visualizing sports rivalry with social network analysis
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North American Society for Sport Management. Pittsburgh, PA. Friday, May 30, 2014
Visualizing rivalry intensity:
A social network analysis of fan perceptions
Joe B. CobbsNorthern Kentucky
University
B. David TylerWestern Carolina
University
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
Tyler & Cobbs 3NASSM 2014, Pittsburgh, PA
Why do we care? – Demand estimation
𝐴=𝛽0+𝐵𝑋+𝑒
Tyler & Cobbs 4NASSM 2014, Pittsburgh, PA
Why do we care? – Behavior toward rivals
Tyler & Cobbs 5NASSM 2014, Pittsburgh, PA
Why do we care? – Driving consumption
Tyler & Cobbs 6NASSM 2014, Pittsburgh, PA
Why do we care? – Limit fan aggression
Tyler & Cobbs 7NASSM 2014, Pittsburgh, PA
Why do we care? – Sponsor activation
Tyler & Cobbs 8NASSM 2014, Pittsburgh, PA
Why do we care? – Contract incentives
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)
Tyler & Cobbs 10NASSM 2014, Pittsburgh, PA
METHOD
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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)
Tyler & Cobbs 12NASSM 2014, Pittsburgh, PA
Method – Rivalry points allocation
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RESULTS
Tyler & Cobbs 14NASSM 2014, Pittsburgh, PA
Dyadic relationships
44.2
2.959.3
32.5
25.4
0.7
Sees other as a rival
Tyler & Cobbs 15NASSM 2014, Pittsburgh, PA
Dyadic relationships
16.8
4.066.8
68.8
90.7
0.3
Sees other as a rival
Tyler & Cobbs 16NASSM 2014, Pittsburgh, PA
National Rivalry Network
Line width: average point allocation (> 5; 100 max)Node size: in-degree centralityNode color: conference
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Most focused rivalriesBased on aggregate score
159.6
171.8
182.6
#3
#2
#1
Tyler & Cobbs 18NASSM 2014, Pittsburgh, PA
Biggest rivals
Line width: average point allocation (>50 in either direction; 100 max)
Node size: in-degree centralityNode color: conference
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Ego networksWisconsin – most ‘cohesive’ ego network
(density=81.9)Rivals connected to other rivalsTie strength > 5
Tyler & Cobbs 20NASSM 2014, Pittsburgh, PA
Ego networksWisconsin – most ‘cohesive’ ego network
(density=81.9)Rivals connected to other rivalsTie strength > 3
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 powerfulTie strength > 5
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SEC Network (tie >5)
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DISCUSSION
Map from http://plvcolin.blogspot.com
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
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)
North American Society for Sport Management. Pittsburgh, PA. Friday, May 30, 2014
Visualizing rivalry intensity:
A social network analysis of fan perceptions
Joe B. CobbsNorthern Kentucky
University
B. David TylerWestern Carolina
University
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