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North American Society for Sport Management. Pittsburgh, PA. Friday, May 30, 2014
Visualizing rivalry intensity:
A social ...
Tyler & Cobbs 2NASSM 2014, Pittsburgh, PA
What is rivalry? What’s a rival?
…an actor which increases the
focal actor’s psy...
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
• Div...
Tyler & Cobbs 10NASSM 2014, Pittsburgh, PA
METHOD
Tyler & Cobbs 11NASSM 2014, Pittsburgh, PA
Method - Population
 Surveyed college football fans (n=5,317)
 122 FBS (DI-A)...
Tyler & Cobbs 12NASSM 2014, Pittsburgh, PA
Method – Rivalry points allocation
Tyler & Cobbs 13NASSM 2014, Pittsburgh, PA
RESULTS
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
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
Tyler & Cobbs 16NASSM 2014, Pittsburgh, PA
National Rivalry Network
Line width: average point allocation (> 5; 100 max)
No...
Tyler & Cobbs 17NASSM 2014, Pittsburgh, PA
Most focused rivalries
Based 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; 1...
Tyler & Cobbs 19NASSM 2014, Pittsburgh, PA
Ego networks
Wisconsin – most ‘cohesive’ ego network
(density=81.9)
Rivals conn...
Tyler & Cobbs 20NASSM 2014, Pittsburgh, PA
Ego networks
Wisconsin – most ‘cohesive’ ego network
(density=81.9)
Rivals conn...
Tyler & Cobbs 21NASSM 2014, Pittsburgh, PA
Social capital
Notre Dame – 2nd most powerful
network
Bonacich power: est. soci...
Tyler & Cobbs 22NASSM 2014, Pittsburgh, PA
SEC Network (tie >5)
Tyler & Cobbs 23NASSM 2014, Pittsburgh, PA
DISCUSSION
Map from http://plvcolin.blogspot.com
Tyler & Cobbs 24NASSM 2014, Pittsburgh, PA
Implications
 Nature of “rivalry”
 Start of a parsimonious measure of rivalry...
Tyler & Cobbs 25NASSM 2014, Pittsburgh, PA
Next steps
 Refine survey based on findings
 Extend to other sport leagues
 ...
North American Society for Sport Management. Pittsburgh, PA. Friday, May 30, 2014
Visualizing rivalry intensity:
A social ...
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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. 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. 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. 3. Tyler & Cobbs 3NASSM 2014, Pittsburgh, PA Why do we care? – Demand estimation 𝐴 = 𝛽0 + 𝐵𝑋 + 𝑒
  4. 4. Tyler & Cobbs 4NASSM 2014, Pittsburgh, PA Why do we care? – Behavior toward rivals
  5. 5. Tyler & Cobbs 5NASSM 2014, Pittsburgh, PA Why do we care? – Driving consumption
  6. 6. Tyler & Cobbs 6NASSM 2014, Pittsburgh, PA Why do we care? – Limit fan aggression
  7. 7. Tyler & Cobbs 7NASSM 2014, Pittsburgh, PA Why do we care? – Sponsor activation
  8. 8. Tyler & Cobbs 8NASSM 2014, Pittsburgh, PA Why do we care? – Contract incentives
  9. 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. 10. Tyler & Cobbs 10NASSM 2014, Pittsburgh, PA METHOD
  11. 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. 12. Tyler & Cobbs 12NASSM 2014, Pittsburgh, PA Method – Rivalry points allocation
  13. 13. Tyler & Cobbs 13NASSM 2014, Pittsburgh, PA RESULTS
  14. 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. 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. 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. 17. Tyler & Cobbs 17NASSM 2014, Pittsburgh, PA Most focused rivalries Based on aggregate score 159.6 171.8 182.6 #3 #2 #1
  18. 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. 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. 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. 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. 22. Tyler & Cobbs 22NASSM 2014, Pittsburgh, PA SEC Network (tie >5)
  23. 23. Tyler & Cobbs 23NASSM 2014, Pittsburgh, PA DISCUSSION Map from http://plvcolin.blogspot.com
  24. 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. 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. 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

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