

Notes: CB = center back, FB = full back, MF = midfielder, AT = attacker; TOT = total, SD = standard deviation.

Multilevel analyses

The final multilevel models, separately for each physical outcome measure, are illustrated in Table 3. The five final multilevel models, including age group and SSG were significantly stronger than the empty models without predictors. Improved model fit was found by comparing the −2*Log likelihood of the models (p < 0.001). The final multilevel models showed most variation at level-1, this indicates that most variance is present within the players (between the measurements). The explained variance for the final models is calculated for all models; total distance R² = 0.38, ƒ² = 0.61, high-speed running distance R² = 0.46, ƒ² = 0.85, sprinting distance R² = 0.24, ƒ² = 0.32, accelerations R² = 0.39, ƒ² = 0.64 and for decelerations R² = 0.35, ƒ² = 0.54.

Table 3.

Multilevel model for the physical demands.

	Total Distance (TD/min)	High-Speed Running Distance (HSRD/min)	Sprint Distance (SD/min)	Accelerations (acc/min)	Decelerations (dec/min)
Fixed Effects	Estimate (SE)	Estimate (SE)	Estimate (SE)	Estimate (SE)	Estimate (SE)
Intercept	129.185 (1.699)	9.970 (0.415)	2.291 (0.154)	2.102 (0.091)	2.113 (0.090)
Age Group (Reference Group: U21)
U18	1.219 (2.328)	−2.155* (0.539)	−0.983* (0.194)	0.444* (0.116)	0.441* (0.118)
Sided Game (Reference Group: Large)
Small	−20.562* (1.153)	−6.934* (0.364)	−1.607* (0.204)	1.332* (0.084)	1.119* (0.075)
Medium	−6.787* (1.123)	−4.053* (0.355)	−1.147* (0.174)	0.924* (0.082)	0.738* (0.073)
Sided Game (Reference Group: Small)
Medium	13.774* (1.224)	2.881* (0.386)	0.461* (0.224)	−0.407* (0.089)	−0.381* (0.080)
Variance level 1	86.327 (6.355)	8.664 (0.637)	1.674 (0.142)	0.465 (0.034)	0.368 (0.027)
Variance level 2	40.428 (11.465)	1.765 (0.611)	0.119 (0.076)	0.075 (0.028)	0.089 (0.029)
Deviance
Final model	3027.165	2070.861	1063.194	877.420	792.759
Empty model	3305.224	2350.302	1154.369	1100.131	1008.783

Notes: *p < 0.05 compared to the reference group.

The models revealed that for age category significant differences were present between the U21 and U18 in sprint distance, high intensity sprint distance, accelerations and decelerations. U21 performed more HSRD and SD during the SSGs compared to the U18. In contrast the U18 performed more accelerations and decelerations compared to the U21. No significant differences were found in TD between the U18 and the U21 (p = 0.326).

Adding SSGs to the multilevel models significantly improved model fit for all physical outcome measure models. The large-sided games showed significantly more TD, HSRD and SD compared to the medium- and small-sided games. In addition, medium-sided games showed significantly higher values on these physical outcome measures compared to small-sided games. In contrast, in the large-sided games the players showed the least accelerations and decelerations. In the small-sided games most accelerations and decelerations were found. Differences between the SSGs were all significant (p < 0.05).

Discussion

This study aimed to explore potential disparities in physical performance between late-academy age categories (U18 and U21) during various SSGs. To answer our research question and related sub-questions, we conducted a comprehensive analysis of physical performance data. Our findings confirm that there is a substantial difference in physical performance between U18 and U21 teams, particularly in larger game formats, with older players covering greater distances in higher speed zones (high-speed running and sprinting) but not in total distance This indicates that late-stage academy players should regularly engage in high-intensity training sessions on larger pitch sizes to better prepare themselves for the transition to the next stage.

As outlined in the introduction, there is a lack of studies on the physical training demands of late-stage academy players and, therefore, a lack of knowledge on how intensely players need to train to be prepared for senior-level competition. Earlier studies solidified the idea that physical demands increase with age (U12 to U18) in SSGs¹⁵ as well as in match play.¹³ However, similar evidence is lacking for late-stage academy players. Our results partially confirm the findings of Rábano-Muñoz et al.,¹⁶ who examined physical performance during a conditioned-sided game (4v4 + 2 floaters, RPA = 150 m²) between elite youth categories (U17 & U19) and semiprofessional senior players. Similar to their results, we also found higher values in the older age group for high-speed running, and sprinting distances. These findings are also in line with the study by Buchheit et al.,¹³ which indicates that distances covered increase with age in general. In line with our results, no difference in total distance between the older age groups (U17 to U18) was found either Buchheit et al.¹³ The results of Rábano-Muñoz et al.¹⁶ are thus comparable to our findings in medium-sided games (RPA = 125–225 m²). It is also noteworthy that these age-related patterns in physical performance only emerged in medium and large-sided games but not in small-sided (RPA < 125 m²) ones.

The larger relative playing area (RPA) obviously affects how players perform, as larger pitch sizes and fewer included players allow more space for high-speed efforts, contributing to improved physical performance. This study shows that these physical performance disparities are more pronounced within larger RPAs (>125 m²), with older players covering more distance in both high-speed running and sprinting zones. For the number of accelerations and decelerations, differences were observed only within medium-sided games between the age categories. Specifically, the younger team (U18) performed a greater number of accelerations and decelerations than the older team (U21). This contrasts with earlier research, which indicated a higher rate of accelerations and decelerations for the older team.¹⁶

The observed differences in physical performance between age categories may be associated with various factors. Firstly, biological maturation can contribute to the observed variability, with older players maximizing their maturation advantage to achieve more high-intensity efforts.^13,36 Secondly, tactical behavior can impact players’ physical performance. Literature states that older and more experienced players are more aware of the opportunities offered in the lateral direction, possibly due to improved perceptual and cognitive skills³⁷^,³⁸ Subsequently, Olthof et al.²⁷ study demonstrated the integration of these tactical variances with different physical performances between similar age categories. The study by Olthof et al.²⁷ associated the increase in physical performance (high demanding HID and sprinting) of the older age category with the increase in inter-team distance and goalkeeper-defender distance on a large pitch. In the context of this study, the increased tactical proficiency among older players might translate into more high-intensity efforts, particularly in larger pitches where the larger space allows them to exploit the provided space. Furthermore, this greater tactical awareness may also explain why the older players performed fewer accelerations and decelerations, with younger players more likely relying on frequent reactive movements, while older players anticipate play better and therefore move more effectively.

The presented findings give a comprehensive overview of the relative external load imposed by different SSGs per age category. This information is valuable for coaches and practitioners in tailoring training sessions to specific team loads, particularly considering the differences between late-stage categories in elite soccer academies. It becomes crucial to identify these age-specific differences, so coaches can set goals to achieve the physical demands observed in older teams.²³ The load can be regulated by manipulating the RPA as increasing the RPA leads to greater demands in high-speed running and sprinting, suggesting that coaches should reduce the number of players or increase the pitch size to reach the stimulus that will develop the players’ physical performance.²⁰ However, coaches should consider that the intensity of play will not be achieved simultaneously, as the duration of play needs to be gradually increased to reach and maintain the same performance level. Therefore, by using this knowledge, coaches should design training plans aimed at progressively reaching and sustaining the desired physical load. By matching the age-appropriate physical performance, coaches can determine the optimal transition point, ensuring an effective progression for players going to the older team. This tailored approach not only optimizes physical performance but also ensures a well-informed transition between age categories in the late-stage elite academy teams.

Nevertheless, the proposal for practical implementation should not imply that the RPA must be increased for all SSGs to be played. Instead, consideration of RPA manipulation should be given to the set training goal. Large RPA game formats stress different physiological and physical components of performance compared to small RPA game formats.³⁹ The large RPA game formats can be used to maintain aerobic capacity, specifically suggesting enhancing perseverance of explosiveness (i.e. the ability to act explosively, even at the end of the match), insisting on quick recovery (i.e. the ability to make numerous sprints, even at the end of the match) and develop maximum speed over longer distances.⁴⁰ Subsequently, SSGs have different physical and physiological benefits.⁴¹ These can be used as an aerobic conditioning stimulus and contribute to the improvement of explosiveness (i.e. the ability to act quickly and explosively in a small space) and repeat-sprint ability over a short duration of time (i.e. the ability to recover quickly after an intensive sprint, to be able to achieve more sprints in a short time).⁴² Therefore, coaches should take into account that the presented findings primarily highlight the differences and recommendations for the physical performance benefits in larger formats.

From a broader perspective, it is important to consider that SSGs serve more than just physical conditioning purposes. While the present study primarily examined physical performance differences, coaches often design SSGs with technical and tactical objectives. From a technical perspective, smaller formats can emphasize quick decision-making and close ball control, whereas larger formats afford greater opportunities for passing and dribbling actions, often with more time available for execution.^21,43 Recent work by Deuker et al.⁴⁴ demonstrated that manipulating pitch dimensions changes the effective playing space, which in turn shapes the opportunities available for collective tactical behaviors. Thus, larger formats promote tactical learning opportunities that smaller formats cannot replicate. Therefore, the choice of format should always be aligned with the training objective, whether the focus is physical, technical, tactical or a combination thereof.

Limitations and future directions

The sample size of this study is sufficient to answer the main research question based on the conducted power analysis. However, this study has a relatively low sample size for potential sub-analysis by playing position. We therefore did not perform such a sub-analysis. A further limitation is that all players were recruited from a single elite academy. While this homogeneity ensured consistency, it limits the generalizability of the findings to broader academy settings, as training cultures and coaching strategies differ across academies and countries.⁴⁵ Additionally, the observational design of the study can be a limitation, as it allows for variations in the buildups to the SSGs played (e.g. number of positional games) and possibly influences players’ physical performance. This aspect should be considered when interpreting the study's findings. While our categorization in small, medium, and large SSGs was based on literature, they might not represent fundamentally different game formats. This means that other cutoff values might present a better representation. We invite future researchers to address this issue. Finally, the offside rule was not applied during SSGs. This likely increased the effective playing space compared to official matches, which may have influenced both tactical behaviors and the physical demands.

As a result, future research should first aim to increase sample sizes to allow sub-analyses by playing position and include players from multiple academies to improve generalizability of findings and account for different coaching strategies and training cultures. To reduce variability in execution, experimental designs should be standardized training sessions with conditioned-sided games, to avoid any influencing physical performance constraints such as unequal length and width ratios and playing time. In addition, incorporating rule-based conditions, such as the offside rules, would better replicate the constraints of official matches and allow clearer comparisons with competitive play. Furthermore, it is essential to conduct comparisons based on official matches to evaluate whether the physical performance differences observed in sided games are similar to those in official matches. Lastly, exploring a wider range of age categories, considering the long-term development of players, could provide more insights into the physical readiness of youth players for elite adult level soccer. In this context, future research may also examine whether younger players benefit more from a stronger technical-tactical focus, with a gradual shift towards conditioning as physical maturation progresses.

Conclusion

This study revealed physical disparities between elite late-academy soccer teams during medium- and large-sided games, with greater physical performance and higher intensities within the older team. These insights, particularly in larger RPA formats, emphasize the importance of a strategic approach to player promotion from U18 to U21 teams for optimal development.

Furthermore, this study provides an understanding of the physical load variations in different game formats across late-stage age categories, valuable for tailoring training sessions in elite academies. Coaches can set age-specific goals to match the physical demands of older teams by stepwise increasing the RPA. This approach can optimize physical development and support a well-informed transition between late-stage elite academy teams.

Supplemental Material

sj-docx-1-spo-10.1177_17479541251407273 - Supplemental material for Physical disparities in elite late-stage academy soccer players: An examination of demands across age categories in small-sided games

Supplemental material, sj-docx-1-spo-10.1177_17479541251407273 for Physical disparities in elite late-stage academy soccer players: An examination of demands across age categories in small-sided games by V.P. Meulenkamp, B.C.H. Huijgen, Y. Geurkink and M. Kempe in International Journal of Sports Science & Coaching

Footnotes

ORCID iD

V.P. Meulenkamp

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Matthias Kempe is on the Editorial Board of the International Journal of Sports Science & Coaching.

Data availability

Anonymous data can be provided on request towards the corresponding author.

Statements and declarations

The authors report there are no competing interests to declare.

Supplemental material

Supplemental material for this article is available online.

Appendix A

Table A1.

Typical weekly training content and organization for both teams.

Day	Match Day Relation	Main Training Content
Saturday	MD	Official match
Sunday	MD +1	Rest
Monday	MD +2	Technical/tactical training
Tuesday	MD +3	Physical training
Wednesday	MD −3	Physical training
Thursday	MD −2	Technical/tactical training
Friday	MD −1	Technical/tactical training

Appendix B

Table B1.

Detailed description of SSGs characteristics played by participants.

Team	Type of Sided Game	Number of Players (incl. GKs)	Length (m)	Width (m)	RPA (m²)	Duration (min)	Bouts	Rest period (min)
U18	Small	4v4	29	20	97	1:15	3 × 4	2:00
		5v5	29	20	73	1:15	3 × 4	2:00
		5v5	32	28.4	114	2:30	2 × 2	2:30
		5v5	32	30.3	121	1:15	4 × 3	2:00
	Medium	4v4	32	23.5	125	1:15	2 × 8	0:45
		4v4	31	28.3	146	1:30	3 × 4	2:00
		5v5	32	28	112	1:40	3 × 4	0:50
		5v5	32	32.4	130	1:30	15 × 1	0:30
		5v5	32	31.5	126	1:15	2 × 8	0:45
	Large	7v7	68	50.4	286	8:00	4 × 1	2:00
		7v7	78	42.4	276	7:30	4 × 1	2:00
		8v8	73	44	229	8:00	4 × 1	2:00
		8v8	70	54.1	271	8:00	1 × 4	2:00
		9v9	78	52.1	254	8:00	4 × 1	2:00
		9v9	84	52.1	274	9:00	4 × 1	2:00
U21	Small	4v4	32	18.3	97	1:30	2 × 3	2:00
		4v4	32	18.3	97	1:30	2 × 3	2:00
		4v4	34	18	102	2:00	2 × 3	1:00
		5v5	34	18	77	2:00	4 × 3	1:00
		5v5	32	18.3	73	1:30	2 × 3	2:00
		6v6	43.3	21.7	94	4:30	5 × 1	2:00
		6v6	43.4	24.7	107	3:00	6 × 1	2:00
	Medium	6v6	52.5	30.3	159	2:30	3 × 2	2:30
		6v6	65.8	30.3	199	6:30	2 × 1	2:00
		6v6	52.5	32.3	170	6:30	4 × 1	2:00
		6v6	60	30	180	6:00	2 × 1	2:00
		7v7	65.8	28.3	155	6:30	4 × 1	2:00
		7v7	61.7	40.3	207	6:30	4 × 1	2:00
		9v9	105	28.3	186	8:00	3 × 1	3:00
	Large	5v5	56.5	32	226	6:00	3 × 1	2:00
		7v7	66.5	41	227	5:30	2 × 1	2:30
		7v7	66.5	52	288	7:00	4 × 1	2:00
		7v7	105	40.3	352	6:00	2 × 1	2:00
		8v8	105	40.3	302	12:00	2 × 1	2:00
		10v10	105	68	397	11:30	2 × 1	2:00

References

Clemente

Afonso

Sarmento

. Small-sided games: an umbrella review of systematic reviews and meta-analyses. PLoS One 2021; 16: e0247067.

Hill-Haas

Dawson

Impellizzeri

, et al. Physiology of small-sided games training in football. Sports Med 2011; 41: 199–220.

Sarmento

Clemente

Harper

, et al. Small sided games in soccer–a systematic review. Int J Perform Anal Sport 2018; 18: 693–749.

Morgans

Orme

Anderson

, et al. Principles and practices of training for soccer. J Sport Health Sci 2014; 3: 251–257.

Impellizzeri

Marcora

Castagna

, et al. Physiological and performance effects of generic versus specific aerobic training in soccer players. Int J Sports Med 2006; 27: 483–492.

Hill-Haas

Coutts

Rowsell

, et al. Generic versus small-sided game training in soccer. Int J Sports Med 2009; 30: 636–642.

Gregson

Drust

. The physiology of football drills. Insight 2000; 3: 1–2.

Little

. Optimizing the use of soccer drills for physiological development. Strength Cond J 2009; 31: 1–8.

Machado

Ribeiro

Palheta

, et al.

Changing rules and configurations during soccer small-sided and conditioned games. How does it impact teams’ tactical behavior?

Front Psychol 2019; 10: 1554.

10.

Barnes

Archer

Hogg

, et al. The evolution of physical and technical performance parameters in the English Premier League. Int J Sports Med 2014; 35: 1095–1100.

11.

Bradley

Archer

Hogg

, et al. Tier-specific evolution of match performance characteristics in the English Premier League: it’s getting tougher at the top. J Sports Sci 2016; 34: 980–987.

12.

Bujalance-Moreno

Latorre-Roman

García-Pinillos

. A systematic review on small-sided games in football players: acute and chronic adaptations. J Sports Sci 2019; 37: 921–949.

13.

Buchheit

Méndez-Villanueva

Simpson

, et al. Match running performance and fitness in young soccer players. Int J Sports Med 2010; 31: 818–825.

14.

Lloyd

Oliver

. The youth physical development model. Strength Cond J 2012; 34: 61–72.

15.

Oliveira

Brito

Moreno-Villanueva

, et al. Reference values for external and internal training intensity monitoring in young male soccer players: a systematic review. Healthcare (Basel) 2021; 9: 1567.

16.

Rábano-Muñoz

Asian-Clemente

Sáez de Villareal

, et al. Age-related differences in the physical and physiological demands during small-sided games with floaters. Sports (Basel) 2019; 7: 79.

17.

Glazier

Robins

. Self-organisation and constraints in sports performance. In: McGarry

O’Donoghue

Sampaio

(eds) Routledge handbook of sports performance analysis. 1st ed. London: Routledge, 2013, pp.42–51.

18.

Aguiar

Botelho

Gonçalves

, et al. Physiological responses and activity profiles of football small-sided games. J Strength Cond Res 2013; 27: 1287–1294.

19.

Lacome

Simpson

Cholley

, et al.

Small-sided games in elite soccer: does one size fit all?

Int J Sports Physiol Perform 2018; 13: 568–576.

20.

Riboli

Coratella

Rampichini

, et al. Area per player in small-sided games to replicate the external load and estimated physiological match demands in elite soccer players. PLoS One 2020; 15: e0229194.

21.

Owen

Wong

Paul

, et al. Physical and technical comparisons between various-sided games within professional soccer. Int J Sports Med 2014; 35: 286–292.

22.

Olthof

SBH

Frencken

WGP

Lemmink

. A match-derived relative pitch area facilitates the tactical representativeness of small-sided games for the official match. J Strength Cond Res 2019; 33: 523–530.

23.

Riboli

Olthof

SBH

Esposito

, et al. Training elite youth soccer players: area per player in small-sided games to replicate the match demands. Biol Sport 2022; 39: 579–598.

24.

Bradley

Sheldon

Wooster

, et al. High-intensity running in English Premier League soccer matches. J Sports Sci 2009; 27: 159–168.

25.

Zhou

Lorenzo

Gómez

, et al. Players’ match demands according to age and playing position in professional male soccer players. Int J Perf Anal Spor 2020; 20: 389–405.

26.

Savoia

Iellamo

Caminiti

, et al. Rethinking training in elite soccer players: comparative evidence of small-sided games and official match play in kinematic parameters. J Sports Med Phys Fitness 2021; 61: 763–770.

27.

Olthof

SBH

Frencken

WGP

Lemmink

. Match-derived relative pitch area changes physical and team tactical performance of elite soccer players in small-sided soccer games. J Sports Sci 2018; 36: 1557–1563.

28.

Castillo-Rodriguez

Durán-Salas

Giménez

, et al. The influence of pitch dimensions during small-sided games to reach match physical and physiological demands on the youth soccer players. Sensors 2023; 23: 1299.

29.

Rumpf

Jäger

Clemente

, et al. The effect of relative pitch size on physiological, physical, technical and tactical variables in small-sided games: a literature review and practical guide. Front Sports Act Living 2025; 7: 1592536.

30.

Scott

MTU

Scott

Kelly

. The validity and reliability of global positioning systems in team sport: a brief review. J Strength Cond Res 2016; 30: 1470–1490.

31.

Nikolaidis

Clemente

van der Linden

CMI

, et al. Validity and reliability of 10-Hz global positioning system to assess in-line movement and change of direction. Front Physiol 2018; 15: 228.

32.

Clemente

Nikolaidis

Van Der Linden

CMIN

, et al. Effects of small-sided soccer games on internal and external load and lower limb power: a pilot study in collegiate players. Hum Mov 2017; 18: 50–57.

33.

Rasbash

Steele

Browne

, et al. A user’s guide to MLwiN v2.33. Bristol, UK: University of Bristol, 2015.

34.

Snijders

TAB

Bosker

. Multilevel analysis. An introduction to basic and advanced multilevel modeling. 2nd ed.

London:

Sage Publications Inc, 2011.

35.

Cohen

. A power primer. Quantitative methods in psychology. Psychol Bull 1992; 112: 155–159.

36.

Radnor

Staines

Bevan

, et al. Maturity has a greater association than relative age with physical performance in English male academy soccer players. Sports (Basel) 2021; 9: 171.

37.

Olthof

SBH

Frencken

WGP

Lemmink

. The older, the wider: on-field tactical behavior of elite-standard youth soccer players in small-sided games. Hum Mov Sci 2015; 41: 92–102.

38.

Williams

. Perceptual skill in soccer: implications for talent identification and development. J Sports Sci 2000; 18: 737–750.

39.

Zlojutro

Eler

Joksimovic

, et al. Kinematic parameters and metabolic power in elite soccer players: a small sided a large sided games comparison. Front Physiol 2023; 14: 1150713.

40.

Praça

Chagas

Bredt

SDGT

, et al. Small-sided soccer games with larger relative areas result in higher physical and physiological responses: a systematic and meta-analytical review. J Hum Kinet 2022; 81: 163–176.

41.

Clemente

Praça

Aquino

, et al. Effects of pitch size on soccer players’ physiological, physical, technical, and tactical responses during small-sided games: a meta-analytical comparison. Biol Sport 2023; 40: 111–147.

42.

Owen

Wong

Paul

, et al. Effects of a periodized small-sided game training intervention on physical performance in elite professional soccer. J Strength Cond Res 2012; 26: 2748–2754.

43.

Rodrigues

MCJ

Figueiredo

De Lira

CAB

, et al. Cognitive processes in small-sided games (procesos cognitivos en pequeños juegos). Retos (Madrid) 2022; 44: 897–906.

44.

Deuker

Wittkugel

Dublin

, et al. Beyond the pitch: unveiling the concave hull as soccer’s ecological niche in practice design. Res Q Exerc Sport 2024; 96: 463–474.

45.

Lazic

Clarke-Williams

Munafò

. What exactly is ‘N’ in cell culture and animal experiments? PLoS Biol 2018; 16: e2005282.