Can contextual factors affect match running performance in elite youth soccer players? A case study in Brazil

doi:10.21203/rs.3.rs-1950718/v1

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Can contextual factors affect match running performance in elite youth soccer players? A case study in Brazil

https://doi.org/10.21203/rs.3.rs-1950718/v1

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This study investigated the effects of match location (i.e., home vs. away), quality of opposition (i.e., weak vs. intermediate vs. strong), and the match outcome (i.e., loss vs. draw vs. win) on match running performance of starters and non-starters young elite Brazilian soccer players. Thirty-five male outfield players were observed during the U20 Brazilian National League and were classified into starters (played ≥ 60 minutes: n = 133 individual observations) and non-starters (played < 60 minutes; n = 59 individual observations). The distance- and accelerometry-based measures were recorded during 17 matches using a global position system (10 Hz). The main results were: i) non-starters covered greater absolute sprinting distance (SPRabs: p = 0.02; Effect Size [ES] = large), SPR distance relative per minute played (SPRrel: p = 0.005; ES = very large), and high-acceleration and deceleration relative per minute played (ACC+DECrel: p < 0.001; ES = very large) in the home compared to away matches; ii) in starters, matches played against strong opponents resulted in higher values of absolute total distance in high-speed running (HSRabs) (p = 0.04; ES = very large) and high-speed running per minute played (HSRrel) (p = 0.04; ES = very large) compared to matches played against weaker opponents; iii) non-starters covered greater absolute total distance in matches against strong vs. intermediate opponents (p = 0.02; ES = very large); iv) starters (p = 0.03; ES = moderate) and non-starters (p = 0.04; ES = very large) covered greater absolute ACC+DEC distances in matches that resulted in win vs. draw. These findings suggest that match contextual factors influenced both starters and non-starters young elite soccer players. Coaches and practitioners should consider these aspects when interpreting the match running outputs and use them to inform team tactics, selection, and substitution strategies. Abbreviations GPS: global positioning systems. TD: total distance covered. HSR: total distance covered under high-speed running. Acc: total distance covered under high acceleration. Dec: total distance covered under high deceleration. Abs: absolute. Rel: relative. ES = effect size.

Match analysis is currently a vital process during the researchers’ and practitioners’ daily practices¹. In soccer, global positioning systems (GPS) and accelerometer devices have contributed to understanding the physical requirements of match play based on the distance covered at different speeds and acceleration/deceleration measures². In addition, a myriad of factors can influence the match running performance, especially match location, quality of opposition, and match outcome^3–6. However, a previous systematic review showed that the potential impact of contextual factors on match running outputs is frequently overlooked⁷.

In youth soccer, Varley et al.2016⁸ showed that very high-speed running distance with ball possession was lower when playing against bottom opponents while the same locomotor variable when the opponent was in possession was higher. However, additional research is necessary to investigate other factors, including match location, level of participation (starters or non-starters) and match outcome on match running performance^9,10. This analysis could help coaches and staff to optimize the match running performance during the competitions and plan the training process and recovery strategies.

Most coaches use substitutions due to tactical reasons and to reduce fatigue effects on the team’s physical performance, considering contextual factors (e.g., score line) and playing position^11,12. A previous study reported that professional players with less participation in the match (i.e., non-starters) displayed higher physical performance compared with the player who completed the entire match¹³. In addition, Giménezes et al.¹⁴ demonstrated that non-starter professional soccer players covered greater distances at 3.3-6.9 m·s^− 1 than starters. It is important to highlight the limited number of studies regarding match running performance in elite youth soccer athletes¹⁵, especially concerning the effects of their participation status on locomotor performance.

Descriptions of soccer team’s performance in different matches or scenarios using different outcomes in interaction with different moderators (i.e., contextual factors, the quality of the teams or opponents, location of match, or playing position or status, i.e., starters and non-starters) have become a popular topic of research^16–18. In the case of physical performance, it is relatively different contextual factors such as match location, match results, playing position, playing status and competitive/opponent level influence data interpretation in match running distance and accelerometry based variables^19,20, however, the information is not complete yet. 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 match running performance of starters and non-starters belonging to an elite Brazilian soccer club. We hypothesized that the contextual factors mentioned above can affect the running performance of youth U20 players.

Observational design

Distance variables were monitored during a full season of the U20 Brazilian National League (edition 2020-21). Twenty soccer clubs competed for the League, in home or away matches, totalizing 19 matches during the “group” phase. The best four teams disputed a knockout stage (semi-final and final). The reference team observed was positioned in the top-5 of the final classification. Two matches were excluded due the data noise. Therefore, 17 matches were fully analyzed. This study investigated the effects 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 of starters (players who played > 60 minutes) and non-starters (players who played ≤ 60 minutes)²¹.

Participants

Thirty-five elite U20 male outfield soccer players participated in this study (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 seconds; 20-m sprint time: 2.94 ± 0.12 seconds; squat jump: 39.69 ± 4.83 cm; countermovement jump: 40.85 ± 5.12; 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 National team of the respective age-category during last two years. The study was approved by the local Human Research Ethics Committee (Centre of Physical Education and Sports, Federal University of Espírito Santo, reference number: 10954/2021).

Dependent measures

The distance measures were recorded during the matches using a wearable 10-Hz GPS integrated with a 100-Hz Tri-Axial accelerometer, gyroscope, and magnetometer (Catapult Sports, Melbourne, Australia). The validity and accuracy of the devices were previously reported in the literature²². The devices were fixed on the upper back of each player using adjustable harnesses and were activated 15 min before the data collection, in accordance with the manufacturer's instructions to optimize the acquisition of satellite signals. Throughout the season, the players used the same device to avoid inter-unit errors ²². 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 studies²³.

Independentmeasures

Three contextual factors were included in this study⁵: 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 – determined according to k-means cluster analysis based on the final classification. The grouping is performed by minimizing the sum of squares of distances between data and the corresponding cluster centroid, which is the arithmetic mean for each dimension separately over the ranking differences in the cluster²⁴. The results identified three clusters: “best ranking”, featuring strong opponents (1st to 4th position; starters: 23 individual observations; non-starters: 13 individual observations); “intermediate ranking”, including the intermediate opponents (5th to 16th position; starters: 76 individual observations; non-starters: 30 individual observations), and “worst ranking” representing 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

The Kolmogorov–Smirnov test revealed that match running performance data were not normally distributed for some variables (p < 0.05). The non-normally distributed variables were corrected using a natural log transform. The data are described by the median (minimum-maximum). The effects of match location (home vs. away) on running outputs were calculated using the t-test for the independent sample. ANOVA one way was used to compare the match running performance according to the quality of opposition (weak vs. intermediate vs. strong) and the match outcome (loss vs. draw vs. win). 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)²⁵. The significance level was set at p < 0.05. Data were analyzed using the SPSS for Windows statistical software package version 22.0 (SPSS Inc., Chicago, IL, USA).

Starters

Table 1 shows the values of the match running performance according to match location and 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 higher 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, we observed greater values of ACC + DECabs in matches played against intermediate vs. weak opponents (p = 0.04; ES = 0.49 [moderate]).

***Please, insert Table 1 near here***

Table 2 presents the influences of match outcome on match running 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.

***Please, insert Table 2 near here***

Non-starters

The effects of match location and quality of opponents are present 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]) compared to away matches. In addition, quality of opposition presented main effects on TDrel (F_2,56 = 4.128; p = 0.02). The pairwise comparisons showed higher values of TDrel (p = 0.02; ES = 0.86 [very large]) in matches against strong vs. intermediate opponents.

***Please, insert Table 3 near here***

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.

***Please, insert Table 4 near here***

This is the first study that investigated the effects of contextual factors on match running performance of starters and non-starters elite young soccer players. The main results were: 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 compared to matches played against weak opponents; iii) non-starter players covered greater 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, greater running outputs (e.g., SPR and ACC + DEC) were reported in home compared with away matches. This finding confirms the results of a previous studies performed in the 3rd and 4th divisions in professional Brazilian league (season 2015; 2016; 2017), demonstrating the home advantage in soccer^5,26,27. In other soccer Leagues (European continent), running performance also presented greater values in home compared to away matches^10,28,29. Previous researches reported some factors that may explain this behavior, such as: local crowd support and familiarity with local conditions, which are associated with more aggressive playing style. In addition, factors like absence of travel, travel fatigue for opposition, and psychological factors^30–32 might also have influenced the results.

In the current study, the quality of opponents influenced the match running performance. These findings contrast with the previous studies showing that this contextual variable did not influence the running performance during match days of the Brazilian National 2nd Division League (season 2019)²¹. On the other hand, previous studies showed similar results to the ones presented here. Aquino et al.⁵ and Rampinini et. al.³³ demonstrated greater total distances and high-intensity distance against strong opponents in Brazilian and Italian professional soccer players, respectively. In addition, Castellano et al.¹⁰ reported that when playing against more successful teams, the professional reference team covered greater distances in different speed zones (Strong > Intermediate > Weak). One possible explanation for these results is that playing against strong opponents seems to be more physically demanding due to the need to “surprise” and “disrupt” the opposing team's style of play, making it difficult for opponents to recover the ball. This suggests that players must be physically prepared to play against strong opponents^34,35 while demanding more of their physical capacities. In relation to a match outcome, greater intensity running was observed in matches that the team won as opposed to draw. This result can be related to different styles of play during the matches. For example, Lago et al.³⁶ showed that ball possession is greater when the team is losing than when winning or drawing. Therefore, it is suggested that when winning matches, the team adopted a counterattacking style (i.e., a direct style of play), and this can induce higher match running intensity (e.g., high acceleration). However, it is important to acknowledge that multidimensional aspects influence the temporary and final score of the match, such as the technical, tactical, and psychological factors³⁷.

In relation to the level of matches’ participation (starters and non-starters status), the current results showed significant differences in TD and SPR for non-starters in home vs. away and in matches against strong vs. intermediate opponents. The findings related to non-starters are in accordance with a previous study that investigated friendly matches¹⁴. Usually, coaches and practitioners used players’ substitutions for physical or tactical reasons^11,13. Bradley et al.¹³ reported that non-starter players adopted an unconstrained style of effort compared to when they started a match, imposing high relative players’ physical demands. In addition, starters may employ more effective pacing strategies, due to the anticipation of longer playing time.

This study presented some limitations. First, we did not include internal load (e.g., heart rate) and technical-tactical indicators (e.g., team surface area, offensive/defensive tactical principles) in the analyses. Further studies may include these aspects for deep understanding of the influence of match contextual factors on soccer performance. In addition, the interpretation of our results should be done with caution because the analyses were carried out considering only 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 is the first study that investigated the influence of match contextual factors on distance variables across participation status; ii) this study included young elite level players.

In general, the findings revealed that match contextual factors influenced both starters and non-starters young Brazilian soccer players from an elite club. Specifically, non-starters covered greater SPRabs, SPRrel, and ACC + DECrel in home vs. away matches. When the non-starters and starters won the matches, higher running demands were noted than when they drew. Coaches and practitioners should consider these aspects when interpreting the match running outputs and use them to inform team tactics, selection, and substitution strategies.

GPS: global positioning systems. TD: total distance covered. HSR: total distance covered under high-speed running. Acc: total distance covered under high acceleration. Dec: total distance covered under high deceleration. Abs: absolute. Rel: relative. ES = effect size.

The training coaches of the club, after obtaining permission from the relevant authorities and the head coach 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.

Consent for Publication

Not applicable.

Availability of data and materials

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

Competing interests

The authors declare that they have no competing interests.

Funding

Not applicable.

Acknowledgments

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

Authors’ Contributions:

L.G.G., A.A.R. and R.A. led the project, 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.

Sydney, M. G., Ball, N., Chapman, D., Wollin, M. & Mara, J. K. 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. 16, 360–369 (2021).
Buchheit, M. & Simpson, B. M. Player-Tracking Technology: Half-Full or Half-Empty Glass? Int. J. Sports Physiol. Perform. 12, S235–S241 (2017).
Lago, C., Casais, L., Dominguez, E. & Sampaio, J. The effects of situational variables on distance covered at various speeds in elite soccer. Eur. J. Sport Sci. 10, 103–109 (2010).
Bradley, P. S. & Noakes, T. D. Match running performance fluctuations in elite soccer: indicative of fatigue, pacing or situational influences? J. Sports Sci. 31, 1627–1638 (2013).
Aquino, R., Munhoz Martins, G. H., Palucci Vieira, L. H. & Menezes, R. P. Influence of match location, quality of opponents, and match status on movement patterns in Brazilian professional football players. J. Strength Cond. Res. 31, 2155–2161 (2017).
Martín-García, A., Gómez Díaz, A., Bradley, P. S., Morera, F. & Casamichana, D. Quantification of a professional football team’s external load using a microcycle structure. J. Strength Cond. Res. 32, 3511–3518 (2018).
Palucci Vieira, L. H., Carling, C., Barbieri, F. A., Aquino, R. & Santiago, P. R. P. Match Running Performance in Young Soccer Players: A Systematic Review. Sports Med. 49, 289–318.
Varley, M. C. et al. Physical and technical performance of elite youth soccer players during international tournaments: influence of playing position and team success and opponent quality. 1, 18–29 (2016).
Bellistri, G. et al. Match running performance and physical capacity profiles of U8 and U10 soccer players. Sport Sci. Heal. 2016 132 13, 273–280 (2016).
Castellano, J., Blanco-Villaseñor, A. & Álvarez, D. Contextual variables and time-motion analysis in soccer. Int. J. Sports Med. 32, 415–421 (2011).
Reilly, T., Drust, B. & Clarke, N. Muscle fatigue during football match-play. Sports Med. 38, 357–367 (2008).
Siegle, M. & Lames, M. Game interruptions in elite soccer. J. Sports Sci. 30, 619–624 (2012).
Bradley, P. S., Lago-Peñas, C. & Rey, E. Evaluation of the match performances of substitution players in elite soccer. Int. J. Sports Physiol. Perform. 9, 415–424 (2014).
Giménez, J. V, Leicht, A. S. & Gomez, M. A. Physical Performance Differences Between Starter and Non-Starter Players During Professional Soccer Friendly Matches. J. Hum. Kinet. 69, 283–291 (2019).
Nobari, H. 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. 225, (2020).
Diez, A. 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. 13, (2021).
Nobari, H., Castillo, D., Clemente, F. M., Carlos-Vivas, J. & Pérez-Gómez, J. Acute, chronic and acute/chronic ratio between starters and non-starters professional soccer players across a competitive season. Proc. Inst. Mech. Eng. Part P J. Sport. Eng. Technol. (2021).
Nobari, H., Fani, M., Mainer-Pardos, E. & Oliveira, R. Comparison of GPS derived variables based on home versus away matches in the Asian professional soccer team. Proc. Inst. Mech. Eng. Part P J. Sport. Eng. Technol. 17543371221109728 (2022).
Díez, A. 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. 13, 157 (2021).
Nobari, H., Keshish Banoocy, N., Oliveira, R. & Pérez-Gómez, J. Win, Draw, or Lose? Global Positioning System-Based Variables’ Effect on the Match Outcome: A Full-Season Study on an Iranian Professional Soccer Team. (2021).
Gonçalves, L. G. C. et al. Effects of match-related contextual factors on weekly load responses in professional Brazilian soccer players. Int. J. Environ. Res. Public Health 17, 1–13 (2020).
Jennings, D., Cormack, S., Coutts, A. J., Boyd, L. J. & Aughey, R. J. Variability of GPS units for measuring distance in team sport movements. Int. J. Sports Physiol. Perform. 5, 565–569 (2010).
Hands, D. E. & Janse de Jonge, X. Current time-motion analyses of professional football matches in top-level domestic leagues: a systematic review. Int. J. Perform. Anal. Sport 20, 747–765 (2020).
Almeida, C. H., Duarte, R., Volossovitch, A. & Ferreira, A. P. Scoring mode and age-related effects on youth soccer teams’ defensive performance during small-sided games. J. Sports Sci. 34, 1355–1362 (2016).
Cohen, J. Statistical Power Analysis for the Behavioral Sciences. (Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers., 1988).
Aquino, R. 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. 34, 808–817 (2020).
Aquino, R. 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 17, 695–705 (2017).
Taylor, J., Mellalieu, S., James, N. & Shearer, D. The influence of match location, quality of opposition, and match status on technical performance in professional association football. 26, 885–895 (2008).
Zhou, C., Hopkins, W. G., Mao, W., Calvo, A. L. & Liu, H. Match Performance of Soccer Teams in the Chinese Super League—Effects of Situational and Environmental Factors. Int. J. Environ. Res. Public Health 16, (2019).
Agnew GA, C. A. Crowd effects and the home advantage. Int J Sport Psychol 25, 53–62 (1994).
Pollard, R. & Gómez, M. A. Comparison of Home Advantage in College and Professional Team Sports in the United States. Coll. Antropol. 39, 583–589 (2015).
Pollard, R. & Gómez, M. A. Components of home advantage in 157 national soccer leagues worldwide. Int. J. Sport Exerc. Psychol. 12, 218–233 (2014).
Rampinini, E., Coutts, A. J., Castagna, C., Sassi, R. & Impellizzeri, F. M. Variation in top level soccer match performance. Int. J. Sports Med. 28, 1018–1024 (2007).
Almeida, C. H., Ferreira, A. P. & Volossovitch, A. Effects of match location, match status and quality of opposition on regaining possession in UEFA Champions League. J. Hum. Kinet. 41, 203 (2014).
Lago-Peñas, C. & Dellal, A. Ball possession strategies in elite soccer according to the evolution of the match-score: The influence of situational variables. J. Hum. Kinet. 25, 93–100 (2010).
Lago, C., Casáis, L., Domínguez, E., Lago, J. & Rey, E. The effect of match location, quality of opposition and match status on work rate in elite soccer. Eur. J. Hum. Mov. 23, 107–121 (2009).
Augusto, D. et al. Contextual Variables Affect Running Performance in Professional Soccer Players: A Brief Report. Front. Sport. Act. living 3, 778813 (2021).

Table 1

Mean (minimum-maximum) of the match running performance of starters (players who played ≥ 60 minutes) 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

Mean (minimum-maximum) of the match running performance of starters (players who played ≥ 60 minutes) 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)*
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.

Table 3

Mean (minimum-maximum) of the match running performance of non-starters (players who played < 60 minutes) 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)
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). * home > away. ** strong > intermediate

Table 4

Mean (minimum-maximum) of the match running performance of non-starters (players who played < 60 minutes) 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.

No competing interests reported.

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Can contextual factors affect match running performance in elite youth soccer players? A case study in Brazil

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Abstract

Introduction

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Results

Starters

Non-starters

Discussion

Conclusions

Abbreviations

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References

Tables

Additional Declarations

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