Energy cost and game dynamics in soccer: comparing sided games and repeated sprint training

Abstract

Background

This study aims to propose an alternative solution for determining the optimal training load to meet the high physiological demands of football, encompassing both metabolic and locomotor loads. To this end, two different formats, repetitive sprint training (RST) and sided games (SGs), were evaluated in terms of energy cost (EC), and their similarities were revealed.

Methods

The study was conducted with 21 men soccer players (age: 18.24 ± 0.63 yrs, training age: 7.24 ± 0.63 yrs, body weight: 73.01 ± 7.47 kg). RST and various-sided games (4 × 4 small-sided games and 6 × 6 medium-sided games) were played according to the rules, with goalkeeper and ball possession rules tailored to different field dimensions. Both training methods were performed separately on matchday + 4 / -3 within a weekly cycle. While all SGs work for 420 s and rest for 4 min, RST performed 720 m. with 20 s rest between bouts and 4 min between sets. EC was measured using global positioning system technology during all training sessions, considering total and equivalent running distance.

Results

The study demonstrated that the 4v4 + Goalkeeper SSG and RST energy cost results EC cost results in similar conditioning improvement (5917.76 J/kg and 6181.00 J/kg, respectively).

Conclusion

As a result of the study, 4v4 + Gk SSG showed similar results regarding energy cost with RST. Additionally, it was determined that metabolic demands increased as the number of players decreased in SGs with constant m² per player.

Introduction

Soccer, a sport that continuously evolves tactically, has become characterized by increased high-intensity running, accelerations, and decelerations, as shown in various match and training analyses [1–9]. Performance analyses indicate that elite soccer players cover approximately 9–14 km during a 90-minute match. Notably, around 30–60% of this distance is covered through walking, jogging, and low-to-moderate intensity activities, while high-intensity running and sprints account for about 10% of the total distance. These figures can vary based on factors such as the league, level of play, player positions, and match circumstances (e.g., result, red cards) [1, 2, 10, 11]. It has also been observed that player’s maximum oxygen consumption (VO2max) reaches about 70% during a match, highlighting the high level of aerobic capacity required [1, 2, 6, 12]. Considering the match duration and the activities performed during the match, it is noted that while aerobic energy pathways are utilized, anaerobic energy sources are also required [1, 2, 6, 13].

Recent research has questioned the adequacy of traditional GPS-based methods for assessing external training load, which typically rely on distance covered and time spent in predefined speed zones. Such approaches fail to capture the full physiological demands of soccer-specific movements, particularly those involving frequent accelerations and decelerations. Unlike steady-state running, these dynamic actions significantly increase energy expenditure, even at relatively low speeds [14]. In this context, Gaudino et al. reported that the mean energy cost (EC) during training was approximately 25 kJ·kg⁻¹, whereas match-play values reached ~ 60 kJ·kg⁻¹. When converted to metabolic power (MP), the average was ~ 7.5 W·kg⁻¹, with central midfielders displaying higher MP values (~ 8.5 W·kg⁻¹) than attackers (~ 7.7 W·kg⁻¹), despite comparable total EC. This suggests that central midfielders engage in more frequent high-intensity actions. Furthermore, when high-intensity effort was defined via MP thresholds (> 20 W·kg⁻¹) rather than speed thresholds (> 14.4 km·h⁻¹), a greater portion of total distance was classified as high-intensity (19% vs. 13%) [14]. These findings imply that speed-based measures may underestimate high-intensity demands by ~ 6%, reinforcing MP as a more sensitive and comprehensive indicator of physical load [15]. In addition to these insights, Osgnach et al. (2010) reported that the energy cost of high-intensity activities during match-play may be two to three times greater than estimates based solely on speed-based analysis [16].

Di Prampero et al. introduced a biomechanical model demonstrating that accelerated running on flat terrain is equivalent to uphill running, while deceleration mirrors downhill running [17]. In this model, the concept of “equivalent slope” (ES) is calculated based on the athlete’s body angle during acceleration (ES = tan(90– α)), and the “equivalent mass” (EM) reflects the additional muscular force required during acceleration (EM = g’/g) [17].

Building on this, Minetti et al. formulated an equation to estimate EC based on terrain slope. Savoia et al. later adapted this model for football-specific contexts, determining that the EC during constant-speed running on a football pitch was 4.66 J·kg⁻¹·m⁻¹.³⁰ The corresponding EC equation was refined to account for these conditions, and metabolic power (P) was then calculated as the product of EC and running speed (P = EC × v) [18].

In terms of total EC, Osgnach et al. calculated an average value of 61.12 ± 6.57 kJ·kg⁻¹ (14.60 ± 1.57 kcal·kg⁻¹) per player over a 90-minute match, with over 26% of the total EC attributed to high-MP efforts [16]. Complementing this, the concept of equivalent distance (ED) has been proposed to translate the total energy expended into a theoretical distance that would be covered at constant velocity on grass [14, 16]. This measure offers a clearer reflection of total workload by considering the variable energetic demands of different in game activities.

Although physiological (e.g., heart rate, blood lactate) and kinematic (e.g., speed, acceleration) indicators are widely used in monitoring soccer training loads [19–22] the combined application of MP and ED remains limited in the literature [14]. Integrating MP (metabolic load) and ED (locomotor load) provides a more complete picture of the physical demands of soccer. Both metrics respond sensitively to sudden, high-intensity movements such as accelerations, making them especially valuable for real-time load monitoring.

To address these demands, coaches implement various training methods to improve both physiological capacity and performance efficiency. One widely used method is Repeated Sprint Training (RST), involving short bursts of maximal or near-maximal effort followed by recovery periods. This method has been shown to enhance VO₂max in soccer players [23, 24]. To address these demands, coaches implement various training methods aimed at enhancing both physiological capacity and performance efficiency. Among these, Repeated Sprint Training (RST) which consists of short bursts of maximal or near-maximal effort interspersed with recovery periods, has been widely used and shown to improve VO₂max in soccer players [23, 24]. In addition, coaches frequently incorporate game-based formats such as small-sided games (SSGs), medium-sided games (MSGs), and large-sided games (LSGs), which simultaneously target physical conditioning alongside technical and tactical skill development [4].

Although small-sided games (SSG) and running-based training (RBT) differ in structure, several studies have compared these modalities in terms of aerobic fitness, especially VO₂max improvements [25, 26]. These studies typically ensured equal training duration across conditions to allow a fair comparison. While most previous research has focused on aerobic adaptations, the current study aims to compare the two formats based on energy cost (EC), providing a novel perspective on the physiological demands of each training type.

Therefore, the aim of the present study is to compare energy cost (EC), total running distance, and equivalent running distance among young male soccer players during repeated sprint training (RST), small-sided games (SSGs), and medium-sided games (MSGs).

Materials and methods

Participants

A total of 21 male soccer players (Fullback: 4 players, Centre back: 4 players, Center midfield: 6 players, Winger: 4 players, Center forward: 3 players) participated in the study. All participants were actively competing in the national elite U19 league. The players had an average age of 18.24 ± 0.625 years, an average training experience of 7.24 ± 0.63 years, and a mean body weight of 73.01 ± 7.47 kg, representing a sample appropriate for assessing performance-related variables.

To determine the appropriate sample size, G*Power software (version 3.1.7) was used for a repeated-measures analysis of variance (ANOVA) within factors [24]. The following parameters were applied: effect size, 0.30; alpha error, 0.05; power, 0.85; number of conditions, 5; number of measurements, 5; correlation between measures, 0.5; and nonsphericity correction. Based on this calculation, a minimum of 20 participants were required for the study, and a total of 21 participants were included.

Written informed consent was obtained from all participants after a detailed explanation of the study’s objectives and protocol. The study adhered to the ethical principles outlined in the Declaration of Helsinki and was approved by the Akdeniz University Clinical Research Ethics Committee (Approval Date: 20/04/2022, Protocol No.: 245).

Study design

Repeated Sprint Training and SG sessions were incorporated into the weekly soccer training program in alignment with match schedules. Each participant performed only one session per week, scheduled around + 4 / -3 days from match day. This spacing minimized fatigue or learning effects that could cause order effects.Each session included in the study began with a 10-minute dynamic warm-up, following the team’s regular training routine.

The study was designed as a block randomized controlled trial involving 21 participants. This random assignment was conducted using software for randomization (Research Randomizer) that assigned randomly the players before the initial assessment, ensuring equal chances of group placement for each player. The participants were administered four interventions (4v4 + Goalkeepers SSG, 4v4 Ball Possession SSG, 6v6 + Goalkeepers MSG, 6v6 Ball Possession MSG), with training methods were performed on designated physical performance training days (match + 4, −3) within the weekly cycle.

Small-sided games (SGs) such as 4v4 and 6v6 have playing areas determined by players per square meter (m²/player) following established literature [27], with game durations set accordingly [28]. In this study, the area per player was standardized at 271 m²/player across formats to minimize total field size while preserving relative spatial proportions. Nonetheless, despite equivalent area per-player, different formats may exhibit distinct tactical and spatial dynamics affecting movement patterns and energy cost (EC). Goalkeepers were excluded from area calculations in SGs with keepers.

As the players regularly performed small-sided games and repeated sprint training during their usual in-season training sessions, no additional familiarization phase was required prior to study.

Repeated sprint training

Players were instructed to sprint 40 m at maximum speed and then rest passively for 20 s at the finish line before sprinting back at maximum speed upon signal. Repeated Sprint Training comprised 3 sets, with 6 repetitions per set and 4 min of passive rest between sets [23, 29, 30].

40 m × 6 repetitions × 3 sets = 720 m. The total duration of RST was 7 min.

Sided games

In this study, all players participated in the small-sided game formats without being assigned to fixed positional roles. The absence of positional constraints allowed players to freely move and engage in various areas of the pitch. This design choice aimed to isolate the effects of the game formats on players’ physical and metabolic responses, minimizing potential confounding influences arising from positional demands.

In possession-based SGs, participants played without adhering to specific positions. In goalkeeper-inclusive SGs, players followed their assigned positions. Table 1 provides an overview of the player count, game format, playing area, and game duration in SGs. The total duration of both SGs and RST was identical (7 min). The entire process of study is presented in Fig. 1.

Table 1.

Details of sided games

Player Count and Format	Playing Area (m2/player)	Duration
4v4 + Goalkeepers	62 m × 35 m = 271 m2/player	3 sets × 140 s × 4 min rest
4v4 Ball Possession	62 m × 35 m = 271 m2/player	3 sets × 140 s × 4 min rest
6v6 + Goalkeepers	72 m × 45 m = 270 m2/player	2 sets × 3.5 min × 4 min rest
6v6 Ball Possession	72 m × 45 m = 270 m2/player	2 sets × 3.5 min × 4 min rest

Fig. 1.

Flowchart of research

Collection of performance data

Player movement data were collected using GPEXE LT GPS devices (Exelio Srl, Italy), which operate at a 10 Hz sampling rate via a u-blox NEO-M8Q GNSS receiver with multi-constellation support (GPS, GLONASS, Galileo) to enhance signal stability and accuracy. Under optimal conditions, the system reports a horizontal accuracy of approximately 2.5 m, velocity accuracy of ± 0.05 m/s, and heading accuracy of ± 0.3°. Key performance metrics are derived from carrier-phase Doppler shift rather than positional fixes, ensuring improved precision during high-intensity movements typical in team sports. The validity and reliability of this class of devices have been confirmed in previous studies, showing inter-unit variability below 1% for distance and ~ 0.1–0.3 m/s for instantaneous velocity [31–33].

This system, adapted for soccer players and grass surfaces, utilized Di Prampero’s metabolic EC approach based on previous study of Minetti et al. [34] Energy cost of walking and running at extreme uphill and downhill slopes. Journal of applied physiology) and modified by Savoia for to more accurately calculate the cost of energy in soccer players on grass surface [35].

The GPS system calculated EC separately for walking and running and then determined the total EC using the following equations:

Energy Cost of Running (ECr) Equation = 30.4 × 4–5,0975 × 3 + 46.3 × 2 + 17.696x + 4.66

In this equation, “x” represents the slope of terrain, while 4.66 is the energy cost of a constant speed running on grass and is expressed j.kg-1.m-1 [35]. The result of the equation is expressed as j.kg-1.m-1 [35].

Energy Cost of Walking (ECw) Equation = 280.5 · i5–58.7 · i4–76.8 · i3 + 51.9 · i2 + 19.6 · i + 2.5

In this equation, “i” corresponding to the slope of terrain, while 2.5 is the energy cost of walking at the optimal speed on compact, flat terrain j.kg-1.m-1 [18].

The integration of metabolic power and kinematic metrics allows for an efficient integrated measurement of acceleration and velocity, providing better indication of the physiological effort soccer players perform during trainings and matches.

In order to calculate MP, the researchers used the following formulas:

The “EC” represents the energy cost of running uphill on an incline derived from the running acceleration vector and “v” represents the instantaneous velocity.

Equivalent Distance (ED) indicates the total energy expended by an athlete during a match, which is essentially the running distance equivalent to running at a constant pace on grass [18].

Data analysis

The effect sizes were assessed using partial eta squared (η2) as trivial (η2 = 0.10), moderate (η2 = 0.25–0.39), or large (η2 = 0.40). with Bonferroni post-hoc analysis and Cohen’s d Ess were interpreted as trivial (0.00–0.19), small (0.20–0.59), moderate (0.60–1.19), large (1.20–1.99), and very large (≥ 2.00). Absolute reliability was interpreted from the upper 95% confidence interval for the CV (CV + 95) interpreted as ≥ 15%, 10–15%, 5–10%, and ≤ 5% that were considered to represent poor, moderate, good, and excellent absolute reliability, respectively [36].

The mean ± standard deviation of the collected data was calculated, and the Shapiro–Wilk test was applied to assess normal distribution. To compare EC between RST and the four SG conditions (4v4 and 6v6, both with goalkeepers and ball possession formats), a repeated measure ANOVA with Bonferroni post-hoc test was employed. Partial eta squared (η²) values were computed to determine effect size (ES) in variance analysis. The effect sizes were assessed using partial eta squared (η2) as trivial (η2 = 0.10), moderate (η2 = 0.25–0.39), or large (η2 = 0.40). All statistical analyses were performed using the software package SPSS (version 22.0), and significance was set at p <.05.

Results

Table 2 shows a significant difference in time-dependent changes in repeated measurements for the EC value (F(4;80) = 19.68 partial η² = 0.50 (large), p =.001).

Table 2.

EC incurred by players during RST and SSG

SG	SG Total EC (j/kg)	p	RST Total EC (j/kg)
4v4 + goalkeeper SSG	5917.76 ± 96.23	0.15	6181.00 ± 13.54
4v4 ball possession SSG	5788.14 ± 110.21	0.02*
6v6 + goalkeeper MSG	5141.95 ± 114.43	0.001*
6v6 ball possession MSG	5570.71 ± 110,18	0.001*
F(4;80) = 19.68 η2 = 0.50, p =.001*

*p <.05

EC: Energy Cost, SG: Sided Game, RST: Repeated Sprint Training, η²: Partial Eta Squared

Figure 2 shows that EC value was significantly different from RST in all SGs variations except 4v4 + goalkeeper SSG. Total EC values for SGs and RST for all SGs conditions are presented in Table 2. The highest EC value was determined during 4v4 + goalkeeper SSG and this value was found to be similar to the value obtained during RST (p >.15). The lowest EC value was determined during 6v6 + goalkeeper MSG.

Fig. 2.

Energy cost during different sided games and repeated sprint training

(*p <.05, ***p <.001)

Bonferroni post hoc analysis showed that EC were similar between 4v4 + goalkeeper and 4v4 ball possession games (p = 1.00) and 4v4 ball possession and 6v6 ball possession games (p =.83) but were significantly lower 6v6 + goalkeeper than 6v6 ball possession games (p =.01), and the effect size of the difference (d = -3.82) was very large and lower 4v4 + goalkeeper than 6v6 + goalkeeper games (p =.001), and the effect size of the difference (d = 7.34) was very large.

Table 3 shows a significant difference in time-dependent changes in repeated measurements for the total running distance value (F(4;80) = 118.18, η² = 0.85 (large), p =.001).

Table 3.

Total running distances covered by players during sided games and repeated sprint training

SG	SG Total Running Distances (m)	p	RST Total Running Distance (m)
4v4 + goalkeeper SSG	1132.52 ± 16.43	0.001*	720
4v4 ball possession SSG	1084.19 ± 17.33	0.001*
6v6 + goalkeeper MSG	1040.90 ± 21.02	0.001*
6v6 ball possession MSG	1102.76 ± 18.16	0.001*
F(4;80) = 118.18, η2 = 0.85, p =.001*

*p <.05

SSG: Small-sided Game, RST: Repeated Sprint Training, η²: Partial Eta Squared (Effect Size)

When the results are evaluated in terms of total distance covered, it is seen that the running distance obtained in all SG conditions is significantly different from that obtained during RST (p <.05). It was determined that the running distances decreased with the increase in the number of players in the games where the goalkeeper was added (p <.05). The running distance values ​​obtained in each SG situation were significantly greater and different from the value obtained during RST (p <.05) (Table 3).

Total running distances were similar between 4v4 + goalkeeper and 4v4 ball possession games (p =.54), between 6v6 + goalkeeper and 6v6 ball possession games (p =.45) and between 4v4 ball possession and 6v6 ball possession games (p = 1.00) but were significantly lower 66v6 + goalkeeper than 4v4 + goalkeeper (p =.01), and the effect size of the difference (d = -4.85) was very large.

Table 4 shows a significant difference in time-dependent changes in repeated measurements for the equivalent running distance value (F(4;80) = 22.58, η² = 0.53 (large), p =.001).

Table 4.

Equivalent total running distance covered by players during sided games and repeated sprint training

SG	SG Equivalent Running Distance (m)	p	RST Equivalent Running Distance (m)
4v4 + goalkeeper SSG	1359.86 ± 82.58	0.01*	1423.00 ± 15.78
4v4 ball possession SSG	1318.62 ± 102.02	0.001*
6v6 + goalkeeper MSG	1201.67 ± 98.55	0.001*
6v6 ball possession MSG	1295,67 ± 95,68	0.001*
F(4;80) = 22.58, η2 = 0.53, p =.001*

*p <.05

SG: Sided Game, RST: Repeated Sprint Training, η²: Partial Eta Squared (Effect Size)

The maximum equivalent running distance value, as in the total running distance values, was determined in the 4v4 + goalkeeper SSG and was found to be significantly lower than the value obtained during the RST (p <.05). The equivalent running distance value obtained in all SG conditions was determined to be significantly lower than the RST equivalent running distance values (Table 4).

Equivalent total running distances were similar between 4v4 + goalkeeper and 4v4 ball possession games (p = 1.00) and 4v4 ball possession and 6v6 ball possession games (p = 1.00), but both were significantly lower 6v6 + goalkeeper than 6v6 ball possession (p =.001), and the effect size of the difference (d = -0,97) was moderate and both were significantly lower 6v6 + goalkeeper than 4v4 + goalkeeper (p =.001), and the effect size of the difference (d =-1.74) was large.

Table 5.

Coefficient of variation (± 95% CI) for each outcome measure within each condition

SG	CV (± 95% CI)
	Total Enerji Cost (j/kg)	Total Running Distances (m)	Equivalent Running Distance (m)
4v4 + goalkeeper	7.5 (5717.03 to 6118.50)	6.6 (1098.25 to 1166.80)	6.1 (1322.27 to1397.45)
4v4 ball possession	8.7 (5558.26 to 6018.03)	7.3 (1048.05 to 1120.33)	7.7 (1272.18 to 1365.06)
6v6 + goalkeeper	10.2 (4903.25 to 5380.65)	9.3 (997.06 to 1084.75)	8.2 (1156.81 to 1246.53)
6v6 ball possession	9.1(5340.90 to 58.00.52)	7.5 (1064.88 to 1140.64)	7.4 (1252.11 to 1339.22)
RST	1.0 (6152.75 to 6181.00)	-	1.1 (1415.82 to 1430.18)

SG: Sided Game, RST: Repeated Sprint Training

Average intra-individual variability is presented in Table 5

Discussion

This study aimed to compare the EC during RST and various SGs in young soccer players. To our knowledge, this is the first study to directly compare EC during RST and SGs using GPS-based data in soccer. For this purpose, total running and equivalent running distances were evaluated in RST and different SG formats. The results show that the EC increases with the number of players (6v6 ball possession games). This result is similar to previous studies [37]. However, unlike previous studies [37, 38] interestingly, with the inclusion of goalkeepers in the games, the more EC was determined with fewer players (4v4 + goalkeeper) than with the other (6v6 + goalkeeper). This difference may be because area sizes are kept constant across all SGs.

The game’s acceleration and deceleration increase as the number of players decreases, raising the EC [39]. In addition, the fact that such games impose a mechanical load similar to official match conditions [40] may also impact the EC.

Studies in the literature have shown that extended field games have higher metabolic demands than competitions, and that average pulse, oxygen consumption and blood lactate accumulation are higher [41, 42]. However, it has also been observed that football players cover more distance in total and run more intensely in competitions [16, 18, 42]. Therefore, when determining the field dimension for SSG, MSG, and LSG, the meter square per player approach is more appropriate in terms of conditional motoric characteristics and technical aspects [28, 43]. In our study, the amount of field per player was fixed at the same level in all formats (271 m²/number of players). These dimensions are to minimize the dimensions of the field where official matches are played and to change the amount of space per player to the same limit ratio as the official match field.

Research has shown that ball possession games in SGs result in higher EC compared with games with goalkeepers [39]. These studies have indicated that as the playing area and the number of players increases, EC also rises. Moreover, certain studies suggest that SGs can impose higher metabolic demands than official matches, with elevated heart rate, oxygen consumption, and blood lactate concentrations [18, 42]. However, it was also observed that players cover more total distance and engage in more high intensity running in official matches than in SGs [16, 18, 42].

In this study, the field sizes for all games were kept constant, and it was found that increasing the number of players resulted in increased EC (Table 1). Gaudino et al. also demonstrated that the inclusion of goalkeepers in games reduced EC. However, in their study, field sizes were not fixed, and as the number of players increased, the area per player also increased [37] Sarmento et al. emphasized that larger field sizes are better suited for replicating the physical demands of soccer matches [38].

The study also compared the total running distances covered by players in different SGs. No significant differences in total running distance were found between 4v4 and 6v6 games, regardless of whether goalkeepers were involved. However, when comparing the total running distance between the 4v4 and 6v6 ball possession games, no significant difference was observed. Notably, soccer players covered more distance in the 4v4 + goalkeeper game than in the 6v6 + goalkeeper game. This may be due to the decrease in the number of players in the 4v4 + goalkeeper game.

No recent studies compare different SGs in terms of equivalent running distance. Based on the total equivalent running distance covered by players during various training sessions in our study, it can be suggested, as highlighted in the literature, that the use of equivalent distance is a more appropriate method for determining training load in SGs [14, 44]. In our study, although the total distance covered during RST was 720 m, the EC was equivalent to a running distance of 1423 m. This value is approximately double the designated training distance. While no significant difference in EC was found between RST and 4v4 + goalkeeper SSG, the equivalent running distance was higher during RST compared with all SGs. Therefore, using an equivalent running distance as a method for determining locomotor exercise load could provide trainers with a different perspective for managing and planning training intensity.

According to our study results, a similar conditioning improvement achieved in RST can be obtained from the 4v4 + goalkeeper SSG in terms of EC. This would enable players to develop both their physical performance and their technical and tactical skills simultaneously. Furthermore, when determining training intensity in soccer, it is recommended that coaches prioritize equivalent running distance rather than total running distance derived from GPS analysis.

In this study, players participated in all training formats under free role conditions rather than fixed positions. Therefore, no position-specific analysis was conducted, and all players were evaluated with equal movement opportunities.

In this study, training sessions were consistently scheduled relative to match days (+ 4 and − 3). However, potential influences of external factors such as environmental conditions (e.g., temperature, humidity), accumulated fatigue, and timing within the training week should be considered when interpreting the findings. All sessions were conducted at the same time of day, under similar weather conditions, and with a standardized warm-up protocol to minimize such effects as much as possible.

Although group mean values were used for comparison, this approach does not fully account for within-player or session-to-session variability—factors that may influence the interpretation of EC-based training recommendations. These sources of variability could be addressed in future studies to provide more individualized insights.

One limitation of the present study is that it was conducted with a relatively small sample of 21 male players from a single team, which limits the generalizability of the findings. Future research involving larger and more diverse samples, including different teams, age groups, and genders, is warranted to further validate and extend these results.

It is important to acknowledge that neuromuscular, psychological, and technical loads were not assessed in the current study, which constitutes a limitation. While our primary focus was on comparing running distance metrics as measurable indicators of external load, these additional factors may also significantly influence overall training demands. Therefore, future studies should incorporate comprehensive evaluations of neuromuscular, psychological, and technical loads to provide a more holistic understanding of training load and its effects.

Conclusions

In this study, EC, total running distance, and equivalent running distance during acute RST and SGs were compared using GPS analysis with current calculation formulas. The results showed that the EC value was similar during RST and 4v4 + goalkeeper SSG and greater than all other SG conditions. Additionally, while the total running distance in various SGs was greater than that in RST, the equivalent running distance was found to be lower in SGs.

Future research could focus on the chronic effects of these two training methods on the measured parameters. Studies involving female soccer players, different age groups, and athletes at various training levels could offer valuable insights into the effectiveness of using EC data during soccer training.

Finally, investigating the training load imposed by soccer training and matches on metabolism is essential for providing practitioners with important information to enhance and maintain player performance. This study, which uses a GPS analysis system to assess EC and equivalent running distance in different soccer training sessions, offers a significant reference for future research.

Author contributions

Conceptualization, A.Ş; E.A.; E.Ç.Ö.; methodology, A.Ş.; E.A.; validation, E.A.; A.Ş.; E.Ç.Ö.; investigation, E.A.; A.Ş.; data curation, E.A.; A.Ş.; E.Ç.Ö.; writing—original draft preparation, A.Ş.; E.A.; writing—review and editing, A.Ş.; E.A.; and E.Ç.Ö.; visualization, A.Ş.; supervision, A.Ş.; E.A.; and E.Ç.Ö. All authors have read and agreed to the published version of the manuscript.

Funding

No funding was received to conduct this research.

Data availability

Data are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Written informed consent was obtained from all participants after a detailed explanation of the study’s objectives and protocol. Children under 18 years of age and their legal representatives completed written informed consent forms, respectively. The study adhered to the ethical principles outlined in the Declaration of Helsinki and was approved by the Akdeniz University Clinical Research Ethics Committee (Approval Date: 20/04/2022, Protocol No: 245).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.