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DOI: 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 Alves2
, Paulo
Roberto Pereira Santiago2,3,4
, Enrico Fuini Puggina ( )2,3
1
Faculty of Sport Sciences, Porto University, Porto, Portugal.
2
Post-graduate Program in Rehabilitation and Functional Performance, Medicine School of
Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil.
3
School of Physical Education and Sport of Ribeirão Preto, University of São Paulo, Ribeirão
Preto, Brazil.
4
Laboratório 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: enrico@usp.br
Running tittle: Periodization in soccer.
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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).
*** Table 2 near here ***
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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).
*** Table 3 near here ***
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).
*** Table 4 near here ***
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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6. Barros RML, Misuta MS, Menezes RP, Figueroa PJ, Moura FA, Cunha SA, Anido R, and
Leite NJ. Analysis of the Distances Covered by First Division Brazilian Soccer Players
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35. Rampinini E, Bishop D, Marcora SM, Ferrari Bravo D, Sassi R, and Impellizzeri FM.
Validity of simple field tests as indicators of match-related physical performance in top-
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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.
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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
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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.
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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.
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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 ∆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.
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Periodization training

  • 1. Journal of Strength and Conditioning Research Publish Ahead of Print DOI: 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 Alves2 , Paulo Roberto Pereira Santiago2,3,4 , Enrico Fuini Puggina ( )2,3 1 Faculty of Sport Sciences, Porto University, Porto, Portugal. 2 Post-graduate Program in Rehabilitation and Functional Performance, Medicine School of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil. 3 School of Physical Education and Sport of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil. 4 Laboratório 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: enrico@usp.br Running tittle: Periodization in soccer. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 2. 1 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. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 3. 2 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 4. 3 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 5. 4 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 * Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 6. 5 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 7. 6 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. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 8. 7 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 9. 8 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). Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 10. 9 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 11. 10 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 12. 11 (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). *** Table 2 near here *** Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 13. 12 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). *** Table 3 near here *** 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). *** Table 4 near here *** 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 14. 13 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 15. 14 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 16. 15 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 17. 16 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 18. 17 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 19. 18 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. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 20. 19 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. REFERENCES 1. Abdel-Aziz YI and Karara HM. {Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry}. Presented at Proceedings of the Symposium on Close-Range photogrammetry, 1971. 2. Abt G and Lovell R. The use of individualized speed and intensity thresholds for determining the distance run at high-intensity in professional soccer. J Sports Sci 27: 893- 898, 2009. 3. Alves AL, Garcia ES, Morandi RF, Claudino JG, Pimenta EM, and Soares DD. Individual analysis of creatine kinase concentration in Brazilian elite soccer players. Rev Bras Med Esporte 21: 112-116, 2015. 4. Aquino RLQT, Gonçalves LGC, Vieira LHP, Oliveira LP, Alves GF, Santiago PRP and Puggina EF. Biochemical, physical and tactical analysis of a simulated game in young soccer players. J Sports Med Phys Fitness, 2016. 5. Barros RM, Menezes RP, Russomanno TG, Misuta MS, Brandao BC, Figueroa PJ, Leite NJ, and Goldenstein SK. Measuring handball players trajectories using an automatically trained boosting algorithm. Comput Methods Biomech Biomed Engin 14: 53-63, 2011. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 21. 20 6. Barros RML, Misuta MS, Menezes RP, Figueroa PJ, Moura FA, Cunha SA, Anido R, and Leite NJ. Analysis of the Distances Covered by First Division Brazilian Soccer Players Obtained with an Automatic Tracking Method. J Sports Sci Med 6: 233-242, 2007. 7. Bloomfield J, Polman R, and O'Donoghue P. Physical Demands of Different Positions in FA Premier League Soccer. J Sports Sci Med 6: 63-70, 2007. 8. Brentano MA and Martins Kruel LF. A review on strength exercise-induced muscle damage: applications, adaptation mechanisms and limitations. J Sports Med Phys Fitness 51: 1-10, 2011. 9. Buchheit M, Mendez-Villanueva A, Simpson BM, and Bourdon PC. Match running performance and fitness in youth soccer. Int J Sports Med 31: 818-825, 2010. 10. Carling C, Bloomfield J, Nelsen L, and Reilly T. The role of motion analysis in elite soccer: contemporary performance measurement techniques and work rate data. Sports Med 38: 839-862, 2008. 11. Carling C and Dupont G. Are declines in physical performance associated with a reduction in skill-related performance during professional soccer match-play? J Sports Sci 29: 63-71, 2011. 12. Castagna C, D'Ottavio S, and Abt G. Activity profile of young soccer players during actual match play. J Strength Cond Res 17: 775-780, 2003. 13. Castagna C, Impellizzeri F, Cecchini E, Rampinini E, and Alvarez JC. Effects of intermittent-endurance fitness on match performance in young male soccer players. J Strength Cond Res 23: 1954-1959, 2009. 14. Correa UC, Alegre FA, Freudenheim AM, Dos Santos S, and Tani G. The game of futsal as an adaptive process. Nonlinear Dynamics Psychol Life Sci 16: 185-203, 2012. 15. Dellal A, Chamari K, Wong DP, Ahmaidi S, Keller D, Barros R, Bisciotti GN, and Carling C. Comparison of physical and technical performance in European soccer match-play: FA Premier League and La Liga. Eur J Sport Sci 11: 51-59, 2011. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 22. 21 16. Di Salvo V, Baron R, Tschan H, Calderon Montero FJ, Bachl N, and Pigozzi F. Performance characteristics according to playing position in elite soccer. Int J Sports Med 28: 222-227, 2007. 17. Faude O, Koch T, and Meyer T. Straight sprinting is the most frequent action in goal situations in professional football. J Sports Sci 30: 625-631, 2012. 18. Figueroa PJ, Leite NJ, and Barros RML. Background recovering in outdoor image sequences: An example of soccer players segmentation. Image Vis Comput 24: 363-374, 2006. 19. Figueroa PJ, Leite NJ, and Barros RML. Tracking soccer players aiming their kinematical motion analysis. Comput Vis Image Und 101: 122-135, 2006. 20. Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sports Exerc 30: 1164-1168, 1998. 21. Garcia JD-C, Román IR, Calleja-González J, and Dellal A. Quantification and Analysis of Offensive Situations in Different Formats of Sided Games In Soccer. J Hum Kinet 44: 193- 201, 2014. 22. Gonzaga AdS, Albuquerque MR, Malloy-Diniz LF, Greco PJ, and Teoldo da Costa I. Affective Decision-Making and Tactical Behavior of Under-15 Soccer Players. PLoS ONE 9: e101231, 2014. 23. Heisterberg MF, Fahrenkrug J, Krustrup P, Storskov A, Kjaer M, and Andersen JL. Extensive monitoring through multiple blood samples in professional soccer players. J Strength Cond Res 27: 1260-1271, 2013. 24. Krustrup P and Bangsbo J. Physiological demands of top-class soccer refereeing in relation to physical capacity: effect of intense intermittent exercise training. J Sports Sci 19: 881- 891, 2001. 25. Lazarim FL, Antunes-Neto JM, da Silva FO, Nunes LA, Bassini-Cameron A, Cameron LC, Alves AA, Brenzikofer R, and de Macedo DV. The upper values of plasma creatine kinase Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 23. 22 of professional soccer players during the Brazilian National Championship. J Sci Med Sport 12: 85-90, 2009. 26. MacLeod H, Morris J, Nevill A, and Sunderland C. The validity of a non-differential global positioning system for assessing player movement patterns in field hockey. J Sports Sci 27: 121-128, 2009. 27. McHugh MP. Recent advances in the understanding of the repeated bout effect: the protective effect against muscle damage from a single bout of eccentric exercise. Scand J Med Sci Sports 13: 88-97, 2003. 28. Meister S, Faude O, Ammann T, Schnittker R, and Meyer T. Indicators for high physical strain and overload in elite football players. Scand J Med Sci Sports 23: 156-163, 2013. 29. Meyer T and Meister S. Routine blood parameters in elite soccer players. Int J Sports Med 32: 875-881, 2011. 30. Mohr M, Krustrup P, and Bangsbo J. Match performance of high-standard soccer players with special reference to development of fatigue. J Sports Sci 21: 519-528, 2003. 31. Moura FA, Martins LE, Anido Rde O, de Barros RM, and Cunha SA. Quantitative analysis of Brazilian football players' organisation on the pitch. Sports Biomech 11: 85-96, 2012. 32. Moura FA, Martins LE, Anido RO, Ruffino PR, Barros RM, and Cunha SA. A spectral analysis of team dynamics and tactics in Brazilian football. J Sports Sci 31: 1568-1577, 2013. 33. Nosaka K, Newton M, Sacco P, Chapman D, and Lavender A. Partial protection against muscle damage by eccentric actions at short muscle lengths. Med Sci Sports Exerc 37: 746- 753, 2005. 34. Nosaka K, Sakamoto K, Newton M, and Sacco P. The repeated bout effect of reduced-load eccentric exercise on elbow flexor muscle damage. Eur J Appl Physiol 85: 34-40, 2001. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 24. 23 35. Rampinini E, Bishop D, Marcora SM, Ferrari Bravo D, Sassi R, and Impellizzeri FM. Validity of simple field tests as indicators of match-related physical performance in top- level professional soccer players. Int J Sports Med 28: 228-235, 2007. 36. Rebelo A, Brito J, Seabra A, Oliveira J, and Krustrup P. Physical match performance of youth football players in relation to physical capacity. Eur J Sport Sci 14 Suppl 1: S148-156, 2014. 37. Silva B, Garganta J, Santos R, and Teoldo I. Comparing Tactical Behaviour of Soccer Players in 3 vs. 3 and 6 vs. 6 Small-Sided Games. J Hum Kinet 41: 191-202, 2014. 38. Tiidus PM. Skeletal muscle damage and repair. Human Kinetics, 2008. 39. Vieira LHP, Pagnoca EA, Milioni F, Barbieri RA, Menezes RP, Alvarez L, Déniz LG, Santana-Cedrés D, and Santiago PRP. Tracking futsal players with a wide-angle lens camera: accuracy analysis of the radial distortion correction based on an improved Hough transform algorithm. Comput Methods Biomech Biomed Engin Imaging Vis: 1-11, 2015. 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). Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 25. 24 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. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 26. 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. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 27. 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 Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 28. 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. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 29. 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. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 30. 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. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 31. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED
  • 32. Copyright ª 2016 National Strength and Conditioning Association A C C EPTED