Do Consumer Perceptions of Tanking Impact Attendance at National Basketball Association Games? a Sentiment Analysis Approach

Do Consumer Perceptions of Tanking Impact Attendance at National Basketball Association Games? A Sentiment Analysis Approach

Hua Gong Nicholas M. Watanabe Rice University University of South Carolina

Brian P. Soebbing Matthew T. Brown and Mark S. Nagel University of Alberta University of South Carolina

The use of big data in sport and sport management research is increasing in popularity. Prior research generally includes one of the many characteristics of big data, such as volume or velocity. The present study presents big data in a multidimensional lens by considering the use of sentiment analysis. Speciﬁcally focusing on the phenomenon of tanking, the purposeful underperformance in sport competitions, the present study considers the impact that consumers’ sentiment regarding tanking has on game attendance in the National Basketball Association. Collecting social media posts for each National Basketball Association team, the authors create an algorithm to measure the volume and sentiment of consumer discussions related to tanking. These measures are included in a predictive model for National Basketball Association home game attendance between the 2013–2014 and 2017– 2018 seasons. Our results ﬁnd that the volume of discussions for the home team and sentiment toward tanking by the away team impact game attendance.

Keywords: big data, demand for sport, machine learning

In recent years, academics placed increased attention on the Prior research into tanking predominantly focused on examining concept of tanking, often defined as when teams purposefully whether tanking exists as well as the mechanisms that may lead to underperform to enhance long-term objectives (Taylor & Trogdon, tanking (e.g., Lenten, 2016; Medcalfe, 2009; Price et al., 2010; 2002). Generally, tanking is employed by professional sport Taylor & Trogdon, 2002). In this sense, there exists a gap regarding franchises seeking certain rewards that will help them to develop the impact tanking has in terms of consumer interest in a sport. a competitive advantage, typically highly regarded draft picks who Recent scholarship focused on consumer demand for sport pro- are considered to be able to change the direction of an organization vides evidence that sport fans may not simply just be deterred by (Lenten, Smith, & Boys, 2018; Price, Soebbing, Berri, & poor performance but actually may have an aversion to losing, Humphreys, 2010). At the same time, tanking is often considered indicating a greater decline in interest as teams accumulate more an undesirable behavior by many as purposefully underperforming losses (Coates, Humphreys, & Zhou, 2014). Considering this, the goes against the general ethos of sport (McManus, 2019) and also is current research advances the research into tanking by analyzing seen as potentially degrading the integrity and quality of the sport whether consumer interest in attending sporting events is impacted product (Vamplew, 2018). In this manner, tanking presents an by their awareness and sentiments toward tanking. One major intriguing issue for professional sport organizations (Soebbing & reason that prior research has not examined the link between tanking and consumer demand has been the difficulty in measuring Mason, 2009) as franchises must consider the tradeoffs between whether teams are truly tanking as well as whether consumers are underperforming to increase their chances at obtaining better talent aware of such information. As such, this research focuses on and the potential negative impact that poor on-court performance advancing tanking research by utilizing a big data approach to could have on consumer interest in their product. Moreover, measure consumer discussion and sentiments toward tanking on because it is the individual teams that make the choice of whether social media and then analyzing whether these factors are related to engage in tanking, leagues are also highly focused on monitoring to attendance demand at National Basketball Association (NBA) and creating policies to deter tanking precisely because of the games. fl concerns that such practices will in uence consumer decisions. The adoption of big data in management research is an At the same time, although it is evident that tanking is of important advancement, allowing researchers to either find better concern to professional sport organizations and a variety of sta- answers to existing questions or even to examine new questions keholders, the research on tanking has been somewhat limited. that previously could not be studied to better understand individuals, organizations, and related phenomena (George, Osinga, Lavie, & Scott, 2016). The argument presented is that as the Gong is with the Department of Sport Management, Rice University, Houston, data available to researchers increase in scope—volume/size— TX, USA. Watanabe, Brown, and Nagel are with the Department of Sport and fi “ Entertainment Management, University of South Carolina, Columbia, SC, USA. and granularity, de ned by George et al. (2016)as the most Soebbing is with the Faculty of Kinesiology, Sport, and Recreation, University of theoretically proximal measurement of a phenomenon or unit of Alberta, Edmonton, Alberta, Canada. Gong ([email protected]) is corresponding analysis” (p. 4), they afford researchers new opportunities to better author. understand the world through a quantitative lens. One of the 1 2 Gong et al. approaches emerging in recent years is the use of sentiment within the data themselves but also in merging them with other data analysis on large volumes of social media data as a way to improve to provide further levels of insight. the understanding of individuals or specific interest groups (Fang & Second, from an empirical standpoint, the present study Zhan, 2015). advances the use of sentiment analysis in sport research through In sport management research, there has, likewise, been a introducing the use of the Linear Support Vectors Machine recent increase in the use of large data sets, especially as a way to (LSVM) as a method to examine texts. Previous sport studies better understand the feelings and behaviors of consumers using sentiment analysis either relied on preprogramed machine (e.g., Chang, 2019; Fan, Billings, Zhu, & Yu, 2020; Naraine, learning algorithms that were built for general language use 2019; Yan, Steller, Watanabe, & Popp, 2018). In examining this (e.g., Chang, 2019; Naraine, 2019) and not for those conducted growing literature, big data research could be partitioned into two by consumers discussing sport or manually coded text as sample groups. The first group, rooted in economics, examines direct and sizes were small and manageable (Burton, 2019). We get around derived demand (e.g., Bradbury, 2020; Shapiro, DeSchriver, & the issue of preprogramed algorithms by using an LSVM, which Rascher, 2012; Tainsky & Jasielec, 2014), behaviors of individuals utilizes specialized coding based on the research context it is being (Mills, 2014; Tainsky, Mills, & Winfree, 2015), and the produc- used in. In doing so, it allows the researcher to examine large data tivity/performance of individuals and organizations (Lopez, 2020; sets while taking into consideration the unique aspects of the Watanabe, Wicker, & Yan, 2017). context. Moreover, by adopting a big data approach, the present The second group analyzes social media using a variety of research contributes to the theoretical and empirical understanding theoretical and methodological approaches (e.g., Chang, 2019; of tanking and its potential impact on consumer behavior. That is, Naraine, 2019; Yan, Watanabe, Shapiro, Naraine, & Hull, 2019). by utilizing millions of tweets discussing tanking on social media, Considering the enormous volume of data constantly being pro- we advance the tanking literature by considering whether the duced by users of various social media platforms, it is natural for volume and/or sentiment of these discussions impacts attendance these digital platforms to draw increasing attention from scholars at games. In this, the results from our models highlight that higher and practitioners. The reason is that these platforms provide access volumes of tanking discussion for a specific team can reduce to texts that potentially reveal the attitudes and behaviors of masses attendance at future home games for that team. of consumers (Van Dijck & Poell, 2013). Thus, social media provides scholars, including those scholars focused on sport, new ways to examine consumer behavior. This examination includes sentiments and how they may be influenced by certain Literature Review events (Chang, 2019; Naraine, Pegoraro, & Wear, 2019) in addi- Big Data and Social Media Research in Sport tion to the formation of networks and the importance of certain entities in these networks (Abeza, O’Reilly, & Seguin, 2019; Yan The emergence of social media has drastically changed the way et al., 2019). fans consume sport products (Billings, Broussard, Xu, & Xu, 2019) Despite the growth in big data and analytics within sport as it represents a dynamic digital environment that provides the management, most studies present a rather straightforward treat- potential for sport fans to become prosumers—individuals who ment of big data. Specifically, whereas typical definitions of big both produce and consume—especially on social media sites such data include either three or four elements (volume, velocity, as Twitter and Facebook (Filo, Lock, & Karg, 2015; Yan et al., variety, and veracity; George et al., 2016; McAfee, Brynjolfsson, 2018). From this perspective, social media becomes an important Davenport, Patil, & Barton, 2012), sport research predominately aspect of the sport marketplace not only as a promotional tool focuses on the size (volume) of the data (e.g., Chang, 2019) and/or widely used by organizations, athletes, governing organizations, the speed (velocity) at which the data were created (e.g., Yan et al., and other such stakeholders but also as a way to improve the 2019). As a result, sport management research can be considered connections and understanding of consumers (Filo et al., 2015). It one dimensional with the focus on reporting results in terms of is precisely such dynamics that have prompted researchers to utilize simple statistics, such as counts and frequencies (e.g., Chang, 2019; and focus on social media as a way of understanding the behaviors Yan, Pegoraro, & Watanabe, 2020), or clustering of topics/groups and perceptions of sport consumers (Abeza, O’Reilly, & Reid, (e.g., Naraine, 2019). With some exceptions (e.g., Mills, 2014; Yan 2013). From this perspective, a growing body of sport studies has et al., 2018), sport management research incorporating big data sets considered a variety of aspects of these digital sites, including the tends to ignore the concept of variety—the use of different forms of strategic use of social media by organizations (Abeza et al., 2019), data that can be matched together to help further develop new the connectivity and networks of relations between users (Naraine, insights into behaviors and other phenomena (George et al., 2016). 2019; Yan et al., 2019), and even the structural changes of It is against this backdrop of big data that the present research consumer attention (Pérez, 2013; Watanabe, Yan, & Soebbing, situates itself to make a number of contributions to the sport 2015; Watanabe, Yan, Soebbing, & Pegoraro, 2017). As such, literature. The present study utilizes a two-stage approach to social media has emerged as an important context and tool for examine the concept of tanking (Taylor & Trogdon, 2002). Spe- social science researchers as it allows for multiple theoretical and cifically, in the first stage of this study, we utilize a unique methodological approaches in examining behaviors and social sentiment analysis approach to examine both the volume and phenomena (Felt, 2016). sentiment of discussions focused on each NBA team regarding Most relevant to the focus of the present study is the fact that tanking for draft picks. These data are then combined with atten- social media sites have come to represent one of the main sources of dance information from regular season NBA games to analyze big data for scholars focused on examining consumer behaviors, whether consumer perceptions in relation to tanking impacts game sentiment, interactions, and communication (Kennedy & Moss, attendance. As such, the first contribution of the present research is 2015). That is, by their very nature, social networking sites in further emphasizing the concept of variety within big data constitute digital environments with large masses of users who research in sport by not only examining the patterns of behavior are constantly producing and publishing information that is readily

(Ahead of Print) Consumer Sentiment of Tanking 3 available to the public (Felt, 2016). Taking this argument a step Humphreys, and Mason’s(2013) broad findings confirmed earlier further, Kitchin (2014) has followed the formal definition of big research regarding tanking behavior by NBA teams when presented data and argued that social media is “huge in volume, high in with the incentives to do so. They also uncovered differences in velocity, diverse in variety, exhaustive in scope, fine grained in behavior by teams in conference games compared with nonconfer- resolution, relational in nature” (p. 68). In other words, social ence games. Soebbing and Humphreys (2013), examining betting media does not just represent one aspect of big data but, rather, is markets for NBA games, found that bookmakers adjusted point multifaceted in regard to fitting the definition of big data, and it is spreads in games featuring teams with incentives to tank. this very nature that provides a rich data source for examining Looking at amateur draft formats outside of the NBA, scholars complex behaviors in society (Hill, Kennedy, & Gerrard, 2016). found mixed evidence for tanking. In the Australian Football In addition to its capacity to generate large and complex data League, Borland, Chicu, and Macdonald (2009) did not uncover sets, social media is noted for its ability to provide critical under- evidence of tanking. However, Fornwagner (2019)didfind evidence standing and insights. As mentioned earlier, George et al. (2016) of tanking in the National Hockey League (NHL) along with NHL argued that one of the most important aspects of utilizing big data in coaches shifting their players less after being eliminated from the management research is in the ability to provide better answers to playoff contention. This reduction of shifts is one potential strategy existing questions or even to develop new questions that could not be for the manifestation of tanking within games. There is also evidence previously examined. Specifically, it is the accessibility provided by of tanking outside of the amateur draft format discussion. Medcalfe the advent of new methods and technology to collect and examine (2009) looked at minor league baseball and found that different social media data that allows researchers to process massive volumes scheduling systems (i.e., full season vs. a split season) led to changes and various forms of consumer data in a timely manner. In turn, this in team behavior as it related to exerting effort to win games. allows for larger data sets that enhance the accuracy of findings as Balsdon, Fong, and Thayer (2007) found that regular season well as advance research in new and unique directions while also men’s college basketball teams exerted less effort to win postseason having important implications for communities and society as a conference tournaments prior to the National Collegiate Athletic whole (George et al., 2014). Indeed, in the context of sport, the Association March Madness basketball tournament. increased focus on big data and analytics research in sport has further In summary, previous research in amateur and professional emphasized these digital sites (Abeza et al., 2013); for example, a sports leagues has found evidence of tanking (Kendall & Lenten, number of studies recently utilized social media data to develop a 2017). Little research extends this lineage to understand potential better understanding of how consumers react to protests conducted consequences of tanking. The present study looks at regular season by athletes (e.g., Park, Park & Billings, 2020; Yan et al., 2020). game attendance to begin to understand potential consequences. fi One prominent example of the power of social media, pre- More speci cally, we utilize big data collected from social media to sented by George et al. (2016), is the use of sentiment analysis, an advance the understanding of the relationship between consumer important methodology through which researchers can extend perceptions of tanking and the demand for attendance. From this, beyond existing research into consumer behavior by attempting we develop four research questions: to understand opinions and feelings through analyzing textual data (Kiritchenko, Zhu, & Mohammad, 2014). In general, three popular RQ1: Do consumer perceptions of tanking by home teams sentiment analytical techniques exist, some previously applied to affect NBA attendance in the short term? — sport social media research (Filo et al., 2015) manually analyzing RQ2: Do consumer perceptions of tanking by home teams social media posts (Delia & Armstrong, 2015), lexicon-based affect NBA attendance in the long term? approaches (Chang, 2019; Yu & Wang, 2015), and machine learning (Neethu & Rajasree, 2013; Pang & Lee, 2004). In the RQ3: Do consumer perceptions of tanking by away teams present study, we apply a machine learning-based sentiment affect NBA attendance in the short term? analysis to analyze a large Twitter data set to understand sports RQ4: Do consumer perceptions of tanking by away teams fans’ attitudes toward the concept of tanking. We then incorporate affect NBA attendance in the long term? this sentiment into a predictive demand model. As prior research We answer these questions in the “Results” section. has demonstrated that social media conversations contain valuable information regarding consumers’ attitudes toward sport products (Filo et al., 2015), the use of this approach allows for a unique Methodology examination of not only consumer sentiment toward tanking but also its potential impact on the decision to attend games. To examine the effect of consumer perceptions of tanking on NBA attendance, the present study analyzed NBA game attendance Tanking in Sport between the 2013–2014 and 2017–2018 seasons. The unit of observation was home team–away team–game–season. During the The phenomenon of tanking has received considerable empirical sample period, there were 5,945 unique regular season NBA games attention in sport with scholars typically focused on detecting from 29 NBA teams across five seasons available to examine in this evidence of tanking and understanding its causes. Most of the study. We excluded the Toronto Raptors home games from the research has revolved around amateur drafts and used the NBA as sample due to their location in Canada and the differences between the empirical setting. The seminal work by Taylor and Trogdon how federal governments calculate variables such as population. (2002) found that the NBA draft reforms in the1980s and 1990s Attendance data were collected from basketball-reference.com. led to teams not exerting effort to win games after being eliminated from playoff contention. Encompassing a longer sample Twitter Data period, later research by Price et al. (2010) provided evidence that eliminated NBA teams tanked when the league presented draft To examine the perceptions of tanking by consumers, we col- formats that rewarded the behavior by these clubs. Soebbing, lected data containing the discussion of tanking by NBA teams

(Ahead of Print) 4 Gong et al. through the Twitter search Application Programming Interface, For the purpose of our research, we utilized a machine which allowed access to historical data based on time frames learning approach. As such, to measure the sentiment toward specified by the user. Specifically, we conducted a keyword tanking, we calculated the average sentiment of tanking discus- search including the term “tanking” and the nickname of every sions posted within a set period of time on Twitter. Three types NBA team from July 1, 2012 to June 30, 2018. After specifying of sentiment can be identified: positive, neutral, and negative. search queries in the search Application Programming Interface, In identifying sentiment, we can understand the opinions or Twitter will return most if not all matching tweets (Premium feelings of individuals in textual messages (Kiritchenko, Zhu, & Search APIs, n.d.). Following this, we utilized Python to scrape Mohammad, 2014). In the context of the present study, a positive all the search results, yielding 166,875 specific tweets over this sentiment of a tweet regarding tanking expresses support for time period. the behavior, negative sentiment would highlight frustration Next, we constructed variables from these collected tweets to or displeasure with tanking, and neutral sentiment would not measure the volume of tanking discussions by team and the include any specific opinion or feeling regarding tanking. Table 1 sentiment toward tanking by team. The volume of tweets discuss- contains examples for each type of sentiment in tweets regarding ing tanking within a time period can be considered as a measure of tanking. fan awareness of tanking. The volume has to be analyzed with To extract sentiments embedded in the 166,875 tweets dis- caution as the sheer number of tweets is likely to be confounded by cussing tanking for NBA clubs, we performed machine learning- the size of the local market. To address this issue, we normalized based sentiment analysis on Twitter posts with the following the volume of tanking-related tweets by utilizing a sample of procedures. First, we randomly selected 5,000 tweets from the 6,389,698 NBA team tweets. To collect these data, we adopted sample, and the first author manually labeled each of the randomly the same approach used to collect the tanking data, this time selected 5,000 tanking tweets with one of the aforementioned employing a keyword search including the nickname of every sentiments: positive, neutral, or negative. This decision was based NBA team and #NBA. From this, we normalized the data by using on the first author’s expert knowledge of fan perceptions of tanking the proportion of tweets discussing each specific team to adjust for in professional basketball. In Step 2, we trained an LSVM, a the size of their respective digital markets. popular machine learning algorithm, with the 5,000 labeled tanking As mentioned earlier, there are three popular approaches to tweets. Notably, the LSVM yielded 70.1% prediction accuracy, using sentiment analysis within the literature. The first analytical meaning that 70.1% of sentiments could be correctly identified technique involves manually analyzing social media posts. For in labeled tanking tweets. In general, 70% prediction accuracy is example, Delia and Armstrong (2015)usedTwitterdatatostudy considered as a benchmark for successful sentiment analysis sponsorship effectiveness in the 2013 French Open and found that (Kirilenko, Stepchenkova, Kim, & Li, 2018). Finally, we used most tweets mentioning sponsors had positive sentiment. Like- the trained LSVM to predict sentiments in the remaining 161,875 wise, Burton (2019) sought to understand consumer sentiment tweets in our sample. The value of the sentiment of tweets toward ambush marketing from the 2018 FIFA World Cup Finals. regarding tanking ranged from −1 to 1 as we assigned the value The second technique is the lexicon-based approach, using a of 1, −1, and 0 to positive, negative, and neutral tweets, respec- predefined dictionary of positive and negative words (Hong & tively. For example, if there were 20 positive tweets, 10 neutral Skiena, 2010). The measure of sentiment is derived by counting tweets, and 10 negative tweets posted within a certain period of the number of positive words minus the number of negative words time, then the average sentiment of tanking tweets would be 0.25, in a message. A handful of studies in sport management have implying that fans, on average, held positive views toward tanking adopted this approach (Chang, 2019; Yu & Wang, 2015). For on Twitter. example, Chang (2019) explored the relationship between sports Utilizing these data, we turned our attention to answering the spectators’ emotional reaction and team performance in Super four proposed research questions. As consumer perceptions of Bowl 50. tanking could impact their decision to attend games in both the The final sentiment analysis technique involves machine short and long term, we delineated two different timeframes learning wherein statistical models are used to learn patterns in through which to measure volume and sentiment within this study. textual data (Dhaoui, Webster, & Tan, 2017). There are a few Specifically, we considered short term as the 31-day period before advantages compared with the first two methods. First, machine the tipoff of the observed NBA game. The reason we used the learning models can automatically extract sentiments from tex- 31-day window was due to prior research suggesting that fans, on tual data (Neethu & Rajasree, 2013), allowing researchers to average, purchase tickets to NBA games within a month prior to the process massive data sets with ease (Witkemper, Lim, & game (Mills, Salaga, & Tainsky, 2016). We measured long term as Waldburger, 2012). Second, the machine learning method can the entire duration of the previous NBA season, with July 1 in the take the research context into consideration when estimating year where the observed season began as the season cutoff date. sentiments (Pang & Lee, 2004). Unlike the lexicon-based These calculations for both volume and sentiment of tanking tweets approach, the machine learning approach does not rely on a were done separately for both the home and away teams to account predefined dictionary. Instead, a training set consisting of a for differences in consumer perceptions of tanking for the home randomly selected sample from the entire data set with manually and away teams. labeled sentiments is used (Ge, Alonso Vazquez, & Gretzel, 2018). This technique is especially important for sport research Control Variables as certain words may have special meanings (e.g., destroy, sick, explode) in the context of sport (Ma, Cheng, & Hsiao, 2018) To understand the impact of consumer perceptions of tanking compared with their traditional use. Although there are limited on game attendance, the present research included a number of studies in sport using machine learning, a wide range of research control variables. The control variables followed the five major from other fields relies on machine learning models to analyze determinant categories for attendance outlined by Borland and social media content (e.g., Ma et al., 2018). Macdonald’s(2003) review. These categories were consumer

(Ahead of Print) Consumer Sentiment of Tanking 5

Table 1 Tanking Tweet Examples Twitter ID Date Text Team Sentiment xxxx March 6, Whether by design or through incompetence (more likely) I love that @Sixers are tanking! Sixers Positive (1) 7872 2013 #SixersTalk #SixersNation #Sixers @SixersCEOAdam xxxx January 26, I hate to admit it as a diehard laker fan but @Lakers I know your tanking. I hate it cause its not within Lakers Negative 5472 2016 our blood. I’ll never support it (−1) xxxx July 8, @JakeJ29 @zero_chill I’m ﬁne with tanking. But we wont be top 5 worst teams either. We wont land Bucks Neutral (0) 3424 2013 Wiggins or Glen Robinson III. #Bucks

preferences, economic factors, quality of viewing, supply capacity, perceptions of tanking and the other control variables. Equation 1 and quality of sporting contest. presents the full model. First, we included two dummy variables indicating whether ð Þ = β ln Attendanceijts 1Short Volume Homeijts home teams or away teams had been eliminated from playoff þβ þ 2Short Sentiment Homeijts contention (Eliminated_Home, Eliminated_Away) in a given β þ β þ game. The elimination date was calculated by using a magic 3Long Volume Homeijts 4Long Sentiment Homeijts β number formula (Price et al., 2010). We also employed the 5Short Volume Awayijts þβ þ closing point spread to quantify game uncertainty (Soebbing & 6Short Sentiment Awayijts β þ β þ Humphreys, 2013). The point spread (Point_Spread) was a 7Long Volume Awayijts 8Long Sentiment Awayijts β þ continuous variable, projecting the score difference between 9Eliminated Homeijts β þ β þ two teams in a given match. The lower absolute value of point 10Eliminated Awayijts 11Facility Ageijts β 2 þ spreads indicated greater game uncertainty. Considering a possi- 12Facility Age ijts β þ β 2 þ ble nonlinear relationship between point spreads and game atten- 13Point Spreadijts 14Point Spread ijts dance, a quadratic term for the point spread (Point_Spread2)was β þ β þ 15Elo Homeijts 16Elo Awayijts added. Historical point spread data came from goldsheet.com. β þ β þ β þ 17Populationijts 18Coincident Indexijts 19Competitionijts In addition, to account for overall team strength, we used the β Holiday þ β Day þ β Month þ θ þ γ þ ε , pregame Elo rating for home and always teams (Elo_Home, 20 ijts 21 ijts 22 ijts s i ijts Elo_Away). Elo ratings were gathered from fivethiryeight.com (1) and set to 1,500 for an average team. where i represents the home team, j represents the away team, fl Next, to account for the economic factors that may in uence t indexes game, s indexes season, and εijts is the error term. attendance, we included measures for market size, economic con- During our sample period, 46% of the observations were sold ditions, and within-market competition. First, market size was out, defined as the arena was filled to greater than or equal to 100% measured for each NBA market using the total metropolitan of listed capacity. Estimating an Ordinary Least Squares regression statistical area (MSA) population (Population) from the U.S. given the large number of sold-out games will lead to inconsistent Bureau of Economic Analysis. Next, we included the observed estimates (Wooldridge, 2016). Thus, the present study estimated team’s state coincident indexes (Coincident Index) for the month of a censored normal regression model consistent with previous the observed game. The monthly coincident index is published by research (e.g., Humphreys & Johnson, 2020). the Philadelphia Federal Reserve to measure economic conditions. Considering the panel nature of the data, the model also Finally, to account for the competition within a market, we included included home team (γi) and season (θt) fixed effects to account a count variable for the number of NFL, MLB, and NHL teams in for the individuality of each home team and season. Despite some the same MSA of the observed NBA team (Competition). debate that nonlinear regression models with fixed effects may To control for the quality of the viewing, we included a produce inconsistent estimators due to the incidental parameters number of variables. First, we included the age of the facility problem (Lancaster, 2000; Neyman & Scott, 1948), Monte Carlo (Facility_Age), measured as the age (in years) at the time of the simulations conducted by Greene (2004) found that estimators observed game. Considering that attendance may surge in later from censored normal regression models with fixed effects were years of the facility’s lifespan (e.g., McEvoy, Nagel, DeSchriver, consistent. Finally, as the unobserved error term in econometric &Brown,2005), the quadratic term for facility age (Facility_ models may be serially correlated with one another within clusters Age2) was included. Facility data came from NBA media guides (teams), we addressed this concern and derived efficient estimators and basketball-reference.com. Second, we included dummy using cluster-robust SEs in estimating the results (Wooldridge, variables for the day of the week (e.g., Monday) and the month 2016). of the year (e.g., January). Finally, we included a dummy variable if the observed game occurred on a public holiday (Holiday). Results

Empirical Analysis Table 2 provides the summary statistics. The average attendance during the sample period was 17,734. After conducting sentiment To estimate the impact of consumer perceptions of tanking on analysis on the tweets discussing tanking, we found that 75% of demand for attendance, we modeled the natural logarithm of NBA tweets were labeled as negative, 18% were neutral, and approxi- game attendance as a function of a vector of variables measuring mately 7% were positive. This tabulation was consistent with the

(Ahead of Print) 6 Gong et al.

Table 2 Summary Statistics (n = 5,945) of the regular season to save energy for the playoffs (Price et al., 2010; Taylor & Trogdon, 2002). Thus, some tanking discussions Variable Mean SD Min Max for these playoff teams on Twitter were less likely to be related to Attendance 17,734 2,294 7,244 23,152 tanking for draft picks. For this reason, subsets of games featuring Short_Volume_Home 0.074 0.183 0.000 2.755 nonplayoff teams with the motivation to compete for draft picks – Short_Sentiment_Home −0.628 0.264 −1.000 1.000 were estimated in Models (2 5). To begin with, Model (2) in Table 3 contained the estimation Long_Volume_Home 0.042 0.079 0.0002 0.597 results in regard to the first research question, examining whether − − − Long_Sentiment_Home 0.674 0.115 0.909 0.197 consumer perceptions of tanking for home teams will affect Short_Volume_Away 0.072 0.176 0.000 2.685 attendance in the short run. Recall that short-term variables Short_Sentiment_Away −0.629 0.265 −1.000 1.000 were calculated by using tweets published within 31 days before Long_Volume_Away 0.041 0.078 0.0002 0.597 the start of the observed game. Considering this, Model (2) used Long_Sentiment_Away −0.673 0.116 −0.909 −0.197 2,870 games featuring home teams that did not make the playoffs in the observed season. The results show that the estimated coefficient Eliminated_Home 0.053 0.223 0 1 on the short-term volume of tanking tweets for home teams was Eliminated_Away 0.049 0.216 0 1 negative and significant, meaning that more tanking discussions for Facility_Age 19.8 10.1 1 52 home teams prior to an NBA game reduced demand for attendance. Point_Spread −1.7 7.0 −18.5 21.0 The parameter estimate on the short-term sentiment of tanking Elo_Home 1,502.8 112.4 1,174.7 1,835.7 tweets for home teams was insignificant, indicating that fans’ ’ Elo_Away 1,504.5 111.6 1,175.5 1,838.6 attitudes toward the home teams tanking strategy did not affect demand for NBA games in the short term. Population (in 10,000) 55.6 50.8 5.3 203.2 Model (3) in Table 3 reported the estimation results for the Coincident_Index 120.5 9.6 98.3 144.8 second research question, which concerned the long-term effect on Competition 2.2 1.9 0 7 attendance of fans’ perceptions of tanking for home teams, with Holiday 0.016 0.125 0 1 the long-term effect measured using tweets posted in the previous Monday 0.140 0.347 0 1 season. From this, the Model (3) used 2,829 games featuring Tuesday 0.117 0.321 0 1 observed home teams that did not participate in the playoffs in the prior season. The estimated parameter on the long-term volume Wednesday 0.205 0.404 0 1 of tanking tweets for home teams was negative and significant, Thursday 0.075 0.264 0 1 suggesting that a higher volume of tanking discussions related to Friday 0.191 0.393 0 1 home teams in the previous season resulted in lower demand for Saturday 0.150 0.357 0 1 attendance in the observed game within the current season. The Sunday 0.121 0.326 0 1 estimated coefficient on the long-term sentiment of tanking tweets fi ’ October 0.038 0.191 0 1 for home teams was not signi cant, implying that fans views of fl November 0.181 0.385 0 1 tanking for home teams formed in the past did not in uence their attendance decisions in the current season. December 0.185 0.388 0 1 Further considering the results in Table 3, Model (4) contained January 0.184 0.387 0 1 the findings for the third research question, asking what impact February 0.136 0.342 0 1 fans’ short-term perceptions of tanking by away teams had on March 0.191 0.393 0 1 attendance. These estimations came from 2,769 games featuring April 0.085 0.28 0 1 away teams that did not enter the playoffs in the observed season. 2014 0.2 0.4 0 1 The parameter estimate on the short-term sentiment of tanking tweets for away teams was positive and significant, indicating that 2015 0.2 0.4 0 1 more negative views toward away teams’ tanking behavior prior to 2016 0.2 0.4 0 1 an NBA game may decrease attendance. The estimated coefficient 2017 0.2 0.4 0 1 on the short-term volume of tanking tweets for away teams was not 2018 0.2 0.4 0 1 significant, meaning that the quantity of tanking discussions related to away teams did not affect fans’ decisions to attend games. Model (5) documented the findings for the fourth research mean sentiment in the short and long term for both the home and question, addressing the long-term impact on attendance of fans’ away teams being negative. The mean point spread was −1.7, perceptions of tanking related to away teams. The model used meaning that the observed home team is almost a 2-point favorite 2,774 matches featuring away teams that did not qualify for the compared with the away team. The average facility age was 19 playoffs in the prior season. The estimated coefficients on the long- years. Wednesday was the day of the week on which the highest term volume and sentiment of tanking tweets for away teams were percentage of NBA games were played, and <2% of the sample not significant, signaling that consumer perceptions of tanking for observations occurred on public holidays. away teams did not influence home fans’ attendance decisions in Table 3 reports the main findings. The base model presented in the long term. Model (1) used the entire data set of 5,945 NBA games played from We use Model (1), the full sample, to discuss the findings of the 2013–2014 to 2017–2018 seasons. One potential issue associ- the control variables. First, the estimated coefficients on elimina- ated with using the complete number of observations was that not tion variables were not significant, indicating that knowing that all teams in the sample had incentives to tank for draft picks. For home teams or away teams have been eliminated from playoff example, after clinching a playoff berth, teams may tank for the rest contention did not affect attendance, holding other variables

(Ahead of Print) Consumer Sentiment of Tanking 7

Table 3 Regression Analysis Results for Main Models Nonplayoff Nonplayoff (home Nonplayoff Nonplayoff (away Full (home) prior season) (away) prior season) Variable (1) (2) (3) (4) (5) Short_Volume_Home −0.073*** −0.074*** −0.101*** −0.056** −0.053* (0.015) (0.016) (0.021) (0.022) (0.023) Short_Sentiment_Home −0.010 −0.010 0.006 −0.001 −0.014 (0.009) (0.013) (0.012) (0.012) (0.012) Long_Volume_Home −0.214*** −0.088 −0.301*** −0.195*** −0.163** (0.039) (0.046) (0.044) (0.056) (0.057) Long_Sentiment_Home −0.018 −0.056 0.015 0.028 0.024 (0.025) (0.043) (0.044) (0.035) (0.036) Short_Volume_Away 0.005 −0.001 −0.018 −0.000 −0.029 (0.015) (0.018) (0.019) (0.016) (0.022) Short_Sentiment_Away 0.011 0.003 0.008 0.042** 0.036** (0.009) (0.012) (0.012) (0.014) (0.013) Long_Volume_Away 0.011 0.018 0.040 −0.046 −0.051 (0.034) (0.044) (0.045) (0.037) (0.038) Long_Sentiment_Away −0.036 −0.031 −0.042 −0.017 0.021 (0.020) (0.026) (0.027) (0.030) (0.030) Eliminated_Home −0.013 −0.012 −0.038* −0.002 −0.008 (0.012) (0.014) (0.015) (0.017) (0.017) Eliminated_Away 0.025 0.039* 0.022 −0.002 0.003 (0.013) (0.017) (0.017) (0.016) (0.017) Facility_Age −0.011*** 0.014*** 0.004 −0.016*** −0.006 (0.003) (0.004) (0.004) (0.004) (0.004) Facility_Age2 0.000 −0.001*** −0.000* 0.000* 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) Point_Spread −0.001* −0.001 −0.001 0.000 −0.000 (0.000) (0.001) (0.001) (0.001) (0.001) Point_Spread2 0.000*** 0.000*** 0.000*** 0.000 0.000* (0.000) (0.000) (0.000) (0.000) (0.000) Elo_Home 0.001*** 0.000*** 0.001*** 0.001*** 0.001*** (0.000) (0.000) (0.000) (0.000) (0.000) Elo_Away 0.000*** 0.000*** 0.000*** 0.000 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) Population 0.000* −0.001*** 0.002*** 0.000* 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) Coincident_Index 0.004** 0.008*** 0.004** 0.006*** 0.004* (0.001) (0.002) (0.001) (0.002) (0.002) Competition −0.053* 0.013 0.060 −0.051 −0.036 (0.023) (0.031) (0.037) (0.030) (0.030) Holiday 0.112*** 0.084** 0.098** 0.118*** 0.102*** (0.021) (0.030) (0.030) (0.029) (0.027) Observations 5,945 2,870 2,829 2,769 2,774 Note. The dependent variable is log (attendance). We do not include the ﬁndings for day of the week, month of the year, and season. However, they are available upon request. *p < .05. **p < .01. ***p < .001.

constant. Second, the estimated parameter on the square of facility parameter on the quadratic term of the point spread was signifi- age was not significant, whereas the estimate on the linear term of cantly positive and was significantly negative on the linear term, facility age was negative and significant. These results imply that indicating that local fans loved to see home teams’ wins and upsets. attendance decreases as NBA arenas age. Third, the estimated This finding is consistent with Coates et al. (2014). Fourth, the

(Ahead of Print) 8 Gong et al. parameter estimates for the Elo rating of home and away teams Utilizing a large volume of data from social media, the going into the observed game were positive and significant, present study also highlights that the negative impact on meaning that overall higher team quality led to increased atten- attendance of fans’ perceptions of tanking exists not only in dance.1 Fifth, it appeared that economic factors, such as the MSA the short term but also in the long term. Specifically, the population and monthly coincident index, had a positive relation- findings from our study conclude that greater volumes of ship with attendance, whereas having more competitors in the same tanking discussions for home teams from the prior NBA season MSA generally reduced attendance. Finally, the parameter esti- will undermine fans’ interest in games in the current season. mates for games played on holidays was positive and significant, That is, the awareness of tanking formed in the past by fans can suggesting higher attendance on public holidays. potentially carry over to the next season and continue to have a negative impact on demand, even when there may no longer be Robustness Checks perceptions that the team is still engaged in tanking. In this sense, the results from this study help to advance the theoretical To test the strength of the findings, we performed several robust- and empirical understanding of tanking, especially in relation to ness checks. First, we recalculated the short-term volume and consumer behavior, as it seems that there is a prolonged or sentiment of tanking tweets by using 15-, 62-, and 93-day windows lingering effect that can carry over and continue to have a instead of the 31-day window used in Table 3. We estimated damaging impact on teams. different short-term windows to acknowledge that perceptions of Next, the present research finds evidence that fans’ attitudes tanking formed in other time frames may also affect attendance toward the home teams’ tanking does not affect their attendance decisions. Overall, the results in Table 4, Models (1–6), are decisions. In other words, more positive sentiment or negative consistent with the findings in Table 3 in terms of the sign and sentiment on home teams’ tanking behavior will not affect game fi 2 signi cance. For the second robustness check, we recalculated the attendance. This finding seems surprising as numerous studies have long-term variables by using tanking tweets posted only during the indicated that consumer sentiment is a key predictor of purchase prior regular season (October through April) instead of the entire behavior (Huth, Eppright, & Taube, 1994; Nguyen & Claus, 2013). prior season (July through June). It is likely that fans develop their As such, the results from the present research may further illustrate perceptions of tanking during the regular season when games are that the understanding of sport consumer behavior cannot neces- fi occurring. The ndings for these checks are reported in Table 4, sarily be exactly the same as that exhibited in the consumption of Models (7) and (8). Overall, the estimation results are consistent other products and could be the result of many of the unique aspects fi with the ndings in Table 3, except that the estimated parameter on of the sport product, such as the habitual nature of sport consump- the long-term sentiment of tanking tweets for home teams becomes tion (Ge, Humphreys, & Zhou, 2020). At the same time, our fi positive and signi cant. findings suggest that negative consumer sentiments toward tanking The last robustness check examined teams that ranked ninth to conducted by the away team lead to a decline in attendance for the 15th in each conference over the course of the observed NBA home team. season. Recall that the samples in Table 3, Models (2) and (4), Beyond the contribution to the sport management literature, included nonplayoff teams in the observed season due to the the present research provides a number of advances from the concern that playoff teams, at some point in the season, may perspective of utilizing big data and analytics in sport. Notably, tank for reasons other than draft picks. However, fans may not through introducing the use of the LSVM methodology, we recognize nonplayoff teams early in the season. Thus, the last provide a new approach to conduct sentiment analysis to analyze robustness check used low-seeded teams based on daily standing large volumes of textual data. This new approach improves on prior changes to ensure that tanking discussions were relevant to the research (Chang, 2019; Naraine, 2019) by utilizing specialized behavior of tanking for draft picks. The estimation results for these fi fi coding to take into account context speci c language. In addition, nal robustness checks can be found in Models (9) and (10) in whereas most prior research utilizing big data in sport has applied a Table 4 and are consistent with those in the main models. single layer of analysis in terms of measuring sentiment or mapping other such consumer behaviors (Chang, 2019; Yan et al., 2019), the Discussion present study highlights the variety of applications within big data research. By first measuring consumer sentiments through a large The focus of the present study was to use sentiment analysis to data set and then combining them with other measures of consumer analyze a big data set of tweets discussing tanking for draft picks in behavior in sport, the present research provides a model that both the NBA to consider whether consumer perceptions of tanking highlights the sentiments of consumers and illustrates how they can impact game attendance. To begin with, the present study provides impact other consumption behaviors. As such, we provide a important contributions to the sport management and economics research design for extending the use of big data in sport research literature. Prior research provided some anecdotal evidence that the beyond just volume and velocity to considering other aspects, such perception of match fixing in sport may not influence fan interest in as variety (George et al., 2016). sporting events (Numerato, 2016; Preston & Szymanski, 2003). At the same time, there are limitations in the present research. Specifically, studies have argued that although many fans question Perhaps the biggest one is the accuracy level of the sentiment the purity of the sports competition, they often consider practices analysis approach that is utilized. Although the accuracy level such as match fixing in sport as part of the nature of competition. As achieved through using the LSVM method (70.1%) fits within the a result, they feel their decision not to attend games will have no acceptable benchmarks set by scholars in machine learning and impact on the team to change its behavior, and thus, fans will artificial intelligence (Kirilenko et al., 2018), it still leaves the maintain their current behavior (Numerato, 2016). However, the potential for errors within the research. In addition to the modeling overall findings presented here suggest that an increase in the challenges, the data labeling process may contribute to sentiment volume of social media posts regarding tanking can lower future estimation errors (Ebrahimi, Yazdavar, & Sheth, 2017). When home game attendance for clubs. coding the sentiment of tanking related tweets, a second coder was

(Ahead of Print) Table 4 Robustness Checks Nonplayoff (home Nonplayoff (away Ninth to 15th Ninth to 15th Nonplayoff (home) Nonplayoff (away) prior season) prior season) seed (home) seed (away) 15 days 62 days 93 days 15 days 62 days 93 days Regular season Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Short_Volume_Home −0.040** −0.140*** −0.186*** −0.022 −0.115*** −0.177*** −0.109*** −0.052* −0.050** −0.077*** (0.013) (0.021) (0.025) (0.016) (0.027) (0.031) (0.021) (0.023) (0.016) (0.021) Short_Sentiment_Home −0.013 −0.003 −0.000 −0.007 −0.009 −0.001 0.001 −0.015 −0.010 −0.006 (0.010) (0.015) (0.017) (0.010) (0.014) (0.015) (0.012) (0.012) (0.013) (0.012)

Aedo rn)9 Print) of (Ahead Long_Volume_Home −0.081 −0.090* −0.093* −0.190*** −0.193*** −0.195*** −0.212*** −0.082 −0.149*** −0.195*** (0.046) (0.046) (0.046) (0.056) (0.056) (0.056) (0.033) (0.044) (0.044) (0.055) Long_Sentiment_Home −0.049 −0.059 −0.063 0.033 0.026 0.027 −0.209*** 0.016 −0.058 0.014 (0.043) (0.044) (0.044) (0.035) (0.035) (0.035) (0.042) (0.036) (0.038) (0.035) Short_Volume_Away 0.008 −0.005 −0.022 0.009 0.000 −0.009 −0.016 −0.034 0.010 −0.004 (0.013) (0.024) (0.027) (0.012) (0.020) (0.023) (0.019) (0.022) (0.018) (0.016) Short_Sentiment_Away 0.001 −0.009 −0.004 0.027* 0.037* 0.050** 0.008 0.037** 0.007 0.035* (0.009) (0.013) (0.014) (0.011) (0.015) (0.016) (0.012) (0.013) (0.011) (0.015) Long_Volume_Away 0.017 0.019 0.026 −0.050 −0.046 −0.038 0.050 −0.021 −0.015 −0.038 (0.044) (0.044) (0.044) (0.037) (0.038) (0.038) (0.037) (0.031) (0.041) (0.037) Long_Sentiment_Away −0.030 −0.033 −0.035 −0.018 −0.015 −0.019 −0.044 0.010 −0.014 −0.042 (0.026) (0.026) (0.026) (0.030) (0.030) (0.030) (0.026) (0.029) (0.026) (0.030) Observations 2,870 2,870 2,870 2,769 2,769 2,769 2,829 2,774 2,840 2,722 Note: The dependent variable is log (attendance). The control variables and fixed effects are not included for space considerations but are available upon request. *p < .05. **p < .01. ***p < .001. 10 Gong et al. asked to label the same set of tweets as the first author to ensure the Notes consistency of the labels as prior sentiment analysis studies have done when utilizing machine learning (Alfaro, Cano-Montero, 1. Additional models were estimated that replaced the Elo rating with Go´mez, Moguerza, & Ortega, 2016). Following this, all disagree- team winning percentage (WinPec_Home, WinPec_Away) to measure ments between the two coders were identified, and the differences team quality in the models. The reason for this robustness check rests on were discussed to reach agreement on the sentiment labels. Despite the fact that Elo ratings may already factor tanking behavior by teams the use of two coders, it should be acknowledged that this process is (Silver & Fischer-Baum, 2015). The results from these models also not perfect as subjectivity may play a role in the labeling process returned similar results to the other models that were presented; thus, and, thus, affect the overall accuracy of sentiment analysis we kept the Elo rating as the measure of team strength. (McGurk, Nowak, & Hall, 2020). Furthermore, we also acknowl- 2. In particular, the first robustness check showed that the short-term edge limitations in the use of Twitter as the data source to measure volume of tanking tweets for home teams was negatively related to game sentiment in regard to tanking. That is, although it does provide us attendance and the short-term sentiment of tanking tweets for away teams with a large data set through which to measure consumer senti- was positively linked to demand for attendance. 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