periodization training

Journal of Strength and Conditioning Research Publish Ahead of PrintDOI: 10.1519/JSC.0000000000001381

PERIODIZATION TRAINING FOCUSED ON TECHNICAL-TACTICAL ABILITY IN

YOUNG SOCCER PLAYERS POSITIVELY AFFECTS BIOCHEMICAL MARKERS

AND GAME PERFORMANCE

Rodrigo Leal de Queiroz Thomaz de Aquino1,2

, Luiz Guilherme Cruz Gonçalves2, Luiz

Henrique Palucci Vieira2,4

, Lucas de Paula Oliveira2, Guilherme Figueiredo Alves

2, Paulo

Roberto Pereira Santiago2,3,4

, Enrico Fuini Puggina ( )

2,3

1Faculty of Sport Sciences, Porto University, Porto, Portugal.

2Post-graduate Program in Rehabilitation and Functional Performance, Medicine School of

Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil.

3School of Physical Education and Sport of Ribeirão Preto, University of São Paulo, Ribeirão

Preto, Brazil.

4Laboratório de Biomecânica e Controle Motor (LaBioCoM), University of São Paulo, Ribeirão

Preto, Brazil.

Authors: There are no conflicts of interest

The study was approved by the Research Ethics Committee of the Ribeirão Preto

Medical School (protocol 710.998) and was conducted in accordance with the

Declaration of Helsinki.

Enrico Fuini Puggina, Ph.D. ( )

University of São Paulo. Av. Bandeirantes, 3900 – Monte Alegre, 14040-907 – Ribeirão Preto,

SP, Brazil.

Phone: +55 16 3315-0342

Fax: 55 19 3526-4100

E-mail: [email protected]

Running tittle: Periodization in soccer.

Copyright ª 2016 National Strength and Conditioning Association

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mailto:[email protected]

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ABSTRACT

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 ± 0.2 years, height 172.8 ± 3.6 cm;

body mass 61.9 ± 2.9 kg; % fat 11.7 ± 1.6; VO2max 48.67 ± 3.24 ml.kg-1.min-1) underwent four

stages of evaluation: pre-preparatory stage - T0; post-preparatory stage - T1; post-competitive stage

I - T2 and; post-competitive 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/L; T3: ̴ 150 U/L).

Significant increases were observed (p ≤ 0.05) in the intensity of the game, high intensity activities

(T0: ̴ 22 %; T3: ̴ 27%), maximum speed (T0: ̴ 30 km.h-1; T3: ̴ 34 km.h-1) 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 variation in high intensity running (r = -0.85; p < 0.05

and r = -0.84; p < 0.01 respectively) and high intensity activities (r = -0.71 and r = -0.70; 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 in-game 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.


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KEY WORDS: Muscle damage; Computational tracking; Game analysis; Technical-tactical;

Periodization; Soccer.

INTRODUCTION

Analyses of in-game displacement patterns performed by soccer players have been fully explored in

the literature (6, 7, 11, 12, 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 years 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.

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


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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). Due to this openness (which is

also complex, hierarchical and adaptive) the dynamics of decreases/increases in uncertainty and the

mutual advantage of one 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


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with identifying surrogate markers of muscle damage, in order to gather information about possible

negative effects of the damaging agents in the process of conditioning/training/preparation. Thus,

this study 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/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/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 four weeks of assessments (totaling a 26 week macrocycle). The

macrocycle was divided into three stages: preparatory stage - six weeks; competitive stage I - 8

weeks and competitive stage II - 8 weeks. The players trained four 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 *


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volume), as well as training monotony and tension at every training stage. The PSE 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 prior to blood collection. At the end of the assessment weeks (Thursday), the

simulated matches were held (30 'x 30') (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). Prior to 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 ± SD; age 15.4 ± 0.2 years, height 172.8 ± 3.6 cm; body mass 61.9 ± 2.9

kg; 11.7 ± 1.6% fat; VO2 max 48.67 ± 3.24 ml.kg-1.min-1) and members of a soccer club that plays in

the first division of the state of São Paulo, Brazil; 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 Ribeirão Preto

(protocol 710.998/2014) and was conducted in accordance with the Declaration of Helsinki.

Procedures


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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 four 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, since 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 rating of perceived exertion (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 total time of the session (by volume) was

120 min. In competitive stage I, intensity was measured as an average of 6.1 and total time as 110

min. In competitive stage II, intensity averaged 6.3 and volume measured 100 min. After

calculating the RPE and training volume, variables of monotony and strain training were calculated.


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The monotony was obtained by dividing the average weekly load (RPE * Volume) and its standard

error and the tension by multiplying the sum of the weekly load and monotony (20) (Table 1).

*** Figure 1 near here ***

*** Table 1 near here ***

Analysis of indirect markers of muscle damage

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. 10 mL of

blood were taken from each participant, and the sample was collected in a vial containing an

anticoagulant. Immediately afterwards, the blood was centrifuged at 2000 g 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 -80 ° C for later biochemical analysis (25).

The plasma activities of CK and LDH were determined using commercial Bioliquid® (Pinhais,

Brazil) kits, following the manufacturer’s suggested methodology—which involved adding N-

Acetyl-Cysteine (NAC) to the reaction medium to ensure full activation of CK-MM (muscle

isoform). The procedures for biochemical analyses were carried out through the addition of the

buffer solution (2.5 ml bottle) to a specific reactive, and placed in a water bath at 37° C during one

minute. Shortly thereafter, 20 µl of plasma were added to the reactive solution, and the mixture left

in a water bath at 37° C for another minute. Immediately afterwards, four readings were taken at

measured intervals: immediately, at one minute, two minutes and three minutes. Readings were


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taken at a wavelength of 340 nm and 37° C. The calculations of CK and LDH activity in the

samples were performed via the following equations: CK (U/L) = 8252 x ∆ absorbance/minute and

LDH (U/L) = 8321 x ∆ absorbance/minute, respectively.

Performance analysis in the field

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 x 50 m) at the usual time of

team training lasted 60 minutes (30' x 30' with 15 minutes of passive recovery).

The game was fully monitored by two digital video cameras (CASIO EX-FH25; 720 x 480 pixel)

with an acquisition frequency of 30 Hz, each of which covered about 3/4 of the total area of the

field. After the transfer of image sequences to the computer, the DVIDEOW computational tracking

environment (4, 5, 6, 31, 32, 39) was used to obtain the players’ trajectories. The average error in

determination of the positions on the pitch and distances covered of the soccer players using this

software is approximately 0.3 m and 1% (6, 19).

Synchronization of the images from the cameras was performed by identifying common events in

overlapping areas of the cameras (32, 39). Calibration was obtained from six points on the surface

of the field using previously measured distances to the origin of the adopted coordinate system.

Next, using a specific algorithm (5) segmentation based on morphological filtering (18) was

performed. Tracking youth soccer players (i.e. marking of frames) was conducted with an

automation of 75%. Finally, the data arrays containing the 2-D positions as a function of time, for

each player on the field were obtained by reconstruction via the Direct Linear Transformation

(DLT) method (1, 5, 6, 32).


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In Matlab enviroment (The MathWorks, Inc., USA), the data were smoothed by a third order low-

pass Butterworth digital filter, using a cut-off frequency of 0.4 Hz (31, 32, 39). Specific routines,

the players’ movement patterns were calculated: total distance traveled (km), average speed (km.h-

1), maximum speed (km.h-1) and percentage of the total distance covered in seven ranges of speed,

based on the study by Castagna et al. (13)Error! Hyperlink reference not valid.: V1 ≤ 0.4 km/h

(stopped); 0.4 < V2 ≤ 3 km/h (walking); 3.1 < V3 ≤ 8 km/h (low intensity running [LIR]); 8.1 < V4

≤ 13 km/h (medium intensity running [MIR]); 13.1 < V5 ≤ 18 km/h (high intensity running [HIR]);

V6 > 18 km/h (sprinting [SPR]); and V7 = V5 + V6 (high intensity activities [HIA]). The number

of sprints (u. a.) was defined by the frequency of runs at V6 (4).

The selected tactical variables were the team surface area, defined as a convex polygon having as

vertices the two-dimensional positions of the players on the pitch, and the spread of the players (i.e.

the distance between players and all teammates) calculated at each moment of time (i.e. for each

frame analyzed) using a technique previously adopted in professional soccer players (31, 32) and

more recently in youth soccer players (4).

Statistical analysis

For analysis of the results, SPSS (Statistical Package for Social Sciences) software for Windows,

version 17.0 was used. Data normality was verified using the Shapiro-Wilk test. Comparison of

displacement patterns, tactical variables and indirect markers of muscle damage between the four

stages of data collection (T0, T1, T2, T3) was performed using ANOVA 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) (∆) in CK


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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 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.

*** Figure 2 near here ***

Displacement patterns and tactical variables from the 1st half of gameplay with the 2nd half

of gameplay throughout the periodization

Table 2 shows that the percentage of the total distance in the sprinting (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 high intensity activities (HIA) ,

a significant increase in the same comparison as above (first half x 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


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(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). As

regards the tactical variable, the behavior of the team surface area showed a significant increase

from the 1st half to the 2nd 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 p = 0.01).

Tactical variables and Movement patterns from the 1st half and the 2nd half, in isolation,

throughout the periodization

Table 2 shows that when comparing the 1st 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 x T1 - p

<0.001; T0 x T2 - p <0.001; T0 x T3 - p = 0.002) and spread (T0 x T1- p = 0.008; T0 x T2 - p =

0.02), where significant increases were observed. Moreover, when comparing the above variables

between stages in the 2nd half, there was a significant increase in team surface area (T0 x T1 - p

<0.001; T0 x T2 - p <0.001; T0 x T3 - p <0.001; T1 x T3 - p = 0.006; T2 x T3 - p = 0.007) and

spread (T0 x T2 - p <0.001; T0 x T3 - p <0.001; T1 x T3 - p = 0.004).

In addition, when comparing the 2nd half between stages, there was a significant increase (p = 0.03)

in SPR when comparing stage T0 (pre-training) 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 2nd

half at stages T0 and T3 (p = 0.007), T1 and T3 (p <0.001) and T2 and T3 (p = 0.004).



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Movement patterns during the simulated game (1st + 2nd 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

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 CK and LDH with the movement patterns

Table 4 shows the correlation matrix regarding the ∆CK and ∆LDH plasmatic activities with ∆ in

movement patterns variables. We emphasize the significant inverse correlations between the ∆CK

and ∆LDH with ∆HIR (r = -0.85; p < 0.05 and r = -0.84; p < 0.01 respectively) and ∆HIA (r = -0.85

and r = -0.70; 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


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verifying the effectiveness of the training as regards physical and tactical performance in real game

situations, so as to provide scientific evidence for effective training methodologies aimed at

improving on-field performance.

As regards the plasma activity behavior of the indirect markers of muscle damage (CK and LDH)

there was 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.

_ENREF_28Meyer 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)_ENREF_23 studied professional players from five clubs in the first division of

the Brazilian championship for five months during the game season, and the results showed that CK

serum levels decreased significantly over the months (time of collection - two days after games).

Another study by _ENREF_3Alves 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 the present study and


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demonstrating that young Brazilian soccer players seem to present similar behaviors when

compared to 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, since

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 which 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 high intensity activities (HIA),

maximum velocity (Vmax) and the team surface area and spread of the players. When comparing the

T0 stage (pre-training) 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 1st and the 2nd halves in isolation during the

periodization (T0, T1, T2, T3) there was a significant increase in the variables related to the


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intensity of the game (SPR; HIA; Vmax), although only in the 2nd half. That is, when comparing the

2nd half between the pre- (T0) and post training (T3) stages, there was a significant increase in these

variables. However, when comparing the 1st half this increase was not observed (Table 2). Thus, it

is suggested that players achieve a higher intensity during the 2nd half by the end of the

periodization. This is confirmed when comparing the 1st to the 2nd 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 carried out by the players is the best variable for

determining physical performance during matches (29), this brings us to reflect on the criteria

adopted in the literature to characterize high intensity. _ENREF_2Abt and Lovell (2) suggested

adopting individualized values to define high intensity. However, Krustrup and Bangsbo (24),

by_ENREF_22 defining an intensity above 15 km.h-1 as high intensity, enable future comparisons

with the study by Abt and Lovell (2)_ENREF_2, verifying that the absolute value (15 km.h-1)

corresponds to the median value of the second threshold of physiological transition, thus

characterizing it as a suitable absolute indicator. Since this study used the sum of the values

obtained in high intensity running (HIR - 13.1 < V5 ≤ 18 km/h) and sprinting (SPR - V6 > 18 km/h)

as high intensity activities (HIA), it can be considered an appropriate value, since these are young

players and the absolute value of 15 km.h-1 was obtained by adult players.

Rampinini et al. (35)_ENREF_34 found that, among high-level professional soccer players (UEFA

European Champions League), about 20-30% of the total distance is covered at high intensity.

These values corroborate those found in the present 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


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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 to

the beginning or middle (30).

_ENREF_13Castagna et al. (13) studied young soccer players (age: 14.04 ± 0.01 years) of high

level (four years of experience in 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 the

present 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

_ENREF_13Castagna et al. (13) the authors used GPS technology. Another explanatory variable

may be the quality of the rated game [this study - simulated game x _ENREF_13Castagna et al. (13)

- official game].

When performing comparisons between the 1st and 2nd halves of game time, the literature reports

that there is a reduction in total distance, to the tune of 3.8 to 5.0%, in the second half of gameplay

(13). According to _ENREF_16Di Salvo et al. (16) this reduction appears 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 the present study, we found an increase for

variables related to high intensity running (SPR, HIA, Vmax and number of sprints) when comparing

the 1st to the 2nd 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 towards the opponent's goal and seeking alternatives


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for finalization. On the other hand, 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 post-training in the present 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, 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 high intensity activities). Furthermore, this

relationship was evidenced by large significant inverses correlations between ∆CK and ∆LDH with

∆HIR and ∆HIA.

In short, the periodization training applied in this study, with emphasis on concentrating the

majority of the training on technical-tactical ability, led to positive muscle adaptation, as 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


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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 two 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

carried out 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 present study we verified that the reduction related

was associated with increased work rate during game, especially high intensity activities, through a

technical-tactical periodization training, showing the importance of monitoring these parameters in

long-term.


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In summary, longitudinal experimental designs, as in the case of the present 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 due to training excess, preserving the athletes for effective participation and maximum

performance throughout the competitive season.

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Legends

Figure 1. Representative layout of the experimental protocol.

Note: PS: Preparatory Stage; CSI: Competitive Stage I; CSII: Competitive Stage II.

Figure 2. Dynamics of changes in the plasmatic activity of Creatine Kinase - CK (A) and Lactate

Dehydrogenase - LDH (B) throughout the periodization (n = 15).

Note: A: a T0 x T1 (p = 0.023); b T0 x T2 (p < 0.001); c T0 x T3 (p < 0.001); d T1 x T2 (p < 0.001);

e T1 x T3 (p < 0.001). B: a T0 x T1 (p < 0.001); b T0 x T2 (p < 0.001); c T0 x T3 (p < 0.001); d T1 x

T2 (p < 0.001); e T1 x T3 (p < 0.001).


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Table 1. Distribution of capabilities, load, monotony and training strain applied in the preparatory

stage and competitive stages I and II.

Table 2. Movement patterns and tactical variables in the 1st and 2nd halves of games throughout the

course of periodization (n = 10).

Table 3. Movement patterns and tactical variables during the simulated game (1st + 2nd half).

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.


ACCEPTED

Table 1. Distribution of capabilities, load, monotony and training strain applied in the

preparatory stage, competitive I and II.

Preparatory Stage Competitive I Stage Competitive II Stage

Aerobic Power Training (%) 10 0 0

Coordination-Flexibility Training (%) 15 10 10

Strenght Training (%) 21 23 15

Speed Training (%) 16 23 25

Technique-Tactic Training (%) 38 44 50

RPE Avarage (U/A) 5.1 6.1 6.3

Volume Avarage (min) 120 110 100

Load Avarage (U/A) 2.453 2.674 2.757

Training Monotony Avarage (U/A) 1.21 1.24 1.26

Training Strain Avarage (U/A) 2.961 3.324 3.477

Note: RPE = Rating of perceived exertion.


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Table 2. Movement patterns and tactical variables in the 1st and 2nd halves of games throughout the course of periodization (n = 10).

T0 T1 T2 T3 Variables

1st Time 2sd Time 1st Time 2sd Time 1st Time 2sd Time 1st Time 2sd Time

Stopped (%) 0.09 ± 0.06 0.10 ± 0.05 0.08 ± 0.03 0.09 ± 0.05 0.09 ± 0.05 0.08 ± 0.05 0.08 ± 0.04 0.06 ± 0.02

Walking (%) 6.75 ± 2.46 6.67 ± 1.92 6.82 ± 1.22 6.26 ± 1.28 6.21 ± 1.55 5.84 ± 1.21 6.20 ± 1.35 5.88 ± 1.33

LIR (%) 41.90 ± 4.31 42.18 ± 2.72 43.81 ± 6.19 41.60 ± 6.79 40.93 ± 4.27 38.66 ± 4.34 38.83 ± 2.85 39.77 ± 5.01

MIR (%) 29.86 ± 3.47 28.28 ± 2.33 27.84 ± 3.13 26.73 ± 2.23 28.22 ± 2.88 27.79 ± 3.38 29.97 ± 2.45 30.50 ± 3.20

HIR (%) 14.50 ± 4.70 14.30 ± 1.95 14.19 ± 2.70 15.55 ± 3.37 16.42 ± 2.86 17.10 ± 1.91 16.71 ± 1.88 16.33 ± 2.62

SPR (%) 6.90 ± 2.12 8.47 ± 2.59n 7.26 ± 3.01* 9.76 ± 3.23 8.14 ± 1.34* 10.53 ± 2.81 8.20 ± 1.64* 11.93 ± 2.21

HIA (%) 21.39 ± 5.73 22.77 ± 3.76o,q 21.46 ± 5.29* 25.31 ± 5.45 24.55 ± 3.33* 27.62 ± 3.46 24.92 ± 3.33* 28.26 ± 3.38

Total Distance (km) 3.01 ± 0.23 3.10 ± 0.29 3.16 ± 0.32 3.20 ± 0.31 3.12 ± 0.27* 3.26 ± 0.21 3.17 ± 0.20 3.26 ± 0.31

Vavarage (km.h-1) 6.02 ± 0.47 6.22 ± 0.57 6.34 ± 0.41 6.51 ± 0.62 6.23 ± 0.55* 6.50 ± 0.42 6.40 ± 0.61 6.48 ± 0.59

Vmax (km.h-1) 29.63 ± 2.92* 31.51 ± 2.20r 30.90 ± 2.84 30.02 ± 3.62s 30.12 ± 2.52 31.25 ± 3.46t 31.37 ± 2.80* 36.76 ± 3.88

Number of Sprints 38 ± 10* 48 ± 11 42 ± 9 48 ± 11 39 ± 14* 54 ± 14 42 ± 11* 63 ± 14

Team Surface Area (m2) 501.83 ± 51.54a,b,c 527.54 ± 51.70 f,g,h 631.79 ± 20.49 663.54 ± 52.96i 638.52 ± 30.73 665.69 ± 23.81j 613.64 ± 62.21* 752.50 ± 22.71

Spread (m) 127.58 ± 7.17*,d,e 132.07 ± 6.11k,l 140.12 ± 2.65 142.61 ± 6.29m 138.24 ± 4.40* 153.88 ± 8.39 136.35 ± 7.95* 159.79 ± 8.64


ACCEPTED

Note: LIR: Low Intensity Running; MIR: Medium Intensity Running; HIR: High Intensity Running; SPR: Sprinting; HIA: High Intensity Activity; Vavarage:

Avarage Speed; Vmax: Maximum Speed. * Significant differences when comparing the 1st to the 2nd time – p ≤ 0.05. SPR: n = T0 x T3 – p = 0.03. HIR: o = T0 x T2 –

p = 0.05; q = T0 x T3 – p = 0.02. Vmax: r = T0 x T3 – p = 0.007; s = T1 x T3 – p < 0.001; t = T2 x T3 – p = 0.004. Team Surface Area: a = T0 x T1 – p < 0.001; b = T0

x T2 – p < 0.001; c = T0 x T3 – p = 0.002; f = T0 x T1 – p < 0.001; g = T0 x T2 – p < 0.001; h = T0 x T3 – p < 0.001; i = T1 x T3 – p = 0.006; j = T2 x T3 – p = 0.007.

Spread: d = T0 x T1 – p = 0.008; e = T0 x T2 – p = 0.02; k = T0 x T2 – p < 0.001; l = T0 x T3 – p < 0.001; m = T1 x T3 – p = 0.004.


ACCEPTED

Table 3. Movement patterns and tactical variables during the simulated game (1st + 2nd

half) throughout the periodization (n = 10).

Variables T0

Game

T1

Game

T2

Game

T3

Game

Stopped (%) 0.10 ± 0.05 0.09 ± 0.03 0.09 ± 0.04 0.07 ± 0.01

Walking (%) 6.71 ± 1.27 6.54 ± 1.02 6.03 ± 1.13 6.04 ± 1.06

LIR (%) 42.04 ± 3.16 42.71 ± 6.01 39.80 ± 3.66 39.30 ± 3.01

MIR (%) 29.07 ± 2.60 27.29 ± 2.25 28.00 ± 3.66 30.24 ± 2.31

HIR (%) 14.40 ± 2.34 14.87 ± 2.68 16.76 ± 1.96 16.52 ± 1.86

SPR (%) 7.68 ± 1.95 8.51 ± 2.87 9.33 ± 26.09 10.06 ± 1.45

HIA (%) 22.08 ± 3.52a 23.38 ± 4.97 26.09 ± 2.76 26.59 ± 2.70

Total Distance (km) 6.11 ± 0.48 6.35 ± 0.60 6.38 ± 0.46 6.43 ± 0.38

Vavarage (km.h-1) 6.11 ± 0.49 6.43 ± 0.38 6.37 ± 0.46 6.44 ± 0.58

Vmax (km.h-1) 30.57 ± 2.36b 30.46 ± 2.63c 30.69 ± 2.64d 34.06 ± 2.11

Number of Sprints 86 ± 17 90 ± 15 93 ± 26 105 ± 23

Team Surface Area (m2) 514.68 ± 51.02e,f,g 647.66 ± 41.72 652.10 ± 29.80 683.07 ± 85.16

Spread (m) 129.82 ± 6.77h,i,j 141.37 ± 4.78 146.06 ± 4.40 148.07 ± 14.58

Note: LIR: Low Intensity Running; MIR: Medium Intensity Running; HIR: High Intensity Running;

SPR: Sprinting; HIA: High Intensity Activity; Vavarage: Avarage Speed; Vmax: Maximum Speed. AAI: a =

T0 x T3 – p = 0.05. Vmax: b = T0 x T3 – p = 0.01; c = T1 x T3 – p = 0.01; d = T2 x T3 – p = 0.02. Team

Surface Area: e = T0 x T1 – p < 0.001; f = T0 x T2 – p < 0.001; g = T0 x T3 – p < 0.001. Spread: h = T0

x T1 – p = 0.03; i = T0 x T2 – p = 0.001; j = T0 x T3 – p < 0.001.


ACCEPTED

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 ∆CK (%) ∆LDH (%)

∆Stopped (%) 0.51 0.51

∆Walking (%) 0.65* 0.49

∆LIR (%) 0.75* 0.80**

∆MIR (%) 0.02 0.08

∆HIR (%) -0.85* -0.84**

∆SPR (%) -0.20 -0.22

∆HIA (%) -0.71* -0.70*

∆Total distance (%) -0.32 -0.28

∆Vaverage (%) -0.07 -0.07

∆Vmax (%) 0.55 0.53

∆Number of sprints (%) -0.18 -0.18

Note: ∆: 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: Medium Intensity Running; HIR: High Intensity Running; SPR: Sprinting;

HIA: High Intensity Activities; Vaverage: Average speed; Vmax: Maximum speed; Significant correlation

between variables * p < 0.05 ** p < 0.01.


ACCEPTED


ACCEPTED