Match Play Intensity Distribution in Youth Soccer

Training & Testing

Authors

A. Mendez-Villanueva, M. Buchheit, B. Simpson, P. C. Bourdon

Affiliation

ASPIRE Academy for Sports Excellence, Sport Science, Doha, Qatar

Key words ▶ association football ● ▶ match analysis ● ▶ exercise intensity ● ▶ heart rate ● ▶ training load ●

Abstract

▼

The purpose of this study was to quantify match play intensity distribution in young soccer players in relation to age, playing position and physical fitness. Distance covered and heart rate were measured (global positioning system) on 103, highly-trained young players (Under13 to Under 18) during 42 international club games. Maximal sprinting speed (MSS), estimated maximal aerobic speed (MAS) and maximal heart rate (HRmax) were assessed via field test measures. Distance covered and heart rate (HR) were categorized into 5 intensity zones relative to MSS and MAS and HRmax, respectively. Intensity distribution was significantly influenced by both age and playing position with younger groups, wide-midfield-

Introduction

▼ accepted after revision February 03, 2012 Bibliography DOI http://dx.doi.org/ 10.1055/s-0032-1306323 Published online: 2012 Int J Sports Med © Georg Thieme Verlag KG Stuttgart · New York ISSN 0172-4622 Correspondence Dr. Alberto Mendez-Villanueva ASPIRE Academy for Sports Excellence Sport Science, Physiology Unit 22287 Doha Qatar Tel.: +974/66/711 375 Fax: +974/41/360 60 [email protected]

Competitive soccer play requires a fine interaction between tactical, technical, psychological and physiological components. However, limited research is available on how best to structure the long-term development of these distinct components in young soccer players [49]. To develop the physiological aspects relevant to soccer performance, coaches and scientists must have a solid understanding about the complex interactions of multiple factors affecting the physiological responses to match play in players of all ages. Recent match analyses have provided important information on the physical demands associated with soccer match play in young players [5, 9, 15, 16]. However, the intensity of effort that competition places on a player has mainly been studied by analyzing the distance covered at given running intensities. That is, speeds used to define match running intensities have mostly been based on “player-independent” absolute values [5, 8, 14–16]. However, due to age- and/or

ers and strikers covering the greatest distance above the MAS. There was a significant, negative, large-to-very large correlation (r = − 0.52–0.74) between MAS and the distance run at speeds above MAS for all positions except strikers. HR responses were not different across age groups and playing positions. Distance covered below MAS were lower in the second half for all positions (P < 0.05; 0.08 < η2 < 0.20), while distance covered at intensities above MAS were maintained (P > 0.1; 0.00 < η2 < 0.03). This reduction in distance covered below MAS was not related to a player’s physical capacity. Except for strikers, a superior aerobic fitness level was unlikely to affect total distance covered but was associated with a reduced individual running demand during the game.

maturity-related improvements in physical fitness in young players [36, 37] absolute match physical performance is generally better in older players [8]. However, when individual [9] or agerelated [23] speed-thresholds are used, younger players tended to do more sprinting [9] and run the same total distance [23] as their older counterparts. Age-related differences in between-half variations in match running performance have also been suggested [15], with younger players being able to maintain high-intensity running in the second half better than older players [14, 15]. Interestingly, the better performance during the second half in younger players does not seem fitness dependent [15]. Playing position can also impact on physical performance in youth soccer match play, independently of physical fitness capabilities [8]. As the stimulus for training induced adaptation results from the physiological load imposed on athletes (and not necessarily the external training load) [29], understanding the relative physiological load imposed on the players during match play according to their age,

Mendez-Villanueva A et al. Intensity level in soccer players … Int J Sports Med

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Match Play Intensity Distribution in Youth Soccer

Training & Testing

Methods

▼

Subjects Time-motion match data was collected on 103 young players belonging to 6 different age groups, ranging from Under 13 to ▶ Table 1). All the players, Under 18 in an elite soccer academy (● irrespective of the age group, participated on average in ~14 h of combined soccer (6–8 sessions), strength (1 session) and condi-

tioning (1–2 sessions) training and competitive play (1 domestic local league game per week and 2 international club games every 3 weeks) per week. All players were well accustomed to this training/competition load. Written informed consent was obtained from the players and their parents. The study met the ethical standards of this journal [24] and was approved by an institutional ethics committee.

Experimental procedures Match data was collected 1–9 times on each outfield player across 42 international club matches played over a period of 4 months. Every week, 2 high-level, international club teams (mainly from Europe) visited the academy to play 2 matches against the same aged academy team. By providing high level opposition and in keeping the competition format consistent, match-by-match variability in running performance was likely reduced [44]. All matches were performed on 2 identical 100 × 70 m outdoor natural grass fields using 11 players per side. Playing time was 2 × 35 min for U13 and U14, 2 × 40 min for U15, U16 and U17, and 2 × 45 min for U18. All players undertook a series of running tests to determine maximal sprinting speed (MSS) and to estimate maximal cardiorespiratory performance and more precisely maximal aerobic speed (MAS) and HRmax. To account for the potentially confounding effect of growth on physical performance, these tests were repeated at least once within the 4-month investigation period. Thus, the testing results nearest in time to the match of interest were employed for the subsequent analysis of the match play intensity (see below for more details). To avoid fatigue unduly influencing the results, performance tests were performed over 2 testing sessions with at least 1 day between them. All performance tests were performed in an indoor facility maintained at standard environmental conditions (22 ± 0.5 °C, 55 % relative humidity). All testing sessions were preceded by a 20-min standardized warm-up and all players were familiar with the test procedures.

Anthropometric measurements All anthropometric measurements were taken in the morning (~ 8:00 a.m.). Measures included stretch stature, body mass, sitting height and sum of 7 skinfolds (triceps, subscapular, biceps, supraspinale, abdominal, front thigh and medial calf). Height was measured using a wall-mounted stadiometer ( ± 0.1 cm, Holtain Limited, Crosswell, UK), sitting height with a stadiometer mounted on a purpose-built table ( ± 0.1 cm, Holtain Limited, Crosswell, UK), body mass with a digital balance ( ± 0.1 kg, ADE Electronic Column Scales, Hamburg, Germany) and skinfold thickness using a set of Harpenden type skinfold calipers ( ± 0.1 mm, Baty International, Burguess Hill, UK). Landmarking

Table 1 Age related physical characteristics and performance measures in young soccer players. Age (y)

Height (cm)

Body mass

MAS (km·h − 1)

MSS (km·h − 1)

ASR (km·h − 1)

U13 U14 U15 U16 U17 U18 p-value η2

n = 16 n = 20 n = 17 n = 18 n = 15 n = 17

12.5 ± 0.3 a, b, c, d, e 13.4 ± 0.3 b, c, d, e 14.3 ± 0.3 c, d, e 15.6 ± 0.3 d, e 16.4 ± 0.2 e 17.3 ± 0.3 < 0.001 0.97

149.2 ± 6.8 a, b, c, d, e 158.3 ± 7.2 c, d, e 161.3 ± 7.4 d, e 164.2 ± 8.1 171.5 ± 6.2 171.1 ± 9.5 < 0.001 0.59

38.2 ± 5.4 b, c, d, e 43.8 ± 5.1 c, d, e 48.9 ± 10.1 d, e 51.1 ± 6.9 d 57.5 ± 4.8 56.3 ± 7.5 < 0.001 0.56

HRmax (beats·min − 1)

(kg) 14.0 ± 0.9 a, b, c, d, e 15.2 ± 1.3 b, c, d, e 15.8 ± 1.1 c, d, e 16.5 ± 1.0 e 16.6 ± 1.0 e 17.2 ± 0.8 < 0.001 0.40

24.9 ± 1.0 a, b, c, d, e 27.0 ± 1.7 b, c, d, e 28.7 ± 1.7 d, e 29.3 ± 1.4 d, e 30.9 ± 0.9 e 31.9 ± 1.7 < 0.001 0.65

10.9 ± 1.5 a, b, c, d, e 11.7 ± 2.2 b, c, d, e 12.9 ± 1.9 d, e 12.7 ± 1.5 d, e 14.3 ± 1.0 14.7 ± 1.9 < 0.001 0.32

206 ± 8 b, c, d, e 205 ± 8 b, c, d, e 201 ± 9 e 200 ± 6 198 ± 8 197 ± 9 < 0.001 0.13

Mean ± SD. U13 – Under 13, U14 – Under 14, U15 – Under 15, U16 – Under 16, U17 – Under 17 and U18 – Under 18. MAS, maximal aerobic speed (see Methods); MSS, maximal sprinting speed; ASR, anaerobic speed reserve. a : significant difference vs. U14 (P < 0.05), b : vs. U15, c : vs. U16, d : vs. U17, e : vs. U18. η2: effect size



positional role or fitness level is necessary to develop both sport and player specific training protocols that mimic and overload the physiological demands imposed by the game [20]. Heart rate (HR) has been commonly used to assess the internal load during soccer training [26, 47] and competition [50]. Measurements of HR during soccer match play have shown that it imposes a high physiological load in most outfield players, with average exercise intensities around 85 % of maximum HR (HRmax) and peak heart HRs close to HRmax [14–15, 30]. However, in young soccer players, no significant differences were observed in the HR responses to different types of small-sided soccer games, which elicited markedly different running demands [26, 47]. This suggests that factors other than running speed can influence HR responses during soccer match play. Osgnach et. al. [42] have shown that even low running speeds can be associated with high metabolic demands (and probably HR levels) whenever the acceleration is elevated. As a consequence, matchrelated activities that are often performed at low running speeds such as jumps, turns, physical contacts, lateral movements, shuffling, tackling and running with ball [3, 6, 48], as well as psychological factors [25], can increase the HR responses above that expected for a given running speed. Thus, the association between external (i. e., running speed ) and internal (i. e., HR) load measures in football is not straightforward. It therefore follows that the concurrent assessment of both external and internal measures of intensity would provide a better indication of the exercise load experienced by soccer players during competition and it could help in establishing appropriate training criteria. No published studies have described the relative external and internal exercise intensity distribution during match play in youth soccer players. Accordingly, the aim of this study was to quantify match play intensity distribution in highly-training young players as a function of age, playing position and physical fitness. For this purpose, we used individualized speed and HR thresholds based on physical performance measures designed to assess both the external and internal load of soccer match play, respectively.

Training & Testing

of each skinfold measurement was in accordance with international standards [41]. The same anthropometrist conducted all measurements. The technical errors of stretch stature, body mass, sitting height and skinfolds in our lab are 0.21 cm, 0.07 kg, 0.21 cm and 0.18 mm, respectively.

Anaerobic speed reserve The anaerobic speed reserve (ASR) was quantified as the difference between MSS and MAS [11]. MSS and MAS are empirically determined quantities that are representative of the body’s functional limits for sprint and endurance performance, respectively [11, 39].

Physical performance assessments

Maximal sprinting speed MSS was defined as the fastest speed over any 10-m sector measured during a maximal 40-m sprint using dual-beam electronic timing gates set at 10-m intervals (Swift Performance Equipment, Lismore, Australia) [10]. Split times were measured to the nearest 0.01 s and MSS subsequently calculated in km · h − 1. Players commenced each sprint from a standing start with their front foot 0.5 m behind the first timing gate and were instructed to sprint as fast as possible over the 40-m distance. The players started when ready, thus eliminating reaction time and completed 2 trials with the best performances used as the final result. The intraclass correlation coefficient in a 40-m sprint has been shown to be 0.94–0.99 [52]. Participants performed 2 trials with at least 3 min of rest between them. The best performance of the 2 tests was used for analysis.

Incremental running test Players performed an incremental running test to estimate maximal cardiorespiratory function and more precisely maximal aerobic speed. The test was a modified version of the University of Montreal Track Test [34] (i. e., the Vam-Eval maximal incremental running test, as previously used [37]). The Vam-Eval test began with an initial running speed of 8 km · h − 1 followed by consecutive speed increases of 0.5 km · h − 1 each minute until exhaustion. The players adjusted their running speed to match auditory signals set to correspond to 20-m intervals as delineated by marker cones around a 200-m indoor athletics track. The end of the test was taken to be when players failed to reach the next cone in the required time 3 times in succession. Throughout the test, HR was recorded beat-to-beat with a Polar S810 HR monitor (Polar Electro, Kempele, Finland). Players were also verbally encouraged throughout the test by testers and coaches. The speed of the last 1-min stage completed by the subjects was retained as the estimated players’ MAS (km · h − 1). If the last stage was not completed entirely, the MAS was calculated as MAS = Sf + (t/60 · 0.5), where Sf was the last completed speed in km · h − 1 and t in the time in seconds of the uncompleted stage. HRmax was defined as the highest 5-s average recorded during the test.

Match running performance measurements

▼

Materials A global positioning system (GPS) unit capturing data at 1 Hz (SPI Elite, GPSports, Canberra, Australia) was fitted to the upper back of each player using an adjustable neoprene harness. This GPS system utilised signals from at least 3 earth-orbiting satellites to determine the players position at a given time and therefore allow the calculation of movement speeds and distance covered [33]. Despite a possible underestimation of high intensity running distance with the time-resolution of 1 Hz [46], good accuracy (r = 0.97) and more importantly reliability (CV = 1.7 %) were reported for the assessment of short sprints and repeatedsprint exercise performance for this GPS device compared with a infra-red timing system [2, 18]. While the accuracy of the GPS s units used for total distance has been reported to be good (3–7 %), they have shown to be only moderately accurate for assessing high intensity running (11–30 %) [18]. However, in the absence of “gold standard” method, the current system has been reported to be capable of measuring individual movement patterns in soccer [46]. HR was also continuously measured (1 Hz) throughout the games (SPI Elite, GPSports, Canberra, Australia).

Analyses Time-motion data of all the players who participated in the entire first and/or second halves in the same playing position (n = 550 files from 103 different players, n = 360 for 1st half and n = 190 for 2nd half) were retained for analysis. All the second half files we obtained only from players who completed the entire first half. Tactically, all teams used a 4-4-1-1 formation, a variation of 4-4-2 with one of the strikers playing as a “second striker”, slightly behind their partner. Since player roles within the team structure changed little during the games analysed, all players were assigned to one of 6 positional groups; full backs (FB, n = 20 players, yielding 100 files), centre backs (CB, n = 15 players, yielding 127 files), midfielders (MD, n = 19 players, yielding 115 files), wide midfielders (W-MD, n = 24 players, yielding 84 files), second strikers (2ndS, n = 11 players, yielding 56 files) and strikers (S, n = 14 players, yielding 57 files). All match data was analysed using a custom-made Microsoft Excel program designed to provide objective measures of physical match running performance. Five running intensity zones were established to describe each player’s individual external load in the matches: speed zone 1 (S1): below 60 % of MAS, speed zone 2 (S2): from 61 % to 80 % of MAS, speed zone 3 (S3): from 81 % to 100 % of MAS, speed zone 4 (S4): from 101 % of MAS to 30 % of ASR and, speed zone 5 (S5): above 31 % of ASR. Using these relative speed zones, total distance (i. e., S1 + S2 + S3 + S4 + S5), distance run at intensities below MAS (i. e., S1 + S2 + S3) and distance run at intensities above MAS (i. e., S4 + S5) were calculated. HR data were classified based on percentage of total playing time spent in each of the following intensity zones: HR1, < 60 % HRmax; HR2, 61–70 % HRmax; HR3, 71–80 % HRmax; HR4, 81–90 % HRmax; HR5, > 91 % HRmax[28]. To examine the HR-running speed Mendez-Villanueva A et al. Intensity level in soccer players … Int J Sports Med


While football-specific tests (i. e., which replicate soccer movement patterns and efforts, such as Hoff and Yo-Yo tests) are often favoured [50], they usually evaluate different physical qualities simultaneously (e. g., the performance at the Yo-Yo IR1 test is the result of, among others, cardiovascular fitness, intra-effort recovery capacities and change of direction ability) [8]. For the purpose of the present study, we chose to evaluate 2 isolated physiological variables that can be directly measured on players [11]: the MSS and the estimated MAS. Thus, relative exercise intensity can be quantified in relation to these 2 individual and well-defined physiological parameters [11]. In addition, both MSS [38] and the estimated MAS [8, 43] have been shown to impact player’s locomotor performance in actual playing conditions.

Training & Testing

Statistical analysis Data are presented as means ± standard deviations (SD). The distribution of each variable was examined with the KolmogorovSmirnov normality test. Homogeneity of variance was verified with a Levene test. To account for differences in playing time between age-groups (i. e., 70 vs. 90 min for U13 vs. U18, respectively), the effect of age on match running intensity was analysed by 1-way ANCOVA, with data adjusted on individual playing time. A separate 1-way ANCOVA was also used to examine the effect of playing position on match running performance, with data adjusted for both age and individual playing time. The effect of age and playing position on HR responses was examined with 1-way ANOVAs. For all analyses, when a significant interaction was found, Bonferroni’s post hoc tests were applied. When the assumptions of normality and/or equal variance were not met (i. e., HR), data were analysed using the Kruskal-Wallis’ test followed by Dunn’s post hoc tests. For each ANCOVA, partial eta-squared (η2) was calculated as a measure of effect size. Values of 0.01, 0.06 and above 0.15 were considered as small, medium and large, respectively [17]. Differences between the first and second halves were examined using Student’s independent t-test. The relationships between match running speeds, match HR and physical fitness variables (MSS, MAS and

ASR) were assessed using partial correlations adjusted for age and individual playing time. This was performed independently for each playing position [8]. In addition to measures of statistical significance, the following criteria were adopted to interpret the magnitude of the correlation (r) between test measures: ≤ 0.1, trivial; > 0.1–0.3, small; > 0.3–0.5, moderate; > 0.5– 0.7, large; > 0.7–0.9, very large; and > 0.9–1.0, almost perfect. If the 90 % confidence limits overlapped small positive and negative values, the magnitude of the correlations was deemed unclear; otherwise the magnitude was deemed to be the observed magnitude [27]. All analyses were carried out using SPSS 15.0 (SPSS Inc, Chicago, USA) software with the level of significance set at P ≤ 0.05.

Results

▼

Players’ physical and performance characteristics according to ▶ Table 1, 2, respecage and playing position are presented in ● tively.

Age-related differences Age-related match play intensity distribution during the first and second halves, adjusted for individual playing time, are pre▶ Fig. 1. During the first half, there was a trend for the sented in ● older players to cover greater total distance (η2 = 0.09). U16, U17 and U18 players covered more distance at S1 than the other 3

Table 2 Players’ physical characteristics and performance measures according to playing position.

FB CB MD W-MD 2ndS S p-value η2

n = 20 n = 15 n = 19 n = 24 n = 11 n = 14

Age (y)

Height (cm)

Body mass (kg)

MAS (km · h − 1)

MSS (km · h − 1)

ASR (km · h − 1)

14.5 ± 1.6 14.6 ± 1.7 14.3 ± 1.5 14.9 ± 1.7 14.6 ± 1.7 14.4 ± 1.7 0.80 0.02

159.6 ± 9.8 166.3 ± 9.7 162.0 ± 9.4 162.6 ± 11.7 161.9 ± 8.1 166.6 ± 11.2 0.30 0.05

46.8 ± 8.6 54.9 ± 9.9 48.6 ± 9.8 49.9 ± 11.8 48.5 ± 7.6 56.7 ± 15.7 0.06 0.09

16.0 ± 1.3 15.6 ± 2.4 16.0 ± 1.7 16.4 ± 1.1 16.1 ± 1.7 15.9 ± 2.0 0.69 0.02

28.4 ± 2.5 29.9 ± 2.3 28.7 ± 2.9 29.0 ± 2.8 28.7 ± 1.8 29.6 ± 2.9 0.32 0.04

12.3 ± 2.2a 14.3 ± 2.5 12.7 ± 2.1 12.6 ± 2.3 12.6 ± 1.3 13.7 ± 1.7 0.02 0.10

Mean ± SD. FB – Full backs, CB – centre backs, MD – midfielders, W-MD – wide midfielders, 2ndS – second strikers and S – strikers. MAS, maximal aerobic speed (see Methods); MSS, maximal sprinting speed; ASR, anaerobic speed reserve. a : significant difference vs. CB (P < 0.05), η2 : effect size

5 000 4 500

Distance Covered (m)

4 000 b,c,d,e 3 500 3 000 2 500

#

d

e

#

e

b,e e

e

# e e #

c

#

#

#

#

#

#

c,d #

2 000 1 500

#

e

e #

a,b,c, d,e

1 000

cde

cde

U14

U15

a,b,c, d,e #

d,e #

U13

U14

d,e #

e #

#

#

U15

U16

U17

U18

500 0

U13

U16

U17

U18

First Half 0 –60 % MAS

61 –80 % MAS

Second Half 81 –100 % MAS

101 % MAS–30 % ASR


> 31 % ASR

Fig. 1 Least squared means for match play running intensity distribution in U13, U14, U15, U16, U17 and U18 soccer players. Values are adjusted on total playing time. a: significant difference vs. U14 (P < 0.05), b: vs. U15, c: vs. U16, d: vs. U17, e: vs. U18. # Significant lower vs. first half. MAS, maximal aerobic speed (see Methods). ASR, anaerobic speed reserve.


relationship, the average running speed for each given HR zone was also computed. Due to logistical reasons, HR data was not collected in the U13 group.

groups, with U13 players covering less distance at S1 than all other age groups (η2 = 0.25). U18 players covered lower distances at S3 than U13, U14, U15 and U16 players (η2 = 0.06). U13 players covered greater distances at S5 than most of all other age groups (i. e., U15, U16, U17, U18; η2 = 0.08). Similar between-age group results were obtained in the second half with older teams covering more distances at lower running speeds (i. e., S1) (η2 = 0.39) and younger teams covering greater distances at S3 (η2 = 0.12). Total distance covered in the second half was lower than in the first half for all the age groups (0.09 < η2 < 0.48). The distance covered at S1 (4–11 %; 0.04 < η2 < 0.28) and S2 (12–24 %; 0.05 < η2 < 0.27) was lower in the second half than in the first, irrespective of the age group. U13, U15, U17 and U18 players also covered less distance in the second half at S3 (14–19 %; 0.06 < η2 < 0.13). Running distance covered at intensities higher than S4 during the second half were significantly lower in U13 (33 %; η2 = 0.14), U15 (27 %; η2 = 0.06) and U16 (24 %; η2 = 0.06). The percentage of playing time spent in each HR zone is shown ▶ Table 3. Irrespective of age, players spent most of their playin ● ing time at HR intensities above 81 % HRmax (i. e., HR4 and HR5). Time spent at exercise intensities higher than 81 % HRmax tended to decrease during the second half while time spent at lower ▶ Table 4 outexercise intensities (i. e., HR2 and HR3) increased. ● lines the running speed-HR relationship for each age-group. Irrespective of the age-group, higher average speeds were recorded at HR4 zone compared with any other HR zone. Younger groups (U14 and U15) tended to have lower average speeds recorded at HR4 zone than the older groups (U17 and U18). For most age groups, higher HRs (i. e., higher than 81 % HRmax) were associated with lower running speeds during the second half.

Position-related differences Position-related match play intensity distribution, adjusted by ▶ Fig. 2. Playing position age and playing time, are displayed in ●

substantially impacted on distance covered during the first half at any running intensity (0.03 < η2 < 0.29). CB and S presented the lowest total distance covered (η2 = 0.26). Conversely, MD and W-MD covered greater total distance than FB, CB and S (η2 = 0.26). S covered significantly less distance at S2 and S3 than all other positions. On the contrary, MD covered the greatest distance at S2 (η2 = 0.26). CB and S recorded the lowest distance at S3 (η2 = 0.27). W-MD covered more distance at S4 than FB, CB and MD (η2 = 0.08). MD displayed the lowest distance covered at S5 compared with the other positions (η2 = 0.29), while S covered more distance at this intensity than FB, CB, MD and 2ndS. Between-position differences were less evident in the second half (i. e., η2 values were lower: 0.16, 0.09, 0.17, 0.16, 0.07 and 0.02 for total distance, S1, S2, S3, S4 and S5, respectively). Total distance covered in the second half was lower than in the first half, independent of the playing position (0.12 < η2 < 0.31). With the exception of 2ndS, players in all the other positions covered between 5–8 % less (0.07 < η2 < 0.18) at S1 during the second half. Distance covered at S2 and S3 was lower in the second half than in the first half for all positions (15–18 %; 0.08 < η2 < 0.20). Running distances covered at intensities above MAS (i. e., S4 and S5) were not reduced in the second half (0.00 < η2 < 0.03), irrespective of playing positions. There were no position differences in the time spent in each HR ▶ Table 5). Irrespective of playing position, players spent zone (● most of their playing time at HR intensities above 81 % HRmax (i. e., HR4 and HR5). Time spend in HR5 tended to decrease in the second half while time spent at HR3 tended to increase. There were no position differences in average speed recorded at any ▶ Table 6). Irrespective of playing position, higher HR interval (● average speeds were recorded at HR intensities above 81 % HRmax (i. e., HR4 and HR5) compared with any other HR zone. For most playing positions, average speeds at high HRs (i. e., HR4 and HR5) tended to decrease during the second half.

Table 3 Age related percentages of playing time spent in the different HR zones during each half of international club level youth soccer matches. < 60 % HRmax 1st Half U14 U15 U16 U17 U18 p η2

0.5 ± 0.6 0.5 ± 0.5 3.1 ± 4.9 3.2 ± 4.4 2.8 ± 5.8 0.35 0.05

2nd Half 4.2 ± 4.7* 1.6 ± 1.5 0.9 ± 1.2 3.8 ± 6.0 1.0 ± 1.6 0.03 0.14

61–70 % HRmax 1st Half 1.7 ± 2.9 2.0 ± 2.9 7.3 ± 8.7 6.5 ± 7.2 6.6 ± 6.6 0.08 0.08

2nd Half 7.6 ± 5.4* 6.9 ± 5.6* 4.6 ± 4.2 12.7 ± 8.0* 8.5 ± 5.7 0.05 0.13

71–80 % HRmax 1st Half 11.0 ± 6.1 12.7 ± 7.7 17.7 ± 10.8 16.5 ± 10.3 17.3 ± 9.6 0.25 0.05

2nd Half 20.8 ± 13.6* 22.3 ± 10.4* 20.6 ± 11.6 24.3 ± 13.1* 26.5 ± 9.9 * 0.59 0.04

81–90 % HRmax 1st Half d

48.9 ± 14.1 38.0 ± 14.3 35.3 ± 10.6 b 37.7 ± 13.8 36.7 ± 13.3 0.05 0.09

> 91 % HRmax

2nd Half

1st Half

2nd Half

30.6 ± 14.5* 39.6 ± 10.8 37.6 ± 6.8 39.4 ± 12.1 40.8 ± 8.1 0.15 0.09

34.6 ± 12.3 44.9 ± 21.0 36.4 ± 20.9 36.1 ± 24.0 36.5 ± 22.8 0.85 0.01

32.1 ± 23.8 25.8 ± 17.1* 35.1 ± 22.4 19.2 ± 18.8* 22.6 ± 14.4* 0.26 0.07

Mean ( ± SD). U14 – Under 14, U15 – Under 15, U16 – Under 16, U17 – Under 17 and U18 – Under 18. HRmax, maximal heart rate. b

: vs. U15, d : vs. U17, * Significant difference vs 1st Half. η2 : effect size

Table 4 Age related running speeds (km · h − 1) in the different HR zones during each half of international club level youth soccer matches.

U14 U15 U16 U17 U18 p η2

Speed at < 60 % HRmax

Speed at 61–70 % HRmax



Speed at > 91 % HRmax

1st Half

1st Half

1st Half

1st Half

1st Half

2nd Half

6.2 ± 1.0 6.1 ± 0.8 6.3 ± 1.7 6.5 ± 1.0 6.6 ± 1.0 0.62 0.03

5.0 ± 15* 5.6 ± 0.8 5.7 ± 0.9 5.7 ± 1.6* 6.0 ± 1.2* 0.15 0.10

1.7 ± 1.1 1.3 ± 0.8 1.3 ± 0.9 2.1 ± 1.1 1.7 ± 1.1 0.12 0.08

2nd Half 2.0 ± 1.7 1.6 ± 0.9 1.9 ± 1.7 2.1 ± 1.4 1.5 ± 0.8 0.53 0.05

3.5 ± 2.3 3.5 ± 1.3 4.0 ± 1.4 4.5 ± 1.5 3.8 ± 1.7 0.40 0.04

2nd Half 4.1 ± 1.7 3.1 ± 1.1 3.2 ± 1.7 4.4 ± 1.3 3.9 ± 1.5 0.31 0.07

6.3 ± 1.0 5.6 ± 1.6 5.6 ± 1.3 6.3 ± 1.4 5.9 ± 1.2 0.23 0.06

2nd Half 5.1 ± 1.0* 5.2 ± 0.9 5.4 ± 0.4 5.7 ± 1.2 5.5 ± 0.9 0.60 0.04

6.5 ± 0.5 6.2 ± 0.9 6.6 ± 0.7 6.8 ± 0.8 6.8 ± 0.6 0.18 0.07

2nd Half d, e

5.3 ± 0.8 * 5.5 ± 0.8 d 6.0 ± 0.3 6.7 ± 0.9 6.3 ± 0.6* < 0.001 0.3

Mean ( ± SD). U14 – Under 14, U15 – Under 15, U16 – Under 16, U17 – Under 17 and U18 – Under 18. HRmax, maximal heart rate. d

: vs. U17, e : vs. U18. * Significant difference vs 1st Half. η2: effect size



Training & Testing

Training & Testing

Fig. 2 Least squared means for match play running intensity distribution as a function of playing position (full backs (FB), centre backs (CB), midfielders (MD), wide midfielders (W-MD), second strikers (2ndS) and strikers (S)) in young soccer players. Values are adjusted for total playing time and age. a: significant difference vs. CB (P < 0.05), b: vs. MD, c: vs. W-MD, d: vs. 2ndS, e: vs. S. # Significant lower vs. first half. MAS, maximal aerobic speed (see Methods). ASR, anaerobic speed reserve.

5 000 4 500

Distance Covered (m)

4 000

c,d,e c

b,c,e c

b,c,e c

3 500 b,c,d,e

d

e

c,d,e b

e e

e

b,c,d

3 000

c,d,e

b,e

e

b,e

2 500

c,e

#

b,c #

#

b #

#

b #

FB

CB

e

e

#

#

e

#

#

e #

#

2 000 1 500 b

1 000

#

#

MD

W -MD

#

500

FB

CB

MD

W -MD

2ndS

S

0 –60 % MAS

2ndS

S

Second Half

First Half 61 –80 % MAS

81 –100 % MAS

101 % MAS–30 % ASR

> 31 % ASR

Table 5 Percentage of playing time in each half spent in the different HR zones during international club level youth soccer matches as a function of playing position. < 60 % HRmax FB CB MD W-MD 2ndS S p η2

61–70 % HRmax

71–80 % HRmax

81–90 % HRmax

> 91 % HRmax

1st Half

2nd Half

1st Half

2nd Half

1st Half

2nd Half

1st Half

2nd Half

1st Half

2nd Half

1.5 ± 3.2 2.6 ± 4.2 2.1 ± 3.6 3.9 ± 6.8 0.8 ± 0.8 2.0 ± 3.7 0.38 0.05

1.0 ± 1.0 3.5 ± 5.0 1.0 ± 1.2 3.4 ± 5.3 0.7 ± 0.7 2.4 ± 4.8 0.24 0.09

6.7 ± 6.6 6.0 ± 7.0 5.8 ± 8.0 7.3 ± 7.4 2.6 ± 2.3 5.6 ± 8.0 0.71 0.03

11.1 ± 7.7 8.3 ± 5.7 9.0 ± 7.2 11.8 ± 7.3 4.0 ± 3.7 8.5 ± 5.5 0.27 0.90

17.8 ± 7.9 19.0 ± 13.1 15.7 ± 10.4 19.2 ± 11.2 10.8 ± 5.4 17.2 ± 10.5 0.47 0.04

27.2 ± 11.8* 22.1 ± 9.2 24.6 ± 13.1* 31.2 ± 8.8* 19.8 ± 16.7 24.0 ± 8.1 0.48 0.07

43.8 ± 11.4 40.3 ± 12.4 38.7 ± 17.9 37.0 ± 12.5 31.2 ± 12.6 39.2 ± 8.6 0.14 0.08

38.7 ± 8.0 38.9 ± 10.5 37.9 ± 9.0 42.0 ± 11.0 37.1 ± 9.7 38.5 ± 14.0 0.97 0.01

29.3 ± 17.1 30.8 ± 18.6 36.7 ± 25.8 31.2 ± 21.6 52.1 ± 16.3 34.1 ± 14.5 0.10 0.09

20.6 ± 18.9 25.9 ± 16.0 25.5 ± 16.7* 11.7 ± 9.9* 37.1 ± 24.7 21.3 ± 16.3* 0.28 0.09

Mean ( ± SD). FB – Full backs, CB – centre backs, MD – midfielders, W-MD – wide midfielders,2ndS – second strikers and S – strikers. Main playing position effect: all P < 0.001. HRmax, maximal heart rate. *Significant difference vs 1st Half. η2 : effect size

Table 6 Running speed in the different HR zones during international club level youth soccer matches as a function of playing position.

FB CB MD W-MD 2ndS S P η2

Speed at < 60 % HRmax




Speed at > 91 % HRmax

1st Half

2nd Half

1st Half

2nd Half

1st Half

2nd Half

1st Half

2nd Half

1st Half

2nd Half

1.6 ± 0.8 2.0 ± 1.0 2.4 ± 1.9 1.3 ± 1.1 1.5 ± 0.9 1.3 ± 0.8 0.02 0.13

2.1 ± 1.7 2.0 ± 1.3 1.8 ± 1.3 2.3 ± 1.2 1.8 ± 1.0 1.7 ± 0.9 0.89 0.03

3.7 ± 1.4 3.9 ± 1.9 4.0 ± 2.2 4.5 ± 1.9 3.4 ± 0.8 4.1 ± 1.4 0.66 0.03

3.7 ± 1.2 3.9 ± 1.4 4.1 ± 1.5 5.1 ± 0.7 3.4 ± 1.8 4.3 ± 1.7 0.29 0.09

5.8 ± 1.0 5.7 ± 0.9 5.8 ± 1.8 6.5 ± 1.1 5.3 ± 1.4 6.0 ± 0.9 0.20 0.07

5.4 ± 0.8 5.4 ± 0.8 6.0 ± 0.6 6.0 ± 0.6 4.9 ± 1.5 5.4 ± 0.7 0.13 0.13

6.6 ± 0.6 5.9 ± 0.5 6.8 ± 0.9 6.9 ± 0.9 6.7 ± 0.7 6.5 ± 0.6 0.001 0.20

6.1 ± 0.6* 5.8 ± 0.5 6.7 ± 0.6 6.4 ± 0.6* 6.2 ± 0.7 6.4 ± 0.7 0.02 0.20

6.2 ± 0.7 5.4 ± 1.7 6.6 ± 1.2 6.4 ± 1.2 6.8 ± 0.6 6.2 ± 0.9 0.02 0.13

6.0 ± 1.2 5.9 ± 0.9 6.6 ± 0.6 5.3 ± 1.5* 5.5 ± 1.4* 6.0 ± 0.9 0.27 0.10

Mean ( ± SD). FB – full backs, CB – centre backs, MD – midfielders, W-MD – wide midfielders, 2ndS – second strikers, S – strikers. Main playing position effect: all P < 0.001. HRmax, maximal heart rate. *Significant difference vs. 1st Half. η2: effect size

Fitness-related correlations The correlations between MAS and match running performance ▶ Table 7. There was a for each playing position are reported in ● positive, significant correlation between MAS and total distance run only for S. For all playing positions (except W-MD) there was a significant, positive, moderate-to-large correlation between MAS and the distance run at speeds below MAS. In addition, for all playing positions (with the exception of S) there was significant, negative, large-to-very large correlation between MAS and distance run at speeds above MAS. There were no significant and/or clear correlations between any other marker of physical Mendez-Villanueva A et al. Intensity level in soccer players … Int J Sports Med

fitness (i. e., MSS, and ASR), internal physiological load ( % HRmax) and match running performance.

Discussion

▼

The aim of the present study was to quantify the exercise intensity distribution during match play in highly-trained young soccer players relative to individual physical capacities (i. e., MSS and MAS) and a physiological marker (HRmax). The main findings were as follows: 1) younger players exercised at higher relatively


0

Training & Testing

Table 7 Relationships between match running performance and estimated maximal aerobic speed (MAS) obtained during the incremental field test. MAS FB TD TD < 100 % MAS TD > 100 % MAS Diff 1st–2nd Half TD < 100 % MAS Diff 1st–2nd Half TD > 100 % MAS

0.18 ( − 0.12;–0.45) 0.59* (0.36; − 0.75) − 0.74* ( − 0.85; − 0.57) − 0.28 ( − 0.52; − 0.02) − 0.13 ( − 0.40; − 0.17)

CB 0.13 ( − 0.12;–0.37) 0.46* (0.24; − 0.64) − 0.69* ( − 0.80; − 0.54) − 0.18 ( − 0.40; − 0.06) 0.06 ( − 0.18; − 0.30)

MD

W-MD

0.18 ( − 0.10;–0.43) 0.36* (0.10; − 0.57) − 0.61* ( − 0.76; − 0.41) 0.01 ( − 0.25; − 0.26) 0.16 ( − 0.11; − 0.40)

− 0.09 ( − 0.48;–0.34) 0.10 ( − 0.32;0.50) − 0.52* ( − 0.77; − 0.13) − 0.11 ( − 0.50; − 0.32) − 0.25 ( − 0.60; − 0.18)

2ndS 0.42 ( − 0.03;–0.69) 0.60* (0.28; − 0.80) − 0.64* ( − 0.83; − 0.34) − 0.07 ( − 0.45; − 0.33) − 0.19 ( − 0.54; − 0.22)

S 0.51* (0.14;–0.76) 0.70* (0.42; − 0.86) − 0.37 ( − 0.67; − 0.03) − 0.31 ( − 0.63; − 0.10) − 0.14 ( − 0.52; − 0.27)

Correlation coefficients (90 % confidence limits) between match running performance at different intensities and maximal aerobic speed (MAS, see Methods) obtained during an incremental field test, adjusted for age and individual playing time. TD, total distance; TD < 100 %MAS, total distance covered below MAS; TD > 100 % MAS, total distance covered below MAS; Diff 1st–2nd Half, differences between the first and second half. FB – full backs, CB – centre backs, MD – midfielders, W-MD – wide midfielders, 2ndS – second

running intensities than older players; 2) the substantially different relative running demands for each playing position were not influenced by a player’s physical capacity; 3) despite the marked differences in relative running demands for the different age groups and playing position, there were no differences in HR responses between age groups and playing positions; 4) average relative running speeds were not different between age groups and playing positions at HR intensities above 80 % of HRmax; 5) a superior aerobic fitness level was unlikely to affect total distance covered but was associated with a reduced individual running demand; 6) in the second half, the reduction of total distance covered was explained by covering significant less distance below MAS, irrespective of the playing position, while distances covered above MAS were maintained; 7) the reduction in total distance covered below MAS in the second half was not related to a player’s physical capacity.

The effects of age on match play intensity distribution Previous studies have examined the physical and physiological demands of competitive soccer matches in young players using either running distances covered at fixed speed thresholds and/ or HR responses [5, 8, 14, 15, 23, 51]. In the present study, we utilised individual running speeds and HR values determined via field testing (i. e., MSS, MAS and HRmax). Younger age groups (i. e., U13, U14 and U15) covered less distances in the lowest intensity ▶ Fig. 1). In zone (S1) than the older groups (U16, U17 and U18, ● combination with the greater running distances covered in S5 by the U13 team, the present results indicate that younger players competed at higher relative running intensities than their older counterparts, confirming previous findings related to sprinting performance during match play [9]. However, despite these agerelated differences in running intensity distribution, HR ▶ Table 3). More responses were similar across all age groups (● precisely, all players, irrespective of age, spent most of their playing time in HR4 and HR5, which is in line with data previously reported for elite male and female adult soccer players [30, 50] and young soccer players [5, 15, 51]. Thus, the increase in absolute physical capacity (i. e., higher MAS and MSS values, ▶ Table 1) associated with growing older enabled players to see ● reduce their individual running demands (i. e., running speed) during match play rather than covering more distance. However, this reduced individual running demand did not translate into a lower internal load (HR), which might suggest that some matchrelated movements which are not captured by the GPS were

conducted often enough and/or at a high enough intensity to elevate players’ HRs (discussed below). Irrespective of the age group, players covered less distance in the ▶ Fig. 1). Specifically, the second half than in the first half (● present study demonstrates that some age groups (U13, U15 and U16) showed a significant decrease in distance covered in S5 ▶ Fig. 1), while all age groups had a during the second half (● reduction in the distance covered at low relative running speeds (i. e., S1 and S2). We also observed a reduction in the percentage of time spent in the higher HR zones in the second half with subsequently more time spent in the lower HR zones. The lower overall running demands, the drop in exercise HR and the higher HRs associated with lower running speeds during the second half support the previous findings of reduced work rates in the second halves in adult [40, 46] and young [5, 16] soccer players, and suggest that decrements in match running performance as the match progresses are similar across ages. Whether these match running performance decrements seen in young soccer players are related to physiological [1] and/or tactical and strategic [31, 32] factors needs to be examined in future studies.

The effects of position and fitness level on match play intensity distribution It has been previously suggested that the activity profile during match play is related to a player’s physical ability [1, 30]. For example, it has been reported that physically fit players, irrespective of their playing position, perform more high-intensity running during a game that their less-fit counterparts [15, 16, 30, 40]. However, playing position has been shown to impact physical performance in youth soccer match play independent of physical fitness capabilities [7]. Similarly, in the present study we observed substantial differences in match running performance and intensity distribution across all playing positions ▶ Table 2). Generally, despite no differences in MAS and MSS (● MD covered the greatest distance at low relative speeds (i. e., S2) while S displayed the lowest distance at intensities below MAS. These differences might represent the more “sustained” nature of playing as a MD [21], who are required to act as the link between attack and defense, as well as potentially being involved in more technical actions, especially more balls received and passed than any other position [3]. On the contrary, W-MD and S covered more distance in the highest intensity zone (i. e., S5). This seems to reflect the high speed requirements when accelerating to create spaces or to evade an opponent and to be in the Mendez-Villanueva A et al. Intensity level in soccer players … Int J Sports Med


strikers, S – strikers. *P < 0.05

position to receive a pass from a teammate [3]. Our data on physical fitness and match running performance serve to highlight that playing position can exert more of an influence than physical fitness in determining a player’s running demands [7]. The current results may also provide a framework for constructing competition-specific training programs that, in the case of WM and S, should ensure adequate focus is placed on anaerobicfitness traits (e. g., > S5). Consistent with this dissociation between physical capacity and match running demands, the distance covered at high intensity (i. e., running speeds above MAS) was negatively correlated with ▶ Table 7). Moreover, individual MAS for all positions except S (● distances covered at running speeds below MAS were positively correlated with MAS. These correlations show that aerobically fitter players ran a greater proportion of their total distance at intensities below their individual MAS and with lower proportions at intensities above MAS. Overall, these correlations further indicate that during high-level soccer match play, playing position [8, 9, 38] and other game-related tactical, situational and strategic factors [12, 31, 32] are likely to dictate to a great extent the absolute amount and relative intensity (present results) of running efforts that players have to perform. Thus, a superior aerobic fitness level would result, for most playing positions excepting the S, in similar distances covered [8] but in a reduced relative running demand during the game. Our results also emphasize that caution must be taken when testing for physical capacity-match performance relationships since final match results (i. e., winning or losing) are not directly related to the distance covered during the match [8, 35]. Nevertheless, reduced individual running demands as a result of higher fitness levels might protect against fatigue-associated decrements in technical skills [45] and factors associated with injury risk [13]. The HR measurements revealed that all players, irrespective of their position, spent most of their playing time above 81 % HRmax ▶ Table 5). This exercise intensity is similar to what has previ(● ously been reported for elite male and female adult [30, 50] and young soccer players [5, 15, 51], although no previous study has reported HR responses for each playing position. As indicated by the relative match running data, while the different playing positions have different physical demands during matches, the corresponding positional HR responses were similar and could have suggested the opposite. Similarly, no differences in HR responses were found during small-sided games using different formats despite marked differences in running demands [26]. Thus, while HR measurements have been suggested to provide a valid indication of the metabolic stress associated with soccer practice [22], the present results suggest that HR does not precisely reflect the running demands recorded during match play. The similar HR responses observed across playing positions, despite differences in running demands, suggest that running speed together with factors other than those tracked by the GPS units (i. e., jumps, body contact, psychological pressures, etc) might influence HR responses during soccer match play. Nevertheless, caution is needed when generalizing these data, as a high interindividual variability in the HR responses was observed (i. e., the standard deviations, representing individual differences, were large). Additionally, in the present study, similar running speeds were ▶ Table 4, 6) and most likely associated with very different HRs (● very different metabolic demands [42, 48]. For example, average running speeds were not different between playing positions at ▶ Table 6). In HR intensities ranging from 80 to 100 % of HRmax (● Mendez-Villanueva A et al. Intensity level in soccer players … Int J Sports Med

this regard, recent research has shown that low running speeds can be associated with high metabolic demands whenever additional accelerations [42] and/or changes in direction are involved [4, 6]. Also, it has been reported that for a given speed of locomotion the metabolic demands are higher when running with the ball than during normal running [48]. These match-play activities, which register as low running speed activities using the current GPS technology, can however increase the HR responses above what is expected for a given running speed during ‘normal’ running conditions (e. g., continuous runs with no changes of direction or pace). Thus, position-related differences in the number and/or frequency of soccer specific activities such as accelerating, decelerating, turning, jumping, moving backwards/ sideways, kicking the ball, tackling or running with the ball as well as psychological and emotional stress [25] may account for these discrepancies between markers of internal and external physiological load as observed in the present study. Therefore, even when accounting for the lag between the response of a player’s HR to the running speed demands, the discrepancy between relative running intensity and HR-derived exercise intensity casts some doubt upon the validity of HR measures alone for describing the exercise performed during soccer match play. These results should also be considered when prescribing and monitoring specific, soccer-based drills (e. g., small-sided games) based on running and/or HR measurements [26]. Future studies that combine objective motion analysis data with qualitative analysis of time spent in other non-running activities are required to provide a full understanding of the specific demands of soccer match play.

Changes in match play intensity distribution during the second half Our results showed a significant reduction in the distance covered at running intensities below MAS in the second half of ▶ Fig. 2). On the matches, irrespective of the playing position (● contrary, distances covered in the second half at running intensities above MAS were generally maintained. Despite the variable response (as reflected by the large SDs), there was also a trend towards lower average running speeds in the higher HR zones (above 71 % HRmax) in the second half for most playing ▶ Table 6). Our running results contrast with the findpositions (● ings from recent studies in adults using set speed thresholds which showed a decrease in the amount of high-intensity running and sprinting in the second half [40, 46]. Di Salvo et al. [20] however reported that elite adult Spanish players covered significantly more distance in the first half compared with the second at medium intensities (11.1–19 km · h − 1) with no significant decrements in total high-intensity and sprinting distance. The ability to maintain high-intensity running performance in the second halves is also consistent with results reported for young players [14, 16]. Thus, as previously suggested [14, 16], it is possible that the young players examined in the present study adopted a subconscious pacing strategy by limiting lower intensity activities (i. e., more standing and walking and less jogging) and reducing metabolic load (i. e., lower HR values) [22] to help sustain high speeds as the game proceeds [19]. The strategy apparently adopted by our players does not seem to be fitnessdependent because no relationships were found between MAS and first- vs. second-half decrements in running distance ▶ Table 5). Lending support to these findings, Carling and (● Bloomfield [12] showed that during a match in which one team was forced to play with 10 players due to an early player dis-


Training & Testing

Training & Testing

Practical applications The knowledge of a player’s intensity distribution during soccer match play can be used by coaches and support staff as starting point in the design of effective physical fitness programs and to help manage training and competition loads. The fact that players have similar HR values despite the marked differences in relative running intensities has important implications for the physical fitness preparation of players. For example, it is possible that many training drills elicit similar or more intense internal loads (i. e., HR levels) than actual match play and can therefore, provide sufficient training stress to stimulate the desired physiological (i. e., cardiovascular and metabolic) adaptation. However, such training drills may result in the selection of inappropriate or suboptimal external loads (relative running demands and their associated neuromuscular and mechanical stress). Albeit speculative, if this occurs frequently, it could compromise longer term training adaptations and diminish a player’s ability to cope with the mechanical demands associated to soccer match play. Therefore, both individual HR- and runningderived intensities should ideally be controlled and manipulated in order to provide the desired internal and external training loads.

Conclusions

▼

In summary, the present results provide the first extensive insight into the relative exercise intensity requirements of young soccer players of different ages and playing positions. Our results confirm the influence of tactical and strategic factors on physical performance during matches, as evidenced by the finding that playing position impacted more on relative running demand than did a player’s physical fitness. Aerobically fitter players appear to benefit more from reduced relative running demands during the game rather than being able to cover more distance at high intensity. The present results also showed that distance covered at running intensities below MAS during the second half was reduced in most age groups and playing positions and this may have assisted the players to maintain intensities above MAS (i.e., high intensity running). Lastly, the present results also support the concurrent assessment of both individual internal and external loads during soccer matches and practice. These data may provide a better characterization of the exercise loads required to elicit appropriate training responses in soccer players and thereby optimize the development of physical performance.

Acknowledgements

▼

The authors thank Gil Orriols Jansana, Jesus Pinedo Otaola, Nicholas Poulos, Ben Haines and Dino Palazzi for their assistance for GPS data collection.

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