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Vázquez-Guer rero1, J. et al.: COMPARING EXTERNAL TOTAL LOAD,... Kinesiology 50(2018)2:xxx-xxx
1
COMPARING EXTERNAL TOTAL LOAD, ACCELERATION
AND DECELERATION OUTPUTS IN ELITE BASKETBALL
PLAYERS ACROSS POSITIONS DURING MATCH PLAY
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
Abstract:
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.
Key words: team sports, accelerometer, sport performance, GPS, training
Introduction
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 inuence 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,...
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include total distance run, number of sprints, high
speed running, accelerations, decelerations, jumps
or impacts (Soligard, et al., 2016). Individual player
proles (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 specic 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,
2007).
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,
Bloomeld, 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). Specically, 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 ofcial 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
classied into three playing specic-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 specic 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 specic posi-
tions in elite senior basketball players during
competitive matches.
Methods
Participants
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
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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.
Procedure
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 ofcial 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
comparisons.
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
(n=5).
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 (coefcient 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 condently 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 coefcient [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
2.6.0.0 Software). As in previous studies (Aughey,
2011), the occurrences of moderate accelerations/
decelerations (< 3 m∙s
-2
) and maximal accelerations/
decelerations (> 3 m∙s
-2
) 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 identied must be
greater than or equal to the user-dened 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,...
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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% condence 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).
Results
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
(n=4)
Shooting guards
(n=6)
Small forwards
(n=4)
Power forwards
(n=4)
Centers
(n=5)
# Accelerations
(<3 m∙s-2)
#/min
*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)
#/min
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)
#/min
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)
#/min
+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. 5± 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
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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 specic 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
signicantly 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
m·s
-2
) 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). Specically, 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
-2
in basketball, likely as a consequence
of court dimensions.
When interpreting the position-specic 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 difcult 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 ofcial 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,...
6
load requirements in ofcial 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 ofcial 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 inuenced 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 signicantly 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
Department
Football Club Barcelona
Av. Aristides Maillol
08028 Barcelona, Spain
Phone: +34
Fax: +34
E-mail: jairo.vazquez@fcbarcelona.cat
