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Activity Profile of High-Level Australian Lacrosse Players

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Despite lacrosse being one of the fastest growing team sports in the world, there is a paucity of information detailing the activity profile of high level players. Microtechnology systems (global positioning systems (GPS) and accelerometers) provide the opportunity to obtain detailed information on the activity profile in lacrosse. Therefore, this study aimed to analyze the activity profile of lacrosse match-play using microtechnology. Activity profile variables assessed relative to minutes of playing time included relative distance (mmin), distance spent standing (0-0.1 ms), walking (0.2-1.7 ms), jogging (1.8-3.2 ms), running (3.3-5.6 ms), sprinting (≥5.7 ms), number of high, moderate, low accelerations and decelerations and player load (PLmin), calculated as the square root of the sum of the squared instantaneous rate of change in acceleration in three vectors (medio-lateral, anterior-posterior, vertical). Activity was recorded from fifteen lacrosse players over four matches during a national tournament. Players were separated into positions of attack, midfield, or defense. Differences (ES ± 90% CI) between positions and periods of play were considered likely positive when there was ≥75% likelihood of the difference exceeding an effect size threshold of 0.2. Midfielders had likely higher (mean ± SD) m·min (100 ± 11) compared to attackers (87 ± 14; ES = 0.89 ± 1.04) and defenders (79 ± 14; ES = 1.54 ± 0.94) and more moderate and high accelerations and decelerations. Almost all variables across positions were reduced in quarter four compared to quarter one. Coaches should accommodate for positional differences when preparing lacrosse players for competition.
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ACTIVITY PROFILE OF HIGH-LEVEL AUSTRALIAN
LACROSSE PLAYERS
CHRIS S. POLLEY,
1
STUART J. CORMACK,
1
TIM J. GABBETT,
2
AND TED POLGLAZE
3,4
1
School of Exercise Science, Australian Catholic University, Melbourne, Australia;
2
School of Exercise Science, Australian
Catholic University, Brisbane, Australia;
3
School of Exercise Science, University of Western Australia, Australia; and
4
Department of Physiology, Australian Institute of Sport, Western Australia, Australia
ABSTRACT
Polley, CS, Cormack, SJ, Gabbett, TJ, and Polglaze, T. Activity
profile of high-level Australian lacrosse players. J Strength Cond
Res 29(1): 126–136, 2015—Despite lacrosse being one of the
fastest growing team sports in the world, there is a paucity of
information detailing the activity profile of high-level players. Micro-
technology systems (global positioning systems and accelerome-
ters) provide the opportunity to obtain detailed information on the
activity profile in lacrosse. Therefore, this study aimed to analyze
the activity profile of lacrosse match-play using microtechnology.
Activity profile variables assessed relative to minutes of playing
time included relative distance (meter per minute), distance spent
standing (0–0.1 m$min
21
), walking (0.2–1.7 m$min
21
), jogging
(1.8–3.2 m$min
21
), running (3.3–5.6 m$min
21
), sprinting
($5.7 m$min
21
), number of high, moderate, low accelerations
and decelerations, and player load (PL per minute), calculated
as the square root of the sum of the squared instantaneous rate
of change in acceleration in 3 vectors (medio-lateral, anterior-
posterior, and vertical). Activity was recorded from 14 lacrosse
players over 4 matches during a national tournament. Players were
separated into positions of attack, midfield, or defense. Differences
(effect size [ES] 690% confidence interval) between positions
and periods of play were considered likely positive when there
was $75% likelihood of the difference exceeding an ES threshold
of 0.2. Midfielders had likely covered higher (mean 6SD) meters
per minute (100 611) compared with attackers (87 614; ES =
0.89 61.04) and defenders (79 614;ES=1.5460.94) and
more moderate and high accelerations and decelerations. Almost
all variables across positions were reduced in quarter 4 compared
with quarter 1. Coaches should accommodate for positional differ-
ences when preparing lacrosse players for competition.
KEY WORDS global positioning systems, match-play, analysis,
performance, positional differences, tournament
INTRODUCTION
Originating in North America in the 15th century,
lacrosse has enjoyed substantial growth over the
past decade (7,24) and seen a 150% increase in
participation since 2002 (18). Considered one of
the fastest field-based invasion games (7,28,32), match-play is
characterized by intermittent high-intensity activity, collisions,
and rapid changes of direction (15,28). Matches comprise 4
quarters of 15-minute (National Collegiate Athletic Associa-
tion) or 20-minute duration (Federation of International
Lacrosse). Teams consist of 1 goal keeper and 9 field players
divided into 3 positional groups (midfield n= 3, attack n=3,
and defense n= 3) with unlimited interchange allowable.
Midfielders generally cover the entire playing field, whereas
attackers and defenders usually remain in their respective
halves (15). Considering the positional restrictions placed
upon the team, potential exists for differences in the activity
profile of match-play between positions and between quarters.
Despite minimal research examining the activity profile of
lacrosse (25,30), an abundance of activity profile research
exists in other field-based team sports (e.g., soccer, rugby
league, hockey, and Australian football) (3,9,21). Much of
this recent work has been made possible by the advent of
microtechnology including global positioning systems (GPS)
and accelerometers (19). This technology has enabled the
quantification of gross-fatiguing movements and locomotor
patterns (4,6,14). Specifically, analysis commonly includes
the determination of positional differences (3,21) and differ-
ences between periods of match-play (1,9,23,26,29).
In Australian football, relative distance (first half: 137.8
m$min
21
vs. second half: 132.3 m$min
21
) and low-speed activ-
ity(rsthalf:97.7m$min
21
vs. second half: 95.2 m$min
21
)are
shown to be significantly reduced in the second half of matches
when compared with the first (23), whereas total distance (TD)
and high-intensity running (HIR) are significantly reduced after
the first quarter (9). Similarly, in rugby league, HIR and very
HIR expressed relative to game time have been shown to
significantly reduce in the fourth quarter compared with the
first. Relative distance was also reduced in both the second
(96.1 612 m$min
21
) and fourth quarters (95.5 613.2
m$min
21
) when compared with the first (100.7 68.1
m$min
21
) (29). Additionally, it has been noted that maximal
Address correspondence to Chris S. Polley, chris.s.polley@outlook.com.
29(1)/126–136
Journal of Strength and Conditioning Research
Ó2015 National Strength and Conditioning Association
126
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Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
TABLE 1. Match activity profile of different positional groups during a national lacrosse tournament (n = 51; mean 6SD).*
Midfield Attack Defense Midfield vs. attack Midfield vs. defense Attack vs. defense
Mean 6SD Mean 6SD Mean 6SD ES 6CI ES 6CI ES 6CI
Playing time (min) 36.15 613.16 48.18 614:2959.03 620:0720.82 60.96 21.14 60.93 20.43 61.02
Relative distance (m$min
21
) 100 611z§87614 79 614 0.89 61.04 1.54 60.94 0.53 61.03
Relative PL (PL$min
21
) 9.9 61.5z§ 8.2 62.1 7.6 62.7 0.83 61.07 1.04 60.98 0.27 61.02
Stand (m$min
21
) 3.1 62.1 3.6 61.9 3.1 61.1 20.38 60.92 20.21 60.87 0.24 61.05
Walk (m$min
21
) 28.8 63.1 29.1 63.4 32.4 64.8z20.08 61.01 20.78 60.94 20.67 61.01
Jog (m$min
21
) 28.8 64.5z§ 25.0 65.2§ 19.8 62.4 0.70 61.04 2.48 60.90 1.02 61.10
Run (m$min
21
) 31.7 65.8z§ 25.9 66.4§ 20.3 613.3 0.80 61.04 1.28 60.97 0.75 60.99
Sprint (m$min
21
) 5.4 61.8z§ 3.4 61.7 3.5 64.4 1.12 61.04 1.03 61.00 0.38 61.00
Dec
HI
(no$min
21
) 0.7 60.2z§ 0.5 60.1 0.5 60.2 0.59 60.94 0.66 60.92 0.16 61.01
Dec
MO
(no$min
21
) 2.7 60.3z§ 2.2 60.3 2.1 60.5 1.75 61.04 1.54 60.98 0.23 61.00
Dec
LO
(no$min
21
) 18.5 61.2 18.4 62.1 19.2 62.0 0.09 61.07 20.36 60.96 20.37 61.03
Acc
HI
(no$min
21
) 0.7 60.2z§ 0.5 60.2 0.5 60.2 0.95 61.01 0.76 60.94 20.11 61.02
Acc
MO
(no$min
21
) 3.0 60.4z§ 2.5 60.5§ 2.1 60.4 1.00 61.04 2.11 60.93 0.89 61.03
Acc
LO
(no$min
21
) 25.4 62.2 25.6 62.7 26.6 62.5 20.08 61.01 20.46 60.92 20.35 61.03
*Stand = 0–0.2 m$s
21
; walk = 0.2–1.8 m$s
21
; jog = 1.8–3.3 m$s
21
; run = 3.3–5.7 m$s
21
; sprint = .5.7 m$s
21
; Dec
HI
=$22.78 m$s
22
; Dec
MO
=22.78 to 21.11 m$s
22
;
Dec
LO
=21.11 to 0 m$s
22
;Acc
HI
=$2.78 m$s
22
; Acc
MO
= 1.11–2.78 m$s
22
; Acc
LO
= 0–1.1 m$s
22
;ES690% CI = effect size 690% confidence interval. Differences (ES 6
90% CI) were considered likely positive when there was more than a 75% likelihood of the difference exceeding an ES threshold of 0.2.
A$75% likely positive difference from midfield.
zA$75% likely positive difference from attack.
§A$75% likely positive difference from defense.
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accelerations in Australian football and decelerations in rugby
league were reduced in the latter part of matches indicative of
fatigue (1,26).
To date, only 2 studies using video-based time motion
analysis (TMA) exist on the activity profile of lacrosse
(25,30). Compared with other team ball sports, lacrosse ex-
hibited the second longest duration of high-velocity move-
ments (lacrosse, 1.05 60.60 s; soccer, 1.33 60.86 s; field
hockey, 0.96 60.5 s; handball, 0.67 60.36 s; basketball,
0.64 60.37 s) and recorded a high-average speed of
5.25 61.06 m$min
21
(30). When compared by position,
midfielders played less game time (53%) but had covered
higher relative distance (133 m$min
21
) and a greater contri-
bution of running (18%) and sprinting (7%) than attackers
(97 m$min
21
; 10% and 4%, respectively) (25). Although
a basic knowledge of the movement performed by lacrosse
players exists, no detailed activity profile has been conducted
with objective analysis between positions or quarters of
match-play. Furthermore, the use of video-based TMA used
in these previous studies has numerous limitations including
poor-to-moderate reliability as opposed to GPS (12,27).
Therefore, the aim of this study was to quantify the activity
profile of top level Australian lacrosse players in match-play
using GPS and associated microtechnology.
METHODS
Experimental Approach to the Problem
The activity profile patterns of lacrosse match-play were
measured using microtechnology devices (GPS and accel-
erometers). Data were col-
lected from outfield players in
1 team during 4 matches of
a National Championship tour-
nament. Matches were 80
minutes in duration (4 320-
minute quarters) played on
consecutive days. Similar to
previous studies, players were
required to have played at least
50% of game time compared
with their positional average
to be included in data analysis
(1,9). Players were classified as
midfield, attack, and defense
for positional comparisons.
Subjects
Fifteen male lacrosse players
from a state team participating
in the National Championships
provided data for this study. All
participants were outfield play-
ers (goalkeepers were excluded)
and played in each match of the
tournament. Before the com-
mencement of the study, participants were provided with
a written description outlining the purposes, procedures,
benefits, and risks associated with the study. Written informed
consent was then obtained from all participants. The study
was approved by the Australian Catholic University Human
Research Ethics Committee.
Procedures
Microtechnology Device. Activity profile data were collected
using MinimaxX, GPS units (Catapult Innovations, Team S4,
Melbourne, Australia), sampling at 10 Hz, that also con-
tained triaxial accelerometers (KXP94; Kionix, New York,
NY, USA) sampling at 100 Hz. Accelerometers measured
the magnitude and frequency of movement in 3 planes
(anterior-posterior, medio-lateral, and vertical) reported as
player load (PL) (5). Player load is calculated as the square
root of the sum of the squared instantaneous rate of change
in acceleration in each of the 3 vectors (4). A high validity
and reliability for MinimaxX 10 Hz GPS and accelerometers
(coefficient of variation [CV] ,2%) has previously been re-
ported (4,8,33). The microtechnology device used in this
study (88 350 319 mm) was positioned between the
scapulae in a custom built harness worn under playing attire.
Participants had previously worn the same units in compe-
tition and each player was assigned the same device in each
match to minimize potential error (20).
Activity Profile Data. Players were divided into 3 positional
groups (midfield, attack, and defense) for comparison.
Variables were expressed per minute of playing time and
Figure 1. The relative mean 6SD comparisons for velocity within positions. Differences (ES 690% CI) were
considered likely positive when there was more than a 75% likelihood of the difference exceeding an ES threshold
of 0.2. ES = effect size; CI = confidence interval; Q1 = quarter 1; Q2 = quarter 2; Q3 = quarter 3; Q4 = quarter 4;
S = stand; W = walk; J = jog; R = run; Sp = sprint; 6indicates $75% likely positive difference from all quarters; *
indicates $75% likely positive difference from Q2; uindicates $75% likely positive difference from Q3; #
indicates $75% likely positive difference from Q4.
Activity Profile of High-Level Australian Lacrosse Players
128
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included relative distance (meter per minute) and relative PL
(PL per minute). Velocity categories included stand (0–0.1
m$s
21
), walk (0.2–1.7 m$s
21
), jog (1.8–3.2 m$s
21
), run (3.3–
5.6 m$s
21
), and sprint ($5.7 m$s
21
) (13). The number of ac-
celerations and decelerations per minute was recorded and
categorized as high accelerations (Acc
HI
no$min
21
,$2.78
m$s
22
), moderate accelerations (Acc
MO
no$min
21
, 1.11–2.78
m$s
22
), low accelerations (Acc
LO
no$min
21
, 0–1.11 m$s
22
)
and high decelerations (Dec
HI
no$min
21
,$22.78 m$s
22
),
moderate decelerations (Dec
MO
no$min
21
,22.78 to 21.11
m$s
22
), low decelerations (Dec
LO
no$min
21
,21.11 to
0m$s
22
). The velocity ranges in this study are based on the
velocity distribution curves of team sport athletes (13). Maximal
accelerations of $2.78 m$s
22
were used as described previously
(1). Overall position data were calculated as the mean of all 4
tournament matches. Data were analyzed from a total of 51
samples (midfield, n=18;attack,n= 15; and defense, n=18).
Statistical Analyses
Descriptive data relative to playing time is presented as mean 6
SD. Data were log transformed to reduce bias due to nonuni-
formity of error and analyzed using the effect size (ES) statistic
690% confidence interval (CI). Comparisons between positions
(midfield vs. attack, midfield vs. defense, and attack vs. defense)
and comparisons within positions between quarters (e.g., mid-
field quarter 1 vs. midfield quarter 2) were assessed and mean
differences in activity profile variables analyzed using custom-
ized spreadsheets (16,17). Differ-
ences (ES 690% CI) were
considered likely positive or neg-
ative when there was more than
a 75% likelihood of the difference
exceeding an ES threshold of 0.2
(16). Differences with less cer-
tainty were considered trivial,
and where the 90% CI crossed
boundaries of likely positive and
negative, the difference was con-
sidered unclear (16).
RESULTS
Match Activity Profile
The mean 6SD absolute
distances per match for posi-
tionswereasfollows:mid-
field, 3591 61180 m; attack,
4038 6884 m; and defense,
4427 61198 m. The mean
6SD absolute PL per match
for positions were as follows:
midfield, 348 698 au; attack,
380 691 au; and defense,
409 6100 au. Total mean 6
SD absolute distances for
each of the velocity ranges
were as follows: (a) stand—midfield: 127 6113 m, attack:
189 6114 m, and defense: 194 6100 m; (b) walk—mid-
field: 1061 6383 m, attack: 1385 6366 m, and defense:
1991 6854 m; (c) jog—midfield: 1065 6387 m, attack:
1150 6261 m, defense: 1130 6342 m; (d) run—midfield:
1138 6332 m, attack: 1170 6286 m, and defense: 968 6
219 m; and (e) sprint—midfield: 200 689 m, attack: 143 6
63 m, and defense: 139 695 m. Differences in playing
time, relative distances covered in various locomotor cat-
egories, and relative number of accelerations and deceler-
ations between positions are shown in Table 1.
Quarter Comparisons: Midfield
Figures 1–3 display the relative mean 6SD for all activity
profile variables per quarter and the likelihood ($75% likely)
that differences exist between quarters within the respective
positional groups. Midfielders produced the highest meters
per minute in quarter 1 compared with all other quarters
(ES = 1.95 60.91 [Q2]; 2.61 60.90 [Q3]; 2.03 60.97
[Q4]) and the highest PL per minute in quarter 1 compared
with all other quarters (ES = 1.95 60.92 [Q2]; 2.00 61.00
[Q3]; 1.74 60.93 [Q4]) (Figure 3). However, there were no
clear differences in distances covered standing (0–0.1 m$s
21
)
and walking by midfielders throughout a match. Midfielders
covered greater meters per minute in the following locomo-
tor categories in quarter 1 in comparison with all other quar-
ters: jog (ES = 0.72 60.86 [Q2]; 0.95 60.51 [Q3]; 0.92 6
Figure 2. The relative mean 6SD comparisons of accelerations and decelerations within positions. Differences
(ES 690% CI) were considered likely positive when there was more than a 75% likelihood of the difference
exceeding an ES threshold of 0.2. ES = effect size; CI = confidence interval; Q1 = quarter 1; Q2 = quarter 2;
Q3 = quarter 3; Q4 = quarter 4; Acc
HI
= high accelerations ($2.78 m$s
22
); Acc
MO
= moderate accelerations
(1.11–2.78 m$s
22
); Dec
HI
= high decelerations ($22.78 m$s
22
); Dec
MO
= moderate decelerations (22.78 to
21.11 m$s
22
); 6indicates $75% likely positive difference from all quarters; aindicates $75% likely positive
difference from Q1; * indicates $75% likely positive difference from Q2; uindicates $75% likely positive
difference from Q3; # indicates $75% likely positive difference from Q4; xindicates trivial difference from Q4.
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0.72 [Q4]), run (ES = 1.07 60.99 [Q2]; 1.14 60.50 [Q3];
1.31 60.60 [Q4]), and sprint (ES = 0.92 61.01 [Q2]; 0.90 6
0.93 [Q3]; 1.13 60.72 [Q4]) (Figure 1). The highest num-
ber of Dec
HI
no$min
21
and Acc
HI
no$min
21
occurred in
quarter 1 and was reduced in quarters 2 (ES = 20.77 6
0.90; 20.30 60.98) and quarter 4 (ES = 20.69 60.47;
20.55 60.58).Similarly,therespectivenumberofDec
HI
no$min
21
and Acc
HI
no$min
21
was higher in quarter 3 and
reduced in quarter 2 (ES = 20.47 60.69; 20.34 61.37)
and quarter 4 (ES = 20.39 60.46; 20.43 60.57). Further-
more, Acc
MO
no$min
21
was highest in quarter 1 and
reduced in quarters 3 (ES = 20.92 60.66) and 4 (ES =
20.83 60.49). Similarly, Dec
MO
no$min
21
also reduced in
quarters 3 (ES = 21.04 60.71) and 4 (ES = 20.69 60.72)
compared with quarter 1 and
was also reduced in quarter 3
when compared with quarter
2(ES=20.53 60.76). The
difference in Acc
LO
no$min
21
and Dec
LO
no$min
21
bet-
ween all quarters was unclear
(Figure 2).
Quarter Comparisons: Attack
The highest meter per minute
(ES = 0.58 60.74 [Q2]; 0.65 6
0.70 [Q3]; 0.68 60.34 [Q4])
and PL per minute (ES = 0.47
60.56 [Q2]; 0.58 60.67 [Q3];
0.63 60.39 [Q4]) occurred in
quarter 1 when compared with
all other quarters (Figure 3).
Attackers also performed the
highest amount of walk meter
per minute in quarter 1 in com-
parison with all other quarters
(ES = 1.02 60.81 [Q2]; 1.34 6
1.17 [Q3]; 0.50 60.51 [Q4]) as
well as a greater amount of
walk meter per minute in quar-
ter 4 when compared with
quarter 3 (ES = 0.84 61.08).
The highest amount of jog
meter per minute was observed
in quarter 1 and reduced in
quarters 2 (ES = 20.57 6
0.78) and 4 (ES = 20.48 6
0.19) with amounts of run
meter per minute similarly
highest in quarter 1 but only
reduced in quarter 4 (ES =
20.59 60.54). Sprint meter
per minute was highest for at-
tackers in quarter 2 and
reduced in quarter 4 (ES =
20.73 61.35) (Figure 1). The number of Dec
HI
no$min
21
was highest in quarter 1 and reduced in all subsequent quar-
ters (ES = 21.13 60.71 [Q2]; 21.05 61.51 [Q3]; 20.95 6
0.79 [Q4]), whereas Acc
HI
no$min
21
was highest in quarter
1 but only reduced in quarter 3 (ES = 20.55 60.72). Sim-
ilarly, Dec
LO
no$min
21
was greatest in quarter 1 and
reduced in comparison with all quarters (ES = 20.73 6
0.85 [Q2]; 20.83 60.67 [Q3]; 20.38 60.27 [Q4]) with
the subsequent Acc
LO
no$min
21
highest in quarter 1 but
only reduced in quarters 2 (ES = 20.63 60.85) and 4 (ES
=20.74 60.93). Acc
MO
no$min
21
and Dec
MO
no$min
21
were highest in quarter 1 and reduced in quarter 4 (ES =
20.88 60.72; 20.54 60.38) (Figure 2). There was no clear
change in standing between quarters.
Figure 3. Relative distance (meter per minute) (A) and relative PL (PL per minute) (B) comparisons between
quarters by position. Differences (ES 690% CI) were considered likely positive or when there was more than
a 75% likelihood of the difference exceeding an ES threshold of 0.2. ES = effect size; CI = confidence interval;
Q1 = quarter 1; Q2 = quarter 2; Q3 = quarter 3; Q4 = quarter 4; 6indicates $75% likely positive difference
from all quarters; * indicates $75% likely positive difference from Q2; # indicates $75% likely positive
difference from Q4.
Activity Profile of High-Level Australian Lacrosse Players
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TABLE 2. Midfield vs. attack positional comparison between quarters of a national lacrosse tournament.*
Midfield vs. attack
Q1 Q2 Q3 Q4
ES 6CI Qual ES 6CI Qual ES 6CI Qual ES 6CI Qual
Relative distance (m$s
21
) 1.45 61.04 Likely positive 0.29 61.04 Unclear 0.25 60.95 Unclear 0.37 60.88 Unclear
Relative PL (PL$min
21
) 0.95 61.07 Likely positive 0.34 61.07 Unclear 0.39 60.95 Unclear 0.70 60.95 Likely positive
Stand (m$s
21
)20.71 60.92 Likely negative 20.23 60.91 Unclear 20.47 61.06 Unclear 21.13 60.92 Likely negative
Walk (m$s
21
)20.88 60.87 Likely negative 0.28 60.87 Unclear 0.28 60.88 Unclear 20.52 60.98 Unclear
Jog (m$s
21
) 1.03 620.95 Likely positive 0.40 60.96 Unclear 0.19 60.95 Unclear 0.25 60.88 Unclear
Run (m$s
21
) 1.25 61.04 Likely positive 0.16 60.98 Unclear 0.40 60.93 Unclear 0.44 60.87 Unclear
Sprint (m$s
21
) 1.89 61.07 Likely positive 0.60 60.97 Likely positive 0.66 61.01 Likely positive 0.80 61.01 Likely positive
Dec
HI
(no$min
21
) 0.56 60.90 Likely positive 0.32 60.90 Unclear 0.66 61.12 Likely positive 0.64 60.87 Likely positive
Dec
MO
(no$min
21
) 2.00 60.90 Likely positive 0.67 61.04 Likely positive 0.31 61.04 Unclear 1.28 60.91 Likely positive
Dec
LO
(no$min
21
)20.34 60.93 Unclear 0.27 60.89 Unclear 0.83 60.98 Likely positive 20.11 61.00 Unclear
Acc
HI
(no$min
21
) 1.04 60.99 Likely positive 0.70 60.93 Likely positive 1.36 61.01 Likely positive 0.51 60.98 Unclear
Acc
MO
(no$min
21
) 1.34 60.95 Likely positive 0.36 60.93 Unclear 0.06 61.07 Unclear 0.96 60.87 Likely positive
Acc
LO
(no$min
21
)20.02 60.93 Unclear 0.23 60.86 Unclear 0.63 60.96 Likely positive 20.45 61.04 Unclear
*PL$min
21
= player load per minute; stand = 0–0.2 m$s
21
; walk = 0.2–1.8 m$s
21
; jog = 1.8–3.3 m$s
21
; run = 3.3–5.7 m$s
21
; sprint = .5.7 m$s
21
; Dec
HI
=$22.78 m$s
22
;
Dec
MO
=22.78 to 21.11 m$s
22
; Dec
LO
=21.11 to 0 m$s
22
; Acc
HI
=$2.78 m$s
22
; Acc
MO
= 1.11–2.78 m$s
22
; Acc
LO
= 0–1.1 m$s
22
;ES690% CI = effect size 690%
confidence interval; Qual = qualitative outcome. Differences (ES 690% CI) were considered likely positive or when there was more than a 75% likelihood of the difference exceeding
an ES threshold of 0.2.
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TABLE 3. Midfield vs. defense positional comparison between quarters of a national lacrosse tournament.*
Midfield vs. defense
Q1 Q2 Q3 Q4
ES 6CI Qual ES 6CI Qual ES 6CI Qual ES 6CI Qual
Relative distance (m$s
21
) 2.29 60.96 Likely positive 1.04 60.89 Likely positive 0.43 60.91 Unclear 0.88 60.86 Likely positive
Relative PL (PL$min
21
) 0.95 61.07 Likely positive 0.77 60.96 Likely positive 0.43 60.92 Unclear 0.99 60.91 Likely positive
Stand (m$s
21
)20.69 60.86 Likely negative 20.02 60.86 Unclear 0.00 60.96 Unclear 21.14 60.88 Likely negative
Walk (m$s
21
)20.59 60.93 Likely negative 0.08 60.86 Unclear 20.18 60.87 Unclear 20.63 60.96 Likely negative
Jog (m$s
21
) 1.23 61.07 Likely positive 1.08 60.86 Likely positive 0.49 60.90 Unclear 1.14 60.85 Likely positive
Run (m$s
21
) 1.67 60.96 Likely positive 0.82 60.93 Likely positive 1.04 60.93 Likely positive 0.93 60.91 Likely positive
Sprint (m$s
21
) 1.38 60.97 Likely positive 0.98 60.95 Likely positive 0.66 60.96 Likely positive 0.79 60.95 Likely positive
Dec
HI
(no$min
21
) 1.30 60.91 Likely positive 0.05 60.86 Unclear 0.39 60.93 Unclear 0.43 60.93 Unclear
Dec
MO
(no$min
21
) 1.37 60.98 Likely positive 0.96 60.89 Likely positive 0.25 60.90 Unclear 2.31 60.86 Likely positive
Dec
LO
(no$min
21
)20.08 60.92 Unclear 0.23 60.86 Unclear 0.22 60.95 Unclear 20.12 60.93 Unclear
Acc
HI
(no$min
21
) 1.14 60.89 Likely positive 0.51 60.87 Unclear 0.72 60.93 Likely positive 0.38 60.90 Unclear
Acc
MO
(no$min
21
) 1.58 60.94 Likely positive 1.54 60.86 Likely positive 0.56 60.86 Likely positive 1.02 60.90 Likely positive
Acc
LO
(no$min
21
)20.05 60.90 Unclear 0.22 60.86 Unclear 0.32 60.92 Unclear 20.46 60.93 Unclear
*PL$min
21
= player load per minute; stand = 0–0.2 m$s
21
; walk = 0.2–1.8 m$s
21
; jog = 1.8–3.3 m$s
21
; run = 3.3–5.7 m$s
21
; sprint = .5.7 m$s
21
; Dec
HI
=$22.78 m$s
22
;
Dec
MO
=22.78 to 21.11 m$s
22
; Dec
LO
=21.11 to 0 m$s
22
; Acc
HI
=$2.78 m$s
22
; Acc
MO
= 1.11–2.78 m$s
22
; Acc
LO
= 0–1.1 m$s
22
;ES690% CI = effect size 690%
confidence interval; Qual = qualitative outcome. Differences (ES 690% CI) were considered likely positive or when there was more than a 75% likelihood of the difference exceeding
an ES threshold of 0.2.
Activity Profile of High-Level Australian Lacrosse Players
132
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TABLE 4. Attack vs. defense positional comparison between quarters of a national lacrosse tournament.*
Attack vs. defense
Q1 Q2 Q3 Q4
ES 6CI Qual ES 6CI Qual ES 6CI Qual ES 6CI Qual
Relative distance (m$s
21
) 0.76 61.03 Likely positive 0.41 61.09 Unclear 0.21 61.02 Unclear 0.79 61.03 Likely positive
Relative PL (PL$min
21
) 0.44 61.02 Unclear 0.29 61.03 Unclear 0.08 61.02 Unclear 0.37 61.02 Unclear
Stand (m$s
21
) 0.12 61.07 Unclear 0.30 61.05 Unclear 0.43 61.05 Unclear 0.02 61.03 Unclear
Walk (m$s
21
) 0.01 60.98 Unclear 20.33 61.01 Unclear 20.66 61.02 Likely negative 20.24 61.00 Unclear
Jog (m$s
21
) 1.11 61.03 Likely positive 0.58 61.08 Unclear 0.32 61.03 Unclear 1.50 61.04 Likely positive
Run (m$s
21)
0.66 61.01 Likely positive 0.65 61.02 Likely positive 0.79 60.99 Likely positive 0.72 60.99 Likely positive
Sprint (m$s
21
) 0.02 61.00 Unclear 0.55 60.99 Unclear 0.10 61.01 Unclear 0.06 61.02 Unclear
Dec
HI
(no$min
21
) 0.98 60.99 Likely positive 20.38 61.03 Unclear 20.43 61.09 Unclear 0.00 60.99 Unclear
Dec
MO
(no$min
21
) 0.42 61.01 Unclear 0.02 61.09 Unclear 20.13 61.07 Unclear 1.36 61.03 Likely positive
Dec
LO
(no$min
21
) 0.22 61.01 Unclear 20.06 61.03 Unclear 20.45 61.01 Unclear 20.02 61.02 Unclear
Acc
HI
(no$min
21
) 0.13 61.03 Unclear 20.29 61.05 Unclear 20.75 61.05 Likely positive 20.13 61.03 Unclear
Acc
MO
(no$min
21
) 0.65 60.99 Likely positive 1.34 61.08 Likely positive 0.27 61.11 Unclear 0.27 61.00 Unclear
Acc
LO
(no$min
21
)20.3 61.02 Unclear 0.00 61.02 Unclear 20.25 61.02 Unclear 0.06 61.05 Unclear
*PL$min
21
= player load per minute; stand = 0–0.2 m$s
21
; walk = 0.2–1.8 m$s
21
; jog = 1.8–3.3 m$s
21
; run = 3.3–5.7 m$s
21
; sprint = .5.7 m$s
21
; Dec
HI
=$22.78 m$s
22
;
Dec
MO
=22.78 to 21.11 m$s
22
; Dec
LO
=21.11 to 0 m$s
22
; Acc
HI
=$2.78 m$s
22
; Acc
MO
= 1.11–2.78 m$s
22
; Acc
LO
= 0–1.1 m$s
22
;ES690% CI = effect size 690%
confidence interval; Qual = qualitative outcome. Differences (ES 690% CI) were considered likely positive when there was more than a 75% likelihood of the difference exceeding an
ES threshold of 0.2.
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Quarter Comparisons: Defense
Similar to midfielders and attackers, meter per minute and PL
per minute were highest in quarter 1. However, both were
only reduced in quarters 2 (ES = 20.63 60.45; 20.33 60.28)
and quarter 4 (ES = 20.89 60.38; 20.51 60.35) with differ-
ences between quarters 1 and 3 considered unclear (Figure 1).
The contribution of sprint meter per minute was highest for
defenders in quarter 1 compared with all other quarters (ES =
0.42 60.57 [Q2]; 0.44 60.66 [Q3]; 0.51 60.72 [Q4]),
whereas stand meter per minute seemed greatest in quarter
4 compared with all other quarters (ES = 0.34 60.30 [Q1];
0.56 60.63 [Q2]; 0.43 60.65 [Q3]). Jog meter per minute
values were highest in quarters 1 and 3 but reduced in quarters
2(ES=20.86 61.16; 20.76 61.31) and 4 (ES = 20.94 6
0.56; 20.85 60.71). Additionally, run meter per minute was
highest in quarter 1 and reduced after half time in quarters 3
(ES = 20.39 60.48) and 4 (ES = 20.48 60.29). The differ-
ence in walking between quarters was unclear (Figure 1).
Acc
HI
no$min
21
was greatest in quarter 3 compared with
all other quarters (ES = 0.37 60.44 [Q1]; 0.42 60.45
[Q2]; 0.48 60.32 [Q4]) with trivial differences existing
between quarters 2 and 4 (ES = 0.06 60.24). Similarly, Dec
HI
no$min
21
was greatest in quarter 3 compared with quarters 1
(ES = 0.51 60.51) and 4 (ES = 0.56 60.47). In contrast, both
Acc
LO
no$min
21
and Dec
LO
no$min
21
were highest in quar-
ter 1 and reduced in quarter 2, respectively (ES = 20.53 6
0.68; 20.58 60.59). Acc
MO
no$min
21
were highest in quar-
ters 1 and 3 and reduced in quarters 2 (ES = 20.84 61.10;
20.75 60.62) and 4 (ES = 20.47 60.67; 20.47 60.67). The
number of Dec
MO
no$min
21
was lowest in quarter 4
compared with all other quarters (ES = 21.02 60.49 [Q1];
20.62 60.66 [Q2]; 20.90 60.51 [Q3]) (Figure 2).
Quarter Comparisons Between Positions
The mean 6SD of all activity profile variables by position
are shown in Figures 1–3. Likely differences in activity pro-
file variables are illustrated between midfield and attack
(Table 2), midfield and defense (Table 3), and attack and
defense (Table 4). Midfielders performed more sprinting
when compared with attackers across all quarters (ES =
1.89 61.07 [Q1]; 0.60 60.97 [Q2]; 0.66 61.01 [Q3]; Q4
0.80 61.01) (Table 2) and performed more sprinting (ES =
1.38 60.97 [Q1]; 0.98 60.95 [Q2]; 0.66 60.96 [Q3]; 0.79 6
0.95 [Q4]) and running (ES = 1.67 60.96 [Q1]; 0.82 60.93
[Q2]; 1.04 60.93 [Q3]; 0.93 60.91 [Q4]) than defenders
(Table 3). Likely differences were also observed in a range of
accelerations and decelerations across quarters with the great-
est magnitude observed with high accelerations and decelera-
tions. Both attackers and defenders performed greater amounts
of standing and walking when compared with midfielders with
differences between attackers and defenders unclear.
DISCUSSION
This is the first study to objectively quantify lacrosse match-
play using GPS and associated microtechnology. Our
findings demonstrate that midfielders exhibited the greatest
activity per minute of playing time (100 m$s
21
) and greater
contributions of jog meter per minute, run meter per minute,
and sprint meter per minute in comparison with attackers
and defenders. Furthermore, it was established that midfield-
ers performed a greater number of Acc
MO
no$min
21
, Acc
HI
no$min
21
, and Dec
HI
no$min
21
per match relative to play-
ing time and exhibited higher PL per minute values. Defend-
ers played the greatest amount of field time (;59 minutes)
and had similar activity profiles to attackers.
The present findings are in contrast to the only other
TMA study of lacrosse (25). Previous research determined
that midfielders covered greater relative distance compared
with attackers and exhibited greater contributions from jog-
ging, running, and sprinting to TD. However, despite the
similarities in the relative contributions of jogging, running,
and sprinting, values in this study (attack: TD = 4038 m, 87
m$s
21
; midfield: TD = 3591 m, 100 m$s
21
) were consider-
ably lower than those reported previously (attack: TD =
10,906 m, 97 m$s
21
; midfield: TD = 7429 m, 133 m$s
21
)
(25). The disparity in absolute results may be explained by
the shorter matches (20- vs. 25-minute quarters) and
reduced playing time of participants in this study (midfield,
;36 minutes; attack, ;48 minutes) as opposed to percent-
age of time spent on the field for midfield (53%) and attack
(100%) in previous studies. Although it is not clear why
relative intensity was lower in this study particularly given
it was a higher level of competition, it is possible that the
differences in methodology in each study and the measure-
ment error of subjective interpretation in video TMA could
account for the large variations in results (12,27).
A novel aspect of this investigation is the analysis of the
activity profile between positions by quarter. It is evident from
other field sports that differences can exist due to the various
roles required by position (6,26,34). In many sports (e.g., Aus-
tralian football and soccer), midfielders are required to cover
the entire field and provide support to both defending and
attacking players (6,10). Differences in the activity profiles
between lacrosse positions might therefore be expected (10).
This was observed in this study as midfielders had a likely
higher activity profile and likely positive differences in a num-
ber of activity profile variables when compared with both
attackers and defenders. Despite this, numerous differences
between positions in quarter 1 became unclear in quarters 2
and 3, respectively. It is possible that due to the higher early
activity of midfielders in quarter 1, they may reduce their
activity profile to a greater extent than attackers and defend-
ers, thus reducing the likelihood of a clear difference in activity
profiles between positions (9). Interestingly, the likely differ-
ences that occurred in quarter 1 reappeared in quarter 4. It is
possible that a combination of several factors may account for
this. First, the lower total game time for midfielders
(;36 minutes) may allow the maintenance of their activity
profile later in matches (23), whereas the higher game time for
attackers (;48 minutes) and defenders (;59 minutes) may
Activity Profile of High-Level Australian Lacrosse Players
134
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induce fatigue and subsequent reductions in their activity pro-
file (1,9,26). Furthermore, consideration must be given to the
score and tactics of the match as the activity profile for at-
tackers and defenders may be somewhat dependent on posi-
tional restrictions, the location of the ball, and the amount of
time spent actively involved in match-play (11,22).
As stated previously, it seems evident that first half activity
affects second half activity in team sport (9,26), with high-
intensity activity shown to decrease in the latter stages of
matches (1,11,22,26). In this investigation, a number of activity
profile variables across all positions were reduced in the latter
part of matches. Future research could be directed to investi-
gate whether fatigue, physical capacity, or number of inter-
changes has a relationship with the magnitude of change
within positions, similar to research conducted in Australian
football (23). It is noted that frequent rotations allowing for
recovery may help resynthesize phosphocreatine in prepara-
tion for high-intensity efforts (23). Because of the high-intensity
intermittent nature of lacrosse and frequency of interchange, it
is possible that the physical capacity of players and an effective
interchange strategy may influence these findings (23).
A limitation of previous TMA techniques is that the
assessment of brief high-intensity activities such as acceler-
ations and decelerations has been difficult (2,19). Higher sam-
ple rate (i.e., 10 Hz) GPS units have now improved the
accuracy of measurement of these activities (33). In this study,
both the number of high and moderate accelerations and
decelerations per minute for midfielders and attackers were
reduced toward the end of the game. These findings concur
with those in rugby and Australian football that determined
maximal decelerations and accelerations were reduced in the
latter part of matches (2,26). Reasons for this seem unclear but
could include the higher physiological cost of these activities
(31), fatigue (1,9), and match context. Interestingly, although
and in contrast to other positions, defenders performed their
highest number of maximal accelerations and decelerations in
quarter 3. The precise mechanism for this is difficult to deter-
mine but may be related to match situation including score.
PRACTICAL APPLICATIONS
These findings suggest that differences in activity profile exist
both within matches and between positions in high-level
Australian lacrosse players. As activity profiles are somewhat
position dependent, this should be considered when pre-
scribing conditioning sessions. Coaches should be aware of
the greater meter per minute, PL per minute and relative
contribution to locomotion from high-intensity movement for
midfielders, and the reduction in the activity profile in all
positions after the first quarter. Furthermore, players with
higher meter per minute, PL per minute, and greater amounts
of Acc
HI
no$min
21
and Dec
HI
no$min
21
may require periods
of longer recovery and efficient interchange strategies to
maintain higher intensity activity (23). It is recommended that
individualized programs be developed to suit the specific posi-
tional and tactical requirements of lacrosse.
ACKNOWLEDGMENTS
The authors thank the players and staff of the Victorian
Lacrosse team for their participation in this study. There has
been no financial assistance for this study, and no conflicts of
interest are declared.
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Activity Profile of High-Level Australian Lacrosse Players
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... Studies using Player Load™ have found that data from microtechnology devices produced by Catapult Sports can discriminate between playing levels (Boyd et al., 2013), playing positions (Boyd et al., 2013;Gabbett, Jenkins, & Abernethy, 2012;Jones, West, Crewther, Cook, & Kilduff, 2015;Polley, Cormack, Gabbett, & Polglaze, 2015), periods of a game (Cormack, Smith, Mooney, Young, & O'Brien, 2014;Jones et al., 2015;Polley et al., 2015), training and competition (Boyd et al., 2013;Gabbett et al., 2012;Montgomery, Pyne, & Minahan, 2010), and is related to technical skills (Sullivan et al., 2014b). It is also found that Player Load™ may be a practical measure to quantify external training load (Gallo et al., 2014;Scott et al., 2013), and may be an indicator of muscle damage (Young, Hepner, & Robbins, 2012). ...
... Studies using Player Load™ have found that data from microtechnology devices produced by Catapult Sports can discriminate between playing levels (Boyd et al., 2013), playing positions (Boyd et al., 2013;Gabbett, Jenkins, & Abernethy, 2012;Jones, West, Crewther, Cook, & Kilduff, 2015;Polley, Cormack, Gabbett, & Polglaze, 2015), periods of a game (Cormack, Smith, Mooney, Young, & O'Brien, 2014;Jones et al., 2015;Polley et al., 2015), training and competition (Boyd et al., 2013;Gabbett et al., 2012;Montgomery, Pyne, & Minahan, 2010), and is related to technical skills (Sullivan et al., 2014b). It is also found that Player Load™ may be a practical measure to quantify external training load (Gallo et al., 2014;Scott et al., 2013), and may be an indicator of muscle damage (Young, Hepner, & Robbins, 2012). ...
... Researchers frequently divide games into shorter periods, or quarters where this is natural, in order to more accurately describe changes in activity throughout matches. Declines in activity measures, relative to playing time, are reported across periods of five to 15 minutes in soccer (Bradley & Noakes, 2013), rugby union (Jones et al., 2015), netball , lacrosse (Polley et al., 2015), Australian football Aughey, 2010), and rugby league (Sykes, Twist, Nicholas, & Lamb, 2011;Waldron, Highton, Daniels, & Twist, 2013). Absolute or high-intensity distance also decreased through periods of ten to 15 minutes in soccer (Bradley et al., 2009;Carling & Dupont, 2011;Mohr et al., 2008;Mohr et al., 2003), rugby league (Kempton, Sirotic, & Coutts, 2015) and rugby union (Roberts et al., 2008). ...
Thesis
Handball matches place diverse physical demands on players, which over the course of games may result in fatigue and decreased activity levels. However, studies are limited, and activity profiles are often obtained using video recordings, although this instrument may not be sensitive for capturing short-lasting handball-specific movements. The purpose of this master thesis is therefore to examine activity profiles and fatigue development, for teams and individual players of both genders, using modern microtechnology devices. A microtechnology device (Catapult OptimEye S5) was worn by elite players in a female national team (6 matches, n = 55 samples), and a male national recruit team (3 matches, n = 36 samples), during international tournament matches. Activity profiles were examined on a team- and individual player level, with special regards to possible fatigue development during games. Analyses were performed for Player Load™, accelerations, decelerations, and changes of direction (CoD), as well as high- and moderate-intensity efforts combined (HMI) and lowintensity efforts (LI), all relative to playing time.
... Lacrosse matches comprise 4 × 15-min quarters (total = 60 min) with 2 × 2-min intervals and a 10-min half-time break 5) . Match play is characterized by intermittent highintensity activity, collisions, and rapid changes in direc-tion 6,7) . Because the rate of metabolic heat production is a function of the intensity of physical exertion 1) , intermittent high-intensity exercise such as lacrosse results in greater heat production, which leads to a rapid rise in core body temperature 8,9) . ...
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Japanese collegiate and club lacrosse games are often held under environmental conditions of extreme heat in the summer. Lacrosse players are also required to wear protective equipment, which is a risk factor for heat-related illness. Nevertheless, the thermoregulatory responses of lacrosse players under such conditions are poorly understood. The present study compared the thermoregulatory responses in subjects wearing the mens’ lacrosse uniform or general athletic clothes during an exercise-simulated lacrosse match played in the heat (high temperatures). Eight men performed two experimental trials of 4 × 15-min intermittent cycling exercise in the heat (35°C, 50% relative humidity). All participants performed two trials wearing a lacrosse uniform (WEAR) or general athletic clothes (CON). The physiological strain index was significantly higher (p < 0.05) in the WEAR condition than in CON, and exceeded the high risk level for heat-related illness in WEAR during the 3rd quarter. Additionally, rectal temperature, skin temperature, heart rate, and ratings for perceived exertion, thermal sensation, and thermal comfort were significantly higher (p < 0.05) in the WEAR condition than in CON. Rectal temperature continued to increase during half-time in the WEAR condition, but not in CON (p < 0.05). These findings indicate that lacrosse players are at a higher risk of heat-related illness compared to other team sport athletes during a match because of the protective equipment worn, especially after half-time. Although further studies are required to confirm the thermoregulatory responses during an actual lacrosse match, our data will be useful in developing strategies to minimize the risk of heat-related illness.
... Most of them focused on experiments for practice strategies as described below. Polley et al. [57] provided velocity band settings and measured workload. Based on the measurement, they suggested that higher intensity activities may require more recovery times. ...
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Background Wearable tracking devices are commonly utilised to quantify the external acceleration load of team sport athletes during training and competition. The ability to accelerate is an important attribute for athletes in many team sports. However, there are many different acceleration metrics that exist in team sport research. This review aimed to provide researchers and practitioners with a clear reporting framework on acceleration variables by outlining the different metrics and calculation processes that have been adopted to quantify acceleration loads in team sport research. Methods A systematic review of three electronic databases (CINAHL, MEDLINE, SPORTDiscus), was performed to identify peer-reviewed studies that published external acceleration load in elite team sports during training and/or competition. Articles published between January 2010 and April 2020 were identified using Boolean search phrases in relation to team sports (population), acceleration/deceleration (comparators), and competition and/or training (outcome). The included studies were required to present external acceleration and/or deceleration load (of any magnitude) from able-bodied athletes (mean age ≥ 18 years) via wearable technologies. Results A total of 124 research articles qualified for inclusion. In total, 113/124 studies utilised GPS/GNSS technology to outline the external acceleration load of athletes. Count-based metrics of acceleration were predominant of all metrics in this review (72%). There was a lack of information surrounding the calculation process of acceleration with 13% of studies specifying the filter used in the processing of athlete data, whilst 32% outlined the minimum effort duration (MED). Markers of GPS/GNSS data quality, including horizontal dilution of precision (HDOP) and the average number of satellites connected, were outlined in 24% and 27% of studies respectively. Conclusions Team sport research has predominantly quantified external acceleration load in training and competition with count-based metrics. Despite the influence of data filtering processes and MEDs upon acceleration, this information is largely omitted from team sport research. Future research that outlines acceleration load should present filtering processes, MEDs, HDOP, and the number of connected satellites. For GPS/GNSS systems, satellite planning tools should document evidence of available satellites for data collection to analyse tracking device performance. The development of a consistent acceleration filtering method should be established to promote consistency in the research of external athlete acceleration loads.
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International Journal of Exercise Science 13(4): 1190-1205, 2020. The purpose of this study was to review acute physiological responses induced by repeated running sprint ability (RRSA) tests that could serve as references for practitioners utilising repeated sprints as a performance measure with athletes. This research was conducted following the PRISMA methodology. The systematic search was conducted in November 2019 and yielded 26 different scientific articles. Only peer-reviewed full-text article were included as abstracts are too short to allow proper explanation of the RRSAT methodology that was employed. According to the present literature, practitioners should use the following assessments: the 6x40m RRSA protocol with one Change of Direction (COD) (20+20 m with a 180° COD) and 25s of passive recovery between sprints with soccer players; the Intensive Repeated Sprint Ability (IRSA) test with men basketball players; the Futsal Intermittent Endurance Test (FIET) with futsal players; the Repeated Shuttle Sprint Test (RSST) with men handball players; and the Multiple Repeated Sprint Ability test for Badminton players (MRSAB). The present review should serve as a reference standard for RRSA tests. Further research should be directed towards creating and validating more specific RRSA tests protocols to each sports physiological and physical demands.
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Accelerometry is a recent method used to quantify workload in team sports. A rapidly increasing number of studies supports the practical implementation of accelerometry monitoring to regulate and optimize training schemes. Therefore, the purposes of this study were: (1) to reflect the current state of knowledge about accelerometry as a method of work-load monitoring in invasion team sports according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, and (2) to conclude recommendations for application and scientific investigations. The Web of Science, PubMed and Scopus databases were searched for relevant published studies according to the following keywords: "accelerometry" or "accelerometer" or "microtechnology" or "inertial devices", and "load" or "workload", and "sport". Of the 1383 studies initially identified, 118 were selected for a full review. The main results indicate that the most frequent findings were (i) devices' body location: scapulae; (b) devices brand: Catapult Sports; (iii) variables: PlayerLoad TM and its variations; (iv) sports: rugby, Australian football, soccer and basketball ; (v) sex: male; (vi) competition level: professional and elite; and (vii) context: separate training or competition. A great number of variables and devices from various companies make the comparability between findings difficult; unification is required. Although the most common location is at scapulae because of its optimal signal reception for time-motion analysis, new methods for multi-location skills and locomotion assessment without losing tracking accuracy should be developed.
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The use of GPS technology for training and research purposes requires a study of the reliability, validity and accuracy of the data generated (Petersen et al., 2009). To date, studies have focused on devices with a logging rate of 1 Hz and 5 Hz (Coutts and Duffield, 2010; Duffield et al., 2010; Jennings et al., 2010; MacLeod et al., 2009; Petersen et al., 2009; Portas et al., 2010), although it seems that more frequent sampling can increase the accuracy of the information provided by these devices (Jennings et al., 2010; MacLeod et al., 2009, Portas et al., 2010). However, we are unaware of any study of the reliability and accuracy of GPS devices using a sampling frequency of 10 Hz. Thus, the aim of the present research was to determine the reliability and accuracy of GPS devices operating at a sampling frequency of 10 Hz, in relation here to sprints of 15 m and 30 m and using both video and photoelectric cells.Nine trained male athletes participated in the study. Each participant completed 7 and 6 linear runs of 15 m and 30 m, respectively (n = 117), with only one GPS device being used per participant. Each repetition required them to complete the route as quickly as possible, with 1 min recovery between sets. Distance was monitored through the use of GPS devices (MinimaxX v4.0, Catapult Innovations, Melbourne, Australia) operating at the above mentioned sampling frequency of 10 Hz. In addition, all tests were filmed with a video camera operating at a sampling frequency of 25 frames. Data were collected during what were considered to be good GPS conditions in terms of the weather and satellite conditions (number of satellites = 10.0 ± 0.2 and 10.3 ± 0.4 for sprints of 15 m and 30 m, respectively).Distance was measured using a tape measure. Electronic timing gates (TAG- Heuer, CP 520 Training model, Switzerland) were used to obtain a criterion sprint time accurate to 0.01 s, with gates being placed at the beginning and end of the route (Petersen et al., 2009). Logan Plus v.4.0 software was used to synchronize the GPS files with the video, establishing the beginning of action when the participant crossed the initial photocell; this was then added to the duration obtained through the photoelectric cells.The accuracy of data within and between devices is shown in Table 1. The average values are close to those established in tests of 15 m and 30 m, with errors getting smaller when the devices were used over 30 m.The intra-device reliability is depicted in Figure 1, showing greater stability over 30 m than 15 m. The inter-device reliability yielded a CV = 1.3% and CV = 0.7% for sprints over 15 m and 30 m, respectively.To our knowledge this is the first study to assess the reliability and accuracy of GPS devices operating at a sampling frequency of 10 Hz. A further point of note is that studies of intra- and inter-device reliability for the same model of device (and therefore the same sampling rate) have traditionally used only two devices (Duffield et al., 2010; Petersen et al., 2009), whereas here a total of nine devices were studied.The distance data were found to be highly accurate and only slightly underestimated by the GPS devices. Furthermore, high intra- and inter-device reliability was observed. Accuracy improved with increased distance, and the mean SEM of 10.9% when running 15 m was reduced by half over 30 m (Table 1). Using similar statistics and methodology, Petersen et al., 2009 found SEM values of between 5% and 24% for MinimaxX devices, and between 3% and 11% with SPI-Pro devices, both at a sampling frequency of 5 Hz. Here, only one device (number 1) produced values above 6% in the 15 m test, while another device (number 2) did so for runs of 30 m. We conclude that the increase in sampling frequency led to increased accuracy of the devices.As regards intra-device reliability, high values were obtained in all cases, and increased when used over 30 m (Figure 1). Some studies have reported differences between devices, even of the same model, suggesting that a player must always be monitored with the same device (Coutts and Duffield, 2010; Duffield et al., 2010). However, we only found small variations between devices, with a CV of 1.3% and 0.7% in runs of 15 m and 30 m, respectively. Therefore, we conclude that it is not always necessary to monitor players with the same device.
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Purpose: The purpose of this study was to determine if Yo-Yo Intermittent Recovery level 2 (Yo-Yo IR2) and the number of interchange rotations affected the match activity profile of elite Australian footballers. Method: Fifteen elite Australian footballers completed the Yo-Yo IR2 before the beginning of the season and played across 22 matches in which match activity profiles were measured via microtechnology devices containing a global positioning system (GPS) and accelerometer. An interchange rotation was counted when a player left the field and was replaced with another player. Yo-Yo IR2 results were further split into high and low groups. Results: Players match speed decreased from 1st to 4th quarter, while average-speed (m/min: P = .05) and low-speed activity (LSA, <15 km/h) per minute (LSA m/min; P = .06) significantly decreased in the 2nd half. Yo-Yo IR2 influenced the amount of m/min, high-speed running (HSR, >15 km/h) per minute (HSR m/min) and accelerometer load/min throughout the entire match. The number of interchanges significantly influenced the HSR m/min and m/min throughout the match except in the 2nd quarter. Furthermore, the low Yo-Yo IR2 group had significantly less LSA m/min in the 4th quarter than the high Yo-Yo IR2 group (92.2 vs 96.7 m/min, P = .06). Conclusions: Both the Yo-Yo IR2 and number of interchanges contribute to m/min and HSR m/min produced by elite Australian footballers, affecting their match activity. However, while it appears that improved Yo-Yo IR2 performance prevents reductions in LSA m/min during a match, higher-speed activities (HSR m/min) and overall physical activity (m/min and load/min) are still reduced in the 4th quarter compared with the 1st quarter.
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Due to the emphasis on preventive sports medicine, it has become increasingly important to identify physiological indices ofathletic performance and predisposition to injury. Although physiological testing is typically used to assess physical status of athletes in numerous sports, no research has been published that address the physiological profile of college lacrosse athletes.Thus this study sought to quantify body composition, maximal aerobic power, anaerobic capacity, blood chemistry, and coronary risk factors on 30 college male club lacrosse athletes. Data were grouped by player position (attack, n = 5; defense, n = 8; goalie, n= 2; midfield, n = 15) and skill level (Ist team, n = 14; 2nd team, n= 16). Results indicated the lacrosse players were above average in some indices of maximal aerobic power during exercise stress testing. They had higher maximal power, mean power, and totalwork output, collectively, than reported for other college athletes. Analysis of body composition revealed a mean body fat of 15% for the entire sample. Mean resting blood chemistries were within normal limits. Based on their exercise response, body composition, and blood lipid profile, these athletes are at low risk for developing coronary heart disease. They also exhibit high anaerobic capacity reflective of this sport.
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Despite the recent popularity and growth of lacrosse, there has been a lack of specific training programs for the sport. This article provides one example of a preseason resistance training program for male lacrosse athletes. The program utilizes a variety of training strategies, including complex and interval weight training, specific to the energy demands of each position.
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We investigated and compared isokinetic strength, anaerobic and intermittent capacity of men’’s lacrosse players at various playing positions. The subjects were 33 men’’s lacrosse players (attacker; AT n = 11, defender; DF n = 10, midfielder;MF n = 12). Measurements contained physical characteristics, isokinetic strength, anaerobic capacity (maximal anaerobic power; Pmax, Wingate anaerobic power; WAnP), and 10-s (R10IC) and 20-s (R20IC) rest intervals of intermittent capacity.Knee flexor strength at angular velocities of 60deg/s and 180deg/s was significantly greater in DF players than in AT players (p <0.05), 300deg/s was signicantly higher in MF players than in AT players (p < 0.05). With regard to Pmax, MF players showed significantly higher values compared with AT players (p < 0.05), and WAnP of MF players showed significantly higher values compared with that of AT and DF players (AT; p < 0.01, DF; p < 0.05). R10IC was significantly higher in MF players than in both AT and DF players (AT:p <0.05, DF:p <0.05), whereas R20IC was signicantly higher in MF players than in AT players (p < 0.05). These results indicate that knee flexor strength, anaerobic capacity, and intermittent capacity are greater in MF than the other positions in men’’s lacrosse.
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The purpose of this investigation was to examine factors influencing college selection by NCAA Division I, II and III softball players. The Influential Factors Survey for Student-Athletes was used to collect data from 21 teams representing 323 female collegiate softball student-athletes. Descriptive statistics showed the most influential factors were: university offers specific major of interest, academic reputation of university, coach's personality/style, academic facilities, opportunity to play early in career, and graduation rate of athletes. Descriptive analysis further revealed the academics category to have the greatest overall influence in the college selection process. A MANOVA revealed significant differences in the college selection process by softball student-athletes at NCAA Division I, II, and III institutions ( p < .05). Recommendations for collegiate softball coaches and athletic department personnel as well as for future research are discussed.
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The purpose of this study was to compare the movement characteristics and intermittent exercise pattern in five ball games: basketball, handball, soccer, field hockey, and lacrosse. In the respective events, six to twelve male college players in Division I were chosen as subjects, and their movement distances and velocities were measured using two-dimensional direct linear transformation (DLT).The main results were as follows:1. Movement distances during the first half were the longest in soccer, followed in order by field hockey, handball, basketball, and lacrosse. However, the movement distance per playing time in each game was longer in field hockey and lacrosse than in the others.2. The rates of low-velocity movement were higher in basketball and soccer, and those of high-velocity movements were higher in handball and lacrosse, than in the others.3. Mean movement durations during low-velocity and high-velocity movements were: basketball 2.78 and 0.64 s; handball 3.54 and 0.67 s; soccer 6.67 and 1.33 s; field hockey 3.70 and 0.96 s; and lacrosse 2.67 and 1.05 s.These results clarified the characteristics of intermittent exercise patterns in each event. In basketball, there were many changes in movement velocity and direction, but the subjects hardly reached top speed. In handball, there was alternation between brief high-velocity movements and prolonged low-velocity movements. In soccer, movement duration was longer, and the mean velocity during low-velocity movement was faster than in the other events. In field hockey, in both low- and high-velocity movements, velocity and duration were comparatively fast and long, respectively. In lacrosse, players performed prolonged high-velocity movement in the field, and took sufficient recovery in the bench area.
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To describe the external load of Australian football matches and training using accelerometers. Nineteen elite and 21 subelite Australian footballers wore accelerometers during matches and training. Accelerometer data were expressed in 2 ways: from all 3 axes (player load; PL) and from all axes when velocity was below 2 m/s (PLSLOW). Differences were determined between 4 playing positions (midfielders, nomadics, deeps, and ruckmen), 2 playing levels (elite and subelite), and matches and training using percentage change and effect size with 90% confidence intervals. In the elite group, midfielders recorded higher PL than nomadics and deeps did (8.8%, 0.59 ± 0.24; 34.2%, 1.83 ± 0.39 respectively), and ruckmen were higher than deeps (37.2%, 1.27 ± 0.51). Elite midfielders, nomadics, and ruckmen recorded higher PLSLOW than deeps (13.5%, 0.65 ± 0.37; 11.7%, 0.55 ± 0.36; and 19.5%, 0.83 ± 0.50, respectively). Subelite midfielders were higher than nomadics, deeps, and ruckmen (14.0%, 1.08 ± 0.30; 31.7%, 2.61 ± 0.42; and 19.9%, 0.81 ± 0.55, respectively), and nomadics and ruckmen were higher than deeps for PL (20.6%, 1.45 ± 0.38; and 17.4%, 0.57 ± 0.55, respectively). Elite midfielders, nomadics, and ruckmen recorded higher PL (7.8%, 0.59 ± 0.29; 12.9%, 0.89 ± 0.25; and 18.0%, 0.67 ± 0.59, respectively) and PLSLOW (9.4%, 0.52 ± 0.30; 11.3%, 0.68 ± 0.25; and 14.1%, 0.84 ± 0.61, respectively) than subelite players. Small-sided games recorded the highest PL and PLSLOW and were the only training drill to equal or exceed the load from matches across positions and playing levels. PL differed between positions, with midfielders the highest, and between playing levels, with elite higher. Differences between matches and training were also evident, with PL from small-sided games equivalent to or higher than matches.