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Understanding the game demands encountered in basketball provides useful insight for developing specific, individualized and team-based training sessions. This study quantified and compared the game activity demands encountered by basketball players of different playing positions: i) strictly when in possession of the ball and ii) overall during live playing time (irrespective of ball possession). The activity demands encountered by 44 (22 guards, 14 forwards, 8 centres) adult, professional, male basketball players were assessed across 10 official games. Time-motion analysis was used to determine the frequency and proportion (%) of playing time performing recovery (REC), low- (LIA), moderate- (MIA), and high- (HIA) intensity activities. Linear mixed models were constructed to examine differences in dependent variables between playing positions, accounting for repeated measures. Guards, forwards, and centres spent 11.9±5.9%, 3.5±1.3%, and 2.9±1.1% of live playing time in possession of the ball, respectively. Guards performed more activities at all intensities (total movements, REC, LIA, MIA, and HIA) than forwards (P < 0.05) and centres (P < 0.05) when in possession of the ball. The proportion of time spent performing HIA in possession of the ball was greater for forwards (P = 0.001) and centres (P = 0.001) than guards. During live playing time overall across games, centres performed more HIA per minute (P = 0.049) and spent a greater proportion of time performing HIA (P = 0.047) than guards. Activities performed when in possession of the ball and during live playing time across basketball games are affected by playing position. These data highlight the need to develop position-specific training drills, particularly with ball possession.
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Biology of Sport, Vol. 37 No3, 2020 269
Physical demands of basketball games
INTRODUCTION
Understanding the demands imposed on basketball players during
games provides useful insight for developing specic individualized
and team-based training sessions[1]. Game demands can be as-
sessed in terms of physiological responses (e.g. heart rate, meta-
bolic measurements) and physical activities performed (e.g. fre-
quency and duration of activities, distance covered, PlayerLoad)[2, 3].
Current evidence suggests that an increasing number of studies are
focusing on quantifying the external demands encountered by bas-
ketball players across games[3–5]. One of the most frequently used
approaches for measuring activity demands in basketball is time-
motion analysis (TMA)[3, 6]. Typically, TMA is employed to calculate
the frequencies of, and durations spent performing, various activities
across basketball games. Existing literature has strongly established
the intermittent nature of basketball games, during which players
perform changes in activity type every 1–3 s[4, 7–10]. Within TMA
studies[4, 6, 7], basketball movements are usually classified
Inuence of ball possession and playing position on the physical
demands encountered during professional basketball games
AUTHORS: Davide Ferioli1,2, Ermanno Rampinini2, Marco Martin2, Diego Rucco1, Antonio
LaTorre1, Adam Petway3, Aaron Scanlan4
1 Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milano, Italy
2 Human Performance Laboratory, MAPEI Sport Research Centre, Olgiate Olona, Varese, Italy
3 Philadelphia 76ers, Philadelphia, PA, USA
4 Human Exercise and Training Laboratory, School of Health, Medical, and Applied Sciences, Central Queensland
University, Rockhampton, Queensland, Australia
ABSTRACT: Understanding the game demands encountered in basketball provides useful insight for developing
specic, individualized and team-based training sessions. This study quantied and compared the game activity
demands encountered by basketball players of different playing positions: i) strictly when in possession of the
ball and ii) overall during live playing time (irrespective of ball possession). The activity demands encountered
by 44 (22 guards, 14 forwards, 8 centres) adult, professional, male basketball players were assessed across
10ofcial games. Time-motion analysis was used to determine the frequency and proportion (%) of playing
time performing recovery (REC), low- (LIA), moderate- (MIA), and high- (HIA) intensity activities. Linear mixed
models were constructed to examine differences in dependent variables between playing positions, accounting
for repeated measures. Guards, forwards, and centres spent 11.9±5.9%, 3.5±1.3%, and 2.9±1.1% of live
playing time in possession of the ball, respectively. Guards performed more activities at all intensities (total
movements, REC, LIA, MIA, and HIA) than forwards (
P
<0.05) and centres (
P
<0.05) when in possession of
the ball. The proportion of time spent performing HIA in possession of the ball was greater for forwards
(
P
=0.001) and centres (
P
=0.001) than guards. During live playing time overall across games, centres
performed more HIA per minute (
P
=0.049) and spent agreater proportion of time performing HIA (
P
=0.047)
than guards. Activities performed when in possession of the ball and during live playing time across basketball
games are affected by playing position. These data highlight the need to develop position-specic training
drills, particularly with ball possession.
CITATION: Ferioli D, Rampinini E, Martin M et al. Inuence of ball possession and playing position on the physical
demands encountered during professional basketball games. Biol Sport. 2020;37(3):269–276.
Received: 2020-03-23; Reviewed: 2020-04-14; Re-submitted: 2020-05-12; Accepted: 2020-05-12; Published: 2020-06-08.
according to their relative intensity into recovery (REC, including
standing/walking), low-intensity activities (LIA, including jogging and
low-intensity specic movements), moderate-intensity activities (MIA,
including running and moderate-intensity specic movements), and
high-intensity activities (HIA, including sprinting, high-intensity spe-
cic movements, and jumping). Systematic evidence shows that
male basketball players spend ~28–63% of time recovering and
~14–40%, ~11–28%, and ~11–20% of time performing LIA, MIA,
and HIA respectively during games[3].
While existing data provide detailed information regarding the
physical demands imposed on basketball players during games,
dribbling activities have been quantied in alimited number of stud-
ies[8, 9, 11]. This observation is surprising considering that team
success partially depends on the activities performed by the player
in possession of the ball (e.g. controlling possession, scoring)[12].
In this regard, adult, male basketball players spend up to ~10% of
Original Paper
DOI: https://doi.org/10.5114/biolsport.2020.95638
Key words:
Time-motion analysis
Activity demands
Playing roles
External load
Team sports
Match analysis
Corresponding author:
Davide Ferioli
University of Milan
via G. Colombo n.71,
20133, Milano, Italy
E-mail: ferio89@hotmail.it
270
Davide Ferioli et al.
MATERIALS AND METHODS
Subjects
Data were collected from 44 professional, adult, male basketball
players (age: 26.5±4.4 years, stature: 197.7±8.2 cm, body mass:
94.7±10.7 kg) competing in the Italian rst (n=25) and second
(n=19) divisions (i.e. Serie Aand Serie A2). The players were
grouped according to playing positions including guards (n=22,
age: 26.6±4.9 years, stature: 191.3±5.3 cm, body mass: 87.7±7.7
kg), forwards (n=14, age: 25.8±3.1 years, stature: 201.2±3.1
cm, body mass: 98.4±5.9 kg), and centres (n=8, age: 27.3±5.1
years, stature: 208.9±4.4 cm, body mass: 107.4±9.8 kg). Players
were recruited from 6separate basketball teams (i.e. 3teams for
each division). Throughout the data collection period, coaching staff
reported players to train 6–10 times per week, with session duration
typically lasting between 60 and 120 min. In addition to on-court
basketball training, players performed strength sessions twice per
week and specic conditioning sessions once per week. First division
teams played 1–2 games per week, while second division teams
completed 1game per week. All players included in this study were
members of the teams since the start of the preparation period and
were required to have played 10 min in at least 1game to be
considered for the individual player analysis. All reserve players (those
who play<10 min per game) were excluded from the study.[4]
After verbal and written explanation of the experimental design and
potential risks and benets of the study, written informed consent
was gathered from all players. The study was approved by the Inde-
pendent Institutional Review Board of MAPEI Sport Research Centre
in accordance with the Helsinki Declaration (2013).
Design
A between-subject, observational study design was used to compare
activity demands when in possession of the ball and during live playing
time between playing positions during ofcial games throughout the
regular competitive period (season 2015–16 and 2016–17). Atotal of
70 individual game samples were collected across 10 ofcial games
(i.e. each player was analysed on 1–2 occasions). Each individual
player (and team) was only monitored during one of the two seasons
considered, with amaximum of 3weeks separating games when play-
ers were considered on 2occasions. Games were randomly selected
for analysis during the competitive season; however, games with differ-
ences in the nal score exceeding 20 points were excluded apriori.
Consequently, the analysed game had relatively consistent score differ-
ences (mean=11±5points). All games were administered following
FIBA rules, using a24-s shot clock, and 4x 10-min quarters with 2-min
inter-quarter breaks and a15-min half-time break.
Time-motion analysis
All games were video-recorded using axed camera (GoPro hero
4silver edition, San Mateo, CA, USA), positioned to allow afull view
of the court. All games were captured at asample rate of 30 Hz and
resolution of 1080 p. Games were recorded for their entire duration,
live playing time dribbling the ball[8]. Despite the important contri-
bution of dribbling to the overall activity demands faced during bas-
ketball games, no studies have described the intensities at which
these dribbling activities, or other activities when in possession of
the ball (e.g. making an offensive move to score, passing the ball,
securing arebound), are performed. Understanding the precise
physical activities performed when players are in possession of the
ball will provide important insights for developing more specic train-
ing strategies.
Differences in basketball game demands between competitive
levels,[4, 8, 10, 13] countries,[3] sex,[3, 9] and game quar-
ters[7, 10, 14] have been well established in the literature. Likewise,
some studies have compared game activity demands between play-
ing positions in basketball[7, 8]. In this regard, guards have been
reported to perform more frequent changes in movement types per
minute compared to forwards and centres, demonstrating agreater
intermittent prole during basketball games[3, 7, 8]. Furthermore,
guards spend more time performing HIA (i.e. sprints and high-inten-
sity shufes) and spend less time in REC than forwards and centres
during games[3, 7, 11, 15]. Agreater proportion of playing time
performing sprinting and shufing movements was also observed in
forwards compared to centres[3]. Despite some initial insights being
provided regarding differences in game demands according to play-
ing position, systematic evidence[3] suggests that further research
is still needed to denitively understand positional differences in
basketball game demands.
Indeed, some limitations should be acknowledged when interpret-
ing the results of previous research examining basketball game de-
mands according to playing position. First, most existing TMA stud-
ies exploring position differences in basketball game demands
analysed collegiate or junior players[15–17] thus available knowledge
does not sufciently include adult, professional players. This discern-
ment is important given that adult, professional basketball players
may perform activities differently during games given that they like-
ly possess better developed physical characteristics and technical-
tactical skills compared to younger players[18, 19] and players
competing at lower levels[19–22]. Secondly, the limited TMA stud-
ies comparing game demands between playing positions in adult,
professional basketball players included small sample sizes
(n=10–13) from asingle team[8, 23]. To overcome these limita-
tions, studies recruiting alarge sample of adult, professional players
from various teams are needed to develop amore holistic understand-
ing of basketball game activity demands according to playing position.
Acomprehensive set of position-specic TMA game data will further
assist in developing more specic training programmes according to
positional needs.
Therefore, the aim of this study was to quantify and compare the
activity demands between playing positions: i) when players are in
possession of the ball and ii) overall during live playing time (irrespec-
tive of ball possession) across ofcial games in professional, adult,
male basketball players.
Biology of Sport, Vol. 37 No3, 2020
271
Physical demands of basketball games
including all stoppages in play. Manual frame-by-frame software
(SICS VideoMatch Basket, version 5.0.5) was used to determine
player activities when in possession of the ball and during live play-
ing time. As previously described,[4, 24, 25] player physical demands
were classied into 8movement categories as follows: (i) standing/
walking: activity of no greater intensity than walking without any
distinction between standing still and walking or between different
intensities of walking; (ii) jogging: movement (forwards or backwards)
at an intensity greater than walking but without urgency; (iii) running:
forwards or backwards movement at an intensity greater than jogging
and amoderate degree of urgency but which did not approach an
intense level of movement; (iv) sprinting: forward or backwards move-
ment at ahigh intensity, characterized by effort and purpose at or
close to maximum; (v) low-: (vi) moderate-: (vii) high- specic move-
ments: movements differing from ordinary walking or running per-
formed respectively at low intensity without urgency, at medium
intensity with amoderate degree of urgency and at high intensity
with urgency and (viii) jumping: the time from the initiation of the
jumping action to the completion of landing. Specic movements
mainly included the stance position, shufing, rolling, reversing,
screening, and cross-over running activities[25]. Movements were
then grouped according to their relative intensity into REC (standing/
walking), LIA (jogging and low-intensity specic movements), MIA
(running and moderate-intensity specic movements), and HIA (sprint-
ing, high-intensity specic movements, and jumping)[4, 6, 7]. The
frequency of occurrence and the duration of each movement were
determined when players were in possession of the ball and during
live playing time (i.e. game activity when the game clock was run-
ning). Activity frequencies were calculated as the total number of
events (n) performed when in possession of the ball and during live
playing time, and normalized according to duration (n/min) for each
player. Activity durations were determined as apercentage (%) of
time when each player was in possession of the ball and during live
playing time. All video analyses were performed by two expert mem-
bers of the research team. All measures possessed acceptable intra-
and inter-tester reliability (Table 1).
Statistical analysis
The TMA descriptive results are reported as means±standard devia-
tions (SD). Before running linear mixed effect models, boxplots and
histograms were used to determine potential inuential data points.
Following analysis, visual inspections of residual plots were used to
determine deviations from homoscedasticity or normality. Linear
mixed models were constructed to examine activity differences be-
tween playing positions, accounting for individual repeated measures.
Each playing position (3 levels) and the different leagues (2 levels)
were included as xed effects in the model, while individual players
were included as arandom effect. ‘Step-up’ model construction strat-
egies were employed, similar to that used in previous team sports
research[26]. Each process began with an unconditional model
containing only axed intercept and the random factor. The model
was then implemented by adding each single xed effect one at
atime. The order in which each xed effect was added to the mod-
el was guided by extensive experience in team sports. The Akaike
information criterion (AIC) and degrees of freedom for each model
were visually compared with the previous model, in which alower
AIC represented abetter model t. For all models, the best t for the
data was found by including both the playing position and league.
However, no differences between leagues were found, conrming the
similar game activity demands faced by players across leagues and
the professional status of both leagues. The tstatistics from the mixed
model were converted into Cohen’s deffect sizes (ES) and associ-
ated 95% condence limits (CL). ES were interpreted as follows:
0.20, trivial; >0.20–0.60, small; >0.60–1.2, moderate;
TABLE 1. Intra- and inter-tester reliability of time-motion analysis variables.
Variable Activity
category
ICC (90% CI) CV% (90% CI)
Inter-operator Intra-operator Inter-operator Intra-operator
Frequency
REC 0.97 (0.91–0.99) 0.99 (0.97–1.00) 9.1 (6.6–15.5) 5.3 (4.1–7.8)
LIA 0.96 (0.89–0.99) 0.99 (0.97–1.00) 8.0 (5.7–13.4) 4.1 (3.1–6.1)
MIA 0.88 (0.67–0.96) 0.91 (0.77–0.97) 14.9 (10.6–25.6) 12.5 (9.5–18.7)
HIA 0.93 (0.80–0.98) 0.95 (0.89–0.98) 15.2 (10.9–26.3) 12.1 (9.2–18.1)
Duration
REC 0.99 (0.98–1.00) 1.00 (1.00–1.00) 3.4 (2.4–5.6) 1.9 (1.4–2.7)
LIA 0.98 (0.94–0.99) 0.99 (0.99–1.00) 6.8 (4.9–11.5) 3.7 (2.8–5.4)
MIA 0.93 (0.80–0.98) 0.90 (0.75–0.96) 11.8 (8.5–20.1) 14.0 (10.6–21.1)
HIA 0.96 (0.87–0.99) 0.97 (0.92–0.99) 13.2 (9.5–22.7) 10.7 (8.2–16.1)
Abbreviations: ICC, intraclass correlation coefcient; CI, condence intervals; CV, coefcient of variation; REC, Recovery; LIA, low-
intensity activities; MIA, medium-intensity activities; HIA, high-intensity activities.
272
Davide Ferioli et al.
more total activities per minute compared to forwards (all P<0.05,
ES range: 0.67–1.38, moderate-large) and centres (all P<0.05,
ES range: 0.74–1.63, moderate-large). Non-signicant differences
(ES range: 0.06–0.25, trivial-small) were evident in activity frequen-
cies when in possession of the ball between forwards and centres.
Furthermore, the percentage of time spent performing HIA when in
possession of the ball was greater for forwards (P  =0.001,
ES±95%CL=-1.02±0.57, moderate) and centres (P =0.001,
-1.21±0.72, large) compared to guards. Conversely, guards spent
agreater proportion of time performing LIA when in possession of
the ball compared to centres (P=0.002, 1.10±0.72, moderate)
and MIA compared to both forwards (P =0.003, 0.85±0.56,
moderate) and centres (P=0.001, 1.17±0.72, moderate).
Statistical outcomes for positional comparisons in game activities
during live playing time are presented in Table 4. Regarding live
playing time, centres performed more HIA per minute (P=0.049,
-0.68±0.69, moderate) and spent agreater proportion of time per-
forming HIA (P =0.047, -0.69±0.69, moderate) compared to
guards. Non-signicant, trivial-moderate differences were observed
between positions for all other comparisons in game activities during
live playing time.
>1.20–2.0, large; >2.0–4.0, very large; >4.0, extremely large[27].
Statistical signicance was set at P<0.05. All statistical analyses
were conducted using the lme4, lmerTest and compute.es pack-
ages in Rstatistical software (version 3.6.2)[28].
RESULTS
Guards, forwards, and centres spent 11.9±5.9%, 3.5±1.3%, and
2.9±1.1% of live playing time in possession of the ball, respec-
tively. Pairwise comparisons between positions showed that agreat-
er proportion of live playing time was spent in possession of the ball
for guards compared to forwards (P < 0.001, estimated
±95%CL=7.95±3.04, ES±95%CL=1.39±0.59, large) and
centres (P<0.001, 8.68±3.69, 1.58±0.75, large). No signicant
differences in the proportion of live playing time in possession of the
ball were found between forwards and centres (P=0.71, 0.13±0.72,
0.73±3.95, moderate).
Mean±SD for each game activity variable when in possession of
the ball and during live playing time according to playing position
are presented in Table 2. Statistical outcomes for positional com-
parisons in game activities when in possession of the ball are pre-
sented in Table 3. When in possession of the ball, guards performed
TABLE 2. Frequency and duration of game activities according to playing position when in possession of the ball and during live
playing time in professional, male basketball players.
REC LIA MIA HIA All movements
In possession of the ball
Frequency (n/min)
Guards 1.17±0.64 1.70±1.01 0.84±0.49 1.18±0.48 4.88±2.02
Forwards 0.51±0.29 0.55±0.29 0.22±0.17 0.89±0.36 2.16±0.67
Centers 0.60±0.29 0.38±0.23 0.15±0.11 0.86±0.37 1.99±0.72
Duration (%)
Guards 28.3±10.7 34.6±13.9 18.0±9.2 19.0±13.2 -
Forwards 25.9±11.3 28.2±11.9 10.7±7.8 35.2±16.0 -
Centers 33.5±16.1 21.5±10.8 8.3±6.4 36.7±11.4 -
Live playing time
Frequency (n/min)
Guards 6.55±0.88 11.37±1.59 3.81±1.26 3.47±1.46 25.20±3.62
Forwards 6.00±1.09 10.99±1.16 3.42±1.01 3.54±1.27 23.95±2.84
Centers 5.82±1.41 10.52±1.21 3.43±1.01 4.23±1.55 24.01±2.27
Duration (%)
Guards 36.6±8.0 44.4±6.4 10.6±3.6 8.4±4.2 -
Forwards 35.6±9.7 45.3±4.6 10.1±3.2 9.0±3.7 -
Centers 36.1±11.6 42.7±6.2 10.4±3.7 10.8±5.3 -
Abbreviations: REC, recovery; LIA, low-intensity activities; MIA, medium-intensity activities; HIA, High-intensity activities.
Note: Live playing time encompasses game activities when players were on the court and the clock was running.
Biology of Sport, Vol. 37 No3, 2020
273
Physical demands of basketball games
TABLE 3. Comparison in frequency and duration of game activities when in possession of the ball between playing position in
professional, male basketball players.
Guards vs. Forwards Guards vs. Centers Forwards vs. Centers
Estimate
±95% CI ES±95% CI
P
value Estimate
±95% CI ES±95% CI
P
value Estimate
±95% CI ES±95% CI
P
value
Frequency (n/min)
REC 0.60±0.34 0.94±0.56 0.001 0.55±0.42 0.88±0.70 0.013 -0.06±0.45 -0.09±0.72 0.805
LIA 1.04±0.49 1.13±0.57 <0.001 1.27±0.60 1.42±0.74 <0.001 0.23±0.64 0.25±0.72 0.475
MIA 0.60±0.23 1.38±0.59 <0.001 0.69±0.28 1.63±0.76 <0.001 0.09±0.30 0.20±0.72 0.572
HIA 0.31±0.25 0.67±0.55 0.017 0.34±0.31 0.74±0.69 0.033 0.03±0.32 0.06±0.72 0.861
All movements 2.53±1.05 1.28±0.58 <0.001 2.88±1.28 1.51±0.75 0.000 0.35±1.17 0.18±0.72 0.613
Duration (%)
REC 1.46±7.74 0.10±0.53 0.709 -6.32±9.47 -0.45±0.69 0.191 -7.77±10.11 -0.54±0.73 0.133
LIA 5.98±6.70 0.48±0.54 0.082 13.74±8.37 1.10±0.72 0.002 7.76±8.85 0.62±0.73 0.088
MIA 7.22±4.55 0.85±0.56 0.003 9.85±5.66 1.17±0.72 0.001 2.63±6.00 0.31±0.72 0.386
HIA -15.19±7.92 -1.02±0.57 0.001 -17.72±9.86 -1.21±0.72 0.001 -2.53±10.45 -0.17±0.72 0.631
Abbreviations: ES, effect size (values above zero: greater for guards compared to forwards and centers or greater for forwards compared
to centers); CI, condence intervals; REC, Recovery; LIA, low-intensity activities; MIA, medium-intensity activities; HIA, high-intensity
activities.
Note: Pvalue is bolded when<0.05.
TABLE 4. Comparison in frequency and duration of game activities during live playing time between playing position in professional,
male basketball players.
Guards vs. Forwards Guards vs. Centers Forwards vs. Centers
Estimate
±95% CI ES±95% CI
P
value Estimate
±95% CI ES±95% CI
P
value Estimate
±95% CI ES±95% CI
P
value
Frequency (n/min)
REC 0.44±0.74 0.31±0.53 0.245 0.71±0.90 0.53±0.69 0.124 0.27±0.96 0.20±0.72 0.579
LIA 0.35±0.87 0.21±0.53 0.431 0.82±1.07 0.52±0.69 0.133 0.47±1.14 0.29±0.72 0.412
MIA 0.45±0.64 0.38±0.54 0.166 0.26±0.78 0.22±0.68 0.517 -0.19±0.84 -0.16±0.72 0.644
HIA -0.00±0.80 0.00±0.54 0.995 -0.99±0.98 -0.68±0.69 0.049 -0.99±1.05 -0.67±0.74 0.066
All movements 1.26±1.72 0.39±0.54 0.153 0.80±2.11 0.25±0.67 0.452 -0.46±2.25 -0.14±0.72 0.687
Duration (%)
REC 0.48±6.19 0.04±0.53 0.878 1.46±7.52 0.13±0.68 0.700 0.98±8.05 0.09±0.72 0.809
LIA -0.95±3.92 -0.13±0.53 0.631 1.75±4.76 0.25±0.68 0.467 2.70±5.10 0.37±0.72 0.297
MIA 0.80±2.01 0.21±0.53 0.430 -0.03±2.46 -0.01±0.67 0.981 -0.83±2.62 -0.22±0.72 0.531
HIA -0.40±2.52 -0.09±0.53 0.751 -3.16±3.08 -0.69±0.69 0.047 -2.75±3.29 -0.59±0.73 0.102
Abbreviations: ES, effect size (values above zero: greater for guards compared to forwards and centers or greater for forwards compared to
centers); CI, condence intervals; REC, Recovery; LIA, low-intensity activities; MIA, medium-intensity activities; HIA, high-intensity activities.
Note: Pvalue is bolded when<0.05.
live playing time. Generally, playing position inuenced player activ-
ity when in possession of the ball. Specically, guards performed
more activities at all intensities than forwards and centres (moderate-
large ES), while the proportion of time spent undergoing HIA was
greater for forwards and centres compared to guards (moderate-large
ES). Conversely, the activity demands during live playing time
DISCUSSION
This is the rst study providing normative data representing the
activity demands encountered by basketball players when in posses-
sion of the ball. Furthermore, the present study presents the most
comprehensive set of available data (44 players, 70 game samples)
describing player game activity according to playing position across
274
Davide Ferioli et al.
HIA in the present study, forwards (14.3%) and centres (15.2%)
spent roughly three times as much time jumping when in possession
of the ball compared to guards (5.1%), and approximately double
the proportion of time performing specic movements at high inten-
sities (forwards=21.0%; centres=14.6%) when in possession of
the ball compared to guards (8.3%). Considering that this is the rst
study to describe the activity demands performed when in possession
of the ball during basketball games, comparison of the ndings with
previous studies is not possible. Future studies should investigate
the activity demands carried out when in possession of the ball dur-
ing basketball games across various player samples (e.g. youth, fe-
male, and amateur players) and using the time course of specic
actions (e.g. fast break, isolations, ball screens) to further expand
the evidence base on this topic.
The present study also investigated differences in game activity
demands between playing positions overall during live playing time
(irrespective of being in possession of the ball). Afew studies[8, 23]
have described differences in physical demands between playing
positions during professional, male basketball games. Despite the
practical limitations associated with use of TMA (i.e. time- and labour-
intensive data analysis and interpretation), this approach has been
readily adopted in the literature to quantify the activity demands
encountered by players during basketball games, as the use of mi-
crosensors is not always permitted[3, 31]. The results of the present
study demonstrate that centres perform greater HIA per minute and
spend agreater proportion of time performing HIA compared to guards
(moderate ES) during live playing time in games. Accordingly, García
et al.[23] recently reported that professional Spanish centres perform
more jumps and reach greater peak velocities (measured with mi-
crosensors) than professional Spanish guards during ofcial games.
In line with this nding, the centres in the present study performed
agreater number of jumps (1.39 vs 0.94) and high-intensity spe-
cic movements per minute (2.19 vs 1.63) compared to guards. In
addition, the proportion of live time spent performing HIA was mod-
erately greater for centres than guards, likely as aconsequence of
the larger contribution of high-intensity specic movements (6.6%
vs 4.6%) and jumps (2.2% vs 1.5%) performed by centres compared
to guards. While several physical (e.g. strength and power production)
and technical-tactical (e.g. technical skill and coaching staff decisions)
factors contribute to basketball performance, these results conrm
the importance of sustaining high-intensity efforts during profes-
sional basketball games. Consequently, basketball practitioners are
encouraged to consider the pronounced differences between playing
positions. Specically, the greater number of movements performed
at high intensities and the higher proportion of time spent carrying
out intense movements such as screening, positioning to secure
rebounds, and 1-on-1 situations likely underpin the moderately
greater HIA performed by centres compared to guards across live
playing time. Guards are usually less involved in scenarios involving
high-impact body contact and collisions with opponents than forwards
and centres[23]. Consequently, basketball practitioners should
(irrespective of being in possession of the ball) were similar between
playing positions (trivial-small ES), except for centres, who more
frequently performed and spent agreater proportion of time perform-
ing HIA than guards (moderate ES).
A thorough understanding of the physical activities performed
when players are in possession of the ball is fundamental for devel-
oping specic individual and team-based drills during basketball
training. As expected, guards spent the greatest time in possession
of the ball (large ES) as they are required to dribble from the defen-
sive to the offensive half-court during transitions with the overall aim
of driving fast breaks or leading offensive plays[18]. As such, guards
are usually selected according to their physical characteristics (e.g.
agility and ability to sustain high-intensity efforts and changes of
direction) and technical skills (e.g. shooting, passing, and drib-
bling)[20, 29, 30]. In line with the results of the present study,
Scanlan et al.[8] showed that guards were in possession of the ball,
executing only dribbling tasks, for agreater proportion of live time
during games than frontcourt players (i.e. forwards and centre)
(~9.0% vs ~1.5% of live playing time). However, this is the rst
study quantifying all scenarios when players are in possession of the
ball, not strictly dribbling activities as previously quantied[8, 9].
The specic physical demands encountered when in possession
of the ball were greatly affected by playing position. In this regard,
guards performed more than double the activities per minute in pos-
session of the ball than forwards and centres (~5 vs ~2 n/min;
large), reinforcing their importance in pushing the ball and keeping
the pace/tempo of the offensive play[18]. Furthermore, we found
that guards completed more REC, LIA, MIA, and HIA per minute
than forwards and centres when in possession of the ball (moderate-
large ES). These ndings are likely aconsequence of the greater
proportion of time spent in possession of the ball by guards, high-
lighting the importance of developing position-specic drills for guards
dribbling at various intensities with frequent changes in movement
type.
When comparing the proportion of time spent performing different
game activities in possession of the ball, guards performed amod-
erately greater proportion of LIA compared to centres and amoder-
ately greater proportion of MIA compared to both forwards and
centres. However, forwards and centres spent ~35% of time in pos-
session of the ball performing HIA, which is considerably higher
(moderate-large ES) than the 19±13% of time in possession of the
ball spent by guards performing HIA. This result is likely aconse-
quence of the existing differences in technical and physical charac-
teristics of players occupying different playing positions[20, 30] and
may also be attributed to the tactical strategies adopted across
teams[18]. For example, forwards and centres are not typically
involved in driving the ball across the court during transitions at
varied intensities, and therefore when they gain possession of the
ball they carry out rapid, intense movements (e.g. making an of-
fensive move to score, securing arebound). In support of this notion,
when further analysing the different types of activities constituting
Biology of Sport, Vol. 37 No3, 2020
275
Physical demands of basketball games
include position-specic exercises at high intensities during training
drills to ensure that players are prepared to meet the game demands
likely to be encountered.
There are some limitations of the present study that must be
acknowledged. First, despite TMA representing avalid[2, 3, 24]
and reliable[4, 24] approach to quantify game demands, issues may
arise from the qualitative denition of player activity classications.
Hence, future studies should adopt other available technologies (e.g.,
wearable microsensors) to further explore differences in playing po-
sitions on this topic. Second, the recruited basketball players in this
study were competing in the same male national tournament, and
therefore the ndings might not be generalizable to basketball play-
ers competing in other male or female competitions. Third, activity
demands were determined as average values across entire games in
the present study. Thus, this positional differences in game activities
representing the most demanding passages of the play (worst-case
scenario) were not explored.
PRACTICAL APPLICATIONS
The present study permits some useful evidence-based practical
recommendations to be generated. Accordingly, the large data set
we provided regarding game activity frequencies and durations for
professional, adult, male basketball players may permit more precise
conditioning exercises to be developed by high-performance staff for
optimal player preparation across different seasonal phases. Further-
more, basketball practitioners could consider the data we provided
indicating player activity demands strictly when in possession of the
ball for the development of individual and team-based training ses-
sions. Specically, when training offensive skills in possession of the
ball, forwards and centres should perform the required tasks at high
intensities (e.g. 1-on-1 play on aquarter court or rebound exercises),
while guards should develop their dribbling ability at both higher
(e.g. sprinting, accelerating, decelerating, and changing directions)
and lower (e.g. stationary or low-velocity dribbling skills) intensities.
In contrast, when considering the activity demands encountered by
players during live playing time overall across games, considerable
overlap exists across positions, and therefore subsequent positional
training plans are likely to possess overlap across positions when
administered in team environments. However, drills for centres should
specically focus on developing high-intensity specic movements,
body contacts, collisions, and jumps given the heightened HIA de-
mands observed in this position, while guards should spend sufcient
time performing varied exercises with the ball given the high propor-
tion of playing time they spend in possession.
CONCLUSIONS
The activity demands encountered when in possession of the ball
and overall during live playing time in adult, professional basketball
games are affected by playing position. When in possession of the
ball, guards perform more activities at all intensities per minute than
forwards and centres, while the proportion of time spent at high
intensities is greater for forwards and centres compared to guards.
The activity demands overall during live playing time (irrespective of
being in possession of the ball) are similar between playing positions,
except for centres, who more frequently perform and spend agreat-
er proportion of time performing HIA than guards. These data high-
light the need to develop position-specic training drills, particularly
when in possession of the ball.
Acknowledgements
The authors would like to thank SICS s.r.l. and Michele Crestani for
technical support with the TMA and Marco Maria Manfredi for his
support in the data analysis. The authors would like also to thank
all the clubs involved in the study.
Conict of interest declaration
The authors report no conict of interest for this manuscript.
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... In basketball, activities performed while holding the ball and during live play are affected by the position of the game. ese data underscore the need to develop position-specific training, especially with the ball, but not very practical [2]. In this study, Nakashima pointed out that "ball of string" and "basketball with postman and edge zone" can improve the decision-making ability of sixth-grade students compared with the ordinary game of basketball. ...
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Modern competitive basketball has developed into a skilled international competitive sport. The situation in basketball is changing rapidly. At the same time, tactics are one of the main factors affecting game performance in athletic basketball. Whether the athletes successfully use the techniques and tactics in the game or not will often become the decisive factor for the outcome of the game. Therefore, it is essential to grasp the situation of basketball games, to study the technical and tactical rules of basketball competitive games, and to provide scientific support for the daily technical and tactical training of competitive basketball and technical and tactical decision-making during the game, which is an important way to improve the level of basketball competition in China. As a decision support technology, data mining plays an important role in decision support systems. This decision-making method is closely related to the amount of data, the way of organization, and the structure of the organization. Based on this, this study studies the improved Bayesian algorithm classification algorithm in data mining, and it also reorganizes and restructures the large, intricate, and unorganized data from the database of the system according to the decision-making demand. After a series of data processing processes are completed, the latest data organization mode is obtained, and new data correlation information is found from it, by finding the relevance of knowledge in the decision support system of the database-based relational database management system, and presenting the implicit structural information in the data to provide users with more accurate decision-making information. Finally, in the investigation and analysis of the on-site decision-making system of basketball games, it is found that 48% of sport coaches think it is very helpful, indicating that the system has merit.
... Although shooting indicators and game-related statistics provide useful insight, further data can be acquired using comprehensive video analyses to examine the demands of 3 3 3 basketball games. Specifically, team possession data such as the frequency, efficiency (to score), duration, and player involvement per possession can identify the most suitable team strategies to adopt to increase chances of winning (3,8). For example, these data might identify the most efficient form of shot to take (1-or 2-point shot) and optimal pace to play, which could inform the development of offensive and defensive schemes for successful implementation in competition. ...
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Despite the popularity of 3 × 3 basketball rapidly growing on a global scale, a paucity of data exist on player demands during competition, particularly considering various factors. This study aimed to quantify the technical-tactical demands of international-level 3 × 3 basketball games according to game outcome, player sex, and competition phase. Overall, 96 players from 24 national teams (48 players across 12 teams in each sex) competing at the 2019 European Basketball Cup 3 × 3 were included in this study. Technical-tactical demands during games including shooting, game-related, and possession-related statistics were retrospectively gathered from public sources or analyzed using video analyses. Linear mixed models and effect size analyses were used to determine differences in demands according to game outcome (wins vs. losses), player sex (males vs. females), and competition phase (group games vs. finals games). Winning teams (p < 0.05, small-large) scored more shots, shot more efficiently, secured more rebounds, committed fewer turnovers and fouls, and drew more fouls to shoot free-throws. Differences between sexes (p < 0.05, small-moderate) showed male teams shot more efficiently, scored more 2-point shots, and scored more points, whereas female teams attempted more 1-point shots, committed more turnovers, and had more possessions. Considering the competition phase, more blocks were completed during group games, and more points per possession were achieved during finals games (p < 0.05, small). This study provides foundation normative values regarding the technical-tactical demands of 3 × 3 game-play during an international competition, with reported data able to be used by practitioners in developing precise, sex-specific training and tactical strategies to optimize team success.
... The results indicate diagnostic validity regarding offensive game involvement as PG were more involved in passing, dribbling, and total ball-bound actions than SG/SF and PF/C (H2a). These findings are in line with former research of position-dependent differences in activity demands demonstrating that Guards are more involved in movements with the ball, especially in passing and dribbling (Abdelkrim et al., 2007;Scanlan et al., 2011Scanlan et al., , 2012Delextrat et al., 2015;Ferioli et al., 2020a). Further, the results match with those of Ortega et al. (2006), who found that PG made more passes compared to other playing positions in Spanish youth basketball. ...
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The Basketball Learning and Performance Assessment Instrument (BALPAI) has been initially developed and evaluated to assess the performance of students or youth basketball players on the entry level. As it is currently the only observational instrument that allows an overall assessment of players’ in-game performance, it might represent a valuable tool for talent identification and development purposes. To investigate this potential field of application, this study aimed to evaluate the BALPAI regarding reliability and diagnostic validity when assessing youth basketball players within a competitive setting. The study sample comprised N = 54 male youth players (Mage = 14.36 ± 0.33 years) of five regional selection teams (Point Guards, PG: n = 19; Shooting Guards and Small Forwards, SG/SF: n = 21; and Power Forwards and Centers, PF/C: n = 14) that competed at the annual U15 national selection tournament of the German Basketball Federation (n = 24 selected; n = 30 non-selected). A total of 1997 ball-bound actions from five games were evaluated with BALPAI. The inter-rater reliability was assessed for technical execution, decision making, and final efficacy. The diagnostic validity of the instrument was examined via mean group comparisons of the players’ offensive game involvement and performance regarding both selection-dependent and position-dependent differences. The inter-rater reliability was confirmed for all performance-related components (κadj ≥ 0.51) while diagnostic validity was established only for specific the BALPAI variables. The selection-dependent analysis demonstrated higher offensive game involvement of selected players in all categories (p < 0.05, 0.27 ≤ Φ ≤ 0.40) as well as better performance in shooting and receiving (p < 0.05, 0.23 ≤ Φ ≤ 0.24). Within the positional groups, the strongest effects were demonstrated among PG (p < 0.05, 0.46 ≤ Φ ≤ 0.60). The position-dependent analysis revealed that PG are more involved in total ball-bound actions (p < 0.05; 0.34 ≤ Φ ≤ 0.53), passing (p < 0.001; 0.55 ≤ Φ ≤ 0.67), and dribbling (p < 0.05, 0.45 ≤ Φ ≤ 0.69) compared to players in other positions. Further differences between players according to selection status and playing position were not detected. The results of this evaluation indicate that the instrument, in its current form, is not yet applicable in competitive youth basketball. The findings highlight the importance of optimizing BALPAI for reliable and valid performance assessments in this context. Future studies should investigate the application of stricter and position-specific criteria to use the observational tool for talent identification and development purposes.
... However, the guard position showed better sprint and agility than the center position [21]; this advantage means that guards can frequently perform repetitive high-intensity activities, such as fast attacks and quick returns to defense [23]. In fact, the guard spends a lot of time in possession of the ball during the game compared to other positions and performs more activities at all intensities than forwards and centers, so a guard position requires a high level of agility and the ability to perform high-intensity interval movements [32]. The roles of basketball positions are clearly defined, and the standards of physique and physical strength suitable for successful performance are standardized to some extent. ...
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No studies have measured the physical strength and lower extremity stability of elite male high school basketball players. This study aimed to measure the physique, physical strength, and lower extremity stability of such athletes in Korea and analyze the differences according to their play positions. Overall, 204 male elite basketball players participated and were classified as guard (n = 97), forward (n = 69), and center (n = 38) according to their main playing position. All sub-variables of physique were significantly higher in the forward and center groups than in the guard group, and were significantly higher in the center group than in the forward group. Strength was significantly higher in the forward and center groups than in the guard group. Agility and speed were significantly faster in the guard group than in the forward and center groups. Y-balance analysis showed that the composite score of both feet tended to be higher in the order of center, forward, and guard, and it was significantly higher in the guard group than in the center group. These results could be used as basic data for selecting players, determining positions, and setting specific training goals for players of each position to improve physical strength and prevent injuries.
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This study aimed to (1) characterize the physical demands of 3 × 3 basketball games during live playing time and ball possession and (2) assess the differences in physical demands between male and female players. Following an observational design, video footage from 27 games of the International Basketball Federation 3 × 3 World Cup 2019 were analyzed from 104 international 3 × 3 basketball players (n = 52 male and n = 52 female players) resulting in a total of 216 (104 male and 112 female) individual game samples. Manual frame-by-frame time-motion analyses determined the relative frequency (n·min-1) and duration (%) for several physical demands at different intensities, according to sex, during the live playing time and in ball possession phases. Linear mixed models for repeated measures and effect size (ES) analyses revealed small non-significant differences in the intermittent profile of 3 × 3 basketball games according to sex (total movements per minute, male = 39.3 (38.6-40.1); female = 40.2 (39.5-41.0), estimated marginal means with 95% confidence intervals). Female competitions had significantly greater number of low-intensity activities (LIA, small ES) and high-intensity activities (HIA, small ES) performed per minute over longer games (small ES), whereas male players had more recovery activities (small ES). During ball possession, male players spent a larger amount of time performing LIA (small ES) than female players, who displayed both the greatest number of HIA and the highest percentage of playing time performed at high intensity (small ES). Overall, these findings suggest that basketball coaches should design sex-specific training sessions based on the specific match demands.
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Basketball includes a wide range of physical actions with and without the ball, which puts forward certain requirements for motor fitness, moral, volitional and mental qualities, as well as creativity and decision-making skills in rapidly changing and often unforeseen circumstances. The scientific novelty is determined by the fact that in order to categorically define the term of “features of physical and mental development and their connection with regular physical exercise”, the authors analysed the works of leading scientists on the theory and methods of physical education, grouped by the authors in accordance with the main concepts. The aim of the article is to study of the phenomenon of physical culture of the student’s personality, namely, the targets of the modern system of physical education and the structure of personal physical culture of a person; the humanisation and democratisation of the system of physical education; the development of the concept of the theory of physical culture and its implementation in the conditions of reformation of higher education. The practical significance of the study is determined by the fact that the elements of basketball are included in the programmes of physical education of preschool children, in the curricula for physical culture of all degrees of general secondary education (primary, secondary, high school), in the programme for physical education of higher education institutions.
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Purpose: The aim of this study was to quantify changes in physical capacities of thirty-eight basketball players selected from different teams, as well as from varying competitive levels (i.e. Division I, Division II and Division III) during the preparation and in-season periods. Methods: Pre (T1) and post (T2) preparation period and during regular season (T3), the players completed a Yo-Yo Intermittent Recovery test-level 1. Following a 3 to 8 days-break, players performed a 6-min continuous running test (Mognoni's test), a counter-movement jump test and a 5-min high-intensity intermittent running test. Results: Blood lactate concentration measured after the Mognoni's test was significantly reduced from T1 to T2, and from T2 to T3 (P<0.001, ƞ2 = 0.424). The distance covered during the Yo-Yo Intermittent Recovery test was significantly increased only from T1 to T2 in Division II and III (P<0.001, ƞ2 = 0.789). Similarly, the physiological responses to high-intensity intermittent running test were improved only from T1 to T2 (all P<0.001, ƞ2 = 0.495 to 0.652). Despite significant changes observed in running tests from T1 to T2, at individual level 35-55% of players did not show a very likely improvement. Relative peak power produced during vertical jumps at T3 by Division I players was increased compared to T1 (ANOVA interaction, P = 0.037, ƞ2 = 0.134). Conclusions: The main improvements in physical capacities occurred during the preparation period, when the aerobic fitness and the ability to sustain high-intensity intermittent efforts were moderately-to-largely improved. However, it appears that the preparation period does not consistently impact on vertical jump variables. Aerobic fitness and force/power production during vertical jumps appear to improve across the competitive season (slightly-to-moderately). Physical tests should be used to identify weaknesses in physical performance of players and to monitor their fatigue status, with the aim to develop individualized training programs.
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The purpose of this study was to compare physical demands between game quarters and specific playing positions during official basketball competition. Thirteen professional male basketball players from the Spanish 2 nd Division were monitored across all 17 regular-season home games. Physical demands were analyzed using a local positioning system (WIMU PRO™, Realtrack Systems S.L., Almería, Spain) and included peak velocity, total distance covered, high-speed running (>18 kmꞏh-1), player load, jumps (>3G), impacts (>8G) and high-intensity accelerations (≥2 m•s-2) and decelerations (≤-2 m•s-2). A linear mixed model was used to test statistical significance (p < 0.05) between independent variables. Furthermore, standardized Cohen's effect size (ES) and respective 90% confidence intervals were also calculated. There was an overall decrease in all variables between the first and fourth quarter during competition. Specifically, total distance covered (p < 0.001; ES =-1.31) and player load (p < 0.001; ES =-1.27) showed large effects between the first and last period. Regarding differences between positions, guards presented significant increased values compared to centers (p = 0.04; ES = 0.51), whereas centers achieved significant larger results and moderate effects in comparison to guards in peak velocity (p = 0.01; ES = 0.88) and jumps (p = 0.04; ES = 0.86). In conclusion, physical demands vary between game quarters and playing positions during official competition and these differences should be considered when designing training drills to optimize game performance .
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Purpose: The aim of this study was to describe the physical demands during U18 elite basketball games according to the game quarter and to identify a smaller subset of variables and threshold scores that distinguish players' physical performance in each quarter. Methods: Data was collected from ninety-four players who participated in the study (age: 17.4 ± 0.74 years; height: 199.0 ± 0.1 cm; body mass: 87.1 ± 13.1 kg) competing in the Euroleague Basketball Next Generation Tournament. Players' movements during the games were measured using a portable local positioning system (LPS) (WIMU PRO®, Realtrack Systems SL, Almería, Spain) and included relative distance (total distance / playing duration), relative distance in established speed zones, high-intensity running (18.1-24.0 km·h-1) and sprinting (> 24.1 km·h-1). player load, peak speed (km·h-1) and peak acceleration (m·s-2) number of total accelerations and total decelerations, high intensity accelerations (> 2 m·s-2) and decelerations (< -2 m·s-2). Results: There was an overall decrease in distance covered, player load, number of high intensity accelerations and decelerations between the first and last quarter of the games in all playing positions. A classification tree analysis showed that the first quarter had much influence of distance covered (above 69.0 meters), distance covered <6.0 km·h-1 and accelerations (> 2 m·s-2), whereas the fourth quarter performance had much influence of distance covered (below 69.0) and distance covered 12.1-18.0 km·h-1. Conclusions: A significant reduction in physical demands occurs during basketball, especially between first and last quarter for players in all playing positions during basketball games of under 18 elite players.
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Purpose:: To examine the collective independent influence of a range of individual characteristics on physical and technical match performance during international rugby sevens matches. Methods:: Data was collected from 20 international rugby sevens players from one team across one season. Activity profiles were measured using wearable microtechnology devices, and technical performance measures were collected from match video analysis. Subjective wellbeing measures were collected using a wellbeing questionnaire completed in the morning of main training days, and groin squeeze assessments at 0° and 60° knee flexion were also conducted using a sphygmomanometer. Assessments of aerobic fitness were completed periodically across the season, including time to complete a two-kilometre run, and final velocity during the 30:15 intermittent fitness test (VIFT). A principal components analysis was conducted to reduce the dimensionality of the physical and technical variables into single factor values. Linear mixed models were then constructed to examine the collective influence of a range of individual contextual variables on physical and technical performance factors. Results:: Increased muscle soreness, stress, and VIFT were associated with trivial to small increases in physical and technical performance values; whilst trivial to small decreases were associated with higher perceived recovery, bodyweight, and groin squeeze (0° knee flexion). Conclusions:: A range of wellbeing metrics are required to account for a significant portion of the variance in physical and technical performance. These factors may be manipulated by coaches or practitioners to achieve favourable physiological readiness which may lead to improved match performance.
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This study examined the physical differences in adult male basketball players of different competitive level and playing position using a large cohort. In the middle of the regular season, 129 players from four different Divisions completed a Yo-YoIR1 and, after 3-to-8 days, they performed a 6-min continuous running test (Mognoni’s test), a counter-movement jump (CMJ) test and a 5-min High-intensity Intermittent running test (HIT). Magnitude-based inferences revealed that differences in HIT were very likely moderate between Division I and II and likely small between Division II and III. The differences in absolute peak power and force produced during CMJs between Division I and II and between Division II and III were possibly small. Differences in Yo-YoIR1 and Mognoni’s test were very likely-to-almost certain moderate/large between Division III and VI. We observed possibly-to-likely small differences in HIT and Mognoni’s test between guards and forwards and almost certainly moderate differences in absolute peak power and force during CMJs between guards and centres. The ability to sustain high-intensity intermittent efforts (i.e. HIT) and strength/power characteristics can differentiate between competitive level, while strength/power characteristics discriminate guards from forwards/centres. These findings inform practitioners on the development of identification programs and training activities in basketball.
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Purpose: To examine the physiological, physical, and technical demands of game-based drills (GBDs) with regular dribble (RD) or no dribble (ND) involving a different number of players (3 vs 3, 4 vs 4, and 5 vs 5). Methods: Ten regional-level male basketball players performed 6 full-court GBD formats (each consisting of 3 bouts of 4 min and 2 min rest) on multiple occasions. The physiological and perceptual responses were measured through heart rate and rating of perceived exertion. Video-based time-motion analysis was performed to assess the GBD physical demands. The frequencies of occurrence and the duration were calculated for high-intensity, moderate-intensity, low-intensity, and recovery activities. Technical demands were assessed with a notional-analysis technique. A 2-way repeated-measures analysis of variance was used to assess statistical differences between GBD formats. Results: A greater perceptual response (rating of perceived exertion) was recorded during 3 versus 3 than 5 versus 5 formats (P = .005). Significant interactions were observed for the number of recovery (P = .021), low-intensity activity (P = .007), and all movements (P = .001) completed. Greater time was spent performing low-intensity and high-intensity activities during RD than ND format. Greater technical demands were observed for several variables during 3 versus 3 than 4 versus 4 or 5 versus 5. A greater number of turnovers (P = .027), total (P ≤ .001), and correct passes (P ≤ .001) were recorded during ND than RD format. Conclusions: The number of players predominantly affected the perceptual response to GBD, while both the number of players and rule modification (RD vs ND) affected activities performed during GBD. Reducing the number of players increases the GBD technical elements, while ND format promotes a greater number of turnovers and passes.
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The aims of this study were (a) to compare players’ physical demands between different playing positions in elite U18 basketball games and (b) to identify different clusters of performance. Data were collected from 94 male subjects (age: 17.4 ± 0.7 years), competing in a Euroleague Basketball Tournament. Guards covered a greater relative distance than centres and forwards (small to moderate effect). Forwards and guards had more peak accelerations, high accelerations and high decelerations than centres (moderate to large effects). A cluster analysis allowed to classify all cases into three different groups (Lower, Medium and Higher activity demands), containing 37.4%, 52.8% and 9.8% of the cases, respectively. The high accelerations, high decelerations, peak accelerations and total distance covered were the variables that most contributed to classify the players into the new groups. The percentage of cases distributed in the clusters according to playing position, game type (worst vs worst, mixed opposition, best vs best) and team were different. Centres have lower physical demands specially related with the number of accelerations and decelerations at high intensity and the peak acceleration when compared with guards. Each team has a different activity profile, that does not seem to influence the tournament outcome.
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This study examined the (a) differences in the activity demands of official basketball games between different competitive levels (from elite to amateur levels) among a large cohort of adult male players and (b) match-to-match variations of basketball physical demands. Video-based time-motion analysis (TMA) was performed to assess the players' physical activity among 136 players. Match-to-match variations were determined analyzing 2 consecutive matches of the same level on 35 players. The frequency of occurrence (n per minutes) and the duration in percentage of playing time were calculated for high-intensity activity (HIA), moderate-intensity activity (MIA), low-intensity activity (LIA), and recovery (REC). Division I performed an almost certain greater number of HIA, MIA, and total actions per minutes of playing time compared with Division II that performed similarly to Division III. Division VI performed a likely-to-very likely lower number of LIA, MIA, and total actions per minute compared with Division III. Division I spent almost certain greater playing time competing in HIA and MIA compared with lower divisions. Time spent at REC was very likely greater in Division VI compared with all other Divisions. The frequency of occurrence was less reliable than percentage duration of game activities. Matches of different competitive levels are characterized by different physical activities. The ability to sustain greater intermittent workloads and HIA, and the ability to quickly recover from high-intensity phases during competitions should be considered as key components of basketball. The match-to-match variations values observed in this study might be useful to correctly interpret individual TMA data.
Article
Purpose: To examine differences between adult male basketball players of different competitive levels (study 1) and changes over a basketball season (study 2) of knee extensor peripheral muscle function during a multi-stage changes of direction exercise (MCODE). Methods: In study 1, 111 players from 4 different divisions completed the MCODE during the regular season. In study 2, the MCODE was performed before (T1) and after (T2) the preparation period and during the competitive season (T3) by 32 players from division I, II and III. The MCODE comprised 4 levels of increasing intensity for each player. The peak twitch torque (PT) of knee extensors was measured after each level. PTmax (the highest value of PT) and fatigue were calculated. Results: In study 1, we found possibly small differences (ES±90%CI: -0.24±0.39) in fatigue between division I and II. Division I was characterized by likely (ES: 0.30 to 0.65) and very likely-to-almost certain (ES: 0.74 to 1.41) better PTmax and fatigue levels compared to division III and VI, respectively. In study 2, fatigue was very likely reduced (ES: -0.91 to -0.51) among all divisions from T1 to T2, while PTmax was likely-to-very likely reduced (ES: -0.51 to -0.39) in division II and III. Conclusions: Professional basketball players are characterized by a better peripheral muscle function during a MCODE. Most of the seasonal changes in peripheral muscle function occurred after the preparation period. These findings inform practitioners on the development of training programs to enhance the ability to sustain repeated changes of direction efforts.
Article
The aim of this study was to analyze the game-related statistics and tactical profile in winning and losing teams in NCAA division I men's basketball games. Twenty NCAA division I men's basketball close (score difference: 1-9 points) games were analyzed during the 2013/14 season. For each game, the game-related statistics were collected from the official teams' box scores. Number of ball possessions, offensive and defensive ratings and the Four Factors (effective field goal percentage; offensive rebounding percentage, recovered balls per ball possession, free throw rate) were also calculated. The tactical parameters evaluated were: ball reversal, dribble in key area, post entry, on-ball screen, off-ball screen, and hand off. Differences between winning and losing teams were calculated using a magnitude-based approach. Winning teams showed a likely higher percentage of 3-point goals made, number of defensive rebounds and steals and a very likely higher number of free throws made and free throws attempted. Furthermore, winning teams showed a likely higher team offensive rating and effective field goal percentage and a very likely higher free throw rate compared to losing teams. Finally, the results revealed a likely higher number of ball reversals and post entries in winning teams compared to losing teams. This study highlighted the game-related statistics and the tactical actions differentiating between winning and losing teams in NCAA Division I men's basketball close games. Coaches should use these results to optimize their training sessions, focusing on those variables that might increase the possibility to win close games.