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.2015.205
swingman 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 1707
of 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 1708
undiluted 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 1709
is 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! 1710
Cavaliers 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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