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Comparing external total load, acceleration and deceleration outputs in elite basketball players across positions during match play


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The aim of this study was to compare external load, calculated by an accelerometer training load model, the number and intensity of accelerations and decelerations, and the acceleration:deceleration ratio between playing positions during basketball matches. Twelve elite male basketball players (mean±SD, age: 25.5±5.2 years; (range: 19-36 years); body height 201.4±8.6 cm; body mass: 98.4±12.6 kg) were monitored during two official matches. An accelerometer training load model and the number of accelerations and decelerations were used to assess physical demands imposed on basketball players. Magnitude-based inferences and effect sizes (ES) were used to assess possible differences between positions: point guards (PG), shooting guards (SG), small forwards (SF), power forwards (PF) and centers (C). Elite basketball players in all positions presented higher maximal decelerations than accelerations (ES=2.70 to 6.87) whereas the number of moderate accelerations were higher than the number of moderate decelerations (ES=0.54 to 3.12). Furthermore, the acceleration:deceleration ratio (>3 m∙s-2) was significantly lower in players on the perimeter (PG and SG) than in PF and C (ES=1.03 to 2.21). Finally, PF had the lowest total external load (ES=0.67 to 1.18). These data allow us to enlarge knowledge of the external demands in basketball matches and this information could be used in the planning of training programs
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Vázquez-Guer rero1, J. et al.: COMPARING EXTERNAL TOTAL LOAD,... Kinesiology 50(2018)2:xxx-xxx
Jairo Vázquez-Guerrero1, Luis Suarez-Arrones2,
David Casamichana Gómez3, and Gil Rodas1
1Medical Department, Football Club Barcelona, Barcelona, Spain
2Faculty of Sport. Pablo de Olavide University, Sevilla, Spain
3Faculty of Physiotherapy and Speech Therapy Gimbernat-Cantabria University
School associated with the University of Cantabria (UC), Torrelavega, Spain
Original scientific paper
UDC: 796.011.3: 796.323.2
The aim of this study was to compare external load, calculated by an accelerometer training load model,
the number and intensity of accelerations and decelerations, and the acceleration:deceleration ratio between
playing positions during basketball matches. Twelve elite male basketball players (mean±SD, age: 25.5±5.2
years; (range: 19-36 years); body height 201.4±8.6 cm; body mass: 98.4±12.6 kg) were monitored during two
official matches. An accelerometer training load model and the number of accelerations and decelerations
were used to assess physical demands imposed on basketball players. Magnitude-based inferences and effect
sizes (ES) were used to assess possible differences between positions: point guards (PG), shooting guards
(SG), small forwards (SF), power forwards (PF) and centers (C). Elite basketball players in all positions
presented higher maximal decelerations than accelerations (ES=2.70 to 6.87) whereas the number of moderate
accelerations were higher than the number of moderate decelerations (ES=0.54 to 3.12). Furthermore, the
acceleration:deceleration ratio (>3 m∙s
) was significantly lower in players on the perimeter (PG and SG)
than in PF and C (ES=1.03 to 2.21). Finally, PF had the lowest total external load (ES=0.67 to 1.18). These
data allow us to enlarge knowledge of the external demands in basketball matches and this information could
be used in the planning of training programs.
Key words: team sports, accelerometer, sport performance, GPS, training
High demands in basketball produce major phys-
iological and neuromuscular loads on players during
competition (McInnes, Carlson, Jones, & McKenna,
1995). Modern basketball matches comprise around
1000 actions (Ben Abdelkrim, El Fazaa, & El Ati,
2007), of which 11.5% require maximum intensity
(Ben Abdelkrim, Castagna, et al., 2010). The dura-
tion of high intensity actions ranges between 2 and
5 s (Hoffman & Maresch, 2000), with a mean work-
to-rest ratio of 1:10 when considering maximum
effort actions (Ben Abdelkrim, Castagna, et al.,
2010; Hoffman & Maresch, 2010; McInnes, et al.,
1995). In this way, basketball players are required
to perform lower-body explosive actions such as
sprints, jumps, accelerations and decelerations (Ben
Abdelkrim, et al., 2007).
Monitoring and management of the training
load in team sports has received increased attention
in recent years (Soligard, et al., 2016). It is impor-
tant to monitor individual training load during
training sessions and competitive matches to deter-
mine whether athletes are achieving the physical
targets proposed (Scott, Lockie, Knight, Clark,
& Janse de Jonge, 2013). Orchard (2012) hypoth-
esized excessive and unsuitable workloads would
lead to reduced sport performance or augmented
injury occurrence. Understanding the inuence of
training load outcomes on sports performance and
injury prevention should be considered as vital in
sport medicine and for strength and conditioning
coaches and sports scientists (Soligard, et al.,
2016). Essentially, sports scientists acquire meas-
urements of a programmed external training load
(i.e., physical ‘work’ based on movement), accom-
panied by an internal training load response (i.e.,
perceived fatigue or physiological changes) (Soli-
gard, et al., 2016). External training loads may
Kinesiology 50(2018)2:xxx-xxxVázquez-Guer rero1, J. et al.: COMPARING EXTERNAL TOTAL LOAD,...
include total distance run, number of sprints, high
speed running, accelerations, decelerations, jumps
or impacts (Soligard, et al., 2016). Individual player
proles (e.g., training age, chronological age, phys-
ical capacity and injury history) associated with
the internal and external training loads achieved
determine the training and/or match-play outcome
(Soligard, et al., 2016).
While the internal load includes ratings of
perceived exertion (Manzi, et al., 2010) and heart
rate responses (Ben Abdelkrim, et al., 2007; Manzi,
et al., 2010) and has been studied in elite basket-
ball match-play, the external load has been assessed
in few research studies (Montgomery, Pyne, &
Minahan, 2010; (Puente, Abián-Vicén, Areces,
López, & Del Coso, 2017) using specic technolo-
gies in this setting. Although recent advances in
methods have created various external measures
such as global positioning systems (GPS) to esti
mate the intensity of exercise and training external
loads in outdoor team sports (Scott, M.T., Scott,
T.J., & Kelly, 2016), this approach is impractical
for indoor sports such as basketball due to inability
to interact with signal satellites (Dobson & Keogh,
Accelerometry overcomes some of the limita-
tions associated with time-motion video analyses
and GPS technologies such as the low sampling
frequency and the non-consideration of move-
ments made in the frontal plane, such as jumps
(Scott, Scott, & Kelly, 2016). The use of smart
sensor devices such as triaxial accelerometers,
which are relatively unobtrusive during training
and competition, has aroused interest as a method
to monitor external training load in team sports
(Boyd, Ball, & Aughey, 2011; Montgomery, et al.,
2010; Scanlan, Wen, Tucker, & Dalbo, 2014; Scott,
Lockie, et al., 2013). When studying the phys-
ical demands of basketball match-play with video
analyses, gross locomotion is categorized using
several speed thresholds ranging from motion-
less standing to sprinting (Ben Abdelkrim, et al.,
2007). However, this method neglects accelerations
and decelerations (Akenhead, Hayes, Thompson,
& French, 2013; Varley, Fairweather, & Aughey,
2012). Acceleration precedes high speed running
(di Prampero, et al., 2005) and requires high rates
of force development, but it is a distinct phenom-
enon (di Prampero, et al., 2005) which requires
higher neural activity of the working muscles than
it does constant speed sprinting (Mero & Komi,
1987) as described by Akenhead, et al. (2013).
Acceleration is vital in decisive activities in order
to obtain advantages during team sports (Carling,
Bloomeld, Nelsen & Reilly, 2008). Therefore, in
order to quantify external load in team sports it is
important to establish the number of high inten-
sity actions such as accelerations and decelerations
(Gabbett, Wiig, & Spencer, 2013). Specically, an
accelerometer training load model has been used
to monitor external training load during basket-
ball training (Montgomery, et al., 2010). Scanlan
et al. (2014) used a similar accelerometer training
load algorithm to monitor external training load
during the preparatory phase of the annual training
plan in semiprofessional male basketball players. To
date, however, no model has been used to assess the
external load, the number of accelerations (concen-
tric muscular action) and decelerations (eccentric
muscular action), and the acceleration:deceleration
ratio in professional male basketball players during
an ofcial match-play. This ratio could be an inter-
esting indicator to know the importance of one type
of action relative to the other.
Basketball team playing positions are in general
classied into three playing specic-positions:
guards, forwards and centers (Ben Abdelkrim,
Chaouachi, Chamari, Chtara, & Castagna, 2010).
Prior research has reported differences between
positional roles in aerobic and anaerobic power
(Pojskić, Šeparović, Užičanin, Muratović, &
Mačković, 2015), maximal aerobic power (Cormery,
Marcil, & Bouvard, 2008; Ostojic, Mazic, & Dikic,
2006; Sallet, Perrier, Ferret, Vitelli, & Baverel,
2005), speed (Smith & Thomas, 1991), height
(Ackland, Schreiner, & Kerr, 1997; Bale, 1991;
Ostojic, et al., 2006; Sallet, et al., 2005), body mass
(Latin, et al., 2009; Ostojic, et al., 2006; Sallet, et
al., 2005) and body fat (Latin, Berg, & Baechle,
2009; Ostojic, et al., 2006; Sallet, et al., 2005) in
men’s senior basketball. However, positions should
attend to the specic role within the competition:
point guard (PG), shooting guard (SG), small
forward (SF), power forward (PF) and center (C);
(Ben Abdelkrim, et al., 2010). Although the anal-
ysis of competition is vital to train most effectively
(Gabbet, 2016), no information is currently avail-
able on the total external loading and the number
of accelerations and decelerations experienced by
elite basketball players during match-play relative
to playing position.
Thus, the aim of this study was to compare the
total external load calculated by an accelerometer
training load model, the number of accelerations
and decelerations and the acceleration:deceleration
ratio at different velocities between specic posi-
tions in elite senior basketball players during
competitive matches.
Twelve elite male basketball players (mean±SD,
age: 25.5±5.2 years [range: 19-36 years]; body height
201.4±8.6 cm; body mass: 98.4±12.6 kg) volun-
teered to participate in the study. The players played
for the team ranked in one of the top three teams in
the Spanish Basketball League (ACB) in the 2014/15
Vázquez-Guer rero1, J. et al.: COMPARING EXTERNAL TOTAL LOAD,... Kinesiology 50(2018)2:xxx-xxx
Championship. All players were screened for health
conditions and injuries that contraindicated partici-
pation. All players were verbally informed of the
study requirements, and provided written informed
consent before commencement. The research proce-
dures were approved by an Institutional Human
Research Ethics Committee in accordance with
the Helsinki Declaration.
In this study, an observational design was
used to examine the physical demands imposed on
elite male basketball players during competitive
matches. All players belonged to the same team,
which competed in two ofcial matches during a
2-day tournament. The matches were played with
24 hours of rest between and conducted on the same
court. Triaxial accelerometers (model ADXL326,
Analog Devices, Inc., Norwood, U.S.A) were used
to assess the physical demands of the two matches
(N=23 match les). Variables were expressed rela-
tive to playing time in order to allow positional
Player data were included for analysis provided
they met the following criteria: (i) they did not
suffer injury during the game and (ii) they played
in the same position throughout the entire game (the
players who occupied different positions within the
same match were excluded from analyses). With
these inclusion criteria, data were generated for 12
separate players, assigning each player to a certain
position (Delextrat, et al., 2015): PG (n=4 individual
match les), SG (n=6), SF (n=4), PF (n=4) and C
The players wore a triaxial accelerometer unit
(Figure 1) which detected and measured move-
ment using a micro-electromechanical system. The
unit was tted to the upper back of each player
using an adjustable harness (Figure 1). The devices
were switched on ~10 minutes before each game
and switched off immediately following the game
completion. Accelerometer data were recorded at
100 Hz and transferred to a SD memory card for
further analysis. As previously described in detail
(Scanlan, et al., 2014), to validate the accelerom-
eter’s measurements of whole body movement,
pilot tests were performed with same participants,
with a correlation (r=0.96) between accelerometer
external total load and running speed during tread-
mill-based incremental running (7-15 km∙h-1). The
reliability of the accelerometers has been accept-
able both within (coefcient of variation, CV=1%)
and between devices (CV= 1%) under controlled
laboratory conditions, and between devices during
eld testing (CV=2%; Boyd, et al., 2011). Thereby,
accelerometers can be condently utilized as a
reliable tool to measure physical activity in team
sports across multiple players and repeated bouts
of activity (Boyd, et al., 2011; Scott, Black, et al.,
2016). Further pilot data supported the reliability
(intraclass correlation coefcient [ICC]=0.89 and
standard error of the mean [SEM]=0.08) of the
accelerometer total load model in the same players
(n=8) during a typical 11-man fast break basket-
ball exercise. This drill consisted of three players
trying to score against two defenders on the court
(28 x 14 m) with rest/work time ~1:1. When a shot
has been taken (even if it results in a basket), the
rebounder makes a quick outlet pass to either of the
two players waiting near to the baseline who step
inbounds from the sidelines to receive the outlet
pass. The rebounder and these two players then
break down the oor where the other two defenders
are waiting to engage in play. After the shot, two
new players enter the court for the outlet pass and
the cycle continues.
Figure 1. Triaxial accelerometer unit fitted to the upper
back between the shoulder blades of each player using an
adjustable harness.
The number of accelerations, decelerations,
and external total load data from all players who
participated during a game (at least 10 min) were
retained (n=23 les from 12 different players). After
collection, all the match data were analysed with
a software designed to provide objective measures
of movement patterns and training load (Viper Software). As in previous studies (Aughey,
2011), the occurrences of moderate accelerations/
decelerations (< 3 m∙s
) and maximal accelerations/
decelerations (> 3 m∙s
) were recorded (Delaney,
Cummins, Thornton, & Duthie, 2017). Accelera-
tions and decelerations in each band were calculated
by identifying sudden increases/decreases in speed
and continued increasing/decreasing for at least 0.5
s. To be recognized as an acceleration/deceleration,
the increases/decreases in speed identied must be
greater than or equal to the user-dened value for
moderate and maximal bands. As described previ-
ously, the set of body movements performed during
match-play was expressed as the accumulated total
external load (Montgomery, et al., 2010). This esti-
mate of physical demand combines the instanta-
neous rate of change in acceleration in three axes
of body movement:
Kinesiology 50(2018)2:xxx-xxxVázquez-Guer rero1, J. et al.: COMPARING EXTERNAL TOTAL LOAD,...
up/down (z), side/ side (y) and forward/backward
(x) according to the formula:
[(x)pf + (y) pf + (z)pf ]* 0.0001
where x=g force measure along the x axis, y=g force
measure along the y axis, and z=g force measure
along the z axis are the orthogonal components
of acceleration measured from the triaxial accel
erometer directions at 100 Hz. These values were
then accumulated over the length of the match-play
activity to obtain the external total load, which
is reported in arbitrary units per minute of play.
Only the number of accelerations and decelerations,
external total load and the acceleration:deceleration
ratio data during live time on the court were
included in the analysis.
Statistical analysis
Data are presented as means±standard deviation
(SD). All data were rst log-transformed to reduce
bias arising from non-uniformity error. Possible
differences or changes in variables within- and
between-group were analysed for clinical signi-
cance using magnitude-based inferences by pre-
specifying 0.2 between-subject SDs as the smallest
worthwhile effect (Hopkins, Marshall, Batterham,
& Haninn, 2009). The standardized difference or
effect size (ES, 90% condence limit [90%CL])
in the selected variables was calculated (Cohen,
1977). Threshold values for assessing magnitudes
of the ES (changes as a fraction or multiple of base-
line standard deviation) were <0.20, 0.20-0.60,
0.6-1.2, 1.2-2.0 and 2.0 for trivial, small, moderate,
large and very large, respectively (Hopkins, et al.,
2009). Quantitative chances of higher or lower
changes were evaluated qualitatively as follows:
<1% , almost certainly not; 1 −5%, very unlikely;
5−25%, unlikely; 25−75%, possible; 75−95%,
likely; 95−99%, very likely; >99%, almost certain
(Hopkins et al., 2009). A substantial effect was set
at >75% (Suarez-Arrones, et al., 2015).
Moderate accelerations were almost certainly
greater than moderate decelerations (ES=0.54
to 3.12), and maximal decelerations were almost
certainly greater than maximal accelerations in all
playing positions (ES=2.70 to 6.87) (Table 1).
SF showed fewer moderate accelerations than
PG (ES=0.37±0.21, likely), SG (ES = 0.65±1.00,
likely) and C (ES=0.67±1.32, likely), and fewer
moderate decelerations than C (ES=0.82±1.23,
likely). SF showed fewer maximal accelerations
than PG (ES=0.67±1.28, likely), PF (ES=1.29±1.26,
likely) and C (ES=1.88±1.13, very likely), and SG
showed fewer maximal accelerations than PF
(ES=0.83±1.17, likely) and C (ES=1.20±1.03, likely).
SG had more maximal decelerations than SF
(ES=1.35±1.17, likely), PF (ES=0.91±1.17, likely) and
C (ES=0.64±1.07, likely), and PG had more maximal
decelerations than SF (ES=0.93±1.23, likely) and PF
(ES=0.72±1.2 3, likely) (Table 1).
Table 1. Acceleration and deceleration profiles (accelerometer-based) in elite professional basketball players and their external
total load during competitive matches (n=23); data are mean±SD
Playing positions
Variables Point guards
Shooting guards
Small forwards
Power forwards
# Accelerations
(<3 m∙s-2)
*29.6±3.9 *32 .7± 11.0 *26.7±2.6a,b,e *28.0±5.0 *28 . 3±1.1
# Accelerations
(>3 m∙s-2)
1.4±0.9 1.0±0.4d ,e 0.8±0.3a,d,e 1.4 ±0. 5 1.5±0.4
# Decelerations
(<3 m∙s-2)
23.8±3.6 25.7±10.0 21.7±2 .2 e24.0±4.6 23.4±1.3
# Decelerations
(>3 m∙s-2)
+4.5 ±1.4 +4 .1± 0.5 +3. 2±0.7a,b +3.5±0.7a,b +3.7±0 .8 b
Acc : Dec Ratio
(<3 m∙s-2)1 : 0.80±0.0 4d,e 1 : 0.78±0.06c,d,e 1 : 0.81±0.01d,e 1 : 0.86±0.02 1 : 0.83±0.02d
Acc : Dec Ratio
(>3 m∙s-2)1 : 3.94±1.3 1 : 4.87±1.8 1 : 4.26±0.8 1 : 2.67±0.4a,b,c 1 : 2.57±0.5a,b,c
External total
load (AU/min) 4. 8 ±1.1 4 .6±1.7 4.8±0.8 3. 1.1a,b,c,e 4.4±0.3
Note. #: Number; Acc: accelerations; Dec: decelerations; AU: arbitrary units. *: Almost cer tainly difference vs. decelerations (<3m/
s2); +: Almost cer tainly higher vs. accelerations (>3 m∙s-2).
a: substantial difference vs. point guards; b: substantial difference vs. shooting guards; c: substantial difference vs. small forwards;
d: substantial difference vs. power for wards; e: substantial difference vs. centers.
Vázquez-Guer rero1, J. et al.: COMPARING EXTERNAL TOTAL LOAD,... Kinesiology 50(2018)2:xxx-xxx
PF presented the highest acceleration-deceler-
ation ratio <3 m∙s-2 (ES=1.40 to 2.22, very likely in
all cases), and C had a higher acceleration-deceler-
ation ratio <3 m∙s-2 than PG, SG and SF (ES=0.71
to 1.02, likely in all cases). PG, SG and SF had
a higher acceleration-deceleration ratio >3 m∙s-2
than PF and C (ES=1.03 to 2.21, from likely to very
likely). PF presented the lowest external total load
of all specic playing positions (ES=0.67 to 1.18,
likely in all cases) (Table 1).
Discussion and conclusions
In this study, we examined the external total
load, number of accelerations and decelerations,
and acceleration:deceleration ratio between playing
positions during basketball match-play for the rst
time in professional male senior players. The main
ndings of the current study were: 1) the number
of moderate accelerations was higher than the
number of moderate decelerations, whereas the
number of maximal decelerations was greater
than the number of maxima accelerations in all
playing positions; 2) the number of accelerations
and decelerations at moderate and maximal speeds
differed between playing positions (PG=SG>SF);
3) the acceleration:deceleration ratio (>3 m∙s-2) was
signicantly lower in players on the perimeter (PG
and SG) than in PF and C, and 4) PF had the lowest
total external load.
The observation of more moderate accelerations
than moderate decelerations aligns with previous
research (Puente, et al., 2017). Unfortunately, past
research provides no information about the accel-
eration/deceleration threshold during basketball
match-play (Puente, et al., 2017). On the other hand,
the presence of higher maximal decelerations than
maximal accelerations in all playing positions likely
highlights the importance of decelerations during
high intensity actions in order to obtain advantages
during match-play. The use of maximal decelera-
tions may be especially necessary to perform high
intensity offensive actions such as quick changes of
direction to avoid defenders during dribbling and
before shooting. Maximal decelerations may also
be used during defense to react quickly to actions
performed by the opposing team during attacking
play. Although maximal accelerations may be used
to perform high intensity actions, maximal deceler-
ations may be more frequently required by players
in all positions to obtain advantage during tech-
nical and tactical tasks execution (Kempton, Sirotic,
& Coutts, 2016). This nding is mirrored in other
sports where top team rugby players were shown
to execute more high intensity decelerations (<-2.78
) than sub-elite team rugby players (Kempton,
et al., 2016). Accelerations and decelerations may
be produced in different axes that correspond to
different skills such as sprinting, changing direc-
tion, or jumping. Furthermore, more maximal
decelerations can place notable mechanical stress
on the basketball players and may be associated
with a greater eccentric muscular work that should
be included in strength and conditioning programs,
and in injury prevention programs.
Another prominent nding in the present
study was the substantial difference in moderate
and maximal accelerations and decelerations
between playing positions. This result may partly
be explained by the inherent technical and tactical
requirements of each position and players’ indi-
vidual physical characteristics (Ben Abdelkrim, et
al., 2007). Specically, PG performed the highest
number of decelerations at >3 m∙s-2, probably
because players in this position make a high number
of picks and hand to hand tactical actions. Similarly,
SG performed more maximal decelerations than SF
and PF, also likely due to a higher number of picks
and hand to hand actions, but also possibly due to
a higher number of screens they received to create
open shooting opportunities. Maximal accelera-
tions appear to be used by PG, PF and above all by
C, probably because of the shorter duration of the
actions in which they are involved (Puente, et al.,
2017), with a lower maximal speed achieved during
match-play (Puente, et al., 2017). These accelera-
tions and decelerations may be required in both
offensive and defensive drills and may include hori-
zontal acceleration and deceleration actions. For
example, dribbling and crossover dribbling actions,
but also vertical accelerations such as attacking the
rim during offensive play.
The substantial difference in the acceleration:
deceleration ratio (>3 m∙s-2) between players on
the perimeter (PG and SG) and PF and C suggests
that these positions may require different playing
styles, because PF and C may have to occupy
space closer to basket. From another perspec-
tive, the moderate acceleration:deceleration ratio
per minute (<3 m∙s-2) was similar to that recorded
during a study of professional football match play
(1.23 vs. 1.16) (Akenhead, et al., 2013), but the
maximal acceleration:deceleration ratio (>3 m∙s-2)
was almost three times higher than in the football
study (1.10 vs 0.34; Akenhead, et al., 2013). These
results highlight the greater relevance of decelera-
tions >3 m∙s
in basketball, likely as a consequence
of court dimensions.
When interpreting the position-specic total
external load data, PF possessed the lowest value.
This parameter contemplates the accelerations made
by the basketball player in the three planes of move-
ment, which makes it difcult to identify the cause
of results obtained. However, the lowest demand
in PF during the match-play should be considered
when design training exercises or sessions.
The data obtained in studies of ofcial games
should be borne in mind, particularly in the
management of the training load. If the external
Kinesiology 50(2018)2:xxx-xxxVázquez-Guer rero1, J. et al.: COMPARING EXTERNAL TOTAL LOAD,...
load requirements in ofcial matches are quanti-
ed, in-season training load can be better perio-
dized and prescribed (Gabbet, et al., 2016). This
approach would allow us to determine the external
total training load percentage for each training
session relative to ofcial game loads.
However, these results should be interpreted
with caution. The small sample size of the current
study is a limitation, especially the number of
players per position; however, subjects were
recruited from the Spanish First Division (ACB),
which constitutes a small exclusive convenience
sample. In the future, it is necessary to research
the physical demands with more players evaluated.
Moreover, the external load demands of basketball
competition may be inuenced by tactical factors,
the score at a particular point in the game, and the
quality of the opposition. Further research should
try to determine whether there are any differences
between professional and non-professional players,
and also between different leagues (NBA vs. FIBA),
ages (senior vs. junior) and sexes.
In conclusion, more maximal decelerations than
accelerations were performed in all playing posi-
tions during elite basketball competition games.
Furthermore, the acceleration:deceleration ratio
(>3 m∙s-2) was signicantly lower in players on the
perimeter (PG and SG) than in PF and C. This infor-
mation should be taken into account in the design
of strength/conditioning programs, emphasizing
maximal deceleration movements in perimeter
players and reducing the total external load on PF
to best prepare players for match demands.
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Submitted: March 1, 2017
Accepted: January 8, 2018
Published Online First: September 21, 2018
Correspondence to:
Jairo Vázquez-Guerrero
Medical Department and Performance & Science
Football Club Barcelona
Av. Aristides Maillol
08028 Barcelona, Spain
Phone: +34
Fax: +34
... These differences are also found when adding the covariate specific positions, being generally higher in the forwards. The load placed on basketball players during competition vary depending on the game phase [13], game period [21,25], and specific player positions [25][26][27][28]. ...
... However, in other non-professional contexts, it was determined that perimeter players, including guards and forwards, performed more accelerations and decelerations than inside players [27,56,57]. The disparities in accelerations and decelerations between perimeter and inside players could be attributed to differences in playing style due to their proximity to the basket [28]. Modern professional basketball is beginning to transcend the traditional classifications of players into specific positions. ...
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Basketball players should train at intensities similar to those recorded in competition, but are the intensities really similar? This study aimed to quantify and compare the internal and external intensities assimilated by professional basketball players, both in training and in competition, according to context and the specific player position. Players from the same team in the Spanish ACB competition were monitored for three weeks. The sample recorded intensities in 5 vs. 5 game situations in both training (n = 221) and competition (n = 32). The intensities, as dependent variables, were classified into kinematic external workload demands (distances, high-intensity displacements, accelerations, decelerations, the acceleration:deceleration ratio, jumps, and landings), neuromuscular external workload demands (impacts and player load), and internal workload demands (heart rate). They were measured using inertial measurement devices and pulsometers. The playing positions, as independent variables, were grouped into guard, forward, and center. According to the context, the results reported a significant mismatch of all training intensities, except jumps, with respect to competition; these intensities were lower in training. According to the playing position, inside players recorded more jumps and landings per minute than point guards and outside players in training. In turn, inside players recorded a higher average heart rate per minute than outside players in this same context. There were no significant differences in intensity according to the playing position in the competition. Considering the context–position interaction, no differences were observed in the intensities. Adjusting and optimizing training intensities to those recorded in competition is necessary.
... When considering accelerometry data collected during elite competition, in most research, it can be found that they divide such actions between total accelerations or decelerations and high intensity accelerations or decelerations [51,52]. In the case of highintensity accelerations, it is again observed that the criterion to classify them as such varies depending on the research, the criterion to qualify them as such being, in the case of Svilar et al. [16], when the acceleration is >3.5 m/s 2 , while in the case of Vázquez-Guerrero et al. [52], the criterion is above 3 m/s 2 . ...
... When considering accelerometry data collected during elite competition, in most research, it can be found that they divide such actions between total accelerations or decelerations and high intensity accelerations or decelerations [51,52]. In the case of highintensity accelerations, it is again observed that the criterion to classify them as such varies depending on the research, the criterion to qualify them as such being, in the case of Svilar et al. [16], when the acceleration is >3.5 m/s 2 , while in the case of Vázquez-Guerrero et al. [52], the criterion is above 3 m/s 2 . The same criteria were established for high-intensity decelerations. ...
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Personalization of workloads is essential for optimizing training processes and minimizing the risk of injuries in sports. Precise knowledge of the external load demands borne by basketball players during competition is necessary for this purpose. The objective of this research was to determine the objective external load demands of five variables during a basketball competition, three kinematic (speed, accelerations, and decelerations) and two neuromuscular variables (impacts/min and Player Load/min), and subsequently establish workload ranges. Six official matches from preparatory tournaments involving professional basketball players from the Spanish first division, Liga ACB, were analyzed. Inertial devices and an UWB system were used for variable localization and recording within indoor spaces. Two methods, two-step and k-means clustering, were employed for workload range classification. The results revealed different workload thresholds clusters based on the data analysis technique used. The following speed ranges were identified in professional basketball players: Standing, <2.95 km/h; Walking, 2.96 to 7.58 km/h; Jogging, 7.59 to 12.71 km/h; Running, 12.72 to 17.50 km/h; and Sprinting, >17.51 km/h. The center of cluster 5 was found to determine the concept of a sprint (>19 km/h) as well as high-speed running (>17.50 km/h). Acceleration and deceleration ranges displayed few cases but with considerably high values, which must be considered when designing injury prevention tasks. The distribution of impacts showed a normal pattern, with identified periods during which players withstood significant G-forces (14%). Finally, the Player Load value at which an activity is considered to be very high, 1.95 au/min, was identified. Considering the obtained results, basketball is proposed as a sport with a high neuromuscular load. Coaches should choose the classification method that best suits their needs. These reference values are the first of their kind for this population of top-level professional players and should aid in adjusting training processes to match competition demands.
... Therefore, when programming strength and conditioning programs for athletes, the characteristics of the sport should be considered. In this sense, different team sports (soccer, handball, Australian football, hockey, rugby league, rugby sevens, rugby union, and basketball) have not only shown to have a significant amount of high and very-highintensity decelerations, but also a higher frequency when compared with accelerations (41,55,120). In any respect, not every team sport shows this tendency. ...
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Eccentric resistance training has been shown to elicit beneficial effects on performance and injury prevention in sports because of its specific muscular and neural adaptations. Within the different methods used to generate eccentric overload, flywheel eccentric training has gained interest in recent years because of its advantages over other methods such as its portability, the ample exercise variety it allows and its accommodated resistance. Only a limited number of studies that use flywheel devices provide enough evidence to support the presence of eccentric overload. There is limited guidance on the practical implementation of flywheel eccentric training in the current literature. In this article, we provide literature to support the use of flywheel eccentric training and present practical guidelines to develop exercises that allow eccentric overload. See Supplemental Digital Content 1, for a video abstract of this article.
... Bianchi et al., 2017;Drinkwater et al., 2008;Dežman et al., 2001;Trninić and Dizdar, 2000) and seven online articles (e.g. Get Hyped Sports, 2022; Lab, 2021;Mac, 2021;Cornberg, 2021;Judy, 2020;Defining the Positions, 2015;Basketball Positions, 2012) dealt with technical variables, and ten of these previous studies (e.g Ivanović et al., 2022;García et al., 2020;Cui et al., 2019;Vázquez-Guerrero et al., 2018;Pion et al., 2018;Kucsa and Mačura, 2015;Abbas and Abbas, 2012;Abdelkrim et al., 2010;Delextrat and Cohen, 2009;Ziv and Lidor, 2009) dealt with physical variables for basketball players' positions, was analyzed.  After that, select the technical variables that got 50% and above in analyzing and the physical variables that got 30% and above in analyzing previous studies of basketball players' positions. ...
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A strong team foundation in any team game, such as basketball, is the process of selecting the appropriate players for all playing positions scientifically and objectively. This study aims to identify the key technical and physical determinants as well as set standardized tests necessary for selecting and directing players to different play positions in basketball. The descriptive design was followed by using the survey method on a sample of 57 female basketball players under 16 years old who achieved the top three places in the Cairo region league season 2022/2023. The results revealed that the key technical determinants of position 1 were dribbling skills and ball control, passing skills, outside shots, and defensive pressure; position 2 was outside shots, moves without the ball, dribbling skills, and ball control, defensive pressure; position 3 was inside and outside shots, defensive rebound; position 4 was inside and outside shots, offensive and defensive rebound; and position 5 was inside shots, offensive and defensive rebound, and block shots. Furthermore, the key physical determinants for position 1 were agility, acceleration, legs and arms power, speed “short distance”, and deceleration; position 2 was acceleration, legs and arms power, speed “relatively long distances”, and agility; position 3 was legs and arms power, speed “relatively long distance”, and agility; position 4 and 5 were legs power and arms strength. Also, appropriate tests were shown to assess these determinants: Dribble Skill with its entire Types Test, Speed and Accuracy Passing Test, Spot Up Shooting Test, Shooting From Close to the Basket Test, Rebound Shooting Task Test, Moving Without the Ball Test, Defense Against Dribbler Test, Defensive Rebound Test, Block Shots Test, Illinois Agility Test, 10-m, 20-m, and 28-m Sprint Test, 5-0-5 m Sprint Test, Vertical Jump Test, Push Up Test, and Pushing a Medicine Ball Test. These tests were evaluated based on a seven-level standard ranging from excellent, very good, good, above-average, average, weak, to very poor.
... Different studies report that accelerations, together with jumps, are the type of key effort responsible for scoring in basketball and, thus, the ones on which play performance depends [47][48][49][50]. For this reason, accelerations and decelerations have been used to determine intensity zones during competition [51]. Therefore, it is observed that the acceleration capacity is one of the determining variables of basketball performance and, after analyzing the results obtained in this research, it is also the main variable that is affected by the MC. ...
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The menstrual cycle can be seen as a potential determinant of performance. This study aims to analyze the influence of the menstrual cycle in women on sports performance, more specifically on the internal and external load of professional women basketball players. The sample consisted of 16 women players and 14 training sessions were recorded. A descriptive analysis of the mean and standard deviation of the variables according to the different phases of the menstrual cycle was performed, as well as an ANCOVA, partial Eta2 effect size criteria, and Bonferroni's Post Hoc test to identify differences among phases. The results establish that ovulation is the phase in which higher values of external load are recorded and, therefore, the late follicular phase is the time of the cycle where a greater intensity in explosive distance, accelerations and decelerations are recorded. Considering women's hormonal cycles, understanding their function and the individual characteristics of each athlete is essential since it allows for the development of specific training, the prevention of injuries and therefore positively affects the performance of women players. To this end, individual training profiles should be created in specific contexts, not following general rules. In addition, psychological factors and the specific position of the athletes should be monitored.
Background/aim: Oral injuries such as oral soft tissue lacerations and contusions can occur in basketball by mechanisms such as running into other players or falling. Given a high enough impact force, dental injuries such as tooth fractures and avulsions can occur. Previous research has studied the different types of oral injuries as well as the mechanisms that cause them. Yet, the mechanisms resulting in dental injuries have remained unexplored. The aims of this study were to investigate the distribution of different oral injuries within each injury mechanism and evaluate which mechanisms were most likely to lead to a dental injury. Materials and methods: This is a retrospective cohort study using the National Electronic Injury Surveillance System (NEISS). Subjects who experienced oral injuries from basketball between January 1, 2003 and December 31, 2022 were included in this study. The independent variable was the injury mechanism. The dependent variable was the dental injury outcome (yes/no). Multivariate logistic regression was used to measure the association between the injury mechanism and the dental injury outcome. A p < .05 was considered statistically significant. Results: This study included 4419 subjects who experienced oral injuries (national estimate, 138,980). Approximately 14.7% of oral injuries were dental injuries. Subjects experiencing collisions with objects such as walls or the basketball hoop (odds ratio (OR), 4.39; p < .001), falls (OR, 3.35; p < .001), or contact with the basketball (OR, 1.77; p = .006) had significantly higher odds of sustaining a dental injury relative to those experiencing contact with another player. Conclusions: Basketball players experiencing contact to the mouth have high odds of sustaining a dental injury. An understanding of injury mechanisms is important for medical teams to manage these injuries and for coaches to educate athletes on safe and proper playing styles. Furthermore, healthcare providers and basketball staff should encourage athletes to wear mouthguards to reduce the risk of traumatic dental injuries.
This study aimed to analyse the influence of different physical fitness levels of youth basketball players on match-related physical performance, using Random Forest clustering to distinguish between high-fitness level players and low-fitness level players. Twenty male youth basketball players completed the following physical performance tests in two separate sessions: bilateral and unilateral countermovement jumps, bilateral and unilateral horizontal jumps, single leg lateral jumps, the 20 m linear straight sprint test, the 505 test and a repeated sprint ability test. 1 week after the second testing day, players completed a simulated match while external loads were monitored using an ultra-wide band-based Local Positioning System. A Random Forest clustering was used to create two different clusters composed of players with similar physical fitness attributes (high-and low-fitness level players). Results indicate that the Random Forest clustering adequately discriminated among the players in different groups according to their physical fitness attributes. High-fitness level players covered more distance per min in all intensity thresholds and reached higher maximal speed and acceleration intensity during the simulated matches (p \ 0.05). These results may assist basketball practitioners in understanding running performance variations during matches and can be used to optimise preparation for individual players.
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Training load monitoring provides information about the physical requirements of the sport in which the athlete competes. Coaching staff should use this information to make decisions about the periodization and planning of the training process to optimize performance and prevent injuries. The following review presents the current state of knowledge on monitoring of external and internal loads in basketball. The purpose of the study is to examine the current experience, content and specifics of training load monitoring in basketball in terms of its applicability to player qualifications, the methodology used, the type of data recorded, and the relationship with performance and injury. Materials and methods of the study. To solve the set purpose of the research we used the following methods: analysis and generalization of data of the special scientific and methodical literature; monitoring of information resources of the Internet; method of systematization. Results. The main provisions of modern monitoring of influence of a load on high-class basketball players as a single system of a peculiar management of adaptation processes of their organism are defined. Conclusions. On the basis of the conducted researches the tendencies of formation of the modern system of research of load on an organism of high-class basketball players and methods and technologies of their use for the prevention of occurrence of occupational diseases and injuries are allocated.
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The aim of our study was to determine the predictors of physical performance evaluated by the mean of acceleration capacity (10-m and 20-m sprint) and jumping ability (CMJ) during three consecutive days' semiprofessional basketball tournament. For this, 24 male players (24,3±3,4 years) were monitored during the tournament to assess the percentage of maximal actions (PMA) and T and C concentrations. Test were conducted 24 h before the first game started, after the end of each of the 3 games and 24 h after the last game. The results showed that the decrease in the physical variables through the tournament can be predicted by the mean of a panel data model including the level of exertion evaluated both by perceived exertion load and C, CMJ models were the most significant (Within-R 2 = 0,60 and Within-R 2 = 0,54 respectively). Therefore, it is recommended the use of cortisol monitoring on testing the demands of exercise in these competitive contexts. In addition, results allow us to enlarge knowledge of the internal and external demands in basketball matches.
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zet Bu çalışmanın amacı, genç erkek basketbolculara uygulanan akselerasyon ve deselerasyon antrenmanlarının sürat ve çeviklik performansı üzerindeki etkisini incelemektir. Toplamda 20 erkek basketbolcunun dâhil edildiği çalışmada deney (n=10) ve kontrol (n=10) grupları oluşturulmuş, 6 hafta boyunca kontrol grubuna basketbol antrenmanı; deney grubuna ise basketbol antrenmanlarına ek olarak akselerasyon ve deselerasyon antrenmanı uygulatılmıştır. Çalışma öncesinde (ön test) ve sonrasında (son test) 5 m, 10 m ve 20 m sürat ölçümü; İllinois, T-çeviklik, 505-çeviklik ve Lane çeviklik test ölçümleri değerlendirilmiştir. Normallik sınaması Shapiro-Wilk testi ile yapılmış, tekrarlanan ölçümler için doğrusal karma modeller, grupları (kontrol ve deney) ve zamanı (ön ve son testler) sabit faktörler olarak dikkate alınarak fiziksel performanstaki farklılıkları analiz etmek için kullanılmıştır. Elde edilen bulgulara göre deney grubuna uygulanan 6 haftalık akselerasyon ve deselerasyon antrenmanının 20 m sürat performansını geliştirdiği, tüm çeviklik testlerinde ön test ve son test arasında gelişim sağlandığı belirlenmiştir. Kontrol grubuna uygulanan basketbol antrenmanlarının performans üzerinde etki sağladığı tespit edilmiş olsa da deney grubuna basketbol antrenmanlarına ek olarak uygulanan akselerasyon ve deselerasyon antrenmanlarıyla birlikte gelişimin daha etkili olduğu görülmektedir. Böylece, mevcut çalışma akselerasyon ve deselerasyon antrenmanlarının sürat ve çeviklik performansını geliştirebileceğinin sonucunu ortaya koymaktadır. Abstract The aim of this study is to examine the effect of acceleration and deceleration training applied to young male basketball players on speed and agility performance. Twenty young male basketball players were divided into two groups as an experiment (n=10) and control (n=10). Basketball training only for the control group for 6 weeks; in addition to basketball training, acceleration and deceleration training was applied to the experimental group. 5 m, 10 m, and 20 m speed tests, Illinois, T-agility, 505-agility, and lane agility test measures were evaluated before (pre-test) and after (post-test) training. Data were tested for normality with the Shapiro-Wilk test. Linear mixed models for repeated measurements were used to analyze differences in physical performance, taking groups (control and experiment) and time (pre-test and post-test) as fixed factors. According to the findings, it was determined that the 6-week acceleration and deceleration training applied to the experimental group improved the 20 m sprint performance, and an improvement was achieved between the pre-test and post-test in all agility tests. Although it has been determined that basketball training applied to the control group has an effect on performance, it is seen that the development is more effective with the acceleration and deceleration training applied in addition to the basketball training in the experimental group. Thus, the current study reveals that acceleration and deceleration training can improve speed and agility performance.
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The ability to accelerate, decelerate and change direction efficiently is imperative to successful team-sports performance. Traditional intensity-based thresholds for acceleration and deceleration may be inappropriate for time-series data, and have been shown to exhibit poor reliability, suggesting other techniques may be preferable. This study assessed movement data from one professional rugby league team throughout two full seasons and one pre-season period. Using both 5 Hz and 10 Hz global positioning systems (GPS) units, a range of acceleration-based variables were evaluated for their inter-unit reliability, ability to discriminate between positions, and associations with perceived muscle soreness. The reliability of 5 Hz GPS for measuring acceleration and deceleration ranged from good to poor (CV = 3.7-27.1%), with the exception of high-intensity deceleration efforts (CV = 11.1-11.8%), the 10 Hz units exhibited moderate to good inter-unit reliability (CV = 1.2-6.9%). Reliability of average metrics (average acceleration/deceleration, average acceleration and average deceleration) ranged from good to moderate (CV = 1.2-6.5%). Substantial differences were detected between positions using time spent accelerating and decelerating for all magnitudes, but these differences were less clear when considering the count or distance above acceleration/deceleration thresholds. All average metrics detected substantial differences between positions. All measures were similarly related to perceived muscle soreness, with the exception of high-intensity acceleration and deceleration counts. This study has proposed that averaging the acceleration/deceleration demands over an activity may be a more appropriate method compared to threshold-based methods, due to a greater reliability between units, whilst not sacrificing sensitivity to within and between-subject changes.
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Athletes participating in elite sports are exposed to high training loads and increasingly saturated competition calendars. Emerging evidence indicates that poor load management is a major risk factor for injury. The International Olympic Committee convened an expert group to review the scientific evidence for the relationship of load (defined broadly to include rapid changes in training and competition load, competition calendar congestion, psychological load and travel) and health outcomes in sport. We summarise the results linking load to risk of injury in athletes, and provide athletes, coaches and support staff with practical guidelines to manage load in sport. This consensus statement includes guidelines for (1) prescription of training and competition load, as well as for (2) monitoring of training, competition and psychological load, athlete well-being and injury. In the process, we identified research priorities.
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Background: There is dogma that higher training load causes higher injury rates. However, there is also evidence that training has a protective effect against injury. For example, team sport athletes who performed more than 18 weeks of training before sustaining their initial injuries were at reduced risk of sustaining a subsequent injury, while high chronic workloads have been shown to decrease the risk of injury. Second, across a wide range of sports, well-developed physical qualities are associated with a reduced risk of injury. Clearly, for athletes to develop the physical capacities required to provide a protective effect against injury, they must be prepared to train hard. Finally, there is also evidence that under-training may increase injury risk. Collectively, these results emphasise that reductions in workloads may not always be the best approach to protect against injury. Main thesis: This paper describes the 'Training-Injury Prevention Paradox' model; a phenomenon whereby athletes accustomed to high training loads have fewer injuries than athletes training at lower workloads. The Model is based on evidence that non-contact injuries are not caused by training per se, but more likely by an inappropriate training programme. Excessive and rapid increases in training loads are likely responsible for a large proportion of non-contact, soft-tissue injuries. If training load is an important determinant of injury, it must be accurately measured up to twice daily and over periods of weeks and months (a season). This paper outlines ways of monitoring training load ('internal' and 'external' loads) and suggests capturing both recent ('acute') training loads and more medium-term ('chronic') training loads to best capture the player's training burden. I describe the critical variable-acute:chronic workload ratio)-as a best practice predictor of training-related injuries. This provides the foundation for interventions to reduce players risk, and thus, time-loss injuries. Summary: The appropriately graded prescription of high training loads should improve players' fitness, which in turn may protect against injury, ultimately leading to (1) greater physical outputs and resilience in competition, and (2) a greater proportion of the squad available for selection each week.
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The aim of the present study was to compare the aerobic and anaerobic power and capacity of elite male basketball players who played multiple positions. Fifty-five healthy players were divided into the following three different subsamples according to their positional role: guards (n = 22), forwards (n = 19) and centers (n = 14). The following three tests were applied to estimate their aerobic and anaerobic power and capacities: the countermovement jump (CMJ), a multistage shuttle run test and the Running-based Anaerobic Sprint Test (RAST). The obtained data were used to calculate the players' aerobic and anaerobic power and capacities. To determine the possible differences between the subjects considering their different positions on the court, one-way analysis of variance (ANOVA) with the Bonferroni post-hoc test for multiple comparisons was used. The results showed that there was a significant difference between the different groups of players in eleven out of sixteen measured variables. Guards and forwards exhibited greater aerobic and relative values of anaerobic power, allowing shorter recovery times and the ability to repeat high intensity, basketball-specific activities. Centers presented greater values of absolute anaerobic power and capacities, permitting greater force production during discrete tasks. Coaches can use these data to create more individualized strength and conditioning programs for different positional roles.
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The use of global positioning systems (GPS) has increased dramatically over the last decade. Using signals from orbiting satellites, the GPS receiver calculates the exact position of the device and the speed at which the device is moving. Within team sports GPS devices are used to quantify the external load experienced by an athlete, allowing coaches to better manage trainings loads and potentially identify athletes who are overreaching or overtraining. This review aims to collate all studies that have tested the validity and/or the reliability of GPS devices in a team sport setting, with a particular focus on 1) measurements of distance, speed, velocities and accelerations across all sampling rates and 2) accelerometers, player/body load and impacts in accelerometer-integrated GPS devices. A comprehensive search of the online libraries identified 22 articles that fit search criteria. The literature suggests that all GPS units, regardless of sampling rate, are capable of tracking athlete's distance during team sport movements with adequate intra-unit reliability. 1Hz and 5Hz GPS units have limitations in their reporting of distance during high intensity running, velocity measures and short linear running (particularly those involving changes of direction), although these limitations seem to be overcome during measures recorded during team sport movements. 10Hz GPS devices appear the most valid and reliable to date across linear and team sport simulated running, overcoming many limitations of earlier models, while the increase to 15Hz GPS devices have had no additional benefit.
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Purpose: The objectives of this study were to describe the repeated high-intensity activity and internal training load of rugby sevens players during international matches and to compare the differences between the first and second halves. Methods: Twelve international-level male rugby sevens players were monitored during international competitive matches (n=30 match files) using global positioning system technology and heart rate monitoring. Results: The relative total distance covered by the players throughout the match was 112.1 ± 8.4 m·min-1. As a percentage of total distance, 35.0% (39.2 ± 9.0 m·min-1) was covered at medium-speed and 17.1% (19.2 ± 6.8 m·min-1) at a high-speed. A substantial decrease in the distance covered at >14.0 km·h-1 and >18.0 km·h-1, the number of accelerations of >2.78 m·s-2 and >4.0 m·s-2, repeated sprint sequences interspersed with ≤60 s rest and repeated acceleration sequences interspersed with ≤30 s or ≤60 s rest was observed in the second half compared with the first half. A substantial increase in the mean heart rate, maximal heart rate (HRmax), percentage of time at >80% HRmax and at >90% HRmax, and Edwards's training load was observed in the second half compared with the first half. Conclusion: This study provides evidence of a pronounced reduction in high-intensity and repeated high-intensity activities and increases in internal training load in rugby sevens players during the second half of international matches.
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The primary aim of this study was to identify the effects of playing position on match activities of female basketball players. A secondary aim was to compare these match activities between quarters of play. Forty-two elite females players (25.9±4.3y, 183.4±9.0 cm) were studied during competitive matches. The frequency, duration and percentage of live time (%LT) were calculated. Differences between playing positions (PG, SG, SF, PF and C) and quarters were analyzed using repeated measures ANOVA. Results showed significantly more movements in PG than other positions (25.6±2.8 per min vs. 21.2±2.2 to 23.6±2.6 per min, P = 0.022), and more sprints in PG than PF and C (0.4±0.2 per min vs. 0.1±0.1 and 0.1±0.1, respectively for PG, PF and C, P = 0.040). Furthermore, PF and C performed more jumps and static exertion than other positions (jump frequencies and %LT static exertion of 1.2±0.2 per min and 4.8±2.1% for PF, 1.5±0.3 per min and 7.1±2.2% for C vs. 0.7±0.3 to 1.0±0.2 per min and 1.7±0.6 to 3.4±1.2 % in other positions, P = 0.002 to 0.028). A decrease in %LT for high-intensity movements was observed in the 4th compared to 1st and 3rd quarters (P = 0.048). These results highlight that physical conditioning by playing position might be worth considering by coaches, although longitudinal training studies are necessary to confirm this observation.
Purpose: This was the first study to examine differences in physical and technical performance profiles using a large sample of match observations drawn from successful and less-successful professional rugby league teams. Methods: Match activity profiles were collected using global positioning satellite (GPS) technology from 29 rugby league players from a successful team during 24 games and 25 players from a less-successful team during 18 games throughout two separate competition seasons. Technical performance data were obtained from a commercial statistics provider. A progressive magnitude based statistical approach was used to compare differences in physical and technical performance variables between the reference teams. Results: There were no clear differences in playing time, nor absolute and relative total distances or LSR distances between successful and less-successful teams. The successful team had possibly to very likely lower higher-speed running demands and likely fewer physical collisions than the less-successful team, although they likely to most likely demonstrated more accelerations and decelerations and likely higher average metabolic power. The successful team very likely gained more territory in attack, very likely had more possession and likely committed fewer errors. In contrast, the less-successful team was likely required to attempt more tackles, most likely missed more tackles and very likely had a lower effective tackle percentage. Conclusions: In the present study, successful match performance was not contingent on higher match running outputs or more physical collisions, rather proficiency in technical performance components better differentiated between successful and less-successful teams.
The aim of this investigation was to analyze the physical and physiological demands of experienced basketball players during a real and competitive game. Twenty-five well-trained basketball players (8 guards, 8 forwards, 9 centers) played a competitive game on an outdoor court. Instantaneous running speeds, the number of body-impacts above 5-g as well as the number of accelerations and decelerations were assessed by means of a 15 Hz GPS accelerometer unit. Individual heart rate was also recorded using heart rate monitors. As a group mean, the basketball players covered 82.6±7.8 m/min during the game with a mean heart rate of 89.8±4.4% of maximal heart rate. Players covered 3±3% of the total distance running at above 18 km/h and performed 0.17±0.13 sprints per minute. The number of body impacts was 8.2±1.8 per minute of play. The running pace of forwards was higher than centers (86.8±6.2 vs. 76.6±6.0 m/min; p<0.05). The maximal speed obtained during the game was significantly higher for guards than centers (24.0±1.6 km/h vs. 21.3±1.6 km/h; p<0.05). Centers performed a lower number of accelerations/decelerations than guards and forwards (p<0.05). In conclusion, the extraordinary rates of specific movements performed by these experienced basketball players indicate the high physiological demands necessary to be able to compete in this sport. The centers were the basketball players that showed lower physiological demands during a game while there were no differences between guards and forwards. These results can be used by coaches to adapt basketball training programs to the specific demands of each playing position.