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Abstract

This study investigated the effects of match location, quality of opposition (classified into: strong [1st to 4th position]; intermediate [5th to 15th position]; weak [16th to 20th position]), and match outcome on the match running performance of starters and non-starters from a top elite Brazilian soccer team. Absolute measures were calculated using total distance, high-speed (19.8–25.2 km·h⁻¹), sprinting (≥ 25.2 km·h⁻¹), total distance high-acceleration (> 2m·s²), and deceleration (< −2m·s²) were recorded by GPS units from a sample of young soccer players (N = 25) in a total of 17 matches. Relative measures were calculated by dividing absolute measures by the total duration of the matches. Non-starters covered greater total distance (p = 0.02), sprinting (p = 0.02), high-acceleration and deceleration (p = 0.04), sprinting distance relative per minute played (p = 0.005), and high-acceleration and deceleration relative per minute played (p < 0.001) when the team plays at home, strong opponents, and wins the matches. Starters covered greater total distance in high-speed running (p = 0.04), high-acceleration and deceleration (p = 0.03), and high-speed running relative per minute played (p = 0.04) when the team plays strong opponents and wins the matches. These findings highlight the impact of contextual factors during matches on the locomotor performance of young soccer players.

Keywords

Team sports external load physical performance GPS sports science

Introduction

Match analysis is crucial for researchers and practitioners in sports.¹ In soccer, the integration of different technologies (e.g. global positioning systems (GPS) and microtechnology) is a useful aid in understanding the external loads of training and matches, including distance covered at various speeds and acceleration/deceleration measures.² However, soccer match play demands can vary considerably due to the various potential constraints (e.g. match location, quality of opposition, and match outcome) that can also affect running performance.^3–6 However, previous research on youth soccer has overlooked the possible impact of contextual factors on running outputs during the matches.⁷

Over the recent years, there has been an increasing interest in exploring the relationship between contextual factors and the locomotor demands in soccer.⁸ For example, consider professional soccer player covering a more total distance, sprinting distances, and a greater number of sprints when the team plays at home,^9,10 playing against intermediate and strong opponents,¹¹ or winning the match.¹² However, further research is needed to investigate other factors that may influence physical demands, including match players’ participation (i.e. starters vs non-starters),¹³ congested fixture,¹⁴ tactical behaviors,¹⁵ and match style adopted by the coach.¹¹ Notably, there is a lack of studies focusing physical demands in elite youth soccer players, particularly regarding the effects of their participation status on locomotor performance.¹⁶ Therefore, it is essential to gather empirical evidence to ascertain the significance of these factors regarding the locomotor and mechanical demands experienced by players.

Several studies compare the physical demands between starters and non-starters throughout the season and matches.^17–19 For instance, a previous study conducted monitoring training analysis on junior soccer matches in elite domestic leagues revealed that players who started a match exhibited larger physical demands (i.e. total distance, high-speed running, and sprinting distances) than non-starting players, and this difference is mainly caused by accumulated playing time.²⁰ In contrast, Giménez et al.²¹ demonstrated that non-starter professional soccer players covered large distances at speeds ranging from 3.3 to 6.9 m·s⁻¹ compared to starters. Thus, it seems that current literature has drawn attention to comparing physical performance between starters and non-starters. However, it appears that there is a gap between understanding how contextual factors influence different levels of match participation.¹³ These findings have practical implications as they can assist coaches and technical staff in optimizing the planning of their weekly routines.

To date, no previous studies have explored the match running performance of young soccer players from South American countries regarding the influence of contextual variables such as quality of opposition, match location, and outcome. Furthermore, to the best of our knowledge, studies have predominantly focused on players in lower age categories (i.e. U-14) and have been conducted in various countries worldwide.^22–24 Therefore, there is a need for additional research to compare data across different players profiles and leagues, enhancing our understanding to the implications of talent development in soccer. Therefore, this study aimed to investigate the effects of match location (home vs away), quality of opposition (weak vs intermediate vs strong), and the match outcome (loss vs draw vs win) on the match running performance of starters and non-starters from a top elite Brazilian soccer team. The hypothesis posits that contextual factors can influence the running performance of U20 youth players.

Methods

Observational design

Distance and accelerometry variables were monitored throughout the entire 2020–2021 edition of the U20 Brazilian National League. The League comprised 20 soccer clubs participating in-home or away matches, totalizing 19 matches during the “group” phase. The top four teams engaged in a knockout stage consisting of semi-finals and a final. The reference team under observation finished within the top-5 of the final classification. Two matches were excluded due to the data noise. Therefore, 17 matches were fully analyzed. This study investigated the influences of match location (home vs away), quality of opposition (three clusters: strong [1st to 4th position] vs intermediate [5th to 15th position] vs weak [16th to 20th position]), and the match outcome (win vs draw vs loss) on match running performance. Players were categorized into starters and non-starters based on their total playing time in the competition match. To qualify as a starter, a player had to complete a minimum of 60 min of play during the competition.^25,26

Participants

This study involved the participation of 35 elite male outfield soccer players in the U20 category (age 19 ± 1 years; height 177 ± 6 cm; body mass 70 ± 8 kg; % body fat: 12 ± 2; 10-m sprint time: 1.72 ± 0.08 s; 20-m sprint time: 2.94 ± 0.12 s; squat jump: 39.69 ± 4.83 cm; countermovement jump: 40.85 ± 5.12 cm; n = 6 central defenders; n = 6 external defenders; n = 12 central midfielders; n = 6 external midfielders; n = 5 forwards). Five of those players were called by the National team of the respective age category during the last two years. The inclusion criteria consisted of the following: (i) at least having played the match. The exclusion criteria were as follows: (ii) The players who were expelled from the match. (iii) The physical performance data of goalkeepers were excluded. The research received approval from the Human Research Ethics Committee (Centre of Physical Education and Sports, Federal University of Espírito Santo, reference number: 10954/2021).

Dependent measures

During the matches, the distance and accelerometry measures were captured using a wearable 10-Hz GPS integrated with a 100-Hz Tri-Axial accelerometer, gyroscope, and magnetometer (Catapult Sports, Melbourne, Australia). The literature has previously reported on the validity and accuracy of these devices.²⁷ Each player wore the devices fixed on their upper back using adjustable harnesses, and activation occurred 15 min before data collection, adhering to the manufacturer's instructions to maximize the acquisition of satellite signals. The horizontal dilution of precision presented average values of 1 ± 0.2 during the matches. Furthermore, the average number of available satellites was 14 ± 0.3. Throughout the season, the players utilized a consistent device to prevent inter-unit errors.²⁷ The following metrics were obtained: (i) TDabs = absolute total distance covered (meters); TDrel = total distance covered relative per minute played (meters/minute); HSRabs = absolute total distance covered in high-speed running (19.8˗25.2 km·h⁻¹; meters); HSRrel = total distance covered in high-speed running relative per minute played (19.8˗25.1 km·h⁻¹; meters/minute); SPRabs = absolute total distance covered in sprinting (≥ 25.2 km·h⁻¹; meters); HSRrel = total distance covered in sprinting relative per minute played (≥ 25.2 km·h⁻¹; meters/minute); ACC + DECabs = absolute total distance covered in high-acceleration (> 2m·s²) and high-deceleration (< −2m·s²) (meters); ACC + DECrel = relative distance covered in high-acceleration (> 2m·s²) and high-deceleration (< −2m·s²) per minute played (meters/minute). The speed thresholds used were reported according to previous studies.²⁸

Independent measures

This study incorporated three contextual factors⁵: (i) match location—characterized as home (starters: 69 individual observations; non-starters: 26 individual observations) and away (starters: 64 individual observations; non-starters: 33 individual observations); (ii) quality of opposition—assessed through k-means cluster analysis based on the final classification. The grouping involved minimizing the involved sum of squares of distances between data and the corresponding cluster centroid. The centroid, which represents the arithmetic mean for each dimension, was calculated over the ranking differences within cluster.²⁹ The finding revealed three clusters: “best ranking,” characterized by strong opponents (1st to 4th position; starters: 23 individual observations; non-starters: 13 individual observations); “intermediate ranking,” encompassing the intermediate opponents (5th to 16th position; starters: 76 individual observations; non-starters: 30 individual observations), and “worst ranking” indicating the weaker opponents (16th–20th position; 34 individual observations; non-starters: 16 individual observations); (iii) match outcome—win (starters: 59 individual observations; non-starters: 20 individual observations), draw (starters: 46 individual observations; non-starters: 18 individual observations), loss (starters: 28 individual observations; non-starters: 21 individual observations).

Statistical analysis

Descriptive statistics were presented in terms of the median (minimum–maximum). The Kolmogorov–Smirnov test revealed that match running performance data was not normally distributed for some variables (p < 0.05). To address this non-normal distribution, a natural log transformation was applied to the relevant variables. To assess the impact of match location (home vs away) on running outputs, an independent sample t-test was conducted. Furthermore, the comparison of match running performance based on the quality of opposition (weak vs intermediate vs strong) and match outcome (loss vs draw vs win) was carried out using one-way ANOVA. We used the “post hoc” Bonferroni when necessary. Additionally, effect sizes (ES) were calculated for pairwise comparisons using the Cohens’ d and classified as trivial (<0.1), small (0.1–0.29), medium (0.3–0.49), large (0.5–0.69), and very large (≥0.70).³⁰ The significance level of p < 0.05 was established. Data analysis was conducted utilizing SPSS for Windows statistical software package version 22.0 (SPSS Inc., Chicago, IL, USA).

Results

Starters

Table 1 shows the values of the match running performance based on both match location and the quality of opponents. The match location did not influence the running demands (t = 0.054–1.796; p = 0.07–0.97; ES = 0.01–0.26 [trivial-small]). The quality of opposition presented effects on HSRabs (F_2,130= 3.748; p = 0.03), HSRrel (F_2,130= 3.364; p = 0.04), and ACC + DECabs (F_2,130= 3.248; p = 0.04). The pairwise comparisons showed that matches played against strong opponents resulted in greater values of HSRabs (p = 0.04; ES = 0.77 [very large]) and HSRrel (p = 0.04; ES = 0.72 [very large]) compared to matches played against weak opponents. In addition, higher values of ACC + DECabs were noted in matches played against intermediate compared weak opponents (p = 0.04; ES = 0.49 [moderate]).

Table 1.

Mean (minimum–maximum) of the match running performance of starters (players who played ≥ 60 min) according to match location and quality of opponents.

	Match location		Quality of opponents
	Home	Away	Weak	Intermediate	Strong
TDabs (m)	9043.8 (6152.0–11127.0)	9160.3 (2569.0–12559.0)	8839.2 (6152.0–11060.0)	9030.0 (2569.0–12559.0)	9715.9 (7316.0–11408.0)
TDrel (m·min⁻¹)	100.4 (64.1–140.4)	100.2 (26.2–128.1)	100.0 (64.0–140.4)	98.2 (26.2–128.1)	107.5 (86.0–126.3)
HSRabs (m)	387.7 (88.0–805.0)	360.2 (0.0–730.0)	358.9 (128.0–744.0)	360.0 (0.0–805.0)	459.9 (210.0–730.0)*
HSRrel (m·min⁻¹)	4.3 (0.9–8.5)	4.0 (0.0–9.2)	3.9 (1.2–8.5)	4.0 (0.0–9.0)	5.1 (2.3–9.2)*
SPRabs (m)	105.4 (0.0–351.0)	107.1 (0.0–348.0)	93.9 (3.0–347.0)	105.9 (0.0–351.0)	125.5 (0.0–330.0)
SPRrel (m·min⁻¹)	1.2 (0.0–3.6)	1.1 (0.0–3.7)	1.0 (0.0–3.6)	1.1 (0.0–3.5)	1.4 (0.0–3.7)
ACC + DECabs (m)	365.5 (165.0–728.0)	432.6 (66.0–862.0)	324.9 (165.0–673.0)	433.9 (66.0–862.0)**	385.9 (173.0–698.0)*
ACC + DECrel (m·min⁻¹)	4.1 (1.6–8.7)	4.7 (0.7–10.9)	3.7 (1.6–8.7)	4.3 (1.7–7.2)	4.7 (0.7–10.9)

Note: TDrel: total distance covered relative per minute played (meters/minute); HSRabs: absolute total distance covered in high-speed running (19.8-25.2 km·h⁻¹; meters); HSRrel: total distance covered in high-speed running relative per minute played (19.8-25.1 km·h⁻¹; meters/minute); SPRabs: absolute total distance covered in sprinting (≥ 25.2 km·h⁻¹; meters); HSRrel: total distance covered in sprinting relative per minute played (≥ 25.2 km·h⁻¹; meters/minute); ACC + DECabs: absolute total distance covered in high-acceleration (> 2m·s²) and high-deceleration (< −2m·s²) (meters); ACC + DECrel: total distance covered in high-acceleration (> 2m·s²) and high-deceleration relative (< −2m·s²) per minute played (meters/minute). * Strong > weak. ** Intermediate > weak.

Table 2 presents the influences of match outcome on locomotor performance. The match outcome presented effects on ACC + DECabs (F_2,130= 3.477; p = 0.03) and ACC + DECrel (F_2,130= 4.140; p = 0.01). The pairwise comparisons showed higher values of ACC + DECabs (p = 0.03; ES = 0.45 [moderate]) and ACC + DECrel (p = 0.01; ES = 0.51 [large]) in win compared to draw matches.

Table 2.

Mean (minimum–maximum) of the match running performance of starters (players who played ≥ 60 min) according to match outcome.

	Match outcome
	Loss	Draw	Win
TDabs (m)	9102.7 (3167.0–11408.0)	9038.4 (6152.0–10872.0)	9146.4 (2569.0–12559.0)
TDrel (m·min⁻¹)	102.2 (33.3–126.3)	97.3 (64.0–138.6)	101.7 (26.2–140.4)
HSRabs (m)	405.9 (0.0–730.0)	382.0 (88.0–680.0)	353.6 (22.0–805.0)
HSRrel (m·min⁻¹)	4.6 (0.0–9.1)	4.1 (0.8–7.2)	3.9 (0.2–8.5)
SPRabs (m)	108.0 (0.0–285.0)	114.2 (0.0–330.0)	99.1 (0.0–351.0)
SPRrel (m·min⁻¹)	1.2 (0.0–3.7)	1.2 (0.0–3.3)	1.0 (0.0–3.6)
ACC + DECabs (m)	370.0 (66.0–643.0)	347.2 (138.0–398.0)	450.3 (137.0–862.0)*
ACC + DECrel (m·min⁻¹)	4.1 (0.6–6.5)	3.7 (1.4–7.1)	4.9 (1.4–10.9)*

Non-starters

The impacts of match location and the quality of opponents are presented in Table 3. Home matches resulted in greater SPRabs (t = 2.412; p = 0.02; ES = 0.66 [large]), SPRrel (t = 2.950; p = 0.005; ES = 0.84 [very large]), and ACC + DECrel (t = 3.903; p < 0.001; ES = 1.02 [very large]) in comparison to away matches. In addition, the quality of opposition presented the main effects on TDrel (F_2,56= 4.128; p = 0.02). The pairwise comparisons showed greater values of TDrel (p = 0.02; ES = 0.86 [very large]) in matches against strong versus intermediate opponents.

Table 3.

Mean (minimum–maximum) of the match running performance of non-starters (players who played < 60 min) according to match location and quality of opponents.

	Match location		Quality of opponents
	Home	Away	Weak	Intermediate	Strong
TDabs (m)	3242.7 (449.0–6425.0)	3284.0 (555.0–7018.0)	3100.0 (569.0–5906.0)	3097.2 (449.0–6425.0)	3859.2 (1439.0–7018.0)
TDrel (m·min⁻¹)	111.6 (89.8–165.9)	103.2 (45.0–130.8)	105.0 (70.3–125.2)	101.8 (45.0–165.9)	120.9 (113.7–130.8)**
HSRabs (m)	170.5 (6.0–424.0)	160.5 (0.0–408.0)	165.2 (25.0–424.0)	147.9 (0.0–418.0)	203.7 (69.0–408.0)
HSRrel (m·min⁻¹)	5.3 (1.2–10.3)	5.3 (0.0–12.9)	5.3 (2.4–8.4)	4.7 (0.0–11.8)	6.6 (3.0–12.9)
SPRabs (m)	71.8 (0.0–206.0)*	37.1 (0.0–130.0)	66.3 (0.0–206.0)	47.9 (0.0–165.0)	45.6 (0.0–130.0)
SPRrel (m·min⁻¹)	2.4 (0.0–6.3)*	1.1 (0.0–6.3)	2.1 (0.0–6.3)	1.6 (0.0–4.7)	1.4 (0.0–6.3)
ACC + DECabs (m)	174.4 (28.0–412.0)	133.2 (0.0–331.0)	148.6 (28.0–393.0)	155.9 (0.0–412.0)	144.2 (40.0–331.0)
ACC + DECrel (m·min⁻¹)	6.2 (2.8–10.8)*	4.3 (0.0–7.5)	5.0 (2.4–7.5)	5.4 (0.0–10.8)	4.5 (2.8–7.0)

Table 4 shows the values of the match running outputs according to the match outcome. Two variables presented main effects: TDabs (F_2,56= 3.367; p = 0.04) and ACC + DECabs (F_2,56= 3.332; p = 0.04). The pairwise comparisons showed greater values of TDabs (p = 0.04; ES = 0.85 [very large]) and ACC + DECabs (p = 0.04; ES = 0.80 [very large]) in win compared to draw matches.

Table 4.

Mean (minimum–maximum) of the match running performance of non-starters (players who played < 60 min) according to match outcome.

	Match outcome
	Loss	Draw	Win
TDabs (m)	3486.7 (555.0–7018.0)	2401.4 (449.0–5930.0)	3811.9 (831.0–6425.0)*
TDrel (m·min⁻¹)	107.0 (54.3–130.8)	102.4 (45.0–165.9)	110.8 (70.3–138.6)
HSRabs (m)	167.5 (38.0–408.0)	131.0 (0.0–338.0)	192.7 (21.0–424.0)
HSRrel (m·min⁻¹)	5.6 (1.1–12.9)	5.0 (0.0–10.3)	5.2 (2.4–8.4)
SPRabs (m)	37.1 (0.0–130.0)	49.9 (0.0–179.0)	70.6 (0.0–206.0)
SPRrel (m·min⁻¹)	1.2 (0.0–6.3)	2.1 (0.0–6.3)	1.8 (0.0–4.5)
ACC + DECabs (m)	136.1 (0.0–331.0)	121.2 (28.0–272.0)	194.4 (43.0–412.0)*
ACC + DECrel (m·min⁻¹)	4.2 (0.0–7.0)	5.6 (3.3–10.8)	5.6 (2.4–8.6)

Note: TDrel: total distance covered relative per minute played (meters/minute); HSRabs: absolute total distance covered in high-speed running (19.8-25.2 km·h⁻¹; meters); HSRrel: total distance covered in high-speed running relative per minute played (19.8-25.1 km·h⁻¹; meters/minute). SPRabs: absolute total distance covered in sprinting (≥ 25.2 km·h⁻¹; meters); HSRrel: total distance covered in sprinting relative per minute played (≥ 25.2 km·h⁻¹; meters/minute); ACC + DECabs: absolute total distance covered in high-acceleration (> 2m·s²) and high-deceleration (< −2m·s²) (meters); ACC + DECrel: total distance covered in high-acceleration (> 2m·s²) and high-deceleration relative (< −2m·s²) per minute played (meters/minute). * Win > draw.

Discussion

To the best of our knowledge, this is the first study that investigated the effects of contextual factors on the match running performance of starters and non-starters elite young soccer players. The main results were as follows: (i) non-starters covered greater SPRabs, SPRrel, and ACC + DECrel in the home compared to away matches; (ii) in starter players, matches played against strong opponents resulted in higher values of HSRabs and HSRrel in comparison to matches played against weak opponents; (iii) non-starter players covered higher TDabs in matches against strong vs intermediate opponents; (iv) starters and non-starter players covered greater ACC + DECabs distances in matches resulted in win vs draw; (v) win matches presented greater values of TDabs for non-starters and higher values of ACC + DEC for starters compared to draw matches.

In this study, starters demonstrated greater/physical match demands were reported at home compared with away matches. This finding corroborates the findings of prior studies conducted in the 3rd and 4th divisions of the professional Brazilian league (season 2015; 2016; 2017), demonstrating the home advantage in soccer.^5,12,31 In other soccer Leagues (European continent), running performance also presented greater value in the home compared to away matches.^32–34 Previous research reported some factors that may explain this behavior, such as local crowd support and familiarity with local conditions, which are associated with a more aggressive playing style. In addition, factors like absence of travel, travel fatigue for the opposition, and psychological factors^35–37 might also have influenced the results. However, further studies are required to explore additional match location variables (e.g. total number of fans at the match, type of soccer lawn) that could elucidate this effect on match running performance.

In the current study, the quality of opponents influenced the physical demands of starters and non-starters. These results diverge from previous studies, which indicated that this contextual variable did not impact running performance during match days in the Brazilian National 2nd Division League (season 2019).²⁵ Conversely, previous studies exhibited findings comparable to those presented in this study. Aquino et al.⁵ and Rampinini et al.³⁸ demonstrated large total distances and high-intensity distances against strong opponents in Brazilian and Italian professional soccer players, respectively. In addition, Castellano et al.³⁴ reported that when playing against more successful teams, the previously mentioned professional team exhibited higher values in distances covered across different speed zones (Strong > Intermediate > Weak). This heightened demand may arise from the necessity to strategically “surprise” and “disrupt” the opposing team's style of play, creating challenges for opponents in ball recovery. This suggests that players need to be physically prepared to face strong opponents,^39,40 requiring a higher utilization of their physical capacities.

About a match outcome, both starters and non-starters revealed greater intensity running in matches won compared to drawn matches. This result may be associated which distinct styles of play employed during the matches. For example, Lago et al.⁴¹ showed that ball possession is higher when the team is losing compared to when winning or drawing. Therefore, it is suggested that in winning matches, the team adopts a counterattacking style (i.e. a direct style of play), potentially leading to increased match running intensity, such as high acceleration. Nevertheless, it is crucial to recognize that various multidimensional factors, including technical, tactical, and psychological elements, influence both the temporary and final score of the match.⁴²

Concerning the level of match participation (whether players were starters and non-starters status), the present findings showed significant variations in TD and SPR for non-starters in-home versus away and in matches against strong versus intermediate opponents. The findings related to non-starters are from a previous study that investigated friendly matches.²¹ Usually, coaches and practitioners used players’ substitutions for physical or tactical reasons.^43,44 Bradley et al.⁴³ suggested that non-starter players exhibited an unconstrained style of effort than when they started a match, imposing high relative players’ physical demands. In addition, starters may indicate greater fatigue and/or employ more effective pacing strategies, due to the anticipation of longer playing time. However, pacing strategies by different team roles may, therefore, influence overall team performance, an aspect that remains to be examined in future studies.⁴⁵

This study presented some limitations. First, the analyses did not include internal load (e.g. heart rate) and technical-tactical indicators (e.g. team surface area, offensive/defensive tactical principles). Secondly, our results did not consider a real-time assessment of the quality of opposition throughout the competition. Additionally, there is an absence of verification of the score line of the matches. Further studies may include these aspects for a deep understanding of the impact of match contextual factors on running performance in soccer. In addition, caution is advised in interpreting our results, as the analyses were conducted solely based on the matches of one season and a single team. Finally, the sample difference in the number of matches for each independent variable analyzed can be considered a limitation of this study. However, this study has important strengths: (i) this study represents the inaugural investigation into the impact of match contextual factors on distance and accelerometry variables across participation status; (ii) this study included young elite-level players.

Conclusions

Overall, the results indicated that match contextual factors exerted an impact on both starters and non-starters among young Brazilian soccer players from an elite team. Specifically, non-starters covered greater SPRabs, SPRrel, and ACC + DECrel in-home compared to away matches. Additionally, both starters and non-starters displayed increased running demands in matches won compared to draw matches.

Our results reinforce the coaches substitute players throughout the match to increase physical performance, including high-intensity efforts such as sprinting, accelerations, and decelerations. However, when analyzing the moments of substitution, coaches must consider the physical, behavioral, strategic, technical, and tactical variables within the context of a match. In addition, these findings have the potential to assist coaches in enhancing the team's overall performance during the match.

Footnotes

Acknowledgments

The authors would like to thank the coaching staff and players of the Fluminense Football Club (Rio de Janeiro, Brazil).

Authors’ Note

After obtaining permission from the relevant authorities and the head coach of the club, the training coaches of the club conducted this research. This study received the approval of the research ethics committee from the Federal University of Espírito Santo (10954/2021). All players were informed of the purpose of the study before completing the informed consent. All stages of this study were carried out based on the ethical principles in the Helsinki Declaration.

Availability of data and materials

The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.

Author contributions

L.G.G., A.A.R., and R.A. led the project, and methodological assessment, analyzed and interpreted the data, wrote the statistical report, and wrote and revised the original manuscript. H. N., F.Y.N., and G.R.G. wrote and revised the original manuscript. All authors have read and agreed to the published version of the manuscript.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was finded in part, by the Coordenação de Aperfeiçoamento de Pessoal de Npivel Superior (financial code: 001).

ORCID iD

Luiz Guilherme Gonçalves

Author biographies

Luiz Guilherme Gonçalves is a PhD student in Sports Training and Soccer. His research focuses on Sports Science, primarily in Sports Training, including Load Monitoring, Training Methodology, and Match Analysis.

Hadi Nobari (PhD in exercise physiology) is a Professor in Exercise Physiology. His research areas are Soccer Training, Injury Prevention, Training Load, Sports Nutrition and Supplements, and Sport Technologies & AI.

Alex Ambrosio Rites is a master's degree student in molecular biology and human motricity. His research focuses on physical performance, with special reference to soccer.

Fábio Yuzo Nakamura is Associate Professor at University of Maia. His research focuses on sports-related training and performance, with special reference to team sports.

Gabriel Rodrigues Garcia is a master's degree student in exercise and sports sciences at the Institute of Physical Education and Sports, State University of Rio de Janeiro, Laboratory of Soccer Studies (LABESFUT) in Rio de Janeiro, Brazil. His research focuses on sports science, with an interest in the effects of the environment on the running performance of female soccer players.

Rodrigo Aquino (PhD in Sport Science) is a Professor in Sports Training and Soccer. His research focuses on Sports Science, primarily in Sports Training, including Load Monitoring, Training Methodology, and Match Analysis.

References

Sydney

Ball

Chapman

, et al.

Does competition standard and player position influence the match-play physical demands of Australian elite youth male soccer players within a single squad?

Int J Sport Sci Coach 2021; 16: 360–369.

Buchheit

Simpson

. Player-tracking technology: half-full or half-empty glass? Int J Sports Physiol Perform 2017; 12: S235–S241.

Lago

Casais

Dominguez

, et al. The effects of situational variables on distance covered at various speeds in elite soccer. Eur J Sport Sci 2010; 10: 103–109.

Bradley

Noakes

. Match running performance fluctuations in elite soccer: indicative of fatigue, pacing or situational influences? J Sports Sci 2013; 31: 1627–1638.

Aquino

Munhoz Martins

Palucci Vieira

, et al. Influence of match location, quality of opponents, and match status on movement patterns in Brazilian professional football players. J Strength Cond Res 2017; 31: 2155–2161.

Martín-García

Gómez Díaz

Bradley

, et al. Quantification of a professional football team’s external load using a microcycle structure. J Strength Cond Res 2018; 32: 3511–3518.

Palucci Vieira

Carling

Barbieri

, et al. Match running performance in young soccer players: a systematic review. Sports Med 2019; 49: 289–318. doi:https://doi.org/10.1007/s40279-018-01048-8

Paul

Bradley

Nassis

. Factors affecting match running performance of elite soccer players: shedding some light on the complexity. Int J Sports Physiol Perform 2015; 10: 516–519.

Díez

Lozano

Arjol-Serrano

, et al. Influence of contextual factors on physical demands and technical-tactical actions regarding playing position in professional soccer players. BMC Sport Sci Med Rehabil 2021; 13: 157.

10.

Lago-Peñas

. The role of situational variables in analysing physical performance in soccer. J Hum Kinet 2012; 35: 89–95.

11.

Aquino

Guimarães

Junior

GOC

, et al. Effects of match contextual factors on internal and external load in elite Brazilian professional soccer players through the season. Sci Rep 2022; 12: 21287.

12.

Aquino

Carling

Palucci Vieira

, et al. Influence of situational variables, team formation, and playing position on match running performance and social network analysis in Brazilian professional soccer players. J Strength Cond Res 2020; 34: 808–817.

13.

Los Arcos

Mendez-Villanueva

Martínez-Santos

. In-season training periodization of professional soccer players. Biol Sport 2017; 34: 149–155.

14.

Vieira

LHP

Aquino

Lago-Peñas

, et al. Running performance in Brazilian professional football players during a congested match schedule. J Strength Cond Res 2018; 32: 313–325.

15.

Aquino

Gonçalves

Galgaro

, et al. Match running performance in Brazilian professional soccer players: comparisons between successful and unsuccessful teams. BMC Sport Sci Med Rehabil 2021; 13: 93.

16.

Nobari

Aquino

Clemente

, et al. Description of acute and chronic load, training monotony and strain over a season and its relationships with well-being status: A study in elite under-16 soccer players. Physiol Behav Epub ahead of print 2020; 225: 113117. https://doi.org/10.1016/j.physbeh.2020.113117

17.

Nobari

Oliveira

Clemente

, et al. Comparisons of accelerometer variables training monotony and strain of starters and non-starters: a full-season study in professional soccer players. Int J Environ Res Public Health 2020; 17: 1–14.

18.

Castillo-Rodríguez

González-Téllez

Figueiredo

, et al.

Starters and non-starters soccer players in competition: is physical performance increased by the substitutions?

BMC Sport Sci Med Rehabil 2023; 15: 33.

19.

Furtado Mesa

Stout

Redd

, et al. Accumulated workload differences in collegiate women’s soccer: starters versus substitutes. J Funct Morphol Kinesiol 2023; 8: 78–89. https://doi.org/10.3390/jfmk8020078

20.

Dalen

Lorås

. Monitoring training and match physical load in junior soccer players: starters versus substitutes. Sports 2019; 7: 70–77. https://doi.org/10.3390/sports7030070

21.

Giménez

Leicht

Gomez

. Physical performance differences between starter and non-starter players during professional soccer friendly matches. J Hum Kinet 2019; 69: 283–291.

22.

Algroy

Grendstad

Riiser

, et al. Motion analysis of match play in U14 male soccer players and the influence of position, competitive level and contextual variables. Int J Environ Res Public Health 2021; 18: 7287–7296. https://doi.org/10.3390/ijerph18147287

23.

Curtis

Huggins

Benjamin

, et al. Contextual factors influencing external and internal training loads in collegiate men’s soccer. J Strength Cond Res 2020; 34: 374–381.

24.

Varley

Gregson

McMillan

, et al. Physical and technical performance of elite youth soccer players during international tournaments: influence of playing position and team success and opponent quality. Sci Med Football 2016; 1: 18–29.

25.

Gonçalves

LGC

Kalva-Filho

Nakamura

, et al. Effects of match-related contextual factors on weekly load responses in professional Brazilian soccer players. Int J Environ Res Public Health 2020; 17: 1–13.

26.

Anderson

Orme

Di Michele

, et al. Quantification of seasonal-long physical load in soccer players with different starting status from the English premier league: implications for maintaining squad physical fitness. Int J Sports Physiol Perform 2016; 11: 1038–1046.

27.

Jennings

Cormack

Coutts

, et al. Variability of GPS units for measuring distance in team sport movements. Int J Sports Physiol Perform 2010; 5: 565–569.

28.

Hands

Janse de Jonge

. Current time-motion analyses of professional football matches in top-level domestic leagues: a systematic review. Int J Perform Anal Sport 2020; 20: 747–765.

29.

Almeida

Duarte

Volossovitch

, et al. Scoring mode and age-related effects on youth soccer teams’ defensive performance during small-sided games. J Sports Sci 2016; 34: 1355–1362.

30.

Cohen

. Statistical power analysis for the behavioral sciences. 2nd ed. New York: Hilldales, 1988.

31.

Aquino

Vieira

LHP

Carling

, et al. Effects of competitive standard, team formation and playing position on match running performance of Brazilian professional soccer players. Int J Perform Anal Sport 2017; 17: 695–705.

32.

Taylor

Mellalieu

James

, et al. The influence of match location, quality of opposition, and match status on technical performance in professional association football. J Sports Sci 2008; 26: 885–895.

33.

Zhou

Hopkins

Mao

, et al. Match performance of soccer teams in the Chinese super league—effects of situational and environmental factors. Int J Environ Res Public Health 2019; 16: 4238–4251. https://doi.org/10.3390/IJERPH16214238

34.

Castellano

Blanco-Villaseñor

Álvarez

. Contextual variables and time-motion analysis in soccer. Int J Sports Med 2011; 32: 415–421.

35.

Agnew

Carron

. Crowd effects and the home advantage. Int J Sport Psychol 1994; 25: 53–62.

36.

Pollard

Gómez

. Comparison of home advantage in college and professional team sports in the United States. Coll Antropol 2015; 39: 583–589.

37.

Pollard

Gómez

. Components of home advantage in 157 national soccer leagues worldwide. Int J Sport Exerc Psychol 2014; 12: 218–233.

38.

Rampinini

Coutts

Castagna

, et al. Variation in top level soccer match performance. Int J Sports Med 2007; 28: 1018–1024.

39.

Almeida

Ferreira

Volossovitch

. Effects of match location, match status and quality of opposition on regaining possession in UEFA Champions League. J Hum Kinet 2014; 41: 203–214.

40.

Lago-Peñas

Dellal

. Ball possession strategies in elite soccer according to the evolution of the match-score: the influence of situational variables. J Hum Kinet 2010; 25: 93–100.

41.

Lago

Casáis

Domínguez

, et al. The effect of match location, quality of opposition and match status on work rate in elite soccer. Eur J Hum Mov 2009; 23: 107–121.

42.

Augusto

Brito

Aquino

, et al. Contextual variables affect running performance in professional soccer players: a brief report. Front Sport Act Living 2021; 3: 778813.

43.

Bradley

Lago-Peñas

Rey

. Evaluation of the match performances of substitution players in elite soccer. Int J Sports Physiol Perform 2014; 9: 415–424.

44.

Reilly

Drust

Clarke

. Muscle fatigue during football match-play. Sports Med 2008; 38: 357–367.

45.

Waldron

Highton

. Fatigue and pacing in high-intensity intermittent team sport: an update. Sports Med 2014; 44: 1645–1658.

Influence of contextual factors on match running performance of starters and non-starters in elite youth male soccer players

Abstract

Keywords

Introduction

Methods

Observational design

Participants

Dependent measures

Independent measures

Statistical analysis

Results

Starters

Non-starters

Discussion

Conclusions

Footnotes

Acknowledgments

Authors’ Note

Availability of data and materials

Author contributions

Declaration of conflicting interests

Funding

ORCID iD

Author biographies

References