NAME: CHRISTOPHER BAKER
UNIVERISTY NUMBER:
ST08002627
DEPARTMENT: CARDIFF
SCHOOL OF SPORT
INSTITUTION: UNIVERSITY OF
WALES INSTITUTE, CARDIFF
THE DISCRIMINATORY POWER OF GAMERELATED STATISTICS IN TERMS OF WINNING
AND LOSING IN THE NATIONAL BASKETBALL
ASSOCIATION
CONTENTS
Acknowledgements
i
Abstract
ii
CHAPTER ONE
1. Introduction and Literature Review
1
1.1.
Statistics
2
1.2.
Previous Research
3
CHAPTER TWO
2. Methodology
8
2.1.
Sample and Variables
8
2.2.
Data Analysis
9
2.3.
Reliability of the Data Source
10
CHAPTER THREE
3. Results
12
CHAPTER FOUR
4. Discussion
4.1.
All Games
20
20
4.1.1. Field Goal Efficiency
21
4.1.2. Defensive Rebounds
23
4.1.3. Assists
24
4.2.
Balanced Games
4.2.1. Fouls and Free Throws
25
25
4.3.
Unbalanced Games
26
4.4.
Implications
27
4.4.1. For the Coach
27
4.4.2. Training Focus
28
4.4.3. Player Recruitment
30
4.5.
Reliability of the Source
31
4.6.
Game-rhythm Contamination
32
CHAPTER FIVE
5. Conclusion
5.1.
References
Future Research and Direction
33
34
36
TABLES
Table 1: Percentage error values generated from the reliability testing of
NBA.com
11
Table 2: Descriptive statistics of the game-related variables of the winning
and losing teams in all games (mean ± SD)
12
Table 3: Descriptive statistics of the game-related variables of the winning
and losing teams in balanced and unbalanced games (mean ± SD)
13
Table 4: Discriminant function coefficients of all game-related statistics in
all games
16
Table 5: Discriminant function coefficients of all game-related statistics in
balanced and unbalanced games
17
Table 6: Classification of the winning and losing teams based on the
obtained discriminant functions
18
FIGURES
Figure 1: Mean differences between the winning and losing teams in all
games
15
Figure 2: Mean differences between the winning and losing teams in
balanced (≤10 points) and unbalanced games (>10 points)
15
Figure 3: Mean differences between the winning and losing teams across all
game-related statistics in all games, and their corresponding discriminant
function coefficients
19
ACKNOWLEDGEMENTS
Many thanks to the official website of the National Basketball Association for
continuing to provide such detailed statistics of all games. Also much gratitude to
Darrell Cobner, who provided excellent guidance throughout the completion of this
dissertation.
i
ABSTRACT
The aim of this study was to investigate the discriminatory power of the
game-related statistics commonly found in a basketball box-score, determining
which statistics best discriminate between winning and losing teams in the
National Basketball Association (NBA). The sample comprised of all 1,230 games
from the 2009-2010 NBA regular season. Based on the final score differential of
the two teams, the sample was divided into either balanced (≤10 points) or
unbalanced (>10 points) games. The mean differences between each team were
calculated for all games, balanced games and unbalanced games. Mann-Whitney
U tests were performed to identify those game-related statistics that were
significantly different between the winning and losing teams.
A discriminant
analysis identified those variables that most distinguish a winning team from a
losing team, recognising field goal percentage (discriminant function coefficient =
0.48), defensive rebounds (0.39), field goals made (0.36), assists (0.36) and 3point field goal percentage (0.30) as the most discriminative statistics in all games.
The statistics of field goal percentage (0.49), assists (0.42), field goals made
(0.42) and defensive rebounds (0.37) were again deemed discriminative in
unbalanced games. The discriminant values differed when interpreting balanced
games, particularly pinpointing the importance of free throws made (0.31) and free
throws attempted (0.31). Coaches and players can use these results to dictate
their game-strategies and direct their training procedures in the search for more
victories. Moreover, scouts can use this information to inform their recruitment of
players, something hugely important in all leagues and sports across the world.
ii
CHAPTER I
INTRODUCTION AND
LITERATURE REVIEW
1. Introduction
Performance analysis of sport is the examination of actual sports
performance (Hughes & Bartlett, 2008; O’Donoghue, 2010). It is this notion of
investigating actual performance that separates performance analysis from other
disciplines whereby activity may be analysed within a laboratory setting or data
may be gathered from self-reports (O’Donoghue, 2010). Performance analysis in
team sports such as basketball is an essential tool for coaches, allowing them to
have in-depth data concerning their team or their opponents (Hughes & Bartlett,
2008; Ibáñez et al., 2009). It has been acknowledged that quantitative analysis of
sports is a growing branch of science, with the study of game-related statistics
increasingly becoming a subject of practical and scientific interest to coaches and
sports scientists (Kubatko et al., 2007; Ibáñez et al., 2008).
The analysis of
basketball performance through the use of these game statistics is being more
extensively used by coaches in an attempt to analyse team and player
performance in different game contexts, with more valid and reliable data
(Sampaio & Janeira, 2003; Hughes & Franks, 2004; Kubatko et al., 2007; Ibáñez
et al., 2009). Given that basketball is a dynamic game involving a variety of
technical skills and tactical solutions (Mikolajec et al., 2005), comprehensive
analysis gives the coach a greater understanding of which elements of the game
have particular significance when separating a win from a loss. Previous research
studies in basketball have used game-related statistics in a multitude of ways. For
example, they are commonly used to highlight the differences between successful
teams and unsuccessful teams in basketball leagues across the world. Different
studies have also used statistics to observe the impact of various effects such as
game quality and season period on performance (Sampaio et al., 2010a). They
1
have been used to differentiate between starters and non-starters (Gomez et al.,
2009), and also to examine the differences in positional playing roles in an attempt
to improve the effectiveness of the player recruitment process (Sampaio et al.,
2006).
The following research that has been conducted within the field of basketball
focuses on a common set of game-related statistics.
1.1. Statistics
In each study, the statistics of 2-point field goals made, 2-point field goals
attempted, 3-point field goals made, 3-point field goals attempted, free throws
made, free throws attempted, offensive rebounds, defensive rebounds, steals,
assists, blocks, fouls and turnovers have been interpreted.
Definitions for these statistics have been devised using an array of basketball
coaching literature (Mikes, 1987; Marcus, 1991; Goldstein, 1995; Wissel, 2004;
Krause et al., 2008) and the official rulebook of the National Basketball
Association (NBA, 2010). These definitions can be found in Appendix A.
These are the statistics that are commonly found within the official box-scores of
basketball leagues across the world (Sampaio & Janeira, 2003; Gomez et al.,
2008; Csataljay et al., 2008; Ibáñez et al., 2009).
2
1.2. Previous Research
Sampaio and Janeira (2003) aimed to investigate the discriminatory power
of these game-related statistics between winning and losing teams in the
Portuguese Professional Basketball League.
A large sample of 353 regular
season games and 56 playoff games was initially divided into 3 sub categories
based on final score differential using cluster analysis. These categories were
close games (point differential of 1-8 points), balanced games (8-18 points) and
unbalanced games (more than 18 points).
Importantly, these different game
outcomes could be looked at in isolation to identify more specific results.
A
discriminant analysis showed that in both balanced and unbalanced matches,
losing teams performed poorly in all game statistics. Specifically, Sampaio and
Janeira (2003) found that in close games in the regular season, match outcome
was best discriminated by successful free throws.
Conversely, close playoff
games were determined by offensive rebounding. Despite the in-depth findings,
this research was based in the Portuguese Basketball League, recognised as an
inferior level of play (Sampaio et al., 2006). Gomez et al. (2008) used a sample of
306 games from the 2004-2005 season of the Spanish Men’s Professional League
(ACB League), recognised as one of the best leagues in European basketball
(Sampaio et al., 2006).
Similarly to Sampaio and Janeira (2003), this study
separated the games according to the final score differential. However, different
outcomes were used as games were deemed either balanced (differential of 12
points or less) or unbalanced (more than 12 points).
Discriminant analysis
performed by Gomez et al. (2008) concluded that defensive rebounds best
discriminated between winning and losing in balanced games.
Conversely,
winning and losing in unbalanced games was best differentiated by successful 23
point field goals and assists.
Similar findings were discovered in a study by
Sampaio et al. (2010a), which also focused on the Spanish Men’s Professional
League. Stronger teams were found to be superior in terms of 2-point field goals
and assists.
Moreover, weaker teams were considerably worse at defensive
rebounding. This research indicated that securing rebounds and committing few
errors are important determinants of success in basketball.
Ibáñez et al. (2008) aimed to identify the game-related statistics that best
differentiate between season-long successful and unsuccessful basketball teams
participating in the Spanish Basketball League (LEB1).
A large sample of
statistics from 870 games from the 2000-2001 and 2005-2006 regular seasons
was analysed, ultimately discovering that season long basketball success is
discriminated best by performance on blocks, assists and steals.
Essentially,
Ibáñez et al. (2008) found that the profile of success is shaped by better overall
passing skills and better defensive pressure. Conversely, Csataljay et al. (2008)
aimed to identify those critical performance indicators that most distinguish
between winning and losing within matches, as opposed to across seasons
(Ibáñez et al., 2008). As with the research performed by Sampaio and Janeira
(2003), this study also used a cluster analysis to disseminate between tight,
balanced and unbalanced games. Csataljay et al. (2008) discovered that in tight
games (9 points or less), free throw percentage was the most crucial variable that
distinguished between winning and losing. This provides support for the findings
of Sampaio and Janeira (2003). In the balanced games (10-22 points), the most
significant performance indicators were 2-point and 3-point field goal percentage.
Winning and losing in unbalanced games (22 point differential and higher) was
most significantly distinguished by defensive rebounds and 2-point field goal
4
percentage. These findings are particularly useful to a coach who, for example,
should pay particular attention to free throw shooting in the hope of achieving
success in close games. However, a small sample of just 57 games from the
European Basketball Championships in 2007 was used by Csataljay et al. (2008).
Furthermore, in contrast to research showing the importance of 3-point field goal
shooting (Csataljay et al., 2008), Ibáñez et al. (2009) showed that winning teams
were superior in all game-related statistics with the exception of 3-point field goals
made, as well as turnovers and free throws missed. This study by Ibáñez et al.
(2009) aimed to identify those statistics that discriminated between winning and
losing performances when playing 3 consecutive games.
However, effect of
consecutive games was only identified in turnovers, with a statistically significant
decrease between the second and third games. Ancillary to the aim of the study,
Ibáñez et al. (2009) did find that 2-point field goals made, defensive rebounds and
assists were discriminators of winning and losing in all 3 games.
Successful 2-point field goals and assists were also deemed to be discriminant
statistics in a study by Gomez et al. (2009).
This study used statistics to
discriminate between starters and non-starters in the Women’s National
Basketball Association (WNBA) when related to winning or losing teams over the
course of a season.
Similar to the study performed by Ibáñez et al. (2008),
season long success was determined by playoff qualification. Assists, 2-point field
goals made and successful free throws were the most significant variables
discriminating between starters and non-starters in the WNBA (Gomez et al.,
2009). Research from Trninic et al. (2002) gives further support to the importance
of efficient shooting. This study aimed to identify parameters among the indicators
of situation-related efficiency that differentiated between the winning and losing
5
teams of European Club finals from 1992-2000. The differences were confirmed
by discriminant analysis, with the highest discriminative power being obtained in
the variable defensive rebounds, then also field goal percentage and free throw
percentage. This study concluded that winning teams showed more of a tactical
discipline in controlling the inside to secure defensive rebounds, as well as
controlling the ball on offense until an open shot became available.
This
subsequently results in a higher shooting percentage.
Interestingly, Krause et al. (2008) report of a longitudinal study examining winning
and rebounding over a 10 year period in the United States. In this research, it was
found that 80 per-cent of the time, teams that out-rebounded their opponents went
on to win the game. Evidently, these high winning percentages give coaches
great impetus to develop rebounding among their players.
Previous research in this field has evidently provided some important information
about the game of basketball; however, the use of game-related statistics in
research has been associated with important threats to internal validity (Turcoliver,
1991). The analysis of game statistics can offer particular insight for a coach who
may be studying team performance against a particular opponent (Wissel, 2004).
However, such statistical analysis may lack validity when team performance needs
to be analysed across the duration of a season for example. This is due to gamerhythm contamination (Sampaio & Janeira, 2003). Game-rhythm contamination
ultimately pertains to the simultaneous existence of fast paced and slow paced
games throughout the season (Sampaio & Janeira, 2003; Oliver, 2004; Kubatko et
al., 2007; Sampaio et al., 2010). For example, one team may employ a fast paced
offense and thus attempt a high number of field goals within a game. On the
contrary, one team may be known for slowing the game down and employing a
6
slow paced offense. With this, they may attempt fewer field goals during the game
and thus achieve fewer possessions.
This incidence highlights the need to
normalise all game statistics according to game rhythm. The previous research
identified that has analysed these statistics across a wide number of games has
appropriately normalised all statistics according to game ball possessions
(Sampaio & Janeira, 2003; Gomez et al., 2008; Ibáñez et al., 2008; Gomez et al.,
2009; Ibáñez et al., 2009).
Evidently, a range of significant statistics have been interpreted in a variety of
studies.
The differences in fundamental styles of basketball across the world
(Sampaio et al., 2006; Sampaio et al., 2010) can conceivably contribute to these
varied findings. As further highlighted by Sampaio et al. (2006), different rules in
different leagues have an impact on styles of play. For example, the 3-point line in
the National Basketball Association (NBA) is 1 meter more distant from the basket.
This is deemed to further accentuate the roles of different positional players such
as the centre and the guard (Sampaio et al., 2006). These differences in styles of
play present issues of external validity (Mitchell & Jolley, 2001) when examining
game statistics in basketball. The findings are useful to all coaches and players
alike, yet only particularly relevant to those coaching and playing in the league
within which the research has taken place.
The aim of this study will be to investigate the discriminatory power of gamerelated statistics in terms of winning and losing in the National Basketball
Association (NBA). Noticeably, previous research in the area has mainly been
rooted in European basketball, and as of yet, no research available has focused
on and covered the duration of an entire NBA regular season.
The National
Basketball Association is globally recognised as an elite, superior basketball
7
league which tends to dominate international play (Sampaio et al., 2006; James,
2008; Sampaio et al., 2010). As highlighted by Sampaio et al. (2010), there have
been an increasing number of foreign players entering the NBA in recent years,
further implying the supremacy of the league in global terms. These reasons have
impacted the decision to solely focus on the NBA for this research.
8
CHAPTER II
METHODOLOGY
2. Methodology
2.1. Sample and Variables
The following game-related statistics were gathered from the official boxscores of all 1,230 games from the 2009-2010 National Basketball Association
regular season: Points scored, offensive rebounds; defensive rebounds; total
rebounds; assists; blocks; fouls; steals; turnovers; free throws made; free throws
attempted; free throw percentage; field goals made; field goals attempted; field
goal percentage; 3-point field goals made; 3-point field goals attempted; 3-point
field goal percentage. Importantly, the official NBA website provides up-to-date
information and data throughout the season (James, 2008), offering live statistics
for all 1,230 games. The data from both teams in each game was thus taken from
the official website and collated in Microsoft Excel. Given the format of the data
collected, various functions within Microsoft Excel were utilised to break down the
figures to a form that could be easily analysed. For example, field goal shooting
figures were displayed on the official website as the following:
Field goals made – field goals missed, field goal percentage
Essentially, each aspect of this line needed to be isolated for analysis reasons.
By further utilising various functions within Microsoft Excel, the differences
between each team in each of the statistics was calculated for each game. Here,
all games were separated into two categories based on the final score differential.
These categories were balanced games, whereby the score differential was 10
points or less; and unbalanced games, whereby the score differential was greater
than 10 points. These figures were determined based upon previous research
within this field (Sampaio & Janeira, 2003; Cstaljay et al., 2008; Gomez et al.,
8
2008). Given the separation of all shooting statistics and categorisation of final
score differentials, data analysis could subsequently be performed.
2.2. Data Analysis
Data analysis was performed using SPSS software version 17.0. Here,
descriptive statistics of arithmetic mean and standard deviation were calculated for
winning and losing teams across all game-related variables. These statistics were
collected for balanced games (10 points or fewer), unbalanced games (more than
10 points) and all games. Mann-Whitney U tests were then performed to compare
these means and identify the differences between balanced games and
unbalanced games (Field, 2009). Significant statistical differences between the
winning and losing teams in each instance were identified. Statistical significance
was appropriately set at p ≤ 0.05 (Altman, 1991; Pallant, 2007; Field, 2009).
A discriminant analysis was then performed to identify the variables that best
discriminate between winning and losing teams (Field, 2009). Interpretation of the
discriminant functions was based on examination of the canonical function
coefficients greater than 0.30 (Tabachnick & Fidell, 2007).
Game-related
variables with a higher absolute value have a powerful contribution to discriminate
between winning teams and losing teams. The discriminant models were then
validated using the leave-one-out method of cross validation (Norušis, 2008; Field,
2009). Cross validation analysis is required to understand the usefulness of the
discriminant functions when classifying data (Field, 2009).
9
2.3. Reliability of the Data Source
Reliability in performance analysis pertains to the extent to which the event
codes that have been notated by the analyst reflect what happened in the game
(James et al., 2007). The accuracy of the measurements recorded by NBA.com
were assessed using percentage error techniques. This form of reliability testing
will be clearly outlined.
The reliability testing took place using ESPN on Sunday January 2 nd, 2011;
Thursday January 6th, 2011 and Saturday January 8th, 2011.
The specific
matches were the Atlanta Hawks against the Los Angeles Clippers, the San
Antonio Spurs against the Boston Celtics, and the Houston Rockets against the
Orlando Magic respectively. The manual notation for each game was conducted
live. In order to test the events and their operational definitions, a pilot study took
place. It has been acknowledged that pilot studies are vitally important in allowing
for appropriate refinement and the development of understanding (Kezar, 2000;
O’Donoghue, 2010). Thus, a pilot study took place on Thursday December 30th,
2010, during the match between the New Orleans Hornets and the Los Angeles
Lakers on ESPN. Importantly, due to the speed of the game, it became apparent
that the observer would have to avoid spending time looking at the data capture
sheet. Therefore, to overcome the possibility of observational errors whereby the
analyst would fail to code an event (James et al., 2002), the tally boxes were
spatially enhanced. Once all of the results were collected from all three games,
they were entered into a spreadsheet using Microsoft Excel where the reliability
testing took place. Percentage error was used to identify the accuracy of the
coding by comparing the differences in tallies between the official statistics of
NBA.com and the independent observer.
10
It is suggested that a reliability test
should be examined to the same depth as the subsequent data processing
(Hughes et al., 2002; Hughes, 2008). Therefore, percentage error statistics were
calculated for each of the thirteen analysed performance indicators. Percentage
error is calculated using the following equation identified by Hughes and Franks
(2004);
( (mod[V1-V2]) / Vmean)*100%
Where  = sum of; mod = modulus (absolute value); V1 = analyst 1’s code; V2 =
analyst 2’s code (NBA.com); Vmean = mean of all variables. These percentage
error statistics were plotted against each variable to give a powerful and
immediate visual image of the reliability test (Hughes & Franks, 2004).
Table 1 illustrates the percentage error statistics generated from the inter-operator
reliability testing of NBA.com. For the purpose of this test, the level of significance
was set at p ≤ 0.05 (Altman, 1991; Pallant, 2007; Field, 2009). Thus, error scores
of ≤ 5% were deemed significant.
Table 1: Percentage error values generated from the reliability testing of NBA.com
Match
Percentage error
San Antonio -Boston
2.30%
Orlando – Houston
2.85%
Atlanta – Los Angeles
3.41%
Overall
2.87%
11
Evidently, the percentage error values were significant for all analysed games,
giving an overall score of 2.87%. More specific reliability statistics from each of
the analysed games can be found in Appendix B.
12
CHAPTER III
RESULTS
3. Results
A consistent format will be followed in the presentation of all results.
Throughout, data for all games will firstly be presented, followed by more specific
results accounting for the type of game (balanced and unbalanced). Table 2
shows that across all games, the winning teams’ average more of each statistical
variable with the exception of offensive rebounds, fouls, turnovers, field goals
attempted and 3-point field goals attempted. Table 2 also shows that the variable
of 3-point field goals attempted is the only statistic that does not differ significantly
between the winning and losing teams (see Appendix C).
Table 2: Descriptive statistics of the game-related variables of the winning and losing
teams in all games (mean ± SD)
WINNING TEAMS
106.0 ± 10.8
LOSING TEAMS
94.9 ± 10.2*
Offensive rebounds
10.8 ± 3.9
11.1 ± 3.9*
Defensive rebounds
32.7 ± 4.9
28.8 ± 4.6*
Total rebounds
43.5 ± 6.2
39.9 ± 6.1*
Assists
23.1 ± 5.1
19.4 ± 4.5*
Blocks
5.3 ± 2.6
4.4 ± 2.4*
Fouls
20.2 ± 4.0
21.6 ± 4.0*
Steals
7.6 ± 3.0
6.8 ± 2.7*
Turnovers
13.9 ± 3.8
14.6 ± 3.8*
Free throw percentage
76.2 ± 9.2
74.4 ± 10.1*
Free throws made
19.9 ± 6.1
17.4 ± 5.6*
Free throws attempted
25.9 ± 7.4
23.2 ± 6.8*
Field goal percentage
48.4 ± 5.4
43.2 ± 4.9*
Field goals made
39.5 ± 4.9
35.9 ± 4.4*
Field goals attempted
81.0 ± 7.0
82.4 ± 7.1*
3-point field goal percentage
38.4 ± 12.1
31.0 ± 11.3*
3-point field goals made
7.1 ± 3.1
5.8 ± 2.8*
3-point field goals attempted
18.0 ± 5.7
18.2 ± 5.8
Points
* Significantly different from winning teams
12
Table 3 examines balanced (≤10 points) and unbalanced games (>10 points)
separately. Evidently, all game-related statistics are significantly different among
winning and losing teams in balanced games with the exception of offensive
rebounds, turnovers and 3-point field goals made (see Appendix D for more
detail).
Conversely, in unbalanced games, field goals attempted is the only
statistical variable that does not differ significantly (see Appendix E for more
detail). Also noticeable are the increases in shooting efficiency for the winning
teams, specifically mean field goal percentage (+3.9%) and mean 3-point field
goal percentage (+4.7%) when going from balanced games to unbalanced games.
Table 3: Descriptive statistics of the game-related variables of the winning and losing
teams in balanced and unbalanced games (mean ± SD)
Balanced
Unbalanced
Winning
teams
Losing
teams
Winning
teams
Losing
teams
Points
103.0 ± 9.8
97.2 ± 9.9*
110.0 ± 10.8
91.9 ± 9.9*
Offensive rebounds
11.0 ± 4.0
11.1 ± 3.9
10.6 ± 3.7
11.1 ± 3.9*
Defensive rebounds
32.0 ± 4.9
29.7 ± 4.5*
33.6 ± 4.9
27.6 ± 4.5*
Total rebounds
43.0 ± 6.2
40.8 ± 6.0*
44.2 ± 6.1
38.7 ± 6.0*
Assists
21.6 ± 4.6
20.3 ± 4.5*
25.0 ± 5.1
18.2 ± 4.2*
Blocks
5.0 ± 2.5
4.7 ± 2.5*
5.5 ± 2.6
4.2 ± 2.3*
Fouls
20.3 ± 4.1
22.0 ± 4.0*
20.0 ± 4.0
21.0 ± 3.9*
Steals
7.3 ± 2.9
6.9 ± 2.6*
8.0 ± 3.0
6.8 ± 2.7*
Turnovers
14.0 ± 3.7
14.3 ± 3.7
13.7 ± 3.8
14.9 ± 3.9*
Free throw percentage
76.3 ± 9.1
75.0 ± 10.2*
76.1 ± 9.4
73.6 ± 9.9*
Free throws made
20.5 ± 6.1
17.5 ± 5.8*
19.1 ± 6.0
17.1 ± 5.5*
Free throws attempted
26.7 ± 7.4
23.2 ± 6.9*
24.9 ± 7.3
23.1 ± 6.8*
Field goal percentage
46.7 ± 4.9
44.1 ± 4.8*
50.6 ± 5.3
42.0 ± 4.7*
Field goals made
38.0 ± 4.3
36.7 ± 4.3*
41.5 ± 5.0
34.7 ± 4.3*
Field goals attempted
80.7 ± 6.9
82.7 ± 7.2*
81.5 ± 7.1
82.0 ± 7.0
3-point field goal percentage
36.4 ± 11.6
31.8 ± 10.9*
41.1 ± 12.3
30.0 ± 11.7*
3-point field goals made
6.5 ± 2.9
6.2 ± 2.9
7.8 ± 3.3
5.3 ± 2.7*
3-point field goals attempted
17.5 ± 5.5
18.9 ± 5.9*
18.7 ± 5.8
17.4 ± 5.7*
* Significantly different from winning teams
13
Figure 1 and Figure 2 visually portray the mean differences between the winning
and losing teams across each variable in all games and then balanced and
unbalanced games respectively.
These mean differences are relative to the
winning team. Therefore, the statistics that are on the negative pole are achieved
more by the losing team. Thus when examining Figure 2, it becomes clear that in
balanced games, the losing team has more 3-point field goal attempts. However,
the winning team has more 3-point field goal attempts in the unbalanced games.
It is also fair to infer that the mean statistical differences commonly increase in the
winning team’s favour when going from balanced to unbalanced games. Visibly,
the largest difference in balanced games is within the variable 3-point field goal
percentage (4.6%). This trend continues into unbalanced games, and evidently
increases (11.1%). Also made apparent in Figure 2 is the fact that the winning
team averages more made free throws and attempted free throws in balanced
games than in unbalanced games. Uniquely, these are the only statistics that
follow this trend.
For comparison reasons, the same vertical axis has been
implemented in both Figure 1 and Figure 2. This effectively shows the extent to
which the differences vary across each type of game. Moreover, the horizontal
axis applied to Figure 1 has also been applied to Figure 2. In Figure 1, the gamerelated statistics are depicted in rank order, moving from the largest mean
differences in favour of the winning team to the largest mean differences in favour
of the losing team. Again, by applying the same order of variables to Figure 2, it
shows the extent to which the type of game affects the mean differences between
the winning and losing teams.
14
-2.00
-4.00
15
Fouls
FG attempted
Turnovers
Off. rebounds
3pt. attempted
Steals
Blocks
3pt. made
FT percentage
FT made
FT attempted
Total rebounds
FG made
Assists
Def. rebounds
FG percentage
-4.00
Fouls
FG attempted
Turnovers
Off. rebounds
3pt. attempted
Steals
Blocks
3pt. made
FT percentage
FT made
FT attempted
Total rebounds
FG made
Assists
Def. rebounds
FG percentage
3pt. percentage
-2.00
3pt. percentage
Mean difference
Mean difference
12.00
10.00
8.00
6.00
4.00
2.00
0.00
Game-related statistic
Figure 1: Mean differences between the winning and losing teams in all games
12.00
10.00
Balanced
Unbalanced
8.00
6.00
4.00
2.00
0.00
Game-related statistic
Figure 2: Mean differences between the winning and losing teams in balanced (≤10 points)
and unbalanced games (>10 points)
Table 4 shows the discriminant values of all analysed variables in all games from
the 2009-2010 NBA regular season. Evidently, the game-related statistic that best
discriminates between winning and losing teams is field goal percentage (0.48).
Similarly, variables of defensive rebounds (0.39), field goals made (0.36), assists
(0.36) and 3-point field goal percentage (0.30) are also discriminative of the two
types of teams. Importantly, the winning teams are on the positive, whereas the
losing teams are on the negative pole of the discriminant function.
Table 4: Discriminant function coefficients of all game-related statistics in all games
Canonical discriminant
function coefficient
Game-related statistic
Field goal percentage
0.48*
Defensive rebounds
0.39*
Field goals made
0.36*
Assists
0.36*
3-point field goal percentage
0.30*
Total rebounds
0.28
Free throws made
0.20
3-point field goals made
0.20
Free throws attempted
0.18
Fouls
-0.16
Blocks
0.15
Steals
0.13
Field goals attempted
-0.09
Free throw percentage
0.09
Turnovers
-0.09
Offensive rebounds
-0.04
3-point field goals attempted
-0.02
* Discriminant value ≥ 0.30
16
Table 5 is more specific in showing the variables that best discriminate between
winning and losing teams in balanced games and unbalanced games. Both field
goal percentage (0.34) and defensive rebounds (0.31) are once again
discriminative in balanced games. Additionally, free throws made (0.31) and free
throws attempted (0.31) are powerful discriminators of success in balanced
games.
When interpreting unbalanced games, the variables of field goal
percentage (0.49), defensive rebounds (0.37) and field goals made (0.42) are
discriminant values. Furthermore, assists (0.42) become discriminative of winning
and losing teams in unbalanced games.
Table 5: Discriminant function coefficients of all game-related statistics in balanced and
unbalanced games
Balanced
Game-related statistic
Unbalanced
Canonical
discriminant function
coefficient
Game-related statistic
Canonical
discriminant function
coefficient
Field goal percentage
0.34*
Field goal percentage
0.49*
Defensive rebounds
0.31*
Assists
0.42*
Free throws made
0.31*
Field goals made
0.42*
Free throws attempted
0.31*
Defensive rebounds
0.37*
Fouls
-0.27
3-point field goal percentage
0.27
3-point field goal percentage
0.26
Total rebounds
0.26
Total rebounds
0.22
3-point field goals made
0.24
Field goals made
0.18
Blocks
0.16
Assists
0.18
Steals
0.12
Field goals attempted
-0.17
Free throws made
0.10
3-point field goals attempted
-0.15
Turnovers
-0.09
Steals
0.10
Free throw percentage
0.08
Blocks
0.09
Free throws attempted
0.07
Free throw percentage
0.08
Fouls
-0.07
3-point field goals made
0.07
3-point field goals attempted
0.07
Turnovers
-0.05
Offensive rebounds
-0.04
Offensive rebounds
-0.03
Field goals attempted
-0.02
* Discriminant value ≥ 0.30
17
Table 6 illustrates the results of the re-classification of the winning and losing
teams based on the discriminant function and their performance in all variables
within a game. Clearly, 97% of winning teams and losing teams from unbalanced
games were correctly classified. In balanced games, 78% of winners and 77.2%
of losers were successfully re-classified. Finally, 84.8% of winning teams and
86.6% of losing teams from all games were correctly classified. Therefore, a
correct classification rate of 85.7% for all games was achieved.
Table 6: Classification of the winning and losing teams based on the obtained discriminant
functions
Correctly re-classified group membership (%)
Games
Winning team
Losing team
84.8
86.6
Balanced
78
77.2
Unbalanced
97
97
All
Additionally, Figure 3 is an amalgamation of data, directly comparing the mean
differences and discriminant values of all variables in all games. As before, the
mean differences of each game-related statistic are relative to the winner and are
in order of magnitude of difference.
Clearly, the variables that have mean
differences in favour of the losing teams also have negative discriminant function
coefficients. Generally, as the mean differences move closer to zero, the line
representing the discriminant values does likewise.
evident discrepancies.
However, there are some
For example, although the highest mean difference is
within the statistic 3-point field goal percentage (7.4%), field goal percentage has
the largest discriminant value (0.48). This clearly shows that the statistics that are
18
the most discriminative between the teams are not simply those which exhibit the
largest differences. Visibly, 3-point field goals attempted is the statistic that boasts
the smallest mean difference and also least discriminates between winning and
losing teams in all games.
8.00
0.8
Mean difference
0.7
Discriminant
function coefficients
0.6
3pt. attempted
Off. rebounds
Turnovers
-2.00
Steals
-1.00
Blocks
0
3pt. made
0.00
FG attempted
0.1
Fouls
1.00
FT percentage
0.2
FT made
2.00
FT attempted
0.3
Total rebounds
3.00
FG made
0.4
Assists
4.00
Def. rebounds
0.5
FG percentage
5.00
3pt. percentage
Mean difference
6.00
Discriminant value
7.00
-0.1
-0.2
Game-related statistic
Figure 3: Mean differences between the winning and losing teams across all game-related
statistics in all games, and their corresponding discriminant function coefficients
19
CHAPTER IV
DISCUSSION
4. Discussion
4.1. All Games
The aim of this study was to investigate the discriminatory power of the
game-related statistics commonly found within a basketball box-score, determining
which statistics best discriminate between winning and losing teams in the
National Basketball Association (NBA). Having analysed the data from all 1,230
matches of the 2009-2010 regular season, the findings show that the statistics of
field goal percentage, defensive rebounds, field goals made, assists and 3-point
field goal percentage are common discriminators between winning and losing
teams.
This study also discovered the average performance by a winning and losing team
across all game-related statistics. Therefore, the mean differences between these
teams within each statistic could be interpreted. It became clear that the winning
teams had higher values in most game-related statistics. Turnovers, fouls, 3-point
field goals attempted, field goals attempted and offensive rebounds were the
exceptions, being achieved more by the losing teams.
Understandably, high
scores in these particular statistics may not be desirable.
For example,
committing more fouls than your opponent effectively results in your opponent
having more free-throw attempts and thus more uncontested chances to score
(Krause et al., 2008).
Similarly, committing more turnovers will reduce your
number of scoring opportunities and give your opponents more chances to score
in the process (Wissel, 2004). However, there is a common emphasis by coaches
on the importance of offensive rebounding (Goldstein, 1995; Trninic et al., 2002;
Wissel, 2004; Krause et al., 2008) and therefore this statistical difference may be
20
unexpected. Although when more closely scrutinised, an offensive rebound is a
consequence of a missed field goal and thus victorious teams that boast more
superior field goal percentages will limit the number of offensive rebounding
opportunities (Trninic et al., 2002).
Specifically, Table 6 shows how indicative a team’s performance in all gamerelated statistics is in terms of winning and losing. Evidently, 85.7% of teams in all
games were successfully classified as a winner or loser given their box-score
performance and the obtained discriminant function coefficients. Ultimately, this
highlights the importance of performing successfully in a multitude of areas within
a game of basketball.
4.1.1. Field Goal Efficiency
One of the two fundamental objectives in basketball is to get a good shot in order
to score a basket (the other is preventing the opposition from doing the same;
Goldstein, 1995; Kloppenburg, 1996; Wootten, 2003; Wissel, 2004; Krause et al.,
2008).
The findings from this research show that in all games, field goal
percentage is the game-related statistic that best discriminates between
successful and unsuccessful teams. The results illustrated in Table 2 also show
that the winning teams attain a mean field goal percentage that is more than 5%
higher than their unsuccessful opponents.
Field goals incorporate any type of shot or tap and exclude free throws (Goldstein,
1995, Krause et al., 2008). The lay-up is regarded as the most high percentage
field goal attempt given its proximity to the basket (Wissel, 2004). It would be fair
21
to suggest that teams with higher shooting percentages tend to create more
chances such as lay-ups in positions closer to the basket (Csataljay et al., 2009).
It is also reasonable to relate high field goal efficiency to poor defence from the
opposition. If the defence is failing to appropriately defend the most dangerous
areas under the basket (Kloppenburg, 1996; Wootten, 2003), the opposing
offence is being able to take more high-percentage shots (Csataljay et al., 2009).
Conversely, if the defence is sufficiently active and executing well, they will be
forcing the opponent’s to take more low-percentage shots from outside
(Kloppenburg, 1996). This notion is supported by the results shown in Table 2, as
not only do the losing teams exhibit poorer shooting percentages, but they also
average more 3-point field goal attempts in all games.
Successful field goal shooting is a common factor that is attributed to successful
teams and players (Trninic et al., 2002; Gomez et al., 2008; Csataljay et al., 2009;
Gomez et al., 2009; Ibáñez et al., 2009; Sampaio et al., 2010a).
To further
highlight the significance of high field goal percentages in the league, the official
website of the National Basketball Association was utilised.
Here, it was
discovered that the top three teams in terms of field goal percentage for the 20102011 season (as of 01/03/2011; NBA, 2011) own the second, third and fourth best
win/loss records in the league (NBA, 2011a). This shows that efficient shooting
certainly equates to overall success.
22
4.1.2. Defensive Rebounds
In all types of match, defensive rebounding was deemed to be an important gamerelated statistic that differentiates between winning teams and losing teams (See
Table 4 and Table 5).
Defensive rebounds are recognised as an indicator of overall defensive
successfulness (Wootten, 2003; Wissel, 2004; Krause et al., 2008). This is due to
the fact that an unsuccessful shot by the opponent will often be a consequence of
sufficient defensive pressure (Trninic et al., 2002). Moreover, good performances
in defensive rebounding are associated with game rhythm, particularly related to
an increase in fast break possessions (Sampaio et al., 2010a). A team can set up
a good shot by running the fast break (Wootten, 2003; Tsamourtzis et al., 2005;
Krause et al., 2008), thus highlighting the relationship between defensive
rebounds and high field goal percentage in terms of winning games.
The fast
break, which will often develop after a defensive rebound is the fastest way to
make the transition from defence to offence (Wootten, 2003; Tsamourtzis et al.,
2005; Krause et al., 2008).
As acknowledged by Krause et al. (2008), certain physical attributes are
advantageous for rebounding. It would be fair to imply that players who are tall,
have long arms, large hips and have well developed upper and lower body
musculature have an undeniable advantage when it comes to collecting rebounds
(Sampaio & Janeira, 2003; Sampaio et al., 2006; Krause et al., 2008; Sampaio et
al., 2010a). It is important to acknowledge that defensive rebounding does require
more than just physical tools. Appropriate skill execution, effort and determination
23
are vitally important (Krause et al., 2008). The positioning of the players and their
ability to be quick to the ball are also essential to successful rebounding.
The importance of defensive rebounds has been identified in much research
(Trninic et al., 2002; Krause et al., 2004; Gomez et al., 2008; Ibáñez et al., 2009;
Sampaio et al., 2010a). Interestingly, when examining the official website of the
National Basketball Association it became clear that the three teams who lead the
league in defensive rebounding rates (NBA, 2011b) have three of the top six
win/loss records thus far in the 2010-2011 season (as of 01/03/2011; NBA,
2011a). This further accentuates the importance of defensive rebounding in the
NBA, providing palpable evidence that it relates to success.
4.1.3. Assists
Assists are an altruistic measure of teamwork (Ibáñez et al., 2008) and are a result
of good decision making, timing, coordination and execution (Melnick, 2001;
Gomez et al., 2006). The results shown in Table 2 illustrate that in all games, the
winning teams average significantly more assists (+3.7) than their opponents.
Plainly, assists relate positively to field goals made and thus teams who shoot
more successfully will subsequently tend to tally more assists.
As discovered by Sampaio et al., (2006), assists are mostly performed by the
guards on the team. With this in mind, it could also be inferred that the winning
teams tend to have guards who are more adept at successfully passing the ball to
create a basket.
24
In research previously identified, assists were commonly deemed to be an
important statistic that highlights success (Gomez et al., 2008; Ibáñez et al., 2008;
Gomez et al., 2009; Ibáñez et al., 2009).
Once again, when interpreting the
official website of the NBA, it becomes evident that 2 of the 3 top leading teams in
assists per game (NBA, 2011) have 2 of the top 3 win/loss records in the 20102011 season thus far (NBA, 2011a).
4.2. Balanced Games
When focusing on balanced games whereby the final score differential is
equal to or fewer than 10 points, field goal percentage, defensive rebounds, free
throws made and free throws attempted best discriminate between winning and
losing teams.
4.2.1. Fouls and Free Throws
Although not deemed significant, the discriminant function coefficient for fouls is
considerably greater in balanced games than in unbalanced games (See Table 5).
It has been recognised by a high proportion of coaches that almost half of all close
basketball games are decided at the foul line (Garfinkel & Klein, 1988), and these
findings appear to support this claim.
Consequently, both free throws made and free throws attempted have been
shown to be important discriminators of winning and losing in balanced games
(see Table 5). In closing moments of balanced games, the trailing team will be
applying as much pressure as possible to try and turn the ball over on defence
25
(Wootten, 2003). If the defence breaks down and the opposition avoid turning the
ball over, a foul will more than likely follow (Wootten, 2003; Krause et al., 2008).
This will stop the clock and give the offence free throw attempts.
Therefore
unsurprisingly, free throw attempts have been shown to favour the winning teams
in balanced games. As a consequence, and given the high success rate of 80%
assigned to free throw shooting at the professional level (Krause et al., 2008), free
throws made by the winning team will increase. On the contrary, there is less
impetus to force a turnover towards the end of an unbalanced game, and thus
fouling and free throws tend to have a much smaller significance.
4.3. Unbalanced Games
In comparison, when looking at unbalanced games whereby the final score
differential is more than 10 points, the discriminant statistics differ.
As with
balanced games, field goal percentage and defensive rebounds are deemed
discriminant. However additionally, as shown in Table 5, field goals made and
assists are discriminators of winning and losing in this type of game.
Understandably, as previously touched upon, these particular statistics will follow
a similar pattern of importance as they are directly influential of one another.
Theoretically, more assists will be recorded as more field goals are made and this
is sufficiently demonstrated in the research findings.
26
4.4. Implications
Fundamental differences in playing styles, positional roles and game rules
(Sampaio et al., 2006; Sampaio et al., 2010) present issues of external validity
(Mitchell & Jolley, 2001) when examining game-related statistics in basketball. It
is therefore important to understand that given the setting of this research,
although they are useful to all, the findings can only be truly applicable within the
National Basketball Association.
However, this research ultimately provided
further backing for certain variables that have been shown to be important in
basketball leagues across the world, namely shooting efficiency (Trninic et al.,
2002; Gomez et al., 2008; Csataljay et al., 2009; Gomez et al., 2009; Ibáñez et al.,
2009; Sampaio et al., 2010a) and defensive rebounding (Trninic et al., 2002;
Krause et al., 2004; Gomez et al., 2008; Ibáñez et al., 2009; Sampaio et al.,
2010a). The results uncovered in this research can have many practical uses,
particularly for the coach in various contexts. Furthermore, they can be applied to
training and preparation and can also act as a guide for player recruitment.
4.4.1. For the Coach
The most successful coaches are those who are the best at preparing their team
for each game (Wootten, 2003) and scouting is an important part of preparing your
team to face your next opponent (Wootten, 2003). With an increased knowledge
of what is particularly necessary to win against particular teams or in particular
situations, an impetus can be applied to training.
This knowledge can also be
highly favourable for a coach during a performance. For instance, if a team is
noticeably under-achieving in an area of the game that is deemed significant in
27
separating winning teams from losing teams, the coach can adjust their line-up
accordingly in an attempt to gain improvement.
Moreover, simply comparing
statistical achievements within a game with the league averages can pinpoint
areas for post-game discussion. For example, it may be that a losing team has
just performed strongly in all but one statistical area when compared to league
averages. This statistical area can then be analysed more thoroughly with the use
of performance analysis software.
In addition, knowledge of significant statistics in different game types is valuable to
the coach.
Perhaps most noticeably, as free throws have more reverence in
closer games, the coach may wisely choose to ensure that their most successful
foul shooters are on the floor towards the end of a balanced game in order to
preserve a lead.
4.4.2. Training Focus
It is important for the team to understand what they want to achieve and what they
need to develop in order to help them achieve (Krause et al., 2008). As the team
becomes more familiar with which aspects of the game are most important when
securing a victory, they can subsequently give them more attention when
practicing. For example, as previously highlighted, shooting efficiency has been
shown to be the game-related statistic that best discriminates between a winner
and a loser. With this knowledge, a coach and his players could wisely opt to
devote more time to field goal shooting during a training session. High confidence
and efficient mechanics are facet’s recognised as highly important to successful
field goal shooting (Garfinkel & Klein, 1988; Wissel, 2004; Krause et al., 2008).
28
These will subsequently be developed when given more attention in training.
Additionally, lay ups were previously acknowledged as being high percentage field
goal attempts (Wissel, 2004) that are more often performed by successful teams
(Csataljay et al., 2009). With this in mind, special attention may be given to this
type of shot when training, particularly focusing on driving the ball to the basket.
Penetrating the opponent’s defence is a primary principle of successful offence
(Trninic et al., 2002). It can not only induce higher shooting percentages, but can
also draw more fouls by the opponent (Trninic et al., 2002; Wootten, 2003). This
coincides with the fact that losing teams average more fouls per game in all types
of game (see Table 2 and Table 3).
Defensive strategies and techniques can also be sufficiently addressed in training
situations.
Knowing that field goal percentage best differentiates between a
winning team and a losing team, applying appropriate pressure on the shooter
may viably become a training focus. As inferred by Wissel (2004), pressuring the
ball is a vital aspect of playing good defence and it can make the act of shooting
considerably more difficult. Similarly, given the significance of defensive rebounds
found in this study, boxing out can become a training focus.
Boxing out or
blocking out involves positioning one’s self in a way that gives them a positional
advantage over an opponent when trying for a rebound (Krause et al., 2008).
Therefore, by successfully boxing out, the defensive team should be maximising
their opportunities to rebound the ball following a missed shot by the opponent.
29
4.4.3. Player Recruitment
A further implication of these results could be within the field of player recruitment.
As previously stated, there are certain physical attributes that have been shown to
be advantageous for rebounding (Sampaio & Janeira, 2003; Sampaio et al., 2006;
Krause et al., 2008; Sampaio et al., 2010a). When creating a line-up, building a
squad or even scouting for new players, understanding the importance of
defensive rebounding and knowing the characteristics that can help facilitate
rebounding can give the coach or scout a foundation for selection. Similarly, the
coach could focus their attention on players who demonstrate the best field goal
shooting efficiency when selecting their squad or their starters, as this variable has
been shown to be particularly significant in determining success. Players who are
creative in providing assists are also necessary in the line-up given the importance
of this game-related statistic.
Sampaio et al. (2006) showed that the best
rebounders in the NBA are the centers, who are typically confined to the area
around the basket. It was also determined that guards are more proficient in
tallying assists and shooting from long range. A balance of sizes and strengths
within the team is therefore necessary if they are to excel (Wootten, 2003) and
successfully perform in these important areas of the game. Hypothetically, a scout
may be focused on two specific players with the intention of bringing one to the
team in the near future.
If for example one demonstrates elite defensive
rebounding ability and the other demonstrates elite blocking ability, the findings
from this research may encourage the scout to plump for the player who excels at
rebounding as this player might help generate more victories for the team.
30
4.5. Reliability of the Source
Generally, as shown in Table 1, NBA.com was shown to be reliable in
correctly coding the statistics of each basketball game.
Despite the low overall percentage error values gathered from the reliability testing
of NBA.com (see Table 1), it is clear in Appendix B that there are large
discrepancies when interpreting discrete values. Noticeably, many variables have
been tallied equally by both Analyst 1 and NBA.com. However, when focusing on
rebounding statistics in particular, large differences equate to considerably high
percentage error values in all analysed games.
Furthermore, consider the variable of steals in the matchup between Orlando and
Houston. Here, although a difference of only one tally has been observed, the
percentage error value is high (11.76%). Clearly, this value exceeds the level of
significance (Altman, 1991; Pallant, 2007; Field, 2009) and thus denotes a
significant difference among observers. This highlights the problem of sensitivity
when using one level of significance within percentage error when dealing with
variables of substantially differing totals (Hughes, 2008). As very few total steals
were recorded in the game, one recorded difference is magnified, producing the
high percentage error value.
The differences in codes by Analyst 1 and NBA.com may be the result of
operational errors (James et al., 2002) whereby the tally may have been placed in
the wrong box by Analyst 1 and observational errors (James et al., 2002) whereby
the analyst simply failed to code the event.
The latter source of error was
highlighted earlier in the method section as being a particular danger to the
reliability testing as focus is needed solely on the fast performance throughout
31
each game. Furthermore, the issue of definitional errors (James et al., 2002)
appears to affect this reliability testing substantially. Significantly different tally
totals within the variables of offensive and defensive rebounds could conceivably
be attributed to vague understanding of their corresponding definitions. By rule,
every missed field goal or free throw is rebounded (see Appendix B). Importantly,
the ball can travel out of bounds, un-touched following a missed shot. These
rebounds are not classified as either offensive or defensive by NBA.com but were
categorised by Analyst 1, resulting in marked discrepancies.
4.6. Game-rhythm Contamination
The issue of game-rhythm contamination (Sampaio & Janeira, 2003) was
previously raised in Chapter 1. It highlights the need to normalise all game-related
statistics given the simultaneous existence of fast and slow paced games within a
season (Sampaio & Janeira, 2003; Oliver, 2004; Kubatko et al., 2007; Sampaio et
al., 2010).
In previous research, all game-related statistics have been
appropriately normalised according to game ball possessions (Sampaio & Janeira,
2003; Gomez et al., 2008; Ibáñez et al., 2008; Gomez et al., 2009; Ibáñez et al.,
2009), however this research is limited by the fact that it does not follow suit.
Given the size of the data sample and the lack of readily available and exact ball
possession statistics, data analysis was carried out overlooking differences in
game rhythm. Equations providing estimations of ball possessions could have
been applied to the sample to overcome this issue (Oliver, 2004; Kubatko et al.,
2007).
32
CHAPTER V
CONCLUSION
5. Conclusion
Having analysed all 1,230 games from the 2009-2010 NBA regular season,
the game-related statistics of field goal percentage, defensive rebounds, field
goals made, assists and 3-point field goal percentage emerged as being
significant discriminators between winning teams and losing teams.
The
significant statistics varied when games were appropriately categorised as either
balanced or unbalanced according to the final score differential.
In balanced
games, successful free throw shooting was found to differentiate between the two
teams, supporting the widespread view that almost half of all close games are
decided at the free throw line (Garfinkel & Klein, 1988). In unbalanced games,
larger differences between each team in the performance of most game-related
statistics were noted.
Consequently, the statistics of field goal percentage,
assists, field goals made and defensive rebounds became even more
discriminative.
With a broad knowledge of what game-related statistics are more central to
winning, coaches of the National Basketball Association can be more proficient in
adapting their game strategies. For instance, it was highlighted that defensive
rebounding is a highly discriminative statistic separating successful teams from
unsuccessful teams. With this in mind, coaches can do what is necessary to
ensure their team will seize every opportunity to secure a rebound on every
missed shot by the opponent. As suggested, boxing out (Krause et al., 2008) can
become a training focus to ensure that players are aware of how said
opportunities can be seized.
33
Furthermore, by having more of an idea about what it takes to win in the NBA,
coaches and scouts can be more specific when recruiting players. For instance,
knowing that certain physical attributes such as long arms and large hips are
advantageous for rebounding (Sampaio & Janeira, 2003; Sampaio et al., 2006;
Krause et al., 2008; Sampaio et al., 2010a), the recruiters can pay more attention
to the anthropometric data of their potential players to influence their scouting.
5.1. Future Research and Direction
Readily available sporting statistics such as those found in a basketball box-score
can often be tweaked or transformed to portray something more powerful and
constructive (Oliver, 2004; Swalgin, 2008). As alluded to in Chapter 4, offensive
rebounds were unexpectedly found to be more often achieved by the losing teams
in all types of game. It was recognised that this can be largely attributed to the
higher incidence of missed field goals by these teams, providing more
opportunities to collect such rebounds (Trninic et al., 2002; Oliver, 2004).
However, a common view prevails among coaches that offensive rebounding is
vital for success (Goldstein, 1995; Trninic et al., 2002; Wissel, 2004; Krause et al.,
2008). To test the grounds for this viewpoint, more in-depth data analysis can be
undertaken.
Specifically, offensive rebounding percentages (Oliver, 2004;
Kutbatko et al., 2007) for winning and losing teams can be determined and
appropriately analysed.
In this instance, they are cast as a percentage of
available rebounds to overcome the fact that offensive rebounds are correlated
highly with missed shots.
By doing this, more comprehensive statistics are
34
attained that more closely reflect rebounding ability (Oliver, 2004; Kubatko et al.,
2007).
Similar ideas have been developed by Oliver (2004), helping to provide more
detailed and meaningful statistics for coaches, players and fans of basketball to
dissect and utilise.
Further examples include effective field goal percentage
(incorporating the value of the 3-point field goal into a single shooting statistic),
turnovers per possession and free throw rate (Oliver, 2004; Kubatko et al., 2007).
All of these statistical examples can be created by adjusting those variables
commonly found within a basketball box-score. Future research could effectively
create a more in-depth box-score, and apply similar testing procedures to discover
which of these more telling adapted statistics hold more significance when
determining successfulness in basketball.
35
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APPENDICES
Appendix A
Field goal
A field goal is an attempt by a player to shoot the ball into the basket in open play.
A field goal can be worth either 2 or 3 points, depending on where the player is
standing when they begin the attempt. In the National Basketball Association, the
3 point arc is 23 feet and 9 inches away from the basket. Thus, a 3 point field goal
is a shot taken from outside of this arc.
Free throw
This is an unopposed attempt awarded to a player by the official to score one
point from behind the free throw line, following a foul by the opposing team. This
free throw line is parallel to the face of the backboard and 15 feet away.
Rebound
A rebound is the act of successfully gaining possession of the ball after a missed
field goal or free throw.
Rebounds are divided into two categories, offensive,
whereby the ball is recovered by the offensive team and thus possession does not
change; and defensive, whereby the defending team gains possession following a
miss from the opposition. By law, every missed free throw or field goal must be
rebounded.
Assist
An assist is attributed to the player who passes the ball to a teammate in a way
that leads to a successful field goal. Importantly, only the pass directly before the
score can be credited as an assist.
Block
A block is made when a defender legally pushes the ball away from the basket
with their hand following an opponent’s attempted shot, thus preventing the field
goal.
Steal
A steal occurs when a defensive player legally gains possession of the ball from
their opponent. This can be done by catching an opponent’s pass or dribble, or
deflecting it and either controlling it or diverting it to a teammate.
Turnover
This occurs when a member of one team gives possession to the opposing team
by losing the ball. This may be done in many ways including stepping or throwing
the ball out of bounds, committing an offensive foul such as travelling and getting
the ball stolen.
Foul
Foul’s can be separated into various forms.
A personal foul involves illegal
physical contact and is the most common type of foul. A technical foul is a penalty
for unsportsmanlike conduct. An offensive foul concerns illegal contact committed
by the offensive player. A loose ball foul can occur when neither team has control
of the ball, yet it is in play.
Appendix B
San Antonio and Boston
FG Made
FG Attempted
3pt Made
3pt Attempted
FT Made
FT Attempted
Off Rebounds
Def Rebounds
Assists
Steals
Blocks
Fouls
Turnovers
Analyst 1
SA
39
85
9
22
16
17
14
26
20
8
2
14
15
BOS
46
75
5
9
8
15
8
32
33
10
4
18
17
NBA.com
SA
39
86
9
22
16
17
15
22
20
8
2
14
15
BOS
46
75
5
9
8
15
5
31
34
10
4
17
18
Mod[V1-V2]
0
1
0
0
0
0
4
5
1
0
0
1
1
13
[V1+V2]/2
Hou
34
82
5
18
22
28
10
29
24
5
5
19
10
NBA.com
Orl
45
87
8
20
12
12
9
39
25
3
5
23
16
Hou
34
82
5
18
22
28
9
26
24
6
5
19
10
Mod[V1-V2]
0
2
0
1
0
0
5
6
1
1
0
0
1
17
[V1+V2]/2
85
160.5
14
31
24
32
21
55.5
53.5
18
6
31.5
32.5
564.5
% Error
0.00%
0.62%
0.00%
0.00%
0.00%
0.00%
19.05%
9.01%
1.87%
0.00%
0.00%
3.17%
3.08%
2.30%
Orlando and Houston
FG Made
FG Attempted
3pt Made
3pt Attempted
FT Made
FT Attempted
Off Rebounds
Def Rebounds
Assists
Steals
Blocks
Fouls
Turnovers
Analyst 1
Orl
45
89
8
21
12
12
13
42
24
3
5
23
15
79
170
13
38.5
34
40
20.5
68
48.5
8.5
10
42
25.5
597.5
% Error
0.00%
1.18%
0.00%
2.60%
0.00%
0.00%
24.39%
8.82%
2.06%
11.76%
0.00%
0.00%
3.92%
2.85%
Atlanta and LA
FG Made
FG Attempted
3pt Made
3pt Attempted
FT Made
FT Attempted
Off Rebounds
Def Rebounds
Assists
Steals
Blocks
Fouls
Turnovers
Analyst 1
Atl
32
70
8
18
35
40
10
28
17
6
4
25
14
LA
37
80
4
18
20
29
21
32
19
3
9
27
18
NBA.com
Atl
32
70
8
18
35
40
7
26
17
6
4
24
14
LA
37
80
4
18
20
29
11
31
20
3
9
25
19
Mod[V1-V2]
0
0
0
0
0
0
13
3
1
0
0
3
1
21
[V1+V2]/2
69
150
12
36
55
69
24.5
58.5
36.5
9
13
50.5
32.5
615.5
% Error
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
53.06%
5.13%
2.74%
0.00%
0.00%
5.94%
3.08%
3.41%
Appendix C
Test Statistics for ALL GAMESa
Points
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Offensive rebounds Defensive rebounds Total rebounds
Assists
Blocks
Fouls
Steals
Turnovers
341690.500
720881.000
420056.000
511482.500 446176.500 616923.000 603611.500 642788.000 680611.500
1098755.500
1477946.000
1177121.000
1268547.500 1203241.500 1373988.000 1360676.500 1399853.000 1437676.500
-23.555
-2.026
-19.132
-13.923
-17.648
-7.980
-8.701
-6.492
-4.319
.000
.043
.000
.000
.000
.000
.000
.000
.000
Free-throw
Free-throws
Free-throws
Field goal
Field goals
Field goals
3-point
3-point shots
3-point shots
percentage
made
attempted
percentage
made
attempted
percentage
made
attempted
675863.500
575680.000
592257.000
357527.500
444262.500
675437.500
495165.500
583995.000
743008.000
1432928.500
1332745.000
1349322.000
1114592.500
1201327.500
1432502.500
1252230.500
1341060.000
1500073.000
-4.578
-10.276
-9.330
-22.681
-17.757
-4.604
-14.842
-9.842
-.764
.000
.000
.000
.000
.000
.000
.000
.000
.445
a. Grouping Variable: Match Outcome
Appendix D
Test Statistics for BALANCED GAMESa
Points
Offensive rebounds Defensive rebounds Total rebounds
Assists
Blocks
Fouls
Steals
Turnovers
Mann-Whitney U
164662.000
240919.500
177618.000
198332.500 207033.000 225090.500 186081.500 225607.000 236843.000
Wilcoxon W
412118.000
488375.500
425074.000
445788.500 454489.000 472546.500 433537.500 473063.000 484299.000
-10.836
-.815
-9.147
-6.415
-5.277
-2.913
-8.037
-2.841
-1.353
.000
.415
.000
.000
.000
.004
.000
.004
.176
Z
Asymp. Sig. (2-tailed)
Free-throw
Free-throws
Free-throws
Field goal
Field goals
Field goals
3-point
3-point shots
3-point shots
percentage
made
attempted
percentage
made
attempted
percentage
made
attempted
Mann-Whitney U
231172.500
179729.000
179217.500
169656.000
206097.500
208265.500
192850.000
233460.000
214664.000
Wilcoxon W
478628.500
427185.000
426673.500
417112.000
453553.500
455721.500
440306.000
480916.000
462120.000
-2.094
-8.862
-8.926
-10.194
-5.401
-5.107
-7.132
-1.803
-4.268
.036
.000
.000
.000
.000
.000
.000
.071
.000
Z
Asymp. Sig. (2-tailed)
a.
Grouping Variable: Match Outcome
Appendix E
Test Statistics for UNBALANCED GAMESa
Points
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Offensive rebounds Defensive rebounds Total rebounds
71229.500
Assists
Blocks
Fouls
Steals
Turnovers
29506.500
128176.000
49098.000
41786.000 95690.000 119167.000 106408.000 114270.000
168634.500
267304.000
188226.000
-22.137
-2.171
-18.195
-13.702
-19.675
-8.804
-3.998
-6.609
-4.993
.000
.030
.000
.000
.000
.000
.000
.000
.000
210357.500 180914.000 234818.000 258295.000 245536.000 253398.000
Free-throw
Free-throws
Free-throws
Field goal
Field goals
Field goals
3-point
3-point shots
3-point shots
percentage
made
attempted
percentage
made
attempted
percentage
made
attempted
Mann-Whitney U
116371.500
112060.500
119447.000
31150.500
42390.000
133297.500
69640.500
76685.000
120489.500
Wilcoxon W
255499.500
251188.500
258575.000
170278.500
181518.000
272425.500
208768.500
215813.000
259617.500
-4.555
-5.432
-3.933
-21.823
-19.552
-1.128
-14.016
-12.644
-3.724
.000
.000
.000
.000
.000
.259
.000
.000
.000
Z
Asymp. Sig. (2-tailed)
a. Grouping Variable: Match Outcome