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Scand J Med Sci Sports 2016; 1–8	 wileyonlinelibrary.com/journal/sms	   |  1© 2016 John Wiley & Sons A/S.
Published by John Wiley & Sons Ltd
Accepted: 14 November 2016
DOI: 10.1111/sms.12816
O R I G I N A L A R T I C L E
Attacking 22 entries in rugby union: running demands and
differences between successful and unsuccessful entries
P. Tierney1,2
  |  D. P. Tobin1
  |  C. Blake2
  |  E. Delahunt1,3
1
Leinster Rugby, Dublin, Ireland
2
School of Public Health, Physiotherapy and
Sports Science, University College Dublin,
Dublin, Ireland
3
Institute for Sport and Health, University College
Dublin, Dublin, Ireland
Correspondence
Peter Tierney, Leinster Rugby, Dublin, Ireland.
Email: peter.tierney@leinsterrugby.ie
Global Positioning System (GPS) technology is commonly utilized in team sports,
including rugby union. It has been used to describe the average running demands of
rugby union. This has afforded an enhanced understanding of the physical fitness
requirements for players. However, research in team sports has suggested that train-
ing players relative to average demands may underprepare them for certain scenarios
within the game. To date, no research has investigated the running demands of at-
tacking 22 entries in rugby union. Additionally, no research has been undertaken to
determine whether differences exist in the running intensity of successful and unsuc-
cessful attacking 22 entries in rugby union. The first aim of this study was to describe
the running intensity of attacking 22 entries. The second aim of this study was to
investigate whether differences exist in the running intensity of successful and un-
successful attacking 22 entries. Running intensity was measured using meters per
minute (m min−1
) for (a) total distance, (b) running distance, (c) high-­speed running
distance, and (d) very high-­speed running distance. This study provides normative
data for the running intensity of attacking 22 entries in rugby union. Forwards
achieved greater high-­speed running intensity in successful (3.6 m min−1
) compared
to unsuccessful (1.8 m min−1
) attacking 22 entries. Forwards should try and achieve
greater high-­speed running intensity in attacking 22 entries to increase the likelihood
of successful outcomes during this period of gameplay.
K E Y W O R D S
football, physical fitness, running, sports
1  | INTRODUCTION
Global Positioning System (GPS) technology has been used
extensively to quantify the average running demands of
rugby union.1–3
Despite the volume of research using GPS
technology in rugby union, no studies have reported simulta-
neously on GPS metrics and performance. In rugby league,
it has been reported that condensed periods of repeated high-­
intensity efforts (RHIE) are common prior to scoring or
conceding a try.4
Also, at varying levels of rugby league com-
petition, it has been reported that lower level teams perform
more RHIE prior to scoring than higher level teams, while
higher level teams complete more RHIE prior to conceding
a try.5
An increased playing intensity, as quantified by an in-
crease in RHIE, often occurs prior to a try being scored in
rugby league,4
but this has yet to be explored in rugby union.
To date, no research in rugby union has attempted to relate
GPS-­derived running metrics to the likelihood of a positive
outcome.
Research has reported on the average running demands of
rugby union games, using GPS technology.1,2,6
Using meters per
minute (m min−1
) as a measure of running intensity, research
has reported similar findings when using total distance (TD); an
average of 65 m min−1
, 69 m min−1
, and 65 m min−1
has been
2 
|     TIERNEY et al.
reported by Jones et al.,2
Cunniffe et al.,1
and Cunningham
et al.,7
respectively. An average of 76 m min−1
and 81 m min−1
has also been reported.6,8
The averages reported by Lindsay
et al.8
and Reardon et al.6
were derived from distance per game
time minutes, which accounts for the higher m min−1
compared
to previous research. Average running game demands do not
reflect the demands of the most intense periods of gameplay.
Unpublished data from our research group has observed that
long periods of ball-­in-­play have a higher running intensity
(125 m min−1
) compared to average running demands.
Attacking 22 entries in rugby union are of key importance;
teams must pass through the opposition 22 zone (complete
a successful attacking 22 entry) to score a try. It has been
shown in previous research that winning teams score signifi-
cantly more tries compared to losing teams.9
Research has
investigated the results of rugby games with winning teams
partaking in significantly greater attacking 22 entries than
losing teams.10
Rather logically, winning teams achieved
points more frequently than losing teams for each attacking
22 entry.10
Despite the knowledge of how important attacking
22 entries are in rugby union, there remains a lack of research
investigating potential ways to increase the likelihood of suc-
cess in these scenarios. Methods that need investigation may
include, but are not limited to, physical output, skill execu-
tion, and style of play.
It is expected that an opposition defense would be more
likely to succumb to attacking pressure following repeated
defensive efforts. The logic is to force the opposition into
more RHIE, causing fatigue and therefore an increased
likelihood of conceding a try or penalty. Unpublished data
from our research group has observed that increased running
distance and total distance are associated with an increased
number of open-­play involvements. It is plausible that higher
GPS-­derived running metrics would increase involvements in
attacking 22 entries, thus increasing the likelihood of a pos-
itive outcome.
The first aim of this study was to describe the running
intensity of attacking 22 entries and compare these to average
game demands. The second aim of this study was to investi-
gate whether differences exist in the running intensity of suc-
cessful and unsuccessful attacking 22 entries. The hypothesis
was that higher GPS-­derived running intensity would result
in an increased likelihood of successful attacking 22 entries.
The running demands of attacking 22 entries described
in this study will provide practitioners with comparative
metrics, which could be utilized to guide training intensity
in attacking 22 entries. It may be recommended that practi-
tioners ensure that athletes are above the intensity of attack-
ing 22 entries in training, to ensure they are prepared for such
scenarios in competition. Should differences be identified in
successful vs unsuccessful attacking 22 entries, this would
provide important metrics for practitioners to focus attention
on with regard to this specific scenario in rugby union.
2  | METHODS
2.1  | Subjects
Forty-­three professional rugby union players were recruited for
this study (age=27.8 ± 4.1 years; height=1.86 ± 0.07 m; body
mass=104.5 ± 12.4 kg). The 43 players provided 470 GPS
files from 11 games, in both the domestic league (Guinness
Pro12) and the European Cup (European Champions Cup) dur-
ing the 2015/2016 season. The average number of attacking
22 entries analyzed from each game was 3 (SD ± 1). The 43
players were subcategorized into position (number of players
in each position): prop (n=8), hooker (n=4), second row (n=5),
back row (n=9), scrum half (n=3), fly half (n=3), centre (n=3),
back three (n=8). The breakdown of GPS files from each po-
sition (number of files) was as follows: prop (n=64), hooker
(n=32), second row (n=60), back row (n=96), scrum half
(n=31), fly half (n=38), centre (n=41), back three (n=108). Of
the 11 games, seven games were won and four games were lost.
Ethical approval for data collection on these players was ap-
proved by the University Human Research Ethics Committee.
2.2  | Procedures
All players wore an individual GPS microtechnology unit
(Catapult S5, 10 Hz, Catapult Innovations, Scoresby, VIC,
Australia) in a bespoke pocket fitted in their jersey, between the
scapulae. Each GPS unit had a sampling frequency of 10 Hz,
which has proved the most reliable in team sports for measur-
ing distances and speeds.11
The GPS units were taped into the
pocket to ensure they were not displaced during competition.
The GPS units were turned on and off upon arrival to the sta-
dium for the game and switched on again 15 minutes prior to the
game to ensure the highest quality of satellite signal, as recom-
mended by the GPS provider. No estimated data were included
in the sample. Data for attacking 22 entries were coded live in
Openfield software (versions 1.8.2 -­1.11.0) during gameplay.
Outcomes from attacking 22 entries were coded as suc-
cessful or unsuccessful, with a description of the event occur-
ring in the final play of each entry. A “successful” attacking
22 entry would entail one of the following outcomes: a try
being scored, a penalty being awarded, a dropkick being
scored, or any retention of possession (eg, being held up over
the try line). An “unsuccessful” attacking 22 entry would en-
tail any form of loss of possession: turnover, penalty con-
ceded, knock-­on, or into touch. A total of 32 attacking 22
entries were analyzed in this study, 19 of which were success-
ful and 13 unsuccessful.
These GPS-­derived metrics investigated in this study were
as follows:
1.	 Total distance: the cumulative distance covered at all
walking and running intensities.
   
| 3TIERNEY et al.
2.	 Running distance: the total amount of distance covered
above 2.2 ms−1
. Long and Srinivasan12
reported 2.2 ms−1
as the mean transitional speed between walking and
running.
3.	 High-speed running distance: the total amount of distance
covered above 60% of the player’s individual max veloc-
ity (Vmax).6
Speed zones were individualized in accord-
ance with the findings of Reardon et al.6
that reported an
under- and overestimation of HSR with the use of absolute
speed zones in forwards and backs, respectively, in rugby
union. The highest velocity achieved by a player in the
past three seasons of data collected from all training ses-
sions and games was used as the individual Vmax.
Training sessions included dedicated speed sessions.
4.	 Very high-speed running distance: the total amount of dis-
tance covered above 80% of the player’s individual Vmax.
Metrics from GPS were investigated in a “per-­minute”
method (m min−1
) to evaluate intensity and to allow for compar-
ison of all entries disregarding duration of attacking 22 entries.
Attacking 22 entries less than 20 s of play were discounted, to
avoid an excessively high per-­minute figure for metrics that
would give a false representation of running intensity. If a maul/
scrum event occurred at the start of an attacking 22 entry, data
from GPS and duration only commenced when the scrum/maul
event was completed. Collisions were not included in this analy-
sis due to current GPS technology’s inability to validly quantify
collisions in rugby union.13
Reardon et al.14
have highlighted
that current GPS technology under-­and overestimates collision
count, when compared to video analysis. Accelerations and de-
celerations were not included in our study, due to reported is-
sues over the validity of quantifying these metrics with current
GPS technology.15,16
2.3  |  Statistical analysis
2.3.1  |  Attacking 22 entry running intensity
compared to average game running intensity
For each position, a separate MANOVA was undertaken to
investigate differences in the running intensity of attacking
22 entries compared to average game demands. The four de-
pendent variables (all described as m min−1
) were as follows:
(a) total distance; (b) running distance; (c) high-­speed run-
ning distance; and (d) very high-­speed running distance.
2.3.2  |  Positional differences in attacking 22
entry running intensity
A one-­way between-­groups MANOVA was performed to
investigate positional differences in the running intensity
of attacking 22 entries. The independent variable was posi-
tion (with eight levels). The four dependent variables (all
described as m min−1
) were as follows: (a) total distance; (b)
running distance; (c) high-­speed running distance; and (d)
very high-­speed running distance.
2.3.3  |  The worst case scenario for
successful and unsuccessful attacking
22 entries
The interquartile ranges for both successful and unsuccessful
attacking 22 entries were determined to describe the upper
range of running intensity (the worst case scenario) for each
of the following variables (all described as m min−1
): (a)
total distance; (b) running distance; (c) high-­speed running
distance; and (d) very high-­speed running distance.
2.3.4  |  Positional differences in
successful and unsuccessful attacking 22 entry
running intensity
For forwards and backs, a multivariate analysis of variance
was undertaken to investigate differences in the running in-
tensity between successful and unsuccessful attacking 22 en-
tries. The four dependent variables (all described as m min−1
)
were as follows: (a) total distance; (b) running distance; (c)
high-­speed running distance; and (d) very high-­speed run-
ning distance. In instances whereby there was a perceived
substantial difference in the estimated marginal means be-
tween successful and unsuccessful attacking 22 entries, the
univariate results were also considered (with the application
of a Bonferroni adjustment).
For each position, a multivariate analysis of variance was
undertaken to investigate differences in the running intensity
between successful and unsuccessful 22 entries. The four de-
pendent variables (all described as m min−1
) were as follows:
(a) total distance; (b) running distance; (c) high-­speed run-
ning distance; and (d) very high-­speed running distance. In
instances whereby there was a perceived substantial difference
in the estimated marginal means between successful and un-
successful attacking 22 entries, the univariate results were also
considered (with the application of a Bonferroni adjustment).
Partial eta squared effect sizes were calculated and re-
ported as recommended by Cohen.17
The cutoffs for effect
sizes used were small (0.01), moderate (0.06), and large
(0.14).17
These have been reported in the results alongside
the numerical effect sizes.
3  | RESULTS
3.1  |  Attacking 22 entry running intensity
compared to average game running intensity
For each position, there was a statistically significant differ-
ence on the combined dependent variables (P≤.01). When
4 
|     TIERNEY et al.
the results of the dependent variables were considered sepa-
rately, both total distance and running distance were statisti-
cally significant for all positions (Table 1). Additionally, for
the second row, back row, scrum half, and back three posi-
tions, very high-­speed running was also significantly differ-
ence (Table 1).
3.2  |  Positional differences in attacking 22
entry running intensity
Regarding positional differences in attacking 22 entry run-
ning intensity, there was a statistically significant difference
on the combined dependent variables, F (28, 1656)=2.16,
P≤.01; Wilk’s lambda=0.88, partial eta squared=0.03
(small). When the results of the dependent variables were
considered separately, both high-­speed running (F [7,
462]=4.31, P≤.01, partial eta squared=0.06 [moderate]) and
very high-­speed running (F [7, 462]=2.62, P≤.01, partial eta
squared=0.04 [small]) were statistically significant. Results
of the Bonferroni-­adjusted pairwise comparisons are detailed
in Table 1.
3.3  |  The worst case scenario for
successful and unsuccessful attacking
22 entries
The median value and interquartile range for successful and
unsuccessful attacking 22 are detailed in Table 2.
3.4  |  Positional differences in successful
and unsuccessful attacking 22 entry
running intensity
For forwards, there was no statistically significant difference
between successful and unsuccessful attacking 22 entries on
the combined dependent variables, F (4, 247)=1.69, P=.15;
Wilk’s lambda=0.97; partial eta squared=0.03 (small)
(Table 3). Interestingly, the results of the univariate analysis
revealed a statistically significant difference (P=.04) for high-­
speed running intensity between successful (3.6 m min−1
)
and unsuccessful (1.8 m min−1
) 22 entries. The associated
effect size was small (partial eta squared=0.02).
For backs there was a statistically significant difference
between successful and unsuccessful attacking 22 entries on
the combined dependent variables, F (4, 213)=3.85, P=.01;
Wilk’s lambda=0.93; partial eta squared=0.07 (moderate)
(Table 3). When the results of the dependent variables were
considered separately, running distance, high-­speed running
distance, and very high-­speed running distance were signifi-
cantly lower in successful attacking 22 entries, when com-
pared to successful entries (Table 3).
For the prop position, there was no significant difference be-
tween successful and unsuccessful attacking 22 entries on the
TABLE 1 Attacking22entryrunningintensitycomparedtoaveragegamerunningintensity
TotalDistance(m min−1
)RunningDistance(m min−1
)High-­speedRunningDistance(m min−1
)VeryHigh-­speedRunningDistance(m min−1
)
AverageAttacking22entriesAverageAttacking22entriesAverageAttacking22entriesAverageAttacking22entries
Prop[Jersey1,3]63.1(59.2-­67.0)97.5(88.8-­106.0)a
29.8(25.6-­34.1)53.7(44.1-­63.4)a
2.2(1.3-­3.0)2.3(−­1.1-­5.8)b
0.2(0.0-­0.4)0.2(−­1.1-­1.4)
Hooker[Jersey2]66.8(59.4-­74.3)103.8(91.7-­116.0)a
34.1(26.0-­42.2)63.2(49.6-­76.9)a
4.2(1.9-­6.5)6.5(1.6-­11.4)0.3(0.1-­0.5)0.1(−­1.7-­1.9)
SecondRow[Jersey4,5]66.1(61.0-­71.1)98.2(89.4-­107.1)a
32.9(26.9-­39.0)51.5(41.5-­61.4)a
3.5(2.4-­4.5)2.3(−­1.3-­5.9)b
0.2(0.1-­0.3)0.0(−­1.3-­1.3)a
BackRow[Jersey6,7,8]68.8(64.8-­72.8)100.9(93.9-­107.9)a
35.5(30.8-­40.2)54.5(46.6-­62.3)a
3.5(2.5-­4.5)2.4(−­0.4-­5.2)b
0.2(0.1-­0.2)0.0(−­1.0-­1.0)a
ScrumHalf[Jersey9]81.8(74.1-­89.4)121.0(108.7-­133.3)a
43.2(34.8-­51.6)76.8(62.9-­90.7)a
6.2(2.4-­10.0)11.5(6.5-­16.4)0.3(0.2-­0.4)0.0(−­1.7-­1.8)a
FlyHalf[Jersey10]70.7(61.8-­79.7)106.7(95.6-­117.8)a
35.8(26.3-­45.4)62.2(49.7-­74.7)a
4.0(0.0-­8.2)7.0(2.5-­11.5)0.5(0.0-­2.9)2.6(1.0-­4.3)
Centre[Jersey12,13]71.0(65.0-­76.9)105.9(95.2-­116.6)a
36.0(29.3-­42.7)61.2(49.2-­73.3)a
4.5(1.7-­7.4)8.1(3.8-­12.4)0.3(0.0-­1.5)1.5(−­0.1-­3.0)
BackThree[Jersey
11,14,15]
70.5(63.4-­77.7)105.3(98.7-­111.9)a
32.9(25.1-­40.7)58.8(51.3-­66.2)a
4.7(1.4-­8.0)10.2(7.5-­12.8)0.7(0.0-­1.9)2.1(1.1-­3.1)a
m min−1
,metersperminute.
a
significantlydifferenttoaverage.
b
significantlydifferenttobackthree.
   
| 5TIERNEY et al.
TABLE 2 Theworstcasescenarioforsuccessfulandunsuccessfulattacking22entries
Totaldistance(m min−1
)Runningdistance(m min−1
)High-­speedrunningdistance(m min−1
)Veryhigh-­speedrunningdistance(m min−1
)
SuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessful
Prop[Jersey1,3]97.5(74.8-­121.0)90.5(81.0-­108.5)52.5(29.0-­78.3)54.5(23.8-­71.3)0.0(0.0-­1.3)0.0(0.0-­0.0)0.0(0.0-­0.0)0.0(0.0-­0.0)
Hooker[Jersey2]100.0(79.0-­126.0)93.0(80.0-123.0)55.0(43.0-­87.0)58.0(14.0-86.0)1.0(1.0-­11.0)0.0(0.0-­0.0)0.0(0.0-­0.0)0.0(0.0-­0.0)
SecondRow[Jersey4,5]96.0(73.0-­118.5)96.0(75.0-­107.0)44.0(22.5-­69.5)44.0(27.0-­61.0)0.0(0.0-­4.5)0.0(0.0-­0.0)0.0(0.0-­0.0)0.0(0.0-­0.0)
BackRow[Jersey6,7,8]95.0(86.0-­115.8)96.0(83.3-­112.5)48.0(34.3-­67.5)46.0(32.3-­76.3)0.0(0.0-­2.0)0.0(0.0-­0.0)0.0(0.0-­0.0)0.0(0.0-­0.0)
ScrumHalf[Jersey9]117.5(95.3-­147.0)127.0(85.5-­153.5)64.0(49.0-­101.5)75.0(44.5-­112.5)0.0(0.0-­12.5)3.0(0.0-­27.0)0.0(0.0-­0.0)0.0(0.0-­0.0)
FlyHalf[Jersey10]96.0(83.3-­112.5)89.5(80.8-­144.5)46.0(32.3-­76.3)54.0(30.8-­103.0)0.0(0.0-­0.0)0.0(0.0-­8.5)0.0(0.0-­0.0)0.0(0.0-­0.0)
Centre[Jersey12,13]92.0(70.0-­115.0)121.5(93.5-­147.8)44.0(21.0-­73.0)80.5(57.3-­104.5)0.0(0.0-­12.0)0.0(0.0-­15.3)0.0(0.0-­0.0)0.0(0.0-­0.0)
BackThree[Jersey11,14,15]96.5(66.3-­122.8)102.5(75.3-­142.0)51.5(16.5-­80.0)53.0(21.0-­95.0)0.0(0.0-­6.5)0.0(0.0-­21.5)0.0(0.0-­0.0)0.0(0.0-­0.0)
Valuesaremedian(interquartilerange(IQR));m min−1
,metersperminute(UpperendofIQRrepresentstheworstcasescenariorunningintensity).
TABLE 3 Positionaldifferencesinsuccessful(n=19)andunsuccessful(n=13)attacking22entryrunningintensity(forwardsandbacks)
Totaldistance(m min−1
)Runningdistance(m min−1
)High-­speedrunningdistance(m min−1
)Veryhigh-­speedrunningdistance(m min−1
)
SuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessful
Forwards[Jersey1-­8](n=252)100.4(95.7-­105.1)98.8(93.2-­104.5)54.7(49.2-­60.2)54.7(48.0-­61.3)3.6(2.5-­4.8)a
1.8(0.3-­3.2)0.1(0.0-­0.2)0.0(0.0-­0.2)
Backs[Jersey9-­15](n=218)102.3(95.3-­109.4)115.6(107.3-­123.9)55.8(48.1-­63.5)a
71.6(62.5-­80.7)6.2(2.9-­9.5)a
13.9(10.1-­17.8)0.4(0.0-­1.6)a
3.8(2.2-­5.3)
Valuesaremean(95%CI);m min−1
,metersperminute;n,numberoffiles.
a
significantlydifferenttounsuccessful.
6 
|     TIERNEY et al.
combined dependent variables, F (4, 59)=0.25, P=.91; Wilk’s
lambda=0.98; partial eta squared=0.02 (small) (Table 4).
For the hooker position, there was no significant difference
between successful and unsuccessful attacking 22 entries on the
combined dependent variables, F (4, 25)=0.39, P=.81; Wilk’s
lambda=0.94; partial eta squared=0.06 (moderate) (Table 4).
For the second row position, there was a statistically
significant difference between successful and unsuccessful
attacking 22 entries on the combined dependent variables,
F (3, 56)=2.89, P=.04; Wilk’s lambda=0.87; partial eta
squared=0.13 (moderate) (Table 3). Results of the Bonferroni-­
adjusted pairwise comparisons are detailed in Table 4.
For the back row position, there was no significant differ-
ence between successful and unsuccessful attacking 22 en-
tries on the combined dependent variables, F (4, 91)=0.65,
P=.63; Wilk’s lambda=0.97; partial eta squared=0.03
(small) (Table 4).
For the scrum half position, there was no significant dif-
ference between successful and unsuccessful attacking 22
entries on the combined dependent variables, F (4, 26)=0.76,
P=.56; Wilk’s lambda=0.90; partial eta squared=0.10 (mod-
erate) (Table 4).
For the fly half position, there was no significant differ-
ence between successful and unsuccessful attacking 22 en-
tries on the combined dependent variables, F (4, 33)=1.32,
P=.28; Wilk’s lambda=0.86; partial eta squared=0.14 (large)
(Table 4). Interestingly, the results of the univariate analysis
revealed a statistically significant difference (P=.05) for high-­
speed running intensity between successful (2.2 m min−1
)
and unsuccessful (13.6 m min−1
) attacking 22 entries. The as-
sociated effect size was moderate (partial eta squared=0.09).
For the centre position, there was a statistically significant
difference between successful and unsuccessful attacking 22
entries on the combined dependent variables, F (4, 36)=2.68,
P=.05; Wilk’s lambda=0.77; partial eta squared=0.23
(large). Results of the Bonferroni-­adjusted pairwise compar-
isons are detailed in Table 4.
For the back three position, there was no significant differ-
ence between successful and unsuccessful attacking 22 en-
tries on the combined dependent variables, F (4, 103)=1.54,
P=.20; Wilk’s lambda=0.94; partial eta squared=0.06 (mod-
erate) (Table 4). Interestingly, the results of the univariate
analysis revealed a statistically significant difference (P<.05)
for very high-­speed running intensity between successful
(0.7 m min−1
) and unsuccessful (4.1 m min−1
) attacking
22 entries. The associated effect size was small (partial eta
squared=0.05) (small).
4  | DISCUSSION
When considering successful and unsuccessful attacking
22 entries, forwards achieved greater high-­speed running
TABLE 4 Positionaldifferencesinsuccessful(n=19)vsunsuccessful(n=13)Attacking22entryrunningintensity(positional)
Totaldistance(m min−1
)Runningdistance(m min−1
)High-­speedrunningdistance(m min−1
)
Veryhigh-­speedrunningdistance
(m min−1
)
SuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessful
Prop(n=64)[Jersey1,3]99.4(90.3-­108.6)94.5(83.4-­105.7)55.6(45.2-­65.9)51.0(38.5-­63.5)2.8(0.7-­4.8)1.7(0.0-­4.2)0.3(0.0-­0.8)0.0(0.0-­0.6)
Hooker(n=32)[Jersey2]107(88.4-­125.6)97(74.3-­119.8)66.2(45.6-­86.8)55.7(30.5-­80.9)7.8(2.1-­13.5)2.3(0.0-­9.3)0.2(0.0-­0.5)0.0(0.0-­0.4)
SecondRow(n=60)[Jersey4,5]96.9(87.4-­106.4)100.4(88.3-­112.5)49.1(37.1-­61.0)55.3(40.1-­70.4)3.5(1.5-­5.4)a
0.4(0.0-­2.9)0.0(0.0-­0.0)0.0(0.0-­0.0)
BackRow(n=96)[Jersey6,7,8]101.6(94.6-­108.6)100(91.7-­108.3)54.2(45.8-­62.5)54.9(45.0-­64.8)2.8(1.1-­4.5)1.9(0.0-­3.9)0.0(0.0-­0.0)0.0(0.0-­0.0)
ScrumHalf(n=31)[Jersey9]117.7(99.2-­136.2)125.5(103.7-­147.2)72.1(52.0-­92.1)83.4(59.8-­107.0)9.1(0.0-­18.5)14.8(3.6-­25.9)0.0(0.0-­0.1)0.1(0.0-­0.2)
FlyHalf(n=38)[Jersey10]104.1(87.4-­120.9)110.2(90.6-­129.8)57.9(40.0-­75.9)68.1(47.1-­89.2)2.2(0.0-­9.8)b
13.6(4.6-­22.5)0.0(0.0-­4.4)6.2(1.1-­11.4)
Centre(n=41)[Jersey12,13]93.6(80.7-­106.4)a
121.7(107.2-­136.2)46.2(31.8-­60.6)a
80.4(64.1-­96.7)5.0(0.0-­11.7)12.1(4.6-­19.7)0.0(0.0-­2.7)3.3(0.2-­6.4)
BackThree(n=108)[Jersey11,14,15]100.5(89.6-­111.5)112.2(99.0-­125.5)53.9(41.9-­66.0)65.8(51.3-­80.3)7.2(2.2-­12.1)14.6(8.6-­20.6)0.7(0.0-­2.5)b
4.1(1.9-­6.3)
Valuesaremean(95%CI);n,numberoffiles;m min−1,metersperminute.
a
significantlydifferenttounsuccessfulonthemultivariateanalysis,b
significantlydifferenttounsuccessfulontheunivariateanalysis.
   
| 7TIERNEY et al.
intensity (3.6 m min−1
vs 1.8 m min−1
; small effect size) in
successful entries. It may be interpreted that the greater high-­
speed running intensity for forwards in successful attacking
22 entries relates to their efforts in getting into position early
to make themselves available for the next phase of play. The
difference in physical output found in the current study op-
poses results found in rugby league, where greater amounts
of high-­intensity running and total distance did not relate to
competitive success.18,19
Backs were characterized by significantly lower running
intensity, high-­speed running intensity, and very high-­speed
running intensity in successful attacking 22 entries compared
to unsuccessful (moderate effect size). We posit that the higher
running intensity for backs in unsuccessful attacking 22 entries
is a direct result of forwards having a reduced high-­speed run-
ning intensity. From observations of the data set, it is specu-
lated that in unsuccessful attacking 22 entries, backs are forced
to work harder to rectify the lower work rate of the forwards.
When comparing the intensity of positional groups (Table 1),
prop, second row, and back row had significantly lower high-
speed running intensity compared to back three players.
Although not significantly different, it is evident that there
are substantial differences in forward positional groups (prop,
hooker, and back row) with regard to high-­speed running inten-
sity in successful vs unsuccessful attacking 22 entries (Table 4).
Second row players showed a significant difference in high-
speed running intensity in successful vs unsuccessful attacking
22 entries (Table 4). When grouping all the forwards together,
there was twice the high-­speed running intensity in successful
attacking 22 entries, compared to unsuccessful (3.6 m min−1
vs 1.8 m min−1
, respectively) (Table 4). Some of the findings,
despite the lack of statistical significance, may be of particular
relevance to the practitioner. It was hypothesized that a higher
intensity of GPS-derived running metrics would be evident in
successful attacking 22 entries, when compared to unsuccessful
entries. With regard to the metrics assessed in the current study,
there were significant differences in successful vs unsuccessful
entries, which are highlighted in Table 4. Gabbett 20
had pre-
viously found that there are no significant differences in high-
speed running intensity (m min−1
) in matches won and matches
lost in rugby league, when looking at the game as whole. It may
be that looking at specific phases would provide a greater insight
into the influence of running intensity on the outcome of sce-
narios, and thus the game. Gabbett 20
did suggest that a team’s
ability to maintain high playing intensity is linked to successful
teams, and it may be that a team’s work rate in phases of play
influences the entire game. High-speed running intensity has
also been compared in two rugby league teams in a tournament,
whereby results showed a likely (81% chance; ES 0.52) higher
high-speed running intensity in the high standard team (first di-
vision) when compared to a low standard team (third division).21
The greater high-­speed running intensity for forwards
in successful 22 entries provides interesting insight for
practitioners to consider. Even within the confined space of
the 22 zone, the importance of achieving greater than 60%
(threshold for determining high-­speed running) of top speed
is clear. It is thought that a certain amount of effort is required
to achieve 60% of top speed, particularly over shorter dis-
tances. Such effort is thought to translate into the increased
likelihood of success in gameplay scenarios. It may be that
players are accelerating harder in the 22 zone, to reach 60%
of top speed and get into position early, thus providing greater
attacking advantage. Limitations of current technology in
quantifying high-­intensity accelerations hinder the accurate
analysis of these events.15
It may be that the use of the high-­
speed running band (>60% Vmax) used in this study provides
an indirect measure of acceleration intensity within attacking
22 entries.
The current findings highlight the specific running de-
mands of attacking 22 entries for rugby union, and the differ-
ences in these compared to average game demands (Table 1).
Allpositionalsubgroupsshowedsignificantlygreatertotaldis-
tance and running distance (m min−1
) in attacking 22 entries
compared to average game intensity (P≤.01). Interestingly,
there were no significant differences in high-­speed running
intensity (HSR m min−1
) between attacking 22 entries and
average game demands (Table 1). Second row, back row,
scrum half, and back three positions showed a significantly
different very high-­speed running intensity in attacking 22
entries compared to average game demands (Table 1). From
observations of the current data set, it is speculated that the
higher very-high speed running intensity seen in the back
three position in attacking 22 entries is from initial entry into
attacking 22 entries and repositioning that may occur behind
the phase of play.
The values in the current study may give practitioners in-
sight into the application of training methods to specifically
train the attacking 22 entry. However, using average demands
may not result in preparedness for the worst case scenario
of an attacking 22 entry. The upper limit of the interquartile
range of both successful and unsuccessful entries (Table 2)
may provide practitioners with the higher end of running in-
tensity within this scenario.
These values may be used to train specific scenarios in
rugby union to ensure that players are adequately conditioned
for such scenarios in games. Using reports on average game
demands to guide training may result in underpreparedness
for specific periods in rugby union, such as attacking 22
entry scenarios. The evidence presented in the current study
is similar to that reported in rugby league, in which different
field position zones were found to have different physical de-
mands.22
The metrics in the current study for attacking 22 en-
tries are below those observed by our research group for long
outfield periods of play in rugby union players (105 m min−1
vs 125 m min−1
respectively; unpublished data The current
running intensity figures may be used as a reference to ensure
8 
|     TIERNEY et al.
that rugby union players are adequately conditioned to han-
dle the demands of attacking 22 entries. Previous research
in Australian Football League (AFL) has shown that fitness
levels (determined using the Yo-­Yo IR2) influenced the
high-speed running instensity of players in games.23
Future
research in rugby union should investigate whether an im-
proved fitness level of players influences their high-speed
running intensity within attacking 22 entries and whether that
further improves the likelihood of success in these scenarios.
This study only investigated the running demands of at-
tacking 22 entries. The addition of collision and acceleration
activity to running intensity would provide practitioners with
an overall picture of the physical output of attacking 22 en-
tries. A measure of internal stress (heart rate) may provide
a further level of player analysis, as the attacking 22 entry
may be of high internal stress to the athlete, considering its
proximity to scoring a try. When interpreting results from this
study, it must be considered that these findings are from one
team and tactics and skill level may differ in other teams.
Future research in rugby union should examine different
running demands in different zones in the field related to at-
tacking and defending. This would further guide practitioners
in developing training that targets the specific running de-
mands of scenarios in rugby union. Validation of acceleration
and collision events derived from GPS microtechnology in
rugby union would enhance the physical profiling of game
events to further inform the practitioner.
5  | PERSPECTIVE
This is the first study to highlight differences in the outcome
of specific scenarios in rugby union gameplay using GPS-­
derived running metrics. There is opportunity for future re-
search to further investigate rugby union gameplay, from
the game as a whole, and with regard to specific scenarios
of gameplay (eg, defensive 22 entries and long periods of
play). The importance of GPS-­derived running metrics for
rugby union in conditioning players for specific scenarios
and evaluating the likelihood of successful outcomes is clear.
In rugby union training and gameplay, forwards should be
encouraged to achieve greater high-­speed running intensity
in attacking 22 entry scenarios, to increase the likelihood of
success.
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  • 1. Scand J Med Sci Sports 2016; 1–8 wileyonlinelibrary.com/journal/sms   |  1© 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Accepted: 14 November 2016 DOI: 10.1111/sms.12816 O R I G I N A L A R T I C L E Attacking 22 entries in rugby union: running demands and differences between successful and unsuccessful entries P. Tierney1,2   |  D. P. Tobin1   |  C. Blake2   |  E. Delahunt1,3 1 Leinster Rugby, Dublin, Ireland 2 School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland 3 Institute for Sport and Health, University College Dublin, Dublin, Ireland Correspondence Peter Tierney, Leinster Rugby, Dublin, Ireland. Email: peter.tierney@leinsterrugby.ie Global Positioning System (GPS) technology is commonly utilized in team sports, including rugby union. It has been used to describe the average running demands of rugby union. This has afforded an enhanced understanding of the physical fitness requirements for players. However, research in team sports has suggested that train- ing players relative to average demands may underprepare them for certain scenarios within the game. To date, no research has investigated the running demands of at- tacking 22 entries in rugby union. Additionally, no research has been undertaken to determine whether differences exist in the running intensity of successful and unsuc- cessful attacking 22 entries in rugby union. The first aim of this study was to describe the running intensity of attacking 22 entries. The second aim of this study was to investigate whether differences exist in the running intensity of successful and un- successful attacking 22 entries. Running intensity was measured using meters per minute (m min−1 ) for (a) total distance, (b) running distance, (c) high-­speed running distance, and (d) very high-­speed running distance. This study provides normative data for the running intensity of attacking 22 entries in rugby union. Forwards achieved greater high-­speed running intensity in successful (3.6 m min−1 ) compared to unsuccessful (1.8 m min−1 ) attacking 22 entries. Forwards should try and achieve greater high-­speed running intensity in attacking 22 entries to increase the likelihood of successful outcomes during this period of gameplay. K E Y W O R D S football, physical fitness, running, sports 1  | INTRODUCTION Global Positioning System (GPS) technology has been used extensively to quantify the average running demands of rugby union.1–3 Despite the volume of research using GPS technology in rugby union, no studies have reported simulta- neously on GPS metrics and performance. In rugby league, it has been reported that condensed periods of repeated high-­ intensity efforts (RHIE) are common prior to scoring or conceding a try.4 Also, at varying levels of rugby league com- petition, it has been reported that lower level teams perform more RHIE prior to scoring than higher level teams, while higher level teams complete more RHIE prior to conceding a try.5 An increased playing intensity, as quantified by an in- crease in RHIE, often occurs prior to a try being scored in rugby league,4 but this has yet to be explored in rugby union. To date, no research in rugby union has attempted to relate GPS-­derived running metrics to the likelihood of a positive outcome. Research has reported on the average running demands of rugby union games, using GPS technology.1,2,6 Using meters per minute (m min−1 ) as a measure of running intensity, research has reported similar findings when using total distance (TD); an average of 65 m min−1 , 69 m min−1 , and 65 m min−1 has been
  • 2. 2  |     TIERNEY et al. reported by Jones et al.,2 Cunniffe et al.,1 and Cunningham et al.,7 respectively. An average of 76 m min−1 and 81 m min−1 has also been reported.6,8 The averages reported by Lindsay et al.8 and Reardon et al.6 were derived from distance per game time minutes, which accounts for the higher m min−1 compared to previous research. Average running game demands do not reflect the demands of the most intense periods of gameplay. Unpublished data from our research group has observed that long periods of ball-­in-­play have a higher running intensity (125 m min−1 ) compared to average running demands. Attacking 22 entries in rugby union are of key importance; teams must pass through the opposition 22 zone (complete a successful attacking 22 entry) to score a try. It has been shown in previous research that winning teams score signifi- cantly more tries compared to losing teams.9 Research has investigated the results of rugby games with winning teams partaking in significantly greater attacking 22 entries than losing teams.10 Rather logically, winning teams achieved points more frequently than losing teams for each attacking 22 entry.10 Despite the knowledge of how important attacking 22 entries are in rugby union, there remains a lack of research investigating potential ways to increase the likelihood of suc- cess in these scenarios. Methods that need investigation may include, but are not limited to, physical output, skill execu- tion, and style of play. It is expected that an opposition defense would be more likely to succumb to attacking pressure following repeated defensive efforts. The logic is to force the opposition into more RHIE, causing fatigue and therefore an increased likelihood of conceding a try or penalty. Unpublished data from our research group has observed that increased running distance and total distance are associated with an increased number of open-­play involvements. It is plausible that higher GPS-­derived running metrics would increase involvements in attacking 22 entries, thus increasing the likelihood of a pos- itive outcome. The first aim of this study was to describe the running intensity of attacking 22 entries and compare these to average game demands. The second aim of this study was to investi- gate whether differences exist in the running intensity of suc- cessful and unsuccessful attacking 22 entries. The hypothesis was that higher GPS-­derived running intensity would result in an increased likelihood of successful attacking 22 entries. The running demands of attacking 22 entries described in this study will provide practitioners with comparative metrics, which could be utilized to guide training intensity in attacking 22 entries. It may be recommended that practi- tioners ensure that athletes are above the intensity of attack- ing 22 entries in training, to ensure they are prepared for such scenarios in competition. Should differences be identified in successful vs unsuccessful attacking 22 entries, this would provide important metrics for practitioners to focus attention on with regard to this specific scenario in rugby union. 2  | METHODS 2.1  | Subjects Forty-­three professional rugby union players were recruited for this study (age=27.8 ± 4.1 years; height=1.86 ± 0.07 m; body mass=104.5 ± 12.4 kg). The 43 players provided 470 GPS files from 11 games, in both the domestic league (Guinness Pro12) and the European Cup (European Champions Cup) dur- ing the 2015/2016 season. The average number of attacking 22 entries analyzed from each game was 3 (SD ± 1). The 43 players were subcategorized into position (number of players in each position): prop (n=8), hooker (n=4), second row (n=5), back row (n=9), scrum half (n=3), fly half (n=3), centre (n=3), back three (n=8). The breakdown of GPS files from each po- sition (number of files) was as follows: prop (n=64), hooker (n=32), second row (n=60), back row (n=96), scrum half (n=31), fly half (n=38), centre (n=41), back three (n=108). Of the 11 games, seven games were won and four games were lost. Ethical approval for data collection on these players was ap- proved by the University Human Research Ethics Committee. 2.2  | Procedures All players wore an individual GPS microtechnology unit (Catapult S5, 10 Hz, Catapult Innovations, Scoresby, VIC, Australia) in a bespoke pocket fitted in their jersey, between the scapulae. Each GPS unit had a sampling frequency of 10 Hz, which has proved the most reliable in team sports for measur- ing distances and speeds.11 The GPS units were taped into the pocket to ensure they were not displaced during competition. The GPS units were turned on and off upon arrival to the sta- dium for the game and switched on again 15 minutes prior to the game to ensure the highest quality of satellite signal, as recom- mended by the GPS provider. No estimated data were included in the sample. Data for attacking 22 entries were coded live in Openfield software (versions 1.8.2 -­1.11.0) during gameplay. Outcomes from attacking 22 entries were coded as suc- cessful or unsuccessful, with a description of the event occur- ring in the final play of each entry. A “successful” attacking 22 entry would entail one of the following outcomes: a try being scored, a penalty being awarded, a dropkick being scored, or any retention of possession (eg, being held up over the try line). An “unsuccessful” attacking 22 entry would en- tail any form of loss of possession: turnover, penalty con- ceded, knock-­on, or into touch. A total of 32 attacking 22 entries were analyzed in this study, 19 of which were success- ful and 13 unsuccessful. These GPS-­derived metrics investigated in this study were as follows: 1. Total distance: the cumulative distance covered at all walking and running intensities.
  • 3.     | 3TIERNEY et al. 2. Running distance: the total amount of distance covered above 2.2 ms−1 . Long and Srinivasan12 reported 2.2 ms−1 as the mean transitional speed between walking and running. 3. High-speed running distance: the total amount of distance covered above 60% of the player’s individual max veloc- ity (Vmax).6 Speed zones were individualized in accord- ance with the findings of Reardon et al.6 that reported an under- and overestimation of HSR with the use of absolute speed zones in forwards and backs, respectively, in rugby union. The highest velocity achieved by a player in the past three seasons of data collected from all training ses- sions and games was used as the individual Vmax. Training sessions included dedicated speed sessions. 4. Very high-speed running distance: the total amount of dis- tance covered above 80% of the player’s individual Vmax. Metrics from GPS were investigated in a “per-­minute” method (m min−1 ) to evaluate intensity and to allow for compar- ison of all entries disregarding duration of attacking 22 entries. Attacking 22 entries less than 20 s of play were discounted, to avoid an excessively high per-­minute figure for metrics that would give a false representation of running intensity. If a maul/ scrum event occurred at the start of an attacking 22 entry, data from GPS and duration only commenced when the scrum/maul event was completed. Collisions were not included in this analy- sis due to current GPS technology’s inability to validly quantify collisions in rugby union.13 Reardon et al.14 have highlighted that current GPS technology under-­and overestimates collision count, when compared to video analysis. Accelerations and de- celerations were not included in our study, due to reported is- sues over the validity of quantifying these metrics with current GPS technology.15,16 2.3  |  Statistical analysis 2.3.1  |  Attacking 22 entry running intensity compared to average game running intensity For each position, a separate MANOVA was undertaken to investigate differences in the running intensity of attacking 22 entries compared to average game demands. The four de- pendent variables (all described as m min−1 ) were as follows: (a) total distance; (b) running distance; (c) high-­speed run- ning distance; and (d) very high-­speed running distance. 2.3.2  |  Positional differences in attacking 22 entry running intensity A one-­way between-­groups MANOVA was performed to investigate positional differences in the running intensity of attacking 22 entries. The independent variable was posi- tion (with eight levels). The four dependent variables (all described as m min−1 ) were as follows: (a) total distance; (b) running distance; (c) high-­speed running distance; and (d) very high-­speed running distance. 2.3.3  |  The worst case scenario for successful and unsuccessful attacking 22 entries The interquartile ranges for both successful and unsuccessful attacking 22 entries were determined to describe the upper range of running intensity (the worst case scenario) for each of the following variables (all described as m min−1 ): (a) total distance; (b) running distance; (c) high-­speed running distance; and (d) very high-­speed running distance. 2.3.4  |  Positional differences in successful and unsuccessful attacking 22 entry running intensity For forwards and backs, a multivariate analysis of variance was undertaken to investigate differences in the running in- tensity between successful and unsuccessful attacking 22 en- tries. The four dependent variables (all described as m min−1 ) were as follows: (a) total distance; (b) running distance; (c) high-­speed running distance; and (d) very high-­speed run- ning distance. In instances whereby there was a perceived substantial difference in the estimated marginal means be- tween successful and unsuccessful attacking 22 entries, the univariate results were also considered (with the application of a Bonferroni adjustment). For each position, a multivariate analysis of variance was undertaken to investigate differences in the running intensity between successful and unsuccessful 22 entries. The four de- pendent variables (all described as m min−1 ) were as follows: (a) total distance; (b) running distance; (c) high-­speed run- ning distance; and (d) very high-­speed running distance. In instances whereby there was a perceived substantial difference in the estimated marginal means between successful and un- successful attacking 22 entries, the univariate results were also considered (with the application of a Bonferroni adjustment). Partial eta squared effect sizes were calculated and re- ported as recommended by Cohen.17 The cutoffs for effect sizes used were small (0.01), moderate (0.06), and large (0.14).17 These have been reported in the results alongside the numerical effect sizes. 3  | RESULTS 3.1  |  Attacking 22 entry running intensity compared to average game running intensity For each position, there was a statistically significant differ- ence on the combined dependent variables (P≤.01). When
  • 4. 4  |     TIERNEY et al. the results of the dependent variables were considered sepa- rately, both total distance and running distance were statisti- cally significant for all positions (Table 1). Additionally, for the second row, back row, scrum half, and back three posi- tions, very high-­speed running was also significantly differ- ence (Table 1). 3.2  |  Positional differences in attacking 22 entry running intensity Regarding positional differences in attacking 22 entry run- ning intensity, there was a statistically significant difference on the combined dependent variables, F (28, 1656)=2.16, P≤.01; Wilk’s lambda=0.88, partial eta squared=0.03 (small). When the results of the dependent variables were considered separately, both high-­speed running (F [7, 462]=4.31, P≤.01, partial eta squared=0.06 [moderate]) and very high-­speed running (F [7, 462]=2.62, P≤.01, partial eta squared=0.04 [small]) were statistically significant. Results of the Bonferroni-­adjusted pairwise comparisons are detailed in Table 1. 3.3  |  The worst case scenario for successful and unsuccessful attacking 22 entries The median value and interquartile range for successful and unsuccessful attacking 22 are detailed in Table 2. 3.4  |  Positional differences in successful and unsuccessful attacking 22 entry running intensity For forwards, there was no statistically significant difference between successful and unsuccessful attacking 22 entries on the combined dependent variables, F (4, 247)=1.69, P=.15; Wilk’s lambda=0.97; partial eta squared=0.03 (small) (Table 3). Interestingly, the results of the univariate analysis revealed a statistically significant difference (P=.04) for high-­ speed running intensity between successful (3.6 m min−1 ) and unsuccessful (1.8 m min−1 ) 22 entries. The associated effect size was small (partial eta squared=0.02). For backs there was a statistically significant difference between successful and unsuccessful attacking 22 entries on the combined dependent variables, F (4, 213)=3.85, P=.01; Wilk’s lambda=0.93; partial eta squared=0.07 (moderate) (Table 3). When the results of the dependent variables were considered separately, running distance, high-­speed running distance, and very high-­speed running distance were signifi- cantly lower in successful attacking 22 entries, when com- pared to successful entries (Table 3). For the prop position, there was no significant difference be- tween successful and unsuccessful attacking 22 entries on the TABLE 1 Attacking22entryrunningintensitycomparedtoaveragegamerunningintensity TotalDistance(m min−1 )RunningDistance(m min−1 )High-­speedRunningDistance(m min−1 )VeryHigh-­speedRunningDistance(m min−1 ) AverageAttacking22entriesAverageAttacking22entriesAverageAttacking22entriesAverageAttacking22entries Prop[Jersey1,3]63.1(59.2-­67.0)97.5(88.8-­106.0)a 29.8(25.6-­34.1)53.7(44.1-­63.4)a 2.2(1.3-­3.0)2.3(−­1.1-­5.8)b 0.2(0.0-­0.4)0.2(−­1.1-­1.4) Hooker[Jersey2]66.8(59.4-­74.3)103.8(91.7-­116.0)a 34.1(26.0-­42.2)63.2(49.6-­76.9)a 4.2(1.9-­6.5)6.5(1.6-­11.4)0.3(0.1-­0.5)0.1(−­1.7-­1.9) SecondRow[Jersey4,5]66.1(61.0-­71.1)98.2(89.4-­107.1)a 32.9(26.9-­39.0)51.5(41.5-­61.4)a 3.5(2.4-­4.5)2.3(−­1.3-­5.9)b 0.2(0.1-­0.3)0.0(−­1.3-­1.3)a BackRow[Jersey6,7,8]68.8(64.8-­72.8)100.9(93.9-­107.9)a 35.5(30.8-­40.2)54.5(46.6-­62.3)a 3.5(2.5-­4.5)2.4(−­0.4-­5.2)b 0.2(0.1-­0.2)0.0(−­1.0-­1.0)a ScrumHalf[Jersey9]81.8(74.1-­89.4)121.0(108.7-­133.3)a 43.2(34.8-­51.6)76.8(62.9-­90.7)a 6.2(2.4-­10.0)11.5(6.5-­16.4)0.3(0.2-­0.4)0.0(−­1.7-­1.8)a FlyHalf[Jersey10]70.7(61.8-­79.7)106.7(95.6-­117.8)a 35.8(26.3-­45.4)62.2(49.7-­74.7)a 4.0(0.0-­8.2)7.0(2.5-­11.5)0.5(0.0-­2.9)2.6(1.0-­4.3) Centre[Jersey12,13]71.0(65.0-­76.9)105.9(95.2-­116.6)a 36.0(29.3-­42.7)61.2(49.2-­73.3)a 4.5(1.7-­7.4)8.1(3.8-­12.4)0.3(0.0-­1.5)1.5(−­0.1-­3.0) BackThree[Jersey 11,14,15] 70.5(63.4-­77.7)105.3(98.7-­111.9)a 32.9(25.1-­40.7)58.8(51.3-­66.2)a 4.7(1.4-­8.0)10.2(7.5-­12.8)0.7(0.0-­1.9)2.1(1.1-­3.1)a m min−1 ,metersperminute. a significantlydifferenttoaverage. b significantlydifferenttobackthree.
  • 5.     | 5TIERNEY et al. TABLE 2 Theworstcasescenarioforsuccessfulandunsuccessfulattacking22entries Totaldistance(m min−1 )Runningdistance(m min−1 )High-­speedrunningdistance(m min−1 )Veryhigh-­speedrunningdistance(m min−1 ) SuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessful Prop[Jersey1,3]97.5(74.8-­121.0)90.5(81.0-­108.5)52.5(29.0-­78.3)54.5(23.8-­71.3)0.0(0.0-­1.3)0.0(0.0-­0.0)0.0(0.0-­0.0)0.0(0.0-­0.0) Hooker[Jersey2]100.0(79.0-­126.0)93.0(80.0-123.0)55.0(43.0-­87.0)58.0(14.0-86.0)1.0(1.0-­11.0)0.0(0.0-­0.0)0.0(0.0-­0.0)0.0(0.0-­0.0) SecondRow[Jersey4,5]96.0(73.0-­118.5)96.0(75.0-­107.0)44.0(22.5-­69.5)44.0(27.0-­61.0)0.0(0.0-­4.5)0.0(0.0-­0.0)0.0(0.0-­0.0)0.0(0.0-­0.0) BackRow[Jersey6,7,8]95.0(86.0-­115.8)96.0(83.3-­112.5)48.0(34.3-­67.5)46.0(32.3-­76.3)0.0(0.0-­2.0)0.0(0.0-­0.0)0.0(0.0-­0.0)0.0(0.0-­0.0) ScrumHalf[Jersey9]117.5(95.3-­147.0)127.0(85.5-­153.5)64.0(49.0-­101.5)75.0(44.5-­112.5)0.0(0.0-­12.5)3.0(0.0-­27.0)0.0(0.0-­0.0)0.0(0.0-­0.0) FlyHalf[Jersey10]96.0(83.3-­112.5)89.5(80.8-­144.5)46.0(32.3-­76.3)54.0(30.8-­103.0)0.0(0.0-­0.0)0.0(0.0-­8.5)0.0(0.0-­0.0)0.0(0.0-­0.0) Centre[Jersey12,13]92.0(70.0-­115.0)121.5(93.5-­147.8)44.0(21.0-­73.0)80.5(57.3-­104.5)0.0(0.0-­12.0)0.0(0.0-­15.3)0.0(0.0-­0.0)0.0(0.0-­0.0) BackThree[Jersey11,14,15]96.5(66.3-­122.8)102.5(75.3-­142.0)51.5(16.5-­80.0)53.0(21.0-­95.0)0.0(0.0-­6.5)0.0(0.0-­21.5)0.0(0.0-­0.0)0.0(0.0-­0.0) Valuesaremedian(interquartilerange(IQR));m min−1 ,metersperminute(UpperendofIQRrepresentstheworstcasescenariorunningintensity). TABLE 3 Positionaldifferencesinsuccessful(n=19)andunsuccessful(n=13)attacking22entryrunningintensity(forwardsandbacks) Totaldistance(m min−1 )Runningdistance(m min−1 )High-­speedrunningdistance(m min−1 )Veryhigh-­speedrunningdistance(m min−1 ) SuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessful Forwards[Jersey1-­8](n=252)100.4(95.7-­105.1)98.8(93.2-­104.5)54.7(49.2-­60.2)54.7(48.0-­61.3)3.6(2.5-­4.8)a 1.8(0.3-­3.2)0.1(0.0-­0.2)0.0(0.0-­0.2) Backs[Jersey9-­15](n=218)102.3(95.3-­109.4)115.6(107.3-­123.9)55.8(48.1-­63.5)a 71.6(62.5-­80.7)6.2(2.9-­9.5)a 13.9(10.1-­17.8)0.4(0.0-­1.6)a 3.8(2.2-­5.3) Valuesaremean(95%CI);m min−1 ,metersperminute;n,numberoffiles. a significantlydifferenttounsuccessful.
  • 6. 6  |     TIERNEY et al. combined dependent variables, F (4, 59)=0.25, P=.91; Wilk’s lambda=0.98; partial eta squared=0.02 (small) (Table 4). For the hooker position, there was no significant difference between successful and unsuccessful attacking 22 entries on the combined dependent variables, F (4, 25)=0.39, P=.81; Wilk’s lambda=0.94; partial eta squared=0.06 (moderate) (Table 4). For the second row position, there was a statistically significant difference between successful and unsuccessful attacking 22 entries on the combined dependent variables, F (3, 56)=2.89, P=.04; Wilk’s lambda=0.87; partial eta squared=0.13 (moderate) (Table 3). Results of the Bonferroni-­ adjusted pairwise comparisons are detailed in Table 4. For the back row position, there was no significant differ- ence between successful and unsuccessful attacking 22 en- tries on the combined dependent variables, F (4, 91)=0.65, P=.63; Wilk’s lambda=0.97; partial eta squared=0.03 (small) (Table 4). For the scrum half position, there was no significant dif- ference between successful and unsuccessful attacking 22 entries on the combined dependent variables, F (4, 26)=0.76, P=.56; Wilk’s lambda=0.90; partial eta squared=0.10 (mod- erate) (Table 4). For the fly half position, there was no significant differ- ence between successful and unsuccessful attacking 22 en- tries on the combined dependent variables, F (4, 33)=1.32, P=.28; Wilk’s lambda=0.86; partial eta squared=0.14 (large) (Table 4). Interestingly, the results of the univariate analysis revealed a statistically significant difference (P=.05) for high-­ speed running intensity between successful (2.2 m min−1 ) and unsuccessful (13.6 m min−1 ) attacking 22 entries. The as- sociated effect size was moderate (partial eta squared=0.09). For the centre position, there was a statistically significant difference between successful and unsuccessful attacking 22 entries on the combined dependent variables, F (4, 36)=2.68, P=.05; Wilk’s lambda=0.77; partial eta squared=0.23 (large). Results of the Bonferroni-­adjusted pairwise compar- isons are detailed in Table 4. For the back three position, there was no significant differ- ence between successful and unsuccessful attacking 22 en- tries on the combined dependent variables, F (4, 103)=1.54, P=.20; Wilk’s lambda=0.94; partial eta squared=0.06 (mod- erate) (Table 4). Interestingly, the results of the univariate analysis revealed a statistically significant difference (P<.05) for very high-­speed running intensity between successful (0.7 m min−1 ) and unsuccessful (4.1 m min−1 ) attacking 22 entries. The associated effect size was small (partial eta squared=0.05) (small). 4  | DISCUSSION When considering successful and unsuccessful attacking 22 entries, forwards achieved greater high-­speed running TABLE 4 Positionaldifferencesinsuccessful(n=19)vsunsuccessful(n=13)Attacking22entryrunningintensity(positional) Totaldistance(m min−1 )Runningdistance(m min−1 )High-­speedrunningdistance(m min−1 ) Veryhigh-­speedrunningdistance (m min−1 ) SuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessfulSuccessfulUnsuccessful Prop(n=64)[Jersey1,3]99.4(90.3-­108.6)94.5(83.4-­105.7)55.6(45.2-­65.9)51.0(38.5-­63.5)2.8(0.7-­4.8)1.7(0.0-­4.2)0.3(0.0-­0.8)0.0(0.0-­0.6) Hooker(n=32)[Jersey2]107(88.4-­125.6)97(74.3-­119.8)66.2(45.6-­86.8)55.7(30.5-­80.9)7.8(2.1-­13.5)2.3(0.0-­9.3)0.2(0.0-­0.5)0.0(0.0-­0.4) SecondRow(n=60)[Jersey4,5]96.9(87.4-­106.4)100.4(88.3-­112.5)49.1(37.1-­61.0)55.3(40.1-­70.4)3.5(1.5-­5.4)a 0.4(0.0-­2.9)0.0(0.0-­0.0)0.0(0.0-­0.0) BackRow(n=96)[Jersey6,7,8]101.6(94.6-­108.6)100(91.7-­108.3)54.2(45.8-­62.5)54.9(45.0-­64.8)2.8(1.1-­4.5)1.9(0.0-­3.9)0.0(0.0-­0.0)0.0(0.0-­0.0) ScrumHalf(n=31)[Jersey9]117.7(99.2-­136.2)125.5(103.7-­147.2)72.1(52.0-­92.1)83.4(59.8-­107.0)9.1(0.0-­18.5)14.8(3.6-­25.9)0.0(0.0-­0.1)0.1(0.0-­0.2) FlyHalf(n=38)[Jersey10]104.1(87.4-­120.9)110.2(90.6-­129.8)57.9(40.0-­75.9)68.1(47.1-­89.2)2.2(0.0-­9.8)b 13.6(4.6-­22.5)0.0(0.0-­4.4)6.2(1.1-­11.4) Centre(n=41)[Jersey12,13]93.6(80.7-­106.4)a 121.7(107.2-­136.2)46.2(31.8-­60.6)a 80.4(64.1-­96.7)5.0(0.0-­11.7)12.1(4.6-­19.7)0.0(0.0-­2.7)3.3(0.2-­6.4) BackThree(n=108)[Jersey11,14,15]100.5(89.6-­111.5)112.2(99.0-­125.5)53.9(41.9-­66.0)65.8(51.3-­80.3)7.2(2.2-­12.1)14.6(8.6-­20.6)0.7(0.0-­2.5)b 4.1(1.9-­6.3) Valuesaremean(95%CI);n,numberoffiles;m min−1,metersperminute. a significantlydifferenttounsuccessfulonthemultivariateanalysis,b significantlydifferenttounsuccessfulontheunivariateanalysis.
  • 7.     | 7TIERNEY et al. intensity (3.6 m min−1 vs 1.8 m min−1 ; small effect size) in successful entries. It may be interpreted that the greater high-­ speed running intensity for forwards in successful attacking 22 entries relates to their efforts in getting into position early to make themselves available for the next phase of play. The difference in physical output found in the current study op- poses results found in rugby league, where greater amounts of high-­intensity running and total distance did not relate to competitive success.18,19 Backs were characterized by significantly lower running intensity, high-­speed running intensity, and very high-­speed running intensity in successful attacking 22 entries compared to unsuccessful (moderate effect size). We posit that the higher running intensity for backs in unsuccessful attacking 22 entries is a direct result of forwards having a reduced high-­speed run- ning intensity. From observations of the data set, it is specu- lated that in unsuccessful attacking 22 entries, backs are forced to work harder to rectify the lower work rate of the forwards. When comparing the intensity of positional groups (Table 1), prop, second row, and back row had significantly lower high- speed running intensity compared to back three players. Although not significantly different, it is evident that there are substantial differences in forward positional groups (prop, hooker, and back row) with regard to high-­speed running inten- sity in successful vs unsuccessful attacking 22 entries (Table 4). Second row players showed a significant difference in high- speed running intensity in successful vs unsuccessful attacking 22 entries (Table 4). When grouping all the forwards together, there was twice the high-­speed running intensity in successful attacking 22 entries, compared to unsuccessful (3.6 m min−1 vs 1.8 m min−1 , respectively) (Table 4). Some of the findings, despite the lack of statistical significance, may be of particular relevance to the practitioner. It was hypothesized that a higher intensity of GPS-derived running metrics would be evident in successful attacking 22 entries, when compared to unsuccessful entries. With regard to the metrics assessed in the current study, there were significant differences in successful vs unsuccessful entries, which are highlighted in Table 4. Gabbett 20 had pre- viously found that there are no significant differences in high- speed running intensity (m min−1 ) in matches won and matches lost in rugby league, when looking at the game as whole. It may be that looking at specific phases would provide a greater insight into the influence of running intensity on the outcome of sce- narios, and thus the game. Gabbett 20 did suggest that a team’s ability to maintain high playing intensity is linked to successful teams, and it may be that a team’s work rate in phases of play influences the entire game. High-speed running intensity has also been compared in two rugby league teams in a tournament, whereby results showed a likely (81% chance; ES 0.52) higher high-speed running intensity in the high standard team (first di- vision) when compared to a low standard team (third division).21 The greater high-­speed running intensity for forwards in successful 22 entries provides interesting insight for practitioners to consider. Even within the confined space of the 22 zone, the importance of achieving greater than 60% (threshold for determining high-­speed running) of top speed is clear. It is thought that a certain amount of effort is required to achieve 60% of top speed, particularly over shorter dis- tances. Such effort is thought to translate into the increased likelihood of success in gameplay scenarios. It may be that players are accelerating harder in the 22 zone, to reach 60% of top speed and get into position early, thus providing greater attacking advantage. Limitations of current technology in quantifying high-­intensity accelerations hinder the accurate analysis of these events.15 It may be that the use of the high-­ speed running band (>60% Vmax) used in this study provides an indirect measure of acceleration intensity within attacking 22 entries. The current findings highlight the specific running de- mands of attacking 22 entries for rugby union, and the differ- ences in these compared to average game demands (Table 1). Allpositionalsubgroupsshowedsignificantlygreatertotaldis- tance and running distance (m min−1 ) in attacking 22 entries compared to average game intensity (P≤.01). Interestingly, there were no significant differences in high-­speed running intensity (HSR m min−1 ) between attacking 22 entries and average game demands (Table 1). Second row, back row, scrum half, and back three positions showed a significantly different very high-­speed running intensity in attacking 22 entries compared to average game demands (Table 1). From observations of the current data set, it is speculated that the higher very-high speed running intensity seen in the back three position in attacking 22 entries is from initial entry into attacking 22 entries and repositioning that may occur behind the phase of play. The values in the current study may give practitioners in- sight into the application of training methods to specifically train the attacking 22 entry. However, using average demands may not result in preparedness for the worst case scenario of an attacking 22 entry. The upper limit of the interquartile range of both successful and unsuccessful entries (Table 2) may provide practitioners with the higher end of running in- tensity within this scenario. These values may be used to train specific scenarios in rugby union to ensure that players are adequately conditioned for such scenarios in games. Using reports on average game demands to guide training may result in underpreparedness for specific periods in rugby union, such as attacking 22 entry scenarios. The evidence presented in the current study is similar to that reported in rugby league, in which different field position zones were found to have different physical de- mands.22 The metrics in the current study for attacking 22 en- tries are below those observed by our research group for long outfield periods of play in rugby union players (105 m min−1 vs 125 m min−1 respectively; unpublished data The current running intensity figures may be used as a reference to ensure
  • 8. 8  |     TIERNEY et al. that rugby union players are adequately conditioned to han- dle the demands of attacking 22 entries. Previous research in Australian Football League (AFL) has shown that fitness levels (determined using the Yo-­Yo IR2) influenced the high-speed running instensity of players in games.23 Future research in rugby union should investigate whether an im- proved fitness level of players influences their high-speed running intensity within attacking 22 entries and whether that further improves the likelihood of success in these scenarios. This study only investigated the running demands of at- tacking 22 entries. The addition of collision and acceleration activity to running intensity would provide practitioners with an overall picture of the physical output of attacking 22 en- tries. A measure of internal stress (heart rate) may provide a further level of player analysis, as the attacking 22 entry may be of high internal stress to the athlete, considering its proximity to scoring a try. When interpreting results from this study, it must be considered that these findings are from one team and tactics and skill level may differ in other teams. Future research in rugby union should examine different running demands in different zones in the field related to at- tacking and defending. This would further guide practitioners in developing training that targets the specific running de- mands of scenarios in rugby union. Validation of acceleration and collision events derived from GPS microtechnology in rugby union would enhance the physical profiling of game events to further inform the practitioner. 5  | PERSPECTIVE This is the first study to highlight differences in the outcome of specific scenarios in rugby union gameplay using GPS-­ derived running metrics. There is opportunity for future re- search to further investigate rugby union gameplay, from the game as a whole, and with regard to specific scenarios of gameplay (eg, defensive 22 entries and long periods of play). The importance of GPS-­derived running metrics for rugby union in conditioning players for specific scenarios and evaluating the likelihood of successful outcomes is clear. In rugby union training and gameplay, forwards should be encouraged to achieve greater high-­speed running intensity in attacking 22 entry scenarios, to increase the likelihood of success. REFERENCES 1. Cunniffe B, Proctor W, Baker JS, Davies B. An evaluation of the physiolog- ical demands of elite rugby union using Global Positioning System tracking software. J Strength Cond Res. 2009;23:1195–1203. 2. Jones MR, West DJ, Crewther BT, Cook CJ, Kilduff LP. Quantifying posi- tional and temporal movement patterns in professional rugby union using global positioning system. 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