periodization training focused on technical-tactical ability in young ...

PERIODIZATION TRAINING FOCUSED ON TECHNICAL-TACTICAL ABILITY IN YOUNG SOCCER PLAYERS POSITIVELY AFFECTS BIOCHEMICAL MARKERS AND GAME PERFORMANCE RODRIGO L. Q. T. AQUINO,1,2 LUIZ G. CRUZ GONC¸ALVES,2 LUIZ H. PALUCCI VIEIRA,2,3 LUCAS P. OLIVEIRA,2 GUILHERME F. ALVES,2 PAULO R. PEREIRA SANTIAGO,2,3,4 AND ENRICO F. PUGGINA2,4 1

Faculty of Sport Sciences, Porto University, Porto, Portugal; 2Post-graduate Program in Rehabilitation and Functional Performance, Medicine School of Ribeira˜o Preto, University of Sa˜o Paulo, Ribeira˜o Preto, Brazil; 3Laboratory of Biomechanical and Motor Control, (LaBioCoM), University of Sa˜o Paulo, Ribeira˜o Preto, Brazil; and 4School of Physical Education and Sport of Ribeira˜o Preto, University of Sa˜o Paulo, Ribeira˜o Preto, Brazil ABSTRACT

Aquino, RLQT, Cruz Gonc¸alves, LG, Palucci Vieira, LH, Oliveira, LP, Alves, GF, Pereira Santiago, PR, and Puggina, EF. Periodization training focused on technical-tactical ability in young soccer players positively affects biochemical markers and game performance. J Strength Cond Res 30(10): 2723– 2732, 2016—The aim of this study was to investigate the effects of 22 weeks of periodized training, with an emphasis on technical-tactical ability, on indirect markers of muscle damage, and the on-field performance of young soccer players. Fifteen players (age 15.4 6 0.2 years, height 172.8 6 3.6 cm; body mass 61.9 6 2.9 kg; % fat 11.7 6 1.6; V_ O2max 48.67 6 3.24 ml$kg21$min21) underwent 4 stages of evaluation: prepreparatory stage—T0; postpreparatory stage—T1; postcompetitive stage I—T2 and; postcompetitive stage II—T3. The plasmatic activity of creatine kinase (CK) and lactate dehydrogenase (LDH) were evaluated, as well as the on-field performance (movement patterns, tactical variables). Regarding the plasmatic activity of CK and LDH, there was a significant reduction (p # 0.05) throughout the periodization training (T0: 350 U$L21; T3: 150 U$L21). Significant increases were observed (p # 0.05) in the intensity of the game, high-intensity activities (HIA) (T0: 22%; T3: 27%), maximum speed (T0: 30 km$h21; T3: 34 km$h21) and tactical performance, team surface area (T0: 515 m2; T3: 683 m2), and spread (T0: 130 m; T3: 148 m). In addition, we found significant inverse correlations between the percentage variation of T0 to T3 in CK and LDH activities with percentage Address correspondence to Rodrigo Leal de Queiroz Thomaz de Aquino, [email protected]. 30(10)/2723–2732 Journal of Strength and Conditioning Research Ó 2016 National Strength and Conditioning Association

variation in high-intensity running (r = 20.85; p # 0.05 and r = 20.84; p , 0.01, respectively) and HIA (r = 20.71 and r = 20.70; p # 0.05, respectively) during the matches. We concluded that there was reduced activity in biochemical markers related to muscle damage, as well as increases ingame high-intensity performance and the tactical performance of the study participants. Furthermore, players who showed greater reduction in plasma activity of CK and LDH also obtained greater increases in-game high-intensity performance along the periodization. These results may contribute to the expansion and future consolidation of the knowledge of coaches and sport scientists to develop effective methodologies for training in soccer.

KEY WORDS muscle damage, computational tracking, game analysis

INTRODUCTION

A

nalyses of in-game displacement patterns performed by soccer players have been fully explored in the literature (6,7,11–13,15,24,26,35) especially in the professional category, however, little has been documented and described in the youth population (10). Recent studies with young soccer players (13–18 year old) suggest that there is an association between training status and physical performance during matches (9,13,36). Castagna et al. (13) investigated soccer players at the U-17 level and found that they run an average distance of 5–7 km during an official match, with 15% of the total distance (0.4–1.5 km) being run at high intensity. Such research has helped coaches and sports scientists to understand how a game is characterized, which is key to better development, prescription, and refining of specific training programs (6,10) in pursuit of enhancing in-game performance. VOLUME 30 | NUMBER 10 | OCTOBER 2016 |

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Periodization in Soccer In addition to analyze displacement patterns (i.e., in-game physical performance variables), recent studies have explored tactical analysis through computational screening (31,32). The team surface area (the area occupied by the team), which is a convex polygon formed by the 2D position of the players on the field and the spread of the players, consists of examining the Euclidean distance between each player and their teammates at every moment and has been demonstrated as a useful method of game analysis to verify the systems and standards of the games in an athletic context, i.e., the technical-tactical approach. Much of the available literature on game dynamics is dedicated to understanding the technical-tactical context (15,21,22,37). These concerns are justified by the dynamic and complex characteristics of the game, which are characterized by the cooperation-opposition relationship between teammates and their opponents. Games played in team sports are characterized as being made up of open systems (i.e., obtaining, using, or exchanging energy and information with the environment). Owing to this openness (which is also complex, hierarchical, and adaptive), the dynamics of decreases and increases in uncertainty and the mutual advantage of 1 team over another are factors that constantly interfere with the patterns of interaction and produce varying degrees of internal disorganization. This causes the team dynamics to fluctuate between stability and instability (14). Thus, during a player’s preparation process, there is a need to provide stimuli aimed at understanding the game in its cognitive dimension, such that the player plays more insightfully. Or rather, the player’s movements must be directly related to the player’s own technical and tactical application. This highlights the importance of bringing considerations regarding technical-tactical ability to bear on the training planning process. Additionally, studies show that a season of soccer training and competition can cause biochemical disturbances that may lead athletes to situations of higher risk of muscle damage, thus causing a decrease in performance (23,28,29). Accordingly, the search for the development of a strategy for periodization training that prevents the onset of negative biological effects (e.g., muscle damage and oxidative stress) can contribute to the athlete making better use of training sessions, as well as performing better in season games. This pushes us to reflect on to what extent periodization training with an emphasis on technical and tactical ability may cause biochemical disturbances. Given that one of the main goals of the above analysis is to contribute to the development of more specific training programs with less stress on the muscular system, minimizing possible musculoskeletal injuries throughout the season, the next step is to verify the effectiveness of a training program using the movement patterns and tactical variables as analytical tools, together with identifying surrogate markers of muscle damage, to gather information about possible negative effects of the damaging agents in the process of training. Thus, this study

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adopted a periodization training schedule of 22 weeks. The proposed adaptation was to assign higher importance to technical and tactical ability over all other aspects of training (aerobic power, coordination and flexibility, strength, speed) at all stages of the periodization training (preparatory, competitive I and II). Thus, the aim of this study was to investigate the effects of 22 weeks of periodization training with an emphasis on technical-tactical ability on indirect markers of muscle damage [Creatine kinase (CK) and lactate dehydrogenase (LDH)] and on-field performance (movement patterns and tactical variables) in young soccer players. It was hypothesized, that along the periodization, the young soccer players studied have a reduction and maintenance in plasma activity of indirect markers of muscle damage and increased intensity and tactical performance in game.

METHODS Experimental Approach to the Problem

A longitudinal study was designed to analyze the effects of a periodization with an emphasis on technical and tactical ability, on indirect markers of muscle damage, and the physical and tactical performance in game situations of dispute. For this objective, 15 young soccer players underwent 22 weeks of training and 4 weeks of assessments (totaling a 26 weeks macrocycle). The macrocycle was divided into 3 stages: preparatory stage—6 weeks; competitive stage I—8 weeks, and competitive stage II—8 weeks. The players trained 4 times a week, totaling 96 sessions. During all daily sessions, rating of perceived exertion (RPE) was monitored and the duration of the training session in minutes recorded for subsequent load quantification (RPE 3 volume), as well as training monotony and tension at every training stage. The RPE was obtained 30 minutes after each session (20). The assessments were performed at weeks 1 (T0), 8 (T1), 17 (T2), and 26 (T3). At the beginning of the weeks of assessment weeks (Monday), subjects underwent venous blood collections for plasmatic activity of CK and LDH. They were instructed not to perform any physical effort for within 72 hours before blood collection. At the end of the assessment weeks (Thursday), the simulated matches were held (300 3 300 ) (4) for further analysis of displacement patterns (total distance covered in different speed ranges, total distance covered in the game, average speed, maximum speed, and number of sprints) and the predictors of tactical performance (team surface area and spread). Before the simulated matches, the players performed a standard warm-up protocol. Subjects

Fifteen young soccer players participated in this study (4 defenders, 4 wingers, 3 midfielders, 4 strikers), all males (mean 6 SD; age 15.4 6 0.2 years, height 172.8 6 3.6 cm; body mass 61.9 6 2.9 kg; 11.7 6 1.6% fat; V_ O2max 48.67 6 3.24 ml$kg21$min21) and members of a soccer club that plays in the first division of the state of Sa˜o Paulo, Brazil;

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the division is considered the leading state-level tournament in the country. The inclusion criteria were that the players participated in 80% of all training sessions and had been associated with, and trained at, the club for a full year. The study was approved by the Research Ethics Committee of the Faculty of Medicine at Ribeira˜o Preto (protocol 710.998/ 2014) and was conducted in accordance with the Declaration of Helsinki. All participants and their legal responsible signed a Assent and Consent Term respectively, with the objectives and purposes of the study.

total time of the session (by volume) was 120 minutes. In competitive stage I, intensity was measured as an average of 6.1 and total time as 110 minutes. In competitive stage II, intensity averaged 6.3 and volume measured 100 minutes. After calculating the RPE and training volume, variables of monotony and strain training were calculated. The monotony was obtained by dividing the average weekly load (RPE 3 Volume) and its SE and the tension by multiplying the sum of the weekly load and monotony (20) (Table 1).

Procedures

Analysis of Indirect Markers of Muscle Damage

The periodization training consisted of 22 weeks of training and 4 weeks of evaluation (T0, T1, T2, and T3) composed of 4 weekly training sessions (a total of 96 sessions). The evaluations were conducted in 4 distinct periodization phases: early preparatory stage (T0), end of preparation stage (T1), final competitive stage I (T2), and final competitive stage II (T3) (Figure 1). As part of planning the training sessions, aerobic power capacity, coordination, flexibility, strength, speed and technique-tactics were considered. Technical-tactical capacity was prioritized at all stages of training. The total weekly volume was considered for the planning of the stages. In the preparatory phase, stimuli were applied at an average of 10% of the total training volume for aerobic power, 15% for coordination and flexibility, 21% for strength, 16% for speed, and 38% for technique-tactics. In competitive stage I, stimuli were applied at an average of 10% for coordination and flexibility, 23% for strength, 23% for speed, and 44% for techniques-tactics. In competitive stage II, 10% was applied for coordination and flexibility, 15% for strength, 25% for speed, and 50% for techniques-tactics. It should be noted that stimuli were not applied at competitive stages I and II for aerobic power, because the percentage of the technical and tactical training load was increased in an effort to increase the training specificity through shorter and formal games (Table 1). The intensity of each session was determined by the degree of RPE, based on the Foster (20) method, collected after 30 minutes of the session. Accordingly, the average intensity of the preparatory stage was 5.1 and the average

Collection of venous blood was conducted at the same time and place, and always early in the week (72 hours after the previous training session). The athletes were instructed not to perform physical exercises between the period of the previous workout and the blood sample collection, to ensure that there were no changes in the results of the samples collected earlier in the week. A total of 10 ml of blood were taken from each participant, and the sample was collected in a vial containing an anticoagulant. Immediately afterward, the blood was centrifuged at 2000g for 15 minutes to obtain the plasma. After the procedure of blood collection and separation, the plasma was separated into several aliquots and immediately frozen at 2808 C for later biochemical analysis (25). The plasma activities of CK and LDH were determined using commercial Bioliquid (Pinhais, Brazil) kits, after the manufacturer’s suggested methodology—which involved adding N-Acetyl-Cysteine to the reaction medium to ensure full activation of CK-MM (muscle isoform). The procedures for biochemical analyses were performed through the addition of the buffer solution (2.5 ml bottle) to a specific reactive, and placed in a water bath at 378 C during 1 minute. Shortly thereafter, 20 mL of plasma were added to the reactive solution, and the mixture left in a water bath at 378 C for another minute. Immediately afterward, four readings were taken at measured intervals: immediately, at 1 minute, 2 minutes, and 3 minutes. Readings were taken at a wavelength of 340 nm and 378 C. The calculations of CK and LDH activity in the samples were performed through the following equations: CK (U$L21) = 8252 3 D absorbance per minute and LDH (U$L21) = 8321 3 D absorbance per minute, respectively. Performance Analysis in the Field

Figure 1. Representative layout of the experimental protocol. PS = preparatory stage; CSI = competitive stage I; CSII = competitive stage II.

For analysis of on-field performance (movement patterns and tactical variables), participants were subjected to a simulated game (4). The game was held on a field (70 3 50 m) at the usual time of team training that lasted 60 minutes (300 3 300 with 15 minutes of passive recovery). The game was fully monitored by 2 digital video cameras

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Periodization in Soccer of time, for each player on the field were obtained by reconTABLE 1. Distribution of capabilities, load, monotony, and training strain applied in struction through the Direct the preparatory stage, competitive I, and II.* Linear Transformation method Preparatory Competitive Competitive (1,5,6,32). stage I stage II stage In Matlab enviroment (The MathWorks, Inc., Natick, MA, Aerobic power training (%) 10 0 0 USA), the data were smoothed Coordination-flexibility training (%) 15 10 10 by a third-order low-pass Strength training (%) 21 23 15 Speed training (%) 16 23 25 Butterworth digital filter, using Technique-tactic training (%) 38 44 50 a cutoff frequency of 0.4 Hz RPE average (U/A) 5.1 6.1 6.3 (31,32,39). Specific routines, the Volume average (min) 120 110 100 players’ movement patterns were Load average (U/A) 2.453 2.674 2.757 calculated: total distance traveled Training monotony Average (U/A) 1.21 1.24 1.26 Training strain Average (U/A) 2.961 3.324 3.477 (km), average speed (km$h21), maximum speed (km$h21), and *RPE = rating of perceived exertion. percentage of the total distance covered in 7 ranges of speed, based on the study by Castagna et al. (13): V1 # 0.4 km$h21 (stopped); 0.4 , V2 # 3 km$h21 (walking); 3.1 , V3 # 8 (CASIO EX-FH25; 720 3 480 pixel) with an acquisition frekm$h21 (low-intensity running); 8.1 , V4 # 13 km$h21 quency of 30 Hz, each of which covered about 3/4 of the total (medium-intensity running); 13.1 , V5 # 18 km$h21 (higharea of the field. After the transfer of image sequences to the intensity running [HIR]); V6 . 18 km$h21 (sprinting [SPR]); computer, the DVIDEOW computational tracking environand V7 = V5 + V6 (high-intensity activities [HIA]). The number ment (4–6,31,32,39) was used to obtain the players’ trajectoof sprints (u. a.) was defined by the frequency of runs at V6 (4). ries. The average error in determination of the positions on The selected tactical variables were the team surface area, the pitch and distances covered of the soccer players using defined as a convex polygon having as vertices the 2this software is approximately 0.3 m and 1% (6,19). dimensional positions of the players on the pitch, and the Synchronization of the images from the cameras was spread of the players (i.e., the distance between players and performed by identifying common events in overlapping areas all teammates) calculated at each moment of time (i.e., for of the cameras (32,39). Calibration was obtained from 6 points each frame analyzed) using a technique previously adopted on the surface of the field using previously measured distances in professional soccer players (31,32) and more recently in to the origin of the adopted coordinate system. Next, using youth soccer players (4). a specific algorithm (5), segmentation based on morphological filtering (18) was performed. Tracking youth soccer players (i.e., Statistical Analyses marking of frames) was conducted with an automation of 75%. For analysis of the results, SPSS (Statistical Package for Social Finally, the data arrays containing the 2D positions as a function Sciences) software for Windows, version 17.0 was used. Data

Figure 2. Dynamics of changes in the plasmatic activity of creatine kinase (A) and lactate dehydrogenase (B) throughout the periodization (n = 15). A) aT0 3 T1 (p = 0.023); bT0 3 T2 (p , 0.001); cT0 3 T3 (p , 0.001); dT1 3 T2 (p , 0.001); eT1 3 T3 (p , 0.001). B) aT0 3 T1 (p , 0.001); bT0 3 T2 (p , 0.001); cT0 3 T3 (p , 0.001); dT1 3 T2 (p , 0.001); eT1 3 T3 (p , 0.001).

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TABLE 2. Movement patterns and tactical variables in the first and second halves of games throughout the course of periodization (n = 10).*† T0 Variables Stopped (%) Walking (%) LIR (%) MIR (%) HIR (%) SPR (%) HIA (%) Total distance (km) Vaverage (km$h21) Vmax (km$h21) Number of sprints Team surface area (m2) Spread (m)

T1

1st time 0.09 6.75 41.90 29.86 14.50 6.90 21.39 3.01 6.02 29.63 38 501.83 127.58

6 6 6 6 6 6 6 6 6 6 6 6 6

2nd time

0.06 2.46 4.31 3.47 4.70 2.12 5.73 0.23 0.47 2.92z 10z 51.54a,b,c 7.17z,d,e

0.10 6.67 42.18 28.28 14.30 8.47 22.77 3.10 6.22 31.51 48 527.54 132.07

6 6 6 6 6 6 6 6 6 6 6 6 6

0.05 1.92 2.72 2.33 1.95 2.59n 3.76o,q 0.29 0.57 2.20r 11 51.70 f,g,h 6.11k,l

1st time 0.08 6.82 43.81 27.84 14.19 7.26 21.46 3.16 6.34 30.90 42 631.79 140.12

6 6 6 6 6 6 6 6 6 6 6 6 6

0.03 1.22 6.19 3.13 2.70 3.01z 5.29z 0.32 0.41 2.84 9 20.49 2.65

T2 Variables Stopped (%) Walking (%) LIR (%) MIR (%) HIR (%) SPR (%) HIA (%) Total distance (km) Vaverage (km$h21) Vmax (km$h21) Number of sprints Team surface area (m2) Spread (m)

6 6 6 6 6 6 6 6 6 6 6 6 6

0.05 1.55 4.27 2.88 2.86 1.34z 3.33z 0.27z 0.55z 2.52 14z 30.73 4.40z

0.09 6.26 41.60 26.73 15.55 9.76 25.31 3.20 6.51 30.02 48 663.54 142.61

6 6 6 6 6 6 6 6 6 6 6 6 6

0.05 1.28 6.79 2.23 3.37 3.23 5.45 0.31 0.62 3.62s 11 52.96i 6.29m

T3

1st time 0.09 6.21 40.93 28.22 16.42 8.14 24.55 3.12 6.23 30.12 39 638.52 138.24

2nd time

2nd time 0.08 5.84 38.66 27.79 17.10 10.53 27.62 3.26 6.50 31.25 54 665.69 153.88

6 6 6 6 6 6 6 6 6 6 6 6 6

0.05 1.21 4.34 3.38 1.91 2.81 3.46 0.21 0.42 3.46t 14 23.81j 8.39

1st time 0.08 6.20 38.83 29.97 16.71 8.20 24.92 3.17 6.40 31.37 42 613.64 136.35

6 6 6 6 6 6 6 6 6 6 6 6 6

0.04 1.35 2.85 2.45 1.88 1.64z 3.33z 0.20 0.61 2.80z 11z 62.21z 7.95z

2nd time 0.06 5.88 39.77 30.50 16.33 11.93 28.26 3.26 6.48 36.76 63 752.50 159.79

6 6 6 6 6 6 6 6 6 6 6 6 6

0.02 1.33 5.01 3.20 2.62 2.21 3.38 0.31 0.59 3.88 14 22.71 8.64

*LIR = low-intensity running; MIR = medium-intensity running; HIR = high-intensity running; SPR = sprinting; HIA = high-intensity activity; Vaverage = average speed; Vmax = maximum speed. †n = T0 3 T3—p = 0.03; o = T0 3 T2—p = 0.05; q = T0 3 T3—p = 0.02; r = T0 3 T3—p = 0.007; s = T1 3 T3—p , 0.001; t = T2 3 T3—p = 0.004; a = T0 3 T1—p , 0.001; b = T0 3 T2—p , 0.001; c = T0 3 T3—p = 0.002; f = T0 3 T1—p , 0.001; g = T0 3 T2—p , 0.001; h = T0 3 T3—p , 0.001; i = T1 3 T3—p = 0.006; j = T2 3 T3—p = 0.007; d = T0 3 T1—p = 0.008; e = T0 3 T2—p = 0.02; k = T0 3 T2—p , 0.001; l = T0 3 T3—p , 0.001; m = T1 3 T3—p = 0.004. zSignificant differences when comparing the first to the second time—p # 0.05.

normality was verified using the Shapiro-Wilk test. Comparison of displacement patterns, tactical variables, and indirect markers of muscle damage between the 4 stages of data collection (T0, T1, T2, and T3) was performed using analysis of variance for repeated measures followed by the “post hoc” Tukey-Kramer test. Regarding the comparisons of the movement patterns and tactical variables between the first and second halves of the gameplay, a paired student’s T test was used. Pearson correlation was used to verify the possible associations between the percentage variation of T0 (first week of tests) to T3 (end of periodization training) (Δ) in

CK and LDH activities with the players’ movement patterns. In all cases, the significance level was preset at p # 0.05.

RESULTS Indirect Markers of Muscle Damage

Figure 2 presents the average values of the plasma activity of CK and LDH throughout the training. There was a significant reduction (p # 0.05) when comparing the T0 stage with the other stages—T1, T2, and T3. Or rather, a significant decrease in CK activity during the periodization demonstrates that there was a reduction in the degree of muscle VOLUME 30 | NUMBER 10 | OCTOBER 2016 |

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Periodization in Soccer damage. For LDH activity, the same dynamic was noted as in the CK analysis. That is, a significant reduction (p # 0.05) when comparing the T0 stage with the others—T1, T2, and T3. Displacement Patterns and Tactical Variables From the First Half of Gameplay With the Second Half of Gameplay Throughout the Periodization

Table 2 shows that the percentage of the total distance in the SPR variable increased significantly from the first half of gameplay to the second half at the T1 (p = 0.001), T2 (p = 0.01), and T3 (p = 0.001) stages. For the percentage of the total distance in HIA, a significant increase in the same comparison as above (first half 3 second half of gameplay) at stages T1 (p = 0.001), T2 (p = 0.02), and T3 (p = 0.01) was observed. The total distance and average speed (Vaverage) variables demonstrated significant increases from first half to second half of gameplay only at stage T2 (p = 0.02 for both). The maximum speed (Vmax) variable demonstrated significant increase at stages T0 and T3 (p = 0.02 and p = 0.01, respectively). When comparing the first half to the second half of gameplay at stages T0, T2, and T3, there were significant increases for the number of sprints (p = 0.01, p , 0.001, and p , 0.001, respectively). Regarding the tactical variable, the behavior of the team surface area showed a significant increase from the first half to the second half at stage T3 (p = 0.003) only, unlike the spread, which showed differences at stages T0, T2, and T3 (p = 0.003, p = 0.006, and p = 0.01).

Tactical Variables and Movement Patterns From the First Half and the Second Half, in Isolation, Throughout the Periodization

Table 2 shows that when comparing the first half across the stages (T0-T1-T2-T3), there were no significant differences (p $ 0.05) for any variables, except for the team surface area (T0 3 T1—p , 0.001; T0 3 T2—p , 0.001; T0 3 T3—p = 0.002) and spread (T0 3 T1—p = 0.008; T0 3 T2—p = 0.02), where significant increases were observed. Moreover, when comparing the above variables between stages in the second half, there was a significant increase in team surface area (T0 3 T1—p , 0.001; T0 3 T2—p , 0.001; T0 3 T3—p , 0.001; T1 3 T3—p = 0.006; T2 3 T3–p = 0.007) and spread (T0 3 T2—p , 0.001; T0 3 T3—p , 0.001; T1 3 T3—p = 0.004). In addition, when comparing the second half between stages, there was a significant increase (p = 0.03) in SPR when comparing stage T0 (pretraining) with T3 (after training). For the HIA variable, a significant increase was found when comparing stages T0 to T2 and T2 to T3 (p = 0.05 and p = 0.02, respectively). For the Vmax variable, a significant increase was observed when comparing the second half at stages T0 and T3 (p = 0.007), T1 and T3 (p , 0.001), and T2 and T3 (p = 0.004). Movement Patterns During the Simulated Game (first + second Halves) Throughout the Periodization

Table 3 shows the analysis of movement patterns collected at different stages during the simulated games. A significant increase in the HIA was observed when comparing the T0 and T3 stages (p = 0.05). For the Vmax, along with the increase from stage T0 to T3 (p = 0.01) there was also an

TABLE 3. Movement patterns and tactical variables during the simulated game (first + second half) throughout the periodization (n = 10).*† Variables Stopped (%) Walking (%) LIR (%) MIR (%) HIR (%) SPR (%) HIA (%) Total distance (km) Vaverage (km$h21) Vmax (km$h21) Number of sprints Team surface area (m2) Spread (m)

T0 game 0.10 6.71 42.04 29.07 14.40 7.68 22.08 6.11 6.11 30.57 86 514.68 129.82

6 6 6 6 6 6 6 6 6 6 6 6 6

T1 game

0.05 1.27 3.16 2.60 2.34 1.95 3.52a 0.48 0.49 2.36b 17 51.02e,f,g 6.77h,i,j

0.09 6.54 42.71 27.29 14.87 8.51 23.38 6.35 6.43 30.46 90 647.66 141.37

6 6 6 6 6 6 6 6 6 6 6 6 6

0.03 1.02 6.01 2.25 2.68 2.87 4.97 0.60 0.38 2.63c 15 41.72 4.78

T2 game 0.09 6.03 39.80 28.00 16.76 9.33 26.09 6.38 6.37 30.69 93 652.10 146.06

6 6 6 6 6 6 6 6 6 6 6 6 6

0.04 1.13 3.66 3.66 1.96 26.09 2.76 0.46 0.46 2.64d 26 29.80 4.40

T3 game 0.07 6.04 39.30 30.24 16.52 10.06 26.59 6.43 6.44 34.06 105 683.07 148.07

6 6 6 6 6 6 6 6 6 6 6 6 6

0.01 1.06 3.01 2.31 1.86 1.45 2.70 0.38 0.58 2.11 23 85.16 14.58

*LIR = low-intensity running; MIR = medium-intensity running; HIR = high-intensity running; SPR = sprinting; HIA = high-intensity activity; Vaverage = average speed; Vmax = maximum speed. †a = T0 3 T3—p = 0.05; b = T0 3 T3—p = 0.01; c = T1 3 T3—p = 0.01; d = T2 3 T3—p = 0.02; e = T0 3 T1—p , 0.001; f = T0 3 T2—p , 0.001; g = T0 3 T3—p , 0.001; h = T0 3 T1—p = 0.03; i = T0 3 T2—p = 0.001; j = T0 3 T3—p , 0.001.

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TABLE 4. Correlation matrix between the percentage variation of T0 (first testing week) to T3 (end of periodization training) variables obtained from computational tracking with the biochemical (CK and LDH) markers evaluated.* Variables

ΔCK (%)

ΔLDH (%)

ΔStopped (%) ΔWalking (%) ΔLIR (%) ΔMIR (%) ΔHIR (%) ΔSPR (%) ΔHIA (%) ΔTotal distance (%) ΔVaverage (%) ΔVmax (%) ΔNumber of sprints (%)

0.51 0.65† 0.75† 0.02 20.85† 20.20 20.71† 20.32 20.07 0.55 20.18

0.51 0.49 0.80z 0.08 20.84z 20.22 20.70† 20.28 20.07 0.53 20.18

*Δ = percentage variation of T0 (first week of tests) to T3 (end of periodization training); CK = plasmatic activity of creatine kinase; LDH = plasmatic activity of lactate dehydrogenase; LIR = low-intensity running; MIR = mediumintensity running; HIR = high-intensity running; SPR = sprinting; HIA = high-intensity activities; Vaverage = average speed; Vmax = maximum speed. †p # 0.05. zp , 0.01.

increase from stage T1 to T3 (p = 0.01) and T2 to T3 (p = 0.02). With regard to the tactical variable, the team surface area and spread increased significantly between stages T0 and T1 (p , 0.001; p = 0.03, respectively), T0 and T2 (p , 0.001; p = 0.001, respectively), and T0 and T3 (p , 0.001 for both). Association Between the Percentage Variation of Creatine Kinase and Lactate Dehydrogenase With the Movement Patterns

Table 4 shows the correlation matrix regarding the ΔCK and ΔLDH plasmatic activities with Δ in movement patterns variables. We emphasize the significant inverse correlations between the ΔCK and ΔLDH with ΔHIR (r = 20.85; p # 0.05 and r = 20.84; p , 0.01, respectively) and ΔHIA (r = 20.85 and r = 20.70; p # 0.05, respectively).

DISCUSSION At this point in the literature, this study is the first to analyze the patterns of in-game movement and tactical variables throughout periodization training in soccer. These findings may contribute to verifying the effectiveness of the training as regard physical and tactical performance in real game situations, so as to provide scientific evidence for effective training methodologies aimed at improving on-field performance. Regarding the plasma activity behavior of the indirect markers of muscle damage (CK and LDH), there was

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a reduction in activities throughout the periodization. This suggests that the body adapted positively to the stimuli provided to the athletes. According to the organization of the periodization training proposed in this study, where the volume of technical-tactical ability training was prioritized at every stage, we can affirm that this model of training significantly reduced muscle damage in the analyzed sample. Meyer and Meister (29) analyzed 532 soccer players over the course of the second-division German championship. The results demonstrated an average elevation in CK, justified by the effect of training and games. This finding contrasts with those found in the present study, where there was a significant reduction in this variable during the season. However, explanatory variables for this difference in findings should include the level of competition and the kind of training in which the teams are engaged. Lazarim et al. (25) studied professional players from 5 clubs in the first division of the Brazilian championship for 5 months during the game season, and the results showed that CK serum levels decreased significantly over the months (time of collection—2 days after games). Another study by Alves et al. (3), analyzed the CK activity of 17 players from a Brazilian soccer club through 25 games of the first division Brazilian championship. In this study, CK was analyzed 36–46 hours after games. The results showed that the CK decreased over the period analyzed in the championship, both corroborating the findings of this study and demonstrating that young Brazilian soccer players seem to present similar behaviors when compared with adult Brazilian players, regarding the response of indirect muscle damage markers. The decrease in CK and LDH over the 22 weeks of training applied in this study can be attributed to an adaptation of the skeletal muscle of these young athletes to their specific training load, because early in the season (at the preparatory stage), they were not accustomed to the stimuli they were presented with but throughout the periodization (competitive stages I and II) these stimuli became more common and less stressful to their bodies (27). One of the mechanisms associated with this adaptation of the muscle is derived from the activation of myogenic satellite cells that operate to repair muscle fibers (38). Another explanatory factor for these results is the “Repeated Bout Effect” (34), which is a phenomenon that leads to decreased muscle damage with progression of training. Corroborating the findings of this study, the literature indicates that after training repeatedly with equivalent loads, there is a decrease in the magnitude of muscle damage given that the muscle tissue is repaired and restructured after microlesions, adapted to training. This generates partial protection for the muscle, strengthening it against possible stresses that lead to further damage conditions (8,33,34). When analyzing the movement patterns and tactical variables, it was seen that periodization training promoted increases in the percentage of total distance covered in HIA, maximum velocity (Vmax), and the team surface area and VOLUME 30 | NUMBER 10 | OCTOBER 2016 |

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Periodization in Soccer spread of the players. When comparing the T0 stage (pretraining) with the T3 stage (after training), a significant increase in the aforementioned variables was observed, which represents increased intensity and baseline play by the end of the periodization (Tables 1 and 2). In addition, it was found that when comparing the first and the second halves in isolation during the periodization (T0, T1, T2, and T3), there was a significant increase in the variables related to the intensity of the game (SPR; HIA; Vmax), although only in the second half. That is, when comparing the second half between the pre (T0) and posttraining (T3) stages, there was a significant increase in these variables. However, when comparing the first half this increase was not observed (Table 2). Thus, it is suggested that players achieve a higher intensity during the second half by the end of the periodization. This is confirmed when comparing the first to the second halves at every stage of the evaluation. In these analyses, at the T0 stage, there were no increases in variables relating to in-game high-intensity activities (i.e., HIA and Vmax), except for the variable number of sprints by the T3 stage, there was an increase in such variables as SPR, HIA, Vmax, and the number of sprints. Moreover, given that the high-intensity activity performed by the players is the best variable for determining physical performance during matches (30), this brings us to reflect on the criteria adopted in the literature to characterize high intensity. Abt and Lovell (2) suggested adopting individualized values to define high intensity. However, Krustrup and Bangsbo (24), by defining an intensity above 15 km$h21 as high intensity, enable future comparisons with the study by Abt and Lovell (2), verifying that the absolute value (15 km$h21) corresponds to the median value of the second threshold of physiological transition, thus characterizing it as a suitable absolute indicator. Because this study used the sum of the values obtained in high-intensity running (HIR— 13.1 , V5 # 18 km$h21) and sprinting (SPR—V6 . 18 km$h21) as HIA, it can be considered an appropriate value, because these are young players and the absolute value of 15 km$h21 was obtained by adult players. Rampinini et al. (35) found that, among high-level professional soccer players (Union of European Football Associations, European Champions League), about 20–30% of the total distance is covered at high intensity. These values corroborate those found in this study (22–27%), which studied young soccer players under training conditions. Thus, given that the literature defends this variable as the best measure of in-game physical performance (30), we can assert that that the athletes in this study presented elevated athletic performance (27% of the total distance was covered at high intensity) by the end of the periodization. Previous studies have also indicated that at the professional level, these distances covered at higher intensity occur at the end of the soccer season, when compared with the beginning or middle (30). Castagna et al. (13) studied young soccer players (age: 14.04 6 0.01 years) of high level (4 years of experience in

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national and international championships) during an official game. The authors found that players covered 15% of the total distance in HIA, which is less than this study. This difference can be attributed to the method used to determine the movement patterns. In this study, we used computational tracking, whereas in the study by Castagna et al. (13), the authors used GPS technology. Another explanatory variable may be the quality of the rated game [this study— simulated game 3 Castagna et al. (13)—official game]. When performing comparisons between the first and second halves of game time, the literature reports that there is a reduction in total distance, to the tune of 3.8–5.0%, in the second half of gameplay (13). According to Di Salvo et al. (16), this reduction seems to be justified by the significant decrease in percentage of the total distance traveled at average intensity and the longer times spent in low-intensity efforts. However, in this study, we found an increase for variables related to HIR (SPR, HIA, Vmax, and number of sprints) when comparing the first to the second half at the T3 stage (after training), which demonstrates positive adaptation to the training, since high-intensity actions are decisive in soccer, enabling quick transitions and creating empty spaces and situations conducive to finalization (17). Regarding the tactical variables in offensive contexts, players must keep the ball and move around in the empty spaces of the field, progressing toward the opponent’s goal and seeking alternatives for finalization. However, defensive players seek recovery of possession, while moving to prevent the progression of the opponent and the completion of their goal. Thus, the tactical variables (team surface area and spread) may reflect the strategies set by the teams, and represent tactical performance indicators (31,32). Therefore, the documented increase in team surface area and player spread from pre to posttraining in this study, could be attributed to a greater in-game effective space, as characterized by the polygonal area that connects all the players on the team involved in the action located on the periphery of the positioning lines at that given moment, and suggests an improved ability to maintain possession, which in turn demonstrates better tactical performance. Recently, Aquino et al. (4) found positive relationships (r = 0.62–0.90) between the movement patterns during a game (e.g., HIR, SPR, HIA, the total distance, Vaverage, and number of sprints) and muscle damage markers (e.g., CK and LDH) in young soccer players (cross-sectional study). However, in this study (longitudinal) were found positive changes across the periodization training (e.g., reducing the activities of the biochemical markers of muscle damage) with a simultaneous improvement in physical performance (increase in HIR). Furthermore, this relationship was evidenced by large significant inverses correlations between DCK and DLDH with DHIR and DHIA. In short, the periodization training applied in this study, with emphasis on concentrating most of the training on technical-tactical ability, led to positive muscle adaptation, as

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Journal of Strength and Conditioning Research evidenced by the reduced activity of indirect markers of muscle damage (CK and LDH). In addition, there was a longitudinal increase in the percentage of the total distance traveled at high intensity, as well as at top speed, the team surface area and spread of the players, which contributed to a greater intensity of play and tactical performance at the end of the proposed periodization training period. These results suggest a strategy for effective training that improves in-game soccer performance, as well as providing a protective effect on the muscular system. The limitation of this study refers to the absence of a control group. However, due to the design of the experimental protocol, which hinders the formation of 2 groups within the same team, this limitation is partially justified. Moreover, even with the absence of a control group, to our knowledge, this is the first study which monitored training loads and content during a macrocycle performed with young soccer players and analyzed the physical and tactical performance through computational tracking, finding increased intensity of the game at the end of the season. Furthermore, there are few studies that have demonstrated a reduction in muscle damage markers during a soccer season (3,25).

PRACTICAL APPLICATIONS Our data suggest that a macrocycle with an emphasis on technical and tactical ability was able to promote increases in physical and tactical performance of young soccer players in real situations of dispute. Thus, the distribution of training loads used in this study, in addition to enabling more specific and contextual training, provided an increased game intensity at the end of the season, a variable directly related to the outcome of the match (17). In addition, it was found that the training protocol caused reductions in muscle damage markers, revealing a beneficial stimulus to the muscular system, which may contribute to the prevention of injuries from overtraining throughout the season. Despite the well-documented importance of evaluation of blood parameters (i.e., damage markers) during soccer practice (3,4,23,29), in this study, we verified that the reduction related was associated with increased work rate during game, especially HIR, through a technical-tactical periodization training, showing the importance of monitoring these parameters in long term. In summary, longitudinal experimental designs, as in the case of this study, dedicated to discussing the training content and organization throughout the season with young soccer players, may provide coaches and sport scientists with information regarding the annual cycle of training in the search for specificity in the daily sessions, optimizing sports performance and preventing injuries because of training excess, preserving the athletes for effective participation and maximum performance throughout the competitive season.

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