Dealing With Statistical Significance in Big Data: The Social Media Value of Game Outcomes in Professional Football

in Journal of Sport Management
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  • 1 University of Duisburg-Essen
  • 2 University of Alberta
  • 3 Bielefeld University
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The identification of relevant effects is challenging in Big Data because larger samples are more likely to yield statistically significant effects. Professional sport teams attempting to identify the core drivers behind their follower numbers on social media also face this challenge. The purposes of this study are to examine the effects of game outcomes on the change rate of followers using big social media data and to assess the relative impact of determinants using dominance analysis. The authors collected data of 644 first division football clubs from Facebook (n = 297,042), Twitter (n = 292,186), and Instagram (n = 312,710) over a 19-month period. Our fixed-effects regressions returned significant findings for game outcomes. Therefore, the authors extracted the relative importance of wins, draws, and losses through dominance analysis, indicating that a victory yielded the highest increase in followers. For practitioners, the findings present opportunities to develop fan engagement, increase the number of followers, and enter new markets.

Weimar is with the Department of Managerial Economics, Mercator School of Management, University of Duisburg-Essen,  Duisburg, Germany. Soebbing is with the University of Alberta, Edmonton, Alberta, Canada. Wicker is with the Department of Sports Science, Bielefeld University, Bielefeld, Germany.

Wicker (pamela.wicker@uni-bielefeld.de) is corresponding author.
  • Abeza, G., O’Reilly, N., Seguin, B., & Nzinduklyimana, O. (2015). Social media scholarship in sport management research: A critical review. Journal of Sport Management, 29(6), 601618. doi:10.1123/JSM.2014-0296

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, M. (1985). Stardom and talent. The American Economic Review, 75(1), 208212.

  • Ahammer, A., Halla, M., & Lackner, M. (2020). Mass gathering contributed to early COVID-19 spread: Evidence from US sports. IDEAS. Retrieved from https://ideas.repec.org/p/jku/cdlwps/wp2003.html

    • Search Google Scholar
    • Export Citation
  • Baerg, A. (2017). Big data, sport, and the digital divide: Theorizing how athletes might respond to big data monitoring. Journal of Sport and Social Issues, 41(1), 320. doi:10.1177/0193723516673409

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baltagi, B.H. (2008). Econometric analysis of panel data (4th ed.). Chichester, UK: John Wiley.

  • Borland, J., & McDonald, R. (2003). Demand for sport. Oxford Review of Economic Policy, 19(4), 478502. doi:10.1093/oxrep/19.4.478

  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662679. doi:10.1080/1369118X.2012.678878

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bring, J. (1994). How to standardize regression coefficients? The American Statistician, 48(3), 209213.

  • Budescu, D.V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542551. doi:10.1037/0033-2909.114.3.542

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burk, V., Grimmer, C.G., & Pawlowski, T. (2016). “Same, same-but different!” On consumers’ use of corporate PR media in sports. Journal of Sport Management, 30(4), 353368. doi:10.1123/jsm.2015-0180

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cardazzi, A., Humphreys, B.R., Ruseski, J.E., Soebbing, B.P., & Watanabe, N. (2020). Professional sporting events increase seasonal influenza mortality in US cities. Economics Faculty Working Papers Series. doi:10.2139/ssrn.3628649

    • Search Google Scholar
    • Export Citation
  • Clavio, G. (2011). Social media and the college football audience. Journal of Issues in Intercollegiate Athletics, 4, 309325.

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.

  • Cornfield, J. (1966). Sequential trials, sequential analysis and the likelihood principle. The American Statistician, 20(2), 1823.

  • Cortes, R., Bonnaire, X., Marin, O., & Sens, P. (2015). Stream processing of healthcare sensor data: Studying user traces to identify challenges from a big data perspective. Procedia Computer Science, 52, 10041009. doi:10.1016/j.procs.2015.05.093

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deutscher, C., Gürtler, O., Prinz, J., & Weimar, D. (2017). The payoff to consistency in performance. Economic Inquiry, 55(2), 10911103. doi:10.1111/ecin.12415

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eagleman, A.N. (2013). Acceptance, motivations, and usage of social media as a marketing communications tool amongst employees of sport national governing bodies. Sport Management Review, 16(4), 488497. doi:10.1016/j.smr.2013.03.004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feddersen, A., Humphreys, B.R., & Soebbing, B.P. (2017). Sentiment bias and asset prices: Evidence from sports betting markets and social media. Economic Inquiry, 55(2), 11191129. doi:10.1111/ecin.12404

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1), 115. doi:10.1177/2053951716645828

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Filo, K., Lock, D., & Karg, A. (2015). Sport and social media research: A review. Sport Management Review, 18,(2) 166181. doi:10.1016/j.smr.2014.11.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Franck, E., & Nüesch, S. (2008). Mechanisms of superstar formation in German soccer: Empirical evidence. European Sport Management Quarterly, 8(2), 145164. doi:10.1080/16184740802024450

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Franck, E., & Nüesch, S. (2012). Talent and/or popularity: What does it take to be a superstar? Economic Inquiry, 50(1), 202216. doi:10.1111/j.1465-7295.2010.00360.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • George, G., Haas, M., & Pentland, A. (2014). From the editors: Big data and management. Academy of Management Journal, 57(2), 321326. doi:10.5465/amj.2014.4002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greenland, S. (2019). Valid P-values behave exactly as they should: Some misleading criticisms of P-values and their resolution with S-values. The American Statistician, 73, 106114. doi:10.1080/00031305.2018.1529625

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grömping, U. (2015). Variable importance in regression models. Wiley Interdisciplinary Reviews: Computational Statistics, 7(2), 137152. doi:10.1002/wics.1346

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hutchins, B. (2016). Tales of the digital sublime: Tracing the relationship between big data and professional sport. Convergence: The International Journal of Research into New Media Technologies, 22(5), 494509. doi:10.1177/1354856515587163

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, J.A., Ervin, S.M., & Dittmore, S.W. (2014). Exploring the factors affecting popularity in social media: A case study of football bowl subdivision head coaches. International Journal of Sport Communication, 7(2), 261278. doi:10.1123/IJSC.2014-0008

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, J.W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35(1), 119. PubMed ID: 26777229 doi:10.1207/S15327906MBR3501_1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kempthorne, O., & Folks, L. (1971). Probability, statistics, and data analysis. Ames, IA: Iowa State University Press.

  • Kiefer, S. (2014). The impact of the Euro 2012 on popularity and market value of football players. International Journal of Sport Finance, 9, 95110.

    • Search Google Scholar
    • Export Citation
  • Kiefer, S., & Scharfenkamp, K. (2018). Does online media put beauty before performance? The impact of physical attractiveness on the popularity of female tennis players in online media. International Journal of Sport Finance, 13, 156182.

    • Search Google Scholar
    • Export Citation
  • Lindeman, R.H., Merenda, P.F., & Gold, R.Z. (1980). Introduction to bivariate and multivariate analysis. Glenview, IL: Scott Foresman and company.

    • Search Google Scholar
    • Export Citation
  • Lindley, D.V., & Scott, W.F. (1984). New cambridge statistical tables. Cambridge, UK: Cambridge University Press.

  • McManus, J. (2018). Ethical considerations & the practice of tanking in sport management. Sport, Ethics, and Philosophy, 13(2), 145160. doi:10.1080/17511321.2018.1483418

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Millington, B., & Millington, R. (2015). ‘The Datafication of Everything’: Toward a sociology of sport and big data. Sociology of Sport Journal, 32(2), 140160. doi:10.1123/ssj.2014-0069

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morgulev, E., Azar, O.H., & Lidor, R. (2018). Sports analytics and the big-data era. International Journal of Data Science and Analytics, 5(4), 213222. doi:10.1007/s41060-017-0093-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mumcu, C., & Fried, G. (2017). Analytics in sport marketing. Sport Management Education Journal, 11(2), 102105. doi:10.1123/smej.2016-0019

  • Murdoch, T.B., & Detsky, A.S. (2013). The inevitable application of big data to health care. Journal of the American Medical Association, 309(13), 13511352. PubMed ID: 23549579 doi:10.1001/jama.2013.393

    • 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, 19411947.

    • Search Google Scholar
    • Export Citation
  • Popp, B., Horbel, C., & Germelmann, C.C. (2018). Social-media-based antibrand communities opposing sport-team sponsors: Insights from two prototypical communities. International Journal of Sport Communication, 11(3), 339368. doi:10.1123/ijsc.2018-0082

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prinz, J., Weimar, D., & Deutscher, C. (2012). Popularity kills the Talentstar? Einflussfaktoren auf Superstargehälter in der NBA. Zeitschrift für Betriebswirtschaft, 82(7–8), 789806. doi:10.1007/s11573-012-0587-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pronschinske, M., Groza, M., & Walker, M. (2012). Attracting Facebook ‘fans’: The importance of authenticity and engagement as a social networking strategy for professional sport teams. Sport Marketing Quarterly, 21, 221231.

    • Search Google Scholar
    • Export Citation
  • Reade, J.J., Schreyer, D., & Singleton, C. (2020). Echoes: What happens when football is played behind closed doors? SSRN Electronic Journal. doi:10.2139/ssrn.3630130

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science. Springer Plus, 5, 1410. doi:10.1186/s40064-016-3108-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richardson, J.T. (2011). Eta squared and partial eta squared as measures of effect size in educational research. Educational Research Review, 6(2), 135147. doi:10.1016/j.edurev.2010.12.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rojas-Valverde, D., Gomez-Carmona, C.D., Gutierrez-Vargas, R., & Pino-Ortega, J. (2019). From big data mining to technical sport reports: The case of inertial measurement units. BMJ Open Sport & Exercise Medicine, 5(1), e000565. PubMed ID: 31673403 doi:10.1136/bmjsem-2019-000565

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Royall, R.M. (1986). The effect of sample size on the meaning of significance tests. The American Statistician, 40(4), 313315.

  • Santos, T., Correia, A., Biscaia, R., & Pegoraro, A. (2019). Examining fan engagement through social networking sites. International Journal of Sports Marketing & Sponsorship, 20(1), 163183. doi:10.1108/IJSMS-05-2016-0020

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sivarajah, U., Mustafa Kamal, M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263286. doi:10.1016/j.jbusres.2016.08.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spaaij, R., & Thiel, A. (2017). Big data: Critical questions for sport and society. European Journal for Sport and Society, 14(1), 14. doi:10.1080/16138171.2017.1288374

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stigler, G.J., & Becker, G.S. (1977). De gustibus non est disputandum. The American Economic Review, 67(2), 7690.

  • Tonidandel, S., & LeBreton, J.M. (2011). Relative importance analysis: A useful supplement to regression analysis. Journal of Business and Psychology, 26(1), 19. doi:10.1007/s10869-010-9204-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watanabe, N.M., 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:10.1123/JSM.2014-0229

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Watanabe, N.M., Yan, G., & Soebbing, B.P. (2016). Consumer interest in Major League Baseball: An analytical modeling of Twitter. Journal of Sport Management, 30(2), 207220. doi:10.1123/jsm.2015-0121

    • 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:10.1123/jsm.2016-0244

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waters, R.D., Burke, K.A., Jackson, Z.H., & Buning, J.D. (2011). Using stewardship to cultivate fandom online: Comparing how National Football League teams use their websites and Facebook to engage their fans. International Journal of Sport Communication, 4(2), 163177. doi:10.1123/ijsc.4.2.163

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weimar, D., Holthoff, L.C., & Biscaia, R. (2020). When sponsorship causes anger: Understanding negative fan reactions to postings on sports clubs’ online social media channels. European Sport Management Quarterly. Advance online publication. doi:10.1080/16184742.2020.1786593

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weimar, D., Wicker, P., & Prinz, J. (2015). Membership in nonprofit sport clubs: A dynamic panel analysis of external organizational factors. Nonprofit and Voluntary Sector Quarterly, 44(3), 417436. doi:10.1177/0899764014548425

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wicker, P., & Breuer, C. (2013). Exploring the critical determinants of organisational problems using Data Mining techniques: Evidence from non-profit sports clubs in Germany. Managing Leisure, 18(2), 118134. doi:10.1080/13606719.2013.752211

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wicker, P., & Breuer, C. (2014). Exploring the organizational capacity and organizational problems of disability sport clubs in Germany using matched pairs analysis. Sport Management Review, 17(1), 2334. doi:10.1016/j.smr.2013.03.005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilkerson, M., & Olson, M.R. (1997). Misconceptions about sample size, statistical significance, and treatment effect. The Journal of Psychology, 131(6), 627631. doi:10.1080/00223989709603844

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, G., Pegoraro, A., & Watanabe, N.M. (2018). Student-athletes’ organization of activism at the University of Missouri: Resource mobilization on Twitter. Journal of Sport Management, 32(1), 2437. doi:10.1123/jsm.2017-0031

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, G., Steller, D., Watanabe, N., & 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:10.1123/ijsc.2017-0113

    • 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:10.1080/16184742.2018.1517272

    • 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 U.S. sports fans’ tweets. Computers in Human Behavior, 48, 392400. doi:10.1016/j.chb.2015.01.075

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ziliak, S.T., & McCloskey, D.N. (2008). The cult of statistical significance (economics, cognition & society). Ann Arbor, MI: University of Michigan Press.

    • Search Google Scholar
    • Export Citation
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