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

in Journal of Sport Management
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  • 1 Rice University
  • 2 University of South Carolina
  • 3 University of Alberta
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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.

Gong is with the Department of Sport Management, Rice University, Houston, TX, USA. Watanabe, Brown, and Nagel are with the Department of Sport and Entertainment Management, University of South Carolina, Columbia, SC, USA. Soebbing is with the Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, Alberta, Canada.

Gong (hg37@rice.edu) is corresponding author.
  • Abeza, G., O’Reilly, N., & Reid, I. (2013). Relationship marketing and social media in sport. International Journal of Sport Communication, 6(2), 120142. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Abeza, G., O’Reilly, N., & Seguin, B. (2019). Social media in relationship marketing: The perspective of professional sport managers in the MLB, NBA, NFL, and NHL. Communication & Sport, 7(1), 80109. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alfaro, C., Cano-Montero, J., Gómez, J., Moguerza, J.M., & Ortega, F. (2016). A multi-stage method for content classification and opinion mining on weblog comments. Annals of Operations Research, 236(1), 197213. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balsdon, E., Fong, L., & Thayer, M.A. (2007). Corruption in college basketball? Evidence of tanking in postseason conference tournaments. Journal of Sports Economics, 8(1), 1938. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Billings, A.C., Broussard, R.M., Xu, Q., & Xu, M. (2019). Untangling international sport social media use: Contrasting US and Chinese uses and gratifications across four platforms. Communication & Sport, 7(5), 630652. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Borland, J., & Macdonald, R. (2003). Demand for sport. Oxford Review of Economic Policy, 19(4), 478502. doi:

  • Borland, J., Chicu, M., & Macdonald, R.D. (2009). Do teams always lose to win? Performance incentives and the player draft in the Australian Football League. Journal of Sports Economics, 10(5), 451484. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bradbury, J.C. (2020). Determinants of attendance in major league soccer. Journal of Sport Management, 34(1), 5363. doi:

  • Burton, N. (2019). Exploring user sentiment towards sponsorship and ambush marketing. International Journal of Sports Marketing and Sponsorship, 20(4), 583602. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, Y. (2019). Spectators’ emotional responses in tweets during the Super Bowl 50 game. Sport Management Review, 22(3), 348362. doi:

  • Coates, D., Humphreys, B.R., & Zhou, L. (2014). Reference‐dependent preferences, loss aversion, and live game attendance. Economic Inquiry, 52(3), 959973. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delia, E.B., & Armstrong, C.G. (2015). #Sponsoring the #FrenchOpen: An examination of social media buzz and sentiment. Journal of Sport Management, 29(2), 184199. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dhaoui, C., Webster, C.M., & Tan, L.P. (2017). Social media sentiment analysis: Lexicon versus machine learning. Journal of Consumer Marketing, 34(6), 480488. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebrahimi, M., Yazdavar, A.H., & Sheth, A. (2017). Challenges of sentiment analysis for dynamic events. IEEE Intelligent Systems, 32(5), 7075. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, M., Billings, A., Zhu, X., & Yu, P. (2020). Twitter-based BIRGing: Big Data analysis of English national team fans during the 2018. FIFA World Cup. Communication & Sport, 8, 317345. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1), 5. doi:

  • Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1), 115. doi:

  • Filo, K., Lock, D., & Karg, A. (2015). Sport and social media research: A review. Sport Management Review, 18(2), 166181. doi:

  • Fornwagner, H. (2019). Incentives to lose revisited: The NHL and its tournament incentives. Journal of Economic Psychology, 75, 102088. doi:

  • Ge, J., Alonso Vazquez, M. & Gretzel, U. (2018). Sentiment analysis: A review. In M. Sigala, & U. Gretzel (Eds.), Advances in social media for travel, tourism and hospitality: New perspectives, practice and cases (pp. 243261). New York, NY: Routledge.

    • Search Google Scholar
    • Export Citation
  • Ge, Q., Humphreys, B.R., & Zhou, K. (2020). Are fair weather fans affected by weather? Rainfall, habit formation, and live game attendance. Journal of Sports Economics, 21(3), 304322. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • George, G., Haas, M.R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321326. doi:

  • George, G., Osinga, E.C., Lavie, D., & Scott, B.A. (2016). Big data and data science methods for management research. Academy of Management, 59(5), 14931507. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greene, W. (2004). The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects. The Econometrics Journal, 7(1), 98119. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hill, R.L., Kennedy, H., & Gerrard, Y. (2016). Visualizing junk: Big data visualizations and the need for feminist data studies. Journal of Communication Inquiry, 40(4), 331350. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, Y., & Skiena, S. (2010). The wisdom of bookies? Sentiment analysis versus the NFL point spread. In M. Hearst & W. Cohen (Eds.), Proceedings of the fourth international AAAI conference on weblogs and social media (pp. 251254). Menlo Park, CA: AAAI Press.

    • Search Google Scholar
    • Export Citation
  • Humphreys, B.R., & Johnson, C. (2020). The effect of superstars on game attendance: Evidence from the NBA. Journal of Sports Economics, 21(2), 152175. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huth, W.L., Eppright, D.R., & Taube, P.M. (1994). The indexes of consumer sentiment and confidence: Leading or misleading guides to future buyer behavior. Journal of Business Research, 29(3), 199206. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kendall, G., & Lenten, L.J. (2017). When sports rules go awry. European Journal of Operational Research, 257(2), 377394. doi:

  • Kennedy, H., & Moss, G. (2015). Known or knowing publics? Social media data mining and the question of public agency. Big Data & Society, 2(2), 111. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, J.W., Magnusen, M., & Lee, H.W. (2017). Existence of mixed emotions during consumption of a sporting event: A real-time measure approach. Journal of Sport Management, 31(4), 360373. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirilenko, A.P., Stepchenkova, S.O., Kim, H., & Li, X. (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 10121025. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiritchenko, S., Zhu, X., & Mohammad, S.M. (2014). Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50, 723762. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1, 112. doi:

  • Lancaster, T. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95(2), 391413. doi:

  • Lenten, L.J. (2016). Mitigation of perverse incentives in professional sports leagues with reverse-order drafts. Review of Industrial Organization, 49(1), 2541. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenten, L.J., Smith, A.C., & Boys, N. (2018). Evaluating an alternative draft pick allocation policy to reduce ‘tanking’ in the Australian Football League. European Journal of Operational Research, 267(1), 315320. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lopez, M.J. (2020). Bigger data, better questions, and a return to fourth down behavior: An introduction to a special issue on tracking data in the National Football League. Journal of Quantitative Analysis in Sports, 16(2), 7379. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, E., Cheng, M., & Hsiao, A. (2018). Sentiment analysis—A review and agenda for future research in hospitality contexts. International Journal of Contemporary Hospitality Management, 30(11), 32873308. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90, 6068. PubMed ID: 23074865

    • Search Google Scholar
    • Export Citation
  • McEvoy, C.D., Nagel, M.S., DeSchriver, T.D., & Brown, M.T. (2005). Facility age and attendance in Major League Baseball. Sport Management Review, 8(1), 1941. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGurk, Z., Nowak, A., & Hall, J.C. (2020). Stock returns and investor sentiment: Textual analysis and social media. Journal of Economics and Finance, 44(3), 458485. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McManus, J. (2019). Ethical considerations & the practice of tanking in sport management. Sport, Ethics and Philosophy, 13(2), 145160. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Medcalfe, S. (2009). Incentives and league structure in minor league baseball. Journal of Sport Management, 23(2), 119141. doi:

  • Mills, B.M. (2014). Social pressure at the plate: Inequality aversion, status, and mere exposure. Managerial and Decision Economics, 35(6), 387403. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mills, B.M., Salaga, S., & Tainsky, S. (2016). NBA primary market ticket consumers: Ex Ante expectations and consumer market origination. Journal of Sport Management, 30(5), 538552. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naraine, M.L. (2019). Follower segments within and across the social media networks of major professional sport organizations. Sport Marketing Quarterly, 28(4), 222233. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naraine, M.L., Pegoraro, A., & Wear, H. (2019). #WeTheNorth: Examining an online brand community through a professional sport organization’s hashtag marketing campaign. Communication & Sport. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neethu, M.S., & Rajasree, R. (2013). Sentiment analysis in Twitter using machine learning techniques. In 2013 fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 15), Tiruchengode, India. New York, NY: IEEE Press. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neyman, J., & Scott, E.L. (1948). Consistent estimates based on partially consistent observations. Econometrica: Journal of the Econometric Society, 16(1), 132. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nguyen, V.H., & Claus, E. (2013). Good news, bad news, consumer sentiment and consumption behavior. Journal of Economic Psychology, 39, 426438. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Numerato, D. (2016). Corruption and public secrecy: An ethnography of football match-fixing. Current Sociology, 64(5), 699717. doi:

  • Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting on Association for Computational Linguistics (pp. 271278), Barcelona, Spain. Stroudsburg, PA: Association for Computational Linguistics. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, B., Park, S., & Billings, A.C. (2020). Separating perceptions of Kaepernick from perceptions of his protest: An analysis of athlete activism, endorsed brand, and media effects. Communication & Sport, 8(4–5), 629650. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pérez, L. (2013). What drives the number of new Twitter followers? An economic note and a case study of professional soccer teams. Economics Bulletin, 33(3), 19411947.

    • Search Google Scholar
    • Export Citation
  • Premium Search APIs. (n.d.). Retrieved from https://developer.twitter.com/en/docs/twitter-api/v1/tweets/search/api-reference/premium-search

    • Search Google Scholar
    • Export Citation
  • Preston, I., & Szymanski, S. (2003). Cheating in contests. Oxford Review of Economic Policy, 19(4), 612624. doi:

  • Price, J., Soebbing, B.P., Berri, D., & Humphreys, B.R. (2010). Tournament incentives, league policy, and NBA team performance revisited. Journal of Sports Economics, 11(2), 117135. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Silver, N., & Fischer-Baum, R. (2015, May 21). How we calculate NBA Elo ratings. FiveThirtyEight.  Retrieved from

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shapiro, S.L., DeSchriver, T., & Rascher, D.A. (2012). Factors affecting the price of luxury suites in major North American sports facilities. Journal of Sport Management, 26(3), 249257. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soebbing, B.P., & Humphreys, B.R. (2013). Do gamblers think that teams tank? Evidence from the NBA. Contemporary Economic Policy, 31(2), 301313. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soebbing, B.P., Humphreys, B.R., & Mason, D.S. (2013). Exploring incentives to lose in professional team sports: Do conference games matter? International Journal of Sport Finance, 8, 192207.

    • Search Google Scholar
    • Export Citation
  • Soebbing, B.P., & Mason, D.S. (2009). Managing legitimacy and uncertainty in professional team sport: The NBA’s draft lottery. Team Performance Management, 15(3/4), 141157. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tainsky, S., & Jasielec, M. (2014). Television viewership of out-of-market games in league markets: Traditional demand shifters and local team influence. Journal of Sport Management, 28, 94108. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tainsky, S., Mills, B.M., & Winfree, J.A. (2015). Further examination of potential discrimination among MLB umpires. Journal of Sports Economics, 16(4), 353374. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, B.A., & Trogdon, J.G. (2002). Losing to win: Tournament incentives in the National Basketball Association. Journal of Labor Economics, 20(1), 2341. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vamplew, W. (2018). Tanking, shirking, and running dead: The role of economics and large data sets in identifying competition corruption and its causes. The International Journal of the History of Sport, 1, 116. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and Communication, 1(1), 214. doi:

  • Watanabe, N., Wicker, P., & Yan, G. (2017). Weather conditions, travel distance, rest, and running performance: The 2014 FIFA World Cup and implications for the future. Journal of Sport Management, 31(1), 2743. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watanabe, N., Yan, G., & Soebbing, B.P. (2015). Major league baseball and twitter usage: The economics of social media use. Journal of Sport Management, 29(6), 619632. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watanabe, N.M., Yan, G., Soebbing, B.P., & Pegoraro, A. (2017). Is there economic discrimination on sport social media? An analysis of major league baseball. Journal of Sport Management, 31(4), 374386. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Witkemper, C., Lim, C.H., & Waldburger, A. (2012). Social media and sports marketing: Examining the motivations and constraints of Twitter users. Sport Marketing Quarterly, 21, 170183.

    • Search Google Scholar
    • Export Citation
  • Wooldridge, J.M. (2016). Introductory econometrics: A modern approach. Mason, OH: Nelson Education.

  • Yan, G., Pegoraro, A., & Watanabe, N.M. (2020). Examining IRA bots in the NFL anthem protest: Political agendas and practices of digital gatekeeping. Communication & Sport, doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, G., Steller, D., Watanabe, N.M., & Popp, N. (2018). What determines user-generated content creation of college football? A big-data analysis of structural influences. International Journal of Sport Communication, 11(2), 219240. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, G., Watanabe, N.M., Shapiro, S.L., Naraine, M.L., & Hull, K. (2019). Unfolding the Twitter scene of the 2017 UEFA champions league final: Social media networks and power dynamics. European Sport Management Quarterly, 19(4), 419436. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, Y., & Wang, X. (2015). World Cup 2014 in the Twitter world: A big data analysis of sentiments in US sports fans’ tweets. Computers in Human Behavior, 48, 392400. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
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