Periodisation and Physical Performance in Elite Female Soccer Players

transcript

Seediscussions,stats,andauthorprofilesforthispublicationat:https://www.researchgate.net/publication/271331357

PeriodisationandPhysicalPerformanceinEliteFemaleSoccerPlayers

ARTICLEinINTERNATIONALJOURNALOFSPORTSPHYSIOLOGYANDPERFORMANCE·JANUARY2015

ImpactFactor:2.66·DOI:10.1123/ijspp.2014-0345·Source:PubMed

CITATION

4AUTHORS:

JocelynKMara

UniversityofCanberra

5PUBLICATIONS4CITATIONS

SEEPROFILE

KevinGThompson

SEEPROFILE

KatePumpa

SEEPROFILE

NickBBall

SEEPROFILE

Availablefrom:JocelynKMara

Retrievedon:05February2016

“Periodisation and Physical Performance in Elite Female Soccer Players” by Mara JK, Thompson KG, Pumpa KL, Ball NB

International Journal of Sports Physiology and Performance

Note. This article will be published in a forthcoming issue of the

International Journal of Sports Physiology and Performance. The

article appears here in its accepted, peer-reviewed form, as it was

provided by the submitting author. It has not been copyedited,

proofread, or formatted by the publisher.

Section: Original Investigation

Article Title: Periodisation and Physical Performance in Elite Female Soccer Players

Authors: Jocelyn K. Mara, Kevin G. Thompson, Kate L. Pumpa, and Nick B. Ball

Affiliations: The authors are with the University of Canberra, Research Institute for Sport

and Exercise (UC-RISE), Bruce, ACT, Australia.

Journal: International Journal of Sports Physiology and Performance

Acceptance Date: January 8, 2015

DOI: http://dx.doi.org/10.1123/ijspp.2014-0345

TITLE PAGE

1. Title: Periodisation and Physical Performance in Elite Female Soccer Players

2. Submission Type: Original Investigation

3. Authors:

1. Jocelyn K. Mara, University of Canberra, Research Institute for Sport and Exercise

(UC-RISE)

2. Kevin G. Thompson, University of Canberra, Research Institute for Sport and

Exercise (UC-RISE)

3. Kate L. Pumpa, University of Canberra, Research Institute for Sport and Exercise

(UC-RISE)

4. Nick B. Ball, University of Canberra, Research Institute for Sport and Exercise (UC-

4. Contact Details:

Name: Jocelyn Mara

Institution: University of Canberra

Mail Address: University of Canberra, Bruce, ACT, 2617, Australia

Telephone: +61 401 282 123

Email: jocelyn.mara@canberra.edu.au

5. Preferred Running Head: Periodisation & Performance in Soccer

6. Abstract Word Count: 220

7. Text-Only Word Count: 3252

8. Number of Figures and Tables: 2 figures, 0 tables

ABSTRACT

Purpose: To investigate the variation in training demands, physical performance and player

wellbeing across a female soccer season. Methods: Seventeen elite female players wore GPS

tracking devices during every training session (n = 90) throughout one national league

season. Intermittent high-speed running capacity, 5, 15 and 25m sprint testing were

conducted at the beginning of preseason, end of preseason, midseason and end of season. In

addition, subjective wellbeing measures were self-reported daily by players over the course

of the season. Results: Time over 5m was lowest at the end of preseason (mean = 1.148s, SE

= 0.017s), but then progressively deteriorated to the end of the season (p < 0.001). Sprint

performance over 15m improved by 2.8% (p = 0.013) following preseason training; while

25m sprint performance peaked at midseason, with a 3.1% (p = 0.05) improvement from the

start of preseason, before declining at the end of season (p = 0.023). Training demands varied

between phases with total distance and high-speed distance greatest during preseason before

decreasing (p < 0.001) during the early and late season phases. Endurance capacity and

wellbeing measures did not change across training phases. Conclusions: Monitoring training

demands and subsequent physical performance in elite female soccer players allow coaches

to ensure training periodisation goals are being met, and related positive training adaptations

are being elicited.

KEYWORDS: Women’s football, training, seasonal variation, sprint, endurance

INTRODUCTION

Periodisation is the systematic planning and variation of training demands, with the

aim of optimising physical condition and minimising injury (1). A typical periodisation plan

involves phases or cycles of varying training demands and goals (power, endurance)

programmed across pre-season, early competition, late competition and transition phases. A

purposeful change in training volume and intensity between training phases subsequently

alters the physical condition of players, warranting the need for regular monitoring of both

external (training prescribed by coaches) and internal (the physiological response elicited by

training) training loads (2). The concept of periodising training into phases is widely accepted

and implemented within elite female soccer, despite a lack of scientific evidence to support

its application.

Training demands can be quantified objectively by player tracking systems (such as

global and local positioning devices) and heart rate monitors, and subjectively through self-

report scales such as rating of perceived exertion (RPE) and muscle soreness (3). Tracking

devices provide comprehensive information related to physical performance such as distances

covered, velocities, accelerations and decelerations. Coupled with match data, inferences can

be made regarding the relative physical demand of training sessions, whether training

stimulus targets were achieved and to avoid overtraining. In addition to objective measures

of training demands, subjective questionnaires that encompass perceived muscle soreness,

fatigue and sleep patterns are economical methods to monitor player wellbeing and thus

provide a holistic approach to elite athlete monitoring (3).

Testing protocols have been designed to mimic and monitor the specific movements

and qualities that are related to high performance in soccer games, such as acceleration,

maximal speed and repeated high-speed running ability. Acceleration and maximal speed are

considered independent characteristics of sprint performance in both male (4) and female (5)

soccer players. As such, typical testing protocols include sprint tests of varying distance (e.g.

5m and 20m) to adequately measure both qualities (6), as well as to represent the typical

distances of sprints observed in elite women’s soccer matches (7). High-speed running

performance, and more notably, the ability to recover from high-speed running bouts, are

considered to be the most distinguishable physical characteristic between playing levels, and

are directly related to training status (8,9). The Yo-Yo Intermittent Recovery (YYIR) tests

(Level 1 and level 2) are commonly used in elite soccer to assess players’ ability to repeat

bouts of intense exercise. The distance achieved by players in the YYIR2 has been shown to

reflect the amount of running performed during the peak five-minute period in elite men’s

matches (10).

The use of field tests to determine match-specific capabilities provide a practical

alternative for laboratory tests and can be easily implemented within the training

environment. It is commonplace for elite teams to undergo baseline testing at the beginning

and end of preseason and then on a number of occasions during the season to determine if a

high physical level is being maintained (11). Research that has investigated the seasonal

variation in physical performance measures of elite and semi-elite male players have shown

improvements in maximal aerobic capacity, multi-stage fitness test performance (MSFT), and

sprint performance from the beginning of pre-season to mid-season (11–13). Following mid-

season, MSFT performance tends to decline (12), while V̇O2max and sprint have remained

stable to the end of the season (11,12). On the other hand, acceleration and sprint

characteristics in youth female players have been shown to decline across the course of the

season (14). From these studies it would appear that there could be sex differences in

seasonal variations for sprint performance, which highlights the need for sex-specific

research.

Despite investigations into the seasonal changes in physical measures, changes in

physical performance and wellbeing that occur from purposeful variation in training demands

have yet to be thoroughly explored, particularly in female players across an entire season. As

such, the main aim of the current study was to investigate the variation in training demands,

physical performance and player wellbeing according to training phase in female soccer

players. A secondary aim of this study was to examine the relationships between training

demands, physical performance and player wellbeing.

METHODS

Subjects

Seventeen elite female soccer players (mean height = 172.9cm ± 5.5cm, mean body

mass = 64.3kg ± 5.9kg) from the same national league team participated in this study.

Goalkeepers were excluded from this study. Prior to commencing the study, institutional

ethical approval was granted and subjects were thoroughly informed of the procedures of the

study by verbal and written communication. All players that participated in the study

provided written informed consent.

Methodology

This study was an observational study. Training periodisation was implemented by

the club’s coaching staff and the training season was divided into three training phases

consisting six weeks each of pre-season, early season and late season (Figure 1). Testing

sessions occurred at the transition of each training phase:

1. At the start of pre-season training (START-PRE)

2. At the end of pre-season training and at the start of the competition (END-PRE)

3. At the midpoint of the season (MS)

4. At the end of the season (ES)

During the competition phases, fitness testing sessions were performed no sooner than

72 hours post-match to allow for adequate recovery from neuromuscular fatigue (15).

Body Composition

Body composition testing was conducted at START-PRE and ES and was determined

by a Dual X-Ray Absorptiometry (DXA) scan (16). All DXA scans were conducted in the

morning between 7am and 9am, and participants were instructed not to eat or drink prior to

being scanned. Total body mass, percent body fat, and percent lean muscle were collected.

Yo-Yo Intermittent Recovery Test

The YYIR2 is an intermittent running test, consisting 2x20m shuttle runs separated by

a 10 second active recovery interval. The test begins at an average running speed of 13 km/h

and progressively increases in speed until participants fail to reach the line by the required

audio bleep (17). Participants’ scores were recorded as the equivalent distance achieved,

excluding the active recovery phase. To analyse the variation in YYIR2 performance

throughout the season, participants were grouped into “high” and “low” YYIR2 scores by a

median split of the equivalent distance achieved at START-PRE. The validity and reliability

of the YYIR2 for measuring the intermittent high-speed running capacity of soccer players

has been reported elsewhere (17,18).

Acceleration and Sprint Testing

Acceleration and sprint speed were assessed by individual 5m, 15m and 25m sprint

tests using electronic timing gates (Smartspeed, Fusion Sport, Cardiff, UK) accurate to 0.01

seconds. Participants were instructed to begin the sprint from a stand-still position half a

meter behind the first timing gate to ensure the timing gate beam was not broken prematurely

(12). Participants completed the test twice each, with adequate active recovery

(approximately 5 minutes) between repetitions. The participants’ fastest times were included

in the data analysis.

Player Wellbeing Questionnaire

Participants were asked to electronically record subjective scores related to their

current wellbeing each morning during preseason, early season and late season using a cloud-

based spreadsheet interface (Google Drive, Google, California, USA). Participants recorded

perceived muscle soreness and fatigue on a scale of 1-10 adapted from the Borg CR-10,

where 1 equals minimal muscle soreness and fatigue and 10 equals maximum muscle

soreness and fatigue (3). The Borg CR-10 has been shown to be a valid method for the

subjective assessment of muscle soreness and fatigue (19). Participants also recorded the

number of hours they had slept the previous night.

Training Demands

Participants wore 15Hz global positioning devices (SPI HPU, GPSports Systems,

Canberra, Australia) during all (n = 90) training sessions across the course of the season.

Total distance (m), high-speed distance (>3.4m/s) (HSD) sprinting (>5.4m/s), high-intensity

acceleration (>2m/s2) and deceleration (<-2m/s2) counts were collected for all training

sessions and were compared based on training phase (preseason, early season and late

season). Velocity thresholds were chosen based on recommendations for determining high-

speed running thresholds in female soccer players (20) and were similar to recently used

thresholds in previous research (21).

Match and Training Schedule

The team played a total of five friendly matches during the pre-season phase and 11

matches (10 round games and a semi-final game) in the competitive season. A typical weekly

in-season training structure consisted of one game, followed by two recovery days, one

conditioning, one skill and two tactical sessions.

Statistical Analysis

Statistical analysis was conducted using SPSS version 21.0 (SPSS Inc, Chicago, IL).

A paired-samples t-test was used to determine any changes in body composition from

START-PRE to ES. Repeated measures analysis of variance (ANOVA) tests were conducted

to determine the variation in YYIR2 and sprint performance across the course of the season.

A one-way ANOVA examined the differences in training variables between training phases,.

Where appropriate, Bonferroni post-hoc analyses were conducted to examine pairwise

comparisons. Friedman’s test assessed the difference in self-report wellbeing scores between

training phases. Pearson’s r and spearman’s rho correlations were used to determine the

relationship between training demands, player wellbeing and physical performance tests. In

addition to acquiring statistical significance (p < 0.05), Cohen’s d and r (small = 0.2; medium

= 0.5, large = 0.8) effect size and partial eta-squared (η2) (small = 0.01; medium = 0.06; large

= 0.14) statistics were also used to measure the magnitude of difference (22).

RESULTS

Body Composition

Mean total body mass was 64.38kg (SD = 5.94kg) at START-PRE and 65.16kg (SD =

6.79kg) at ES (p = 0.432, d = 0.12). Percent body fat was 21.45% (SD = 6.03) at START-

PRE and 22.36% (SD = 6.37) at ES (p = 0.300, d = 0.15), while percent lean muscle was

73.77% (SD = 6.17) at START-PRE and 72.82% (SD = 6.52, d = 0.15) at ES.

Yo-Yo Intermittent Recovery Test

The mean distance achieved during the YYIR2 was 425m (SD = 122m) at START-

PRE, 400m (SD = 95m) at END-PRE, 420m (SD = 122m) at MS, and 450m (SD = 118m) at

ES. However, there were no differences shown in YYIR2 performance between training

phases (p = 0.195, partial η2 = 0.197). The mean distance achieved by “high” YYIR2

performers was 600m (SD = 57m), 520m (SD = 11m), 580m (SD = 85m) and 560m (SD =

57m) for START-PRE, END-PRE, MS and ES, respectively. In addition, “low” YYIR2

performers achieved 392m (SD = 18m), 384m (SD = 46m), 392m (SD = 52m) and 440m (SD

= 102m) for the respective training phases. There were no differences in YYIR2 performance

between training phases for the “high” YYIR2 performers (p = 0.314, partial η2 = 0.648) or

“low” YYIR2 performers (p = 0.313, partial η2 = 0.248).

Acceleration and Sprint Performance

There were differences in 5m (p = 0.001, partial η2 = 0.711), 15m (p = 0.002, partial

η2 = 0.700) and 25m (p < 0.001, partial η2 = 0.775) sprint performance between training

phases (Figures 2a-2c).

Player Wellbeing

There were no differences (p > 0.05, d = 0.00 – 0.14) in mean hours of sleep between

preseason (mean = 7.8, SD = 0.8), early season (mean = 7.8, SD = 0.5) and late season (mean

= 7.7, SD = 0.6). Median fatigue scores were reported to be 3.5, 3.1 and 3.2 for preseason,

early season and late season, respectively (p = 0.056, r = 0.36 - 0.56). Similarly, median

muscle soreness scores were reported to be 3.8, 3.3 and 3.5 for the respective training phases

(p = 0.269. r = 0.31 – 0.41).

Training Demands

The mean distance covered during training sessions were 6646m (SD = 111m) during

preseason, 5437m (SD = 106m) during early season and 4604m (SD = 110m) during late

season (p < 0.001, partial η2 = 0.38). In addition, the mean high-speed running distance was

1415m (SD = 42m) during preseason, 1027m (SD= 40m) during early season and 742m (SD

= 41m) during late season (p < 0.001, partial η2 = 0.32). Similarly, there was a decline in

sprint, acceleration and deceleration counts between training phases. It was observed that

mean sprint counts during training sessions were 27 (SD = 15) for preseason, 24 (SD = 9) for

early season and 15 (SD = 9) for late season. Acceleration counts were 56 (SD = 19) for

preseason, 49 (SD = 14) for early season and 32 (SD = 18) for late season. Deceleration

counts were 22 (SD = 10) during preseason, 20 (SD = 10) during early season and 12 (SD =

9) during late season. A multivariate ANOVA with Bonferroni post-hoc analyses showed

differences in all counts (p < 0.001) between all phases, with the exception of sprint and

Relationships between Training Demands, Wellbeing and Physical Performance

Pearson’s r correlations showed relationships between training variables and YYIR2

performance during the preseason and early season phases, but not the late season phase.

YYIR2 performance at START-PRE was shown to have relationships with total distance (r =

0.599, p = 0.03) high-speed distance (r = 0.708, p = 0.01) and acceleration count (r = 0.554, p

= 0.05) during training sessions in preseason. In addition, Spearman’s rho tests revealed

negative correlations between total distance covered in training and muscle soreness in the

early season phase (r = -0.571, p = 0.041) and high speed distance covered in training and

muscle soreness in the late season phase (r = -0.577, p = 0.039). However, there were no

correlations between training and wellbeing variables in the preseason phase, or wellbeing

and physical testing variables across the season.

DISCUSSION

This is the first study to examine the seasonal variations in physical performance,

body composition and player wellbeing with respect to training demands. The main findings

were 1) Total distance, high-speed distance and acceleration counts during training sessions

declined across all phases from preseason to late season; 2) 5m acceleration peaked following

preseason and then declined at the end of season; 15m sprint performance improved from the

start to the end of preseason and then remained stable throughout the season; and 25m sprint

performance improved from the start of preseason to midseason and then declined at the end

of season; 3) YYIR2 performance and player wellbeing remained stable across the course of

the season. The secondary aim of this study was to examine the relationship between training

demands, physical performance and player wellbeing. It was found that players that

performed better in the YYIR2 at the start of preseason also covered greater total distances,

high-speed distances and performed more acceleration counts during preseason.

Training demands fluctuated between preseason and early season, with observed

declines in all training variables following preseason. These findings were expected as a

traditional preseason phase aims to improve the physical condition of players following the

transition period (i.e. the off-season) in which a detraining effect commonly occurs (12).

Decreasing the training demands during the season can be explained by the increase in

competitive match-play, and thus, an attempt by the coach to taper players in the training

sessions preceding a match and provide adequate recovery in the days following. It can also

be suggested that travelling for away games may have interfered with the regular training

schedule, and may have also influenced the training demands during the early and late season

phases.

Both 5m and 15m sprint performance peaked following the preseason training phase,

with improvements identified between the start and end of preseason for 15m. These results

are in line with previous findings (12) however unlike the current study, 15m sprint

performance has been shown to improve until the middle of season, whilst the present

findings showed decrements in 5m and 15m performance following preseason. Despite this,

25m sprint performance improved and peaked during the middle of season and the greatest

decrements were shown at the end of season. This finding was in line with previous results in

youth (under 13 and under 15 years) female soccer players (14). The varying degrees of

improvement between the shorter (5m and 15m) and longer distance sprint tests (25m)

support the concept that acceleration and maximum speed are specific characteristics, and are

relatively unrelated (4). In light of this, acceleration and maximum speed should be trained

and tested accordingly.

In the current study YYIR2 performance did not improve following preseason

training, which is in contrast to previous research reporting increases in male soccer players

following preseason training before stabilising during the season phases (18). However, the

seasonal changes in YYIR2 performance have not been previously established in female

soccer players and as such comparisons are difficult to draw upon. One study has found that

following the preseason training phase Yo-Yo Intermittent Endurance (YYIE2) test

performance improved in elite female soccer players (23). Similarly, in elite male players

multi-stage fitness test performance (12), YYIR1 performance (18) and V̇O2 max (11) have

been shown to improve following preseason. However, it should be noted that the YYIE2 and

YYIR1 tests begin at a lower intensity than the YYIR2 and the incremental progression in

running intensity is different between tests. As such, this may be a potential reason for the

conflicting findings between previous literature and the current findings. Future research

should examine the training stimulus through heart rate measures to more thoroughly

examine the cardiovascular demands experienced by players and the impact on performance

outcomes. In addition, while 72 hours was allowed between a training session or game and a

testing session, future research should also consider the optimal time to implement a testing

session to ensure full recovery and training adaptations to occur.

Body composition (total body mass, percent body fat and percent lean muscle) did

not change from the start of preseason to the end of the season. This finding is in contrast to a

number of observations made by previous researchers in elite male soccer players which

percent body fat decreased from the beginning to the end of the training season (11–13,24).

However, there are no previously reported information regarding the seasonal changes in

body composition using DXA scans in female soccer players. Future research in this area is

warranted to determine any sex-specific differences in the way body composition changes as

a result of a soccer season.

Subjective player wellbeing scores remained steady throughout the season, with the

exception of a trend (p = 0.056, r = 0.56) showing that fatigue scores decreased from

preseason to early season. A negative moderate relationship was found between total distance

covered in training and muscle soreness during the early season phase and between high-

speed distance and muscle soreness during the late season phase. These findings indicated

that higher muscle soreness values were associated with lower total distance covered at

training sessions earlier in the season, and as players were exposed to prolonged levels of

muscle soreness, decrements were shown more so in high-speed distance.

PRACTICAL APPLICATIONS AND CONCLUSIONS

The physical demands of training sessions declined from preseason to the early

competition phase. This coincided with a decrease in acceleration and sprint performance

suggesting a decrease in power output throughout the season which has been shown

previously. There was a lack of improvement in YYIR2 performance between the preseason

and early season phase, which may indicate training was not providing the desired training

stimulus, and modifying the preseason conditioning plan may be warranted. It is exceedingly

important for coaches to approach athlete monitoring holistically, as these results demonstrate

that declines in training demands following preseason are not always accompanied by a

decrease in muscle soreness and fatigue levels in players. Monitoring training demands and

subsequent physical performance in elite female soccer players allow coaches to ensure

training periodisation goals are being met, and related positive training adaptations are being

elicited.

References

1. Fleck SJ. Non-linear periodization for general fitness & athletes. J Hum Kinet. 2011;

29A:41–5. t

2. Viru A, Viru M. Nature of training effects. In: Garrett W, Kirkendal D, editors.

Exercise and Sport Science. Philadelphia: Lippincott Williams & Williams; 2000. p.

67–95.

3. Montgomery PG, Hopkins WG. The Effects of Game and Training Loads on

Perceptual Responses of Muscle Soreness in Australian Football. Int J Sport Physiol

Perform. 2013;8(3):312–8.

4. Little T, Williams AG. Specificity of Acceleration, Maximum Speed and Agility in

Professional Soccer Players. J Strength Cond Res. 2005;19(1):76–8.

5. Vescovi JD, McGuigan MR. Relationships Between Sprinting, Agility, and Jump

Ability in Female Athletes. J Sports Sci. 2008;26(1):97–107.

6. Vescovi JD. Sprint Speed Characteristics of High-Level American Female Soccer

Players: Female Athletes in Motion (FAiM) study. J Sci Med Sport. 2012

Sep;15(5):474–8.

7. Vescovi JD. Sprint Profile of Professional Female Soccer Players During Competitive

Matches: Female Athletes in Motion (FAiM). J Sports Sci. 2012;30(12):37–41.

8. Mohr M, Krustrup P, Bangsbo J. Match Performance of High-Standard Soccer Players

with Special Reference to Development of Fatigue. J Sports Sci. 2003;21(7):519–28.

9. Krustrup P, Mohr M, Ellingsgaard H, Bangsbo J. Physical Demands During an Elite

Female Soccer Game: Importance of Training Status. Med Sci Sport Exerc.

2005;37(7):1242.

10. Krustrup P, Mohr M, Nybo L, Jensen JM, Nielsen JJ, Bangsbo J. The Yo-Yo IR2 test:

physiological response, reliability, and application to elite soccer. Med Sci Sports

Exerc. 2006;38(9):1666–73.

11. Kalapotharakos VI, Ziogas G, Tokmakidis SP. Seasonal Aerobic Performance

Variations in Elite Soccer Players. J Strength Cond Res. 2011;25(6):1502.

12. Caldwell B. Seasonal Variation in Physiological Fitness of a Semiprofessional Soccer

Team. J Strength Cond Res. 2009;23(5):1370–7.

13. Ostojic SM. Seasonal Alterations in Body Composition and Sprint Performance of

Elite Soccer Players. J Exerc Physiol. 2003;6(3):11–4.

14. Taylor J, Portas M, Wright M, Hurst C, Weston M. Within-Season Variation of Fitness

in Elite Youth Female Soccer Players. J Athl Enhanc. 2012;1(2).

15. Andersson H, Raastad T, Nilsson J, Paulsen G, Garthe I, Kadi F. Neuromuscular

Fatigue and Recovery in Elite Female Soccer: Effects of Active Recovery. Med Sci

Sport Exerc. 2008;40(2):372.

16. Ackland TR, Lohman TG, Sundgot-borgen J, Maughan RJ, Meyer NL, Stewart AD, et

al. Current Status of Body Composition Assessment in Sport. Sport Med.

2012;42(3):227–49.

17. Ingebrigtsen J, Bendiksen M, Randers MB, Castagna C, Krustrup P, Holtermann A.

Yo-Yo IR2 Testing of Elite and Sub-Elite Soccer Players: Performance, Heart Rate

Response and Correlations to Other Interval Tests. J Sports Sci. 2012;30(13):1337–45.

18. Bangsbo J, Iaia FM, Krustrup P. The Yo-Yo Intermittent Recovery Test Intermittent

Sports. Sport Med. 2008;38(1):37–51.

19. Dedering A, Németh G, Harms-Ringdahl K. Correlation between electromyographic

spectral changes and subjective assessment of lumbar muscle fatigue in subjects

without pain from the lower back. Clin Biomech. 1999 Feb;14(2):103–11.

20. Dwyer DB, Gabbett TJ. Global Positioning System Data Analysis: Velocity Ranges

and a New Definition of Sprinting for Field Sport Athletes. J Strength Cond Res.

2012;26(3):818–24.

21. WP M, Hoffman J, Pruna G, Scanlon T, Bohner J, Townsend J, et al. Reduced High

Intensity Running Rate in Collegiate Women’s Soccer When Games are Separated by

42-hours. Int J Sports Physiol Perform. 2014.

22. Richardson JTE. Eta squared and partial eta squared as measures of effect size in

educational research. Educ Res Rev. Elsevier Ltd; 2011;6(2):135–47.

23. Bradley PS, Bendiksen M, Dellal A, Mohr M, Wilkie A, Datson N, et al. The

application of the Yo-Yo intermittent endurance level 2 test to elite female soccer

populations. Scand J Med Sci Sports. 2014;24(1):43–54.

24. Hammami MA, Ben Abderrahmane A, Nebigh A, Le Moal E, Ben Ounis O, Tabka Z,

et al. Effects of a Soccer Season on Anthropometric Characteristics and

PhysicalFfitness in Elite Young Soccer Players. J Sports Sci. 2013;31(6):589–96.

Figure 1. Training demands, periodisation and physical testing structure. Distance and high-

speed distance are represented as mean training values per session for each week.

Figure 2a. 5m acceleration performance during the season

Figure 2b. 15m sprint performance during the season

Figure 2c. 25m sprint performance during the season

* Difference (p < 0.05) compared with END-PRE; # Difference (p < 0.05) compared to

START-PRE; ^ Difference (p < 0.05) compared to ES

Periodisation and Physical Performance in Elite Female Soccer Players

Documents