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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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... However, forwards (12%) spent more time engaged in high-intensity activities than the backs (4%). Lacome et al. [19], who evaluated the physical demands of rugby, found that 40% of movement during a match was of moderate intensity. This may be due to multiple factors (such as the game plan and weather conditions) that can contribute to a change in movement and collisions in a match, as reported by Schoeman et al. [14]. ...
... Lacome et al. [19] noted that activities of high intensity cause fatigue. In agreement, Benardot [20] stated a well-established aerobic capacity and aerobic endurance are required by rugby players, which can only be fueled by macronutrients. ...
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Background Rugby union is a high-contact team sport where professional rugby players are exposed to considerable training and game loads in pre-season and in-season. Some studies have shown that rugby players’ dietary intake remains inadequate for the three macronutrients (carbohydrates [CHO], proteins and fats) required for optimal performance. This study aimed to describe the macronutrient intake of professional male rugby players at Zebre Rugby Club in Parma, Italy, during in-season, and to compare players’ macronutrient intake to international recommendations. Methods Thirty-four professional male rugby players participated in the cross-sectional study. A self-developed questionnaire, a food frequency questionnaire and food records (on training and competition days and off day) were used to investigate players’ macronutrient intake. Anthropometric measurements were obtained using the International Society for the Advancement of Kinanthropometry (ISAK) standardized techniques. Descriptive statistics were calculated, and associations were investigated using chi-square, Fisher’s exact and Wilcoxon rank tests as applicable. Results The players’ median age was 25.8 years (range 20.6–33.0 years) and 47.5% were Italian. Most players (64.7%) held forward positions and had a median of 5 years (range 2–14 years) of professional experience. More than 75.0% of players lived with a spouse or partner and 30.3% earned between 4 000–4 999 euros per month. The median body weight and height of players were 106.9 kg and 186.3 cm, respectively. The forwards weighed heavier (p < 0.0001) than the backs, which was expected due to positional demands, with no significant difference in height distribution. The median body mass index (p < 0.0001), waist circumference (p < 0.001) and waist-to-height ratio (p < 0.03) of forwards were higher than the backs. Additionally, the median body fat percentage of all players exceeded the international recommendation of 8–17% for rugby union players. The American College of Sports Medicine (ACSM), International Olympic Committee (IOC) and International Society of Sports Nutrition (ISSN) recommend an intake of 5.0–8.0 g/kg body weight (BW)/day CHO, 1.5–2.0 g/kg BW/day proteins and 20–35% total energy (TE) from fats for rugby players. The overall median intake of the three-day food records for all the players was 2.7 g/kg BW CHO, 1.7 g/kg BW protein and 35.1% TE from fat. On each of the three reported days, 90.0% of players’ CHO intake fell below the recommended range, with almost all players (>90.0%) consuming less than the recommended amount of carbohydrates and almost 30.0% of players consuming below the recommended amount of protein on competition day. At least 50.0% of players’ protein and fat intake was within the recommended range on each of the three reported days. Conclusion The study’s findings can assist various stakeholders at Zebre Rugby Club to align rugby players’ dietary requirements to their workload, and encourage players’ adherence to dietary guidelines and recommendations. It is advised that attention be focused on accurate dietary education, intake and monitoring to promote individualization and optimal performance and recovery. Future research is needed to adapt standardized macronutrient recommendations for rugby-specific requirements and address obstacles that may impede the optimal intake of macronutrients.
... However, the duration of the WAnT is significantly greater than maximal neuromuscular efforts in team and court sports. Individual effort duration in Rugby Union is typically less than 4 s at all intensities (Lacome et al., 2013). An alternative assessment, the six-second peak power test on a cycle ergometer (6sCS) has increased in popularity as a testing and training tool (Cushman et al., 2018;Jones et al., 2019;Wehbe et al., 2015). ...
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The purpose of this study was to determine relationships between traditional tests of maximal and ballistic strength, with the results of a 6 s cycle sprint (6sCS) in international level Rugby Union (Rugby) players. Thirty-three international level male Rugby players participated in the study. Each player completed the 6sCS, sprint run, standing long jump, weighted and unweighted countermovement jumps, and a 1RM squat test. Pearson’s correlations were carried out to determine relationships between absolute (PPO) and relative peak power output (relPPO) from the 6sCS with the other tests of maximal and ballistic performance for the whole population and for positional groups. For the cohort, significant correlations (p≤0.05) between relPPO and various measures of speed (r=0.63-0.73) and jump performance (r=0.48 to 0.53) were observed. In the Backs, there were large, significant relationships with weighted countermovement jump, standing long jumps, and 10 m sprint time (r=0.58 to 0.74). Large significant correlations were found with sprint and standing long jump performance in the Forwards (r=0.54 to 0.82). These significant correlations are most likely due to similarity in duration, energy system requirements, contraction types, and similarities in muscle groups recruited. Differences between position groups may reflect the physical qualities players possess to meet game demands. The study suggests that 6sCS may be a valuable addition to existing testing to evaluate maximal and ballistic intensity performancebenchmark levels of these physical capacities in elite RU players.
... Such training allows players to limit performance decrements in intense neuromuscular efforts (e.g., the sled push) and thus to be less affected by the repetition of contact efforts in game (as observed with the significant reduction of SPdec for RHIE training in our study). By contrast, RSE training, exclusively based on high-intensity running repetition, leads to metabolic fatigue related to the decrease in PCr, inorganic phosphate accumulation, and H 1 increase (30). This method allows players to increase their average velocity in high-intensity efforts, such as sprints, which facilitates improvements in acceleration and sprinting repetition in rugby union games. ...
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Glaise, P, Rogowski, I, and Martin, C. Effects of repeated high-intensity effort training or repeated sprint training on repeated high-intensity effort ability and in-game performance in professional rugby union players. J Strength Cond Res XX(X): 000–000, 2023—This study investigated the effects of repeated high-intensity efforts (RHIE) training compared with repeated sprint exercise (RSE) training on RHIE ability (RHIEa) and in-game performance in professional rugby union players. Thirty-nine, male, professional, rugby union players were randomly assigned to 3 training groups (RHIE training, RSE training, and control). Repeated high-intensity effort ability and high-intensity effort characteristics (including sprints, acceleration, and contact efforts) during official games were measured before and after a 10-week specific (RHIE, RSE, or control) training period. The results of this study showed that concerning RHIEa, both the RHIE and RSE training significantly increased the players' average sprint velocity ( p < 0.001, d = −0.39 and p < 0.001, d = −0.53 respectively), average sled push velocity (ASPV; p < 0.001, d = −0.81 and p = 0.017, d = −0.48 respectively), and RHIE score ( p < 0.001, d = −0.72 and p < 0.001, d = −0.60 respectively). Repeated high-intensity effort training trended in a smaller increase in average sprint velocity than RSE training, a larger increase in ASPV, and a similar increase in RHIE score. Concerning in-game high-intensity efforts, both the RHIE and RSE training produced significant improvements in the number of sprints ( p = 0.047, d = −0.28 and p < 0.001, d = −0.47 respectively), total distance ( p < 0.001, d = −0.50 and p = 0.002, d = −0.38 respectively), the number of accelerations ( p < 0.001, d = −0.37 and p = 0.003, d = −0.32 respectively), and contact rate ( p < 0.001, d = −0.97 and p = 0.020, d = −0.28 respectively). Conversely, the magnitude of the increase in contact rate was almost twice as high in RHIE compared with RSE training. To conclude, the findings of this study were that both RSE and RHIE training are effective methods for developing RHIEa and in-game high-intensity efforts in professional rugby union. In practical applications, as the gains in certain abilities and game performance data differed depending on the training method chosen, we suggest that coaches choose the most appropriate method according to the profile of the players, their position, and the style of play they want to develop.
... In invasion and combat sports such as rugby union, the ability to cover a distance in the shortest possible time (or the largest distance in a given time) is a key determinant (e.g., for breaking the line, avoiding or tackling an opponent, scoring a try), independently of the player level or position [1][2][3][4][5]. Examining such acceleration capabilities through velocity-time measurements and force-velocity profiling [6][7][8] is of paramount importance to individualize players' training process [6,9,10]. ...
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Background Recently a proof-of-concept was proposed to derive the soccer players’ individual in-situ acceleration-speed (AS) profile from global positioning system (GPS) data collected over several sessions and games. The present study aimed to propose an automatized method of individual GPS-derived in-situ AS profiling in a professional rugby union setting. Method AS profiles of forty-nine male professional rugby union players representing 61.5 million positions, from which acceleration was derived from speed during 51 training sessions and 11 official games, were analyzed. A density-based clustering algorithm was applied to identify outlier points. Multiple AS linear relationships were modeled for each player and session, generating numerous theoretical maximal acceleration (A0), theoretical maximal running speed (S0) and AS slope (ASslope, i.e., overall orientation of the AS profile). Each average provides information on the most relevant value while the standard deviation denotes the method accuracy. In order to assess the reliability of the AS profile within the data collection period, data were compared over two 2-week phases by the inter-class correlation coefficient. A0 and S0 between positions and type of sessions (trainings and games) were compared using ANOVA and post hoc tests when the significant threshold had been reached. Results All AS individual profiles show linear trends with high coefficient of determination (r² > 0.81). Good reliability (Inter-class Correlation Coefficient ranging from 0.92 to 0.72) was observed between AS profiles, when determined 2 weeks apart for each player. AS profiles depend on players’ positions, types of training and games. Training and games data highlight that highest A0 are obtained during games, while greatest S0 are attained during speed sessions. Conclusions This study provides individual in-situ GPS-derived AS profiles with automatization capability. The method calculates an error of measurement for A0 and S0, of paramount importance in order to improve their daily use. The AS profile differences between training, games and playing positions open several perspectives for performance testing, training monitoring, injury prevention and return-to-sport sequences in professional rugby union, with possible transferability to other sprint-based sports. Key Points AS profiles computed from rugby union GPS data provide positional benchmarks during training and competition. This study provides automatic detection of atypical data and the computation of error measurement of theoretical maximal acceleration and speed components. This refinement constitutes a step forward for a daily use of ecological data by considering data collection and method reliabilities. This easy-to-implement approach may facilitate its use to the performance management process (talent identification, training monitoring and individualization, return-to-sport).
... In invasion and combat sports such as rugby union, the ability to cover a distance in the shortest possible time (or the largest distance in a given time) is a key determinant (e.g., for breaking the line, avoiding or tackling an opponent, scoring a try), independently of the player level or position [1][2][3][4][5]. ...
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Background: Recently a proof-of-concept was proposed to derive the soccer players’ individual in-situ acceleration-speed (AS) profile from global positioning system (GPS) data collected over several sessions. The present study aimed to validate an automatized method of individual GPS-derived in-situ AS profiling in professional rugby union setting. Method: AS profiles of forty-nine male professional rugby union players representing 61.5 million positions, from which acceleration was derived from speed during 51 training sessions and 11 official games, were analyzed. A density-based clustering algorithm was applied to identify outlier points. Multiple AS linear relationships were modeled for each player and session, generating numerous theoretical maximal acceleration (A0), theoretical maximal running speed (S0) and AS slope (ASslope, i.e., overall orientation of the AS profile). Each average provides information on the most relevant value while the standard deviation denotes the method accuracy. In order to assess the reliability of the AS profile within the data collection period, data were compared over two 2-weeks phases by the inter-class correlation coefficient. A0 and S0 between positions and type of sessions (trainings and games) were compared using ANOVA and post hoc tests when the significant threshold had been reached. Results: All AS individual profiles show linear trends with high coefficient of determination (r² > 0.81). Good reliability (Inter-class Correlation Coefficient range between 0.92, to 0.72) was observed between AS profiles, when determined 2 weeks apart for each player. AS profiles depend on players’ positions, types of training and games. Training and games data highlight that highest A0 are obtained during games, while greatest S0 are attained during speed sessions. Conclusions: This study provides individual in-situ GPS-derived AS profiles with automatization capability. The method calculates an error of measurement for A0 and S0, of paramount importance in order to improve their daily use. The AS profile differences between training, games and playing positions open several perspectives for performance testing, training monitoring, injury prevention and return-to-sport sequences in professional rugby union, with possible transferability in other sprint-based sports.
... However, to successfully structure rehabilitation and an RTP and RTPerf decision-making process, it is critical to understand the demands of the game (Eaton & George, 2006;Gabbett et al., 2016;Buckthorpe et al., 2019). Such understanding has recently been advanced by techniques that include video analysis, time-motion analysis and heart rate monitoring during matches and practice (Lacome et al., 2014;Gabbett, 2016). Since rugby union is a team sport associated with high velocity impact, high injury rates have been reported on all levels of the sport (Williams et al., 2016;Williams et al., 2017;Fuller et al., 2011). ...
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Models and guidelines on factors associated with the safe return to play (RTP) of an injured athlete have been established, but very limited research has been conducted on components necessary for returning an athlete to their previous level of performance, known as return to performance (RTPerf). The study aimed to establish guidelines applicable to RTP and RTPerf in rugby union. A mixed-methods study design using an e-Delphi survey was conducted to obtain the opinions of medical team members of the Currie Cup rugby unions across South Africa on RTP (Part 1 of the study). In Part 2, medical team members and coaches of the Free State Rugby Union were consulted for RTPerf guidelines. Part 1 of the study comprised a three-step decision-based RTP model used to identify RTP components in rugby. The e-Delphi questionnaire was compiled based on literature analyses and vast experience of the authors. Part 1 involved three steps of integrated guidelines for RTP decision-making in rugby union established by agreement (>80%) (first or second round): Step 1: medical history; Step 2: evaluation of participation risk; and Step 3: decision modifiers. Part 2 focused on components to consider during the RTPerf decision-making process, including psychological readiness, limb symmetry index, acute:chronic training load, external load and internal load. Twelve key performance indicators (KPIs) to measure RTPerf in rugby reached consensus (>80%). The comparison of performance profiles and current KPIs of a rugby player could be used to evaluate the player's performance level and whether they truly achieved RTPerf.
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Abstract Objective: Soccer is an attractive and popular team sport that has high physiological and fitness stress, and therefore requires special and controlled training programs during the season. The aim of this study was to describe the weekly average and changes in training monotony (TM) and training strain (TS) throughout different periods of the season in professional football players based on the number of accelerations and decelerations, and also to analyze the difference between starters and non-starters players in TM and TS. Research Methodology: Nineteen professional players from a soccer team competing in the Iranian Premier League (age, 28 ± 4.6 years; height, 181.6 ± 5.8 cm; body mass, 74.5 ± 5.6 kg, and body mass index, 21.8 ± 1.0 kg/m2 ) participated in a cohort study. Participants were divided into two groups based on the time of participation in the weekly competition: starters (N = 10) or non-starters (N = 9). The physical activities of the players were recorded during the training sessions and competitive matches of 43 weeks using GPSPORTS systems Pty Ltd. During pre- and end-season TS was not significantly different between starters and non-starters, while during early- and mid-season starters showed a higher TS than non-starter (p < 0.05). Results: TS was higher during early- and mid-season compared to pre- and end-season. In all zones on both the TM and TS variables, non-starters experienced higher change percentages and coefficient of variation. TM during the season in all zones of accelerations was not significantly different between starters and non-starters. while during mid-season starters showed a higher TM than non-starters in all zones of decelerations (p < 0.05). TM data showed fluctuations and w-shaped graphs in the week-by-week survey. Conclusion: These results indicate that training during early- and mid-season is not enough for the physical development of non-starters soccer players. Coaches should be more careful when designing training for non-starters players, and they could consider the use of game simulation, preparatory match or intra-team match, or individual training programs.Keywords: acceleration; deceleration; external monitoring; periodization; performance; GPS
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Abstract Objective: The excessive and rapid increases in training load (TL) may be responsible for most non-contact injuries in soccer. This study’s aims were to describe, week(w)-by-week, the acute (AW), chronic (CW), acute: chronic workload ratio (wACWR), total distance (wTD), duration training (wDT), sprint total distance (wSTD), repeat sprint (wRS), and maximum speed (wMS) between starter and non-starter professional soccer players based on different periods (i.e., pre-, early-, mid-, and end-season) of a full-season (Persian Gulf Pro League, 2019–2020). Research Methodology: Nineteen players were divided according to their starting status: starters (n = 10) or non-starters (n = 9). External workload was monitored for 43 weeks: pre- from w1–w4; early- from w5–w17; mid- from w18–w30, and end-season from w31–w43. Results: In starters, AW, CW, and wACWR were greater than non-starters (p < 0.05) throughout the periods of early- (CW, p ≤ 0.0001), mid- (AW, p = 0.008; CW, p ≤ 0.0001; wACWR, p = 0.043), or end-season (AW, p = 0.035; CW, p = 0.017; wACWR, p = 0.010). Starters had a greater wTD (p ≤ 0.0001), wSTD (p ≤ 0.0001 to 0.003), wDT (p ≤ 0.0001 to 0.023), wRS (p ≤ 0.0001 to 0.018), and wMS (p ≤ 0.0001) than non-starters during early-, mid-, and end-season. Conclusion: Starters experienced more CW and AW during the season than non-starters, which underlines the need to design tailored training programs accounting for the differences between playing status Keywords: external training load; technology; soccer; performance; wearable inertial measurement units
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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.