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RESEARCH ARTICLE
Exploring Game Performance in the National
Basketball Association Using Player Tracking
Data
Jaime Sampaio
1☯
*, Tim McGarry
2☯
, Julio Calleja-González
3‡
, Sergio Jiménez Sáiz
4‡
,
Xavi Schelling i del Alcázar
5‡
, Mindaugas Balciunas
6‡
1Research Center in Sports Sciences, Health and Human Development, CIDESD, CreativeLab Research
Community, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal, 2Faculty of Kinesiology,
University of New Brunswick, Fredericton, Canada, 3Faculty of Physical Activity Sport Sciences, University
of the Basque Country, Vitoria, Spain, 4Facultad de Ciencias de la Actividad Física y el Deporte,
Universidad Europea de Madrid, Madrid, Spain, 5Complex Systems and Sport Research Group, National
Institute of Physical Education of Catalonia (INEFC), Barcelona, Spain, 6Lithuanian Sports University,
Kaunas, Lithuania
☯These authors contributed equally to this work.
‡These authors also contributed equally to this work.
*ajaime@utad.pt
Abstract
Recent player tracking technology provides new information about basketball game perfor-
mance. The aim of this study was to (i) compare the game performances of all-star and non
all-star basketball players from the National Basketball Association (NBA), and (ii) describe
the different basketball game performance profiles based on the different game roles. Archi-
val data were obtained from all 2013-2014 regular season games (n = 1230). The variables
analyzed included the points per game, minutes played and the game actions recorded by
the player tracking system. To accomplish the first aim, the performance per minute of play
was analyzed using a descriptive discriminant analysis to identify which variables best
predict the all-star and non all-star playing categories. The all-star players showed slower
velocities in defense and performed better in elbow touches, defensive rebounds, close
touches, close points and pull-up points, possibly due to optimized attention processes that
are key for perceiving the required appropriate environmental information. The second aim
was addressed using a k-means cluster analysis, with the aim of creating maximal different
performance profile groupings. Afterwards, a descriptive discriminant analysis identified
which variables best predict the different playing clusters. The results identified different
playing profile of performers, particularly related to the game roles of scoring, passing,
defensive and all-round game behavior. Coaching staffs may apply this information to differ-
ent players, while accounting for individual differences and functional variability, to optimize
practice planning and, consequently, the game performances of individuals and teams.
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 1/14
OPEN ACCESS
Citation: Sampaio J, McGarry T, Calleja-González J,
Jiménez Sáiz S, Schelling i del Alcázar X, Balciunas
M (2015) Exploring Game Performance in the
National Basketball Association Using Player
Tracking Data. PLoS ONE 10(7): e0132894.
doi:10.1371/journal.pone.0132894
Editor: José César Perales, Universidad de
Granada, SPAIN
Received: February 14, 2015
Accepted: June 22, 2015
Published: July 14, 2015
Copyright: © 2015 Sampaio et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: Data are publicly
available from the NBA website (http://stats.nba.com).
Funding: This study was supported by the
Portuguese foundation for science and technology
(PEst-OE/SAU/UI4045/2015). The funders had no
role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
Introduction
The National Basketball Association (NBA) from the United States of America is the most
competitive basketball league in the world, with a competition period in regular season com-
prising 82 games spanning approximately 24 weeks. The coaching staff must prepare and over-
see the training loads on players throughout the entirety of the competition period, a complex
process that places a great amount of physiological stress on the athletes [1]. This process also
requires managing the significant differences in work demands introduced by position-specific
game behaviors and player status (e.g., starting vs non-starting players), as well as adjusting
throughout the season to several changing unpredictable constraints such as player injuries.
Thus, the ongoing planning and monitoring of practice sessions and game performance is criti-
cal for optimizing the decisions on individual training loads taken by coaching staff.
While each player responds individually to the stress of practice and competition [2], there
remains a clear need to use updated sports performance models to inform starting points for
player preparation. One of the most common methods of monitoring sports performance is
using game-related statistics to evaluate technical and tactical behavior, as well as the efficiency
of players and teams throughout the season. Research reporting these variables frequently uses
data from European league games but not from the NBA. This study using NBA data serves
somewhat to address this imbalance. Performance variables represent duality of the performer
and the environment in order to understand how players engage with others by detecting affor-
dances [3]. For example, the assists are likely a result of affordances to the ball carrier created
by open teammates. In fact, perception-action coupling indicates that information drives
movement and movement drives information available for players to pick up [4]. In this sense,
game-related statistics can provide insight on both perception and action of the players. In
addition, they may provide a measure of co-adaptation, in the way that players function as part
of a larger system (the team) co-adapting to small but important changes in each others struc-
ture and function [5].
Basketball performance depends primarily on shooting 2-point field-goals and on securing
defensive rebounds [6–8]. In close contested games, however, fouls and free-throws exhibit
increased importance for determining game outcome than for lesser contested games [8,9].
Other remaining game statistics such as offensive rebounds, turnovers, steals and assists are
not reported consistently as discriminating performance variables for winning and losing.
When contrasting the best and worst teams, the best performance variables for long term suc-
cess are related to assists, steals and blocks, denoting the importance of passing skills and of
defensive skills along outside and inside court positions [10]. Research from NBA data likewise
reported winning game outcomes to be related to better offensive efficiency, specifically points
scored in the third quarter, as well as the defensive variables of fouls and steals [11]. Thus, as
expected, the results suggest that both offensive and defensive variables are important for win-
ning games.
These descriptions are informative on a team-level basis, however, a need exists to under-
take player-level analysis in order to better understand what performance variables most dis-
criminate elite players from other players. In the NBA context, this aim can be accomplished
by contrasting game performances from the awarded players that comprise the first, second
and third NBA team (all-stars) with the performance statistics of the other players. The all-star
players from these three teams are selected from a voting conducted by a panel of sportswriters
and broadcasters [12]. The players receive five points for a first team vote, three points for a
second team vote, and one point for a third team vote. At the end, and accounting for playing
positions, the five players with the highest point totals make the first team, the next five make
the second team, and the remaining five the third team.
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 2/14
One of the most recent advances in assessing basketball performance is player-tracking
technology [13,14]. This technology uses computer vision systems designed with algorithms
capable of measuring the positions of players with a sampling rate around 25 frames per second
[15]. Of course, kinematic variables such as distance, velocity or acceleration may be derived
from these data, and sampling frequencies might improve in future [16]. Currently, the track-
ing technology is being used with data obtained from notational analysis providing combined
information about sports performance; for example, by analyzing the distance covered by
players when the team is attacking and when the same team is defending. Research in basket-
ball using positional-derived variables however is limited at present to small samples of young
basketball players examining physical demands [17], effects of defensive pressure on move-
ment behavior [18], and how tactical performances are affected by activity workload [19].
These new tracking data open up possibilities that advance understanding of game perfor-
mance by embracing a more holistic approach to analyzing sports behavior. For example,
movement patterns (kinematics) from tracking data complement variables from the physiolog-
ical (e.g., work rate), technical (e.g., actions) and tactical (e.g., individual/team behavioural pat-
terns) domains leading to a more complete description and understanding of sports behavior
in its entirety. As noted, an issue to address in this study using the large amounts of tracking
data at hand concerns different basketball game performance profiles for different players and
teams. That is, to categorize individual player performances into like groupings for use as base-
line reference for the future development and preparation of players. The aim of the present
study then is twofold: (i) to compare basketball game performances from the all-star and non
all-star players, and (ii) to identify and describe the different basketball game performance pro-
files based on different game roles in the NBA.
Regarding the first aim, it was hypothesized that all-star players will outperform the non all-
stars in game statistics. Therefore, the player performances on an actions-by-minute of play
basis were compared, in aim of identifying performance variables that discriminate between
the two separate groups of players. It is expected that all-star players should outperform the
non all-star players in their performance statistics, particularly in scoring and passing related
variables, as these important variables are thought to place higher demands on anticipatory
processes [20–22]. In the second aim it was hypothesized that player performance profiles will
present similarities and dissimilarities that can be used to identify different groups of players
based on playing position. This aim is accomplished by using actions-per-game, in order to
identify different groups of player performances, regardless of minutes of play in the games,
thereby identifying those performance variables that discriminate between different player
groupings.
Finally, it is important to describe the data within these performance-based groupings
according to the players (all-star vs. non all-star) and playing positions. For example, some
groups might have strong presence from all-star players and other groups might comprise both
all-star and non all-star players from specific positions. This information can be useful when
used in planning representative tasks in practice sessions, thereby fine-tuning playing behav-
iors in competition by using representative tasks in training [23,24]. In fact, players are often
divided in practice into smaller groups according to specific positions as well as their playing
standard. Non-starting players, for example, lack the same amount of playing time as starting
players, and this competitive playing deficit likely affects their responses to competition
throughout the season [21,25]. It follows that a detailed description of these different perfor-
mance profiles using available objective measures would serve as an appropriate performance
baseline for optimizing practice planning and, ultimately, for improving game performance.
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 3/14
Methods
Sample and variables
Archival data were obtained from open-access official NBA records for 1230 games played dur-
ing the 2013–2014 regular season (available at http://stats.nba.com, these records contained
both non-tracking and tracking data). A total of 30 teams played 82 games between October
29, 2013 and April 16, 2014. The gathered database had records of game performances from
548 players. The cases of player transfer between teams were counted as two different records.
The variables analyzed included the points per game, minutes played and the following
game actions, as defined by the NBA and the company responsible for the player tracking pro-
cess (SportsVU, Northbrook, IL, USA):
•Pull-up shots: any jump shot outside 10 feet where a player took one or more dribbles before
shooting. Gathered variables include pull-up points per game (PPG) or minute (PPM), field-
goal percentage (FG%) and 3-point field-goal percentage (3FG%).
•Catch and shoot: any jump shot outside of 10 feet where a player possessed the ball for two
seconds or less and took no dribbles. Gathered variables include catch and shoot PPG or
PPM, FG% and 3FG%.
•Close shots: any jump shot taken by a player on any touch that starts within 12 feet of the
basket, excluding drives. Gathered variables include close PPG or PPM and FG%.
•Drives: any touch that starts at least 20 feet of the hoop and is dribbled within 10 feet of the
hoop and excludes fast breaks. Gathered variables include drives PPG or PPM and FG%.
•Passing-variables: the total number of passes a player makes and the scoring opportunities
that come from those passes, whether they lead directly to a teammate scoring a basket
(assists) or free throw (free-throw assists), or if they set up an assist for another teammate
(secondary assists). Gathered variables also include total assists opportunities and total points
created by assists.
•Touches-variables: the number of times a player touches and possesses the ball (touches per
game), where those touches occur on the court (front, close or elbow), how long the player
possessed the ball (time of possession), and the number of points per touch or per half-court
touch. Gathered variables also include blocks, steals and the opponent field goals made at the
rim while being defended.
•Speed and distance: variables that measure the distance covered (expressed in miles) and the
average speed of all movements (expressed in miles per hour) by a player while attacking or
defending.
•Rebounds: the number of rebounds secured (rebounds), the times when the player was
within the vicinity (3.5 feet) of a rebound (chances), the number of rebounds a player recov-
ers compared to the number of rebounding chances available (percentage chances) as well as
if the rebound was uncontested by an opponent (uncontested). These variables were gathered
either for defensive and offensive rebounds.
•Free-throw percentage: the number of free-throws made divided by the number of free-
throws attempted.
Video footage from the entire court was unavailable making assessment of the NBA tracking
data impossible. The NBA non-tracking data (e.g., assists, steals or defensive rebounds) how-
ever was assessed for reliability as follows. Two games were selected at random and analyzed
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 4/14
conjointly through systematic observation by two experts. The minimum Cohen’sκvalue for
all variables exceeded 0.91 demonstrating high inter-rater reliability [26] between the NBA
non-tracking data and the two experts.
Data analysis
Variables expressed as counts per game were divided by average minutes played. Records were
screened for univariate outliers (cases outside the range Mean ± 3SD) and distribution tested,
together with advised assumptions for each following inferential analysis [27]. To identify
which variables best predict the player category (i.e., all-star vs. non all-star), the performance
per minute of play was analyzed using a descriptive discriminant analysis. Structure coefficients
greater than |0.30| were interpreted as meaningful contributors for discriminating between the
two groups [27]. Validation of discriminant models was conducted using the leave-one-out
method of cross-validation [28]. Also, a k-means cluster analysis was performed on the entire
sample with the aim of creating and describing maximal different groups of game performance
profiles. The cubic clustering criterion, together with Monte Carlo simulations, was used to
identify the optimal number of clusters, thereby avoiding using subjective criteria. This statisti-
cal technique requires that all cases have no missing values in any of the variables introduced
in the model; there were a total of 339 cases meeting this condition (62%). Afterwards, a
descriptive discriminant analysis was performed to identify which of the variables best predicts
the playing clusters.
One-way independent measures ANOVA was used to compare the variables not selected in
the discriminant models (i.e., points scored per game and minutes played). Tukey post-hoc
homogeneous subsets were used to describe post-hoc results. Statistical significance was set at
0.05 and calculations were performed using JMP statistics software package (release 11.0, SAS
Institute, Cary, NC, USA) and SPSS software (release 22.0, SPSS Inc., Chicago, IL).
Results
Comparing all-star and non all-star players
The means and standard deviations from the variables according to the all-star vs. non all-star
categories are presented in Table 1. The most important variables for differentiating all-star
and non all-star performances per minute of play were identified using discriminant analysis.
The obtained function was statistically significant (p0.001) with a canonical correlation of
0.59 (Λ= 0.65) and reclassification of 97.2%. The structure coefficients (SC) from the function
reflected emphasis on elbow touches (SC = 0.43), defensive rebounds (SC = 0.35), close touches
(SC = 0.34), close points (SC = 0.33), pull-up points (SC = 0.33) and speed in defense (SC =
-0.33) (see Table 1). There were six cases misclassified (60.0% accuracy) in the all-star group
and seven cases misclassified (97.8% accuracy) in the non all-star group, therefore, the obtained
mathematical model shows high accuracy in classifying the players into their original groups.
Figs 1and 2present the distribution from the discriminant scores in each group of players.
The all-star players presented higher mean scores when compared to non all-star players (3.04
±1.45 and -0.13±0.87, respectively).
Describing different game performance profiles
The cubic clustering criterion (CCC) along with Monte Carlo simulations was used to identify
the optimal number of clusters. The largest value (CCC = 252.6) was obtained for a model of
seven clusters. Therefore, a k-means cluster analysis was performed to create and describe
seven maximal different groups of performance profiles per game. The means and standard
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 5/14
Table 1. Means, standard deviations and structure coefficients from all-star and non all-star categories. The variables expressed as counts were
divided by minutes played.
Group/Variable All-star players Non all-star players Structure coefficients
(n = 15) (n = 324)
Pull-up PPM 0.13±0.09 0.07±0.05 0.33
Pull-up FG% 0.36±0.08 0.34±0.09 0.08
Pull-up 3FG% 0.27±0.13 0.28±0.18 -0.01
Catch and shoot PPM 0.10±0.05 0.12±0.06 -0.09
Catch and shoot FG% 0.43±0.03 0.37±0.07 0.23
Catch and shoot 3FG% 0.36±0.14 0.35±0.11 0.04
Drives PPM 0.09±0.07 0.06±0.05 0.19
Drives FG% 0.50±0.06 0.42±0.15 0.15
Close PPM 0.07±0.07 0.03±0.04 0.33
Close FG% 0.56±0.09 0.55±0.21 0.02
Passes 1.44±0.34 1.22±0.39 0.17
Assists 0.15±0.07 0.09±0.06 0.28
Free-throw assists 0.01±0.01 0.01±0.01 0.14
Secondary assists 0.03±0.02 0.02±0.01 0.15
Assist opportunities 0.28±0.12 0.19±0.11 0.26
Points created assists 0.34±0.15 0.22±0.13 0.29
Blocks 0.02±0.02 0.01±0.01 0.10
Steals 0.03±0.01 0.03±0.01 0.09
Opponents’FGM Rim 0.01±0,00 0.03±0.02 -0.22
Touches 2.10±0.32 1.70±0.43 0.29
Front court touches 1.58±0.41 1.28±0.45 0.20
Close touches 0.09±0.08 0.05±0.04 0.34
Elbow touches 0.13±0.09 0.06±0.05 0.43
Points per touch 0.01±0,00 0.01±0.01 -0.18
Points per half court touch 0.01±0,00 0.02±0.01 -0.17
Time of possession 0.12±0.06 0.08±0.06 0.18
Distance in offense 0.04±0,00 0.04±0,00 -0.18
Distance in defense 0.03±0,00 0.03±0,00 -0.26
Speed in offense (mph) 4.38±0.36 4.50±0.28 -0.13
Speed in defense (mph) 3.65±0.16 3.86±0.20 -0.33
Offensive rebounds 0.04±0.03 0.03±0.02 0.13
OR chances 0.08±0.05 0.06±0.04 0.08
% OR per chance 0.54±0.10 0.51±0.11 0.07
OR uncontested % 0.52±0.17 0.46±0.19 0.10
Defensive rebounds 0.16±0.07 0.11±0.04 0.35
DR Chances 0.24±0.10 0.19±0.07 0.25
% DR per chance 0.66±0.06 0.61±0.06 0.27
DR uncontested % 0.19±0.07 0.18±0.07 0.05
Free-throw % 0.79±0.09 0.71±0.17 0.11
Points per game
a
22.20±4.70 9.20±5.10 *
Minutes played
a
35.90±2.20 22.50±8.80 *
Legend: a—variables not entering the discriminant analysis model
*p0.05.
doi:10.1371/journal.pone.0132894.t001
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 6/14
deviations from the variables according to the cluster solutions are presented in Table 2. The
discriminant analysis revealed four statistically significant functions (p.001), however, the
first two yielded a total of 94.7% from the total variance, with canonical correlations of 0.98
and 0.88, respectively. The reclassification of the cases in the original groups was very high
(96.2%).
The structure coefficients from the functions are presented in Table 2. The first function
had stronger emphasis on total distance covered in offense (SC = 0.83) and defense (SC =
0.80), whereas the second function was emphasized by performance obtained in passing-
related variables (see Table 2).
Table 3 presents the differences between clusters in points scored per game, minutes played
and distance from each case (player) to cluster centroid. The clusters 2 and 4 had more playing
minutes and points per game. The clusters 1 and 5 were the most homogeneous, as identified
in smaller distances to group centroid. In addition, player distributions in the seven clusters
Fig 1. Distribution from the discriminant scores across all-star players.
doi:10.1371/journal.pone.0132894.g001
Fig 2. Distribution from the discriminant scores across non all-star players.
doi:10.1371/journal.pone.0132894.g002
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 7/14
Table 2. Means, standard deviations and structure coefficients from the obtained model of clusters. The variables expressed as counts are averages
per game.
Cluster/Variable Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Function 1
(78.3%)
Function 2
(16.1%)
Pull-up PPG 0.75±0.68 3.44±2.10 1.53±1.10 5.12±2.05 1.04±0.87 3.17±1.82 1.96±1.46 0.20 0.34
Pull-up FG% 0.31±0.13 0.36±0.05 0.34±0.06 0.38±0.04 0.33±0.08 0.35±0.07 0.36±0.07 0.06 0.05
Pull-up 3FG% 0.29±0.27 0.27±0.10 0.28±0.13 0.32±0.06 0.25±0.17 0.31±0.11 0.25±0.14 0.00 0.06
Catch and shoot PPG 1.60±1.14 4.18±1.79 4.32±2.07 2.99±1.15 2.49±1.61 2.60±1.42 3.12±1.43 0.13 -0.12
Catch and shoot FG% 0.35±0.10 0.41±0.04 0.40±0.05 0.39±0.05 0.37±0.06 0.38±0.07 0.39±0.05 0.07 -0.03
Catch and shoot 3FG% 0.35±0.14 0.36±0.09 0.36±0.10 0.39±0.06 0.33±0.11 0.37±0.09 0.33±0.11 0.02 0.04
Drives PPG 0.68±0.64 2.85±1.87 1.37±0.95 4.11±1.43 0.96±0.77 3.06±1.64 1.93±1.53 0.18 0.31
Drives FG% 0.37±0.23 0.49±0.07 0.43±0.07 0.46±0.05 0.44±0.13 0.44±0.06 0.45±0.10 0.06 0.03
Close PPG 0.33±0.18 1.61±1.49 1.60±0.63 0.41±0.28 0.77±0.31 0.73±0.14 1.18±0.19 0.08 -0.15
Close FG% 0.54±0.30 0.57±0.13 0.58±0.09 0.54±0.11 0.56±0.17 0.48±0.28 0.55±0.11 0.01 -0.08
Passes 13.79
±5.71
46.93
±8.24
29.03
±6.32
61.23
±6.42
22.22
±6.63
47.09
±11.29
31.98
±7.75
0.43 0.59
Assists 0.88±0.65 4.09±1.10 1.87±0.69 6.70±1.52 1.47±0.83 5.07±2.02 2.48±1.09 0.31 0.65
Free-throw assists 0.10±0.05 0.43±0.15 0.22±0.12 0.73±0.24 0.18±0.13 0.53±0.20 0.29±0.15 0.24 0.46
Secondary assists 0.25±0.16 0.90±0.25 0.53±0.18 1.51±0.37 0.38±0.21 1.05±0.39 0.59±0.24 0.30 0.54
Assist opportunities 1.81±1.16 8.07±2.03 3.72±1.32 13.29
±2.75
2.96±1.61 10.03
±3.77
4.92±2.01 0.33 0.69
Points created assists 2.10±1.50 9.63±2.59 4.42±1.63 15.69
±3.47
3.49±1.91 11.79
±4.63
5.85±2.56 0.32 0.65
Blocks 0.15±0.04 0.49±0.32 0.58±0.53 0.22±0.15 0.31±0.28 0.29±0.10 0.39±0.34 0.10 -0.14
Steals 0.36±0.22 1.18±0.41 0.95±0.36 1.33±0.50 0.63±0.26 0.96±0.43 0.90±0.31 0.24 0.12
Opponents’FGM Rim 0.55±0.13 0.52±0.04 0.54±0.04 0.57±0.05 0.53±0.06 0.58±0.09 0.55±0.05 0.00 0.09
Touches per game 19.43
±7.56
66.93
±7.94
42.86
±8.56
82.36
±7.49
30.78
±8.35
64.06
±13.17
45.28
±9.69
0.50 0.60
Front court touches 14.44
±6.52
50.49
±6.84
31.01
±6.89
71.00
±7.64
22.14
±7.67
52.78
±12.59
33.81
±9.35
0.44 0.66
Close touches 0.49±0.12 2.09±1.79 1.96±0.98 0.73±0.35 1.00±0.60 1.08±0.20 1.58±0.54 0.09 -0.12
Elbow touches 0.60±0.19 3.68±2.94 1.82±1.32 1.44±0.71 1.20±1.03 1.74±1.58 1.93±0.13 0.12 -0.04
Points per touch 0.23±0.07 0.27±0.09 0.30±0.06 0.20±0.04 0.24±0.08 0.20±0.07 0.26±0.08 0.06 -0.19
Points per half court touch 0.32±0.11 0.35±0.12 0.42±0.08 0.23±0.05 0.35±0.13 0.25±0.10 0.35±0.11 0.02 -0.24
Time of possession (min
per game)
0.84±0.62 3.12±1.30 1.35±0.51 6.53±0.72 1.25±0.94 4.38±1.67 1.97±1.07 0.28 0.72
Distance in offense
(season total)
14.30
±7.84
97.43
±10.97
87.83
±8.78
94.15
±12.41
42.55
±9.53
33.92
±13.92
67.90
±7.20
0.83 -0.29
Distance in defense
(season total)
12.27
±6.68
81.82
±8.82
77.44
±7.62
75.89
±10.40
37.31
±8.83
28.10
±11.45
59.05
±7.02
0.80 -0.38
Speed in offense (mph) 4.52±0.27 4.39±0.31 4.38±0.31 4.62±0.25 4.49±0.27 4.58±0.25 4.50±0.30 -0.02 0.11
Speed in defense (mph) 3.92±0.21 3.71±0.18 3.85±0.19 3.72±0.15 3.90±0.19 3.74±0.17 3.85±0.20 -0.08 -0.11
Offensive rebounds 0.40±0.31 1.17±0.84 1.15±0.81 0.61±0.28 0.75±0.58 0.70±0.57 0.99±0.73 0.10 -0.12
OR chances 0.79±0.57 2.17±1.51 2.17±1.46 1.38±0.64 1.45±1.14 1.35±0.99 1.90±1.33 0.10 -0.11
% OR per chance 0.52±0.15 0.54±0.07 0.53±0.07 0.45±0.08 0.52±0.09 0.51±0.08 0.51±0.07 -0.01 -0.08
OR uncontested % 0.46±0.25 0.50±0.15 0.52±0.14 0.35±0.12 0.48±0.16 0.38±0.16 0.48±0.14 0.00 -0.13
Defensive rebounds 1.40±0.75 4.87±1.94 3.83±1.65 2.85±0.75 2.25±0.96 3.19±1.48 3.23±1.57 0.20 -0.08
DR Chances 2.30±1.18 7.45±2.68 6.26±2.40 4.65±1.34 3.80±1.52 4.93±2.24 5.23±2.33 0.20 -0.09
% DR per chance 0.61±0.08 0.65±0.06 0.61±0.06 0.62±0.05 0.59±0.05 0.65±0.06 0.61±0.06 0.03 0.07
DR uncontested % 0.17±0.09 0.19±0.06 0.20±0.06 0.13±0.04 0.20±0.07 0.15±0.05 0.19±0.06 0.01 -0.15
Free throw % 0.75±0.14 0.80±0.13 0.80±0.08 0.80±0.09 0.77±0.09 0.73±0.11 0.71±0.22 0.19 0.10
doi:10.1371/journal.pone.0132894.t002
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 8/14
were contrasted against player category, presence in the NBA first team, and specific court
position of players. The all-star players were grouped in clusters 2, 3 and 4. The NBA first team
was grouped in clusters 2 and 4.
Fig 3 presents the territorial map from the cases and created clusters within the space from
the first and second discriminant functions. Players from clusters 4 and 2 exhibited better over-
all performances, however, players from cluster 6 also performed well in variables related to
function 2.
Discussion
This study aimed to compare game performances of all-star and non all-star basketball players
and to identify and describe different basketball game performance profiles in the NBA. In gen-
eral terms, key performance indicators were identified that discriminate all-star players from
non all-stars and, also, the different groupings of performance profiles in competition.
Comparing all-star and non all-star players
As expected, all-star players outperformed non all-star players in performance statistics, partic-
ularly in defensive rebounds, close touches and close points, pull-up points and assists. (Note.
These results may be confounded in that the distinction between all-star and non all-star play-
ers is determined by sportswriters and broadcasters. This said, discrimination between these
prejudged player groups is reflected in some game performance variables as reported in this
study.)
Noted previously, the variables obtained from the tracking systems allow use of court loca-
tions for better understanding several game statistics. Therefore, these results increase knowl-
edge of basketball game behavior by identifying key performance variables and by reducing
prior emphasis on the importance of distance covered and velocity. The reclassification
obtained was very high and hence affirms accuracy of the mathematical model.
The close touches and points were identified as key variables, suggesting that all-star players
performed consistently better than non all-star players within 12 feet of the basket. These court
locations are highly concentrated with teammates and opponents with frequent physical
Table 3. Player distributions in the seven clusters contrasted against player category, NBA first team and player specific court position.
Cluster Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 TOTAL
Points per game 4.4±2.3 17.8±6.3 12.8±3.4 16.6±3.7 7.2±2.9 12.9±4.3 11.5±4.1 hs (1)(5)(3,6,7) (2,4)
Minutes played 12.6±5.0 34.5±3.2 30.9±3.4 33.8±2.2 19.7±4.2 28.8±5.2 26.9±4.3 hs (1)(5)(6,7)(6,3)(2,4)
Distance cases to centroid 14.5±5.1 20.2±6.8 20.8±6.8 19.3±8.4 16.1±5.3 22.3±9.6 17.9±7.0 hs (1,5)(2,4,5,7)
(2,3,4,6,7)
Non all-star players 93
(28.7%)
21 (6.5%) 37
(11.4%)
18 (5.6%) 74
(22.8%)
24 (7.4%) 57
(17.6%)
324 (100%)
All-star players 0 (0.0%) 8 (53.3%) 2 (13.3%) 5 (33.3%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 15 (100%)
NBA first team 0 (0%) 4 (80%) 0 (0%) 1 (20%) 0 (0%) 0 (0%) 0 (0%) 5 (100%)
Guards (1–2) 33
(19.1%)
15 (8.6%) 15 (8.6%) 23
(13.3%)
35
(20.2%)
18
(10.4%)
34
(19.6%)
173 (100%)
Forwards (3–4) 28
(24.4%)
13
(11.3%)
23
(20.0%)
0 (0.0%) 31
(27.0%)
1 (0.1%) 16
(15.7%)
115 (100%)
Centers (5) 3 (23.1%) 1 (7.7%) 1 (7.7%) 0 (0.0%) 3 (23.1%) 0 (0.0%) 5 (38.5%) 13 (100%)
Unclear position or missing
values
29
(67.4%)
0 (0.0%) 0 (0.0%) 0 (0.0%) 9 (20.9%) 5 (11.6%) 0 (0.0%) 43 (100%)
Legend: hs—post-hoc homogeneous subsets Tukey test.
doi:10.1371/journal.pone.0132894.t003
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 9/14
contact between players. These complex actions require high anticipatory skills [29] and all-
star players outperform non all-stars in producing these complex skills under extreme adverse
conditions [20–22]. Also, related with these findings, all-star players demonstrated the ability
to score pull-up points, again showing how well these players perceive environmental informa-
tion and adapt their behavior accordingly [30,31], as they strive to reach a better position from
which to score (oftentimes using one or more dribble actions before shooting, for example).
Several studies from basketball [32], football [33] and futsal [34] analyzing space-time dynam-
ics of player dyads inform how the formation of playing patterns are influenced by scoring
targets (i.e., baskets and goals). This higher ability to perceive the environment requires a devel-
oped attention span [35,36], perhaps evidenced in the higher number of assists given that
assists constitute passes to a teammate leading directly to a subsequent field goal.
The distance covered and average speeds were not discriminant variables between the all-
star and non all-star players. Until the availability of recent technology, getting reliable time
motion data in basketball games has been difficult to acquire and, as such, low accuracy in the
measures reported and/or small sample sizes have been a concern since early times [37]. The
present results however provide measures of distance and velocity from an entire NBA season
that are considered reliable [13,14], despite the 25 Hz sampling frequency limitation [16].
Although discriminant analysis only emphasized velocity in defense, there seems to be a ten-
dency for all-star players to cover slightly shorter distances at lower average velocities. This
Fig 3. Territorial map from the cases and created clusters.
doi:10.1371/journal.pone.0132894.g003
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 10 / 14
might be important in that it is consistent with previous observations on the enhanced attune-
ment of players to perceive affordances [38,39]. Thus, all-star players may well make less mis-
takes when deciding when and where to run in both offense and defense, possibly taking
shorter paths to reach their destinations. These fewer mistakes in a game might well result in
lower distances covered by these players. In addition, these considerations might also suggest
that all-star players are more efficient, having less energy demands placed on them during a
game. In fact, research suggests that motor efficiency achieved through intensive training, leads
to improved perception, focus, anticipation, planning and fast responses [40]. The finding of
lower defensive velocities for all-star players may reinforce this observation, but might also sug-
gest that these players might be focusing their efforts more on offensive performances, as they
are more complex and depend more upon their high level expertise [22,41].
Describing different game performance profiles
The results reported different performance profiles for different player groupings. There were
seven different groups identified by the analysis, obtaining very high reclassifications of the
cases (96.2%). These groupings, based on total distance covered in the season and performance
per game, might be used in developing specific playing profiles that, taking into consideration
the influence of individual differences and functional variability, may serve as baseline to facili-
tate optimizing practice planning and game performance.
The clusters 2, 3 and 4 performed best at discriminant variables from function 1 (78.3% of
total variance) and they contained all of the all-star players. These players participated in more
than 30 minutes per game and scored many points per game (from 12.8±3.4 to 17.8±6.3). As
an effect of these higher playing times, the most discriminant variables of this function were
the distances covered either in defense or offense. Other discriminant variables included partic-
ipation in offense (touches and front court touches) and passing-related variables (passes,
assists, secondary assists, assists opportunities and points created by assists). There are also
unique traits from each cluster that could be used to optimize the training process. For exam-
ple, due to their high playing times in game competition, players from cluster 2 are likely high
conditioned players, however, they should also give the most concern for coaches when plan-
ning recovery time between games [42]. Conversely, players from cluster 4 comprised all
guards or shooting guards with extremely high values from time of possession, touches per
game or passing-related variables. This is key information for coaches to optimize representa-
tive task designs that enable players to perceive adequate environmental information and to
subsequently act accordingly [25,30,43]. Finally, players from cluster 3 demonstrated less pos-
session time and touches, despite the higher minutes of play, which suggests a predominant
defensive role for these players. The defensive tasks are particularly related to player fitness var-
iables as high-level defensive performances seem to require higher energy demands [44] and
these kind of tasks are therefore particularly related to player fitness variables.
In addition, the worst performance variables in function 1 belong to players from cluster 1,
as they exhibited lower playing times (12.6±5.0) distributed equally on playing position. In
fact, the most unclear player positions (and missing values), in reference to players that can
play in several different positions, were grouped in this cluster. Therefore, these results might
be suggesting a profile of an all-round player that can be used in a game to serve multiple pur-
poses, or a profile of a very specialized player (e.g. in shooting or rebounding). Together with
workload compensation of reduced playing time, coaching staffs can modify the tasks to opti-
mize the performance produced by these all-round players or specialists.
When adding the results from the second discriminant analysis function, clusters 4 and 6
emerge as active performers in the analyzed variables, such as time in possession, touches,
Game Performance in the National Basketball Association
PLOS ONE | DOI:10.1371/journal.pone.0132894 July 14, 2015 11 / 14
passing, pull-up points and drives per game. These results confirm the guards profile (in cluster
4) identified previously, and also for players in cluster 6. In fact, there is an important require-
ment to adjust the tasks required of these players in order to fine-tune the environmental infor-
mation necessary for information pick-up in game play [30,45]. From the same perspective,
players from cluster 3, identified as defensive-related, demonstrate less activity in these vari-
ables, consistent with their roles in the game.
Conclusions
In summary, this study provided analysis of an NBA regular season using player-tracking vari-
ables and notation data. It was found that all-star players performed consistently better than
non all-star players within 12 feet of the basket, possibly a result of optimized attention pro-
cesses that are key for perceiving the required appropriate environmental information for
action production. In addition, different groupings were identified based on playing perfor-
mance, particularly in relation to the roles of scoring, passing, defensive and all-round duties.
These findings can be used to optimize preparation for individual player groupings and, ulti-
mately, improve game performances of the players and teams.
Acknowledgments
This study was part of a project registered at the Portuguese Foundation for Science (FCT,
PEst-OE/SAU/UI4045/2015).
Author Contributions
Conceived and designed the experiments: JS TM. Performed the experiments: JS TM JC SJ XS
MB. Analyzed the data: JS TM. Contributed reagents/materials/analysis tools: JS TM. Wrote
the paper: JS TM JC SJ XS MB.
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