Measuring NBA Players' Mood by Mining Athlete-Generated Content
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2015 48th Hawaii International Conference on System Sciences
Measuring NBA Players’ Mood by Mining Athlete-Generated Content
Chenyan Xu Yang Yu
The Richard Stockton College of New Jersey Rochester Institute of Technology
chenyan.xu@stockton.edu yyu@saunders.rit.edu
Abstract were using Twitter in the 2012-13 NBA season
Online athlete-generated content in social media which represented an approximate of 78.4% of the
has high potential to become the information source total; they posted 91,659 tweets throughout the
for both team managers and coaches to discern season. With the unique features of Twitter and its
players’ mood status and shaky performance before large user base, NBA players capitalize on this
games. In the existing literature, either in psychology emerging technology to communicate with fans,
or sport analytics, there is a stream of research that journalists, peers, friends and other followers. They
investigated the relationship between athletes’ mood actively voice their opinions, express thoughts and
and the individual sport performance; however, few show feelings through short, direct messages of 140
of them discussed the causality from the social media characters or less on Twitter.
perspective. In this study, we look deep into the The following examples unfold several Twitter
Athlete-generated content (AGC) and aim to provide landmarks in the NBA, which we believe could help
a more comprehensive framework to sport operators readers gain a general idea of how NBA players use
that incorporates players’ social media content into Twitter. In 2011, when the second longest NBA
their administrative decision-making process. We lockout in history came to an end, a lot of players
obtained a unique and extensive dataset of AGC for tweeted their excitement that the new season is
active NBA players (in the 2012-13 season) from finally coming. In 2013, NBA center Jason Collins
Twitter and apply sentiment analysis technique to announced that he is a gay, making him the first
measure the general mood polarity of a player. The openly gay player in the league. He then received
general mood was then incorporated into tremendous support from other players on Twitter. In
econometrics models to examine its effect on players’ April 2014, TMZ released an audio clip that records
individual game performance. The results suggest Los Angeles Clippers owner Donald Sterling’s racist
that the mood of NBA player has significant effect on comment on minorities. Shortly after that, he was
driving sport performance. This paper explores the severely criticized by NBA players on Twitter who
possibility of using social media data to measure expressed their shocking, disappointment and anger
athletes’ mood and predicting the sport performance. towards Sterling and called for an immediate
investigation and action on him. It is worth noting
1. Introduction that players’ use of Twitter is not always positive and
it also produces unpredictable problems for the
league, teams and players themselves. In 2009,
The past decade has seen tremendous shifts in Milwaukee Bucks forward Charlie Villanueva posted
popularity among different browsers, operating a tweet during halftime of a home game against
systems and social networking site (SNSs), Boston Celtics. It is worth noting that this act not
especially, SNSs. Take Twitter as an example. Since
only costs him to receive severe criticism from the
its inception in 2006, Twitter has grown rapidly, media for not paying attention during the game but
gaining worldwide popularity. As of June 2014, there also leads the league to issue a policy that prevents
are 255 million avid users on Twitter who post an team players, coaches and official from using Twitter
average of 500 million Tweets per day 1 . While and other SNSs during games. In June 2009,
Twitter has become an important component of
Minnesota Timberwolves forward Kevin Love
people’s lives, it is not an exception for professional
disclosed on Twitter that header coach Kevin McHale
athletes such as the players of the National Basketball
would not return in the new season and he was sad
Association (NBA). According to the information we
about his leaving. While for the first time, such
gleaned from Tweeting-Athletes.com and Basketball-
breaking news came before any conventional media
Reference.com, there were 353 active players who
reported it, this tweet also put Love in an awkward
position for making McHale’s unofficially-revealed
1
Retrieved June 10, 2014, from https://about.twitter.com/company decision public. Worse, in 2012, San Antonio Spurs
1530-1605/15 $31.00 © 2015 IEEE 1706
DOI 10.1109/HICSS.2015.205swingman Stephen Jackson issued a threatening positive WOM could also contribute to increased
Twitter to another player and was later fined $25,000. stock price. With this line of reasoning, sentiment
With the widespread use of Twitter among NBA analysis, a text mining technique that distills moods
players, an interesting question then arises: how the (e.g., positive vs. negative) underlying textual data,
players generated messages impacts games? was used to analyze tweets. It was found that the
Actually, NBA teams have already recognized the extent to which the overall sentiment of the tweets
market value of social media; all 30 NBA teams have with regard to a movie is positive (indicating positive
created their Twitter accounts and use Twitter as one WOM) has a large effect on the sales of the movie
of important marketing and communication channels. [7]. Taking a step further, other academic works
However, we also noticed that just like some non- found that the overall sentiment of the tweets about
sport organizations which frown upon employees’ IT products is associated with the stock prices of the
use of SNSs for private purposes, the NBA league companies in question [8].
and teams seem to be less friendly on players’ use of Motivated by this stream of works, echoing the
Twitter with a concern that it would distract them prevalence of Twitter usage among NBA players, in
from winning games[1]. For example, in 2009, the this study, we presents a demonstration that uses the
league has enacted a policy stating that coaches and sentiment analysis to gauge players’ moods from
players are not allowed to use SNSs from 45 minutes their tweets and shows that the distilled moods
before game time until after post-game interviews are (positive vs. negative) could be used to predict
completed. Moreover, teams such as Miami Heat, player’s individual game performance. The rest of the
Toronto Raptors, Milwaukee Bucks and Los Angeles paper proceeds as follows. In the next section, we
Clippers have expanded the ban on SNSs to “team review the previous related studies on moods and
times” including team practices and team meetings performance. Next we propose the framework to
[2]. analyze AGC in the NBA scenario and illustrate the
This paper proposes that in addition to Twitter’s process of using this framework on athlete mood
business value to organizations, NBA teams also can detection and sports decision making. We conclude
benefit from the tweets posted and made public by the paper by discussing the limitation and identifying
their players. When governments could listen to, areas for future research.
analyze and understand citizen opinions voiced via
information communication technologies such as 2. Mood and Sport Performance
SMS messages and websites, and absorb the
feedback into administrative decision-making process
[3], operators of NBA teams such as coaching staffs In the sport field, there is a consensus that
and the general managers could use Twitter to athletes’ performance can be fluctuated with their
discern players’ status when they are not on the varied moods. This phenomenon has its root in a
basketball court, particularly their moods, and to use more generic moods-performance relationship well
the information that is otherwise difficult to obtain to established in the psychology field. Research[9]
predict their performance in the upcoming games. To found that moods can affect a range of processes
this end, text mining and sentiment analysis will be when individuals engage in different tasks. These
applied on AGC to extract hidden intelligence from processes include perception, reasoning, memory and
unstructured textual content, which allows teams to behavior, all of which are significant antecedents of
capture players’ moods reflected by their words. performance. In particular, positive moods such as
Tweets have become a source of new intelligence. arousal and pleasure contribute to greater helping
The locations, timing and content of tweets are behaviors, enhanced creativity, improved decision
informative in predicting future events [4, 5]. Twitter making and better team collaboration [10-12]. All
has been demonstrated to explain, detect and predict these outcomes remain critical for basketball players.
a number of phenomenon, including disease For example, we can expect that affected by good
outbreaks, election results, macroeconomic mood, a swing man could be more active in covering
processes, natural phenomena, product sales and up his teammates when they accidently turn the ball
financial markets [6]. For example, tweets posted by over or when they mistakenly leave an open shot
customers are thought of as a new form of word-of- opportunity to opponents; a point guard with positive
mouth (WOM). WOM plays a critical role in shaping mood could be more imaginative in passing the ball
one’s decision to purchase a product. Affected by to penetrates defense that is otherwise impossible;
positive WOM, people are more likely to purchase a when doubled, a center with positive mood could
product. Furthermore, given the close association make better judgment between shooting himself and
between product sales and the market value of a firm, passing the ball to teammates. Observing the effects
1707of athletes’ moods on performance, researchers Wizards. Effectiveness and efficiency aside, such
explored and discussed the mechanisms through interventions cannot succeed without acquiring
which such effects occur. Among them, Lazarus[13] players’ moods in the first place. As noted earlier,
argued that in a competitive sport, an athlete must NBA players made extensive use of Twitter, tweeting
closely anticipate and observe an opponent’s actions their opinions, thoughts and feelings. This stream of
during interactive plays so can s/he instinctively tweets represents an enormous, unfiltered opportunity
know what to do next as a response. While good to characterize players’ moods before games. Similar
moods help the athlete stay focused and vigilant, bad approach that uses tweets to monitor the moods of a
moods could be a major source of the interferences segment of Twitter users was practiced on the
that drive away their attentions from completing participants and journalists involved in the 2011
these perceptual and cognitive tasks. One might Egyptian Revolution [16] and, more frequently,
further wonder why negative moods can shift an existing and potential customers of a particular
athlete’s required focus away. According to product [7] or a company [8].
Totterdell[14], negative moods take extra efforts to The mood detection is important to either team or
repair and consume attentional resources needed by manager, however, acquiring and using players’
the cognitive processing of a sport. As a result, an moods from tweets is not straightforward because
athlete stays less focused in the game and his/her tweets take the form of unstructured text and too
performance is jeopardized by the bad moods. In much noise is hiding in them. We need tools to
addition, Totterdell suggests that positive or negative analyze a sea of textual data in order to distill the
moods lead to different cognitive processing styles, hidden intelligence. Figure 1 outlines a framework
and the cognitive styles incurred by positive moods we recommend to team operators that incorporates
are more conducive to achieving good performance players’ tweets into their administrative decision-
in competitive sports. For example, positive moods making process. It supports listening to and analyzing
stimulate greater efforts, motivation and persistency. the tweets made public by NBA players which could
It is worth noting that latest research[15] reported help the operators of NBA teams know players’
that certain negative moods could increase a type of mental status (e.g., negative moods) and detect
disruptive concentration, namely self-focus. Self- players’ potential sport performance ahead, and then
focus occurs when one spends too much attention to introduce necessary interventions. In this framework,
do the right motion that disrupts the natural or NBA players post tweets as reactions to a number of
automatic ability to perform. For example, in things such as feelings about a game, thoughts about
basketball, self-focus often kicks in when a player coaches’ decisions, or more frequently life
makes free-throw mechanically going through the encounters. At the core of our recommendation is an
sequential steps of making the best free-throw in emerging text-mining technique–sentiment analysis–
mind. that could be employed to sift through players’ tweets
and identify players’ moods before games. The
3. Framework of Mood Detect from AGC results are then summarized as a report which is later
presented to the operation staff of NBA teams. Given
In this study, we put the framework under the the robust mood-performance association in the
basketball scenario. NBA teams have long context of basketball (which we will demonstrate
recognized the importance and necessity of adjusting later), team operators could draw upon necessary
player’s moods before games. They take various approaches that are being practiced to smooth
approaches to accomplish this goal. For example, players’ negative moods or evoke positive moods.
after Donald Sterling’s scandal broke out, there was a The distilled moods from players’ tweets are not
voice that fans and the players of Clippers should free of bias, which could be attributed to a number of
boycott the rest games. Feeling that his players are factors. First, the bias can arise from self-selection.
inundated with the disturbance, the Los Angeles Certain players’ might simply post what they want to
Clippers coach Doc Rivers cancelled the upcoming be seen in Twitter, promoting a good image. While
practice and took one day off to give them a breathe. we cannot rule out the possibility of self-selection,
During the same period of time, the Indiana Pacer’s we believe its confounding effect is minimum. Past
center Roy Hibbert was struggling in his playoff SNS research reported that individuals use SNSs
journey. To help him relax and regain confidence, his primarily for showing affection and expressing
teammate Paul George invited Roy to a fishing trip, themselves [17]; parallel to that, research in the sport
which was paid off by Roy’s 28 points and nine domain[18] confirmed that the same motivations
rebounds in the next game, as well as the Pacer’s continue to hold when athletes use social media.
victory over their opponents – the Washington Specifically, Twitter ensures athletes that their
1708undiluted perspective on events and subjects can be In this study, we built two datasets, namely the
heard immediately. In fact, the spontaneous use of AGC dataset and the sport performance dataset. To
Twitter among athletes brings about an issue that build AGC, we refer to Tweeting-Athletes.com
they barely consider the consequences of the posted which summarizes all NBA players’ twitter accounts.
messages[19]. The problem is so serious that the There were 353 active players in the 2012-13 NBA
ESPN experts suggested that “maybe next time he’ll season. Thus, AGC contains the tweets posted by
think before he Tweets”[19]. On the other hand, these active NBA players during this time period. For
organizations such as teams and leagues have to each tweet, content aside, the dataset also includes
institute regulations to curb the “lack of thought” descriptive attributes such as timestamp when the
issue (recall NBA’s and certain NBA teams’ tweet was posted. AGC reveals that almost all active
restrictive policies on players’ social media use players in the league own his twitter account. They
mentioned earlier). In summary, with all the posted a total of 91,659 tweets in the 2012-13
anecdotal, practical and academic facts taken season 2, there are 51,847 original posts and 47,468
together, tweets record players’ feelings, opinions are written in English. Notice that not every player is
and thoughts that self-revealed with a spontaneous an avid user of Twitter; actually 87 of 353 posted less
intention. While Twitter can truthfully and accurately than 100 tweets or never posted even one. The
record consumers’ moods, likewise, it can be a dataset also identifies some noticeable “activists” of
credible channel to discern players’ moods and Twitter. We consider those players posted at least
Tweets can be also used to capture their status before 100 tweets as active users, who account for 75.3% of
games to a great extent. Second, the procedure of the total.
obtaining players’ moods from large scale of textual We retrieved game by game data in the 2012-13
data is another potential source of bias. The virtues of season and player data from basketball-reference.com
sentiment analysis, however, can compensate such to build the sport performance dataset. The
bias. As noted earlier, this emerging text-mining information of a game is concatenated with the
technique has demonstrated robust validity in information of a player who plays the game, which
gauging customers’ moods in Twitter and the appears as one record in the dataset. The extracted
quantified moods have been used in predicting a game information includes game date, game type,
number of future events. The next section presents a home/away, opponent and win/loss (score). The
demonstration that details the process of applying player information includes age (on the day of the
sentiment analysis to obtain NBA players’ moods played game), games started, minutes played, filed
underlying their tweets and further shows how the goals (percentage), 3-point field goals (percentage),
acquired moods are associated with their free throws (percentage) and Plus/minus (+/-).
performance in the upcoming games. Obviously, the In this study, we select Plus/minus (adjusted) as
results of this study could be summarized into a the individual sport performance measure 3 .
report delivered to team operators for further actions. Plus/minus is a metric that assesses how a team
performs with a certain player on/off the court and
calculates the overall impact that player has on team
success. Plus/minus began as a hockey metric, and
has been kept as an official NHL (National Hockey
League) statistic since 1968. Around 2003, Roland
Beech introduced this concept to NBA. A few years
later, the league made +/- part of its official box score
statistics. This metric is much more meaningful in
Figure 1. A conceptual framework of
basketball than it is in hockey, even though it took
athlete mood detection based on AGC
much longer for the NBA to begin using it. The more
analysis
scoring there is in a game, the more useful this
statistic becomes. Plus/minus is a useful means to
4. An Illustration to NBA 2012-13 Season determine a player's value to the affiliated team
because it takes into account everything a player does
The purposes of demonstration are two-fold. First, on the basketball floor, even things not reflected by
it details a process of applying sentiment analysis on existing NBA statistics. For example, when a player
AGC to identify athletes’ moods before game.
Second, it tests whether the identified moods of a
2
player are associated with individual sport October 30, 2012 – April 17, 2013 April 20 – June 3, 2013
performance in an upcoming game. (Playoffs), June 6 – 20, 2013 (Finals)
3
http://www.82games.com/barzilai2.htm
1709is on the floor and his team scores 7.5 points more results of sentiment analysis applied to collected
than its opponent, his + value is +7.5; when this players’ tweets.
player is not in the game, and his team scores 3.3
points less than the opponent, his – value is -3.3;
taken together, the +/- of the player is +10.8. We also
normalized this performance indicator in testing the
model because we are interested in players’ changed Table 1. List of Emoticons
performance instead of the absolute values. Emoticons mapped to :) Emoticons mapped to :(
In order to represent all the tweets accurately, we :) :(
:-) :-(
started out to preprocess the contents from which :) :(
players’ moods would be obtained. We filtered out :-p or :-q :-e
those pure retweets, and information-oriented tweets ^_^ or =^_^= :-( )
that contain any URL links. We also removed non- (^0^) or ^o^ X-<
English tweets. Given the fact that Twitter is replete =) :[
:-D or :D or =D >:O or >:-O or >:o or >:-o
with non-standard English, we applied a data
Note: Each word in AFINN list has a score from -5 to +5 to
cleansing mechanism to deal with the issue. We describe the sentiment polarity and degree. Here, we mapped all
corrected these misspelled words with minimum these emoticons to either - or / and extent the AFFIN list by
Hamming distance, an automatic error correction assigning -2 to / and +2 to -.
algorithm. The non-standard English scenario could
also originate from repeating letters. For example, Table 2. Examples of players’ tweets with
some arbitrary number of letters would be shown as different mood polarities
“Awwwwful”, “Awfuuuul” and “Ruuuude”. To deal
Mood
with this issue, we replaced any letter occurring more Player Tweet Time AFINN* Polarity
than two times with two consecutive occurrences of Al Nice Win for us 13- 5 Positive
the same letter and then subsequently corrected as Horford on the road vs Nov-12
misspelled words. Position: Portland. Much 6:13:18
To detect the sentiment polarity of each tweet Forward better effort AM
(i.e., a player’s mood hidden behind each tweet), we Team: tonite. Go
used AFINN sentiment lexicons, associated with a Atlanta Hawks!
Hawks Shaq! RT 13- 1 Positive
manually built emoticons list. AFINN is a sentiment
lexicon containing English words manually labeled @owen_then: Nov-12
@Al_Horford 6:53:11
by Finn Arup Nielsen. Words were rated between
who's your AM
minus five (negative) and plus five (positive); the alltime favorite
larger the number, the more positive the sentiment of center??
the word is. While developed based on the Affective Happy birthday! 13- 2 Positive
Norms for English Words lexicon (ANEW) proposed RT @Md813: Nov-12
by Bradley and Lang, AFINN is more focused on the @Al_Horford 8:33:34
language used in microblogging platforms because its Can ur #1 fan get AM
word list includes slangs, obscene words, acronyms a birthday
tweet??? :)
and web jargons which are prevalent in the Internet
Thanks. I really 14- 4 Positive
context. We wrote an application with R that
enjoy having my Nov-12
automatically extracts features related to the AFINN
own segment! RT 4:55:42
lexicon from a given tweet, and then calculates an @KimberMcCart: PM
AFINN score of tweet by summing the ratings of the Love the
positive and negative words that match the lexicon. @Al_Horford
The computed AFINN score reflects the mood of a show on
player underlying the tweet. Moreover, tweets are Tuesdays with
characterized by various emoticons that can express @kingcfb and
@RealMattlanta
moods. For example, either “:)” or “:-)” expresses
Jarrett What's good! 16- 4 Positive
positive mood, and “:(” expresses negative mood. We
Jack Here in Nov-12
also considered the emoticons in our extended list by Position: Sacramento, 4:37:43
mapping them to either “-”or “/”. The full list of Guard getting ready for PM
emoticons could be found in Table 1. The examples Team: the game tonite.
presented in Table 2 provides a snapshot view of the Cleveland Go Hawks!
1710Cavaliers How u gonna fire 28- -1 Negative player’s mood is. Moreover, to enable the
the owner? Nov-12 comparison of moods among different players, we
8:25:12 normalized the daily AFINN scores by using the
AM mean and standard deviation of all his seasonal
available records (Table 3.).
Steve @Naimthestar 28- -2 Negative To test the mood-and-performance relationship,
Nash haha u sound Nov-12 we developed a model that regresses a player’s mood
Position: really upset over 8:39:51 before a game on his performance of the upcoming
Guard there sir and is AM
Team: this a bad time to game. Figure 2 describes the details of the model.
Los bring up that Consistent with prior psychology and sport literature,
Angeles game on it was found that mood has a positive effect on player
Lakers thanksgiving performance (β = 0.014, p < 0.05) (Table 4). This
Spencer nomoreyears! 06- -1 Negative confirms that a player’s mood discerned by his tweets
Hawes Nov-12 posted prior to game is positively associated with his
Position: 8:19:45 performance in the upcoming game. Such finding is
Center PM essential because it justifies the value of applying
Team:
Cleveland sentiment analysis to examine NBA players’ moods.
Cavaliers Philly should be 06- -3 Negative Team operators could draw upon sentiment analysis
better than this. Nov-12 to discern players’ moods by examining their tweets.
Damn shame. 8:43:52 Given the association between players’ moods and
PM their potential performance, coaching staff could turn
to necessary approaches to smooth players’ negative
No hope. No 07- -3 Negative moods or arouse positive moods. In this sense, the
change. Nobama. Nov-12 use of Twitter among NBA players help team
6:58:26 operators stay connected with the status of these
PM players. We took a step further by drilling across
Note: three positions - center, forward and guard. This
* The AFINN score reflects the extent to which a player’s mood additional analysis provides a better understanding of
hidden in a tweet is positive. The larger the AFINN score, the the mood-and-performance relationship across three
more positive the sentiment of a tweet, and vice versa. important positions in basketball. It was found that
moods suggested by players’ tweets continue to
Recall that the second goal of the study is to test
affect performance for centers (β = 0.024, p < 0.1)
the association between the identified moods of a
and forwards (β = 0.031, p < 0.05) whereas such
player and his performance in an upcoming game. A
effect does not hold for guards. While it is beyond the
player might post several tweets before game. Before
scope of this study to discuss why the effect of
we simply aggregate the AFINN scores of the tweets
moods varies by player positions, such analysis
(posted prior to a game) to obtain a players’ moods
shows team operators the customization aspect of
before game, it is important to deal with the
sentiment analysis, demonstrating various
timestamp issue. Typically, an NBA game start at
applications of the technique to discern the status of
8:00 PM of the game day. Also due to the media
players with different traits such as age, salary and
policy issued by the league that prevents team
nationalities.
players, coaches and officials from using Twitter and
other SNSs from 45 minutes before game time until
= + + Eq(1)
after the post-game interviews are finished, we then
selected 7:00 PM (in the game day) as the cutoff time Model Description
to define “before game”. This is to say that, for a represents the performance of player i for a game at
particular game played by a player, his tweets posted day t.
dating back from 7:00 PM in the game day until 7:00 represents the mood of a player i on the day t.
PM one day before will be used to discern his moods represents the player-specific effect that capture the
before the game. The AFINN scores of the tweets idiosyncratic characteristics associated with each player.
submitted during this time frame are summed. The Figure 2. Regression model
total score represents a player’s mood before game.
The higher the aggregated score, the more positive a On the foundation of the base model, we extend
the model by controlling the position of players and
investigate how the patterns shift among different
positions.
1711= + + + Eq.(2) * p
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effects on sport performance.
6. References
[1] Friedman, D.J., "Social Media in Sports: Can Professional
Sports League Commissioners Punish 'twackle Dummies'?", Book
Social Media in Sports: Can Professional Sports League
Commissioners Punish'twackle Dummies'?, 2012, pp. 74.
[2]http://sports.espn.go.com/nba/news/story?id=4520907, accessed
May 16, 2014.
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