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Purpose: To quantify the energetic cost of running and acceleration efforts during rugby league competition to aid in prescription and monitoring of training. Methods: Global Positioning System (GPS) data were collected from 37 professional rugby league players across two seasons. Peak values for relative distance, average acceleration/deceleration and metabolic power (Pmet) were calculated for ten different moving average durations (1-10 min), for each position. A mixed-effects model was used to assess the effect of position for each duration, and individual comparisons were made using a magnitude-based inference network. Results: There were almost certainly large differences in relative distance and Pmet between the 10-min window and all moving averages <5 min in duration (ES = 1.21-1.88). Fullbacks, halves and hookers covered greater relative distances than outside backs, edge forwards and middle forwards for moving averages lasting between 2-10 min. Acceleration/deceleration demands were greatest in hookers and halves compared to fullbacks, middle forwards and outside backs. Pmet was greatest in hookers, halves and fullbacks compared to middle forwards and outside backs. Conclusions: Competition running intensities varied by both position and moving average duration. Hookers exhibited the greatest Pmet of all positions, due to high involvement in both attack and defence. Fullbacks also reached high Pmet, possibly due to a greater absolute volume of running. This study provides coaches with match data that can be used for the prescription and monitoring of specific training drills.
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Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
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: Acceleration-Based Running Intensities of Professional Rugby League Match-
Play
Authors: Jace A. Delaney1,2, Grant M. Duthie1,2, Heidi R. Thornton1,2, Tannath J. Scott1,
David Gay3 and Ben J. Dascombe1
Affiliations: 1Applied Sports Science and Exercise Testing Laboratory, Faculty of Science
and Information Technology, University of Newcastle, Ourimbah, NSW. 2Newcastle Knights
Rugby League Club, Mayfield, NSW. 3School of Electrical Engineering and Computer
Science, University of Newcastle, Callaghan, NSW.
Journal: International Journal of Sports Physiology and Performance
Acceptance Date: December 3, 2015
©2015 Human Kinetics, Inc.
DOI: http://dx.doi.org/10.1123/ijspp.2015-0424
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
Title: Acceleration-based running intensities of professional rugby league match-play.
Submission Type: Original Investigation.
Authors: Jace A. Delaney1,2, Grant M. Duthie1,2, Heidi R. Thornton1,2, Tannath J. Scott1,
David Gay3 and Ben J. Dascombe1.
Institutions and Affiliations:
1. Applied Sports Science and Exercise Testing Laboratory, Faculty of Science and
Information Technology, University of Newcastle, Ourimbah, NSW 2258
2. Newcastle Knights Rugby League Club, Mayfield, NSW 2304
3. School of Electrical Engineering and Computer Science, University of Newcastle,
Callaghan, NSW 2258
Corresponding Author:
Mr Jace A. Delaney
School of Environmental and Life Sciences
Faculty of Science and Information Technology
University of Newcastle
32 Industrial Drive, Mayfield, 2304
Ph: +61 437 600 202
Email: jdelaney@newcastleknights.com.au
Preferred Running Head: Acceleration-based running in rugby league.
Abstract Word Count: 250
Text-only Word Count: 3908
Number of Tables: 4
Number of Figures 1
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
ABSTRACT
Rugby league involves frequent periods of high-intensity running including acceleration and
deceleration efforts, often occurring at low speeds. Purpose: To quantify the energetic cost of
running and acceleration efforts during rugby league competition to aid in prescription and
monitoring of training. Methods: Global Positioning System (GPS) data were collected from
37 professional rugby league players across two seasons. Peak values for relative distance,
average acceleration/deceleration and metabolic power (Pmet) were calculated for ten different
moving average durations (1-10 min), for each position. A mixed-effects model was used to
assess the effect of position for each duration, and individual comparisons were made using a
magnitude-based inference network. Results: There were almost certainly large differences in
relative distance and Pmet between the 10-min window and all moving averages <5 min in
duration (ES = 1.21-1.88). Fullbacks, halves and hookers covered greater relative distances
than outside backs, edge forwards and middle forwards for moving averages lasting between
2-10 min. Acceleration/deceleration demands were greatest in hookers and halves compared to
fullbacks, middle forwards and outside backs. Pmet was greatest in hookers, halves and
fullbacks compared to middle forwards and outside backs. Conclusions: Competition running
intensities varied by both position and moving average duration. Hookers exhibited the greatest
Pmet of all positions, due to high involvement in both attack and defence. Fullbacks also reached
high Pmet, possibly due to a greater absolute volume of running. This study provides coaches
with match data that can be used for the prescription and monitoring of specific training drills.
Keywords: Match analysis, metabolic power, GPS, acceleration, football.
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
INTRODUCTION
The importance of Global Positioning Systems (GPS) for quantifying rugby league
competition has been thoroughly documented1,2. Recently, the most intense periods of match-
play have been described, using a moving average method3. Briefly, this method applied a
moving average to match position-time data to determine the peak relative distance achieved
during competition amongst professional rugby league players, for a range of moving average
durations. It was observed that as the length of the moving average was reduced, the maximal
relative running intensity increased significantly. Such data demonstrated running intensities
as high as 156 ± 12 m.min-1 for a 1-min window. These values present substantially greater
physical demands than previously reported by the relative distances for rugby league match-
play, which typically range between 80-100 m.min-1 4. Whilst such data regarding the running
intensities of rugby league are useful, it could be suggested that they are limited in their ability
to account for the varying match demands of different positions. Gabbett et al.5 reported that
collisions (i.e. hit-ups and tackles) are more frequent in hit-up forwards than any other position.
Subsequently, the ability of forwards to cover large relative distances may become impaired,
due to the constant presence of opposition players6. These positions are regularly required to
accelerate, decelerate and change direction, for which the physical demands are typically not
accounted for by traditional velocity-based methods7.
Previously, di Prampero et al.7 presented a theoretical model that quantified the
energetic cost of accelerations and decelerations. This model considers the energetic cost of
accelerated running on flat terrain to be equivalent to the known physiological cost of uphill
running at a constant pace8. Using the acceleration of a player at any time point, an
instantaneous energy cost can be estimated. This cost can be summated to provide an estimation
of overall energy expenditure throughout the activity, or multiplied by velocity, as an indication
of metabolic power (Pmet; W·kg-1)8. Recently, this model has been applied to team sports such
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
as soccer9, Australian football (AFL)10, rugby sevens11 and rugby league12. For example,
amongst professional soccer players, Osgnach et al.9 estimated the distance players would have
covered at a constant pace, using the total energy expenditure throughout the match (equivalent
distance, ED). It was found that players ED exceeded actual distance by around 20%. Using a
similar analysis amongst AFL players, Coutts et al.10 reported a difference of just 10-11%,
indicating a greater percentage of constant running amongst these athletes. However, when
considering rugby league players, Kempton et al.12 reported higher differences of 27-29%,
suggesting a greater proportion of accelerated running contributed to energy expenditure
compared to soccer and AFL players.
As previously stated, the running demands of certain positions in rugby league are
limited due to the presence of opposition players and as a result may increase the reliance on
acceleration abilities. Fullbacks have been shown to exhibit a greater running intensity than
any other position, due to the open-style running requirements of this position3. In contrast,
Kempton et al.12 compared distance covered over a high-power (HP) threshold of 20 W·kg-1
with distance covered over a traditional high-speed (HS) threshold of 14.4 km·hr-1. The
difference between these two values was strongly influenced by position, with hit-up forwards
covering 76% more distance at HP compared to HS, whilst the difference for outside backs
(wingers and centres) was just 37%. These data outline a significant oversight by previous
match-play analysis techniques, where high-intensity activities performed at low velocities
were unaccounted for. However, the HP and HS data reported by these authors are
representative of absolute match values, and have limited application in the prescription and
monitoring of training. Therefore, the aim of this study was to describe the acceleration-based
duration-specific running demands of rugby league match-play, for the development of precise
training methodologies. The overloading of these demands through an appropriately periodized
program may result in increases in relevant physical capacities, and in turn, match performance.
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
METHODS
Design
GPS data were collected during the 2013 and 2014 National Rugby League (NRL)
competitive seasons, to establish the duration- and position-specific acceleration-based running
demands of rugby league. Prior to the commencement of the study, all subjects were informed
of the aims and requirements of the research, and informed consent was obtained. The
Institutional Human Ethics Committee approved all experimental procedures.
Subjects
Thirty-seven professional rugby league players (age; 27.0 ± 5.1 yr, mass; 98.5 ± 8.8 kg
and stature; 1.84 ± 0.05 m) from the same club volunteered for this study. Data was collected
throughout during 43 matches of the 2013 (12 wins, 10 losses, 1 draw, final position 7th) and
2014 NRL seasons (9 wins, 11 losses, final position 12th). It must be noted that some minor
rule changes were introduced at the beginning of the 2014 season, aimed to increase the amount
of time the ball was active in play (e.g. total game-time once stoppages are removed). However,
data obtained from a commercial statistics provided (Prozone, Sydney, Australia) revealed that
ball-in-play time, for matches involving the team in question, between season was similar
between the 2013 and 2014 season (mean ± SD; 52.7 ± 5.0 min and 53.0 ± 3.9 min,
respectively), and therefore this was deemed to have little effect.
A typical training week consisted of 2-3 field sessions, 1-2 resistance sessions and 1-2
recovery-based sessions. Each match was 80 min in duration that was separated into two 40-
min halves. Players were classified by playing position as follows (n = number of
observations): fullbacks (n = 39), outside backs (n = 153), halves (half-back and five-eighth; n
= 81), middle forwards (props and locks; n = 200), edge forwards (second rowers; n = 81) and
hookers (n = 58). The mean (± SD) number of observations per player was 17 ± 13.
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
Methodology
The match running demands of players were recorded using a portable GPS unit at a
sampling rate of 15 Hz (SPI HPU, GPSports, Canberra, Australia). These units were worn in a
customized padded pouch in the player’s jersey and positioned in the centre of the upper back
area, slightly superior to the scapulae. The number of satellites and HDOP during match play
were 8.3 ± 1.4 and 1.1 ± 0.1, respectively. Whilst the validity and reliability of GPS for
measures of total distance have been established13,14, the inter-unit reliability of GPS for
assessing accelerations during team sport movements has been questioned15. To account for
this issue, each player wore the same unit for the entire study. Lastly, whilst the validity of the
calculations of di Prampero et al.7 for estimating the energetic requirements of team sports
movements has varied between studies16-18, mean Pmet has recently been presented as a stable
marker of locomotor load, where acceleration- and velocity based running are accounted for
(coefficient of variation, CV% = 4.5%)13. As a result, this measure was selected as the most
appropriate measure for quantifying the chaotic nature of rugby league match-play.
Upon completion of each match, GPS data were extracted using the appropriate
proprietary software (Team AMS, Canberra, Australia). A total of 612 individual match files
were obtained. Each file was trimmed to include only match time (excluding extra-time
periods) and within-match stoppages (i.e. decision referred to video referee), and the average
total match duration was 86 ± 13, 84 ± 12, 52 ± 14, 81 ± 15, 47 ± 15 and 87 ± 9 min for
fullbacks, halves, hookers, edge forwards, middle forwards and outside backs, respectively. If
a player’s match time was less than 10 min, the file was removed from analysis. Velocity-time
curves were linearly interpolated to 15 Hz, and a fourth-order Butterworth filter applied with a
1-Hz cut-off frequency. Following this, each file was further analysed using customised
MATLAB® software (Version 8.4.0.150421, MathWorks Inc, MA, USA). This method
allowed the computation of a number of output variables for each player, including relative
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
distance (m·min-1), absolute acceleration/deceleration (m·s-2) and metabolic power (Pmet;
W·kg-1)9. For this study, relative distance was representative of the traditional model, where
accelerated running is ignored. For the acceleration/deceleration measure, all values
(accelerations and decelerations) were made to be positive, and this variable provided an
indication of the total acceleration requirements of the athlete, irrespective of velocity. Finally,
Pmet was calculated by integrating the instantaneous velocity and acceleration, using the
energetic calculations detailed previously7,9.
The customized MATLAB® software was then used for the computation of a moving
average over each output variable, using ten different durations (1, 2, 3, 4, 5, 6, 7, 8, 9 and 10
min), and the maximum value for each duration was recorded. For example, for a 1-min rolling
average, the software identified the 900 consecutive data points (i.e. 15 samples per second for
60 seconds) where the subject exhibited the highest values. For a 2-min rolling average, 1800
samples were used, etc. As a result, for each match, maximum values for each of the three
output variables (relative distance, acceleration/deceleration, Pmet) were calculated for each of
the 10 moving average durations. Data was then collated by playing position, and averaged
across all observations for that positional group, for between-position comparisons.
Statistical Analyses
Data distribution was assessed for normality using the Shapiro-Wilk test. If a dataset
violated the assumption of normality, the data was log-transformed to reduce the non-
uniformity of error. A multilevel linear mixed-effects model was constructed to determine
differences in the individual responses in running intensity between positions (n = 6) for each
moving average duration (n = 10). Individuals were included as a random effect in the model,
to correct for pseudoreplication. When significant main effects were observed, data were
entered into a customized spreadsheet (Microsoft Excel; Microsoft, Redmond, USA), where
pairwise comparisons between groups were made using a magnitude-based inference
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
network19. This method assessed the probability that differences were greater than the smallest
worthwhile difference (SWD), calculated as 0.20 × the between-subject standard deviation
(SD). Further, to examine the effect of moving average duration on running intensities, a
magnitude-based approach was used to compare moving averages 1-9 to the 10-min moving
average, for each outcome variable. Quantitative chances of real differences in variables were
assessed qualitatively as: <1%, almost certainly not; 1-5%, very unlikely; 5-25%, unlikely; 25-
75%, possibly; 75-97.5%, likely; 97.5-99% very likely; >99%, almost certainly19. A difference
was considered substantial when the likelihood that the true value was greater than the SWD
exceeded 75%. Descriptive statistics are presented as mean ± SD, while all other data are
reported as mean and 90% confidence limits (CL), unless otherwise stated. Where necessary,
statistical analyses were performed using R statistical software (R 3.1.0, R foundation for
Statistical Computing)20 using the lme4 package, and significance was set at p < 0.05.
RESULTS
The mixed-model analysis revealed significant main effects duration for each outcome
variable. Figure 1 illustrates the increasing running demands of competition as a function of
moving average duration. Comparisons with the 10-min moving average revealed almost
certainly large increases in relative distance covered and Pmet for moving averages 1 to 4 min
in duration, and almost certainly large increases in acceleration/deceleration for moving
averages 1 to 2 min in duration (Table 1). All windows shorter than 8-min were almost certainly
greater for both acceleration/deceleration and Pmet respectively. For relative distance covered,
all windows except for the 9-min window were almost certainly higher when compared to the
10-min moving average.
A significant effect of position was observed for all moving average durations for both
relative distance and Pmet. For acceleration/deceleration, the model revealed significant effects
for moving averages of 2 to 10 min in duration, but no differences between position for the 1-
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
min window. Maximum relative distances for each moving average duration are displayed in
Table 2. There were likely small to moderate increases in relative distance covered for hookers
and halves compared to edge forwards, outside backs and middle forwards across all moving
averages. Fullbacks exhibited almost certainly large increases in relative distance compared to
outside backs for moving averages of 5 to 10 min in duration.
Table 3 illustrates positional differences in acceleration/deceleration demands across
moving averages 2 to 10 min in duration. Edge forwards exhibited at least likely small increase
in acceleration/deceleration demands compared to fullbacks, outside backs and middle
forwards for moving averages between 2 and 4 min in duration. For moving averages greater
than this, the difference was likely to be moderate. Halves and hookers presented at least likely
moderate increases compared to outside backs and middle forwards for all moving averages at
least 2 min in duration.
Fullbacks and hookers maintained a greater Pmet compared to edge forwards, outside
backs and middle forwards across all moving average durations, and the magnitude of these
differences were at least likely to be moderate (Table 4). Halves were also able to attain a
greater Pmet than outside backs and middle forwards for moving averages 2 to 10 min in
duration, but exhibited poorer values compared to fullbacks for the 1 min window.
DISCUSSION
The present study investigated the acceleration-based running requirements of
professional rugby league competition, concurrently with traditional velocity-based methods,
using a novel rolling average method3. Whilst the duration-specific running demands of rugby
league have been investigated previously3, the present study was able to describe the elevated
accelerated/decelerated running demands of halves and hookers, and the greater Pmet values
achieved by halves, hookers and fullbacks when compared to other positional groups. In
addition, the peak acceleration-based running intensities achieved during match-play increased
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
substantially as the length of the moving average applied decreased. The interactions of peak
running intensity and moving average durations observed in this study provide additional
benefit for coaches and practitioners when attempting to replicate position-specific competition
movement demands using specific training methodologies.
Recently, Furlan et al.11 utilized a 2-min moving average, to determine the peak periods
of Rugby Sevens performance. The authors observed that relative distance underestimated the
intensity of the identified peak period when compared to the Pmet, calculated using the methods
of Gray21, which suggests the incorporation of acceleration-based methods are necessary when
quantifying team sport movement demands. The findings of this study are in support of this
notion, where the inclusion of acceleration-based indices assist in differentiating the varying
positional requirements of rugby league. In the present study, accelerations/decelerations were
calculated as the rate of change in velocity, regardless of the direction of change. This may be
considered a limitation, as the energetic cost of acceleration has been suggested to be far greater
than that of deceleration9. However, this variable was intended to represent the overall
acceleration and deceleration load imposed on the athlete, rather than an estimate of energy
consumption. Recent research has demonstrated that GPS possess poor inter-unit reliability for
both acceleration counts >3 m·s-2 and >4 m·s-2 (CV% = 31% and 43%, respectively), and
deceleration counts <-3 m·s-2 and <-4 m·s-2 (CV% = 42% and 56%, respectively)15. However,
in the present study, each player was assigned the same unit for each match, and this coupled
with the ‘smoothing’ effect of the moving average method, may have provided a more stable
measure for differentiating demands between positions and durations.
This study observed higher average acceleration/deceleration amongst halves and
hookers, compared to outside backs and middle forwards, for moving averages 2 to 10 min in
duration. These findings are similar to whole match acceleration and decelerations counts
(acceleration and deceleration efforts exceeding >2.78 m·s-2 and <-2.78 m·s-2, respectively)
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
observed by Kempton et al.12, where adjustables (halves, hookers and fullbacks) were
substantially different from all other positions. Taken together, these differences would suggest
that for positions where acceleration/deceleration requirements are high, athletes may benefit
from training methodologies that mimic these demands. For improvements in performance to
occur, these qualities should be progressively overloaded through an appropriately periodized
program. This could be facilitated through the incorporation of strength and power training,
due to the well-established links with acceleration22 and change-of-direction23 performance.
Specifically, to improve field sport acceleration, training should be targeted towards improving
the rate of force production22, through explosive power movements such as plyometrics or
resisted sprint training24.
The present study is the first to analyse the duration-specific metabolic demands of
rugby league competition. In theory, the metabolic power method integrates the energetic
demands of accelerated running with traditional velocity-based methods7. In the present study,
the peak metabolic demands of match-play were substantially higher in hookers compared to
outside backs, edge forwards and middle forwards across all moving average durations.
Previously, the hooker position has been grouped with fullbacks and halves due to somewhat
similar competition requirements, in that they are responsible for providing structure and
organisation in both attack and defence. However, modern defensive strategies require the
hooker to be located in the centre of the field, exposing them to a similar number of absolute
collisions compared to hit-up forwards (40 ± 13 vs. 44 ± 13 per game)25, in addition to them
attending most rucks in attack to distribute the ball to other players. As a result of this, it is
common for teams to utilize a second hooker on the interchange bench, in order to maintain
the intensity around the ruck throughout a match. This was evident in the present study, where
although the average match time was similar between hookers (52 ± 14 min) and middle
forwards (47 ± 15 min), hookers exhibited a considerably higher Pmet response compared to
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
other positions. However, it must be noted that the findings of the present study are reflective
of the interchange strategy of the team in question, and this may differ between clubs. Future
research may benefit from examining the factors which may limit players from maintaining
running intensities throughout a match, which may inform individual interchange and
conditioning strategies.
In contrast to the hooker position, halves and fullbacks are commonly required to
complete the entire match. The similarly elevated Pmet values observed for halves would
indicate these positions reach similar peak running intensities to hookers, and although they
are not regularly interchanged, they are not exposed to the same collision loads of interchanged
players25, allowing them to recover from high-intensity periods of match-play more adequately.
However, an interesting finding of the present study was the elevated Pmet response observed
in the fullback position. In defence, for the majority of gameplay fullbacks are positioned
behind the defensive line and are not required to move forward and retreat over 10 m, nor are
they required to be involved in regular physical collisions, as is necessary for most other
positions. As a result, the acceleration/deceleration demands of this position are substantially
lower than that of halves and hookers (Table 3). However, the lower acceleration/deceleration
demands did not translate to a lower Pmet of this position, with fullbacks exhibiting similar Pmet
values to halves and hookers. These findings illustrate the strength of the metabolic power
method for integrating the varying match-play requirements of each position, however the
findings of the present study question the grouping of halves, hookers and fullbacks when
describing competition running requirements. This positional grouping method may affect the
prescription of specific training based on competition demands, as the way an athlete achieves
high-intensity running must be addressed whether that be the open-style running for
fullbacks, or the acceleration-based running of halves and hookers.
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
If athletes are to be adequately prepared for the most intense periods of competition,
training prescription should account for the acceleration-based running requirements common
to rugby league. The novel methodology of the present study may attenuate this implication,
in comparison to that of previous research, where the metabolic power method was used to
describe the mean Pmet sustained in range of team sports, such as rugby sevens (~10 W·kg-1)11,
soccer (~8 W·kg-1)9, rugby league (~9 W·kg-1)12 and AFL (~10 W·kg-1)10. However, these
values represent whole-match averages, and fail to account for the peaks in running intensity
imposed on players throughout a match. Furlan et al. 11 observed that peak Pmet for a 2-min
moving average was significantly greater than the average of the entire period. In the present
study, large increases in Pmet were observed between the 10-min moving average and all
moving averages <5 min in duration (ES 1.21-1.83). This phenomena may be due to athletes
adopting pacing strategies, where energy is distributed across the period to allow for
completion of the entire match26, or possibly the stochastic nature of team sports such as rugby
league. Regardless of the mechanism behind these differences, it would be beneficial to
condition athletes for these peaks in intensity observed throughout a match. However, it is
important to note that these findings are reflective of the tactical strategies of one team only,
and future research may benefit from investigating these running demands across a number of
clubs concurrently.
Despite the theoretical advantages associated with the integration of velocity and
acceleration when quantifying team sport movement demands, the metabolic power method7
is not without limitation. For example, this method assumes the biomechanics, frequency of
movement of the limbs, and environmental conditions to be similar between uphill running on
a treadmill at constant speed and accelerated running on flat terrain7,9. Recently, the validity of
this method in team sports has been questioned, due to the inability to account for the metabolic
cost of sport-specific activities such as dribbling and turning16, or in rugby league, tackling and
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
wrestling12. In addition, this method is unable to account for differences in body size or running
economy16, which may potential influence the metabolic cost of running. However, whilst the
“metabolic” nature of this measure can be questioned, this variable still reflects a relatively
stable measure which collaborates accelerated and decelerated running with traditional
velocity-based techniques13. Future research may benefit from validating this energetic model
in rugby-league specific conditions, potentially accounting for positional differences in body
size and running economy.
PRACTICAL APPLICATIONS
The results of the present study show that the peak running requirements of rugby
league competition differ according to position, and increase as the duration of the moving
average decreases. Using the framework provided by the current study, coaches may
differentiate the training prescribed to each positional group. More specifically, if the aim of
training is to replicate and overload competition demands, specific small-sided games (SSG)
could be used. For example, fullbacks may benefit from open-style games such as offside
touch, played on large field dimensions, as these games have been shown to generate high
velocity-based running intensities27. In contrast, the acceleration-based demands could be
achieved through small, tight games, with a greater importance placed on support plays28.
Lastly, the findings of the current study suggest that the Pmet measure may be useful as a global
measure of external training load, due to the interaction of both acceleration and velocity-based
running.
CONCLUSIONS
The present study has provided a holistic overview of the peak metabolic demands of
rugby league competition. The main findings demonstrated that although the metabolic power
calculations incorporate both acceleration- and velocity-based movements, the method in
which athletes achieve metabolic power differs by position. The findings of this study allow
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
coaches to prescribe and monitor specific training drills according to duration- and position-
specific competition requirements, and appropriately overload athletes to achieve increases in
match performance. The findings of the present study also question the use of a combined
“adjustables” positional group when describing competition movement demands.
ACKNOWLEDGEMENTS
No financial assistance was provided for the current project. There were no conflicts of
interest. The authors wish to thank the Computer Engineering Department at the University
of Newcastle their assistance with this project.
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
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Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
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Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
Figure 1. Maximum running intensities of rugby league match-play by rolling average
duration. Data are presented as mean ± SD for each outcome variable.
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
Table 1: Magnitude of increase in running intensities compared to 10-min moving average. Differences are presented as mean ± 90% confidence
limits (90% CL).
Moving
Average
Length
(min)
Relative Distance (m.min-1)
Acceleration/Deceleration (m.s-2)
Metabolic Power (W.kg-1)
Mean ±
90% CL
likelihood of effect
Mean ±
90% CL
Effect size,
likelihood of effect
Mean ±
90% CL
Effect size,
likelihood of effect
1
64 ± 5
Almost certainly large
0.49 ± 0.04
1.76,
Almost certainly large
7.1 ± 0.5
1.83,
Almost certainly large
2
37 ± 3
Almost certainly large
0.25 ± 0.02
1.39,
Almost certainly large
3.9 ± 0.3
1.65,
Almost certainly large
3
26 ± 2
Almost certainly large
0.17 ± 0.01
1.13,
Almost certainly moderate
2.7 ± 0.2
1.46,
Almost certainly large
4
18 ± 1
Almost certainly large
0.12 ± 0.01
0.90,
Almost certainly moderate
1.9 ± 0.1
1.21,
Almost certainly large
5
13 ± 1
Almost certainly moderate
0.09 ± 0.01
0.70,
Almost certainly moderate
1.4 ± 0.1
0.97,
Almost certainly moderate
6
9 ± 1
Almost certainly moderate
0.07 ± 0.01
0.52,
Almost certainly small
1.0 ± 0.1
0.75,
Almost certainly moderate
7
6 ± 1
Almost certainly moderate
0.04 ± 0.01
0.35,
Almost certainly small
0.6 ± 0.1
0.51,
Almost certainly small
8
4 ± 1
Almost certainly small
0.03 ± 0.01
0.21,
Possibly small
0.4 ± 0.1
0.31,
Very likely small
9
2 ± 1
Possibly trivial
0.01 ± 0.01
0.09,
Very unlikely trivial
0.2 ± 0.1
0.14,
Unlikely trivial
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
Table 2: Peak relative distances (m.min-1) of professional rugby league players by position for each moving average duration (± SD).
Moving
Average
(min)
Fullback
Halves
Hooker
Edge
Forwards
Outside
Backs
Middle
Forwards
Effect Size > 0.60
1
179 ± 15bcdef
168 ± 12ef
172 ± 14def
165 ± 11
164 ± 14
163 ± 14
FB > HA, EF, OB & MF
2
148 ± 13bdef
142 ± 9def
146 ± 11bdef
137 ± 9
137 ± 10
135 ± 10
FB & HK > EF & OB;
FB, HA & HK > MF
3
134 ± 11bdef
131 ± 8def
136 ± 11bdef
127 ± 9
125 ± 9
125 ± 10
FB & HK > EF & MF;
FB, HA & HK > OB
4
127 ± 10def
124 ± 10def
127 ± 11def
119 ± 9
117 ± 9
117 ± 10
FB & HK > EF, FB;
HA & HK > OB & MF
5
122 ± 9def
120 ± 9def
122 ± 11def
114 ± 8
112 ± 8
111 ± 10
FB, HA & HK > EF, OB & MF
6
119 ± 9def
116 ± 8def
118 ± 11def
111 ± 8ef
107 ± 8
108 ± 9
FB, HA & HK > EF, OB & MF
7
116 ± 10def
113 ± 8def
114 ± 11def
108 ± 8ef
104 ± 8
104 ± 9
FB, HA & HK > EF, OB & MF
8
114 ± 9bdef
110 ± 8def
111 ± 11def
106 ± 8ef
102 ± 7
102 ± 9
FB > EF;
FB, HA & HK > OB & MF
9
111 ± 8bdef
108 ± 8def
110 ± 11def
104 ± 8ef
100 ± 7
100 ± 9
FB > EF;
FB, HA & HK > OB & MF
10
109 ± 8def
107 ± 8def
108 ± 11def
102 ± 7ef
99 ± 7
98 ± 11
FB & HK > EF;
FB, HA & HK > OB & MF
FB = Fullback, HA = Halves; HK = Hooker, EF = Edge Forwards; OB = Outside Backs; MF = Middle Forwards, a = greater than FB; b = greater than HA; c = greater than HK;
d = greater than EF; e = greater than OB; f = greater than MF. All observed differences are >75% likelihood of being greater than the SWD (calculated as 0.2 x between-subject
SD).
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
Table 3: Peak average acceleration/deceleration (m.s-2) of professional rugby league players by position for each moving average duration (± SD).
Moving
Average
(min)
Fullback
Halves
Hooker
Edge
Forwards
Outside
Backs
Middle
Forwards
Effect Size > 0.60
1
1.22 ± 0.16
1.26 ± 0.14
1.28 ± 0.13
1.27 ± 0.1
1.23 ± 0.16
1.23 ± 0.14
N/A
2
0.98 ± 0.14
1.05 ± 0.13aef
1.06 ± 0.14aef
1.04 ± 0.11aef
0.96 ± 0.12
0.99 ± 0.13
HA & HK > OB
3
0.89 ± 0.12
0.98 ± 0.12aef
0.99 ± 0.14aef
0.95 ± 0.11aef
0.88 ± 0.12
0.91 ± 0.13
HA & HK > FB & OB
4
0.85 ± 0.12
0.93 ± 0.13aef
0.94 ± 0.14aef
0.90 ± 0.11aef
0.84 ± 0.12
0.86 ± 0.12
HA & HK > FB & OB
5
0.83 ± 0.12
0.90 ± 0.13aef
0.91 ± 0.14aef
0.88 ± 0.11aef
0.8 ± 0.11
0.83 ± 0.12
HK > FB;
HA, HK & EF > OB
6
0.80 ± 0.11
0.87 ± 0.13aef
0.88 ± 0.13aef
0.85 ± 0.1aef
0.78 ± 0.11
0.80 ± 0.13
HA, HK & EF > OB
7
0.79 ± 0.11
0.85 ± 0.12aef
0.85 ± 0.13aef
0.83 ± 0.1aef
0.76 ± 0.11
0.78 ± 0.13
HA, HK & EF > OB
8
0.77 ± 0.11
0.83 ± 0.12aef
0.83 ± 0.13aef
0.81 ± 0.1aef
0.74 ± 0.11
0.76 ± 0.12
HA, HK & EF > OB
9
0.75 ± 0.11
0.82 ± 0.12aef
0.82 ± 0.13aef
0.8 ± 0.1aef
0.73 ± 0.11
0.74 ± 0.13
HA, HK & EF > OB
10
0.75 ± 0.11
0.81 ± 0.12aef
0.81 ± 0.13aef
0.78 ± 0.1aef
0.72 ± 0.11
0.73 ± 0.13
HA, HK & EF > OB
FB = Fullback, HA = Halves; HK = Hooker, EF = Edge Forwards; OB = Outside Backs; MF = Middle Forwards, a = greater than FB; b = greater than HA; c = greater than HK;
d = greater than EF; e = greater than OB; f = greater than MF. All observed differences are >75% likelihood of being greater than the SWD (calculated as 0.2 x between-subject
SD).
Acceleration-Based Running Intensities of Professional Rugby League Match-Play” by Delaney JA et al.
International Journal of Sports Physiology and Performance
© 2015 Human Kinetics, Inc.
Table 4: Peak average metabolic power (W.kg-1) of professional rugby league players by position for each moving average duration (± SD).
Moving
Average
(min)
Fullback
Halves
Hooker
Edge
Forwards
Outside
Backs
Middle
Forwards
Effect Size > 0.60
1
18.1 ± 1.9bcdef
17.0 ± 1.9f
17.4 ± 1.8def
16.7 ± 1.5
16.6 ± 1.9
16.4 ± 1.9
FB > EF, OB & MF
2
14.6 ± 1.5bdef
14.1 ± 1.6ef
14.4 ± 1.6def
13.6 ± 1.2
13.4 ± 1.4
13.3 ± 1.4
FB > EF;
FB & HK > OB & MF
3
13.0 ± 1.2def
12.8 ± 1.3ef
13.3 ± 1.6def
12.5 ± 1.2
12.1 ± 1.3
12.1 ± 1.3
FB & HK > OB & MF
4
12.2 ± 1.2def
12.1 ± 1.4def
12.4 ± 1.5def
11.6 ± 1.2
11.3 ± 1.2
11.3 ± 1.3
FB, HA & HK > OB & MF
5
11.7 ± 1def
11.6 ± 1.3def
11.8 ± 1.5def
11.1 ± 1.1ef
10.7 ± 1.1
10.7 ± 1.3
FB, HA & HK > OB & MF
6
11.4 ± 1def
11.2 ± 1.3def
11.4 ± 1.4def
10.8 ± 1.0ef
10.3 ± 1.0
10.4 ± 1.2
FB, HA & HK > OB & MF
7
11.0 ± 1def
10.9 ± 1.2def
11.0 ± 1.4def
10.5 ± 1.0ef
9.9 ± 1.0
10.0 ± 1.2
FB, HA & HK > OB & MF
8
10.7 ± 1def
10.6 ± 1.2ef
10.8 ± 1.3def
10.2 ± 1.0ef
9.7 ± 1.0
9.8 ± 1.2
FB, HA & HK > OB & MF
9
10.5 ± 1def
10.4 ± 1.2ef
10.6 ± 1.4def
10.0 ± 1.0ef
9.5 ± 1.0
9.6 ± 1.1
FB, HA & HK > OB & MF
10
10.3 ± 1def
10.2 ± 1.2def
10.4 ± 1.4def
9.8 ± 0.9ef
9.3 ± 0.9
9.4 ± 1.1
FB, HA & HK > OB & MF
FB = Fullback, HA = Halves; HK = Hooker, EF = Edge Forwards; OB = Outside Backs; MF = Middle Forwards, a = greater than FB; b = greater than HA; c = greater than HK;
d = greater than EF; e = greater than OB; f = greater than MF. All observed differences are >75% likelihood of being greater than the SWD (calculated as 0.2 x between-subject
SD).
... 23 Despite poor validity for measuring intermittent exercise, metabolic power has been suggested to be of use as a global indicator of external load, encompassing accelerated, decelerated, and speed-based running. 24 Justifying this view, metabolic power displayed good accuracy when compared with a criterion method (radar) utilizing both 5 Hz (coefficient of variation = 4.5%) and 10 Hz (coefficient of variation = 2.4%) GPS devices. 25 Moreover, distances covered above high (>20 W·kg −1 ) and very high (>35 W·kg −1 ) metabolic power thresholds exhibited comparable or reduced variability when compared with high-speed running distances (coefficient of variation = 4.5%-12% vs 4.7%-23%). ...
... 25 During the peak intensity periods of rugby league matches, metabolic power was greater for hookers, half-backs, and fullbacks compared with middle forwards and outside backs. 24 Furthermore, the way in which players accumulated metabolic power (ie, via acceleratory or speed-based movements) differed between playing positions, providing coaches with valuable information that may aid training monitoring and prescription. Although metabolic power should not be used in isolation as a measure of external load as the combination of acceleratory and speed-based running into 1 metric masks the underlying mechanism of the load. ...
... By being able to quantify more of what rugby players physically do (external load), accelerometer-derived PlayerLoad and GNSS-derived metabolic power displayed improved sensitivity in quantifying exercise intensity fluctuations compared with a speed-based metric. Present findings are in support of others 24,44 in recommending the use of accelerationbased indices alongside speed-based metrics to measure the external load of rugby players. ...
The aim of this investigation was to quantify professional rugby union player activity profiles after the most intense (peak) passages of matches. Movement data were collected from 30 elite and 30 subelite professional rugby union athletes across respective competitive seasons. Accelerometer-derived PlayerLoad and global navigation satellite system-derived measures of mean speed and metabolic power were analyzed using a rolling-average method to identify the most intense 5- to 600-second passages (ie, worst-case scenarios) within matches. Player activity profiles immediately post their peak 5- to 600-second match intensity were identified using 5 epoch duration-matched intervals. Mean speed, metabolic power, and PlayerLoad declined sharply (∼29%-86%) after the most intense 5 to 600 seconds of matches. Following the most intense periods of rugby matches, exercise intensity declined below the average match-half intensity 81% of the time and seldom returned to or exceeded it, likely due to a host of individual physical and physiological characteristics, transient and/or accumulative fatigue, contextual factors, and pacing strategies. Typically, player exercise intensities after the most intense passages of matches were similar between match halves, positional groups, and levels of rugby competition. Accurate identification of the peak exercise intensities of matches and movement thereafter using novel methodologies has improved the limited understanding of professional rugby union player activity profiles following the worst-case scenarios of matches. Findings of the present study may inform match-representative training prescription, monitoring, and tactical match decisions (eg, substitutions and positional changes).
... Nevertheless, in field hockey, similar to other sports such as football [7] and rugby [8], the adoption of new training approaches characterized by a greater tactical component and relatively smaller training distances per player compared to actual game play has become prevalent [9,10]. The use of such approaches contributes to the accumulation of a greater number of technical-tactical actions and a greater frequency of high-intensity events. ...
... These approaches, commonly referred to as small-sided games, are highly effective at improving cardiovascular fitness and simulating the acceleration and deceleration patterns of official matches. However, the literature suggests that these methods may not be as effective at increasing total volumes of high-speed running or reproducing the most demanding phases of competition [9]. ...
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Introduction: The implementation of optimal sprint training volume is a relevant component of team sport performance. This study aimed to compare the efficiency and effectiveness of two different configurations of within-season training load distribution on sprint performance over 6 weeks. Methods: Twenty male professional FH players participated in the study. Players were conveniently assigned to two groups: the experimental group (MG; n = 11; applying the microdosing training methodology) and the control group (TG; n = 9; traditional training, with players being selected by the national team). Sprint performance was evaluated through 20 m sprint time (T20) m and horizontal force-velocity profile (HFVP) tests before (Pre) and after (Post) intervention. Both measurements were separated by a period of 6 weeks. The specific sprint training program was performed for each group (for vs. two weekly sessions for MG and TG, respectively) attempting to influence the full spectrum of the F-V relationship. Results: Conditional demands analysis (matches and training sessions) showed no significant differences between the groups during the intervention period (p > 0.05). No significant between-group differences were found at Pre or Post for any sprint-related performance (p > 0.05). Nevertheless, intra-group analysis revealed significant differences in F0, Pmax, RFmean at 10 m and every achieved time for distances ranging from 5 to 25 m for MG (p < 0.05). Such changes in mechanical capabilities and sprint performance were characterized by an increase in stride length and a decrease in stride frequency during the maximal velocity phase (p < 0.05). Conclusion: Implementing strategies such as microdosed training load distribution appears to be an effective and efficient alternative for sprint training in team sports such as hockey.
... Then, sample-to-sample changes were computed before the computation of moving sums over the abovespecified window lengths. For the acceleration metrics, both negative and positive acceleration values were turned into absolute values, changing negative into non-negative values, and moving averages over the above-specified window length were calculated over the whole time series [4]. ...
Article
Full-text available
Purpose Wearables serve to quantify the on-court activity in intermittent sports such as field hockey (FH). Based on objective data, benchmarks can be determined to tailor training intensity and volume. Next to average and accumulated values, the most intense periods (MIPs) during competitive FH matches are of special interest, since these quantify the peak intensities players experience throughout the intermittent matches. The aim of this study was to retrospectively compare peak intensities between training and competition sessions in a male FH team competing in the first german division. Methods Throughout an 8-week in-season period, 372 individual activity datasets (144 datasets from competitive sessions) were recorded using the Polar Team Pro sensor (Kempele, Finland). MIPs were calculated applying a rolling window approach with predefined window length (1–5 min) and calculated for Total distance, High-Intensity-Running distance (> 16 km/h), Sprinting distance (> 20 km/h) and Acceleration load. Significant differences between training and competition MIPs were analysed through non-parametric statistical tests (P < 0.05). Results Analyses revealed higher MIPs during competition for all considered outcomes (P < 0.001). Effect size estimation revealed strongest effects for sprinting distance (d = 1.89 to d = 1.22) and lowest effect sizes for acceleration load (d = 0.92 to d = 0.49). Conclusion The present findings demonstrate that peak intensities during training do not reach those experienced during competitive sessions in a male FH team. Training routines such as manipulations of court-dimensions and team sizes might contribute to this discrepancy. Coaches should compare training and competition intensities to recalibrate training routines to optimize athletes’ preparation for competition.
... 20,21 Additionally, this study included an acceleration-derived GNSS metric. Acceleration (in meters per second squared) was calculated using methods previously explained by Delaney et al, 22 whereby the mean of all rectified acceleration values (in meters per second squared) was determined. This measure has been established as valid 23 and reliable, 20 demonstrating superior reliability to that of traditional acceleration thresholds counts using predefined thresholds (ie, >2.5 m·s −2 ). ...
Article
Purpose: To examine the association between muscle fiber typology and match running performance in professional Australian football (AF) athletes. Methods: An observational time-motion analysis was performed on 23 professional AF athletes during 224 games throughout the 2020 competitive season. Athletes were categorized by position as hybrid, small, or tall. Athlete running performance was measured using Global Navigation Satellite System devices. Mean total match running performance and maximal mean intensity values were calculated for moving mean durations between 1 and 10 minutes for speed (in meters per minute), high-speed-running distance (HSR, >4.17 m·s-1), and acceleration (in meters per second squared), while intercept and slopes were calculated using power law. Carnosine content was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus and expressed as a carnosine aggregate z score (CAZ score) to estimate muscle fiber typology. Mixed linear models were used to determine the association between CAZ score and running performance. Results: The mean (range) CAZ score was -0.60 (-1.89 to 1.25), indicating that most athletes possessed a greater estimated proportion of type I muscle fibers. A greater estimated proportion of type I fibers (ie, lower CAZ score) was associated with a larger accumulation of HSR (>4.17 m·s-1) and an increased ability to maintain HSR as the peak period duration increased. Conclusion: AF athletes with a greater estimated proportion of type I muscle fibers were associated with a greater capacity to accumulate distance running at high speeds, as well as a greater capacity to maintain higher output of HSR running during peak periods as duration increases.
... This novel approach aims to quantify the highest possible demands within brief time intervals, which are also known as the most demanding passages. WCS are defined as short time periods of maximum physical performance (distance covered at high running speed) throughout a match [29,30]. The fixed duration method was the first attempt to quantify WCS [31] and consisted of dividing the match from start to end into fixed 5-minute periods. ...
Article
Full-text available
ABSTRACT: The physical demands of intermittent sports require a preparation based, by definition, on high-intensity actions and variable recovery periods. Innovative local positioning systems make it possible to track players during matches and collect their distance, speed, and acceleration data. The purpose of this study was to describe the worst-case scenarios of high-performance handball players within 5-minute periods and per playing position. The sample was composed of 180 players (27 goalkeepers, 44 wings, 56 backs, 23 centre backs and 30 line players) belonging to the first eight highest ranked teams participating in the European Men’s Handball Championship held in January 2022. They were followed during the 28 matches they played through a local positioning system worn on their upper bodies. Total and high-speed distance covered (m), pace (m/min), player load (a.u.) and high-intensity accelerations and decelerations (n) were recorded for the twelve 5-min periods of each match. Data on full-time player average and peak demands were included in the analysis according to each playing position. A systematic three-phase analysis process was designed: 1) information capture of match activities and context through sensor networks, the LPS system, and WebScraping techniques; 2) information processing based on big data analytics; 3) extraction of results based on a descriptive analytics approach. The descriptive cross-sectional study of worst-case scenarios revealed an ~17% increment in total distance covered and pace, with a distinct ~51% spike in high-intensity actions. Significant differences between playing positions were found, with effect sizes ranging from moderate to very large (0.7–5.1). Line players, in particular, showed a lower running pace peak (~10 m/min) and wings ran longer distances at high speed (> 4.4 m/s) than the rest of the field players (~76 m). The worst-case scenario assessment of handball player locomotion demands will help handball coaches and physical trainers to design tasks that replicate these crucial match moments, thus improving performance based on a position-specific approach.
... Based on current athlete monitoring practices, three GPS metrics of running intensity were assessed: TD, HSD (>19.8 km�h -1 ) and AveAcc [26]. Measures of TD and HSD were made relative to playing time (m�min -1 ), with acceleration profiles calculated through the summation of the absolute value of all accelerations and decelerations, averaged over a defined duration to calculate AveAcc (m�s -2 ) [27]. The amalgamation of both accelerations and decelerations into a singular metric, while concealing the underlying mechanism of load has been suggested to better reflect the overall intensity of match play [4]. ...
Article
Full-text available
Youth footballers need to be developed to meet the technical, tactical, and physical demands of professional level competition, ensuring that the transition between competition levels is successful. To quantify the physical demands, peak match intensities have been measured across football competition tiers, with team formations and tactical approaches shown to influence these physical demands. To date, no research has directly compared the physical demands of elite youth and professional footballers from a single club utilising common formations and tactical approaches. The current study quantified the total match and peak match running demands of youth and professional footballers from a single Australian A-League club. GPS data were collected across a single season from both a professional (n = 19; total observations = 199; mean ± SD; 26.7 ± 4.0 years) and elite youth (n = 21; total observations = 59; 17.9 ± 1.3 years) team. Total match demands and peak match running demands (1–10 min) were quantified for measures of total distance, high-speed distance [>19.8 km·h ⁻¹ ] and average acceleration. Linear mixed models and effect sizes identified differences between competition levels. No differences existed between competition levels for any total match physical performance metric. Peak total and high-speed distances demands were similar between competitions for all moving average durations. Interestingly, peak average acceleration demands were lower (SMD = 0.63–0.69) in the youth players across all moving average durations. The data suggest that the development of acceleration and repeat effort capacities is crucial in youth players for them to transition into professional competition.
... From the GPS data, three commonly assessed measures of running intensity were chosen for assessment: total distance covered, high-speed distance covered (> 19.8 km · h −1 ) and average acceleration, with total and high-speed distance made relative to playing time (m · min −1 ). Average acceleration was calculated through the summation of all absolute acceleration and deceleration speeds which were then averaged over a defined time duration to provide an indication of the total acceleration requirements of match-play [26]. Although the consolidation of both accelerations and decelerations into one variable may conceal the underlying mechanisms responsible for the load, it has been suggested that assessing both variables drill of 5 min in duration. ...
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... Average accelerationbased indices, such as acceleration density (AU) and acceleration load (m·s −2 ), were investigated in addition to effort detection bands previously reported in GPS focused research Malone et al. 2017;Kelly et al. 2019). Acceleration load is the accumulation of absolute acceleration values, and acceleration density is the average of absolute acceleration values over the specified period (Delaney et al. 2016). Similarly, speed exertion is an arbitrary unit which is calculated by GP Sports via the accumulation of maximal speed-and acceleration-based indices. ...
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