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.
On National Teacher Day, meet the 2024-25 Kenan Fellows
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)
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)
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
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