Performance analysis of elite men’s and women’s wheelchair basketball teams

Abstract

The purpose of the present study was to identify which game-related statistics discriminate winning and losing teams in men’s and women’s elite wheelchair basketball. The sample comprised all the games played during the Beijing Paralympics 2008 and the World Wheelchair Basketball Championship 2010. The game-related statistics from the official box scores were gathered and data were analysed in 2 groups: balanced games (final score differences ≤12 points) and unbalanced games (final score differences >13 points). Discriminant analysis allowed identifying the successful 2-point field-goals and free-throws, the unsuccessful 3-point field-goals and free-throws, the assists and fouls received as discriminant statistics between winning and losing teams in men’s balanced games. In women’s games, the teams were discriminated only by the successful 2-point field-goals. Linear regression analysis showed that the quality of opposition had great effects in final point differential. The field-goals percentage and free-throws rate were the most important factors in men’s games, and field-goals percentage and offensive rebounding percentage in women’s games. The identified trends allow improving game understanding and helping wheelchair basketball coaches to plan accurate practice sessions and, ultimately, deciding better in competition.

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Performance analysis of elite men’s and women’s
wheelchair basketball teams
Miguel Ángel Gómeza, Javier Péreza, Bartosz Molikb, Robert J. Szymanc & Jaime Sampaiod
a Faculty of Physical Activity and Sport Sciences, Technical University of Madrid, Madrid
28040, Spain
b Faculty of Rehabilitation, Jozef Pilsudski University of Physical Education, Warsaw, Poland
c Professional Studies and Recreation, Faculty of Secondary Education, Chicago State
University, Chicago, IL, USA
d Sport Sciences Department, Universidade de Trás-Os-Montes e Alto Douro, Vila Real 5000,
Portugal
Published online: 07 Feb 2014.
To cite this article: Miguel Ángel Gómez, Javier Pérez, Bartosz Molik, Robert J. Szyman & Jaime Sampaio , Journal of Sports
Sciences (2014): Performance analysis of elite men’s and women’s wheelchair basketball teams, Journal of Sports Sciences,
DOI: 10.1080/02640414.2013.879334
To link to this article: http://dx.doi.org/10.1080/02640414.2013.879334
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Performance analysis of elite men’s and women’s wheelchair basketball
teams
MIGUEL ÁNGEL GÓMEZ
1
, JAVIER PÉREZ
1
, BARTOSZ MOLIK
2
,
ROBERT J. SZYMAN
3
& JAIME SAMPAIO
4
1
Faculty of Physical Activity and Sport Sciences, Technical University of Madrid, Madrid 28040, Spain,
2
Faculty of
Rehabilitation, Jozef Pilsudski University of Physical Education, Warsaw, Poland,
3
Professional Studies and Recreation,
Faculty of Secondary Education, Chicago State University, Chicago, IL, USA and
4
Sport Sciences Department, Universidade
de Trás-Os-Montes e Alto Douro, Vila Real 5000, Portugal
(Accepted 24 December 2013)
Abstract
The purpose of the present study was to identify which game-related statistics discriminate winning and losing teams in
men’s and women’s elite wheelchair basketball. The sample comprised all the games played during the Beijing Paralympics
2008 and the World Wheelchair Basketball Championship 2010. The game-related statistics from the official box scores
were gathered and data were analysed in 2 groups: balanced games (final score differences ≤12 points) and unbalanced
games (final score differences >13 points). Discriminant analysis allowed identifying the successful 2-point field-goals and
free-throws, the unsuccessful 3-point field-goals and free-throws, the assists and fouls received as discriminant statistics
between winning and losing teams in men’s balanced games. In women’s games, the teams were discriminated only by the
successful 2-point field-goals. Linear regression analysis showed that the quality of opposition had great effects in final point
differential. The field-goals percentage and free-throws rate were the most important factors in men’s games, and field-goals
percentage and offensive rebounding percentage in women’s games. The identified trends allow improving game under-
standing and helping wheelchair basketball coaches to plan accurate practice sessions and, ultimately, deciding better in
competition.
Keywords: wheelchair basketball, game-statistics, gender, performance analysis
Introduction
Since the first World Championship for men’s
wheelchair basketball teams in 1973 (Bruges,
Belgium) and in 1990 for women’s wheelchair bas-
ketball teams (St. Etienne, France), wheelchair bas-
ketball has became one of the most popular team
sports in Paralympics Games. Wheelchair basket-
ball is well developed in number of participants,
spectators’understanding, organisation, physical
training, classification and quality of coaching
(Molik et al., 2009; Vanlandewijck et al., 2003,
2004). The game is played in more than 80 coun-
tries on a competitive level and has attracted the
interest of male and female coaches and perfor-
mance analysts. Particularly, Wang, Chen,
Limroongreungrat, and Change (2005) stated that
wheelchair basketball is an intermittent activity for
wheelchair manoeuvring (i.e., propulsion, starting
and stopping and changing direction of the
wheelchair) and ball handling (i.e., shooting, pas-
sing, dribbling or rebounding). Within this specifi-
city, performance analysis is an interesting tool for a
better interpretation of games for persons with dis-
abilities in terms of technical requirements and tac-
tical responses. Performance analysis allows the
wheelchair basketball coaches to increase the
applicability of findings to improve training plans
and competition management.
Performance analysis in team sports for indivi-
duals with disabilities have been mainly focused on
wheelchair basketball players’physiological and bio-
mechanical performances (Coutts, 1991;Malone,
Gervais, & Steadward, 2002; Perreault &
Vallerand, 2007; Vanlandewijck, Verellen, &
Tweedy, 2011), but little interest has been addressed
regarding other technical or tactical-related variables
(Hindawi et al., 2013). There is, however, research
addressing the game-related statistics in Paralympic
sports such as wheelchair rugby (Molik et al., 2008;
Correspondence: Miguel Ángel Gómez, Faculty of Physical Activity and Sport Sciences, Technical University of Madrid, C/ Martín Fierro, 7., CP/28040,
Madrid 28040, Spain. E-mail: magor_2@yahoo.es
Journal of Sports Sciences, 2014
http://dx.doi.org/10.1080/02640414.2013.879334
© 2014 Taylor & Francis
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Morgulec-Adamowicz et al., 2010;Sarro,Misuta,
Burkett, Malone, & Barros, 2010; Sporner et al.,
2009) and ice sledge hockey (Beckman, Kudlacek,
& Vanlandewijck, 2007;Kudlacek,Dalbesi,Janečka,
Vanlandewijck, & Beckman, 2009; Molik et al.,
2012).
Research describing game-related statistics in elite
wheelchair basketball has differentiated players’clas-
sification points and their playing positions (Molik &
Kosmol, 2001; Molik et al., 2009; Vanlandewijck
et al., 2003, 2004; Vanlandewijck, Spaepen, &
Lysens, 1995). Vanlandewijck et al. (2004) studied
20 games (n= 59 women’s wheelchair basketball
players) during the 1998 Women’s World
Championship Wheelchair Basketball (Sydney,
Australia). The sample was classified according to
playing position (guard, forward and centre) and
functional ability (Class 1 = 1 and 1.5 points; Class
2 = 2 and 2.5 points; Class 3 = 3 and 3.5 points and
Class 4 = 4 and 4.5 points). Their results showed
that high point players performed better than low
point players in most of the game-related statistics,
such as offensive rebounds, assists and last pass, and
the number of successful and unsuccessful 2-point
field-goals. They also found that there were different
performance profiles between positions, but not
when interacting with functional ability (class).
More recently, Molik et al. (2009) analysed 72
women’s wheelchair players competing in the 2006
Women’s World Championship Wheelchair
Basketball (Amsterdam, The Netherlands). The
authors studied the players’performances according
to functional ability (classes) and team quality (the
final ranking obtained by the team during that com-
petition). Their results showed similar performances
for players between adjacent classes, and also, the
players from higher ranked teams obtained better
performances in shooting efficiency than players
from the lower ranked teams.
The available research shows that men and
women’s wheelchair basketball teams have similari-
ties in team scores (Molik et al., 2009;
Vanlandewijck et al., 2004); nevertheless, there are
differences between genders in field-goal and free-
throw shooting, because the men’s players are more
accurate than women (Brasile, 1993; Vanlandewijck
et al., 2004). These studies demonstrated that
women’s teams have physical, and possibly, tactical
and technical differences from men’steams that are
unknown and should be identified in order to
describe accurately the women’s wheelchair basket-
ball performance profiles.
Conversely, research on elite running basketball
has focused its attention on teams’and players’per-
formances in different game contexts (Sampaio,
Lago, & Drinkwater, 2010), identifying the perfor-
mance indicators that discriminate between winning
and losing outcomes (Gómez, Lorenzo, Barakat,
Ortega, & Palao, 2008; Gómez, Lorenzo, Sampaio,
Ibáñez, & Ortega, 2008). The results show that suc-
cessful 2-point field-goals, assists and defensive
rebounds are the best discriminators in men’s
games (Sampaio & Janeira, 2003; Trninić, Dizdar,
&Lukšić,2002) and the successful 3-point field-
goals and assists in women’s games (Gómez,
Lorenzo, Sampaio, & Ibáñez, 2006). These results
point out differences in game styles according to
gender in elite able-bodied basketball (Sampaio,
Ibáñez, & Feu, 2004).
Recent research highlighted four factors to analyse
offensive and defensive performances in elite run-
ning basketball (Kubatko, Oliver, Pelton, &
Rosenbaum, 2007; Oliver, 2004). These factors
were considered in order of importance: (1) field-
goal percentage, (2) offensive rebounding percen-
tage, (3) recovered balls per possession (turnovers)
and (4) free-throws rate. These key factors have not
been studied in elite wheelchair basketball and might
provide new insights to game understanding, by
influencing coaches’decision making during training
(i.e., specific tasks that involve shooting abilities) and
competition (i.e., using different tactics, strategies or
substitutions).
In addition to these factors, the effects of situational
variables have also become a major topic to consider
(Molik et al., 2009; Sampaio, Lago, Casais, & Leite,
2010; Vanlandewijck et al., 2004). The quality of the
opponent, the match status, the game type or the game
location have considerable effects on performance
(Lago, 2009; Lago & Martín, 2007;Sampaio,Lago,
& Drinkwater, 2010; Taylor, Mellalieu, James, &
Shearer, 2008). For example, game type (balanced or
unbalanced) is a very helpful point of reference to
consider, by allowing a better interpretation of the
game, and consequently increasing the applicability
to improve training plans (Gómez, Lorenzo,
Sampaio, et al., 2008;Sampaio&Janeira,2003).
In summary, the aim of the present study is two-
fold: (1) to identify the game-related statistics that
best discriminate winning and losing teams in elite
men and women’s wheelchair basketball according
to different game types (i.e., balanced and unba-
lanced) and (2) to identify how the quality of opposi-
tion and the four game-related factors predict final
point differentials by gender and game types (i.e.,
balanced and unbalanced).
Method
Experimental approach to the problem
The local Institutional Review Board approved this
study to investigate the game-related statistics in elite
men’s and women’s wheelchair basketball. Recently,
2M. Á. Gómez et al.
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the notational analysis has become an interesting
topic for coaches and performance analysts in adapted
team sports such as wheelchair basketball. Although it
seems reasonable to hypothesise that the game-related
statistics are influenced by the game type for each
gender, at present, elite men’s and women’swheel-
chair basketball teams have not been investigated
according to this rationale, so that the above men-
tioned game variables can be inferred only from the
analyses of other elite body able-bodied team sports
(Gómez, Lorenzo, Sampaio, et al., 2008; Sampaio &
Janeira, 2003). Actually, the specific game constraints
during elite wheelchair basketball games are still miss-
ing. Therefore, in this study, the notational analysis
performed on the game-related statistics and key per-
formance indicators can have important effects on
men’s and women’swheelchairgamedynamicsin
balanced and unbalanced games. For this aim, the
discriminant analyses (Stage 1) and the linear regres-
sion models (Stage 2) have been considered to
describe the above mentioned game-related statistics
and key performance indicators on each gender
according to game type.
Sample and variables
Researchers obtained archival data from open-access
official websites for both men and women’s
Championships during 78 games from the World
Wheelchair Basketball Championship (2010) and
76 games from the Beijing Paralympics (2008)
(men, n= 88 games; women, n= 66 games).
The following absolute game-related statistics gath-
ered were: 2- and 3-point field-goals (both successful
and unsuccessful), free-throws (both successful and
unsuccessful), offensive and defensive rebounds,
steals, turnovers, assists, blocks and personal fouls
(committed and received). Afterwards, the variables
were normalised according to game ball possessions
and multiplied by 100, to account for game rhythm
contamination (Gómez, Lorenzo, Sampaio, et al.,
2008; Sampaio & Janeira, 2003). Ball possessions
(BP) were calculated according to the Oliver’sequa-
tion: BP = (field-goals attempted) −(offensive
rebounds) + (turnovers) −0.4 × (free-throws
attempted) (Kubatko et al., 2007;Oliver,2004).
The effective field-goal percentage (FG) was calcu-
lated from the following equation: FG = (field-goals
made + 0.5 × 3-point field-goals made)/field-goals
attempted (Kubatko et al., 2007; Oliver, 2004).
Offensive rebounding percentage (OR) was calcu-
lated by the following equation: OR = offensive
rebounds/(offensive rebounds + opponents’defen-
sive rebounds). Recovered balls per ball possession
(RB) were calculated using the following equation:
RB = (steals + blocks + opponents’turnovers)/ball
possessions. Finally, the free-throw rate (FT) was
calculated from the equation: FT = free-throws
made/field-goals attempted (Kubatko et al., 2007;
Oliver, 2004).
In order to control for the effects of situational
variables, the game types were obtained using a k-
means cluster analysis (Gómez, Lorenzo, Sampaio,
et al., 2008; Sampaio, Lago, Casais, et al., 2010) that
allowed identifying a cut-off value of point differences
in final outcome of a given game. The results identi-
fied a cluster 1 (balanced games) with differences in
the score ranging from 1 to 12 points (men, n=41
games; women, n= 27 games), and a cluster 2 (unba-
lanced games) with differences above 13 points (men,
n= 47 games; women, n= 39 games). The quality of
opposition was measured as the difference in the end-
of-season ranking between teams (differences
between final ranking team A –final ranking team B).
Procedures
Professional statisticians gathered all the data; how-
ever, a sub-sample of 10 games were randomly
selected and observed by an experienced analyst (a
basketball coach with more than 5 years of experi-
ence in performance analysis in wheelchair basket-
ball). The results showed Intraclass Correlation
Coefficients 1.0 for free-throws, 2- and 3-point
field-goals (both successful and unsuccessful), fouls
(both committed and received), defensive and offen-
sive rebounds, turnovers, steals and blocks. For the
assists, the results were lower but still very acceptable
as reliable (ICC = 0.96).
Statistical analyses
Stage 1: Game-related statistics that discriminate
between winning and losing teams according to game
type for each gender. A discriminant analysis was
used to identify the variables that best separate win-
ning and losing teams in balanced and unbalanced
games. The structural coefficients (SCs) above
│0.30│allowed identifying the variables that best
contribute to differentiating the group of game win-
ners from the game losers (Pedhazur, 1982).
Validation of discriminant models was conducted
using leave-one-out classification (Norušis, 2004).
The cross-validation of the discriminant models
was conducted using the leave-one-out classification
(Norušis, 2004). This method evaluates the useful-
ness of discriminant functions when classifying new
data, by generating the discriminant function on all
but one of the participants (n−1) and then testing
for group membership on that participant. The pro-
cess was repeated for each participant (ntimes) and
the percentage of correct classifications was taken as
the mean for the ntrials.
Elite men’s and women’s wheelchair basketball 3
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The validity of the obtained discriminant func-
tions to classify new cases was assessed using a
small sample of 8 games gathered from the open-
access official website of the 2003 European wheel-
chair basketball Championship for both genders
(Frankfurt, Germany). The unstandardised discrimi-
nant functions coefficients (B) were used for predict-
ing the dependent variable (i.e., winning or losing
team), as follows:
D¼β0þβ1V1þ þ β14 V14
where Dis the discriminant score, β0 is the con-
stant value, β1–β14 are the unstandardised coeffi-
cients for each variable and V1–V14 are the values
of game-related statistics. The equation allows cal-
culating the discriminant function scores. The group
prediction was done using the cutting scores estab-
lished by the centroids, calculated as follows:
Cutting score ¼mean centroid 1 þmean centroid 2
2
The cutting score value allows classifying the
cases. The higher Dvalues will be grouped as
group 1 (If D< Cutting point = group 1) and
lower Dvalues will be assigned to group 2 (If D>
Cutting point = group 2) (Tabachnick & Fidell,
2007).
Stage 2: Influence of quality of opposition and perfor-
mance factors on final point differential. Linear regres-
sion models were used to explore the effect of
independent variables on game final outcome.
When estimating the models, no heteroscedasticity
in residuals or multicollinearity among regressors
was observed. Moreover, the RESET test of
Ramsey (1969) did not reveal specification pro-
blems. When interpreting the statistical results, posi-
tive or negative coefficients indicate a greater or
lower propensity to increase/decrease game final out-
come, respectively. Five independent variables were
included: quality of opposition, effective field-goal
percentage, offensive rebounding percentage, recov-
ered balls per ball possession and free-throw rate.
The model is as follows:
FPD ¼β0þβ1QO þβ2FG þβ3OR
þβ4RB þβ5FT þ"i
where FPD = final point differential, QO = quality
of opposition, FG = effective field-goal percentage,
OR = offensive rebounding percentage, RB = recov-
ered balls per ball possession and FT = free-throw
rate. All statistical analyses were performed using
PASW statistics 18 (SPSS Inc., Chicago, IL, USA)
and STATA Release 8.2. (StataCorp LP). Statistical
significance was set at P< 0.05.
Results
Stage 1: Game-related statistics that discriminate
between winning and losing teams according to game type
for each gender
Means and standard deviations for the game-related
statistics for men’s winning and losing teams are
presented in Table I. The univariate analysis showed
Table I. Mean, standard deviations, game-related statistics and univariate differences for winning and losing teams in men’s balanced and
unbalanced games.
Statistics
Balanced Unbalanced
Winners Losers Winners Losers
Mean SMean SMean SMean S
Successful 2-point field-goals
a,b
49.57 9.69 38.85 7.73 54.91 6.49 31.98 7.26
Unsuccessful 2-point field-goals 52.65 11.01 49.68 10.04 47.13 10.41 48.35 10.62
Successful 3-point field-goals 2.86 2.83 4.02 2.73 3.91 3.31 3.14 3.38
Unsuccessful 3-point field-goals
b
8.50 3.21 11.93 6.18 8.25 5.13 12.39 7.10
Successful free-throws
a,b
24.19 13.81 14.26 8.24 23.11 12.84 12.69 7.54
Unsuccessful free-throws
a,b
8.50 3.21 11.93 6.18 8.25 5.13 12.39 7.10
Defensive rebounds
a,b
52.30 14.15 44.01 12.09 50.92 13.78 36.65 6.68
Offensive rebounds
b
18.74 8.72 15.63 6.55 18.44 8.11 13.53 6.42
Assists
a,b
32.25 11.34 24.81 8.32 36.14 11.31 20.30 8.81
Turnovers
b
22.49 8.37 22.44 7.61 20.02 6.56 27.28 10.31
Steals
ab
12.19 5.61 9.45 4.70 13.49 7.90 7.41 4.39
Blocks
b
1.01 1.56 0.91 1.25 1.42 1.91 0.52 0.92
Fouls committed
b
34.28 9.61 36.91 8.83 29.61 8.96 36.05 8.86
Fouls received
a,b
40.93 15.94 31.04 9.18 39.02 14.63 26.52 8.66
Notes:
a
Univariate statistically significant differences between winning and losing teams in balanced games (P< 0.05).
b
Univariate statistically significant differences between winning and losing teams in unbalanced games (P< 0.05).
4M. Á. Gómez et al.
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significant differences for several comparisons.
During balanced and unbalanced games, winning
teams had more successful 2-point field-goals, suc-
cessful free-throws, assists, steals, fouls received and
defensive rebounds, whereas losing teams had more
unsuccessful free-throws. During unbalanced games,
winning teams blocked more shots, secured more
offensive rebounds and were fouled more. Results
also showed that the losing teams had more turn-
overs and unsuccessful 3-point field-goals and com-
mitted more fouls.
For women’s balanced and unbalanced games, the
winning teams had more successful 2-point field-
goals and successful free-throws, whereas losing
teams had more unsuccessful 3-point field-goals.
During unbalanced games, the winning teams
blocked more shots, had more assists, steals, offen-
sive and defensive rebounds and received more
fouls. Conversely, the losing teams had more turn-
overs and committed more fouls (Table II).
The discriminant analyses differentiated winning
and losing teams (see Table III) and cross-validation
percentages were 87.7% for the men’s and 88.9% for
the women’s balanced games. The most powerful
discriminators between men’s winning and losing
teams were the successful 2-point field-goals (SC =
−0.56), unsuccessful 3-point field-goals (SC = 0.31),
successful (SC = −0.40) and unsuccessful free-
throws (SC = 0.31), assists (SC = −0.34) and fouls
received (SC = −0.35). For women’s games, only the
successful 2-point field-goals (SC = 0.37) discrimi-
nated between winning and losing teams.
For the unbalanced games, the cross-validation
percentages were 99.0% for the men’s and 99.7%
for the women’s games. The variables that best dis-
criminated men’s winning and losing teams were the
successful 2-point field-goals (SC = 0.73) and the
assists (SC = 0.34). The same trend was identified
for the women’s games (successful 2-point field-
goals SC = 0.74 and assists SC = 0.42).
Finally, the validation of discriminant function on
new cases was presented in Table IV. The results
showed that all the 8 games were correctly classified.
This reassessment reflects the high accuracy of pre-
diction for all the discriminant functions obtained.
Stage 2: Effects of quality of opposition and performance
factors on final point differential
The effects of the 5 independent variables on final
point differential are presented in Table V.The final
point differential was explained by the quality of
opposition and field-goal percentage in all analyses.
In addition, the offensive rebounding percentage was
also an important predictor in men’s unbalanced
games and women’s balanced games. The recovered
balls per ball possession were important in men and
women’s unbalanced games. Finally, the free-throw
percentage was an important predictor in men’s
balanced games. The second most important predic-
tor was the free-throw percentages for men’s
balanced games and the offensive rebounds for
women’s balanced games.
In men’s games, quality of opposition one ranking
above the opponent increased game final outcome to
0.78 ± 0.2 in balanced games and to 2.04 ± 0.3 in
unbalanced games. In women’s games, quality of
opposition one ranking above the opponent
Table II. Mean, standard deviations, game-related statistics and univariate differences for winning and losing teams in women’s balanced
and unbalanced games.
Game-related statistics
Balanced Unbalanced
Winners Losers Winners Losers
Mean SMean SMean SMean S
Successful 2-point field-goals
a,b
42.57 1.56 36.19 5.74 49.54 7.01 27.59 5.98
Unsuccessful 2-point field-goals 56.58 11.73 56.58 11.73 55.17 8.63 58.08 9.63
Successful 3-point field-goals 2.85 3.27 3.25 4.03 2.67 3.22 3.64 4.91
Unsuccessful 3-point field-goals
a,b
3.84 2.76 5.82 3.91 2.97 2.89 6.08 3.88
Successful free-throws
a,b
13.56 6.43 9.19 5.01 11.84 7.67 5.59 4.39
Unsuccessful free-throws 11.76 10.48 10.71 8.09 6.95 6.51 9.06 7.55
Defensive rebounds
b
49.56 9.55 46.30 10.17 50.84 11.01 40.29 8.22
Offensive rebounds
b
19.24 6.90 15.73 6.21 18.87 7.09 15.46 7.01
Assists
b
26.23 9.58 22.79 9.17 30.95 8.64 15.85 6.88
Turnovers
b
22.11 8.20 25.35 8.87 20.27 6.18 29.22 9.41
Steals
b
11.69 6.68 9.11 6.62 13.89 7.17 7.50 4.37
Blocks
b
1.49 1.99 1.03 1.46 1.69 1.89 0.69 1.15
Fouls committed
b
27.36 8.24 28.32 7.83 20.65 6.75 25.55 7.81
Fouls received
b
29.86 9.84 25.85 8.06 26.37 9.99 19.32 7.64
Notes:
a
Univariate statistically significant differences between winning and losing teams in balanced games (P< 0.05).
b
Univariate statistically significant differences between winning and losing teams in unbalanced games (P< 0.05).
Elite men’s and women’s wheelchair basketball 5
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increased game final outcome to 1.41 ± 0.4 in
balanced games and to 2.84 ± 0.4 in unbalanced
games.
Discussion
This study allowed identifying the game-related sta-
tistics that best discriminate between winning and
losing teams for each studied condition. There
were substantial differences in optimal tactics for
men and women’s wheelchair basketball teams in
balanced games. The quality of opposition was also
a very important variable to account for. The possi-
bility that basketball teams may have used different
game tactics in each of these game types was already
recognised in elite able-bodied team sports (Gómez,
Lorenzo, Sampaio, et al., 2008; Sampaio & Janeira,
2003), but not in wheelchair basketball.
Stage 1: Discriminant analyses
The results from the discriminant analyses support
findings of previous research (Molik et al., 2009;
Vanlandewijck et al., 2004) and add specific perfor-
mance profiles under the studied conditions. The
men’s wheelchair basketball winning teams were bet-
ter at successful 2-point field-goals, unsuccessful 3-
point field-goals, free-throws (both successful and
unsuccessful), assists and fouls received (see Table
I). These results are similar to those found in run-
ning basketball balanced games (Gómez, Lorenzo,
Barakat, et al., 2008; Trninićet al., 2002). However,
when explaining the importance of these perfor-
mance indicators in wheelchair basketball, Wang
et al. (2005) argued that the influence of assists and
field-goal effectiveness was significant and that in
men’s wheelchair basketball players’ball handling
and passing skills are key game components. These
results may suggest that wheelchair basketball
players must improve the skill of moving the ball
around the body, in order to pass or to protect the
ball from the opponents. In wheelchair basketball,
the stationary game depends on strength (i.e., trunk
stability and throwing), the ability to use both sides
of the body (i.e., dominant and non-dominant
hands) and mobility (i.e., wheelchair propulsion,
stopping, pivoting or turning and rebounding).
This fact reinforces the importance of playing tactics
that involve teamwork and passing abilities to
improve shot selection.
The importance of fouls received and free-throws
attempted may be associated to field-goal situations,
as research states that shooting efficiency is the
determinant action in elite wheelchair basketball
(Molik et al., 2009; Vanlandewijck et al., 2004).
Thus, the defensive players have to be prepared for
Table III. Discriminant analysis structure coefficients for game-related statistics of men’s and women’s winning and losing teams in
balanced and unbalanced games.
Game-related statistics
Men Women
Balanced Unbalanced Balanced Unbalanced
Successful 2-point field-goals
a,b,c,d
−0.56 0.73 0.37 0.74
Unsuccessful 2-point field-goals −0.12 −0.02 0.15 −0.07
Successful 3-point field-goals 0.19 0.05 −0.04 −0.05
Unsuccessful 3-point field-goals
a
0.31 −0.14 −0.22 −0.20
Successful free-throws
a
−0.40 0.21 0.28 0.22
Unsuccessful free-throws
a
0.31 −0.15 0.04 −0.06
Defensive rebounds −0.28 0.28 0.14 0.24
Offensive rebounds −0.18 0.14 0.12 0.10
Assists
a,b,d
−0.34 0.34 0.20 0.42
Turnovers −0.24 −0.18 −0.14 −0.24
Steals −0.01 0.21 0.14 0.23
Blocks −0.03 0.13 0.10 0.14
Fouls committed 0.13 −0.15 0.17 0.17
Fouls received
a
−0.35 0.22 −0.04 −0.14
Eigenvalue 1.21 5.23 1.75 5.21
Wilks Lambda 0.45 0.16 0.36 0.16
Canonical correlation 0.74 0.92 0.79 0.91
χ
2
57.66 161.90 45.55 126.01
DF 14 14 14 14
P<0.001 <0.001 <0.001 <0.001
Reclassification % 87.7 99.0 88.9 99.7
Notes:
a
Structure coefficients discriminant value ≥│0.30│in men’s balanced games.
b
Structure coefficients discriminant value ≥│0.30│in men’s unbalanced games.
c
Structure coefficients discriminant value ≥│0.30│in women’s balanced games.
d
Structure coefficients discriminant value ≥│0.30│in women’s unbalanced games.
6M. Á. Gómez et al.
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Table IV. Validation results of the obtained discriminant functions using unstandardised coefficients (UCoef), discriminant function values (D) and group centroid values for each discriminant function
in each context (balanced and unbalanced game) for both genders (men’s and women’s).
Game-related statistics
a
UCoef
Men’s teams
UCoef
Women’s teams
Balanced games
UCoef
Unbalanced games Balanced games
UCoef
Unbalanced games
Game 1 Game 2 Game 3 Game 4 Game 5 Game 6 Game 7 Game 8
Winner Loser Winner Loser Winner Loser Winner Loser Winner Loser Winner Loser Winner Loser Winner Loser
Successful 2-point
field-goals
0.147 53 47.7 58.6 46.1 0.091 72.7 28 69.2 38.6 0.127 48.0 34.9 57.5 44.4 0.125 64.4 30.4 52.5 22.5
Unsuccessful 2-point
field-goals
0.249 62.2 59.2 75.8 81.1 −0.053 53.2 42.3 55.3 54.6 −0.153 54.6 64.7 53.7 74.6 0.040 50.3 78.3 55.6 75.5
Successful 3-point
field-goals
0.140 0 0 3.1 0 0.067 0 1.57 1.5 4.5 0.372 0 1.6 0 1.8 0.183 0 0 0 0
Unsuccessful 3-point
field-goals
0.298 2.3 0 0 2.19 −0.064 2.15 3.14 1.9 8.9 −0.494 4.4 1.6 6.2 1.9 −0.179 0 9.7 0 4
Successful
free-throws
−0.081 21 21 22 15 0.006 16 4.7 16 4.5 0.066 8.7 17 10 24 0.026 13 20 7.7 0
Unsuccessful
free-throws
0.012 12 15 52 20 0.006 30 20 16 6 −0.001 8.7 22 8.3 22 −0.003 21 13 12 0
Offensive rebounds −0.255 23 15 34 31 0.055 27 14 14 16 0.190 8.7 16 10 19 −0.070 26 16 22 9.5
Defensive rebounds −0.049 58 44 83 57 0.045 80 24 73 37 0.186 74 27 68 33 0.012 52 50 52 53
Assists −0.024 37 25 28 33 0.003 49 11 26 8.9 −0.048 20 22 21 28 0.028 41 11 35 10
Fouls committed 0.057 51 36 56 46 −0.060 35 31 28 30 −0.067 44 21 39 24 −0.045 17 37 3.1 27
Fouls received −0.069 51 29 52 26 0.020 55 24 38 22 −0.054 68 19 66 22 0.009 19 33 12 47
Turnovers 0.263 18 23 15 15 −0.035 14 25 16 24 −0.130 8.7 19 10 22 −0.002 2.1 6.5 22 9.1
Steals −0.113 2.3 1.9 0 4.4 0.029 6.8 3.1 0 1.5 0.299 0 0 0 1.9 0.042 0 2.2 0 0
Blocks −0.005 2.3 5.7 9.3 2.2 −0.021 6.8 0 5.9 3 −0.081 0 1.6 0 1.9 0.194 2.1 0 1.5 0
Constant 18.06 −2.16 −0.87 −6.75
Dvalues −1.40 1.43 −1.20 1.36 2.41 −3.67 2.17 −3.21 1.99 −3.45 1.86 −2.13 3.34 −2.57 2.47 −2.21
Cutting score −0.04 −0.02 0 0
Group centroids −1.13 1.05 −1.13 1.05 2.24 −2.28 2.24 −2.28 1.77 −1.77 1.77 −1.77 2.40 −2.40 2.40 −2.40
Group predicted Winner Loser Winner Loser Winner Loser Winner Loser Winner Loser Winner Loser Winner Loser Winner Loser
Note:
a
The game-related statistics were normalised to 100 ball possessions.
Elite men’s and women’s wheelchair basketball 7
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multiple situations (i.e., defending against the shot,
defending against passes or preventing basket pene-
trations). It was suggested that players from losing
teams show poorer reaction times for blocking shots
and, consequently, commit more fouls (Wang et al.,
2005). One interesting result of this study is the lack
of association between defensive rebounds and game
final outcome, which is a very different result from
running basketball. These results may reflect that in
wheelchair basketball the defensive players for both
winning and losing teams take advantage of their
height and wheelchair positioning, which are key
determinants for defensive rebounding (Wang
et al., 2005). These findings highlight the need for
better technical (i.e., shooting and passing) and tac-
tical (i.e., defensive wheelchair position) skills for
men’s players.
On the other hand, women’s wheelchair basketball
winning and losing teams were best discriminated
only by the successful 2-point field-goals, confirming
previous research either in wheelchair (Molik et al.,
2009) and running basketball (Gómez et al., 2006).
In addition, Vanlandewijck et al. (2004) stated that
the use of 3-point field-goals is an exception in elite
women’s wheelchair basketball, and that the main
characteristics of playing tactics are teamwork
focused on better shot selection. Also, this result
reinforces the idea that competition strategies and
tactics in women’s wheelchair basketball games
may not be as advanced as compared to men’s
games (Molik et al., 2009).
The elite men and women’s wheelchair basketball
teams were mainly differentiated by field-goal per-
centages during balanced games (Brasile, 1993;
Vanlandewijck et al., 2004). Conversely, both
men’s and women’s wheelchair basketball teams
showed similar performance profiles during unba-
lanced games, where the winning and losing teams
were differentiated by the successful 2-point field-
goals and assists in either genders. These results are
intriguing due to the great similarities with elite run-
ning basketball (Gómez, Lorenzo, Barakat, et al.,
2008; Sampaio & Janeira, 2003). This fact may sug-
gest that elite wheelchair basketball teams use offen-
sive and defensive tactics and strategies similar to
running basketball teams in unbalanced games. In
these situations, the game outcome is much depen-
dent upon shot selection, as a consequence of
players’decision-making, field-goal execution and
collective coordination (Sampaio et al., 2004).
Stage 2: Regression analyses
The linear regression results of balanced and unba-
lanced games in men and women’s wheelchair basket-
ball enhanced the importance of the quality of the
opposition and field-goal percentages. These results
parallel other studies in wheelchair basketball showing
that the greater differences that exist in final ranking
between teams, the more important shooting efficiency
becomes (Molik et al., 2009; Vanlandewijck et al.,
2004). These results are similar to those found in run-
ning basketball where quality of opposition (Sampaio,
Lago, & Drinkwater, 2010; Sampaio, Lago, Casais,
et al., 2010)andfield-goal percentage (Sampaio,
Drinkwater, & Leite, 2010) are determinants of game
outcome. In men’s unbalanced wheelchair basketball
games, the second most important performance indi-
cator was free-throw percentage. This finding may
reinforce the importance of teamwork to allow select-
ing better field-goal positions when the game is closely
contested, having also in account with an opponent
increased defensive pressure (Sampaio & Janeira,
2003). Conversely, in women’s wheelchair basketball
the second most important performance indicator was
offensive rebounding percentage. This result suggests
that teams have lower field-goal percentages during
balanced games due to higher defensive pressure and
that, teams have an assertive style of play, where offen-
sive players aggressively attack the offensive boards
after a missing shot. Consequently, coaches of
women’s teams should consider training the teams
and players to improve techniques of securing more
offensive rebounds or conversely, improving defence
Table V. The influence of quality of opposition and the four performance factors on final point differential for each gender in each game
type (results from the 4 regression models with standard error in parentheses).
Variables
Men Women
Balanced Unbalanced Balanced Unbalanced
Quality of opposition −0.78 (0.22)** −2.04 (0.32)** −1.41 (0.35)* −2.84 (0.44)**
Field-goal % 44.38 (10.7)** 163.01 (16.3)** 46.29 (15.50)* 150.99 (16.6)**
Offensive rebounding % 3.64 (5.34) 28.91 (6.91)** 20.23 (6.01)* 8.51 (6.84)
Free-throws rate 22.03 (8.57)* −14.56 (11.92) 4.70 (17.9) −0.08 (18.81)
Recovered ball 5.38 (3.17) 19.69 (5.38)** 5.44 (4.70) 14.26 (6.40)*
Intercept −27.65 (5.72)** −93.74 (8.60)** −32.85 (8.36)** −70.71 (9.27)**
Number of observations 81 97 54 78
R
2
0.45 0.89 0.50 0.90
Note:*P< .05; **P< .01.
8M. Á. Gómez et al.
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by blocking out, preventing the opponents from secur-
ing offensive rebounds.
Recovering the ball by steals, blocked shots or
opponent’s turnovers was the most important variable
in both genders’unbalanced games. From a tactical
perspective, the strong relation between field-goal
percentages and recovered balls is seen in points
scored after a recovered ball in fast break situations
(Sampaio, Drinkwater, et al., 2010). During unba-
lanced wheelchair basketball games, the best teams
prevented their opponent’s from gaining this advan-
tage, probably by searching for excellent wheelchair
positions, and quick hand movements, causing turn-
overs as their opponents dribble or pass the ball or
placed the ball on their laps (Wang et al., 2005). In
unbalanced games, the recovered balls generated
more high-percentage shots and more ball possessions
for the winning teams.
Conclusion
Performance analysis of men and women’selite
wheelchair basketball games allows coaches, trainers
and sport scientists to improve the training sessions
and prepare the players for competition according to
real game constraints. The application of these results
emphasises key technical and tactical abilities of
players in either balanced (i.e., field-goals, assists,
free-throws and fouls in men’s teams and successful
2-point field-goals in women’s teams) or unbalanced
games (i.e., field-goal percentage from 2-point
attempts in men’s and women’s teams). The upper
body strength and trunk stability are key determinants
of these actions; therefore, they should be improved
to enhance the players’wheelchair propulsion, stop-
ping, starting, pivoting and turning and rebounding.
Indeed, men’s teams should practice specifically for
the technical and tactical game requirements: (a) free-
throws, (b) 2- and 3-point field-goals efficiency, (c)
assist and (d) fouls after strength training. On the
other hand, women’s teams should participate in
strength training to improve physical fitness and
then focus their attention on (a) field-goal efficiency
(2- and 3-point field-goals), (b) assists and (c) offen-
sive rebounding.
This study presents some limitations related to the
specific nature of wheelchair basketball that differs
from the running basketball, such as classification
points on the floor at one time (running basketball
has the best 5 players on the court most of the time
but wheelchair basketball does not, because of the
classification issue established by the International
Wheelchair Basketball Federation, IWBF). Besides,
classification has a huge impact on winning and los-
ing when it comes to a system that is as dynamic as
the one that is used by the IWBF with 9 classes to
deal with. Also of importance is the variability
concerning players’years of experience and hours
of practice from each national team. In fact, some
of the wheelchair basketball players on these teams
have daily practices all year to be considered for the
national team, whereas other players may practice
only 1 or 2 days per week. Finally, wheelchair bas-
ketball skills necessary to shot, pass and dribble in a
wheelchair are opposed to running basketball; these
skills seem to be more related to body type, position-
ing in chair and ability to manoeuver the chair
around a defender. Therefore, further research is
needed to investigate wheelchair basketball accord-
ing to these classification points.
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