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Abstract The objective of the study was to describe an original approach to assessing individual workload during international rugby union competitions. The difference between positional groups and between the two halves was explored. Sixty-seven files from 30 French international rugby union players were assessed on a computerised player-tracking system (Amisco Pro (®) , Sport Universal Process, Nice, France) during five international games. Each player's action was split up into exercise and recovery periods according to his individual velocity threshold. Exercise-to-recovery (E:R) period ratios and acceleration were calculated. Results indicated that about 65% of exercise periods lasted less than 4 s; half of the E:Rs were less than 1:4, and about one-third ranged between 1 and 1:4 and about 40% of exercise periods were classified as medium intensity. Most acceleration values were less than 3 m·s(-2) and started from standing or walking activity. Back row players showed the highest mean acceleration values over the game (P < 0.05). No significant decrease in physical performance was seen between the first and second halves of the games except for back rows, who showed a significant decrease in mean acceleration (P < 0.05). The analysis of results emphasised the specific activity of back rows and tended to suggest that the players' combinations of action and recovery times were optimal for preventing large decrease in the physical performance.
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A new approach to quantifying physical demand in
rugby union
Mathieu Lacome
a
b
, Julien Piscione
a
, Jean-Philippe Hager
a
& Muriel Bourdin
b
a
Research Department , French Rugby Union Federation (FFR) , Marcoussis , France
b
LBMC, IFSTTAR , University of Lyon 1 , Oullins , France
Published online: 09 Sep 2013.
To cite this article: Mathieu Lacome , Julien Piscione , Jean-Philippe Hager & Muriel Bourdin , Journal of Sports
Sciences (2013): A new approach to quantifying physical demand in rugby union, Journal of Sports Sciences, DOI:
10.1080/02640414.2013.823225
To link to this article: http://dx.doi.org/10.1080/02640414.2013.823225
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A new approach to quantifying physical demand in rugby union
MATHIEU LACOME
1,2
, JULIEN PISCIONE
1
, JEAN-PHILIPPE HAGER
1
,
& MURIEL BOURDIN
2
1
Research Department, French Rugby Union Federation (FFR), Marcoussis, France, and
2
LBMC, IFSTTAR,
University of Lyon 1, Oullins, France
(Accepted 3 July 2013)
Abstract
The objective of the study was to describe an original approach to assessing individual workload during international rugby
union competitions. The difference between positional groups and between the two halves was explored. Sixty-seven les
from 30 French international rugby union players were assessed on a computerised player-tracking system (Amisco Pro
®
,
Sport Universal Process, Nice, France) during ve international games. Each players action was split up into exercise and
recovery periods according to his individual velocity threshold. Exercise-to-recovery (E:R) period ratios and acceleration
were calculated. Results indicated that about 65% of exercise periods lasted less than 4 s; half of the E:Rs were less than 1:4,
and about one-third ranged between 1 and 1:4 and about 40% of exercise periods were classied as medium intensity. Most
acceleration values were less than 3 m·s
2
and started from standing or walking activity. Back row players showed the highest
mean acceleration values over the game (P < 0.05). No signicant decrease in physical performance was seen between the
rst and second halves of the games except for back rows, who showed a signicant decrease in mean acceleration
(P < 0.05). The analysis of results emphasised the specic activity of back rows and tended to suggest that the players
combinations of action and recovery times were optimal for preventing large decrease in the physical performance.
Keywords: Timemotion analysis, Rugby, Acceleration, Ratio
Introduction
In recent years, video graphic motion analysis has
become a common method of estimating the physi-
cal demands of rugby union, as in soccer. The
players displacement can be analysed using two
methods, depending on the video system used. (1)
The notational method denes subjective intensity
zones assessed from the observation of each players
movement characteristics (Austin, Gabbett, &
Jenkins, 2011a; Austin, Jenkins, & Gabbett, 2011b;
Deutsch, Kearney, & Rehrer, 2007; Duthie, Pyne, &
Hooper, 2005). (2) Alternative timemotion techni-
ques are now available that use automatic and semi-
automatic (Eaton & George, 2006; Quarrie,
Hopkins, Anthony, & Gill, 2013) or manual
(Roberts, Trewartha, Higgitt, El-Abd, & Stokes,
2008) player-tracking techniques to provide velocity
data over the course of a match; intensity zones can
thus be dened using absolute velocity values. Both
approaches have limitations. First, the choice of a
single absolute velocity threshold for all players
does not take into ac count the players individual
physiological characteristic despite the noticeable
heterogeneity of physiological proles between posi-
tional groups (e.g. Duthie, Pyne, & Hooper, 2003).
Abt and Lovell (2009) highlighted the importance of
individualising the high-intensity threshold: the dis-
tance covered at high intensity by a professional
soccer player was substantially underestimated if
the same absolute running velocity value was used
for all players. Second, the usual method of data
analysis consists in determining the frequency and
duration of activity in different intensity zones.
However, as illustrated by Figure 1 and pointed out
by Cunniffe, Proctor, Baker, and Davies (2009), this
analysis provides frequency of entry per velocity zone
but not the number of real exercise bouts. Since
velocity almost never reached a steady state, exercise
bouts could be counted twice due to acceleration
and deceleration phases. Likewise, each individual
activity was analysed in terms of duration per velo-
city zone, but never analysed globally as such.
Moreover, neither approach accounts for the fact
that the metabolic cost of running at a consta nt
velocity is signicantly lower than the cost of accel-
eration (di Prampero et al., 2005; Osgnach, Poser,
Bernardini, Rinaldo, & di Prampero, 2010).
Correspondence: Muriel Bourdin, University of Lyon 1, IFSTTAR, LBMC, Oullins, France. E-mail: muriel.bourdin@univ-lyon1.fr
Journal of Sports Sciences, 2013
http://dx.doi.org/10.1080/02640414.2013.823225
© 2013 Taylor & Francis
Downloaded by [88.175.61.234] at 06:52 11 October 2013
Osgnach et al. (2010) also demonstrated that the
energy cost of acceleration increased as a function
of running velocity. To the best of our knowledge,
only one previous study has considered acceleration
in the analysis of a rugby game, but only 2 players
were analysed (Cunniffe et al., 2009). The analysis
of acceleration data will arguably improve the under-
standing of physical demand during rugby match
play.
The intermittent nature of rugby play was
demonstrated by McLean (1992), who rst calcu-
lated the ratio between exerc ise a nd recovery period
durations (E:R) t o quantify exercise density during
the Five Nations matches in 1991. Subsequently,
mean E:R duration was analysed in each positional
group to theori se physiological demand (D eutsch
et al ., 20 07) and training pre scrip tion (Eaton &
George, 2006). However, for a given E:R , physio-
logic al workl oad depe nds on exercise duration
(Åstrand, Åstrand, Christensen, & Hedman, 1960;
Balsom, Seger, Sjodin, & Ekblom, 1992) and, for a
given ratio and exercise duration, on the intensity
of exercise and recovery periods (Åstrand et al.,
1960; Bogdanis, Nevill, Lakomy, Graham, &
Louis , 199 6; Spencer, Bishop, & Dawson, 2006;
Spencer, Dawson, Goodma n, Dascombe, &
Bisho p, 2008).
The primary aim of the present study was to
propose a complementary approach to classic time
motion analysis during international rugby competi-
tions by analysing the mean intensity of each players
activity, acceleration and E:R distribution according
to exercise duration and intensity in different posi-
tional groups.
A secondary aim was to investigate decrease in
physical performance during the course of a match.
Methods
Participants
The expe riment was carried out on 30 French inter-
national rugby pla yers. To make inter-position com-
parisons, players were categ orised as forwards or
backs, and subdivided into 4 positional groups fol-
lowing Deutsch, Maw, Jenkins, and Reaburn (1998):
Front row forwards (props and second rows, n = 9);
back row forwards (hookers and back rows, n = 8);
inside backs (outside halves and centre, n = 6) and
outside backs (wingers and full-backs, n = 7).
Scrum-halves (n = 2) were excluded from the analy-
sis due to the limited sample and unique physical
demands of that position (Deutsch et al., 1998;
Duthie et al., 2005; Roberts et al., 2008 ). The results
of annual physiological testing were used in agree-
ment with the Fédération Française de Rugby (FFR,
French Rugby Federation). Match recordings were
bought by the FFR, which consented to the use of
the data. The study was conducted in accordance
with the guid elines of the ethics committee of the
University of Lyon (France). Anthropometric char-
acteristics of the players are listed in Table I.
Procedures
Physiological tests were conducted in November, in
a single training session.
Five home matches were then analysed a poster-
iori. As can be seen in Table II, games 1 and 2 were
international competitions played in November
while games 35 were video-recorded during the
2010 Six Nations Tournament in February and
March. Players participating in at least 1 complete
half were included in the analysis which represented
Figure 1. Typical evolution of the velocity (km·h
1
) observed during a game. Each horizontal line indicates a different intensity threshold
(MAV: maximal aerobic velocity; MI run.: medium-intensity run).
2 M. Lacome et al.
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67 les. The results, real playing time and number of
les analysed are listed in Table II.
Analysis and calculations
Physiological assessment. Velocity corresponding to a
blood lactate concentration ([La]b) of 4 mmol·l
1
(VLa4) and maximal aerobic velocity were deter-
mined using an intermittent progressive running test
(adapted from the test described by Leger and
Boucher [1980]) consisting of bouts of 3 min inter-
spersed with 1 min passive rest. Velocity was
increased by 2 km·h
1
from 8 to 12 km·h
1
and then
by 1 km·h
1
until voluntary exhaustion. The test was
performed on a Tartan outdoor track. Players were all
fully familiarised with the test procedure. During the
test, heart rate was continuously recorded using a
heart-rate monitor (Polar Team2, Kempele,
Finland). Blood was sampled from the ngertip, dur-
ing each rest period and 3 min after end of test
.
[La]b
was measured using a lactate analyser (YSI2300, YSI
Inc., Yellow Springs, OH, USA). VLa4 was deter-
mined by straight-line interpolation between the two
closest measured lactate values. Body fat was assessed
on whole-body dual-energy X-ray absorptiometry
(D-XA) (Discovery W, Hologic Inc., Marlborough,
MA, US A).
Video data collection and analysis. A computerised
player-tracking system (Amisco Pro
®
, Sport
Universal Process, Nice, France) was used to record
the activity of each player during the game . Six to
eight cameras were positioned under the roof of the
Stade de France stadium (Saint Denis, France) and
subsequently calibrated and synchronised. The sig-
nals and angles obtained by the encoders were
sequentially converted into digital data at 25 Hz
and recorded on 6 computers for post-game analysis.
Processing of the raw data gave the distance covered
every 0.1 s (10 Hz).
In line with Vigne, Gaudino, Rogowski, Alloatti,
and Hautier (2010), displacement at velocities
higher than VLa4 and static activity were considered
as exercise, and activity lower than VLa4 as recovery.
For each period of exercise corresponding to run-
ning, mean exercise velocity was calculated and sub-
divided into medium-intensity running velocity
(medium-intensity run, ranging between VLa4 and
maximal aerobic velocity) and supramaximal run-
ning velocity (higher than maximal aero bic velocity).
Table I. Anthropometric and physiological characteristics of the 4 positional groups.
Positional group (n)
Forwards Backs
Front row (9) Back row (8) All forwards (17) Inside backs (6) Outside backs (7) All backs (13)
Age (years) 29.9 ± 3.3 27.4 ± 4.1 28.7 ± 3.8 25.9 ± 3.5 27.7 ± 3.4 27.1 ± 3.4
Height (m) 1.88 ± 0.09 1.88 ± 0.05 1.88 ± 0.07 1.82 ± 0.05 1.83 ± 0.06 1.83 ± 0.05
Mass (kg) 112.4 ± 6.7 103.7 ± 7.5
a
108.3 ± 8.2 93.0 ± 14.6
a
90.9 ± 6.4
abc
94.0 ± 8.2*
Body fat (%) 17.5 ± 5.8 13.3 ± 4.7 15.5 ± 5.5 15.3 ± 5.8 11.4 ± 1.8 13.5 ± 4.8
MAV (km·h
1
) 14.9 ± 0.5 16.1 ± 1.3 15.5 ± 0.8 15.4 ± 0.8 15.7 ± 0.9 15.4 ± 1.1
VLa4 (km·h
1
) 12.2 ± 0.9 12.5 ± 1.4 12.3 ± 1.1 12.3 ± 0.8 13.2 ± 1.1 12.7 ± 0.9
VLa4 (% MAV) 82.0 ± 5.6 77.5 ± 4.9
d
79.9 ± 5.7 80.0 ± 3.6 83.5 ± 5.1 81.9 ± 4.7
Notes:
a
Signicantly different to front row forwards, P < 0.05;
b
Signicantly different to back row forwards, P < 0.05;
c
Signicantly different
to inside backs, P < 0.05;
d
Signicantly different to outside backs, P < 0.05. *Signicant between group (forwards and backs) differences,
P < 0.05. MAV: maximal aerobic velocity; VLa4: velocity corresponding to blood lactate concentration of 4 mmol·l
1
.
Data are presented as mean ± standard deviation; (n) number of players.
Table II. Result, real playing time and number of les analysed for each of the 5 matches.
Game Result Real playing time (min)
Number of les analysed
Forwards Backs
Front row Back row Inside backs Outside backs
Total les
1 Won 2013 38.7 4 4 2 3 13
2 Lost 1239 36.8 4 4 3 3 14
3 Won 3310 44.5 4 4 3 3 14
4 Won 4620 37.7 4 4 3 2 13
5 Won 1210 44.8 4 4 3 2 13
Total les 20 20 14 13 67
Physical demand in international rugby union 3
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Total distance, duration and mean intensity in the
various previously dened intensity zones were
determined automatically on computerised match
analysis software (Amisco Viewer
®
, Sport Universal
Process, Nice, France). The study of Zu billaga
(2006) compared the distances and velocities
obtained with the Amisco Pro
®
system to that calcu-
lated from time performances over known distances.
It was concluded by the author that the Amisco Pro
®
system demonstrated high level of accuracy and
reliability.
Scrums, rucks and mauls were classied as static
activity. Since the system used was unable to iden-
tify and quantify static activities accu rately and auto-
matically, active participation in a ruck or maul was
monitored manually from the moment a players
shoulder entered into contact with the ruck or maul
to the moment it broke contact. Tackles whereby the
ball carrier was stopped on their feet were also clas-
sied as static activity. Scrums, rucks and mauls
were identied from match videos and listed by an
experienced opera tor. The reliability of a similar
timemotion analysis method has previously been
documented (Deutsch et al., 1998, 2007). The
time spent and distance travelled during static activ-
ities was excluded from the analysis done automati-
cally by Amisco Viewer
®
.
In order to establish a representative prole for
each positional group, all 5 game s were pooled for
analysis (Hughes, Evans, & Wells, 2001).
Calculations. For each player data le , E:R was
calculated by dividing exercise period duration by
the following recovery duration. The results were
related to the total number of E:R ratios (i.e.
exercise + recovery events) and expressed in per-
centages (%). Ratios were divided in to 3 groups:
lower than 1:4 (E:R < 1 :4), ranging betwee n 1:4
and 1 (1:4 < E:R < 1) and higher than 1 (E:R > 1)
and expressed as percentages (%) of the total
number of ratios. For each group, E:Rs were clas-
sied according to exercise duration and e xercise
intensity.
Acceleration values were calculated from distance
data sampled at 10 Hz and derived twice. A
Butterworth low-pass second-order (cut-off fre-
quency: 1 Hz) double phase-lag lter was used
after each derivation. Accord ing to the manufac-
turer, the mean error for a pixel was 75 mm (ranging
between 50 and 100 mm according to the position of
the player relative to the cameras). To test the accu-
racy and the reliability of our calculation, we gener-
ate 30 s noisy displacement data sets (sampled at
10 Hz) using the Monte Carlo method and consid-
ering a standard deviation of 75 mm. We calculated
the standard deviation of the noisy data sets to assess
the accuracy and the reliability of the calculation of
both velocity and acceleration values and to verify
the convergence of the standard deviati on computed
from these noisy sets regarding the number of sets
(between 10 and 500 30 s noisy sets). Standard
deviation was assessed 0.17 m·s
1
and 0.34 m·s
2
for velocity and acceleration, respectively, whatever
the number of noisy sets.
Only acceleration values reaching at least 1 m·s
2
for a duration greater than 0.5 s were taken into
account in the analysis. In accordance with Osgnach
et al. (2010), data were pooled in 3 groups: 12, 23
and greater than 3 m·s
2
. Acceleration values were
then classied into the following activity categories
according to mean exercise-period velocity: standing
walking (07 km·h
1
), jogging (7 km·h
1
VLa4),
medium-intensity run and higher than maximal aero-
bic velocity.
To analys e changes in activity patterns over the
whole match, only players participating in a com-
plete match were analysed. This represented 37
les (front rows, 9 les; back rows, 10 les; inside
backs, 9 les and outside backs, 9 les). The study
parameters were averaged per match and per half.
Statistical analysis
All data are presented as mean ± standard deviation.
The study parameters studied were not normally
distributed on the ShapiroWilk test, and non-
parametric tests were therefore used. Differences
between groups and between the rst and second
halves of the matches were tested using the
Wilcoxon test. Statistical signicance was set at
P < 0.05. All statistics were run on JMP Pro 9
(SAS, Cary, NC, USA).
Results
Anthropometric and physiological characteristics of the 4
positional groups
The results demonstrated that players in the 4 posi-
tional groups had similar heights. On the other hand,
body mass was greater in forwards than in backs.
Front rows were the heaviest and outside backs the
lightest. There was no signicant difference between
positional groups relative to body fat, VLa4, max-
imal aerobic velocity or age. Vla4 related to the
maximal aerobic velocity (VLa4%) was signicantly
lower in back rows than in outside backs (P < 0.05).
Total exercise time and distance covered
Front rows and back rows showed similar mean total
exercise times (11.9 ± 1.3 and 11.2 ± 2.6 min,
respectively). Inside backs showed a mean total exer-
cise time of 10 ± 1.4 min, similar to back rows,
4 M. Lacome et al.
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shorter than front rows (P < 0.05) and longer than
outside backs (7.1 ± 1.3 min, P < 0.05).
Backs covered more distance than forwards
(7,944 ± 659 vs 7,006 ± 356 m, respectively;
P < 0.05). Inside backs and outside backs covered
similar distances. Front rows covered the shortest
distance (6,935 ± 334). Back rows and outside
backs covered similar distances (7,215 ± 266 and
7,764 ± 789, respectively), back rows covered
shorter distance than inside backs (8,079 ± 539;
P < 0.05);
Exercise recovery ratio
The total number of ratios (exercise + recovery
events) per match was signicantly lower in backs
than in forwards (137.1 ± 28.8 vs 178.7 ± 25.7,
respectively; P < 0.05). The total number of ratios
in front rows was similar to that in back rows
(185.9 ± 20.3 and 172.2 ± 29.3, respectively) and
signicantly higher in inside backs than in outside
backs (155.4 ± 13.9 and 118 ± 16.1 respectively,
P < 0.05). Inside backs showed a lower total number
of ratios than front rows (P < 0.05); outside backs
also showed a lower total number of ratios than back
rows (P < 0.05).
To facilitate the reading of Tabl e III , data hig her
than 5% are colour ed in grey. Overall, as illustrated
in Table III and synthesised in Ta ble IV , forwards
were more involve d in static activity and backs were
more involved in supramaximal intensity bouts. No
signicant differe nce was found for medium-
intensity runs. All positional groups had si milar
percentages of E:Rs in per ex ercise duration
category.
Back rows had signicantly more E:R >1 ratios
than front rows or outside backs; outside backs had
the lowest percentage. Outside backs had signi-
cantly more E:R <1:4 ratios than front rows, back
rows or inside backs.
It can be seen that, for exercise durat ions less
than 4 s, whatever the type of exercise, a large
part of the action showed a E:R <1:4. For exercise
durations >4 s and in static activity, forwards
showed a prepon derance of E:R <1. For exercise
intensities greater than maximal aerobic velocity,
backs showed a large percentage of exercise dura-
tions >4 s, r egardless of t he ratio. In the medium-
intensity run category, forwards showed a lower
percentage of E:R <1:4 than backs for exercise
durations 24s.
Acceleration
Mean acceleration duration was signicantly higher
in backs than in forwards (0.85 ± 0.06 vs
0.76 ± 0.03 s, respectively; P < 0.05), as was mean
maximal acceleration duration (3.67 ± 0.35 vs
3.24 ± 0.39 s, respectively; P < 0.05).
Mean acceleration value was signicantly higher in
forwards than in backs (2.46 ± 0.92 vs
2.36 ± 0.93 m.s
2
, respectively). Back rows had the
highest mean acceleration value, compared to front
rows, inside backs and outside backs (2.50 ± 0.95,
2.41 ± 0.89, 2.38 ± 0.90 and 2.34 ± 0.98 m·s
2
,
respectively; P < 0.001). Inside backs and outside
backs both had signicantly lower mean acceleration
values than front rows (P < 0.05), while outside
backs had signicantly lower mean values than inside
backs (P < 0.05).
Overall analysis of results, illustrated in Figure 2,
demonstrated that the majority of match accelera-
tion values (41.2 ± 7.6%) r anged between 1 and
2m·s
2
.Thepercentageofaccelerationvalues
of the category acceleration ranging between 2
and 3 m·s
2
and of the category accelera-
tion >3 m·s
2
represented 37.7 ± 3.9 and
21.1 ± 6% of the total number of accelerations,
respectively. A cceleration started mainly from jog-
ging (31.8 ± 5%) or standingwalking activity
categories (53.4 ± 5.5%); the percentage was sig-
nicantly higher in front rows and outside backs
than in inside backs (86.5 ± 3.0% and 85.7 ± 4.7%
vs 82.7 ± 3.1%, respectively; P < 0.05) but no
signicant difference emerged between forwa rds
and backs as a whole.
As can be seen in Figure 2, back rows had a lower
percentage of acceleration values ranging between 1
and 2 m.s
2
than the other subgroups, with a higher
percentage of acceleration values >3 m.s
2
than
inside backs and outside backs in the medium-inten-
sity run, jogging and walkingstanding zones. Front
rows had the lowest percentage of acceleration values
from running velocities higher than maximal aerobic
velocity.
First Half vs Second Half
As seen in Table V, whatever the positional group,
no signicant differences in the percentage of exer-
cise-period type or intensity, exercise duration or E:
R were observed between the two halves, except that
the percentage of ratios ranging between 1:4 and 1
decreased signicantly between the two halves of the
match for backs.
Mean acceleration, on the other hand, was sig-
nicantly greater during the rst half than the sec-
ond half (2.45 ± 0.95 vs 2.38 ± 0.90 m·s
2
,
respectively; P <0.001),inbothforwardsand
backs (2.49 ± 0.93 vs 2.42 ± 0.91 m·s
2
,
and 2.39 ± 0.97 vs 2.33 ± 0.89 m·s
2
,respectively).
Analysis by subgroup found that mean a cceleration
Physical demand in international rugby union 5
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Table III. Distribution of the ratio of exercise duration to recovery duration (E:R; % of the total number of ratios) classied by exercise duration for each group and subgroup and for each exercise
intensity.
Exercise duration <2 s 24 s >4 s
Ratio E:R < 1:4 1:4 < E:R < 1 E:R > 1 E:R <1:4 1:4 < E:R < 1 E:R > 1 E:R <1:4 1:4 < E:R < 1 E:R > 1
Static activity
Front row 6.2 ± 3.2 1.5 ± 1.1 0.1 ± 0.3 8.7 ± 2.7 3.0 ± 1.4 1.0 ± 1.1 5.5 ± 1.5 4.0 ± 1.8 3.3 ± 2.0
Back row 5.9 ± 2.9 1.5 ± 1.2 0.4 ± 0.5 7.6 ± 3.0 2.8 ± 1.3 1.4 ± 1.0 5.0 ± 2.1 2.9 ± 1.7 4.5 ± 3.1
All forwards 6.0 ± 3.0 1.5 ± 1.2 0.3 ± 0.4 8.2 ± 2.9 2.9 ± 1.3 1.2 ± 1.1 5.2 ± 1.8 3.4 ± 1.8 3.9 ± 2.7
Inside backs 3.4 ± 1.6
ab
0.7 ± 0.6
a
0.3 ± 0.5 2.3 ± 1.7
ab
1.3 ± 1.0
ab
0.4 ± 0.6
b
0.2 ± 0.5
ab
0.4 ± 0.4
ab
0.1 ± 0.3
ab
Outside backs 3.4 ± 2.4
ab
0.5 ± 0.5
ab
0.3 ± 0.5 1.9 ± 1.1
ab
0.2 ± 0.5
abc
0.5 ± 0.6
b
0.7 ± 0.7
ab
0.2 ± 0.5
ab
0.1 ± 0.2
ab
All backs 3.4 ± 2.0* 0.6 ± 0.6* 0.3 ± 0.5 2.1 ± 1.4* 0.8 ± 0.9* 0.4 ± 0.6* 0.4 ± 0.6* 0.3 ± 0.5* 0.1 ± 0.3*
>MAV Front row 3.9 ± 2.3 1.7 ± 1.4 1.3 ± 1.2 3.0 ± 2.2
cd
1.7 ± 1.0
cd
1.9 ± 1.3
c
4.0 ± 2.6
cd
4.6 ± 1.6
cd
5.2 ± 2.1
cd
Back row 2.6 ± 2.5
ac
1.1 ± 0.9
c
0.7 ± 0.9 2.6 ± 1.4
cd
1.4 ± 1.0
cd
2.2 ± 1.2
c
4.1 ± 2.8
cd
4.0 ± 2.0
cd
5.4 ± 2.6
cd
All forwards 3.2 ± 2.4* 1.4 ± 1.2* 1.0 ± 1.1 2.8 ± 1.8* 1.6 ± 1.0* 2.1 ± 1.2* 4.1 ± 2.7* 4.3 ± 1.8* 5.3 ± 2.4*
Inside backs 4.8 ± 1.9 2.3 ± 1.2 0.7 ± 0.7 6.4 ± 2.6
d
3.3 ± 1.6 3.6 ± 1.1 9.7 ± 3.2
d
8.2 ± 3.2 10.2 ± 4.7
Outside backs 5.7 ± 2.8 2.5 ± 2.1 1.4 ± 2.3 10.7 ± 5.1 4.3 ± 2.6 2.9 ± 1.4 12.6 ± 3.8 7.4 ± 3.2 8.0 ± 2.7
All backs 5.2 ± 2.4 2.4 ± 1.7 1.0 ± 1.7 8.5 ± 4.5 3.8 ± 2.2 3.3 ± 1.3 11.1 ± 3.8 7.8 ± 3.2 9.1 ± 4.0
Medium-intensity run Front row 8.8 ± 3.7
c
3.8 ± 2.2 1.7 ± 1.1 7.3 ± 3.7 3.7 ± 2.0 2.5 ± 1.5 4.1 ± 1.8 4.0 ± 2.4 3.6 ± 2.3
Back row 10.4 ± 3.7 4.5 ± 2.5 1.7 ± 1.5 6.3 ± 1.7
cd
4.3 ± 1.8 3.3 ± 2.2 4.6 ± 2.7 4.3 ± 2.1 4.6 ± 2.8
All forwards 9.6 ± 3.8 4.1 ± 2.3 1.7 ± 1.3 6.8 ± 2.9* 4.0 ± 1.9 2.9 ± 1.9 4.4 ± 2.3 4.1 ± 2.2 4.1 ± 2.6
Inside backs 10.9 ± 2.2 3.6 ± 2.5 1.3 ± 1.1 8.3 ± 2.1 4.1 ± 2.1 2.5 ± 1.6 3.1 ± 1.3
b
4.9 ± 2.1 3.2 ± 2.7
Outside backs 10.7 ± 3.6 2.7 ± 1.9
b
0.7 ± 1.0
ab
9.5 ± 3.4 3.4 ± 2.3 1.6 ± 2.1
b
3.9 ± 3.1 2.7 ± 2.3
bc
1.7 ± 1.9
ab
All backs 10.8 ± 2.9 3.2 ± 2.3 1.0 ± 1.1* 8.8 ± 2.8 3.8 ± 2.2 2.1 ± 1.8 3.5 ± 2.3 3.9 ± 2.4 2.5 ± 2.4*
Notes:
a
Signicantly different to front row forwards, P < 0.05;
b
Signicantly different to back row forwards, P < 0.05;
c
Signicantly different to inside backs, P < 0.05;
d
Signicantly different to
outside backs, P < 0.05. *Signicant between group (forward and back) differences, P <0.05.Greycells,percentageofE:Rhigherthan5%.MAV:maximalaerobicvelocity.
6 M. Lacome et al.
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Table IV. Percentage of exercise periods according to exercise intensity, exercise duration and ratio of exercise duration to recovery duration (E:R) for each group and subgroup.
Exercise intensity Exercise duration Ratio
Medium-intensity run >MAV Static activity 02s 24 s >4s E:R < 1:4 1:4 < E:R < 1 E:R > 1
Front row (20) 39.5 ± 12.2 27.2 ± 9.2
cd
33.3 ± 5.6 29.0 ± 4.0 32.8 ± 4.4 38.2 ± 4.2 51.4 ± 6.2
d
27.9 ± 4.8 20.6 ± 4.4
b
Back row (20) 44.0 ± 14.4 24.1 ± 9.0
cd
31.8 ± 7.8 28.7 ± 4.8 32.0 ± 2.8 39.3 ± 4.4 49.1 ± 5.4
d
26.7 ± 5.6 24.2 ± 4.4
Forwards (40) 41.8 ± 13.4 25.7 ± 9.2* 32.6 ± 6.8 28.8 ± 4.4 32.4 ± 3.7 38.8 ± 4.3 50.3 ± 5.9 27.3 ± 5.1 22.4 ± 4.7
Inside back (14) 41.9 ± 7.2 49.0 ± 7.8 9.1 ± 3.1
ab
27.9 ± 4.1 32.1 ± 4.1 40.0 ± 6.4 48.9 ± 5.0
d
28.8 ± 4.0 22.3 ± 6.3
Outside back (13) 37.0 ± 12.2 55.4 ± 13.0 7.6 ± 3.4
ab
27.8 ± 4.6 35.0 ± 6.0 37.2 ± 4.9 59.0 ± 6.6 23.9 ± 7.2 17.1 ± 4.3
abc
Backs (27) 39.6 ± 10.1 52.1 ± 10.9 8.4 ± 3.2* 27.8 ± 4.3 33.5 ± 5.2 38.7 ± 5.8 53.8 ± 7.7 26.4 ± 6.1 19.8 ± 5.9
Notes:
a
Signicantly different to front row forwards, P < 0.05;
b
Signicantly different to back row forwards, P < 0.05;
c
Signicantly different to inside backs, P < 0.05;
d
Signicantly different to
outside backs, P < 0.05. *Signicant between group (forwards and backs) differences, P < 0.05. MAV: maximal aerobic velocity. In parenthesis, the number of les analysed.
Physical demand in international rugby union 7
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values decreased signicantly only for back rows
(2.55 ± 0.96 vs 2.45 ± 0.94; P < 0.001), whereas
only a trend was observed for the o ther subgroups.
Discussion
The purpose of the current study was to propose a
complementary approach to classic timemotion ana-
lysis to improve the understanding of the phys ical
demand of international rugby union match play.
Results indicated that about 65% of exercise periods
lasted less than 4 s; half of the E:Rs were less than 1:4
and about one-third ranged between 1 and 1:4; and
about 40% of exercise periods were classied as med-
ium intensity. Most acceleration values were less than
3 m·s
2
and started from standing or walking activity.
Back rows showed the highest mean acceleration
values over the game. Analysis in subgroups demon-
strated that only back rows showed a signicant
decrease in the mean acceleration between the two
halves. The E:Rs and their repartition in duration and
intensity categories did not vary over the game.
The anthropometric characteristics of the studied
group were in line with those presented in previous
studies concerning elite professional rugby union
(Fuller, Taylor, Brooks, & Kemp, 2013; Higham,
Pyne, & Mitchell, 2013; Holway & Garavaglia,
2009; Sedeaud et al., 2012). To our knowledge,
the current study is the rst to present mean values
of maximal aerobic velocity and of VLa4% in a
group of international rugby union players.
The methodological originality of t he present
study was the choice of individual v elocity thresh-
old. In a recent study by C ahill, Lamb, Worsfold,
Headey, and Murray (2013), i ndividual thresholds
corresponding to maximal running velocity were
used to quantify the movement characteristics of
English premiership players. It is generally
accepted that fatigue occurs quickly during supra-
maximal e xercise intensities (i.e . greater than max-
imal aerobic velocity). Accordingly, the choice of
maximal aerobic velocity as thre shold to de ne
high intensity seems justied, espec ially for asses-
sing physic al demand. I n t he present s tud y,
Front row forwards; Back row forwards; Inside backs; Outside backs
Figure 2. Distribution of acceleration (percentage of total number of acceleration values) according to intensity category (67 les).
a Signicantly different to front row forwards, P < 0.05; b Signicantly different to back row forwards, P < 0.05; c Signicantly different to
inside backs, P < 0.05; d Signicantly different to outside backs, P < 0.05. MAV: maximal aerobic velocity. MI run: medium-intensity
running.
Table V. Evolution of the percentage of exercise periods according to exercise intensity, exercise duration and the ratio of exercise duration
to recovery duration (E:R) between the two halves (H1 and H2).
Exercise intensity Exercise duration Ratio
Medium-
intensity run >MAV
Static
activity 02s 24s >4s E:R < 1:4
1:4 < E:
R < 1 E:R > 1
Forwards (19) H1 36.1 ± 11.6 28.0 ± 8.3 35.9 ± 6.3 28.8 ± 6.1 32.6 ± 4.3 38.6 ± 5.2 51.1 ± 7.6 26.4 ± 5.8 22.4 ± 5.0
H2 36.7 ± 10.2 27.6 ± 10.2 35.8 ± 3.5 29.1 ± 4.8 31.8 ± 3.4 39.1 ± 6.0 51.3 ± 5.8 24.6 ± 4.5 24.0 ± 6.3
Backs (18)
H1 42.0 ± 10.3 49.4 ± 10.7 8.6 ± 3.4 27.0 ± 4.8 36.0 ± 5.1 37.0 ± 6.2 51.7 ± 8.1 28.5 ± 6.4 19.8 ± 6.6
H2 43.1 ± 11.4 48.3 ± 11.3 8.5 ± 5.0 28.7 ± 6.9 34.8 ± 7.0 36.5 ± 8.0 54.4 ± 7.9 23.8 ± 6.0* 21.9 ± 5.9
Notes: *Signicantly different to H1, P < 0.05. MAV: maximal aerobic velocity. In parenthesis, the number of les analysed.
8 M. Lacome et al.
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maximal aerobic ve locity tended to be lower in
front rows. This conrms that by using a single
velocity threshold to dene high intensity tends to
systematically underestimate physical demand in
players with low er maximal aerobic ve locity values
(Abt & Lovell, 2009). It can be assumed that pre-
vious studies, which used a xed-int e n si t y th r e sh -
old, to dene high-intens ity category, did not
dispose of individual reference values such as max-
imal aerobic velocity.
In most studies, active participation in scrums,
rucks and mauls is timed from when the player has
any contact with another player, whereas in the cur-
rent study, participation was only considered to be
when specically the players shoulder was in con-
tact. Consequently, the mean duration of static
activity in the present study (4.15 and 2.21 s for
forwards and backs, respectively) was lower than in
other reports (5 to 7.1 and 3.6 to 4.2 s, respectively)
(Deutsch et al., 2007; Duthie et al., 2005; Roberts
et al., 2008). The mean number of static activity
bouts for forwards (about 53, including 22 scrums)
was signicantly less than in previous studies (ran-
ging between 80 and 105) (Deutsch et al., 2007;
Duthie et al., 2005; Roberts et al., 2008). It is not
possible to determine whether these differences are
due to the methodology and/or to the playing level
and style of the teams studied. Nevertheless, the
methods used in different studies cannot inuence
the total number of scrums. The current results
demonstrated a mean value of 22 scrums per
match. This is consistent with the value of around
25 scrums per match reported by Quarrie et al.
(2013) during south hemisphere international game.
In agreement with the studies of Austin et al.
(2011b), total exercise time was similar for front
rows (11.9 min), back rows (11.2 min) and inside
backs (10.4 min), but signicantly shorter for out-
side backs (7.1 min). Consistent with this, the total
number of ratios (exercise + recovery events) was the
lowest in outside backs (118). Table IV shows that
forwards were more involved in static activity than
backs, while backs were more involved in supramax-
imal runs than forwards. Forwards (7,006 m) were
found to cover a shorter total distance than backs
(7,944 m). This is related to the fact that front rows
covered the shortest total distance (6,935 m). This
nding is in line with those of Austin et al. (2011b)
and Roberts et al. (2008). It is noteworthy that, in
agreement with Austin et al. (2011b), back rows and
outside backs covered similar total distances whereas
inside backs tended to cover the longest. The shorter
distance covered by front rows is consistent with
their lower maximal aerobic velocity. Further studies
are needed to investigate whether maximal aerobic
velocity value could represent the limitation to total
distance covered.
The players competitive level and/or the metho-
dological approach of the present study differs from
previous studies (Austin et al., 2011b; Deutsch et al.,
2007; Duthie et al., 2005; Quarrie et al., 2013;
Roberts et al., 2008), making comparison difcult.
However, it can be concluded that the overall char-
acteristics and between-group differences observed
in the present study were similar to those observed
in elite southern hemisphere and English rugby
union teams.
Nevertheless, total distance covered gives no
information on intensity of displacement, which is
what determines metabolic workload. During inter-
mittent activity, the exercise duration in different
velocity zones or analysis of mean velocity gives par-
tial information on metabolic workload: the accel-
eration should be taken into account in order to
better estimate metabolic workload.
Figure 2 shows that most acceleration values were
less than 3 m·s
2
. In line with the present results,
Cunniffe et al. (2009) reported that most accelera-
tion values attained in 1 s ranged between 1.5 and
2.5 m·s
2
. The present results also demonstrated
that the mean duration of accele ration phases
exceeding 1 m.s
2
was less than 1 s, with a mean
maximal duration ranging between 3 and 4 s. Mean
acceleration value was 4.2% greater in forwards than
in backs, and back rows showed the greatest mean
acceleration over the match as a whole. The studies
of Crewther, Lowe, Weatherby, Gill, and Keogh
(2009) reported that the ability to accele rate over
short distance (10 and 20 m) is greater in backs
than in forwards. It could be supposed that the
higher acceleration value observed in forwards does
not reect a better ability to accelerate but rather
reects the game demands of this position.
Consistent with the nding of Cahill et al. (2013)
and the normal game demands, the present results
also underline the high-intensity workload in back
rows compared to front rows, inside backs or outside
backs.
According to Osgnach et al. (2010), metabolic
power is inuenced by both initial running velocity
and acceleration: for a given acceleration, the higher
the initial running velocity the higher the metabolic
power. As illustrated by Figure 2, most acceleration
values started at intensities corresponding to recov-
ery. It is interesting to note that forwards, and parti-
cularly back rows, were more involved in acceleration
values exceeding 3 m·s
2
, while inside backs were
more involved in acceleration values corresponding
to a medium- or high-intensity run. Consistent with
the nding of Duthie, Pyne, Marsh, and Hooper
(2006), in forwards, most acceleration values have
started when they were standing or walking. This
may due to forwards having to produce higher accel-
eration to attain the same running velocity than backs
Physical demand in international rugby union 9
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involved in acceleration values corresponding to a
medium- or high-intensity run.
As discussed earlier, intermittent activity could be
characterised by the analysis of the distribution of E:
Rs according to exercise durations and intensity cate-
gories. The mean ratio (calculated by dividing mean
exercise duration by mean recovery duration) was
1:6.5 and 1:8.5 in forwards and backs respectively.
This is consistent with the range of 1:4 and 1:25
observed in previous studies (Austin et al., 2011b;
Deutsch et al., 2007; Duthie et al., 2005; Eaton &
George, 2006). An overall analysis of the data shown
in Table IV demonstrates that: (1) in agreement with
Duthie et al. (2005), about 65% of exerc ise periods
lasted less than 4 s; (2) half the E:Rs were less than 1:4
and about one-third ranged between 1 and 1:4; and
(3) about 40% of exercise periods were classied as of
medium intensity. If one considers ratio distribution
and the fact that short-duration high-intensity (near
maximal) phases of static activity or sprints occurred
evenly and were interspersed by exercise and recovery
periods varying in intensity and duration, a decreas e
in the performance due to fatigue is not obvious. In
line with previous studies (Cunniffe et al., 2009 ;
Duthie et al., 2005; Roberts et al., 2008), analysis of
the evolution of ind ividual activity between halves
failed to demonstrate a signicant decrease in the
performance when the characteristics of individual
exercise periods were considered. Nevertheless, a
slight but signicant decrease of 2.9% in the mean
acceleration was found during the second half, which
might be attributed to fatigue and/or to a strategic
decrease in activity level linked to scores (Lago,
Casais, & Dominguez, 2010). Mean acceleration
decreased (by 2.8% and 2.5%, respectively) in both
forwards and backs during the second half, but the
decrease was signicant only in back rows, which may
be related to the signicantly higher percentage of E:
Rs >1 and higher percentage of acceleration values
exceeding 3 m·s
2
in back rows than in the other
subgroups. This decrease in acceleration could also
be related to signicantly lower values of Vla4% in
back rows, a parameter generally associated with
endurance quality.
This result also underlines the interest of analysing
acceleration in intermittent activity. Nevertheless, the
accuracy and reliability of acceleration calculated from
a double derivative of displacement sampled at 10 Hz
and the small number of players within each subgroup
call for caution in generalising the results. Further
studies using new technologies (GPS and accelerome-
try) are needed to conrm the present ndings.
Conclusion
To the best of our knowledge, this is the rst study to
analyse the physical demands of elite rugby union
during ve complete international matches using
semi-automated video analysis, and to consider
mean intensity during each individual exercise per-
iod. Despite methodological limitations, the analysis
of results demonstrates the interest of analysing accel-
eration and E:R ratios to estimate physical demand.
This approach enabled us to show that back rows
were the players with the highest acceleration level
but tended to decrease it during the second half.
These current results tended to show that the combi-
nation of action and recovery times was optimal to
prevent any serious decrease in performance.
Consistent with the earlier studies, the results demon-
strated that international rugby matches place specic
physical demands on players occupying the various
positional groups. This has practical implications.
First, conditioning programmes could differ from a
qualitative point of view between positional sub-
groups: for example, forwards and particularly back
rows should improve their ability to accelerate and to
repeat acceleration. The distribution of ratios pre-
sented in the current study could also be helpful in
designing specic intermittent exercises. Second, the
current results suggested that substitutions of players
during a match should mainly concerned forwards
and particularly back rows.
Acknowledgements
We are very grateful to Paola Sibi, Julien Robineau,
Julien Deloire, Nicolas Barizien and Hugo
Maciejewski for their precious help in eld data col-
lection. We also gratefully thank Professor J.R.
Lacour and the reviewers for their constructive com-
ments that have signicantly improve the manuscript.
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Physical demand in international rugby union 11
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... Analyses using micro-sensor technology such as Global Positioning Systems (GPS) show that the game is intermittent in nature [7]. Players perform frequent bouts of high-speed running and participate in intense physical collisions such as tackling and static actions including scrums, rucks and mauls [8]. In contrast, less information [9,10] exists on the demands in younger elite players, notably in international competition and how these potentially evolve across different age categories. ...
... Backrow players were removed from the scrum coding. Active participation in periods of rucking and mauling was timed from when a player's shoulder entered into contact with the ruck or maul to their detachment from the event [8]. Tackles were considered as actions when a player physically attempted to stop a ball carrier whilst on their feet [8]. ...
... Active participation in periods of rucking and mauling was timed from when a player's shoulder entered into contact with the ruck or maul to their detachment from the event [8]. Tackles were considered as actions when a player physically attempted to stop a ball carrier whilst on their feet [8]. Collision events were counted when a physical contact was made between an attacker with a player in the defensive line [23]. ...
Article
Full-text available
The purpose of the present study was to characterize and compare locomotor and contact loads in U18 and U20 international rugby union competition during matches, and specifically during peak match-play phases using short rolling epochs and continuous ball-in-play (BIP) sequences. 20 international matches from French national teams were analysed in the U18 and U20 Six Nations Tournament respectively and World Rugby U20 Championship. Running loads were quantified using global positioning devices (16 Hz) and contact loads via video match analysis software. Players were split into forward (U18, n = 29; U20, n = 32) and back positional groups (U18, n = 20; U20, n = 24). Compared with U20 peers, U18 players covered a higher total distance (effect size (ES) = 0.76 ± 0.25) and at high-speeds per minute (> 4 m · s-1; ES = -0.55 ± 0.25) and performed more accelerations (ES = -0.71 ± 0.25). While a greater frequency of BIP sequences > 90 s duration was observed in U20s versus U18s match-play, U18s covered more total distance and high-speed distance (ES = -0.42 ± 0.13 and -0.33 ± 0.13 respectively) per minute during these longer sequences. During peak rolling phases shorter than 4 minutes, no clear differences existed between age categories in running activity, while U20 forwards performed more contact actions than U18 peers. The match-play loads observed in the present international U18 players suggest that they are ready to respond to the overall and peak demands observed in U20 competition. Moreover, the present information on peak activity phases can aid design of overload high-intensity conditioning sessions to respond to the running- and contact-demands identified in those competitions.
... Dans ce contexte, les demandes physiologiques de ce sport ont largement été étudiées (Cummins et al., 2013;Deutsch et al., 2007;Lacome et al., 2014;Roberts et al., 2008), permettant une meilleure compréhension des contraintes auxquelles sont soumis les joueurs au cours du match. Ces différentes études avaient notamment fait apparaître la capacité à répéter des sprints (i.e. ...
... avants et arrières). LaEnsemble, tous les éléments qui constituent le test doivent pouvoir, dans la mesure du possible, simuler un ratio E :R correspondant à celui que les athlètes vont rencontrer dans leur discipline(Lacome et al., 2014). Une nouvelle fois, si les expérimentateurs décident de proposer un test générique unique et compte tenu des différences entre les postes, cela induira certainement une sur-stimulation de certains joueurs à prendre en compte au moment de l'analyse des résultats.Comme présenté dans la section précédente, un test de répétition d'effort est composé d'enchainements de sprints et de placages, exécutés de façon à reproduire les plus grandes contraintes physiologiques auxquels peuvent être soumis les joueurs au cours du match.Ce sont les indices utilisés pour évaluer la performance au cours d'un test RSA qui sont également utilisés pour évaluer la performance en sprint au cours d'un test RHIE. ...
Thesis
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The repeated sprint ability (RSA) was considered as a major physical determinant of performance in rugby union. However, some studies from rugby league highlighted that the simple RSA is not sufficiently representative of the physical constraints of the sport and does not prepare properly the players to the game. In this context, the ability to repeat high intensity efforts (RHIE) is suggested as a physical quality more specific to rugby union and thus more discriminant of the performance. The RHIE topic is address in 3 different steps : the evaluation, the development and the optimization. In a first study, the assessment of metrological properties of key outcomes from sprint and tackle performance is made using a RHIE test, specifically modified to represent the physical demands of rugby union. Results show that only sprint indices have a sufficient level of reliability to be used with players. Measures of tackle intensity are too variable for an appropriate interpretation. However, this test allows practitioners to identify the physical qualities associated with RHIE, in order to prescribe coherent development strategies with rugby union players. This topic is discussed during the second study. In this context, body composition, maximal sprinting speed and aerobic capacity are the major performance determinants of the RHIE. Therefore, they should be integrated to specific strength and conditioning programs in rugby union. To verify this hypothesis is the aim of the third study, during which an improvement in RHIE ability is observed after a training block composed of an integrated high intensity interval method. Furthermore, results show that coaches or athletes could benefit from a training methodology based on the alternation of contacts and movements, without limiting the adaptation process. The third part of this thesis focus on the RHIE optimization specially to prepare key games or playoffs, periods during which a taper strategy seems to be preferred by coaches. However, the meta-analysis and review of literature performed during the fourth study of this thesis highlight that although a taper is effective to improve neuromuscular and cardiovascular qualities, there is no information available concerning the RHIE ability. In this context, the fifth study consists in the implementation of a taper strategy following an overload training block, with a focus on the influence of the pre-taper fatigue level on the RHIE supercompensation process. Results confirm the improvement of RHIE after the taper, and highlight an inverted U relationship between the pre-taper fatigue level and the magnitude of improvement in performance. Despite minor performance consequences, players on the left side of the relationship do not benefit from the taper due to a too small accumulated fatigue level. However, the situation of those on the right side of the relationship is more problematic. These players do not benefit from the taper due to an incomplete recovery provoked by a too severe state of accumulated fatigue considering the taper implemented. This phenomenon could be observed during short-term taper, often the only solution available within the context of professional sport. By including sleep quality as a moderator of the taper benefits, results of the sixth study show that poor sleep quality predispose athletes to a severe state of accumulated fatigue and therefore to a reduced taper efficiency with a higher risk of injury and upper respiratory tract infections. This thesis is based on scientific studies providing key information to coaches wishing to focus on the evaluation, development and optimization of their players’ repeated high intensity efforts ability. This work leads to key practical applications, which should guide coaches in their understanding of the RHIE.
... Collected data can be used by trainers and coaches to plan and implement programmes that elicit physiological adaptations specific to the demands of a game (2, 5). There has been extensive research determining the physical demands of rugby in professional male players using time and motion analysis, and GPS (2, [6][7][8][9][10][11][12][13][14]. A study by Jones et al. (5) using GPS found professional European male rugby players typically covered between 3698 m to 6436 m during a game depending on position. ...
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The demands of national and international professional rugby union matches are well established, however, there has not been a comparative study investigating running demands in New Zealand teams playing in club (amateur), Heartland Championship (semi-professional Div 2), the Mitre 10 Cup (semi-professional Div 1) or Super Rugby (professional) competitions. This information could enable specific training and rehabilitation that programmes to be developed to meet the needs of players in the different competitions. Players wore 10 Hz GPS units during games for one rugby season to determine absolute (m) and relative (m.min−1) measures for total distance, running volume (∼≥7 km·h−1) and high intensity running (∼≥16 km·h−1). There were typically minimal differences (1–2 m.min−1) in running distance measures between amateur level front row forwards and inside backs compared to players in these positions at higher levels of competition. Therefore, amateur players in these positions may find the transition to higher competitions less challenging with respect to running load. In contrast, amateur outside backs and back row forwards may find the increased pace of higher levels of competition more challenging due to typically covering significantly less running and high intensity running distances in amateur games. Differences for half backs were more variable between the levels of competition. Based on our results, it cannot be assumed that amateur rugby has lower running demands than higher competitions or that there is a continuum of increased running demands with increasing competition levels, as some playing positions in the semi-professional (Div 2) (second lowest level of competition) team recorded the largest values for total distance, running and high intensity running. Therefore, the specificity of running demands in a position and competition need to be considered individually for each player when transitioning between competitions. The practice and perception of returning a professional player to amateur club rugby due to the belief that running loads being lower may also be flawed, as we found considerable positional variation in running demands within-and-between competitions.
... Furthermore, through the use of micro-captors and video analysis, researchers could observe position-specific activity and determine the physical and physiological demands for each maneuver and/or sailing situation. 38 ...
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Introduction: Evidence regarding the impact of offshore sailing on fatigue and readiness variables is conspicuous by its absence. This study investigated the acute effects of an offshore sailing regatta on anthropometry, muscular performance, subjective recovery, and salivary biomarker cortisol. Methods: Ten professional offshore sailors from a mixed-sex crew partook in the study (N = 10; mean [SD] age = 32.2 [3.96] y; stature = 179.1 [7.30] cm; body mass = 84.2 [12.1] kg). The race involved 3 offshore legs over a 3-week period. Baseline measures of anthropometry, lower- and upper-body muscular function, perceptions of subjective wellness, and salivary cortisol were assessed 3 hours prior to competition (ie, before the first leg). These measures were repeated within 30 minutes after the cessation of each leg. During each leg, boat movements were recorded via global positioning system units. Results: There were significant reductions in lower (effect size [ES] = 0.49) and upper muscular (ES = 0.21) functions, as well as in subjective wellness (ES = 1.65). Salivary cortisol levels increased (ES = 0.84). Conclusion: These results demonstrate that, during an intensified period of sailing competition, fatigue will progressively increase. This may impede sailing performance by reducing physical and cognitive efficiency. Furthermore, countermovement jump, handgrip strength, perception of subjective wellness, and cortisol concentration appear to be sensitive measures for monitoring fatigue and readiness in professional sailors.
... Les systèmes utilisés en milieu extérieur diffèrent de ceux utilisés en milieu intérieur du fait de la plus grande surface de capture, d'une interaction plus grande entre les joueurs et de conditions d'éclairage variables (Barris et Button, 2008). Le plus souvent ces systèmes sont utilisés dans le domaine du football (Ekin et al., 2003;Figueroa et al., 2006;Taki et al., 1996) ou du rugby (Lacome et al., 2014). Les systèmes les plus utilisés sont les systèmes Amisco® (Sport universal process, Nice, France) Il existe beaucoup moins de systèmes de capture utilisables à l'intérieur d'un gymnase (Pers et Kovacic, 2000a,b;Perse et al., 2005;Segen et Pingali, 1996). ...
Thesis
Ce travail de thèse avait pour objectif d'analyser l'influence d'une saison sportive sur les caractéristiques physiques, physiologiques et psychologiques des joueurs de handball du club de Montpellier Agglomération Handball, un des meilleurs clubs européens. Dans un premier temps (Etude 1), nous nous sommes intéressés à l'évolution du profil musculaire isocinétique des membres inférieurs pendant la phase de préparation pré-compétitive (Pc2P). Bien que cette période soit courte (8 semaines), nos résultats montrent que la plupart des valeurs de force, de puissance (à 30±.s¡1, 60±.s¡1 et 240±.s¡1, en concentrique et en excentrique), et des différents ratios (agoniste vs antagoniste, dominant vs non dominant ainsi que le ratio mixte) augmentent significativement pendant Pc2P. Dans un deuxième temps (Etude 2), nous nous sommes intéressés à l'évolution du profil musculaire isocinétique des membres inférieurs pendant la période de compétition. Nos résultats montrent qu'une saison de compétition n'impacte pas significativement l'évolutionde la plupart des paramètres isocinétiques suscités. Enfin, au cours de notre 3e travail, nous avons étudié l'évolution de certains marqueurs (biologiques, physiologiques et psychologiques) au cours d'une saison sportive. Les principaux résultats de nos travaux montrent (i) une baisse des valeurs moyennes de VFC concernant les valeurs de HF et de RMSSD, couplée à une légère augmentation de FC en T4, laissant supposer une baisse de l'activité parasympathique en position couchée, (ii) une augmentation des valeurs au questionnaire d'état de forme en T4 et (iii) une diminution des valeurs de [C]sg , [F]sg , IGF-1 et Hématocrite,respectivement en T5 et T4. Les résultats des valeurs de Testostérone montrent une augmentation significative en T5. Ils ne montrent aucune modification significative des valeurs de CPK et d'IGFBP-3. Ces travaux soulignent la nécessité de développer les qualités de force et de puissance le plus efficacement possible pendant Pc2P et de cibler les marqueurs les plus pertinents pour le suivi longitudinal des joueurs de handball
... X (Lacome et al., 2014) Raw position data was filtered using a 4th order low-pass Butterworth filter with 1HZ cutoff frequency ...
Article
The application of acceleration and deceleration data as a measure of an athlete's physical performance is common practice in team sports. Acceleration and deceleration are monitored with athlete tracking technologies during training and games to quantify training load, prevent injury and enhance performance. However, inconsistencies exist throughout the literature in the reported methodological procedures used to quantify acceleration and deceleration. The object of this review was to systematically map and provide a summary of the methodological procedures being used on acceleration and deceleration data obtained from athlete tracking technologies in team sports and describe the applications of the data. Systematic searches of multiple databases were undertaken. To be included, studies must have investigated full body acceleration and/or deceleration data of athlete tracking technologies. The search identified 276 eligible studies. Most studies (60%) did not provide information on how the data was derived and what sequence of steps were taken to clean the data. Acceleration and deceleration data were commonly applied to quantify and describe movement demands using effort metrics. This scoping review identified research gaps in the methodological procedures and deriving and cleaning techniques that warrant future research focussing on their effect on acceleration and deceleration data.
... Most field sports rely on field-based assessments to derive these outcome parameters of interest, coupled with various logistical constraints (4)(5)(6)(7). Although several field tests exist to estimate ̇2 (e.g. ...
Chapter
Although rugby is a contact collision sport, in all its codes the sport involves considerable running activity. Although backs may be perceived to do more high-speed running and sprinting, however forwards often undertake considerable sprinting more frequently from a standing start and with greater weight leading to higher momentum. High-intensity and sprint training must be specific for players of all positions.
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Background Wearable tracking devices are commonly utilised to quantify the external acceleration load of team sport athletes during training and competition. The ability to accelerate is an important attribute for athletes in many team sports. However, there are many different acceleration metrics that exist in team sport research. This review aimed to provide researchers and practitioners with a clear reporting framework on acceleration variables by outlining the different metrics and calculation processes that have been adopted to quantify acceleration loads in team sport research. Methods A systematic review of three electronic databases (CINAHL, MEDLINE, SPORTDiscus), was performed to identify peer-reviewed studies that published external acceleration load in elite team sports during training and/or competition. Articles published between January 2010 and April 2020 were identified using Boolean search phrases in relation to team sports (population), acceleration/deceleration (comparators), and competition and/or training (outcome). The included studies were required to present external acceleration and/or deceleration load (of any magnitude) from able-bodied athletes (mean age ≥ 18 years) via wearable technologies. Results A total of 124 research articles qualified for inclusion. In total, 113/124 studies utilised GPS/GNSS technology to outline the external acceleration load of athletes. Count-based metrics of acceleration were predominant of all metrics in this review (72%). There was a lack of information surrounding the calculation process of acceleration with 13% of studies specifying the filter used in the processing of athlete data, whilst 32% outlined the minimum effort duration (MED). Markers of GPS/GNSS data quality, including horizontal dilution of precision (HDOP) and the average number of satellites connected, were outlined in 24% and 27% of studies respectively. Conclusions Team sport research has predominantly quantified external acceleration load in training and competition with count-based metrics. Despite the influence of data filtering processes and MEDs upon acceleration, this information is largely omitted from team sport research. Future research that outlines acceleration load should present filtering processes, MEDs, HDOP, and the number of connected satellites. For GPS/GNSS systems, satellite planning tools should document evidence of available satellites for data collection to analyse tracking device performance. The development of a consistent acceleration filtering method should be established to promote consistency in the research of external athlete acceleration loads.
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Objectives: The purpose of the study was to quantify the positional movement patterns of professional Rugby Union players competing in the English Premiership. Design: A cross sectional design was used. Setting: Field based data collection of one professional rugby union club during six league matches. Participants: An incidental sample of 35 professional rugby players with an age range of 20-34 years. Method: Recordings of the positional demands, taken from ten image recognition sensors, were coded for the specified high (HI) and low intensity (LI) tasks. Work-to-rest ratios were also calculated. Statistical assessment used an independent groups one-way ANOVA with post-hoc Scheffe test. Results: For all HI and LI activities there were significant position-related differences (P<0.05). In HI activities there were a range of different post-hoc Scheffe outcomes. The Props sprinted 1±1 time during a game while the Outside Backs sprinted 14±5 times. There were fewer post-hoc differences for the LI activities. For example, the Props jogged 325±26 times and the Outside Backs jogged 339±45 times. There was no significant position-related difference in the work-to-rest ratios for the quantity of HI and LI activities (P>0.05). There was, however, a significant positional difference when comparing the work to rest ratio for time spent in HI and LI activities (P<0.05). The Loose Forwards had the least amount of recovery with a work to rest time ratio, in seconds, of 1:7.5 s. The Outside Backs had the most amount of recovery, 1:14.6 s. Conclusions: There were clear positional differences in the quantity and time spent in rugby specific demands. These differences are most obvious in the HI activities of the game and included position-specific differences within both the Forward and Backs units.
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The aim of this study was to examine the effect of match location, quality of opposition, and match status on distance covered at various speeds in elite soccer. Twenty-seven Spanish Premier League matches played by a professional soccer team were monitored in the 2005–2006 season using a multiple-camera match analysis system. The dependent variables were the distance covered by players at different intensities. Data were analysed using a linear regression analysis with three independent variables: match status (i.e. whether the team was winning, losing or drawing), match location (i.e. playing at home or away), and quality of the opponents (strong or weak). The top-class players performed less high-intensity activity (>19.1 km · h) when winning than when they losing, but more distance was covered by walking and jogging when winning. For each minute winning, the distance covered at submaximal or maximal intensities decreased by 1 m (P
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Abstract The purpose of this investigation was to quantify the movement characteristics of elite rugby union players during competitive play and identify whether position-related differences exist. Ninety-eight elite players from eight English Premiership Clubs were tracked using global positioning systems (GPS) during 44 competitive matches throughout the 2010/2011 season. Player positions were defined as: (1) Backs or Forwards; (2) Front, Second and Back Rows, Scrum Half, Inside and Outside Backs; (3) 15 individual positions (numbers 1-15). Analysis revealed the game is predominantly played at low speeds with little distance covered 'sprinting' by either the Backs (50 ± 76 m) or the Forwards (37 ± 64 m). The Backs travelled greater (P < 0.05) absolute and relative distances than the Forwards. The Scrum Half covered the greatest total distance during a match (7098 ± 778 m) and the Front Row the least (5158 ± 200 m). The Back Row covered the greatest distances at 'sprinting' speeds, particularly the number 8 position (77 m). These findings reflect notable differences in the movement characteristics displayed by elite rugby union players in specific positional roles, and reinforce the contemporary view that training programmes for such players ought to be structured with this in mind.
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To investigate the evolution of anthropometric characteristics in World Cup rugby players and identify elements associated with performance. Age, weight and height were collected for 2692 World Cup rugby players as well as rankings in each World Cup, and collective experience of winners, finalists, semifinalists and quarter finalists in comparison to the rest of the competitors. Anthropometric parameters were compared according to age and position (back and forwards). From 1987 to 2007, forwards and backs have become heavier by 6.63 and 6.68 kg and taller by 0.61 and 1.09 cm, respectively. The collective experience of the forwards' pack is a value increasing with the final ranking attained, as well as the weight of forwards and the height of backs. For all Rugby World Cups, the highest performing teams have the tallest backs and heaviest forwards with the highest percentage of collective experience.
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The aim of the present study was to describe the frequency, duration, and nature of repeated high-intensity exercise in Super 14 rugby union. Time-motion analysis was used during seven competition matches over the 2008 and 2009 Super 14 seasons; five players from each of four positional groups (front row forwards, back row forwards, inside backs, and outside backs) were assessed (20 players in total). A repeated high-intensity exercise bout was considered to involve three or more sprints, and/or tackles and/or scrum/ruck/maul activities within 21 s during the same passage of play. The range of repeated high-intensity exercise bouts for each group in a match was as follows: 11-18 for front row forwards, 11-21 for back row forwards, 13-18 for inside backs, and 2-11 for outside backs. The durations of the most intense repeated high-intensity exercise bouts for each position ranged from 53 s to 165 s and the minimum recovery periods between repeated high-intensity exercise bouts ranged from 25 s for the back row forwards to 64 s for the front row forwards. The present results show that repeated high-intensity exercise bouts vary in duration and activities relative to position but all players in a game will average at least 10 changes in activity in the most demanding bouts and complete at least one tackle and two sprints. The most intense periods of activity are likely to last as long as 120 s and as little as 25 s recovery may separate consecutive repeated high-intensity exercise bouts. The present findings can be used by coaches to prepare their players for the most demanding passages of play likely to be experienced in elite rugby union.
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The purpose of the present study was to describe the match-play demands of professional rugby union players competing in Super 14 matches during the 2008 and 2009 seasons. The movements of 20 players from Super 14 rugby union team during the 2008 and 2009 seasons were video recorded. Using time-motion analysis (TMA), five players from four positional groups (front-row forwards, back-row forwards, inside backs and outside backs) were assessed. Players covered between 4218 m and 6389 m during the games. The maximum distances covered in a game by the four groups were: front row forwards (5139 m), back row forwards, (5422 m), inside backs (6389 m) and outside backs (5489 m). The back row forwards spent the greatest amount of time in high-intensity exercise (1190 s), followed by the front row forwards (1015 s), the inside backs (876 s) and the outside backs (570 s). Average distances covered in individual sprint efforts were: front row forwards (16 m), back row forwards (14 m), inside backs (17 m) and outside backs (18 m). Work to rest ratios of 1:4, 1:4, 1:5, and 1:6 were found for the front row and back row forwards, and inside and outside backs respectively. The Super 14 competition during 2008 and 2009, have resulted in an increase in total high-intensity activities, sprint frequency, and work to rest ratios across all playing positions. For players and teams to remain competitive in Super 14 rugby, training (including recovery practices) should reflect these current demands.
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The purpose of this study was to analyse the activity profile of players in a top-class team in the Italian national football league over the course of a season (n=388). The effect of playing position and the two halves on the number and duration of short, intense bursts of effort and recovery phases was studied. The main results show that midfielders cover significantly more distance than players in other positions (p<0.001). For midfielders, the number of displacements of 2-40 m and the number of sprints covering between 2 and 9 m and between 30 and 40 m are considerably greater than for other positions (p<0.05). The distances covered in the second half compared to the first half are significantly lower for all categories of run (p<0.05). In the second half, the distance covered at very high intensity is significantly lower (p<0.01), whilst the number of recovery times greater than 120 s increases significantly compared to the first half (p<0.01). This study provides data which could be used as a basis for the work of scientists as well as football professionals.
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Abstract The aim of the study was to evaluate changes in the stature, body mass, age and number of players by playing position in the first team squads of English Premiership rugby union teams from 2002 to 2011. Medical personnel at each club reported the individual data for every first team squad player. The average annual number of players included in the study was 485.2 players per season (standard deviation: 58.0). The mean stature of players in all positions increased in the period 2002 to 2011 but statistically significant trends (P < 0.01) were only observed at fly half and prop. While the mean body mass of players increased in most positions only fly half and back row players showed statistically significant (P < 0.01) upward trends. Apart from second row forwards, the average age of players in all positions decreased but this trend was only significant (P < 0.01) at prop. The numbers of registered players in every position increased but these trends were only significant (P < 0.01) at prop. English Premiership professional rugby players are generally getting taller, heavier and younger but statistically significant changes were limited to fly halves (taller and heavier), props (taller and younger) and back row forwards (heavier).
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It is an implicit assumption in notational analysis that in presenting a performance profile of a team or an individual that a ‘normative profile‘ has been achieved. Inherently this implies that all the variables that are to be analysed and compared have all stabilised. Most researchers assume that this will have happened if they analyse enough performances. But how many is enough? In the literature there are large differences in sample sizes. Just trawling through some of the analyses in soccer shows the differences (Table 1). Establishing normative profiles in performance analysis All authors Mike Hughes, Steve Evans & Julia Wells https://doi.org/10.1080/24748668.2001.11868245 Published online 03 April 2017 Table 1 Some examples of sample sizes for profiling in sport CSVDisplay Table There must be some way of assessing how data within a study is stabilising. The nature of the data itself will also effect how many performances are required - 5 matches may be enough to analyse passing in field hockey, would you need 10 to analyse crossing or perhaps 30 for shooting? The way in which the data is analysed also will effect the stabilisation of performance means - data that is analysed across a multi-cell representation of the playing area will require far more performances to stabilise than data that is analysed on overall performance descriptors (e.g. shots per match). It is misleading to test the latter and then go on to analyse the data in further detail. This study aimed to explore strategies in solving these problems in two sports, squash and badminton, in depth and then present further examples from a multiplicity of types of sports. A computerised notation system (Brown and Hughes, 1995) was used to record and analyse play, post event, for elite (N=20), county (N=20) and recreational (N=20) players. T-tests were used to examine the inter- and intra-reliability of the data collection processes. In addition, to establish that a normative profile had been reached, the profiles of 8 matches were compared with those of 9 and 10 matches, using dependent t-tests, for each of the categories of players. This method clearly demonstrated that those studies assuming that 5, 6 or 8 matches or performances were enough for a normative profile, without resorting to this sort of test, are clearly subject to possible flaws. The number of matches required for a normal profile of a subject population to be reached is dependent upon the narure of the data and, in particular, the nature of the performers. A notation system, designed to record rally-end variables in Badminton, was shown to be both valid and reliable. Inter and intra reliability ranged from 98.6% (Rally length) to 91.3% (Position). Percentage differences between data from side, and end observations of the same match were not greater than for the intra-reliability data thus different court viewing angles had little effect on notation. Previous literature declared profiles of performance without adequately tackling the problem of quantifying of the data required in creating a normative template. The badminton notation system was used to examine the cumulative means of selected variables over a series of 11 matches of a player. A template, at match N (E), was established when these means became stable within set limits of error (LE). T-tests on the variable means in games won, and games lost established the existence of winning and losing templates for winners and errors. Match descriptors (rallies, shots and shots per rally) were independent of match outcome. General values of N(E) established for data types, (10% LE), were 3 matches (descriptive variables), 4 (winners/errors (w/e), 6 (smash + w/e), 7 (position + w/e). Respective values at 5% LE were 7, 5, 8 and 10. There was little difference in the values of N (E) when variable means were analysed by game than by match. For the working performance analyst the results provide an estimate of the minimum number of matches to profile an opponent’s rally-end play. Whilst these results may be limited to badminton, men’s singles and the individual, the methodology of using graphical plots of cumulative means in attempting to establish templates of performance has been served. Further examples will be presented from different sports. For the working performance analyst the results provide an estimate of the minimum number of matches to profile an opponent’s rally-end play. Whilst the results may be limited to badminton, men’s singles and the individual, the methodology of using graphical plots of cumulative means in attempting to establish templates of performance has been served.
Article
Unlabelled: In rugby union, published analyses of actions and movements of players during matches have been limited to small samples of games at regional or national level. Objectives: To analyse movements and activities of players in international rugby union matches with a sample size sufficient to clearly delineate positional roles. Design: Observational study. Methods: Actions of 763 players were coded from video recordings of 90 international matches played by the New Zealand national team (the All Blacks) from 2004 to 2010. Movements of players were coded for 27 of these matches via a semi-automated player-tracking system. Movements and activities of all players from both teams were coded. Results: Cluster analysis of activities and time-motion variables produced five subgroups of forwards (props, hookers, locks, flankers, Number 8 forwards) and five subgroups of backs (scrum-half, fly-half, midfield backs, wings and fullbacks). Forwards sustained much higher contact loads per match than backs, via scrums, rucks, tackles and mauls. Mean distance covered per match ranged from 5400 to 6300m, with backs generally running further than forwards. There were marked differences between positional groups in the amount of distance covered at various speeds. The amount of play per match varies by position due to differences in rates at which players are substituted. Conclusions: The distance covered by players at relatively fast running speeds (in excess of 5ms(-1)) appears to be higher during international matches than when competing at lower levels of the professional game. The specific match demands for positional groups need to be considered when managing player workloads.