Visualizing Sports Rivalry with Social Network Analysis
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Tyler, B.D. & Cobbs, J. 2014. "Visualizing rivalry intensity: A social network analysis of fan perceptions," North American Society for Sport Management (NASSM) Annual Conference, Pittsburgh, PA. Central to the conceptualization of rivalry is the process of social categorization and seeing the self and others as members of ingroups and outgroups. For some sport fans—especially those deemed highly identified—a favorite team becomes an extension of one’s self, and opposing teams and their fans are seen as dissimilar outgroups. Akin to other definitions, we view a rival as being a highly salient outgroup that poses an acute threat to the identity of the ingroup. To bring further clarity and consistency to the rivalry discussion, we quantify the perceived rivalries within a closed network of organizations by surveying college football fans (n=5,317) from 122 Football Bowl Subdivision (FBS, or Division I-A) teams using on an online questionnaire posted on 194 fan message boards. Through employing social network analysis (SNA), we graphically map rivalry scores in Netdraw and conduct further statistical analysis via UCINET SNA software. The network analysis results are most interesting when viewed graphically as nodes (universities) with bi-directional ties among them of various magnitude. In the study, we employ SNA measures of ego networks, centrality and power to reveal insights about the nature of rivalry.
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Visualizing Sports Rivalry with Social Network Analysis
- 1. North American Society for Sport Management. Pittsburgh, PA. Friday, May 30, 2014 Visualizing rivalry intensity: A social network analysis of fan perceptions Joe B. Cobbs Northern Kentucky University B. David Tyler Western Carolina University
- 2. Tyler & Cobbs 2NASSM 2014, Pittsburgh, PA What is rivalry? What’s a rival? …an actor which increases the focal actor’s psychological involvement… Kilduff et al., 2010 outgroup Luellen & Wann, 2010 disliked competitor Dalakas & Melancon, 2012 adversarial relationship… gaining significance through competition, incidences, proximity, demographic makeup, or historical occurrences Havard et al., 2013 I know it when I see it Forrest et al., 2005 A highly salient outgroup that poses an acute threat to the identity of the ingroup or to ingroup members’ ability to make positive comparisons between their group and the outgroup Tyler & Cobbs, under review Divisional opponent McDonald & Rascher, 2000 Shared border Morley & Thomas, 2007 Teams under 20 mi. apart Baimbridge et al., 1995
- 3. Tyler & Cobbs 3NASSM 2014, Pittsburgh, PA Why do we care? – Demand estimation 𝐴 = 𝛽0 + 𝐵𝑋 + 𝑒
- 4. Tyler & Cobbs 4NASSM 2014, Pittsburgh, PA Why do we care? – Behavior toward rivals
- 5. Tyler & Cobbs 5NASSM 2014, Pittsburgh, PA Why do we care? – Driving consumption
- 6. Tyler & Cobbs 6NASSM 2014, Pittsburgh, PA Why do we care? – Limit fan aggression
- 7. Tyler & Cobbs 7NASSM 2014, Pittsburgh, PA Why do we care? – Sponsor activation
- 8. Tyler & Cobbs 8NASSM 2014, Pittsburgh, PA Why do we care? – Contract incentives
- 9. Tyler & Cobbs 9NASSM 2014, Pittsburgh, PA How can we know a rivalry’s intensity? Binary approaches • Shared border • Divisional opponent • Naming rivalries Variable approaches • Distance • MRI (hasn’t been done) • Collecting data on specific dyads (current study)
- 10. Tyler & Cobbs 10NASSM 2014, Pittsburgh, PA METHOD
- 11. Tyler & Cobbs 11NASSM 2014, Pittsburgh, PA Method - Population Surveyed college football fans (n=5,317) 122 FBS (DI-A) teams 194 fan message boards Identified with favorite team (µ=5.2/7.0)
- 12. Tyler & Cobbs 12NASSM 2014, Pittsburgh, PA Method – Rivalry points allocation
- 13. Tyler & Cobbs 13NASSM 2014, Pittsburgh, PA RESULTS
- 14. Tyler & Cobbs 14NASSM 2014, Pittsburgh, PA Dyadic relationships 44.2 2.9 59.3 32.5 25.4 0.7 Sees other as a rival
- 15. Tyler & Cobbs 15NASSM 2014, Pittsburgh, PA Dyadic relationships 16.8 4.0 66.8 68.8 90.7 0.3 Sees other as a rival
- 16. Tyler & Cobbs 16NASSM 2014, Pittsburgh, PA National Rivalry Network Line width: average point allocation (> 5; 100 max) Node size: in-degree centrality Node color: conference
- 17. Tyler & Cobbs 17NASSM 2014, Pittsburgh, PA Most focused rivalries Based on aggregate score 159.6 171.8 182.6 #3 #2 #1
- 18. Tyler & Cobbs 18NASSM 2014, Pittsburgh, PA Biggest rivals Line width: average point allocation (>50 in either direction; 100 max) Node size: in-degree centrality Node color: conference
- 19. Tyler & Cobbs 19NASSM 2014, Pittsburgh, PA Ego networks Wisconsin – most ‘cohesive’ ego network (density=81.9) Rivals connected to other rivals Tie strength > 5
- 20. Tyler & Cobbs 20NASSM 2014, Pittsburgh, PA Ego networks Wisconsin – most ‘cohesive’ ego network (density=81.9) Rivals connected to other rivals Tie strength > 3
- 21. Tyler & Cobbs 21NASSM 2014, Pittsburgh, PA Social capital Notre Dame – 2nd most powerful network Bonacich power: est. social capital by centrality of alters Alabama most powerful Tie strength > 5
- 22. Tyler & Cobbs 22NASSM 2014, Pittsburgh, PA SEC Network (tie >5)
- 23. Tyler & Cobbs 23NASSM 2014, Pittsburgh, PA DISCUSSION Map from http://plvcolin.blogspot.com
- 24. Tyler & Cobbs 24NASSM 2014, Pittsburgh, PA Implications Nature of “rivalry” Start of a parsimonious measure of rivalry Marketing & sponsorship Event management League structure • Conference realignment, promotion & relegation
- 25. Tyler & Cobbs 25NASSM 2014, Pittsburgh, PA Next steps Refine survey based on findings Extend to other sport leagues Increase knowledge of rivalries themselves (e.g., antecedents)
- 26. North American Society for Sport Management. Pittsburgh, PA. Friday, May 30, 2014 Visualizing rivalry intensity: A social network analysis of fan perceptions Joe B. Cobbs Northern Kentucky University B. David Tyler Western Carolina University
Editor's Notes
- Many definitions out there Our definition Based in SIT/SCT Classify the self & others One’s group is an extension of oneself Make favorable comparisons with an outgroup (get positive esteem) The words don’t matter to fans Who is a rival is subject to the fans’ perceptions Our focus is on fans, and we did not provide a definition
- Why is it important to know who is/is not a rival? From academic side: Demand estimation models Stadium attendance TV
- More likely to be unethical with rivals than other competitors (Kilduff et al., 2012) Unsportsmanlike behavior Adversarial negotiating
- From managerial side: Driving attendance
- Managing aggression Physiological reaction Yankees fans reacted more on MRI when watching Red Sox plays than Orioles plays (Cikara, Botvinick, & Fiske, 2011) Physical distance perceived as closer Yankees fans estimated Fenway to be closer than non-Yankees fans but this didn’t happen with Camden Yards (Xiao & Van Bavel, 2012)
- Sponsorship activation
- Coaching bonuses in contracts Whether a coach is going to get fired (Holmes, 2011)
- Binary Shared border Country, county Divisional opponent Yankees vs Red Sox is same as Yankees vs Orioles (which we know isn’t true) Naming rivalries By researchers By panel of experts Variable Distance – closer the distance the bigger the rival MRI – hasn’t
- Online survey Focus on fans’ perceptions Identification scale adopted from Mael & Ashforth, 1992
- Calculated average point totals for each direction of a dyadic relationship Also had other, more established measures, which correlated with the points allocation Behavioral measures of bias (Pettigrew & Meertens, 1995) Feelings toward others Schadenfreude toward opponents and/or their fans (Dalakas & Melancon, 2012)
- Two key points Bi-directional Not necessarily discrete (i.e., there can be more than one rival and it’s not a binary construct)
- Two key points Bi-directional Not necessarily discrete (i.e., there can be more than one rival and it’s not a binary construct)
- density measures the existence of ties as a percentage of all possible ties, which is frequently employed in SNA as an indicator of group cohesion (Blau, 1977)
- density measures the existence of ties as a percentage of all possible ties, which is frequently employed in SNA as an indicator of group cohesion (Blau, 1977)
- Nodes sized by directional betweenness centrality. NOTE largest nodes have non-conference rivals (connect/bridge otherwise unconnected alters)
- Map from http://plvcolin.blogspot.com/2012/09/college-football-fan-base-map.html
- Nature of rivalry (academic research) Bi-directional Not binary Not discrete (more than 1 per team) Marketing & sponsorship Generating demand for tickets & TV Activating sponsorships Event management Fan aggression Start of a parsimonious measure We’d love to do MRIs on all fans, but that’s not realistic Need to do more work to validate the point allocation approach