Buchheit Int J Sports Med 2010 Soccer Youth Performance

818 Training & Testing

Match Running Performance and Fitness in Youth Soccer

Authors

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

Affi liations

Physiology Unit, Sport Science Department, ASPIRE, Academy for Sports Excellence, Doha, Qatar

Key words ▶ football ● ▶ high-intensity running ● ▶ field tests ● ▶ adolescents ● ▶ global positioning system ●

Abstract ▼

The activity pro files of highly trained young soccer players were examined in relation to age, playing position and physical capacity. Timemotion analyses (global positioning system) were performed on 77 players (U13– (U13 –U18; fullbacks [FB], centre-backs [CB], mid fielders [MD], wide midfielders [W], second strikers [2 ndS] and strikers [S]) during 42 internation international al club games. Total distance covered (TD) and very high-intensity activities (VHIA; > 16.1 km · h − 1) were computed during 186 entire player-matches. player-matches. Physical capacity was assessed via field test measures (e. g., peak running speed during an incremental field test, VVam-eval). Match running performance showed an increasing trend with age (P ( P < <0.001, partial

Introduction accepted after revision July 05, 2010 Bibliography DOI http://dx.doi.org/ 10.1055/s-0030-1262838 Published online: August 11, 2010 Int J Sports Med 2010; 31: 818– 818 –825 © Georg Thieme Verlag KG Stuttgart · New York ISSN 0172-4622 Correspondence Dr. Martin Buchheit

Physiology Unit, Sport Science Department ASPIRE, Academy for Sports Excellence P.O. Box 22287, Doha Qatar Tel.: + 974 974//4413 4413//6103 Fax: + 974 974//4413 4413//6060 [email protected]

▼

Most professional soccer academies are seeking to optimize the early detection and physical development of their young players [39] . The assessment of the physical determinants of running performance during competitive matches according to age and playing position is therefore required to improve talent detection procedures and long-term training interventions. Nevertheless, while some information is available about the physical and physiological demands of highly trained young soccer players during match play [6, 9, 10, 17, 40], 40], little is known about whether physical capacities are important determinants of physical match performance in these players. Several studies have reported signi ficant correlations between field or laboratory test results and running performance during soccer matches, suggesting suggestin g that individual physical capacities can account for game-related physical performance [10, 17, 23, 34]. 34]. For example, in professional male soccer players, signi ficant correlations have been

eta-squared (η2): 0.20– eta-squared 0.20–0.45). When adjusted for age and individual playing time, match running performance was position-dependent (P ( P < 0.001, η2: 0.13– 0.13–0.40). MD covered the greater TD; CB the lowest (P (P < < 0.05). Distance for VHIA was lower for CB compared with all other positions ( P < < 0.05); W and S displayed the highest VHIA (P ( P < < 0.05). Relationships between match running performance and physical capacities were position-dependent, with poor or non-signi ficant correlations within FB, CB, MD and W (e. g., VHIA vs. V Vam-eval: r = = 0.06 in FB) but large associations within 2 ndS and S positions (e. g., VHIA vs. V Vam-eval: r = = 0.70 in 2ndS). In highly trained young soccer players, the importance of fitness level as a determinant of match running performance should be regarded as a function of playing position.

reported between distance covered at high intensity during a match and both peak running speed during an incremental field test and mean sprint time on a repeated sprint ability (RSA) test [34] . Maximal oxygen uptake [23] and maximal performance for the Yo-Yo Intermittent Recovery Tests were also shown to correlate well with the amount of high intensity activities during games in adult females [23] and young male players [10].. However, these relationships have consist[10] ently been reported with all play players ers from a team pooled together; none of the aforementioned studies considered the correlation between match running performance and physical capacities with regard to playing positions. In young [40] and in top-level adult soccer players [5, 13, 14, 35] 35],, match analyses have demonstrated that match running performance, and especially high-intensity running, are positiondependent. Centre-backs generally undertake less high-intensity running, whereas mid fielders and attackers generally display the most [5, 13, 14, 35]. 35]. Therefore, if physical capacities

. l a i r e t a m d e t h g i r y p o C . k e t o i l b i b s t e t i s r e v i n U s n v a h n e b e o K : y b d e d a o l n w o D

Training & Testing 819

were to significantly account for match running performance, irrespective of playing positions [10, 23, 34], centre-backs would be expected to consistently present the poorest physical tests results, and conversely, mid fielders and attackers, the best ones. While between-position di ff erences in physical capacities have been reported for some physical capacities [15, 22, 44], such differences are not always apparent [20] or even absent [41] . For example, while fullbacks performed more distance in the Yo-Yo test than attackers [22], the mean sprint time on the RSA test conducted by Impellizzeri et al. [20] did not discriminate fullbacks, midfielders and forwards, despite the important di ff erences in their match running performance [5, 13, 14, 35]. It is therefore unclear to what extent physical fitness and/or specific technical/tactical roles assigned to each playing position and playing style can dictate player ’s running activity during the game. In addition, given the reported existence of di ff erent position-specific running patterns [13], it is also unclear which physical capacities (e. g., lower limb explosive strength, maximal aerobic function, RSA [34]) are related with match running performance for a given playing position. The purpose of this study was therefore to 1) examine for the first time match running performance in a wide age range of highly trained young soccer players as a function of age and playing position, 2) determine whether individual di ff erences in match running performances are related to di ff erences in physical capacities (as evidenced via field tests results), and 3) evaluate the magnitude of these relationships for each separate playing position.

▶ Table 1). All Under 13 to Under 18 in an elite soccer academy (● the players participated on average in ~ 14 h of combined soccerspecific training and competitive play per week (6–8 soccer training sessions, 1 strength training session, 1 –2 conditioning sessions, 1 domestic game per week and 2 international club games every 3 weeks). All players had a minimum of 3 years of prior soccer-speci fic training. Written informed consent was obtained from the players and their parents. The study was performed in accordance with the ethical standards of the IJSM [16] and conformed to the Declaration of Helsinki.

Anthropometric measurements and maturity assessment Height (Harpenden, Baty International, Burguess Hill, U.K.), body mass (ADE Electronic Column Scales, Hamburg, Germany) and the sum of 7 skinfold sites (triceps, subscapular, biceps, supraspinale, abdominal, thigh and medial calf; Harpenden skinfold caliper (Baty International, Burguess Hill, U.K.) were measured by an experienced tester [26]. Although the teams di ff ered in chronological age, the possibility of an overlapping of some players’ maturity status was likely given the heterogeneity of biological and physical maturity of children around puberty [25] . The age at peak height velocity (PHV) is an indicator of somatic maturity representing the time of maximum growth in stature during adolescence. Maturity timing (yr) was calculated by subtracting the chronological age at the time of measurement from the age at estimated PHV [28].

Experimental procedures Methods ▼

Subjects Time-motion match analysis data was collected on 99 young football players belonging to 6 di ff erent age groups ranging from

Match analyses were performed on 42 matches against international club teams, played over a 4-month period. Each out field player was assessed 1–9 times. The high level of the opposing teams and the same competition format likely reduced match-

Table 1 Physical capacities and match running performance according to age. U13 number of players year from PHV height (cm) body mass (kg) CMJ (cm) Acc (s) PV (km.h − 1) RSAmean VVam-eval (km.h − 1) number of files playing time TD (m) LIR (m) HIR (m) VHIR (m) Sprinting (m) VHIA (m) peak game speed (km.h − 1)

U14

U15

U16

anthropometric, maturity data and physical capacities n = 17 n = 10 n = 12 − 1.7 ± 0.4 a,b,c,d,e − 0.7 ± 0.5 c,d,e − 0.1 ± 0.7 d,e 0.6 ± 0.8d,e 150 ± 6a,b,c,d,e 159 ± 7d,e 161 ± 6d,e 163 ± 9 b,c,d,e d,e 39.3 ± 5.1 43.9 ± 5.2 48.8 ± 9.8d 52.0 ± 7.2 27.5 ± 2.5a,b,c,d,e 32.0 ± 3.1b,c,d,e 39.2 ± 4.1d,e 37.9 ± 3.7d,e 1.96 ± 0.07 a,b,c,d,e 1.89 ± 0.06 b,c,d,e 1.79 ± 0.08 d,e 1.77 ± 0.05e a,b,c,d,e b,c,d,e d,e 25.4 ± 0.7 27.0 ± 1.5 29.0 ± 1.8 29.4 ± 1.0d,e a,b,c,d,e b,c,d,e d,e 5.15 ± 0.08 4.88 ± 0.16 4.60 ± 0.20 4.51 ± 0.12e 13.7 ± 0.8a,b,c,d,e 15.3 ± 1.4d,e 15.8 ± 1.3e 15.8 ± 1.1e match running performance n = 18 files n = 40 files n = 25 files n = 21 files 2 × 35 min 2 × 35 min 2 × 40 min 2 × 40 min 6549 ± 597a,b,c,d,e 7383 ± 640 b,c,d,e 8129 ± 879 e 8312 ± 1054 b,c,d,e b,c,d,e 5370 ± 470 5799 ± 454 6288 ± 610 6480 ± 845 671 ± 180b,c,d,e 821 ± 231 954 ± 297 968 ± 258 b,c,d,e e 323 ± 87 446 ± 162 477 ± 156 479 ± 180 186 ± 92b,c,d,e 318 ± 183 e 410 ± 204 e 384 ± 163 e a,b,c,d,e d,e e 509 ± 156 763 ± 307 887 ± 311 864 ± 314 e 22.3 ± 1.4a,b,c,d,e 24.4 ± 1.8 b,c,d,e 26.0 ± 2.4e 26.3 ± 2.3 e n=7

U17

U18

η2

n = 17

n = 14

1.6 ± 0.6 e 170 ± 7 58.1 ± 4.7 42.6 ± 4.0 1.74 ± 0.04 31.3 ± 0.7 e 4.39 ± 0.12 16.6 ± 0.9

2.2 ± 0.4 171 ± 9 56.3 ± 7.5 44.5 ± 5.2 1.71 ± 0.06 32.3 ± 1.9 4.31 ± 0.17 17.4 ± 0.9

0.83 0.58 0.51 0.69 0.67 0.74 0.78 0.51

n = 29 files 2 × 40 min 8707 ± 1101 6749 ± 768 991 ± 370 519 ± 155 449 ± 147e 967 ± 221e 26.6 ± 2.2e

n = 53 files 2 × 45 min 8867 ± 859 6650 ± 565 976 ± 240 574 ± 134 666 ± 256 1239 ± 337 28.3 ± 2.2

0.44 0.36 0.28 0.20 0.39 0.39 0.45

Mean ( ± SD) values of anthropometric and match running performances and least squared means ( ± SE) of physical capacities of the Under 13 (U13), Under 14 (U14), Under 15 (U15), Under 16 (U16), Under 17 (U17) and Under 18 (U18) soccer players. PHV peak height velocity. Field tests: counter movement jump (CMJ), acceleration (Acc), peak velocity (PV), mean sprint time on the repeated sprint test (RSA mean) and peak running speed during the incremental field test (V Vam-eval ). Match running performance: total distance covered (TD), low-intensity running (LIR; running speed < 13.0 km· h − 1 ), high-intensity running (HIR; running speed from 13.1 to 16 km · h − 1 ), very high-intensity running (VHIR; running speed from 16.1 to 19 km · h

− 1

) and sprinting distance (Sprinting; running speed > 19.1 km· h − 1 ). Very high-intensity activities (VHIA) = VHIR+ Sprinting. Main

age-group eff ect: all P < 0.001. a: signi ficant diff erence vs. U14 ( P < 0.05), b: vs. U15, c: vs. U16, d: vs. U17, e: vs. U18. η2 : eff ect size


820 Training & Testing by-match variability in running performance [35] . All matches were played on two 100 × 70 m standard outdoor natural grass fields with 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. Since growth can potentially in fluence physical performance [25], all performance tests were reassessed at least once during the 4-month investigation period. To avoid fatigue unduly influencing the results, performance tests were performed over 2 testing sessions with at least 1 day between them. Speed and jump tests were completed on the first day, while the incremental running test was performed in the second testing session. All performance tests were performed in an indoor facility maintained at standard environmental conditions (23–24 ° C). All testing sessions were preceded by a 20-min standardized warmup and players were familiar with all test procedures.

Activity pattern measurements Materials A Global Positioning System (GPS) unit capturing data at 1 Hz (SPI Elite, GPSports, C anberra, Australia) was fitted to the upper back of each player using an adjustable neoprene harness. All players, irrespective of age, wore the same equipment and the same GPS devices. Despite a possible underestimation of highintensity running distance with the time-resolution of 1 Hz [37], good accuracy (r = 0.97) was reported for the assessment of short sprints for this GPS device compared with an infra-red timing system [4]. While the accuracy of the device for total distance has been reported to be good (3–7 %), that for high-intensity running is only moderate (11–30 %) [12]. However, in the absence of “a gold standard” method, the current system has been reported to be capable of measuring individual movement patterns in soccer [37]. More importantly for this study design, the GPS device utilized has been reported to have good reliability (i. e., CV = 1.7 % [4] and < 5 % [12]). Despite possible between-GPS variability [31], we were confident in the results observed since players always wore the same device.

Analyses While 635 player-matches were assessed in total from 99 di ff erent players, only data from players who participated in the full game were retained (n = 186 files from 77 diff erent ▶ Table 1). This unfortunately resulted in a small data players, ● set for the U13 players (n = 7), which was largely a consequence of the high substitution rate of players during games in this age group. Tactically, all teams used a 4-4-1-1 formation, a variation of 4-4-2 with 1 of the strikers playing as a “ second striker”, slightly behind their partner. Since players’ roles within the team structure changed little during the games analysed, all players were assigned to 1 of 6 positional groups; fullbacks (FB, n = 15 players, yielding 36 files), centre-backs (CB, n =16 players, yielding 54 files), midfielders (MD, n = 13 players, yielding 40 files), wide midfielders (W, n = 13 players, yielding 16 files), second strikers (2ndS, n = 9 players, yielding 19 files) and strikers (S, n = 11 players, yielding 21 files). All match data was analysed with a custom-made Microsoft Excel program designed to provide objective measures of physical match performance. Activity ranges selected for analysis were identical for all categories to allow direct between-age comparisons and were adapted from previous studies on young soccer players [6, 10] as follows: 1) total distance covered (TD), 2) low-intensity running (LIR; running speed < 13.0 km.h − 1), 3) high-intensity running (HIR; run-

running (VHIR; running speed from 16.1 to 19 km.h − 1) and 5) sprinting distance (Sprinting; running speed > 19.1 km.h − 1). Very high-intensity activities (VHIA) were also calculated as VHIR plus Sprinting. Peak game running speed (i. e., the highest speed recorded during the game) was also collected. While previous studies have also analysed match running for fixed intervals throughout the game (e. g., 15-min periods) or reported the duration and occurrences of speci fic actions to assess fatigue development [5, 8], we restricted our analyses to the game as a whole so as to focus on possible di ff erences in match running performance as a function of age, playing position and physical fitness.

Physical performance assessment Since football-speci fic tests (i. e., which replicate soccer movement patterns and e ff orts, such as Ho ff [18], Yo-Yo [22] and shuttle RSA [20] tests) evaluate di ff erent physical qualities simultaneously (e. g., the performance at the Yo-Yo IR1 test is the results of, among others, cardiovascular fitness, intra-e ff ort recovery capacities and change of direction ability), we chose, for the purpose of the present study, simple performance tests to evaluate and isolate basic physical qualities of each player.

Lower limb explosive strength A vertical countermovement jump (CMJ; cm) with flight time measured with a contact mat (KMS, Fitness Technology, South Australia) to calculate jump height was used to assess lower limb explosive strength. Players were instructed to keep their hands on their hips with the depth of the counter movement selfselected. Each trial was validated by visual inspection to ensure each landing was without signi ficant leg flexion. Athletes were encouraged to perform each jump maximally. At least 3 valid CMJ’s were performed separated by 25 s of passive recovery, with the best performance recorded.

Acceleration and peak running velocity The player’s acceleration (Acc) as measured by their 10 m sprint time and their peak running velocity (PV) de fined as the fastest 10-m split time were measured during a maximal 40-m sprint (dual-beam electronic timing gates set at 10-m intervals, Swift Performance Equipment, Lismore, Australia) [27]. Split times were measured to the nearest 0.01 s. 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.

Repeated-sprint performance All players performed a RSA test following a 10-min rest break after the 40-m sprint trials. T he RSA test consisted of 10 repeated straight-line 30-m sprints separated by 30 s of active recovery (i. e., jogging back to the starting line within approx 25 s, in order to allow 4–5 s of passive recovery before the commencement of the next sprint repetition). This test is similar to other RSA tests previously used with team sport athletes [33] . Time was recorded to the nearest 0.01 s using 2 sets of electronic timing gates (Swift Performance Equipment, Lismore, Australia). Players used a standing start 0.5 m behind the timing lights. Players were given verbal encouragement to run as fast as possible for each of the 10 sprints and constant verbal feedback was provided during the



recovery run. Mean repeated-sprint time (RSA mean) was determined as a measure of repeated-sprint performance.

Incremental field test A modified version of the University of Montreal Track Test [24] (i. e., Vam-eval) was used to assess cardiorespiratory fitness [27]. Compared with other intermittent tests such as the Yo-Yo test, this test is a better predictor of maximal oxygen uptake, i. e., the correlation between maximal running speed and maximal oxygen uptake is 0.96 [24], while it is only 0.70 for Yo-Yo vs. maximal oxygen uptake [2]. The test begins with an initial running speed of 8 km · h − 1 with consecutive speed increases of 0.5 km · h − 1 each minute until exhaustion. The players adjusted their running speed according to auditory signals timed to match 20-m intervals delineated by cones around a 200-m long indoor athletics track. The test ended when participants twice failed to reach the next cone in the required time. During the test, players were verbally encouraged by testers and coaches. The average velocity of the last 1-min completed was recorded as the players ’ VVam-eval (km.h − 1). If the last stage was not fully completed, the VVam-eval was calculated as V Vam-eval = V + (t /60 · 0.5), where V is the last completed velocity in km.h − 1 and t is the time in seconds of the uncompleted stage [27].

Statistical analysis Data are presented as means ± standard deviations (SD) or means ± standard errors (SE) when stated. The distribution of each variable was examined with the Kolmogorov-Smirnov normality test. Homogeneity of variance was veri fied with a Levene test. To increase statistical power, the e ff ect of age and playing position on match running performance was analysed separately. The e ff ect of age on match running performance was first assessed with a 1-way ANOVA. To account for di ff erences in playing time between age-groups (i. e., 70 vs. 90 min for U13 vs. U18, respectively), match running performance was also analysed by ANCOVA, with each data adjusted for individual playing time. Between-position di ff erences in physical capacities were examined with a 1-way ANCOVA, with data adjusted for age. A separate 1-way ANCOVA was also used to examine the e ff ect of playing position on match running performance, with data adjusted for both age and playing time. For all analyses, when a

9000 ) 8000 m ( d e r 7000 e v o c e c 6000 n a t s i D

5000

4000

LIR HIR

c,d,e

VHIR Sprinting

8254±118

7956±128 8026±143 394±43 e 428±37 e

7497±196 363±35 e 402±39 e 260±53 e

8436±156 8448±135

484±26

487±32

501±28

991±56

946±48

470±29

387±40

617±32 533±24

936±51

869±42

6012±142 6187±93 6218±103 6565±113 6573±98

6235±85

922±46 837±70

c,d

U13

U14

U15

U16

U17

U18

Fig. 1 Least squared means ( ± SE) for match running performance in U13 (n = 18 files), U14 (n = 40 files), U15 (n = 25 files), U16 (n = 21 files), U17 (n = 29 files) and U18 ( n = 53 files) soccer players. Values are adjusted for total playing time. a: signi ficant diff erence vs. U14 (P < 0.05), b: vs. U15, c: vs. U16, d: vs. U17, e: vs. U18.

significant interaction was found, Bonferroni ’s post hoc tests were applied. For each ANCOVA, partial eta-squared ( η2) was calculated as measures of e ff ect size. Values of 0.01, 0.06 and above 0.15 were considered as small, medium and large, respectively [11]. The relationships between match running performance and each physical quality were assessed using partial correlations adjusted for age and individual playing time. This was performed with all players pooled and also within each playing position. In addition to measures of statistical signi ficance, 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 % con fidence limits overlapped, small positive and negative values for the magnitude were deemed unclear; otherwise that magnitude was deemed to be the observed magnitude [19]. All analyses were carried out with Minitab 14.1 (Minitab Inc, Paris, France) and SPSS 12.0 (SPSS Inc, Chicago, USA) software with the level of significance set at P ≤ 0.05.

Results ▼

Age-related match running performances accounting for actual ▶ Table 1. There was a trend for the playing times are detailed in ● older players to cover greater TD and more distance in all running categories (e. g., η2 = 0.44 for TD, and all P < 0.01). U16, U17 and U18 only diff ered in the distance at VHIA, which was significantly higher for the U18 (P <0.05). When adjusted for indi▶ Fig. 1), between-age group di ff erences vidual playing time (● were less evident (i. e., η2 values were 0.10, 0.10, 0.03, 0.05 and 0.17 for TD, LIR, HIR, VHIR and Sprinting, respectively, all P < 0.05). U18 displayed higher total sprinting distance than all other teams (P < 0.05). U13 covered less TD than U16, U17 and U18 and less LIR than U14 and U15 (P < 0.05). Players’ physical capacities, according to their playing position ▶ Table 2. There were no and adjusted by age, are presented in ● position-diff erences in Acc (P = 0.16) and the magnitude of the diff erences was rated as ‘medium’ for VVam-eval. Strikers tended to have the best physical capacities, while FB and CB, the weakest. Between-position diff erences varied as a function of the physical capacities considered (e. g., MD have worse RSA mean values than W and 2 ndS despite similar V Vam-eval ). Position-related match running di ff erences in performance, adjusted by age and playing time, were all rated as ‘ large’ ▶ Table 2). CB presented the lowest TD, which was associated (● with the lowest VHIA values compared with all other positions (P < 0.05). Conversely, MD, 2ndS and S covered the greatest TD (P < 0.05); W and S showing the highest VHIA values (P < 0.05). The correlations between match running performances and ▶ Fig. 2. When players were all physical capacities are shown in ● pooled together, TD was only signi ficantly related to V Vam-eval; VHIA was signi ficantly related to CMJ, PV, RSA mean and VVam-eval . However, all correlations were only small to moderate (e. g., r = ranging from 0.17 (90 % CI, 0.05; 0.29) for VHIA vs. PV to 0.41 (0.30; 0.51) for VHIA vs. V Vam-eval ). Relationships between match running performance and physical capacities were more clearly position-dependent, with trivial and non-signi ficant correlations for FB, CB, MD and W (e. g., VHIA vs. V Vam-eval : r = 0.06 ( − 0.22; 0.33) and 0.022 ( − 0.01; 0.43) in FB and CB) but large associations for 2ndS and S (e. g., VHIA vs. V Vam-eval : r = 0.70 (0.43;


822 Training & Testing

Table 2 Physical capacities and match running performance according to playing positions. FB number of players height (cm) body mass (kg) year from PHV CMJ (cm) Acc (s) PV (km.h − 1) RSAmean VVam-eval (km.h − 1)

CB

n = 15

163 ± 11 50.6 ± 9.8 0.5 ± 1.5 36.0 ± 0.7a,c,e 1.81 ± 0.01 28.7 ± 0.2a,c,e 4.73 ± 0.03 a,c,e 15.8 ± 0.2

number of files n = 36 files TD (m) 8118 ± 103a,b LIR (m) 6197 ± 81b HIR (m) 909 ± 33a,b VHIR (m) 525 ± 20a Sprinting (m) 487 ± 27a,b,e VHIA (m) 1012 ± 41 a,e − 1 peak game speed (km.h ) 25.9 ± 0.3b,e

MD

2ndS

W

anthropometric and maturity data and physical capacities n = 16 n = 13 n = 13 n=9 166 ± 8 164 ± 9 163 ± 9 159 ± 10 53.8 ± 8.6 51.6 ± 7.4 53.2 ± 10.0 50.1 ± 7.8 0.4 ± 1.1 0.6 ± 1.3 0.6 ± 1.3 − 0.6 ± 1.2 e e 38.7 ± 0.6 36.8 ± 0.6 40.3 ± 1.0 37.6 ± 1.0 e 1.78 ± 0.01 1.81 ± 0.01 1.79 ± 0.01 1.82 ± 0.01 30.0 ± 0.2b 29.0 ± 0.2 e 30.0 ± 0.4 29.0 ± 0.3 e 4.59 ± 0.02 4.65 ± 0.03 c,e 4.51 ± 0.04 4.60 ± 0.04 c 15.6 ± 0.2 16.3 ± 0.2 16.8 ± 0.3 16.0 ± 0.3 match running performance n = 54 files n = 40 files n = 16 files n = 19 files 7675 ± 84 b,c,d 8665 ± 98 e 8469 ± 155 e 8429 ± 143e b e 6197 ± 66 6638 ± 77 6231 ± 122 6524 ± 112 e 732 ± 27b,c,d,e 1150 ± 32 d,e 1037 ± 50 e 988 ± 46e b,c,d,e 363 ± 16 552 ± 19 612 ± 30 514 ± 27 384 ± 22c,e 325 ± 26c,e 581 ± 41d 403 ± 38e 747 ± 33c,e 877 ± 38c,e 1200 ± 61 d 917 ± 56e b,e c,e 26.4 ± 0.2 24.6 ± 0.3 27.0 ± 0.5 25.6 ± 0.4 e

S n = 11 163 ± 10 51.3 ± 13.4 − 0.1 ± 1.5 42.4 ± 0.9 1.78 ± 0.01 30.7 ± 0.3 4.51 ± 0.03 16.1 ± 0.3

P

η2

0.35 0.63 0.20 < 0.001 0.16 < 0.001 < 0.001 0.01

0.06 0.04 0.07 0.21 0.05 0.21 0.20 0.09

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

0.29 0.20 0.40 0.35 0.33 0.32 0.13

n = 21 files

7834 ± 136 5867 ± 106 766 ± 44 516 ± 26 686 ± 36 1202 ± 53 28.0 ± 0.4

Mean anthropometric ( ± SD) values and least squared means ( ± SE) for counter movement jump (CMJ), acceleration (Acc), peak velocity (PV), mean sprint time on the repeated sprint test (RSA mean) and peak running speed during the incremental field test (V Vam-eval ), total distance (TD), low-intensity running (LIR), high-intensity running (HIR), very-highintensity running (VHIR), sprint running (Sprinting), very-high-intensity running activities (VHIA) and peak game speed reached as a function of playing position (fullbacks (FB), centre-backs (CB), midfielders (MD), wide mid fielders (W), second strikers (2 nd S) and strikers (S)). Values are adjusted for age and playing time. a: signi ficant diff erence vs. CB (P < 0.05), b: vs. MD, c: vs. W, d: vs. 2 nd S, e: vs. S. η2 : eff ect size

TD

VHIA

FB

FB

*

CB

CB *

MD

MD W

W 2ndS

*

S

*

*

**

**

All pooled

Sprinting

FB CB

** * ** *

** ** CMJ Acc PV RSAmean VVam-eval

**

* **

**

S All pooled

Peak game Speed

** ** * **

* *

*

**

FB

** **

CB MD

MD

W

W 2ndS

2ndS

** *** *

** **

S All pooled

** ** **

**

e g r a L

l l l e a t a i a v r m i r e S T d o M

l e l a t a m r S e d o M

e g r a L

* ** ** ** ** **

-0.9 -0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7 0.9 t e c g e r f r a e l y p r t e s V o m l A

* * **

e g r a l y r e V

t c e f r e p t s o m l A

Partial correlation coefficient (90% CI)

S All pooled

** **

**

2ndS

Fig. 2 Correlation coeffi cients ( 90 % con fidence intervals, CI) between match running performance and performance in field tests, adjusted for age and individual playing time. Match running performance: total distance (TD), very-high-intensity running activities (VHIA), sprint running (Sprinting) and peak game speed reached during the game. Playing positions: fullbacks (FB, n = 36 files), centre-backs (CB, n = 54 files), midfielders (MD, n = 40 files), wide midfielders (W, n = 16 files), second strikers (2 nd S, n = 19 files) and strikers (S, n = 21 files). Field tests: counter movement jump (CMJ), acceleration (Acc), peak velocity (PV), mean sprint time on the repeated sprint test (RSA mean) and peak running speed during the incremental field test (VVam-eval ). * P < 0.05 ; ** P < 0.01.

-0.9 -0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7 0.9 t e c g e r f r a e l p y r t e s V o m l A

e g r a L

l l l e a t a i a v r m i r e S T d o M

l e l a t a m r S e d o M

e g r a L

e g r a l y r e V

t c e f r e p t s o m l A

Partial correlation coefficient (90% CI)

0.86) and 0.64 (0.35; 0.82) in 2ndS and S; VHIA vs. RSA mean: r = 0.66 ( − 0.83; − 0.36) in 2ndS).

Discussion ▼

This is the first time that the activity pro files of highly trained young soccer players are reported in relation to age, playing position and physical capacities. The main findings of the present study were: 1) match running performance was slightly a ff ected by age, with diff erences only apparent between the extreme age

tion-dependent, with the centre-backs tending to cover less distance, 3) match running performance tended to be related to most physical capacities, but 4) the magnitude of the relationships between match running performance and physical capacities (i. e., r values) varied considerably according to physical capacities and playing positions. Since physical capacities are known to improve with growth [30, 32], and given the observed age-diff erences in field test ▶ Table 1), we expected a gradual increase in match results (● running performance with age. This was apparent when consid▶ Table 1), due largely to the younger ering actual playing time (●



However, when adjusted for individual playing time, there was no diff erence in running performance between U14, U15, U16 and U17; diff erences in TD were only signi ficant for the youngest (U13) vs. the 3 oldest teams (U16, U17 and U18), while the U18 diff ered from the younger teams (U14-U17) only by their greater ▶ Fig. 1). While we are not aware of any total sprinting distance (● comparable data in the literature, these small age-related di ff erences are in line with the lack of di ff erence observed in game activity patterns between pre- and post pubertal elite soccer players [40]. A possible overlapping of some players ’ maturity status between teams (as exemplified by the high standard devi▶ Table 1) might also explain the ations of the mean PHV values, ● lack of between-team diff erences observed. These betweenteam comparisons were however not adjusted for PHV to remain consistent with the way players are classi fied in soccer clubs. The present results therefore suggest for the first time that age/ maturation itself (and the associated increase in physical capacities) is not a major determinant of actual match running performance in soccer. While speculative, it can be hypothesized that the complexity of the game itself may constrain a player ’s actual running activity, with older players, due to their greater levels of experience, playing more tactically demanding games and therefore tending to be more restricted in using their full ▶ Table 1). It (greater) physical capacities than younger players ( ● is also possible that the particularly high cardiorespiratory fitness levels of the younger players in the present study (V Vam-eval for U14 and U15 = 15.3 and 15.8 km.h− 1, respectively. [3]) partly explains their ability to achieve high match running performances. Another explanation for the lack of di ff erences in match running performance between the U14 and U17 can be related to the intermittent nature of high-intensity actions during soccer match play, which was likely more favourable to the youngest players [38]. Since using individualized speed thresholds to diff erentiate exercise intensities [1] would have probably weakened between-player di ff erences, absolute speed thresholds were preferred to enable accurate examinations of the impact of physical capacities on match running performance. This also allowed easier comparisons with the existing literature in young players [6, 10]. Further investigations are however required to assess the respective impact of physical /physiological changes resulting from aging/growth vs. match-related tactical aspects on match running performance. In our young highly trained players, match running performance ▶ Table 2). CB was significantly influenced by playing position ( ● covered the lowest TD and VHIA, while W and S showed the highest VHIA values. Since running distances were adjusted for age and total playing time to allow the inclusion of players differing by age, direct comparisons with data from the literature is not possible. The present results however extend the previous findings on positional di ff erences in the occurrence of repeatedsprint sequences [8], as well as in the cardiorespiratory load of competitive games in young players [40] . They also con firm data on position-specific running patterns reported in adult soccer players [5, 13, 14, 35]. The greater amount of high-intensity running in W and S in the present study is probably related to their need to complete sprints away from defending players in order to generate space or capitalize on goal scoring opportunities [14]. These specialized match running patterns appear to be indicative of a mature tactical understanding of position-specific tasks [40]; this is consistent with the highly trained playergroup examined here. Since replicating competitive performance

paring athletes for competition [13], the present information can be used to develop position-speci fic training strategies for developing soccer players. Another practical application that can derive from these position-speci fic match running responses is that playing position can be used as a means of manipulating physiological load during (training) soccer match simulations. While the beneficial impact of good physical capacities on game match running performance has been shown in young moderately-trained [10], adult female [23], elite male [34] soccer players, and top-class referees [21, 43], to our knowledge no study has also considered their e ff ects taking into account playing positions. The respective impact on match running performance of individual physical capacities and/or specific technical/tactical roles assigned to each playing position (see above), was di ffi cult to predict. We observed no di ff erence in VVam-eval values between FB, MD, W, 2ndS and S, despite important di ff erences in game ▶ Table 2). Conversely, FB were slower running performance (● than W on the RSA test, but presented similar high-intensity activities during games. Thus, position-related di ff erences in physical capacities did not always match position-di ff erence in ▶ Table 2), and the magnitudes of match running performance (● the ‘position e ff ect’ were also much lower for physical capacity than for match running performance ( η2 < 0.21 vs. 0.30–0.40 for physical capacities vs. match running performance, respectively). Moreover, while correlations were moderate when all ▶ Fig. 2), players were pooled together (VHIA vs. V Vam-eval : r = 0.41, ● we observed trivial and non-signi ficant correlations within FB, CB, MD and W (e. g., VHIA vs. V Vam-eval: r = 0.06 and 0.022 in FB and CB), but large-to-very large and signi ficant associations within 2ndS and S positions (e. g., VHIA vs. V Vam-eval : r = 0.70 and 0.64 in 2ndS and S; VHIA vs. RSA mean: r =0.66 in 2 ndS). Although present correlations should be considered with care given the limited sample size for some positions (e. g., 2 ndS), they suggest that playing soccer is likely to constrain defenders ’ running activities; conversely, attackers (i. e., 2 ndS and S) have apparently more space and opportunity to express and use their full physical potential. For these latter positions, the fitter players (i. e., having greater PV and V Vam-eval) are therefore likely to bene fit the most from their higher physical capacities. The lack of large correlations between match running performance and physical capacities found for W was however surprising given the high▶ Table 2). We could howintensity nature of this position (see ● ff ever postulate that di erences in individual playing style or specific tactical ploys within the 4-4-1-1 formation used by our teams, might partly explain these findings. Studies matching time-motion analyses to qualitative (technical /tactical) game examinations [36] should however be performed in the future to clarify these observations. Despite of the use of some less soccer-speci fic field tests (e. g., Vam-eval) compared with other studies [10, 23], the present findings show for the first time that the general importance of physical capacities on match running performance is not as evident as previously reported [10, 23, 34]. Playing position and its associated tactical roles need to be taken into consideration when examining the relationship between physical capacities and match running performance. Although speculative with present data restricted to correlations, our results suggest that, from a talent identi fication and development perspective, physical capacities are more likely to be a limiting factor for players who are required to play as attackers. While the precise physical determinants of success in elite soccer are still debated [29, 42],


824 Training & Testing trained young soccer players should target the development of lower limbs explosive strength (in fluencing jumping height, maximal sprinting speed and repeated-sprint ability [7] ) and/or maximal aerobic power (aimed at improving high-intensity intermittent exercise capacity [17]), especially for prospective attacking players. Finally, defining the speci fic physical capacities that are most likely to predict position-speci fic match running performance is also likely to improve the selection/training process of talented soccer players. In elite adult players, Rampinini et al. [34] showed that peak running speed in an incremental field test, mean sprint time on a RSA test, but not jumping height, were predictive of high-intensity activities during competitive games. In the present study, the physical capacities assessed by the performance tests showed signi ficant and moderate-to-large correlations with some match running performance measures ▶ Fig. 2); however the magnitude of these correlations were (● position-dependent. For example, jump height, peak velocity and repeated-sprint performance were signi ficantly and largely related with the peak speed reached during games in FB, 2 ndS and S (e. g., in S, CMJ and PV shared 61 and 55 % of the variance of peak game speed), but not in MD (less than 0.001 %). The correlations for VHIA vs. RSA mean were however lower than those reported for VHIA vs. V Vam-eval, which was consistently the better predictor of game running performance (all correlations rated at least large and V Vam-eval explaining more than 25 % of TD, VHIA, Sprinting and even peak running speed reached in 2 ndS and S). In contrast, acceleration (inferred from a 10-m sprint time) showed the weakest correlations with game running performance, irre▶ Fig. 2). While these findings spective of playing positions (● suggest that all the performance tests used in this study appear to be of interest in the prediction of match running performance, VVam-eval remained the most powerful determinant of physical game performance, at least for the positions where running activity is less likely to be constrained by tactical tasks. It is however possible that larger correlations could have been reported with the use of more soccer-speci fic field tests (e. g., Yo-Yo [10, 23] or shuttle RSA [34] tests) and individualized speed thresholds for assessing match running performance [1]. In conclusion, during international club games in highly trained young soccer players, match running performance is a ff ected by age and playing position; with playing position having a greater impact than age (and its associated changes in physical capacities). Older players, as well as W and S, generally cover greater distances at high-intensities. Although this requires further tactical/technical analysis, the small age-related di ff erences in match running performance, despite the considerable di ff erences observed in physical capacities, suggest that the older players’ ability to use their physical potential might be restricted during games. Moreover, the bene ficial impact of high physical fitness on game r unning performance is likely position-dependent, with attackers (i. e., 2 ndS and S) likely to bene fit the most from their physical capacities. This has important applications, for the detection of talented players, as well as the development of position-specific training strategies in developing players.

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