Compliance by Hong Kong’s National Sport Organizations With the World Anti-Doping Program

Abstract

 

The present study aimed to assess current anti-doping efforts among Hong
Kong’s national sport organizations (NSOs), for example
organizations’ readiness to change and to initiate or strengthen
anti-doping measures. The points of view of administrators, coaches,
and committee members were considered. A great majority of NSOs in Hong Kong appeared to be at the
contemplation stage, concerning anti-doping actions. The major
constraints they faced were limited funds and manpower.


The World Anti-Doping Program, developed by the World Anti-Doping
Agency (WADA), is structured in three levels: a World Anti-Doping
Code, international standards, and models of and guidelines for best
practices. WADA officials state that one purpose of the World
Anti-Doping Program and code is “to ensure harmonized, coordinated,
and effective anti-doping programs at the international and national
level with regard to detection, deterrence, and prevention of doping”
(World Anti-Doping Agency, 2003). We would like to suggest that the
program actually can serve two purposes. On the macro level, it can
provide various international federations and national anti-doping
organizations (NADOs) with a framework for developing anti-doping
policies, rules, and regulations. On a micro level, it can guide
national sport organizations (NSOs) in carrying out anti-doping
functions like educational programming and in adopting appropriate
practices to demonstrate compliance with various anti-doping
regulations.
The World Anti-Doping Code has been in place for over 5 years, so the
roles of international federations and NADOs in promoting and
monitoring athletes’ anti-doping behaviors should be clear to sport
organizations and professionals involved in high-level competition
(e.g., World Games, Olympics). Those not involved at that level may
be less familiar with arrangements, for instance coaches and
administrators of NSOs that have not produced athletes qualifying for
high-level competitions. Even NSOs with experience in high-level
competition may have second- or third-tier athletes lacking the
exposure their elite counterparts have had. Given that NSOs play a
significant role in communicating anti-doping information to athletes
and explaining their role in anti-doping regulations, the evaluation
of NSOs’ current practices is important. The present study provided
such an evaluation, using a case-study approach to determine the
extent of Hong Kong NSOs’ compliance with the anti-doping program.
Specifically, we aimed to assess whether Hong Kong’s NSOs were
implementing anti-doping functions, as well as to identify
constraints on their full compliance. Although the study involved
only Hong Kong organizations, knowledge gained should be applicable
in countries with similar anti-doping experience, and the study
should thus prove useful to international federations, NADOs, and
WADA as they direct resources and efforts.
Since to an extent NSOs are organizations whose anti-doping
compliance or noncompliance can be treated as the adoption of one
management practice over another, their anti-doping compliance can be
modeled as organizational change. We therefore reviewed such models
and chose Prochaska’s transtheoretical model (TTM) (Prochaska,
2000) to analyze NSO anti-doping functions. The popular TTM was
originally developed to explain behavioral change in individuals
(Prochaska, Prochaska, & Levesque, 2001).
Central to the TTM are three theoretical constructs related to
change: (a) stages of change, (b) decisional balance, and (c) process
of change. Intentional change—whether by an individual or an
organization—can occur in stages and so can be seen as a series of
movements along a continuum. There are six such movements or stages:
pre-contemplation, contemplation, preparation, action,
maintenance
, and termination. The terminology process
of change,
in contrast, connotes the belief that change is
influenced by both overt and covert activities that comprise
experiential processes and behavioral processes.
Experiential processes characterize the early-stage transition and
include consciousness raising, dramatic relief, environmental
reevaluation, social liberation,
and self-reevaluation.
Behavioral processes characterize later-stage transition and include
stimulus control, helping relationship, counter conditioning,
reinforcement management,
and self-liberation.
In sum, the TTM provides an opportunity to understand the temporal
ordering of events as an established pattern is changed, which is
what we intended to do in terms of the NSOs’ implementation of
anti-doping functions. It also provides opportunity to explore
mechanisms mediating intentional change (e.g., constraints on
implementation of anti-doping functions). An additional rationale for
adopting the model was its prior successful application in an
analysis of family-service agencies (Prochaska, 2000), a study of the
implementation of a system of “time-limited therapy” that has
notable parallels to the implementation of anti-doping functions.

 

Method

 

Design of Questionnaire

The three versions of the self-report instrument used in the present
study were developed with input from three NSOs of different sizes,
whose staffs were invited to participate in face-to-face interviews
with a member of the research team experienced in anti-doping works.
During these interviews, the purpose and procedures of the study were
clarified for the NSOs, and items for inclusion in the questionnaire,
as well as in a structured interview, were identified. NSOs
participating in these preliminary interviews did not participate in
the study itself.

 

Collection of Survey Data

A letter of invitation to participate in the research project and
three copies of the final questionnaire were delivered to each NSO in
Hong Kong (except the three involved in instrument development).
Follow-up telephone calls were made to confirm the organizations’
interest in participating. NSOs that volunteered to participate were
scheduled for interviews with research team members. Completed
questionnaires were collected during or after an interview session.
The three versions of the study questionnaire included one for NSO
administrators, one for NSO coaches, and one for NSO committee
members. All versions included Part 1 and Part 2; the version for
administrators contained an additional three parts. Part 1 of the
questionnaire represented a modification of the Readiness to Change
Questionnaire (RTCQ) (Rollnick, Heather, Gold, & Hall, 1992). The
original RTCQ, designed to study drinking behavior, is a 12-item
questionnaire that assigns excessive drinkers to either the
precontemplation, contemplation, or action stages
(Heather, Gold, & Rollnick, 1991). For the present study, the
modified questionnaire assessed each NSO’s readiness to increase
its anti-doping efforts. Part 2 of the questionnaire was based on the
early interviews with the three NSOs not generating study data. From
these interviews, a list of pros and cons of increased anti-doping
efforts was developed. Part 2 asked respondents to rate the
importance of these pros and cons as influences on the NSO’s
decisions about increasing or not increasing anti-doping work.
Finally, Parts 3, 4, and 5 of the questionnaire were directed to NSO
administrators only and collected information about (a) spending on
anti-doping works, (b) opinions about anti-doping education programs,
and (c) an NSO’s demographic information.


Collection of Interview Data

Two members of the research team conducted structured face-to-face
interviews with representatives of NSOs who were either
administrators, committee members, or senior coaches. All were
familiar with their NSO’s anti-doping works. Standard questions
were posed initially, with a respondent’s answers guiding a series
of appropriate follow-up questions.

 

Results

A total of 62 invitations were sent to NSOs in Hong Kong to
participate in the research project, and 44 NSOs returned completed
questionnaires, a response rate of 71%. Interviews were completed
with 42 NSOs’ representatives, a response rate of 67.7%.

National
Sport Organization Demographics

The participating NSOs’ demographics provide a rough idea of the
scope of Hong Kong’s locally organized sport. Tables 1–4 present
the numbers of athletes, of coaches, and of competitions organized by
or participated in by our respondents. Most of the NSOs had fewer
than 5 full-time and 5 part-time employees. A majority (77.1%) had
fewer than 50 athletes active in international events that were
endorsed by an international federation. Over half of the surveyed
NSOs (60.6%) had 50–200 Level-1 coaches, while about half (57.6%
and 51.5%, respectively) had fewer than 31 Level-2 coaches and fewer
than 6 Level-3 coaches. About half of the NSOs organized fewer than
10 local competitions per year, and 65% organized 0–1 international
event annually. About 63% of the NSOs sent athletes to 1–5
international competitions each year.

 

Table
1

 

Numbers
of Employees at Hong Kong’s National Sport Organizations, With
Percentage of All Surveyed NSOs Having Similar Numbers

 

Full-time Part-time
Count % Count %
0 2 4.8 20 48.8 1–5 28 66.7 20 48.8 >5 12 28.6 1 2.4 Total 42 100 41 100

Table 2

 

Numbers
of Athletes Within Hong Kong’s National Sport Organizations, By
Competitive Event Type, With Percentage of All Surveyed NSOs Having
Similar Numbers

 

100

26

100

International Eventa Other Event
Count % Count %
0–10 7 20.0 1 3.8 11–50 20 57.1 5 19.2 51–100 4 11.4 9 34.6 101–200 3 8.6 2 7.7 > 200 1 2.9 9 34.6 Total 35

 

aFor
purposes of this study, an international event is a competition
endorsed by an appropriate international federation.
Table 3

 

Numbers
of Coaches Within Hong Kong’s National Sport Organizations (By
Level), With Percentage of All Surveyed NSOs Having Similar Numbers

 

 

Level 1 Level 2 Level 3
Count % Count % Count %
0–50 8 24.2 0–10 13 39.4 0 7 21.2
51–100 9 27.3 11–30 6 18.2 1–5 10 30.3
101–200 11 33.3 31–50 3 9.1 6–10 7 21.2
201–300 4 12.1 51–100 5 15.2 11–20 4 12.1
>300 1 3.03 >100 6 18.2 >20 5 15.2
Total 33 100 Total 33 100 Total 33 100

Table 4

 

Annual
Average Number of Competitions Organized By and Participated in By
NSOs, With Percentage of All Surveyed NSOs Having Similar Numbers

 

17

42.5

3–5

13

31.7

Average # of Local
Competitions Organized
Average # of
International Competitions Organized
Average # of
International Competitions
Participated In
Count % Count % Count %
0–5 14 34.1 0 9 22.5 1–2 13 31.7
6–10 10 24.4 1
11–20 8 19.5 2 6 15 6–10 6 14.6 21–30 1 2.4 3 1 2.5 11–20 6 14.6 >30 8 19.5 >3 7 17.5 >20 3 7.3 Total 41 100 Total 40 100 Total 41 100

 

Resources
Used for Anti-Doping Efforts

Our data suggest that Hong Kong’s national sport organizations have
not invested much, either in terms of finances or manpower, in
anti-doping efforts (Table 5). A majority of our respondents—close
to 88%—had expended no funds for anti-doping efforts within the 3
years preceding the study and anticipated no such spending throughout
the current year. Moreover, 80%–90% of the NSOs had neither any
staff members nor honorary consultants assigned to anti-doping work.
Table 5

 

Average
Annual Spending on Anti-Doping Efforts by Hong Kong NSOs, Over 4-Year
Period, in United States Dollars, With Percentage of All Surveyed
NSOs Spending Similar Amounts

 

 

Average Annual
Spending in 3 Years Preceding Study
Anticipated Spending
During Current Year
0 USD 36 (87.8%) 37 (88.1%)
1–1,000 USD 3 (7.3%) 2 (4.8%)
1,001–2,000 USD 1 (2.4%) 2 (4.8%)
> 2,000 USD 1 (2.4%) 1 (2.4%)

Tables 6

 

NSOs’
Staffing for Anti-Doping Efforts, By Paid Status and Position, With
Percentage of All Surveyed NSOs Providing Similar Numbers of Staff

Paid Staff

 

 

Count %
0 35 85.4
1 5 12.2
2 1 2.4

 

Honorary
Consultant from Medical Profession

 

Count %
0 32 80
1 3 7.5 2 2 5 >2 3 7.5

 

Honorary
Consultant from Legal Profession

 

 

Count %
0 36 90
1 2 5
2 2 5

 

Honorary
Consultant from Technical Field (e.g., Doping Control Officer)

 

 

Count %
0 33 82.5
1 2 5
2 3 7.5
>2 2 5

 

Honorary
Consultant (Unspecified)

 

 

Count %
0 38 95
4 1 2.5
6 1 2.5

 

Opinions
About Anti-Doping Education Programs

The NSO respondents were asked their opinions or perceptions
concerning appropriate content for inclusion in anti-doping
educational programs or informational materials (Table 7). The three
most important content areas, according to our respondents, were
“ways to avoid inadvertent doping,” “rights and
responsibilities of athletes in doping control,” and “anti-doping
rules and regulations.”
Table 7

 

NSO
Respondents’ Rank Ordering of Importance of Content Areas in
Anti-Doping Educational Programs, From Most to Least Important

 

Content Score
Mean SD
Ways to avoid inadvertent doping .97 1.09

Rights and responsibilities of athletes in doping control

.95 1.17 Anti-doping rules and regulations .77 1.02 Responsibilities of NSO in doping control .56 .93 Competitive sports and ethics .47 .69 Therapeutic use exemption for prohibited drugs .45 .92 Drug testing procedures .40 .80 Current international anti-doping practices .39 .84 Whereabouts information of athletes .35 .87 Current Hong Kong anti-doping practices .34 .72

As shown in Table 8, the surveyed respondents indicated that the most
suitable medium to deliver anti-doping educational programs was a web
page. Workshops, pamphlets, and video presentations were also
considered suitable modes of delivery.
Table 8

 

NSO
Respondents’ Rank Ordering of Suitability of Anti-Doping
Educational Program Delivery, From Most to Least Suitable

 

Mean SD

 

 

Web page

2.77

2.02

Workshop

2.58

2.12

Pamphlet

2.15

1.79

VCD

2.13

1.73

Other

.35

1.03

 

Surveyed
NSO associates suggested other suitable media for providing
anti-doping education (Table 9), as well.
Table 9
Other Modes of Anti-Doping Education Suggested by Respondents

 

 

Mode Number of
Respondents Making This Suggestion
TV
Commercial/Program
3
Seminar 1
Newspaper Article 1
Commercial Media 1
Exhibition 1

 

Respondents
were asked what they thought would be a suitable time to conduct an
anti-doping workshop; opinions varied from NSO to NSO. As shown in
Table 10, while 45% preferred weekday evenings, other times also had
support (i.e., weekday “office hours,” 30%; weekends, 25%).
Table 10
Anti-Doping Workshop Times Preferred By Respondents

 

 

Frequency %
Monday–Friday
“Office Hours”
12 30
Monday–Friday
Evenings
18 45
Saturday–Sunday 10 25
Total 40 100

 

 

 
Asked if they would recommend that their NSO staff attend a 6–8-hr
anti-doping workshop costing $300 HKD (about $40 U. S.) per
participant, 68.3% of our respondents said yes (Table 11).
Table 11
Number/Percentage of Respondents Who Would/Would Not Recommend NSO
Staff Attendance at 6–8-Hr, 300 HKD Anti-Doping Workshop

 

 

Frequency %
Yes 28 68.3
No 13 31.7
Total 41 100

 

Readiness
for change

Data from the modified RTCQ completed by NSO administrators, coaches,
and committee members are presented in Table 12. A majority of
respondents of all three types were in the contemplation stage (54.5%
of administrators, 51.1% of coaches, and 47.7% of committee members).
Being in the contemplation stage meant actively considering whether
to initiate or strengthen an NSO’s anti-doping effort.
Table 12

 

Indicated
Readiness to Initiate or Strengthen NSO’s Anti-Doping Efforts, In
Terms of RTCQ “Stage,” With Percentage of All Respondents at Same
“Stage”

 

 

Precontemplation Contemplation Action
Administrators 8 (18.2%) 24 (54.5%) 14 (27.3%)
Coaches 8 (17.8%) 23 (51.1%) 14 (31.1%)
Committee Members 10 (22.7%) 21 (47.7%) 13 (29.5%)

Factors in
Decision Making About Anti-Doping Efforts

Administrators, coaches, and committee members were asked to rate the
importance of a list of pros and cons of initiating or strengthening
anti-doping efforts within their NSO (Tables 13 and 14).

 

Table
13

 

NSO
Respondents’ Rank Ordering of Importance of “Pro” Factors in
Anti-Doping Decisions, From Most to Least Important

 

 

Pros Score
Average SD

 

Administrators

 

It will directly or
indirectly improve professional knowledge of the NSO staff.
5.1 1.17

 

It will help us to
avoid being penalized by an international federation.

3.85

1.61

 

 

It will affect the
professional image of the NSO.

3.69

1.49

It will help to
preserve the health of our athletes.

3.17

1.38

There is a need to
comply with the rules and regulations set forth by the
international sporting community.

2.06

1.17

It will help to
maintain fair play.

2.06

1.21

 

 

Coaches

 

It will directly or
indirectly improve professional knowledge of the NSO staff.
4.11 1.41
It will help us to
avoid being penalized by an international federation.
3.93 1.67
It will affect the
professional image of the NSO.
3.7 1.66
There is a need to
comply with the rules and regulations set forth by the
international sporting community.
2.93 1.6
It will help to
preserve the health of our athletes.
2.7 1.6
It will help to
maintain fair play.
2.41 1.54

Committee
members

It will directly or
indirectly improve professional knowledge of the NSO staff.
4.85 1.24
It will help us to
avoid being penalized by an international federation.
4.1 1.62

 

It will affect the
professional image of the NSO.

3.94

 

1.6

It will help to
preserve the health of our athletes.

2.73

1.58

There is a need to
comply with the rules and regulations set forth by the
international sporting community

2.45

1.11

It will help to
maintain fair play.

2.24

1.28

 

 

 

 

Table
14

 

NSO
Respondents’ Rank Ordering of Importance of “Con” Factors in
Anti-Doping Decisions, From Most to Least Important

 

 

Cons Score
Average SD

 

Administrators

 

It will create
unnecessary hassle for our athletes.
4.98 1.23

It will pose additional
financial pressure on our NSO.

3.81

1.46

Anti-doping work is not
essential to the development of our NSO.

3.36

1.55

 

 

Athletes in our sport
do not use prohibited substances to enhance performance.

3.12

1.66

There is a lack of
professional knowledge to implement such works.

3.07

1.51

 

 

There is a lack of
manpower to implement such works.

2.44

1.38

 

 

 

 

Coaches

 

 

It will create
unnecessary hassle for our athletes.
4.56 1.28
Anti-doping work is not
essential to the development of our NSO.
3.78 1.41
It will pose additional
financial pressure on our NSO.
3.6 1.55
Athletes in our sport
do not use prohibited substances to enhance performance.
3.58 1.76
There is a lack of
professional knowledge to implement such works.
3.06 1.63
There is a lack of
manpower to implement such works.
2.76 1.21

 

Committee
Members

 

It will create
unnecessary hassle for our athletes.
4.92 1.41

Anti-doping work is not
essential to the development of our NSO.

3.92

1.68

 

It will pose additional
financial pressure on our NSO.

3.85

Athletes in our sport
do not use prohibited substances to enhance performance.

3.27

1.71

There is a lack of
professional knowledge to implement such works.
3.52

1.69

 

There is a lack of
manpower to implement such works.

2.85

1.66

 

 

 

 

 

 

 

For
the list of “pros” associated with initiating or strengthening an
anti-doping effort, administrators, coaches, and committee members
alike said the three most important considerations were, in
descending order of importance, “It will directly or indirectly
improve professional knowledge of the NSO staff,” “It will help
us to avoid being penalized by an international federation,” and
“It will affect the professional image of the NSO.” Similarly,
for the list of “cons,” they agreed that the most important
consideration was “It will create unnecessary hassle for our
athletes,” and that the second and third most important factors
were “Anti-doping work is not essential to the development of our
NSO” and “It will pose additional financial pressure on our NSO,”
respectively. However, administrators said financial pressure was a
more important consideration than coaches and committee members said
it was, while the latter groups felt more influenced than
administrators did by anti-doping’s perceived nonessential role in
the development of an NSO.

NSOs’
Present and Upcoming Anti-Doping Efforts

The interviews we conducted with representatives of Hong Kong’s
NSOs allowed for collection of information about their current and

upcoming anti-doping activities, including work in education,
capacity building, drug testing, cooperation with international
federations and anti-doping organizations, and policy. Results
obtained are presented in Table 15.

 

Table
15

 

NSOs’
Present and Upcoming Anti-Doping Work, By Activity, With Percentage
of All Surveyed NSOs Pursuing Same

 

Activity Statusa Count %

 

Education

 

To remind athletes
and athlete support personnel that they are bound by the
anti-doping rules
1 7 16.3
2 1 2.3
4 35 81.4

Total

43

100

To distribute
information on doping control from third parties to your athletes
and athlete support personnel

1

14

32.6

2

1

2.3

4

28

65.1

 

 

Total

43

100

 

 

To distribute
information about education programs on doping control to
athletes/coaches/sport administrators

1

18

41.9

4

25

58.1

Total

43

100

 

 

To include
information on doping control in newsletter, web page, or
correspondence with NSO members

 

 

1

30

69.8

2

5

11.6

 

 

4

8

18.6

Total

43

100

 

 

To seek assistance from
relevant parties to organize education or information sessions for
your athletes and athlete support personnel, on matters related to
doping control

1

28

65.1

2

8

18.6

 

 

 

 

3

2

4.7

4

5

11.6

 

 

Total

43

100

To organize educational
talk or seminar for your athletes/coaches/sport administrators on
anti-doping

1

35

81.4

2

5

11.6

4

3

7

 
Total

43

100

 

 

Capacity Building

 

To upgrade the existing
staff on doping issues, through information/education program
1 32 74.4
2 5 11.6
4 6 14

Total

43

100

To train a doping
control officer for your NSO

1

38

88.4

2

3

7

4

2

4.7

 

 

Total

43

100

 

 

 
Drug Testing (and Related Functions)

 

To conduct drug tests
for locally held international event
1 23 53.5
2 4 9.3
4 16 37.2

Total

43

100

To conduct drug
tests for local competition

1

39

90.7

2

1

2.3

4

3

7

 

 

Total

43

100

 

 

 

To conduct
out-of-competition drug tests on your athletes

1

41

95.3

2

1

2.3

4

1

2.3

 

 

Total

43

100

To keep record of all
drug tests conducted on your athletes (for international
competition and out-of-competition)

1

26

60.5

2

3

7

3

1

2.3

 

 

4

13

30.2

Total

43

100

 

 

To regularly update
your international federation(s) and anti-doping organizations on
the drug test record and results of your athletes

1

36

83.7

 

2

1

2.3

 

 

4

6

14

 

Total

43

100

 

 

To collect or
coordinate the whereabouts information of your athletes

1

24
55.8

4

19

44.2

 

 

Total

43

100

 

 

 

 

To regularly update
your international federation(s) and anti-doping organizations on
the whereabouts information of your athletes

1

30

69.8

4

13

30.2

Total

43

100

 

 

To assist athletes in
the application of the therapeutic use exemption (TUE)

1

34

79.1

2

1

2.3

 

 

4

8

18.6

Total

43

100

 

 

To keep records of TUE
for your athletes

1

35

81.4

2

1

2.3

 

 

4

7

16.3

Total

43

100

 

 

To regularly update
your international federation(s) and anti-doping organizations on
the TUE status of your athletes

1

39

90.7

2

1

2.3

 

 

4

3

7

Total

43

100

 
Cooperation
with International Federations and Anti-Doping Organizations

 

To assist international
federation(s) and anti-doping organizations in conducting drug
tests
1 35 81.4
4 8 18.6
Total 43 100

 

 

Policy

 

To discuss doping
issues in meetings of your NSO
1 25 58.1
2 1 2.3
4 17 39.5

Total

43

100

To include a clause
forbidding use of prohibited substances by athletes in the
constitution of your NSO

1

26

60.5

2

5

11.6

4

12

27.9

 

 

Total

43

100

To prepare a procedural
guideline to handle anti-doping duties (If such a guideline
exists, please provide details on the target group and contents.)

1

33

76.7

2

7

16.3

4

3

7

 

 

 

 

Total

43

100

 

 

aA
numeral 1 in this column indicates an NSO does not intend to pursue
the activity in the foreseeable future; a 2 indicates that an NSO is
seriously considering action within 6 months (i.e., in the
foreseeable future); a 3 indicates that an NSO has developed a plan
to act; and a 4 indicates that the NSO has a system in place and
pursues the activity.
In terms of education, most NSOs (81.4%) had reminded their athletes
and athlete support personnel that they are bound by anti-doping
rules. Answers to our follow-up questions suggested that most of the
reminders were sent prior to major competitions. The majority of Hong
Kong NSOs would distribute to relevant persons information on doping
control obtained from third parties (65.1%) and related educational
programs (58.1%). However, only 18.6% of the NSOs had included
anti-doping information in a newsletter, a web page, or
correspondence with its members. To organize educational programs,
with or without assistance from third parties, was uncommon among the
local NSOs. Programs to enhance an NSO staff’s anti-doping
knowledge were also relatively undeveloped. Only 14% of NSOs had
organized educational programs to upgrade such knowledge, and only
4.7% had a trained doping control officer of their own.

On issues of drug testing and related functions, 37.2% of the NSOs
reported they had experience conducting drug tests at locally held
international events. However, only 7% had conducted drug tests for
local competitions and 2.3% had conducted out-of-competition tests on
athletes. It seems that in Hong Kong only athletes competing at the
international level are monitored via drug testing. Athletes in local
competitions have minimal exposure to drug testing.

In terms of record keeping, about 30.2% of NSOs had records of drug
tests conducted on their athletes, but only 14% reported this
information to an international federation (most federations made no
requests for the information). About half of the NSOs (44.2%) had
experience collecting or coordinating whereabouts information for
athletes. Only 30.2 %, however, updated an international federation
regularly about such information (follow-up questions suggested that
international federations did not request regular updates, especially
from NSOs without athletes competing internationally). Only 18.6% of
NSOs had experience applying the therapeutic use exemption with their
athletes; 16.3% kept records on TUE and 7% regularly updated an
international federation concerning athletes’ TUE status.

Only 8% of NSOs had assisted an international federation or
anti-doping agency in conducting drug testing. Responses to follow-up
questions suggested that both in-competition testing and
out-of-competition testing were involved. In terms of policy, 39.5%
of NSOs had discussed doping issues in their meetings. About one
third (27.9%) had included a clause prohibiting the use of specified
substances by athletes affiliated with them. Response to follow-up
questions indicated that most NSOs addressed the issue only
indirectly, asking individuals to refer to rules and regulations set
forth by international federations. Among the respondents, only 7%
had a procedural guideline for handling anti-doping duties.

 

Discussion and Recommendations

The main purpose of the survey was to evaluate the anti-doping
functions of Hong Kong’s NSOs. Data from a questionnaire and
interview suggest that the majority of NSOs in Hong Kong were at the
contemplation stage in terms of the implementation of anti-doping
functions. According to Prochaska’s transtheoretical model,
individuals at the contemplation stage have started to acknowledge a
target behavior, but they may not be ready to make any change
(Prochaska, 2000). Moreover, if pressured about the behavior,
individuals in the contemplation stage can be very resistant to
change. In the case of Hong Kong’s NSOs in the contemplation stage,
educational workshops and realistic support with resources are
essential to moving them to the next stage, which is the action
stage.
Studies of TTM suggest that “stage-matched interventions”
outperform “action-oriented interventions” (Prochaska et al.,
2001); the former can increase the likelihood of progress to the next
stage, action. For organizational change, TTM dictates that
interventions should be individualized and matched to employees’
readiness to change. This would be a necessary consideration during
development of anti-doping workshops’ content.
According to Prochaska et al. (2001), dramatic relief,
self-reevaluation, and thinking about commitment are processes of
changes that should be emphasized with those in the pre-contemplation
and contemplation stages. The Hong Kong NSOs can, then, be moved to
change their anti-doping functions through the use of emotional
arousal components, for example discussion of fears of sanctioning by
an international federation if noncompliance persists, or discussion
of advantages of successfully implementing the anti-doping code. A
reevaluation of the NSO’s strengths and weaknesses pertaining to
implementation can be helpful. NSOs should also be encouraged to
discuss the possibility of implementing anti-doping programs and to
make a commitment to further anti-doping efforts.
The present study found that resources are the major constraint on
implementation of anti-doping functions by the Hong Kong NSOs. To
provide the needed additional funds and manpower most
cost-effectively, a centralized body could be established to
coordinate anti-doping functions, rather than providing funds to
underwrite various NSOs’ individual efforts.
The present study is the first to study the status of anti-doping
efforts among Hong Kong’s national sport organizations. Apart from
investigating what anti-doping functions the NSOs are currently
fulfilling, we also measured their—the administrators’, coaches’,
and committee members’—readiness to change by starting or
strengthening anti-doping efforts. It appears that a majority of NSOs
in Hong Kong are in the contemplation stage of implementing
anti-doping functions and facing the constraints of limited funding
and manpower. These data provide a starting point for the design of
assistance to the NSOs as they initiate or strengthen anti-doping
efforts to comply with the World Anti-Doping Code. Results are likely
relevant, as well, in countries with similar anti-doping experience.
They should thus be of use to international federations, national
anti-doping organizations, and the World Anti-Doping Agency, in terms
of directing effort and resources.
References

Heather, N., Gold, R., & Rollnick, S. (1991). Readiness to
Change Questionnaire: User’s manual.
(Tech. Rep. No. 15).
Kensington, New South Wales: University of New South Wales, National
Drug and Alcohol Research Centre.

Prochaska, J. M. (2000). A transtheoretical model for assessing
organizational change: A study of family service agencies’ movement
to time-limited therapy. Family in Society, 81, 76–84.

Prochaska, J. M., Prochaska, J. O., & Levesque, D. A. (2001). A
transtheoretical approach to changing organizations. Administration
and Policy in Mental Health
, 28(4), 247–261.

Rollnick, J. O., Heather, N., Gold, R., & Hall, W. (1992).
Development of a short “readiness to change” questionnaire for
use in brief, opportunistic intervention among excessive drinkers.
British Journal of Addiction, 87, 743–754.

World Anti-Doping Agency. (2003). World Anti-Doping Code.
Retrieved August 28, 2006, from http://www.wada-ama.org/en/

Author Note
Lena Fung, Hong Kong Baptist University; Yvonne Yuan, Hong Kong
Sports Institute Limited.
This research was supported by a social science research grant from
the World Anti-Doping Agency.

Music in Sport and Exercise : An Update on Research and Application

Abstract

In spring 1999, almost a decade ago, the first author published in The Sport Journal an article titled “Music in Sport and Exercise: Theory and Practice.” The present article’s origins are in that earlier work and the first author’s research while a master’s student at the United States Sports Academy in 1991–92. To a greater degree than in the original 1999 article, this article focuses on the applied aspects of music in sport and exercise. Moreover, it highlights some new research trends emanating not only from our own publications, but also from the work of other prominent researchers in the field. The content is oriented primarily towards the needs of athletes and coaches.

Music in Sport and Exercise: An Update on Research and Application

With the banning of music by the organizers of the 2007 New York Marathon making global headlines, the potentially powerful effects of music on the human psyche were brought into sharp focus. In fact, music was banned from the New York Marathon as part of the wider USA Track & Field ban on tactical communications between runners and their coaches. The marathon committee upheld this ban, which is often otherwise overlooked, justifying its action in terms of safety.

The response to the ban was emphatic. Hundreds of runners flouted the new regulation and risked disqualification from the event—such was their desire to run to the beat. Experience at other races around the world confirms the precedent set in New York; try to separate athletes from their music at your peril! But why is music so pivotal to runners and to sports people from a wide variety of disciplines?

How Music Wields an Effect

In the hotbed of competition, where athletes are often very closely matched in ability, music has the potential to elicit a small but significant effect on performance (Karageorghis & Terry, 1997). Music also provides an ideal accompaniment for training. Scientific inquiry has revealed five key ways in which music can influence preparation and competitive performances: dissociation, arousal regulation, synchronization, acquisition of motor skills, and attainment of flow.

Dissociation

During submaximal exercise, music can narrow attention, in turn diverting the mind from sensations of fatigue. This diversionary technique, known to psychologists as dissociation, lowers perceptions of effort. Effective dissociation can promote a positive mood state, turning the attention away from thoughts of physiological sensations of fatigue. More specifically, positive aspects of mood such as vigor and happiness become heightened, while negative aspects such as tension, depression, and anger are assuaged (Bishop, Karageorghis, & Loizou, 2007). This effect holds for low and moderate exercise intensities only; at high intensities, perceptions of fatigue override the impact of music, because attentional processes are dominated by physiological feedback, for example respiration rate and blood lactate accumulation.

Research shows that the dissociation effect results in a 10% reduction in perceived exertion during treadmill running at moderate intensity (Karageorghis & Terry, 1999; Nethery, 2002; Szmedra & Bacharach, 1998). Although music does not reduce the perception of effort during high intensity work, it does improve the experience thereof: It makes hard training seem more like fun, by shaping how the mind interprets symptoms of fatigue. While running on a treadmill at 85% of aerobic capacity (VO2max), listening to music will not make the task seem easier in terms of information that the muscles and vital organs send the brain. Nevertheless, the runner is likely to find the experience more pleasurable. The bottom line is that during a hard session, music has limited power to influence what the athlete feels, but it does have considerable leverage on how the athlete feels.

Arousal Regulation

Music alters emotional and physiological arousal and can therefore be used prior to competition or training as a stimulant, or as a sedative to calm “up” or anxious feelings (Bishop et al., 2007). Music thus provides arousal regulation fostering an optimal mindset. Most athletes use loud, upbeat music to “psych up,” but softer selections can help to “psych down,” as well. An example of the latter is two-time Olympic gold medalist Dame Kelly Holmes’s use of soulful ballads by Alicia Keys (e.g., “Fallin’” and “Killing Me Softly”) in her pre-event routine at the Athens Games of 2004. While the physiological processes tend to react sympathetically to music’s rhythmical components, it is often lyrics or extramusical associations that make an impact on the emotions. Ostensibly, fast tempi are associated with higher arousal levels than slow tempi.

Karageorghis and Lee (2001) examined the interactive effects of music and imagery on an isometric muscular endurance task which required participants to hold dumbbells in a cruciform position for as long as possible. Males held 15% of their body weight and females held 5% of their body weight. The authors found that the combination of music and imagery, when compared to imagery only, music only, or a control condition, enhanced muscular endurance (see Figure 1), although it did not appear to enhance the potency of the imagery. The main implication of the study was that employing imagery to a backdrop of music may be a useful performance-enhancement strategy that can be integrated in a pre-event routine.

Figure 1. Bar chart illustrating mean scores (+ 1 SD) for isometric muscular endurance under conditions of imagery only (A), motivational music (B), motivational music and imagery (C), and a no music/imagery control (D).

Synchronization

Research has consistently shown that the synchronization of music with repetitive exercise is associated with increased levels of work output. This applies to such activities as rowing, cycling, cross-country skiing, and running. Musical tempo can regulate movement and thus prolong performance. Synchronizing movements with music also enables athletes to perform more efficiently, again resulting in greater endurance. In one recent study, participants who cycled in time to music found that they required 7% less oxygen to do the same work as compared to cycling with background (asynchronous) music (Bacon, Myers, & Karageorghis, 2008). The implication is that music provides temporal cues that have the potential to make athletes’ energy use more efficient.

The celebrated Ethiopian distance runner Haile Gebrselassie is famous for setting world records running in time to the rhythmical pop song “Scatman.” He selected this song because the tempo perfectly matched his target stride rate, a very important consideration for a distance runner whose aim is to establish a steady, efficient cadence. The synchronization effect in running was demonstrated in an experimental setting by Simpson and Karageorghis (2006), who found that motivational synchronous music improved running speed by ~.5 s in a 400-m sprint, compared to a no-music control condition (see Figure 2).


Figure 2. Mean 400 m times for synchronous motivational music, synchronous oudeterous music, and a no-music control.

Acquisition of Motor Skills

Music can impact positively on the acquisition of motor skills. Think back to elementary school days and your initial physical education lessons, which were probably set to music. Music-accompanied dance and play created opportunities to explore different planes of motion and improve coordination. Scientific studies have shown that the application of purposefully selected music can have a positive effect on stylistic movement in sport (Chen, 1985; Spilthoorn, 1986), although there has been no recent research to build upon initial findings.

There are three plausible explanations for the enhancement of skill acquisition through music. First, music replicates forms of bodily rhythm and many aspects of human locomotion. Hence, music can transport the body through effective movement patterns, the body providing an apparent visual analogue of the sound. Second, the lyrics from well-chosen music can reinforce essential aspects of a sporting technique. For instance, in track and field, the track “Push It” (by Salt-n-Pepa) is ideal for reinforcing the idea that the shot should be put, not thrown; throwing the shot is the most common technical error. Third, music makes the learning environment more fun, increasing players’ intrinsic motivation to master key skills.

Attainment of Flow

The logical implication of study findings concerning music’s effects on motivational states is that music may help in the attainment of flow, the zenith of intrinsic motivation. Recent research in sports settings has indeed found that music promotes flow states. Using a single-subject, multiple-baselines design, Pates, Karageorghis, Fryer, and Maynard (2003) examined the effects of pre-task music on flow states and netball shooting performance of three collegiate players. Two participants reported an increase in their perception of flow, and all three showed considerable improvement in shooting performance. The researchers concluded that interventions including self-selected music and imagery could enhance athletic performance by triggering emotions and cognitions associated with flow. Karageorghis and Deeth (2002), furthermore, investigated the effects of motivational music on flow during a multistage fitness test. The multiple dimensions of the flow experience were represented by the factors incorporated in the Flow State Scale (FSS) developed by Jackson and Marsh (1996). When compared to oudeterous music and a no-music control condition, motivational music led to increases in several FSS factors.

Selecting Music for Sport and Exercise

Type of Activity

An athlete searching for music to incorporate in training and competition should start by considering the context in which he or she will operate (Karageorghis, Priest, Terry, Chatzisarantis, & Lane, 2006). What type of activity is being undertaken? How does that activity affect other athletes or exercisers? What is the desired outcome of the session? What music-playing facilities are available? Some activities lend themselves particularly well to musical accompaniment, for example those that are repetitive in nature: warm-ups, weight training, circuit training, stretching, and the like. In each case, the athlete should make selections (from a list of preferred tracks) that have a rhythm and tempo that match the type of activity to be undertaken. To assess the motivational qualities of particular music, the Brunel Music Rating Inventory (BMRI) may be used (Karageorghis, Terry, & Lane, 1999), as may its derivative, the BMRI-2 (Karageorghis et al., 2006).

One of the latest developments in the music-in-sport field is London’s Run to the Beat half-marathon, an event that will feature scientifically selected motivational music performed live by musicians positioned along the route (Run to the Beat: London’s Half-Marathon, n.d.). Our research team has been instrumental in managing the music policy for Run to the Beat and in ensuring that runners are delivered music that is appropriate to their preferences and sociocultural backgrounds. We have gathered relevant information from the half-marathon’s website and used it in prescribing musical selections contoured to the event’s motivational and physiological demands.

Intensity of Activity

An athlete or exerciser whose goal during warm-up is elevating the heart rate to 120 beats per minute should select accompanying music that has a tempo in the range of 80–130 beats per minute. Successive tracks should create a gradual rise in music tempo to match the intended gradual increase in heart rate. Moreover, segments of music can be tailored to various components of training, so that, for example, work time and recovery time are punctuated by music that is alternately fast and loud or slow and soft. This approach is especially well suited to highly structured sessions such as circuit or interval training. The authors have used this technique with collegiate athletes engaged in a tough weekly circuit training session, and the upshot has been a 20% improvement in attendance.

Our recent research has uncovered the tendency among athletes and exercisers to coordinate bursts of effort with those specific segments of a musical track they find to be especially motivating. We refer to the phenomenon as segmentation (Priest & Karageorghis, 2008). The segmentation effect is particularly strong if the individual knows the musical track very well and can anticipate the flow of the music. It is also beneficial to match the tempo of music with the intensity of the workout. For example, when cycling at around 70% of one’s aerobic capacity, mid-tempo music (115–125 beats per minute) is more effective than faster music (135–145 beats per minute) (Karageorghis, Jones, & Low, 2006; Karageorghis, Jones, & Stuart, 2008).

Delivery of Music

Coaches and athletes must choose how selected tracks will be delivered before or during training or competition. If others are training nearby and might be disturbed by one’s music, it should be delivered via an MP3 player. Music intended to enhance group cohesion or inspire a group of athletes is best delivered with a portable hi-fi system or stadium public address system. If distraction is an important consideration, the volume at which music is played should be set quite high, but not high enough to cause discomfort or leave a ringing in the ears. Indeed, sound at a volume above 75 dB delivered during exercise—when blood pressure in the ear canal is elevated—can cause minor temporary hearing loss (Alessio & Hutchinson, 1991).

Selection Procedure

The researchers suggest accompanying training activities with music, to enable athletes to tap into the power of sound. To start, assemble a wide selection of familiar tracks that meet the following six criteria: (a) strong, energizing rhythm; (b) positive lyrics having associations with movement (e.g., “Body Groove” by the Architects Ft. Nana); (c) rhythmic pattern well matched to movement patterns of the athletic activity; (d) uplifting melodies and harmonies (combinations of notes); (e) associations with sport, exercise, triumph, or overcoming adversity; and (f) a musical style or idiom suited to an athlete’s taste and cultural upbringing. Choose tracks with different tempi, to coincide with alternate low-, medium-, and high-intensity training.

A further consideration is variety among selections. A study we published of data from a major fitness chain in the United Kingdom (Priest, Karageorghis, & Sharp, 2004) indicated that variety in the selections was paramount. Table 1 presents titles of motivational tracks suitable for different components of a single training session with a specific individual in mind.

Table 1

Example Motivational Music for Training-Session Components of Different Exercise Intensities

 
Workout Component

Title

Artist(s)

Tempo, in Beats per Minute

Mental preparation

“Umbrella”

Rihanna Ft. Jay Zee

89

Warm-up activity

“Gettin’ Jiggy With It”

Will Smith

108

Stretching

“Lifted”

The Lighthouse Family

98

Strength component
“Funky Cold Medina”

Tone Loc

118

Endurance component

“Rockafeller Skank
(Funk
Soul Brother)”

Fatboy Slim

153

Warm-down activity

“Whatta Man”

Salt-n-Pepa

88

Conclusion

We have established that there are many ways in which music can be applied to both training and competition. The effects of carefully selected music are both quantifiable and meaningful. As Paula Radcliffe, the world record–holding marathoner, has said, “I put together a playlist and listen to it during the run-in. It helps psych me up and reminds me of times in the build-up when I’ve worked really hard, or felt good. With the right music, I do a much harder workout.”

The findings we have discussed lead to the possibility that the use of music during athletic performance may yield long-term benefits such as exercise adherence and heightened sports performance, through a superior quantity and quality of training. Although many athletes today already use music, they often approach its use in quite a haphazard manner. We hope that through applying the principles outlined in this article, athletes and coaches will be able to harness the stimulative, sedative, and work-enhancing effects of music with greater precision.

References

Alessio, H. M., & Hutchinson, K. M. (1991). Effects of submaximal exercise and noise exposure on hearing loss. Research Quarterly for Exercise and Sport, 62, 414–419.

Bacon, C., Myers, T., & Karageorghis, C. I. (2008). Effect of movement-music synchrony and tempo on exercise oxygen consumption. Manuscript submitted for publication.

Bishop, D. T., Karageorghis, C. I., & Loizou, G. (2007). A grounded theory of young tennis players’ use of music to manipulate emotional state. Journal of Sport & Exercise Psychology, 29, 584–607.

Chen, P. (1985). Music as a stimulus in teaching motor skills. New Zealand Journal of Health, Physical Education and Recreation, 18, 19–20.

Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The Flow State Scale. Journal of Sport & Exercise Psychology, 18, 17–35.

Karageorghis, C. I. (1999). Music in sport and exercise: Theory and practice. The Sport Journal, 2(2). Retrieved March 28, 2007, from http://www.thesportjournal.org/1999Journal/Vol2-No2/Music.asp

Karageorghis, C. I., & Deeth, I. P. (2002). Effects of motivational and oudeterous asynchronous music on perceptions of flow [Abstract]. Journal of Sports Sciences, 20, 66–67.

Karageorghis, C. I., Jones, L., & Low, D. C. (2006). Relationship between exercise heart rate and music tempo preference. Research Quarterly for Exercise and Sport, 26, 240–250.

Karageorghis, C. I., Jones, L., & Stuart, D. P. (2008). Psychological effects of music tempi. International Journal of Sports Medicine, 29, 613-619.

Karageorghis, C. I., & Lee, J. (2001). Effects of asynchronous music and imagery on an isometric endurance task. In International Society of Sport Psychology, Proceedings of the World Congress of Sport Psychology: Vol. 4 (pp. 37–39). Skiathos, Greece.

Karageorghis, C. I., Priest, D. L., Terry, P. C., Chatzisarantis, N. L. D., & Lane, A. M. (2006). Redesign and initial validation of an instrument to assess the motivational qualities of music in exercise: The Brunel Music Rating Inventory–2. Journal of Sports Sciences, 24, 899–909.

Karageorghis, C. I., & Terry, P. C. (1999). Affective and psychophysical responses to asynchronous music during submaximal treadmill running. Proceedings of the 1999 European College of Sport Science Congress, Italy, 218.

Karageorghis, C. I., & Terry, P. C. (1997). The psychophysical effects of music in sport and exercise: A review. Journal of Sport Behavior, 20, 54–68.

Karageorghis, C. I., Terry, P. C., & Lane, A. M. (1999). Development and initial validation of an instrument to assess the motivational qualities of music in exercise and sport: The Brunel Music Rating Inventory. Journal of Sports Sciences, 17, 713–724.

Nethery, V. M. (2002). Competition between internal and external sources of information during exercise: Influence on RPE and the impact of the exercise load. Journal of Sports Medicine and Physical Fitness, 42, 172–178.

Pates, J., Karageorghis, C. I., Fryer, R., & Maynard, I. (2003). Effects of asynchronous music on flow states and shooting performance among netball players. Psychology of Sport and Exercise, 4, 413–427.

Priest, D. L., Karageorghis, C. I., & Sharp, N. C. C. (2004). The characteristics and effects of motivational music in exercise settings: The possible influence of gender, age, frequency of attendance, and time of attendance. Journal of Sports Medicine and Physical Fitness, 44, 77–86.

Priest, D. L., & Karageorghis, C. I. (2008). A qualitative investigation into the characteristics and effects of music accompanying exercise. Manuscript submitted for publication.

Run to the Beat: London’s Half-Marathon (n.d.). Music: The Science behind Run to the Beat. Retrieved July 3, 2008, from http://www.runtothebeat.co.uk/music.html

Simpson, S. D., & Karageorghis, C. I. (2006). The effects of synchronous music on 400-m sprint performance. Journal of Sports Sciences, 24, 1095–1102.

Spilthoorn, D. (1986). The effect of music on motor learning. Bulletin de la Federation Internationale de l’Education Physique, 56, 21–29.

Szmedra, L., & Bacharach, D. W. (1998). Effect of music on perceived exertion, plasma lactate, norepinephrine and cardiovascular hemodynamics during treadmill running. International Journal of Sports Medicine, 19, 32–37.

Factors Affecting Attendance at Bowl Games During the BCS Era

Abstract

Six independent variables combined in a formula that explains 82.2 percent of the variance in attendance (r2 = .822) in all 271 college football bowl games that have been played in the past 10 years. This is despite the fact that during a recent explosion in new bowl games and the creation of the Bowl Championship Series (BCS), attendance to these traditional post-season football exhibitions has varied from 5,494 for the 2004 Silicon Valley Football Classic to 94,392 for the 2001 Rose Bowl. These six variables, out of 11 that were tested, each showed a relationship to attendance that was statistically significant at the 0.01 alpha level (p > 0.01). They include the seating capacity of the stadium (Stadium), the average home game attendance of the participating teams (AHAtt), the age of each bowl game (Age), the winning percentages of the participating teams (Wpct.), the travel distance between the representative institutions and the sites of the bowl games (Distance) and the number of days that elapsed between the participating teams’ final regular season or conference championship game and the bowl game itself (Notice). When the researcher studied only the bowls that were at least six years of age (n = 194), where the attendance track record of the individual bowls could be used as an independent variable, a formula of five independent variables that explain 91 percent of the variance (r2d = .910) was developed. All five variables had a relationship with attendance that was statistically significant at the 0.01 alpha level (p > 0.01). The formula included the attendance average of each bowl game over the previous five years (FiveAtt), Stadium, Distance, Wpct and AHAtt.

Introduction

It would be difficult to find a decade in the 106-year history of college football bowl games in which more dramatic changes have occurred in the major college football postseason. The number of bowl games has increased more than 50 percent from 22 in 1998 to 34 in 2008. The Bowl Championship Series (BCS) has dramatically increased revenues for the elite-level bowl games, and payouts for their participants. The growth of conference championship games and other games being played on the first Saturday in December has decreased the amount of notice academic institutions and fans have in finding out which bowl games their teams will be participating.

Attendance has also taken on an added importance because the growth in number of non-BCS bowl games has created a “clutter” of bowl games on television, creating a potential for a saturated market.

While the National Collegiate Athletic Association (NCAA) does not have an official national championship event for its Division One Football Bowl Subdivision (D1-FBS), the fact that there has been only one bowl game a year that has had an effect on the national championship (in eight out of the past 10 years) makes new marketing approaches all the more necessary for even more bowl games. Bowls such as the Cotton Bowl, which helped determine the national champion in 1978 and hosted the No. 2-ranked team in 1984; the Holiday Bowl, which hosted the No. 1-ranked team in 1984; and the Citrus Bowl (now the Capital One Bowl), which hosted the United Press International’s (UPI) recognized national champion in 1991; were left out of the BCS when it formed in 1998.

The surge in bowl certification has also led to an increasing number of non-traditional bowl teams participating in bowl games. These teams have little or no track record of bowl attendance and fans that are not accustomed to making postseason bowl travel plans. In some cases, football programs that either did not even exist or play at the NCAA Division 1-A level 10 years before the BCS started have participated in non-BCS bowl games during the BCS era.

These facts all indicate a need to research bowl attendance data. The increase in the number of bowls means that 271 such games have been played during the BCS era, enough to create a valid sample for research purposes.

Review of Literature

Many studies have been conducted regarding spectator attitudes and preferences in sporting events, although most have been based on survey data as opposed to fan behavior. A need to study spectator attitudes and preferences based on behavior instead of surveys arises not only from the practicality of obtaining this data (the NCAA lists attendance figures for all bowl games in a record book stored in a PDF file on its website) but also because a ticket to a sporting event differs from other products and the decision to attend an athletic event or support its participants differs from other types of consumer decisions. These decisions are emotionally-based (Hardy et al, 2003), so depending on the rational mind in a survey and the limited number of responses that can be obtained through survey data creates some disadvantages compared to data based on spectator behavior.

Definition of Terms

Bowl Championship Series (BCS)
A partnership involving the Rose Bowl, FedEx Orange Bowl, Tostitos Fiesta Bowl, Allstate Sugar Bowl, Notre Dame and six collegiate athletic conferences (ACC, Big East, Big Ten, Big 12, PAC 10 and Southeastern) to produce an unofficial national championship game for D1-FBS college football and provide the best possible matchups in four major bowl games.
Bowl Games
Special exhibition, All-Star or championship games played at the end of the regular football season. For the purpose of this study, the term will be used in reference to games played after the regular season by D1-FBS teams. As of the end of the 2007 season, there are 32 such games played each year.
National Collegiate Athletic Association (NCAA)
The largest governing body of intercollegiate athletics in the United States.
NCAA Division One
The most competitive level of athletics in the NCAA, with a varying number of schools that usually exceeds 300.
NCAA Division One Football Bowl Subdivision (D1-FBS)
A body of Division One college football teams that play a season culminating in bowl games for 64 of its members. The membership number varies, but usually exceeds 100.

Methodology

Using bowl game attendance as the dependent variable, the researcher analyzed 11 independent variables that theoretically would have an effect on the number of people who attended a bowl game. For the second formula, the researcher analyzed the same 11 independent variables plus one additional independent variable. The researcher used stepwise linear regression analysis as a research method and Statistical Package of Social Science (SPSS) 16.0 as the instrument.

Selection of Subjects

The researcher collected data from all 271 bowls that took place during the BCS era, from December of 1998 to January of 2008. For the second formula, the researcher used data from only those bowl games that were at least six years old at the time they were played (n = 199).

Variables

Dependent

Attendance: The number of spectators who attended each individual bowl game, as reported in the NCAA Football Record Book (NCAA.com, see Table 1).

Table 1
Bowl Attendance

Bowl Attendance
1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08
Alamo 60780 65380 60028 65232 50690 56226 65265 62000 65875 66166
Aloha 46451 40974 24397 N/A N/A N/A N/A N/A N/A N/A
Armed Forces N/A N/A N/A N/A N/A 38028 27902 33505 32412 40975
BCS N/A N/A N/A N/A N/A N/A N/A N/A 74628 79651
Capital One 63584 62011 66928 59693 66334 64565 70229 57221 60774 69748
Champs Sports N/A N/A N/A 28562 21689 26482 28237 31470 40168 46554
Chick-fil-A 72876 73315 73614 71827 68330 75125 69332 65620 75406 74413
Cotton 72611 72723 63465 72955 70817 73928 75704 74222 66777 73114
Emerald N/A N/A N/A N/A 25966 25621 30653 25742 40331 32517
Fiesta 80470 71526 75428 74118 77502 73425 73519 76196 73719 70016
Gator 70791 43416 68741 72202 73491 78892 70112 63780 67704 60243
GMAC N/A 34200 40300 40139 40646 40620 40160 35422 38751 36932
Hawaii N/A N/A N/A N/A 31535 29005 39662 26254 43435 30467
Holiday 65354 57118 63278 60548 58717 61102 66222 65416 62395 64020
Houston N/A N/A 33899 53480 44687 51068 27235 37286 N/A N/A
Humanitarian 19664 29283 26203 23472 30446 23118 28516 30493 28652 27062
Independence 46862 49873 36974 45627 46096 49625 43000 41332 45054 47043
Insight 36147 35762 41813 40028 40533 42364 45917 43536 48391 48892
International N/A N/A N/A N/A N/A N/A N/A N/A 26717 31455
Las Vegas 21429 28227 29113 30894 30324 25437 29062 40053 44615 40712
Liberty 52192 54866 58302 58968 55207 55989 58355 54894 56103 63816
Meinecke N/A N/A N/A N/A 73535 51236 73238 57937 52303 53126
MicronPC.com 44387 31089 28359 N/A N/A N/A N/A N/A N/A N/A
Motor City 32206 44863 52911 44164 51872 51826 52552 50616 54113 60624
Music City 41248 59221 47119 46125 39183 55109 66089 40519 68024 68681
New Mexico N/A N/A N/A N/A N/A N/A N/A N/A 34111 30467
New Orleans N/A N/A N/A 27004 19024 25184 27253 18338 24791 25146
Oahu 46451 40974 24187 N/A N/A N/A N/A N/A N/A N/A
Orange 67919 70461 76835 73640 75971 76739 77912 77773 74470 74111
Outback 66005 54059 65229 66249 65101 65372 62414 65881 65601 65601
Papajohns.com N/A N/A N/A N/A N/A N/A N/A N/A 32023 35258
Poinsettia N/A N/A N/A N/A N/A N/A N/A 36842 29709 39129
Rose 93872 93731 94392 93781 86848 93849 93468 93986 93852 93923
Seattle N/A N/A N/A 30144 38241 N/A N/A N/A N/A N/A
Silicon Valley N/A N/A 26542 30456 10132 20126 5494 N/A N/A N/A
Sugar 76503 79280 64407 77688 74269 79342 77349 74458 77781 74383
Sun 46612 48757 49093 47812 48917 49894 51288 50426 48732 49867
Texas N/A N/A N/A N/A N/A N/A N/A N/A 52210 62097

Source: NCAA
N/A (not applicable) means the bowl game did not exist during the indicated season.

Independent

For the whole group (n = 271)

Age of the bowl (Age): The number of times the bowl game has been played, including the year in question (Table 2). In 2006, the BCS stopped rotating its four bowls as being designated championship games and instead added a fifth bowl, the BCS Championship, on Jan. 8 each year to be hosted by one of the four BCS bowl committees. Two such games have been played during the period of this study, but the age of the BCS title game (1 and 2) would not be reflected in attendance. So in these particular games, the age of the host committee’s bowl game is also used as the age of the BCS Championship game. For example, the 2007 BCS title game was the first one of its kind, but since it was hosted by the Fiesta Bowl Committee, it is listed in this study as being the same age as the Fiesta Bowl (35).

Table 2
Age of the Bowls

Current Bowls Age (in 2007-’08) Discontinued Bowls Final Year Age (in final year)
Rose 106 Aloha 2000-’01 19
Orange 73 Houston 2005-’06 6
Sugar 73 Micron PC.com 2000-’01 10
Cotton 71 Silicon Valley 2004-’05 5
Sun 71 Oahu 2000-’01 3
Gator 61 Seattle 2002-’03 2
Capital One 60
Liberty 48
Chick-fil-A 39
Fiesta 36
Indy 31
Holiday 29
Outback 21
Insight 18
Champs Sports 17
Vegas 15
Alamo 14
Human 11
Motor 11
Music 10
GMAC 9
New O 7
Emerald 6
Hawaii 6
Meinecke Car Care 6
Armed 5
Poinsettia 3
BCS Championship* 2 (35 and 73 in the study)
International 2
New Mexico 2
Papajohns.com 2
Texas 2

*For the purpose of this study, the age of each BCS Championship game will be recorded as the same age as the BCS bowl hosting the event.

Average Home Attendance (AHAtt): The average number of spectators that attended the regular-season home games of the participating teams. The average home attendance for each team is averaged together for this variable (Table 3).

Table 3
Average Home Attendance

Bowl Average Home Attendance (averaged between two participating teams)
1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08
Alamo 47643.0 84813.0 55688.0 55383.0 63621.0 75292.0 75840.5 94200.0 79545.0 95562.0
Aloha 45336.0 38311.5 44969.5 N/A N/A N/A N/A N/A N/A N/A
Armed Forces N/A N/A N/A N/A N/A 32126.5 23594.0 29364.5 32321.5 63136.0
BCS N/A N/A N/A N/A N/A N/A N/A N/A 97752.5 98864.5
Capital One 81782.5 61890.5 96364.0 108375.5 95091.0 75327.5 80803.0 83356.0 77631.5 100326.0
Champs Sports N/A N/A N/A 49084.5 60325.5 46012.0 41942.5 64320.5 52425.0 56265.0
Chick-fil-A 64689.0 58061.5 65243.0 69016.0 76478.0 90558.5 73771.5 68445.0 79489.5 82717.0
Cotton 57412.5 67272.5 78927.5 68044.5 84680.0 50690.5 90571.0 65995.0 85053.5 64969.5
Emerald N/A N/A N/A N/A 51317.5 36617.5 34149.5 46571.5 72743.5 46318.5
Fiesta 93702.0 92284.0 56975.5 46700.5 86513.5 75990.0 42856.0 92906.0 57507.0 72629.0
Gator 60641.5 42517.0 65618.5 66101.5 65147.5 51720.5 69693.0 53058.0 54695.0 55867.5
GMAC N/A 34163.0 30571.5 31976.5 32131.0 32589.5 29299.0 34858.0 22859.0 20309.5
Hawaii N/A N/A N/A N/A 35655.5 31299.0 28703.5 21769.0 45575.5 35937.5
Holiday 62051.0 59820.5 63654.5 77765.5 47513.0 58335.0 58421.0 71382.5 70151.5 74009.5
Houston N/A N/A 39252.5 55841.5 35099.5 39772.0 44552.0 38979.5 N/A N/A
Humanitarian 18925.0 30684.5 35604.0 50292.5 35955.0 37497.0 50635.5 34770.5 29318.0 43285.5
Independence 44674.0 60746.5 60268.5 63742.0 67509.5 57652.5 28630.0 66615.5 66636.0 71413.5
Insight 55735.5 46736.0 41444.0 44840.0 40431.5 50168.0 58564.5 47170.5 51540.5 38514.0
International N/A N/A N/A N/A N/A N/A N/A N/A 19499.0 28374.0
Las Vegas 15165.8 16350.0 18483.0 22080.2 26235.4 27997.4 28799.0 28966.0 31154.0 33898.2
Liberty 45323.0 26705.5 33633.0 50013.5 29137.0 31889.5 35515.0 31066.5 48770.0 46657.0
Meinecke N/A N/A N/A N/A 54330.5 59810.5 47824.0 45895.0 33275.0 35400.0
MicronPC.com 51571.5 45804.0 47050.0 N/A N/A N/A N/A N/A N/A N/A
Motor City 32233.5 46929.5 24725.0 26924.0 31865.9 24823.5 31664.5 25442.0 21800.0 39048.5
Music City 60857.5 57248.5 50019.5 64580.0 51268.5 81844.5 64721.5 54909.0 70161.0 73645.5
New Mexico N/A N/A N/A N/A N/A N/A N/A N/A 23743.5 23380.0
New Orleans N/A N/A N/A 21434.5 21665.5 28681.0 22074.0 23121.5 17785.0 22705.5
Oahu 58505.0 35576.0 70379.0 N/A N/A N/A N/A N/A N/A N/A
Orange 66644.0 97115.5 77953.0 64498.5 64300.0 70642.0 84880.5 93791.5 36998.5 56508.5
Outback 77134.5 74794.0 89830.5 93073.0 97880.5 77987.5 87557.0 80495.5 106678.0 92832.5
Papajohns.com N/A N/A N/A N/A N/A N/A N/A N/A 33695.0 28483.5
Poinsettia N/A N/A N/A N/A N/A N/A N/A 31180.0 26348.5 38605.0
Rose 75568.5 63909.5 67897.0 62468.5 56617.5 94361.0 97059.5 87072.5 100753.0 71174.0
Seattle N/A N/A N/A 46609.5 39716.5 N/A N/A N/A N/A N/A
Silicon Valley N/A N/A 41500.5 58259.0 41144.7 47619.5 24140.0 N/A N/A N/A
Sugar 75897.5 65622.0 71841.5 71843.5 84143.0 87088.0 74100.0 74494.0 86503.5 68130.0
Sun 44040.0 44689.0 72995.5 44611.5 63932.5 50924.5 63095.0 48372.5 48374.0 56007.5
Texas N/A N/A N/A N/A N/A N/A N/A N/A 43903.0 25486.5

Attendance Per Mile (APM): The Average Home Attendance of each team, divided by the number of miles between the bowl’s host city and the city where each academic institution is located, averaged together. An example is in Figure A.

2007 Insight Bowl: Oklahoma State vs. Indiana

Distance from Stillwater, Okla. (home of Oklahoma State University.) to Phoenix, Ariz. (site of the bowl) = 1085.64 miles

Oklahoma State’s Average Home Attendance = 40,024

Oklahoma State’s APM = 40,024/1085.65 = 36.87

Distance from Bloomington, Ind. (home of Indiana University) to Phoenix = 1,747 miles

Indiana’s Average Home Attendance = 37,004

Indiana’s APM = 21.18

2007 Insight Bowl’s APM = (36.87+21.18)/2 = 29.03

Figure A. Example of Attendance Per Mile (APM) Variable.

BCS Status (BCS): The variable that separates BCS bowls from non-BCS bowls, under the hypothesis that a BCS bowl will normally draw higher attendance. The value of “1” is assigned to BCS bowls while “0” is assigned to non-BCS bowls.

Championship Status (CStatus) — The status of a game as it pertains to the unofficial national championship of D1-FBS football. For this study, the BCS-designated national championship game is given a value of “1” and all other bowl games a value of “0” (Table 4).

Table 4
BCS Bowls

Bowl Year Winning Team in Bold, Championship Games in Italics
BCS 2006-’07 Florida Ohio State
BCS 2007-’08 LSU Ohio State
Fiesta 1998-’99 Tennessee Florida State
Fiesta 1999-’00 Nebraska Tennessee
Fiesta 2000-’01 Oregon State Notre Dame
Fiesta 2001-’02 Oregon Colorado
Fiesta 2002-’03 Ohio State Miami
Fiesta 2003-’04 Ohio State Kansas State
Fiesta 2004-’05 Utah Pittsburgh
Fiesta 2005-’06 Ohio State Notre Dame
Fiesta 2006-’07 Boise State Oklahoma
Fiesta 2007-’08 Oklahoma West Virginia
Orange 1998-’99 Florida Syracuse
Orange 1999-’00 Michigan Alabama
Orange 2000-’01 Oklahoma Florida State
Orange 2001-’02 Florida Maryland
Orange 2002-’03 USC Iowa
Orange 2003-’04 Miami Florida State
Orange 2004-’05 USC Oklahoma
Orange 2005-’06 Penn State Florida State
Orange 2006-’07 Louisville Wake Forest
Orange 2007-’08 Virginia Tech Kansas
Rose 1998-’99 Wisconsin UCLA
Rose 1999-’00 Wisconsin Stanford
Rose 2000-’01 Washington Purdue
Rose 2001-’02 Miami Nebraska
Rose 2002-’03 Oklahoma Washington State
Rose 2003-’04 USC Michigan
Rose 2004-’05 Texas Michigan
Rose 2005-’06 Texas USC
Rose 2006-’07 USC Michigan
Rose 2007-’08 USC Illinois
Sugar 1998-’99 Ohio State Texas A&M
Sugar 1999-’00 Florida State Virginia Tech
Sugar 2000-’01 Miami Florida
Sugar 2001-’02 LSU Illinois
Sugar 2002-’03 Georgia Florida State
Sugar 2003-’04 LSU Oklahoma
Sugar 2004-’05 Auburn Virginia Tech
Sugar 2005-’06 West Virginia Georgia
Sugar 2006-’07 LSU Notre Dame
Sugar 2007-’08 Georgia Hawaii

Distance (Distance): The number of miles between the bowl’s host city and the city where each participating team is located (Table 5). The travel distance of each team is averaged out for this variable. The distances were found through the internet using Mapquest.com for mainland bowls and Ask.com for the Aloha, O’ahu and Hawaii bowls.

Table 5
Distance

Bowl Mean of Two Teams’ Travel Distances in Miles
1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08
Alamo 982.46 912.34 1,081.26 765.05 1,147.07 1,167.58 921.44 1,177.24 600.90 912.34
Aloha 2,930.00 3,807.00 4,000.50 N/A N/A N/A N/A N/A N/A N/A
Armed Forces N/A N/A N/A N/A N/A 849.07 1,003.34 388.53 774.47 1,205.70
BCS Title Game N/A N/A N/A N/A N/A N/A N/A N/A 1,982.42 495.28
Capital One 1,117.34 631.92 823.29 901.23 732.99 746.85 975.12 863.40 1,196.24 631.92
Champs Sports N/A N/A N/A 783.90 985.59 937.58 828.90 1,198.63 952.28 1,264.36
Chick-fil-A 292.31 205.01 263.38 244.75 433.94 169.08 501.81 596.95 243.41 116.10
Cotton 375.48 264.93 669.21 261.56 319.94 401.79 513.02 466.01 671.07 468.80
Emerald N/A N/A N/A N/A 2,020.38 2,157.90 1,964.73 1,609.41 1,510.00 1,694.69
Fiesta 1,880.06 1,646.11 1,582.88 1,240.15 2,151.54 1,556.28 1,648.40 1,910.65 1,022.55 1,590.78
Gator 683.30 351.37 489.32 359.76 736.91 741.77 461.25 661.00 552.40 1,005.90
GMAC N/A 717.02 362.91 794.15 700.53 689.82 649.35 1,068.07 489.05 811.33
Hawaii N/A N/A N/A N/A 2,105.00 1,949.00 2,181.00 3,654.50 1,459.00 3,848.50
Holiday 989.72 1,415.66 1,141.56 1,280.59 970.32 1,257.22 769.09 1,175.80 952.31 835.25
Houston N/A N/A 923.12 179.86 470.37 1,011.63 943.21 626.77 N/A N/A
Humanitarian 1,232.12 940.64 612.92 2,106.12 698.79 1,845.16 1,547.59 1,331.75 1,633.05 1,448.64
Independence 460.90 381.80 309.81 584.59 564.92 445.68 818.13 696.13 414.47 735.66
Insight 1,738.43 1,801.57 1,798.01 1,777.19 1,690.28 1,423.95 1,571.16 1,225.81 1,226.59 1,416.23
International N/A N/A N/A N/A N/A N/A N/A N/A 439.50 502.67
Las Vegas 1,318.14 408.33 668.78 345.28 421.74 766.57 527.43 472.05 647.41 323.58
Liberty 1,003.56 723.58 766.93 997.44 817.06 944.98 1,137.04 1,161.31 588.30 471.04
Meinecke N/A N/A N/A N/A 324.03 359.91 504.73 377.72 651.57 437.13
Micron PC.com 406.00 1,151.93 1,293.62 N/A N/A N/A N/A N/A N/A N/A
Motor City 349.84 1,010.91 297.73 157.79 385.69 191.29 368.26 466.09 359.43 246.14
Music City 331.14 531.73 416.63 709.39 706.60 462.30 565.14 714.33 287.00 353.53
New Mexico N/A N/A N/A N/A N/A N/A N/A N/A 524.47 511.44
New Orleans N/A N/A N/A 1,026.25 684.29 477.72 334.72 287.86 330.37 609.61
Oahu 3,012.50 1,270.50 4,658.50 N/A N/A N/A N/A N/A N/A N/A
Orange 883.07 1,086.26 991.30 707.28 2,106.81 240.85 2,119.11 868.32 948.15 1,205.45
Outback 971.59 764.38 756.62 756.62 649.45 701.57 899.16 701.57 887.37 997.65
Papajohns.com N/A N/A N/A N/A N/A N/A N/A N/A 587.35 352.64
Poinsettia N/A N/A N/A N/A N/A N/A N/A 1,924.81 1,675.89 1,730.69
Rose 990.34 1,163.52 1,617.19 2,117.25 1,231.63 1,122.54 1,806.68 695.08 1,122.54 1,004.08
Seattle N/A N/A N/A 1,757.49 1,574.78 N/A N/A N/A N/A N/A
Silicon Valley N/A N/A 759.95 1,260.29 1,296.04 245.97 2,267.72 N/A N/A N/A
Sugar 676.85 610.31 701.90 438.10 463.21 395.50 599.06 344.90 535.07 2,375.18
Sun 706.00 1,521.90 1,117.54 1,509.80 1,598.51 1,521.90 949.15 1,151.98 1,384.94 1,693.86
Texas N/A N/A N/A N/A N/A N/A N/A N/A 1,172.12 131.63

Source of data: Mapquest.com and ask.com. N/A ( not applicable) indicates the game was not played in that particular year.

Improved Winning Percentage (Impct): The regular season winning percentage (including a conference championship game, when applicable) of each team minus its winning percentage from the previous season (including a bowl game, when applicable). The two scores are averaged for this variable (Figure B). It is designed to measure how a team performed compared to expectation, something that would theoretically affect the enthusiasm of the fans and influence their decision to travel.

Improved Winning Percentage:

Regular Season Winning Percentage – Previous year’s total winning percentage

2007-’08 BCS Championship Game: Ohio State vs. LSU

Ohio State

2007 Regular Season = 11-1 = .917

2006-’07 Season = 12-1 = .923

Improved Winning Percentage = .917-.923 = -.006

LSU

2007 Regular Season = 11-2 = .846

2006-’07 Season = 11-2 = .846

Improved Winning Percentage = .846 – .846 = 0

2007-’08 BCS Championship Improved Winning Percentage:

(-.006+0)/2 = -.003

Figure B. Example of Improved Winning Percentage

Market Strength (Market): A measurement of the support for college football in a participating team’s area based on the number of D1-FBS institutions are located in the same state as the participating team. For example, a team from Texas would have a market strength rating of 10 because there are 10 D1-FBS teams in the state of Texas (Table 6). A team from Oklahoma would have a market strength rating of three for the same reason. Therefore, a bowl game played between the University of Tulsa and Texas A&M would have a market strength rating of 6.5.

Table 6
Market Strength

Bowl Market Strength (averaged between the two participating teams)
1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08
Alamo 3.0 6.5 2.0 6.0 2.0 3.0 5.5 3.0 6.0 6.5
Aloha 2.5 3.0 1.5 N/A N/A N/A N/A N/A N/A N/A
Armed Forces N/A N/A N/A N/A N/A 6.0 5.0 6.0 3.0 5.0
BCS N/A N/A N/A N/A N/A N/A N/A N/A 7.5 6.5
Capital One 3.5 6.0 4.5 4.5 3.5 3.0 3.5 2.5 1.5 6.0
Champs Sports N/A N/A N/A 4.0 26.0 5.0 2.5 2.5 3.0 3.0
Chick-fil-A 2.0 2.5 3.5 4.5 3.0 3.0 7.0 6.0 2.0 4.0
Cotton 6.5 6.0 3.0 2.5 7.5 2.5 7.0 7.0 3.0 1.5
Emerald N/A N/A N/A N/A 2.5 2.0 2.0 2.5 7.0 2.0
Fiesta 5.5 2.5 3.0 2.5 7.5 5.0 3.0 6.0 2.5 2.5
Gator 3.0 4.5 2.0 4.5 4.5 2.0 4.5 2.5 2.0 6.0
GMAC N/A 7.5 6.5 3.5 2.0 5.0 6.0 9.0 5.5 5.5
Hawaii N/A N/A N/A N/A 7.5 5.5 5.5 4.5 1.5 3.5
Holiday 1.5 2.0 6.0 6.0 2.0 6.0 8.5 2.5 8.5 6.0
Houston N/A N/A 7.5 10.0 3.0 6.0 6.5 6.0 N/A N/A
Humanitarian 2.5 2.0 6.0 3.5 2.0 2.5 4.5 1.5 4.5 4.5
Independence 6.5 3.0 6.5 3.0 2.0 1.5 5.0 1.5 3.5 3.5
Insight 1.5 2.0 2.5 2.5 2.5 4.5 3.0 1.5 5.5 3.5
International N/A N/A N/A N/A N/A N/A N/A N/A 6.5 2.5
Las Vegas 6.0 5.0 2.0 5.0 4.5 2.0 4.0 5.0 2.5 5.0
Liberty 4.0 3.0 3.0 3.0 6.5 3.0 2.5 5.0 6.0 5.0
Meinecke N/A N/A N/A N/A 2.0 2.5 3.0 6.0 1.5 3.0
MicronPC.com 6.0 2.5 3.0 N/A N/A N/A N/A N/A N/A N/A
Motor City 2.0 2.5 5.0 8.0 4.5 5.5 4.5 6.0 4.5 4.5
Music City 3.0 2.5 2.5 1.5 1.5 2.5 2.0 1.5 2.0 4.5
New Mexico N/A N/A N/A N/A N/A N/A N/A N/A 4.5 2.0
New Orleans N/A N/A N/A 6.5 9.0 7.0 6.5 2.5 7.0 5.5
Oahu 2.5 1.5 2.0 N/A N/A N/A N/A N/A N/A N/A
Orange 5.0 4.5 5.0 4.5 4.5 7.0 5.0 5.0 4.0 2.0
Outback 3.0 3.0 5.0 5.0 6.0 4.5 1.5 4.5 3.5 2.5
Papajohns.com N/A N/A N/A N/A N/A N/A N/A N/A 6.0 5.0
Poinsettia N/A N/A N/A N/A N/A N/A N/A 2.5 6.5 2.5
Rose 4.0 4.0 3.0 7.0 2.5 6.0 7.5 8.5 6.0 5.0
Seattle N/A N/A N/A 4.5 3.5 N/A N/A N/A N/A N/A
Silicon Valley N/A N/A 5.0 6.0 4.5 7.0 3.5 N/A N/A N/A
Sugar 9.0 4.5 7.0 4.0 4.5 4.0 3.0 2.0 4.5 1.5
Sun 8.5 1.5 4.0 3.0 3.0 1.5 3.0 5.0 1.5 4.5
Texas N/A N/A N/A N/A N/A N/A N/A N/A 1.5 10.0

Sources: U.S. Department of Education N/A ( not applicable) indicates the game was not played in that particular year.

Notice (Notice): The number of days between the date of a team’s last regular season or conference championship game and the date of its bowl game. The notice for the two teams is averaged together for this variable. The theory is that the longer notice fans have, the more likely they are to travel to a bowl game (Table 7).

Table 7
Notice

Bowl Notice (averaged between the two participating teams)
1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08
Alamo 31.0 35.0 33.5 35.0 28.0 34.0 35.5 36.0 39.0 39.0
Aloha 31.0 31.5 30.5 N/A N/A N/A N/A N/A N/A N/A
Armed Forces N/A N/A N/A N/A N/A 20.5 30.0 27.0 28.5 37.5
BCS N/A N/A N/A N/A N/A N/A N/A N/A 44 44.0
Capital One 33.5 35.0 37.0 31.0 39.0 33.0 39.0 41.0 37.0 42.0
Champs Sports N/A N/A N/A 22.5 30.0 30.0 24.0 31.0 34.0 34.0
Chick-fil-A 33.0 37.5 34.5 30.0 31.0 37.5 34.0 30.5 35.0 37.0
Cotton 31.0 27.0 33.5 38.5 33.0 38.5 32.0 44.0 37.0 35.0
Emerald N/A N/A N/A N/A 31.0 39.5 33.0 36.5 28.5 30.5
Fiesta 37.0 42.0 40.5 31.0 34.0 34.0 35.0 40.5 33.5 32.0
Gator 33.5 31.5 40.5 31.0 35.5 33.0 39.5 30.0 30.0 41.5
GMAC N/A 29.0 26.0 22.5 14.5 17.0 27.0 27.0 37.5 39.5
Hawaii N/A N/A N/A N/A 24.5 22.5 23.5 25.0 25.5 29.5
Holiday 33.0 39.0 38.0 30.5 31.0 35.0 29.5 36.5 35.0 31.0
Houston N/A N/A 36.0 28.0 27.0 31.0 28.5 42.0 N/A N/A
Humanitarian 34.0 40.0 40.0 34.0 38.0 38.5 38.0 36.5 37.0 34.5
Independence 37.5 35.0 37.5 30.5 28.5 32.5 26.0 36.0 36.5 36.5
Insight 32.0 35.0 37.0 35.0 29.5 30.5 33.5 31.5 41.0 41.0
International N/A N/A N/A N/A N/A N/A N/A N/A 43.0 39.5
Las Vegas 24.5 28.0 27.0 31.0 21.5 25.0 26.0 33.0 27.0 21.0
Liberty 30.5 37.5 42.0 30.5 31.0 36.0 30.5 28.5 31.0 32.0
Meinecke N/A N/A N/A N/A 28.0 28.0 36.5 31.5 32.5 35.0
MicronPC.com 27.5 40.0 36.5 N/A N/A N/A N/A N/A N/A N/A
Motor City 25.5 30.5 32.0 28.5 22.5 28.0 28.5 27.5 28.5 32.0
Music City 34.5 35.5 34.5 30.5 30.0 39.0 44.5 37.5 34.0 37.0
New Mexico N/A N/A N/A N/A N/A N/A N/A N/A 24.5 24.5
New Orleans N/A N/A N/A 24.0 17.5 19.0 18.0 24.0 23.5 23.5
Oahu 27.0 31.5 29.0 N/A N/A N/A N/A N/A N/A N/A
Orange 38.5 35.0 39.0 39.0 40.0 33.0 31.0 38.0 31.0 36.5
Outback 37.5 38.0 44.0 41.5 36.0 36.5 38.5 40.0 41.0 38.0
Papajohns.com N/A N/A N/A N/A N/A N/A N/A N/A 28.0 28.0
Poinsettia N/A N/A N/A N/A N/A N/A N/A 26.0 21.0 22.5
Rose 34.0 42.0 44.0 37.0 25.0 33.0 39.0 32.5 37.0 38.0
Seattle N/A N/A N/A 26.0 33.5 N/A N/A N/A N/A N/A
Silicon Valley N/A N/A 39.5 30.0 28.5 34.5 40.0 N/A N/A N/A
Sugar 34.5 42.0 34.5 32.0 28.5 29.0 30.0 30.0 39.5 34.5
Sun 36.5 41.0 37.5 37.0 38.0 42.5 38.0 34.0 30.5 33.5
Texas N/A N/A N/A N/A N/A N/A N/A N/A 33.0 34.0

Source of data: Division 1A Historical Scores, jhowell.net. N/A ( not applicable) indicates the game was not played in that particular year.

November Winning Percentage (Novpct): The winning percentage of a team in games played during November or the first week of December (Table 8).

Table 8
November Winning Percentage

Bowl November Winning Percentage (averaged between the two participating teams)
1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08
Alamo 0.875 0.333 0.500 0.625 0.425 0.375 0.500 0.583 0.167 0.500
Aloha 0.500 0.666 0.333 N/A N/A N/A N/A N/A N/A N/A
Armed Forces N/A N/A N/A N/A N/A 0.875 0.500 0.625 0.458 0.625
BCS N/A N/A N/A N/A N/A N/A N/A N/A 1.000 0.733
Capital One 0.625 0.750 0.666 0.667 0.875 0.675 1.000 0.667 0.800 0.667
Champs Sports N/A N/A N/A 0.875 0.750 0.292 0.583 0.625 0.625 0.533
Chick-fil-A 0.583 0.583 0.833 0.576 0.800 0.875 0.750 0.775 0.833 0.666
Cotton 0.633 0.500 0.875 0.750 0.625 0.625 0.417 0.500 0.708 0.775
Emerald N/A N/A N/A N/A 0.433 0.500 0.833 0.583 0.625 0.625
Fiesta 1.000 1.000 1.000 1.000 1.000 0.875 0.875 1.000 1.000 0.775
Gator 0.875 0.625 0.583 0.500 0.375 1.000 0.500 0.750 0.600 0.666
GMAC N/A 0.833 0.500 0.567 0.650 0.750 0.708 0.583 0.775 0.900
Hawaii N/A N/A N/A N/A 0.567 0.550 0.650 0.800 0.650 0.750
Holiday 0.833 0.666 0.833 0.625 0.875 0.750 0.833 0.875 0.417 0.583
Houston N/A N/A 0.500 0.333 0.625 0.625 0.708 0.833 N/A N/A
Humanitarian 1.000 0.750 0.833 0.500 0.625 0.625 0.750 0.708 0.500 0.625
Independence 0.375 0.542 0.576 0.625 0.250 0.650 0.708 0.500 0.125 0.167
Insight 0.667 0.500 0.642 0.708 0.625 0.575 0.500 0.500 0.833 0.458
International N/A N/A N/A N/A N/A N/A N/A N/A 0.583 0.583
Las Vegas 0.875 0.666 0.625 0.750 0.625 0.625 0.333 0.500 0.625 0.625
Liberty 0.875 1.000 0.833 0.775 0.666 1.000 1.000 0.576 0.750 0.833
Meinecke N/A N/A N/A N/A 0.750 0.550 0.708 0.675 0.750 0.500
MicronPC.com 0.708 1.000 0.500 N/A N/A N/A N/A N/A N/A N/A
Motor City 1.000 0.667 0.875 0.750 0.800 0.550 0.708 0.625 0.625 0.300
Music City 0.500 0.333 0.500 0.542 0.833 0.417 0.167 0.500 0.542 0.417
New Mexico N/A N/A N/A N/A N/A N/A N/A N/A 0.467 0.500
New Orleans N/A N/A N/A 0.708 0.917 0.875 0.600 0.500 0.900 0.750
Oahu 0.667 0.708 0.333 N/A N/A N/A N/A N/A N/A N/A
Orange 0.708 1.000 1.000 0.868 1.000 0.675 1.000 0.625 0.800 0.875
Outback 0.583 0.417 0.333 0.708 0.750 0.750 0.500 0.666 0.583 0.733
Papajohns.com N/A N/A N/A N/A N/A N/A N/A N/A 0.750 0.750
Poinsettia N/A N/A N/A N/A N/A N/A N/A 0.542 0.833 0.833
Rose 0.708 1.000 0.750 0.833 0.708 1.000 0.917 1.000 0.750 1.000
Seattle N/A N/A N/A 0.600 0.292 N/A N/A N/A N/A N/A
Silicon Valley N/A N/A 0.875 0.700 0.750 0.400 0.750 N/A N/A N/A
Sugar 0.708 1.000 0.875 1.000 0.800 0.900 1.000 0.875 0.875 1.000
Sun 0.666 1.000 0.667 0.433 0.708 0.708 0.666 0.583 0.567 0.425
Texas N/A N/A N/A N/A N/A N/A N/A N/A 0.583 0.750

Stadium Size (Stadium): The seating capacity of the stadium when used for football games (Table 9).

Table 9
Stadiums

Bowl Years Stadium Capacity
Alamo 1993-Present Alamo Dome 65000
Aloha 1982-2000 Aloha Stadium 50000
Armed Forces 2003-Present Amon G. Carter Stadium 43000
BCS 2006-’07 University of Phoenix Stadium 73000
BCS 2007-’08 Louisiana Superdome 72500
Capital One 1986-Present Florida Citrus Bowl 65438
Champs Sports 2002-Present Florida Citrus Bowl 65438
Chick-fil-A 1993-Present Georgia Dome 71990
Cotton 1938-Present Cotton Bowl 71252
Emerald 2002-Present AT&T Park 38437
Fiesta 1971-’06 Sun Devil Stadium 73397
Fiesta 2007-Present University of Phoenix Stadium 73000
Gator 1997-Present Jacksonville Municipal Stadium 77000
GMAC 1999-Present Ladd-Peebles Stadium 40048
Hawaii 2002-Present Aloha Stadium 50000
Holiday 1978-Present Qualcomm Stadium 66000
Houston 2000-2005 Reliant Stadium 69500
Humanitarian 1997-Present Bronco Stadium 30000
Independence 1976-Present Independence Stadium 48947
Insight 1989-’99 Arizona Stadium 57803
Insight 2000-’05 Bank One Ballpark 42915
Insight 2006-Present Sun Devil Stadium 73397
International 2007-Present Rogers Center 53506
Las Vegas 1992-Present Sam Boyd Stadium 40000
Liberty 1965-Present Liberty Bowl Memorial Stadium 62598
Meinecke 2002-Present Bank of America Stadium 73298
MicronPC.com 1996-2001 Joe Robbie Stadium 77823
Motor City 1997-’01 Pontiac Silverdome 80368
Motor City 2002-Present Ford Field 65000
Music City 1998-Present LP Field 68000
New Mexico 2006-Present University Stadium 38634
New Orleans 2001-Present Louisiana Superdome 72500
Oahu 1998-’00 Aloha Stadium 50000
Orange 1996-Present Dolphin Stadium 77823
Outback 1998-Present Raymond James Stadium 65500
Papajohns.com 2006-Present Legion Field 72000
Poinsettia 2005-Present Qualcomm Stadium 66000
Rose 1943-Present Rose Bowl 91887
Seattle 2001-’02 Seahawks Stadium 67000
Silicon Valley 2000-’04 Spartan Stadium 30000
Sugar 1975-’05, 2007-Present Louisiana Superdome 72500
Sugar 2006 Georgia Dome 71990
Sun 1963-Present Sun Bowl Stadium 50426
Texas 2006-Present Reliant Stadium 69500

Data is from NCAA.com. N/A ( not applicable) indicates the game was not played in that particular year.

Winning Percentage (Wpct) — The percentage of games won by each team in the regular season (including conference championship games, when applicable), averaged together (Table 10).

Table 10
Winning Percentage (averaged between the two participating teams)

Bowl 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08
Alamo 0.792 0.739 0.773 0.591 0.615 0.708 0.636 0.636 0.625 0.625
Aloha 0.682 0.545 0.545 N/A N/A N/A N/A N/A N/A N/A
Armed Forces N/A N/A N/A N/A N/A 0.920 0.545 0.591 0.625 0.625
BCS N/A N/A N/A N/A N/A N/A N/A N/A 0.962 0.881
Capital One 0.784 0.784 0.739 0.780 0.708 0.760 0.818 0.826 0.843 0.708
Champs Sports N/A N/A N/A 0.591 0.576 0.542 0.545 0.610 0.671 0.676
Chick-fil-A 0.773 0.682 0.727 0.576 0.718 0.750 0.682 0.826 0.718 0.667
Cotton 0.697 0.626 0.748 0.735 0.833 0.750 0.693 0.576 0.763 0.756
Emerald N/A N/A N/A N/A 0.679 0.583 0.727 0.591 0.542 0.583
Fiesta 0.958 1.000 0.864 0.871 1.000 0.810 0.864 0.826 0.923 0.833
Gator 0.818 0.697 0.864 0.682 0.769 0.708 0.818 0.576 0.763 0.708
GMAC N/A 0.727 0.773 0.689 0.708 0.837 0.727 0.727 0.654 0.679
Hawaii N/A N/A N/A N/A 0.676 0.599 0.610 0.697 0.676 0.708
Holiday 0.833 0.773 0.818 0.780 0.724 0.792 0.682 0.746 0.750 0.792
Houston N/A N/A 0.610 0.591 0.583 0.625 0.655 0.773 N/A N/A
Humanitarian 0.682 0.693 0.773 0.591 0.728 0.583 0.727 0.739 0.583 0.625
Independence 0.545 0.636 0.576 0.591 0.542 0.666 0.606 0.591 0.500 0.500
Insight 0.682 0.636 0.682 0.648 0.667 0.641 0.545 0.591 0.542 0.542
International N/A N/A N/A N/A N/A N/A N/A N/A 0.625 0.583
Las Vegas 0.591 0.697 0.564 0.591 0.561 0.625 0.545 0.591 0.708 0.667
Liberty 0.846 0.727 0.818 0.875 0.818 0.784 0.955 0.576 0.676 0.676
Meinecke N/A N/A N/A N/A 0.683 0.625 0.636 0.591 0.750 0.667
MicronPC.com 0.682 0.636 0.591 N/A N/A N/A N/A N/A N/A N/A
Motor City 0.777 0.864 0.610 0.727 0.679 0.635 0.652 0.564 0.638 0.599
Music City 0.682 0.545 0.591 0.682 0.638 0.583 0.545 0.591 0.667 0.583
New Mexico N/A N/A N/A N/A N/A N/A N/A N/A 0.583 0.583
New Orleans N/A N/A N/A 0.500 0.561 0.708 0.591 0.545 0.583 0.583
Oahu 0.731 0.652 0.591 N/A N/A N/A N/A N/A N/A N/A
Orange 0.773 0.826 0.958 0.864 0.875 0.826 1.000 0.788 0.881 0.881
Outback 0.682 0.636 0.682 0.682 0.576 0.708 0.818 0.682 0.739 0.760
Papajohns.com N/A N/A N/A N/A N/A N/A N/A N/A 0.625 0.667
Poinsettia 0.591 0.667 0.708 N/A N/A N/A N/A N/A N/A N/A
Rose 0.909 0.773 0.818 0.958 0.840 0.875 0.864 1.000 0.875 0.792
Seattle N/A N/A N/A 0.701 0.542 N/A N/A N/A N/A N/A
Silicon Valley N/A N/A 0.682 0.696 0.599 0.558 0.682 N/A N/A N/A
Sugar 0.878 1.000 0.839 0.830 0.808 0.923 0.917 0.871 0.833 0.917
Sun 0.606 0.727 0.606 0.682 0.500 0.708 0.682 0.576 0.679 0.708
Texas N/A N/A N/A N/A N/A N/A N/A N/A 0.708 0.583

For Bowl games that are more than five years old (n = 194)

Five-year Average Attendance (FiveAtt) — The average attendance of a bowl game for the past five years (Table 11).

Table 11
Five-year Average Attendance

Bowl 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08
Alamo 53129.60 56142.40 60397.20 59483.40 61394.40 60422.00 59511.20 59488.20 59882.60 60011.20
Aloha 43592.00 44080.40 43302.80 N/A N/A N/A N/A N/A N/A N/A
Armed Forces N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
BCS* N/A N/A N/A N/A N/A N/A N/A N/A 74872.20 76662.60
Capital One 70171.00 68396.60 66559.80 65786.00 65031.20 63710.00 63906.20 65549.80 63608.40 63824.60
Champs Sports N/A N/A N/A N/A N/A N/A N/A N/A 62519.00 65549.80
Chick-fil-A 66795.40 68687.40 70370.00 70927.80 72568.80 71992.40 72442.20 71645.60 70046.80 70762.60
Cotton 65886.00 66437.20 66938.20 67988.40 68193.80 70514.20 70777.60 71373.80 73525.20 72289.60
Emerald N/A N/A N/A N/A N/A N/A N/A N/A N/A 29662.60
Fiesta 72114.80 73755.00 73266.60 72379.40 74181.80 75808.80 74399.80 74798.40 74952.00 74872.20
Gator 56165.20 56882.40 53125.60 57833.40 61853.20 65728.20 67348.40 72687.60 71695.40 70795.80
GMAC N/A N/A N/A N/A N/A N/A 39181.00 40373.00 39397.40 39119.80
Hawaii N/A N/A N/A N/A N/A N/A N/A N/A N/A 33978.20
Holiday 53624.40 56273.60 55806.60 58252.00 59411.80 61003.00 60152.60 61973.40 62401.00 62770.40
Houston N/A N/A N/A N/A N/A N/A N/A 42073.80 N/A N/A
Humanitarian N/A N/A N/A N/A 24655.50 25813.60 26504.40 26351.00 27209.00 28245.00
Independence 40344.20 42952.80 47479.00 45106.80 45959.00 45086.40 45639.00 44264.40 45136.00 45021.40
Insight 45341.60 42756.00 40884.00 41045.80 40627.00 38856.60 40100.00 42131.00 42475.60 44148.20
International N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
Las Vegas 15165.80 16350.00 18483.00 22080.20 26235.40 27997.40 28799.00 28966.00 31154.00 33898.20
Liberty 40229.40 46448.40 50765.60 52946.40 54907.40 55907.00 56666.40 57364.20 56682.60 56109.60
Meinecke N/A N/A N/A N/A N/A N/A N/A N/A N/A 61649.80
MicronPC.com 39691.40 40865.60 36916.80 N/A N/A N/A N/A N/A N/A N/A
Motor City N/A N/A N/A N/A 43496.80 45203.20 49127.20 50665.00 50206.00 52195.80
Music City N/A N/A N/A N/A N/A 46579.20 49351.40 50725.00 49405.00 53784.80
New Mexico N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
New Orleans N/A N/A N/A N/A N/A N/A N/A N/A 23360.60 22918.00
Oahu N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
Orange 74557.20 71833.80 69575.40 70502.80 72571.40 72965.20 74729.20 76219.40 76407.00 76573.00
Outback 57864.60 60535.80 59070.80 59054.00 61671.60 63328.60 63202.00 64873.00 65003.40 64873.80
Papajohns.com N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
Poinsettia N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
Rose 101088.00 99615.00 97912.00 96770.00 95399.00 92525.00 92520.00 92468.00 92386.00 92401.00
Seattle N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
Silicon Valley N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
Sugar 73515.40 73728.60 74339.80 73164.60 73033.40 74429.40 74997.20 74611.00 76621.20 76639.80
Sun 47080.20 47633.00 47262.00 47257.40 48275.60 48238.20 48894.60 49400.80 49667.40 49851.40
Texas N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

*Figures are based on last five bowls hosted by the local committee
Data source: NCAA.com
N/A (not applicable) indicates the game was not played in that particular year.

Results

When all bowl games (n=271) were counted, six variables combined to explain 82.2 percent of the variance in attendance (r2d = .822). The variables were: Seating Capacity (Stadium), Age of the bowl (Age), Average Home Attendance of the participants (AHAtt), number of miles in travel between the location of the institutions and the bowl games (Distance), and the number of days elapsed from the end of the regular season or conference championship game to the bowl game itself (Notice), as shown in Table 12.

Table 12
Model Summary

Model R r2 Adjusted r2 Std. Error of the Estimate
1 .743a .552 .551 12822.102
2 .841b .707 .704 10399.231
3 .884c .782 .779 8983.648
4 .897d .804 .801 8524.124
5 .903e .816 .813 8279.997
6 .907f .822 .818 8156.106
a. Predictors: (Constant), Stadium
b. Predictors: (Constant), Stadium, Age
c. Predictors: (Constant), Stadium, Age, AHAtt
d. Predictors: (Constant), Stadium, Age, AHAtt, Wpct
e. Predictors: (Constant), Stadium, Age, AHAtt, Wpct, Distance
f. Predictors: (Constant), Stadium, Age, AHAtt, Wpct, Distance, Notice

Each variable had a relationship to attendance that was statistically significant at the 0.01 alpha level (p > 0.01), as noted in Table 13.

Table 13
Coefficients and Relationships

Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -5233.704 3280.305 -1.595 .112
Stadium .944 .052 .743 18.214 .000
2 (Constant) 4910.494 2794.302 1.757 .080
Stadium .636 .049 .501 12.887 .000
Age 318.709 26.845 .461 11.872 .000
3 (Constant) -2534.098 2535.494 -.999 .318
Stadium .527 .044 .415 11.947 .000
Age 237.622 24.682 .344 9.627 .000
AHAtt .294 .031 .327 9.598 .000
4 (Constant) -18954.895 3822.348 -4.959 .000
Stadium .485 .043 .382 11.401 .000
Age 190.531 24.920 .276 7.646 .000
AHAtt .270 .029 .301 9.216 .000
Wpct 30760.395 5564.072 .184 5.528 .000
5 (Constant) -16058.380 3779.075 -4.249 .000
Stadium .448 .042 .353 10.583 .000
Age 189.036 24.209 .274 7.808 .000
AHAtt .271 .029 .302 9.515 .000
Wpct 34305.556 5473.019 .205 6.268 .000
Distance -3.142 .764 -.112 -4.113 .000
6 (Constant) -24353.011 4626.876 -5.263 .000
Stadium .455 .042 .358 10.885 .000
Age 178.672 24.093 .259 7.416 .000
AHAtt .238 .030 .265 7.904 .000
Wpct 34631.879 5392.212 .207 6.423 .000
Distance -3.240 .753 -.115 -4.303 .000
Notice 299.769 99.308 .089 3.019 .003

Dependent Variable: Attendance

So a formula that would explain the variance in bowl attendance would look something like this:

S = Seating Capacity

A = Age of the bowl

T = Average Home Attendance of the Participating teams (The sum Average Home Attendance of each team divided by two)

W = Winning Percentage (the sum of the regular season winning percentages of the two participating teams, including conference championship games when applicable, divided by two).

D = Travel distance (the sum of the travel distance between each participating institution’s home city and the city hosting the bowl game, divided by two)

N = Notice (the sum of the number of days between the last regular season game or conference championship game of each team and the bowl game, divided by two)

Bowl Attendance = .455S + 172.672A + 34631.879W – 3.24D + 299.769N – 24353.011.

On bowls that were at least six years old (n = 194), the five-year attendance average was included as an independent variable. Five variables; Five Year Average Attendance (FiveAtt, Travel Distance (Distance), Winning Percentage (Wpct), Average Home Attendance (AHAtt), and Seating Capacity (Stadium); combined to explain 91 percent of the variance in attendance (r2d = .910), as can be observed in Table 14.

Table 14
Model Summary Bowls that are at least Six Years Old

Model R
VAR00021 > 5 (Selected) r2 Adjusted r2 Std. Error of the Estimate
1 .939a .883 .882 5893.063
2 .946b .894 .893 5604.919
3 .949c .901 .899 5451.683
4 .952d .906 .904 5301.487
5 .954e .910 .908 5202.987
a. Predictors: (Constant), FiveAtt
b. Predictors: (Constant), FiveAtt, Distance
c. Predictors: (Constant), FiveAtt, Distance, Wpct
d. Predictors: (Constant), FiveAtt, Distance, Wpct, AHAtt
e. Predictors: (Constant), FiveAtt, Distance, Wpct, AHAtt, Stadium

As in the previous formula, all variables had a relationship with attendance that was statistically significant (Table 1) at the 0.01 alpha level (p < 0.01).

Table 15
Coefficients and Relationships for bowls at least six years old.

Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 6456.892 1446.943 4.462 .000
FiveAtt .917 .024 .939 37.981 .000
2 (Constant) 9579.045 1533.840 6.245 .000
FiveAtt .913 .023 .935 39.696 .000
Distance -2.996 .650 -.109 -4.610 .000
3 (Constant) 2699.321 2491.405 1.083 .280
FiveAtt .846 .029 .867 28.691 .000
Distance -3.401 .643 -.123 -5.290 .000
Wpct 15344.128 4450.253 .105 3.448 .001
4 (Constant) 537.570 2502.378 .215 .830
FiveAtt .788 .033 .807 23.629 .000
Distance -3.253 .627 -.118 -5.190 .000
Wpct 15823.608 4329.875 .108 3.655 .000
AHAtt .082 .024 .097 3.452 .001
5 (Constant) -3309.442 2798.385 -1.183 .238
FiveAtt .690 .047 .707 14.603 .000
Distance -2.909 .627 -.105 -4.641 .000
Wpct 15173.014 4255.479 .104 3.566 .000
AHAtt .090 .023 .106 3.847 .000
Stadium .142 .050 .117 2.868 .005

The formula for bowls that are at least six years old would include:

S = Seating Capacity

T = Average Home Attendance of the Participating teams (The sum Average Home Attendance of each team divided by two)

W = Winning Percentage (the sum of the regular season winning percentages of the two participating teams, including conference championship games when applicable, divided by two).

D = Travel distance (the sum of the travel distance between each participating institution’s home city and the city hosting the bowl game, divided by two)

F = Average attendance over the past five years.

Attendance = 0.690F – 2.909D + 15173.014W + 0.09T + .142S – 3309.442

Discussion

Bowl committees will publicly state that they invite the most deserving teams more so than those that will bring the highest attendance. It is easy to see why the latter option would be more tempting. On table 12, where all bowls are included, it shows that average home attendance accounts for 7.5 percent of the variance in bowl attendance, while winning percentage only accounts for 2.2 percent. This can be figured from the r2d numbers on step 2, a formula that does not include Average Home Attendance but explains 70.7 percent of the variance, but step 3, which adds Average Home Attendance explains 78.2 percent of the variance, a difference of 7.5 percent. Step 4, which adds winning percentage, explains 80.4 percent of the variance, a difference of 2.2 percent. In Table 14, where only bowls that are at least six years old are studied, winning percentage accounts for 0.7 percent of the variance while Average Home Attendance accounts for 0.5. Notice, which theoretically would become an issue with the increasing number of bowl games played before Christmas and the later invitation dates brought about by the BCS and conference championship games, was not a factor in the bowls that were six years old or more and only explained .6 % of the variance in bowls overall.

Future Studies

Since the adjusted r2 for the first formula is .818, this means the average accuracy of any prediction on bowl attendance would be 81.8 percent (.908/90.8 percent on the second formula). Future studies could focus on bowls that exceed their expected attendance with a qualitative look at the marketing methods used by these bowls compared to other bowls that do not fare as well. Conferences and teams whose bowl appearances draw larger crowds than expected could also be studied.

The second formula, that takes five-year attendance averages into account, could be used by bowl committees to set goals for attendance each year. Since the bowls do not know until December who their participants will be, setting a goal based on this formula’s expectation and using it to measure improvement would be more reasonable.

References

Hardy, Stephen, Bernard J. Mullin and William A. Sutton (2003). Sports Marketing (pp. 55-75). Champaign, Ill: Human Kinetics.

Official Website of the NCAA. Retrieved May 13, 2008 from Ncaa.com.

Ask.com. Retrieved May16, 2008 from Ask.com.

Mapquest. Retrieved May 23, 2008 from Mapquest.com.

Active Versus Passive Recovery in the 72 Hours After a 5-km Race

Abstract

We do not clearly understand what type and duration of recovery works best after a hard run to restore the body to peak racing condition. This study compared 72 hr of active recovery after a 5-km running performance with 72 hr of passive recovery. A sample of 9 male and 3 female runners of above-average ability completed 3 trials within 6 days. Each 5-km trial was followed by 72 hr of passive recovery (PAS) or 72 hr of active recovery (ACT), a counterbalanced protocol. The 2 initial 5-km trials constituted separate PAS and ACT baselines. Mean finishing times did not differ significantly (p = 0.17) between ACT (19:35 + 1.5 min) and baseline (19:41 + 1.7 min); nor was there significant difference (p = 0.21) between PAS (19:30 + 1.5 min) and baseline (19:34 + 1.6 min). Average heart rate for PAS (177.9 + 6.3 b/min) was significantly higher (p = 0.04) than baseline (175.4 + 6.5 b/min), but ACT average heart rate (175.9 + 6.6 b/min) was significantly lower (p = 0.02) than baseline (178.9 + 6.4 b/min). For PAS, perceived rate of exertion at ending (19.8 + 0.6) was significantly greater (p = 0.01) than baseline (19.3 + 0.9), yet for ACT, perceived rate of exertion at ending (19.6 + 0.8) did not differ significantly (p = 0.17) from baseline (19.7 + 0.7). During PAS trials, 2 individuals ran a mean 12.0 + 2.8 s slower, 2 individuals ran a mean 33.0 + 21.0 s faster, and 8 individuals ran within 5.1 + 2.5 s of their first run. During the ACT trials, 1 participant ran 13.0 s slower, 3 participants ran a mean of 34.7 + 13.5 s faster, and 8 nonresponders ran within 5.5 + 2.7 s of baseline. Results indicate that 72 hr of passive and active recovery result in similar mean 5-km performance.

Active Versus Passive Recovery in the 72 Hours After a 5-km Race

In order to improve race day performance, many runners believe, daily running is necessary, with many runs made with intense effort. However, excessive numbers of consecutive hard efforts with no allowance for sufficient recovery can lead to overtraining. Combining appropriate training frequency with adequate rest may optimize race day performance.

Previous studies have focused on recovery from long endurance races, such as marathons and ultra-marathons (Gomez et al., 2002; Martin & Coe, 1997; Noakes, 2003). Recovery from such endurance races revolves around the repairing of damaged muscle fibers and replenishing of glycogen stores (Gomez et al., 2002; Nicholas, Green, Hawkins, & Williams, 1997). However, when it comes to endurance activities of shorter duration, such as a 5-km (3.1 mi) or 10-km race, Foss and Keteyian (1998) indicate that, while muscle and liver glycogen levels may have normalized 24 hr after exercise, muscle function and performance measures may not be fully recovered.

New Zealander Jack Foster indicated a runner should take one recovery day for every mile completed in a race; Joe Henderson indicated that it may be even better to take one easy day per kilometer of racing distance (Brown & Henderson, 2002; Galloway, 1984; Henderson, 2000; Higdon, 1998; O’Conner & Wilder, 2001; Sinclair, Oglesby, & Pierpenburg, 2003). While Henderson’s and Foster’s statements about duration of recovery after a race or hard effort seem appropriate, the most effective length and type (active or passive) of recovery before resuming racing or intense training have yet to be fully determined.

Often—and despite the possibility that athletes who recover relatively longer may actually feel recovered and ready to pick up intense running efforts again—athletes worry about time off (or even lighter training) as they try to balance the need to recover with the fear of reduced fitness or diminished racing performance (Houmard & Johns, 1994; Kubukeli, Noakes, & Dennis, 2002). Active recovery, such as light concentric exercise after strenuous training and racing, is commonly practiced by distance runners, in hopes of enhancing recovery and providing the needed training stimulus to maintain fitness (Brown & Henderson, 2002; Galloway, 1984; Wigernaes, Hostmark, Kierulf, & Stromme, 2000). However, again, the intensity and duration of running needed for sufficient recovery is not well understood.

Some argue that balancing periods of complete rest with periods of active recovery enhances the recovery process, which in turn allows for a greater effort during hard training sessions and on race day (Martin & Coe, 1997; Noakes, 2003; Martin, Zoeller, Robertson, & Lephart, 1998; Wigernaes et al., 2000). However, many runners often complain of feeling “stale” if they haven’t run in a few days; it has been suggested that competitive athletes who for some reason begin to train less often may experience a loss of “feel” during exercise (Mujika et al., 2001). Engaging in active recovery, at an appropriate level of intensity, following a hard session often leads to feeling somewhat invigorated at the start of a subsequent intense effort (Martin & Coe, 1997). Mujika and colleagues (2001) showed that active recovery during a six-day taper preceding a major 800-m race allowed runners to run faster than when they had recovered passively for six days. It may be that pursuing some type of activity tends to improve state of mind prior to a race or intense training effort, which may provide a rationale for improvement (Martin & Coe, 1997; Noakes, 2003).

When a pilot study was conducted for the present research, one participant who took 72 hr of active recovery subsequently ran 90 s faster than the previous 5-km time; the other participant, who also took 72 hr of active recovery, recorded extremely similar times in the two 5-km trials. However, when two trials were instead separated by 72 hr of passive recovery, the first participant ran an identical time in each trial, while the second ran 28 s faster in the trial following 72 hr of passive recovery. This discrepancy between the two runners’ times leaves it unclear which recovery method might be more effective.

The pilot study results along with the lack of established standards as to duration of active and passive recovery, and optimal intensity of activity in the former, suggested further investigation was needed to determine if one type of recovery results in a more effective return to running than another, tending to improve performance during the next race or hard training session. Therefore, the present study compared the relative impacts on 5-km running performance of 72 hr of passive recovery and 72 hr of active recovery.

Method

Participants

Study participants were 12 well-trained male runners (n = 9) and female runners (n = 3) runners currently engaged in rigorous training. Runners from the local road running and track club, local triathlon competitors, and former high school and college competitive runners were recruited by word of mouth. Criteria for selection of participants included the following: (a) current distance-running training program in place; (b) recorded 5-km run time of 16–22 min for males or 18–24 min for females; (c) minimum current weekly average of 20–30 mi running; (d) previous completion of at least five 5-km road or track races; (e) VO2max of at least 45 ml/kg/min for females or 55 ml/kg/min for males; and (f) submitted self-report data indicating good health (e.g., questionnaire over running history, Physical Activity Readiness Questionnaire, Health Readiness Questionnaire).

Participants completed a short questionnaire describing their running background, racing history, and current training mileage. All participants were volunteers and signed an informed consent form outlining the study’s requirements and potential risks and benefits to participants.

Procedures

We assessed participants’ age, height, body weight, and body fat percentage [using a 3-site skinfold technique (Brozeck & Hanschel, 1961; Pollack, Schmidt, & Jackson, 1980)]. Participants were fitted with a Polar brand heart rate monitor and then completed a graded exercise test (GXT) to exhaustion lasting 12–18 min. VO2max, heart rate, and ratings of perceived exertion were collected every minute.

All GXTs were completed on a Quinton 640 motorized treadmill. The test began with a 2 min warm-up at 2.5 mph. Speed was increased to 5 mph for 2 min, followed by 2 min at 6 mph, 2 min at 7 mph, and 2 min at 7.5 mph; from this point on, incline was increased 2% every 2 min, until the participant reached volitional exhaustion (i.e., indicated that continued running at the required speed and grade was impossible). Once participants reached volitional exhaustion, they were instructed to cool down, continuing to run at low intensity until they felt recovered.

Approximately five days after the GXT, participants performed the initial subsequent 5-km race, between 6:30 a.m. and 7:30 a.m. (Time of day of each performance trial was held consistent throughout the study.) All 5-km trials were completed on a flat, hard-surfaced, 0.73 mi loop. Prior to each trial, participants completed a visual analogue scale before and after a 1.5 mi warm-up run, answering questions about fatigue or soreness being experienced within the quadriceps, hamstrings, gastrocnemius, lower body, and total-body muscle groups. The visual analogue scales comprised 15 cm lines on which participants marked an X to indicate fatigue or soreness, from 0 (no fatigue or soreness) to 15 (extreme fatigue or soreness). The subjective visual analogue scales evaluated participants’ status before the start of every time trial. Participants were also required to rate their perceived exertion (RPE) after the warm-up and prior to the start of each 5-km trial, to see if feelings of effort remained consistent between each trial, as well as during each lap and at the end of each performance trial.

Runners underwent a 1.5 mi warm-up prior to every 5-km performance trial (Kaufmann & Ware, 1977). Participants completed three 5-km performance trials within a 6-day period. Two 5-km performance trials were separated by 72 hr of passive recovery (PAS), and two 5-km performance trials were separated by 72 hr of active recovery (ACT), providing a counterbalanced protocol. The first and second 5-km performance trials were considered to provide separate baselines. All subjects were required to have 24 hr of passive recovery prior to the first performance trial. Passive recovery comprised a period of time in which exhausting physical activity was to be avoided; active recovery consisted of running 5 mi on a flat course on two consecutive days, at an intensity of 65%–75% of maximum heart rate. During each time trial, average heart rate (HRave) and ending RPE (RPEend) were recorded at each lap, to determine whether effort during each 5-km trial was consistent. All runners competed with runners of similar ability to simulate race day and hard-training conditions, while verbal encouragement was provided often and equally to each participant. At the end of each performance trial, each runner was instructed to complete a low-intensity, 1.5-mi cool-down run. Each trial session lasted approximately 60 min.

Statistical Analysis

Basic descriptive statistics were computed along with repeated measures of analysis of variance (ANOVA), for comparison of baseline performance trial results with results following active recovery (ACT) and passive recovery (PAS), in terms of (a) finishing times, (b) HRave, (c) RPEend, and (d) visual analogue responses concerning fatigue and soreness. All statistical comparisons were made at an alpha < 0.05. Data were expressed as the group mean ± standard deviation and as individual results.

In order to evaluate individual responses, data from each participant’s first run was compared to that from the second run, using a paired t test. The least significance group mean difference (p < 0.05) was determined, and group mean finishing time was adjusted to determine the amount of change (in seconds) needed to achieve significance. The time change between the first trial run and the adjusted trial run baseline was divided by the first trial run and expressed as either a mean number of seconds or a percentage, for both the ACT trial (8.9 s, or 0.7%) and PAS trial (7.0 s, or 0.6%). The percentages were applied to each individual baseline time, in order to determine, for both ACT and PAS conditions, how many seconds above or below the first trial time (positive or negative) the second trial time needed to be to allow a response to be quantified. Participants were then labeled nonresponders, positive responders (faster on successive trial), and negative responders (slower on successive trial).

Results

Descriptive statistics are found in Table 1. The participants were between 18 and 35 years of age, the majority between 20 and 28. All participants were trained runners or triathletes who specialized in running.

Table 1

Descriptive Statistics for 9 Male and 3 Female Study Participants

 

Males Females Group Males Females Group
Age, in years 25.6 22.0 24.7 5.0 1.0 4.6
Height, in centimeters 175.3 168.0 173.5 6.2 18.2 10.0
Weight, in kilograms 78.0 61.7 73.9 10.9 10.0 12.6
Body fat percentage 10.9 21.9 13.7 1.3 2.0 5.1
VO2max
(ml/kg/min)
63.3 59.7 62.4 5.0 7.9 5.6
Prestudy 5-km
personal best, in minutes
18:57 21:31 20:19 1:54 2:05 2:02
Average weekly mileage 31.7 30.1 30.5 7.4 7.7 7.5
Days per week 4.9 4.6 4.7 1.5 1.1 1.2

Mean finishing times, HRave, and RPEend for ACT and PAS performance trials are presented in Table 2. ACT did not differ significantly (p = 0.17) from the baseline, nor did PAS (p = 0.21). In terms of HRave, ACT was significantly lower (p = 0.02) than baseline, while PAS was significantly higher (p = 0.04) than baseline. For the ACT condition, RPEend was not significantly different (p = 0.17) from the baseline, but for the PAS condition it was significantly difference (p = 0.01) from baseline.

Table 2.

Mean Performance Trial Results After ACT and After PAS

Baseline

PAS

Baseline

ACT

Finish time, in minutes

19:34 +
1.60

19:30 +
1.52

19:41 +
1.66

19:35 +
1.54

Average heart rate, in b/min

175.4 +
6.5

177.9 +
6.3a

178.9 +
6.4

175.9 +
6.6 a

Rating of perceived exertion at
ending

19.3 +
0.9

19.8 +
0.6 a

19.7 +
0.7

19.6 +
0.8

Note. “After ACT” indicates performance trial completed following 72 hr of active recovery; “After PAS” indicates performance trial completed following 72 hr of passive recovery
aTrial results differed significantly from those of baseline trial.

Figure 1 shows individual changes in finishing times for all ACT and PAS performance trials.

Figure 1. Changes in individual finishing times, ACT versus PAS.

A participant labeled a nonresponder had an individual time change that fell within 0.7% of baseline performance for ACT and within 0.6% of baseline performance for PAS. Positive responders and negative responders (Table 3) were those participants whose individual time change surpassed 0.7% for ACT trials or 0.6% for PAS trials. A positive responder’s time improved during the second performance trial (expressed as a negative value), while a negative responder’s time slowed during the second performance trial (expressed as a positive value).

Table 3.

Individual Performance Trial Results After ACT and After PAS
Participant

Baseline

ACT

Time

Baseline

PAS

Time
1

16:47

16:42

-5

16:42

16:36

-6

2

17:32

17:40

+8

17:25

17:32

+7

3

17:37

17:50

+13a

17.44

17:37

-7

4

18:48

18:46

-2

18:38

18:48

+10

5

19:31

19:35

+4

19:35

19:49

+14a

6

19:57

20:05

+8

20:05

20:08

+3

7

20:10

19:49

-21a

19:49

19:48

-1

8

20:37

20:35

-2

20:49

20:37

-12a

9

20:48

20:13

-35a

20:13

20:05

-8

10

21:08

21:14

+6

21:14

21:20

+6

11

21:21

21:30

+9

21:30

20:36

-54a

12

21:53

21:05

-48a

21:05

21:02

-3

M

19:41

19:35

-5.4

19:34

19:30

-4.3

 

Note. “After ACT” indicates performance trial completed following 72 hr of active recovery; “After PAS” indicates performance trial completed following 72 hr of passive recovery. In the change columns, a minus sign indicates a faster time, and a plus sign indicates a slower time.

aParticipant labeled a responder.

Two individuals responded negatively to PAS, running a mean 12.0 + 2.8 s slower during the second PAS trial (Table 3). Two individuals responded positively to PAS, running a mean 33.0 + 21.0 s faster than baseline. Eight individuals were considered nonresponders to PAS, performing within 5.1 + 2.5 s of baseline.

One individual responded negatively to ACT, running 13 s slower during the ACT trial (Table 3). Three individuals responded positively to ACT, running a mean 34.7 + 13.5 s faster than baseline. Eight individuals were nonresponders to ACT, performing within 5.5 + 2.7 s of baseline. It is important to note that neither negative responder to PAS (Participant 3, Participant10) was in addition a negative responder to ACT (only Participant 2 was a negative responder to ACT). Furthermore, no participant was a positive responder to both PAS and ACT. (Participant 8 and Participant 9 were PAS positive responders; Participant 6, Participant 7, and Participant 11 were positive responders to ACT).

Comparing the ACT and baseline trials, there was no significant difference in results of the pre-warm-up and post-warm-up visual analogue scales of soreness and fatigue; neither was a significant difference observed between the PAS and baseline trials, in terms of soreness and fatigue (see Table 4). At the start of each trial, the participants appeared to have been fully recovered from previous exertion.

Table 4. Soreness and Fatigue Surrounding ACT Performance Trials and PAS Performance

Trials


Performance Trial | Analogue Visual Scale | Pre-Warm-up | Post-Warm-up


Soreness

Fatigue

Soreness
Fatigue

ACT

Baseline

6.5 +
1.4

6.1 +
1.1

6.4 +
0.8

6.3 +
1.2

Day 2

6.4 +
0.7

6.0 +
0.8

6.2 +
1.2

6.7 +
0.7

PAS

Baseline

5.8 +
1.3

5.9 +
0.9

6.2 +
0.6

6.3 +
1.4

Day 2

6.3 +
0.6

5.8 +
0.5

6.5 +
0.9

5.9 +
0.8

 

Note. The results showed no significant differences between trials. Participants appeared to be fully recovered prior to the start of each trial.

Discussion and Conclusion

How exactly a subsequent endurance performance is affected by an active recovery as compared to a passive recovery remains something of a mystery. Despite a variety of researchers’ focus on the two types’ effects on anaerobic performance (i.e., repeated short, intense efforts running or cycling), results have been equivocal (Boqdanis, Nevill, Lakomy, Graham, & Louis, 1996; Coffey, Leveritt, & Gill, 2004; Dupont & Berthoin, 2004; Spierer, Goldsmith, Baran, Hryniewicz, & Katz, 2004; Spencer, Bishop, Dawson, Goodman, & Duffield, 2006). Specific, possibly unknown effects of active and passive recovery on individual anaerobic performance tend to support the notion of individual variability: That is, what works for one athlete may not work for another. Individual variability may, then, exist among individuals in terms of their ability to recover from endurance performances like 5-km or 10-km races. Whether recovery should be active or passive has not been fully examined for distance running.

Following an endurance performance, Foss and Keteyian (1998) have noted, 24 hr of passive recovery may allow for normalization of muscle and liver glycogen, yet muscle function and performance measures may not be fully recovered; additional time, for example a 72-hr period, may be sufficient to allow optimal recovery. Running’s catabolic nature results in pain from microtears and edema (swelling) that occur within the muscle (Brown & Henderson, 2002). The damage is only addressed with sufficient passive recovery prior to further intense effort (Bosak, Bishop, & Green, 2004). Moreover, increased recovery time can reduce reflex muscle spasms and spastic conditions that accompany pain (Brown & Henderson, 2002). In the present study, no significant difference (p = 0.17) was observed between baseline and ACT or baseline and PAS. Hence, 72 hr of both active and passive recovery (Table 2, Figure 1) appeared to allow sufficient recovery, permitting most study participants to perform almost identical mean times between baseline trials and PAS and ACT trials.

Although we found no significant mean differences between PAS and baseline or ACT and baseline, it is important to focus on individual differences (Figure 1). Eight individuals, considered nonresponders to PAS, had a mean time change of positive or negative 5.2 + 2.5 s. For these nonresponders, 72 hr of passive recovery seemed sufficient for full recovery prior to the PAS trial. Two participants responded positively to PAS, with a mean 33.0 + 21.0 s faster during the second trial (Table 3). Improved performance during the PAS trial might, however, be attributable to the participants’ being better rested than they were at the time of the first trial. Participants had been encouraged to train normally and then recover passively for a day prior to the first trial; it is therefore possible that the two participants recovered better in advance of the PAS trial than in advance of the baseline trial.

Three individuals responded positively to ACT, running a mean 34.7 + 13.5 s faster during that trial than the prior one; however, eight individuals were nonresponders to ACT, having a mean time change of 5.5 + 2.7 s (Table 3). The roughly equal or slightly faster times recorded during the ACT trial might have been a result of participants’ fuller recovery over the 72-hr period prior to the ACT trial than during the single day of passive recovery preceding baseline trials.

A single participant raced slower during ACT and PAS trials, performance falling off by 13 s following a 72-hr period of active recovery (Table 3). This relative slowness during the second trial could have resulted from the intensity of two 5-mi recovery runs the participant completed on consecutive days in the 72-hr active recovery period preceding the ACT trial. While participants were instructed to run 5 mi at 65%–75% of maximum heart rate, that level of intensity might have prevented the individual’s muscle function from returning to normal (Brown & Henderson, 2002). As for the PAS trials, despite the 72-hr passive recovery period, two participants nevertheless recorded a mean time 12.0 + 2.8 s slower during the PAS performance trial. Slowing during PAS may have been a result of the “stale” feeling in leg muscles reported by Mujika et al. (2001) to affect runners who avoid exercise for some days. It has been suggested that competitive athletes who for some reason begin to train less often may experience a loss of “feel” during exercise, which can diminish subsequent performance (Mujika et al., 2001).

Average heart rate recorded for the ACT trial was significantly lower (p = 0.02) than baseline HRave, despite the similarity of mean ACT finishing times. During the PAS trial, HRave was significantly higher (p = 0.04) than baseline, even though, again, PAS and baseline mean finishing times were similar. The data for individuals show only Participant 8 and Participant10 to have run faster and recorded higher HRave during the ACT and PAS trials as compared to the baseline. Only Participant 4 ran slower and had a lower HRave during the PAS trial than during the baseline trial; during the ACT trial, four participants (3, 4, 5, and 12) ran slower and had lower HRave during the second-day performance. An assumption could be made that low HRave reflects lesser effort, since heart rate and level of intensity tend to be linearly related. But no consistent pattern of HRave and increased or decreased performance was found here for all subjects during all PAS and ACT trials.

Also relating to participants’ level of effort across all trials, we paid particular attention to RPEend and the visual analogue scales of fatigue and soreness. No significant differences (p = 0.17) were found from ACT to baseline for RPEend. In contrast, between PAS RPEend and baseline RPEend a significant difference (p = 0.01) was observed, the PAS score being higher (although a half-unit change in RPEend is potentially meaningless). Soreness and fatigue scores from the visual analogue scales did not differ significantly from baseline to ACT or baseline to PAS, indicating that runners on average tended to feel about the same prior to each 5-km trial (Table 4). Because the study yielded no clear relationship between HRave and RPEend during trials, and because no significant difference in responses were observed between administrations of the fatigue/soreness visual analogue scales, we assume that each participant completed each trial exerting similar effort.

The results of the study indicate that 72 hr of passive or active recovery constituted sufficient recovery to maintain or improve 5-km performance. Previous research does not agree on the best mode of recovery. Noakes (2003) and Henderson (2000) indicate that 72 hrs of passive recovery is potentially superior to active recovery, allowing for restoration of proper muscle function. Martin et al. (1998), however, contend that performing low-intensity aerobic exercise immediately after exercising helps improve subsequent performance. To the discussion, we add this study’s finding, for the sample as a whole, that the passive or active nature of a 72-hr recovery period made no difference in subsequent performance.

To improve race performance, frequent, intense workouts are vital; but it is also important to make optimal use of time between competitive races and strenuous training periods: recovery time. Individual variability has been said to characterize recovery, and it was definitely evident in carrying out this study. Understanding their individual responses to particular recovery protocols may aid runners in determining how often they can complete intense training runs during the week without diminishing their weekend race performance. Future research might examine whether a longer or shorter duration for recovery, passive or active, enhances running performance.

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Cheerleading in the Context of Title IX and Gendering in Sport

Abstract

Existing scholarship on competitive cheerleading’s struggle for sanctioning and recognition under Title IX supports a conclusion that, while cheerleading perpetuates certain stereotypes, it is nevertheless a sport American women created for themselves, and it offers many of the same benefits of participation as other sports do. Research on (a) acceptance of and obstacles to high school and collegiate cheerleading today, (b) regulatory controls, (c) and media interest and hegemonic implications is reviewed. Myths and issues surrounding the safety and health of competitive cheerleaders are examined, including sexual abuse and sport injury.

Cheerleading in the Context of Title IX and Gendering in Sport

As sanctioning and recognition of competitive cheerleading today grows under the protection and regulation of Title IX, one might wonder if the society is regressing. In the 1960s we threw off Betty Crocker and Father Knows Best for the feminist movement and bra burning. Does the new sport of cheerleading represent backpedaling—with the added twist of actual (if limited) tax-dollar support of the phenomenon? How and why did cheerleading become competitive? Who are these new teams and why aren’t their members playing basketball, soccer, softball? Much of this paper is a review of existing research on (a) acceptance of and obstacles to high school and collegiate cheerleading today, (b) cheerleading regulatory controls, (c) and media interest in and hegemonic implications of cheerleading.

Competitive cheerleading hit the scene in the late 1970s, when the television network CBS first televised the Collegiate Cheerleading Championships, in 1978 (Mercer, 2006). Throughout the 1980s, tosses, stunts, and routines evolved increasing originality and difficulty. Safety guidelines were introduced by groups like the National Cheer Conference (Mercer, 2006). New cheerleading organizations formed, eager to develop rules and guidelines for the sport: the Universal Cheerleaders Association, American Association of Cheerleading Coaches and Advisors, National Council for Spirit Safety and Education, NFHS Spirit Association, Recreation Cheer Coaches Association, U. S. All Star Federation. Decades later, however, only 26 states appear to have given high school cheerleading Title IX status, despite its fierce competitiveness, its organization and rules standardization, its popularity and entertainment value, and the multimillion-dollar-value of the cheerleading industry. It seems safe to assume that cheerleading is ingrained in the American psyche as a female pursuit, an activity thus inferior to, for example, football and basketball as far as many social institutions are concerned. Perhaps in supporting cheerleading—insisting that spirit competitive squads, as they may be known, deserve to be treated like other athletic teams, including in their receipt of federal funding—individuals and groups are furthering, after all, the post–Betty Crocker agenda.

Like most sports, cheerleading was born in the male domain (although today 90% of cheerleaders are female). Purportedly, in 1898 one Johnny Campbell stood up before fans at a University of Minnesota football game to organize their enthusiasm. From that germ came today’s competitive sport, although it took until the 1920s for women to participate. In the 1940s, with men at war, women moved into cheerleading in numbers, equaling or exceeding their predecessors’ achievements. After World War II ended, cheerleading remained dominated by women, and so it continued to the millennium (Mercer, 2006).

It is widely known that many American public figures have been cheerleaders, for example presidents Franklin Roosevelt, Dwight D. Eisenhower, Ronald Reagan, and George W. Bush. Other former cheerleaders in U.S. government include Supreme Court Justice Ruth Bader Ginsburg and Mississippi Senator Trent Lott, while celebrity cheerleaders include Ann Margret, Paula Abdul, Halle Berry, Sandra Bullock, Katie Couric, Jamie Lee Curtis, Michael Douglas, Sally Field, Samuel L. Jackson, Madonna, Steve Martin, Reba McEntire, Cybill Shepherd, Jimmy Stewart, Meryl Streep, and Raquel Welch (Ninemire, 2008).

Today’s 3.5 million cheerleading participants over age six (Lin, Huang, & Esposito, 2007) thus have many role models. No doubt, they have contributed to the public acceptance of the sport that has spread to official acceptance and media popularity. The struggle for cheerleading’s recognition as a competitive team sport has slowly brought the activity to most high schools and universities in the country. Enactment of Title IX legislation has helped.

Title IX and Female Athletes

Title IX called for gender equity in school athletics, and part of its challenge was to increase girls’ interest in playing and competing in sports. Since its enactment, sports participation among females has soared, although still trailing the rate of participation by males. The U.S. Department of Education Office of Civil Rights requires schools to pass one of three tests of gender equity in athletic participation. Each school must demonstrate that (a) numbers of male and female athletes are substantially proportional to total numbers of males and females enrolled, or (b) that the school has a history and continuing practice of program expansion for an underrepresented sex, or (c) that the interests and abilities of an underrepresented sex have been fully and effectively accommodated (National Coalition for Women and Girls in Education, 2008). Few institutions can offer evidence of compliance under the first two options; many instead opt to comply by “fully and effectively” accommodating female students’ interests. Since the early 1990s, two thirds of institutions have asserted their compliance this way, but the question, of course, is how to measure those interests (Rhode, 2007).

In 2005, Title IX was amended to allow institutions to use e-mail surveys to measure students’ interest in sports. If survey results indicate “sufficient” student interest, the school’s compliance is allowed to be presumed (unless a relevant “significant” occurrence has taken place). The Department of Education website offers a model survey, which certainly could be found underwhelming by a student pressed for time or hesitant about participating in athletics. Enforcement of survey completion would, furthermore, seem next to impossible—and what qualifications will be required of those who write and update such surveys and analyze the data? Another problem lies in the definition of terms: What exactly do “sufficient” and “significant” mean in this context? The loophole, of course, has critics:

Without any notice or opportunity for comment, the Department of Education issues an “Additional Clarification of Intercollegiate Athletics Policy Guidance: Three-Part Test—Part Three,” allowing colleges to use a single e-mail survey to show that they are meeting women’s interests in playing sports. (National Coalition for Women and Girls in Education, 2008, p. 6)

The Women’s Sports Foundation condemns the survey as a Title IX loophole, and the National Collegiate Athletic Association (NCAA) has asked schools not to use it (Brady, 2007).

Even Title IX’s best intentions can do nothing about recent decreases in the funding of athletics. The general slowdown is part of the reason for disproportionate female participation in sports. At the college level, only a small number of institutions need not worry about their budgets, and where there are competing demands for funds, athletic departments can experience shortfalls. Women’s sports has to compete as men’s sports does for available dollars, and all programs are subject to constraints. Suggs (cited in Rhode, 2007) explains that,

in the relatively well-off Division I institutions, expenditures have been rising faster than higher education budgets. Those budgets have, in turn, been rising faster than inflation. It is by no means self-evident that preserving intercollegiate competition in all male sports should be a priority for all schools or for society generally. Nor is it clear that the increasingly commercialized and competitive model of male sports is the one most women want to replicate. Title IX controversies raise broader questions about the role of sports in higher education that deserve closer public scrutiny.

Perhaps dissatisfaction with the traditional (male) model of athletics does contribute to females’ lesser participation. But that model is still key to such funding as is available. A sport like cheerleading, in order to gain federal funds, must meet the same criteria traditional sports must meet for that sanction. Like other athletes, cheerleaders can expect to have to comply with rules governing seasonality, numbers on squad or team, minimum athletic competence, approved use of equipment, and other matters.

Since the passage of Title IX, the added status women’s sports has gained by complying with the male model has, ironically, diminished women’s role in coaching and athletics-directing, especially at the college level. As Rhode explains (2007),

As opportunities for female students have increased, opportunities for female professionals have declined. The rise in status and financial resources in women’s sports has attracted male competitors for coaching positions. As a result, only 42% of women’s teams have a female head coach, compared to over 90% in 1972. . . . In the pre–Title IX era, women held almost all administrative positions in women’s athletics programs.20 Today, almost all of those programs are merged with men’s, and less than a fifth of athletic directors are women.21 (p. 14)

How must young female athletes view their own importance as they see fewer and fewer women trusted with authority in the world of sports? Advancement of female athletes has clearly had its ups and downs, which have affected the development of cheerleading, as well.

Title IX and Cheerleading

According to the Women’s Sports Foundation (2000) a sport is (a) a “physical activity which involves propelling a mass through space or overcoming the resistance of a mass,” (b) “a contest or competition against or with an opponent,” (c) “governed by rules which explicitly define the time, space and purpose of the contest and the conditions under which a winner is declared,” and (d) intended primarily to “compare[e] . . . the relative skills of the participants” (¶3). Does cheerleading comply with theses requirements? The foundation’s opinion on whether cheerleading and some other activities are indeed sports covered by Title IX and education department Office of Civil Rights (OCR) protections is a guarded one. Sufficient quality opportunities for competition is a Title IX concern, the foundation notes; the OCR assesses “the number of competitive events offered per sport, the number and length of practices and the number of pre-season and post-season competitive opportunities” (¶6). Thus if a cheerleading squad or drill team has as its overarching mission not presentations at male teams’ competitions, but rather

compet[ition] against other drill teams or cheerleaders on a regular season and post season qualification basis in much the same structure as basketball or gymnastics and if the team conducted regular practices in preparation for such competition while under the supervision of a coach, [then] these activities could be considered sports. On occasion, these groups could also put on exhibitions at boys’ or men’s sports events, but these exhibitions could not be their primary purpose. (¶7)

Still, the foundation warns that attempting to relabel girls’ existing, funded programs as sports programs when they are not is “unethical,” and that “danceline, drill team, cheerleading, baton twirling or the marching band are [in many cases] clearly not fulfilling the definitional requirements of sport” (¶8).

The National Federation of State High School Associations (NFHS) appears interested in distinguishing sports competitors from cheerleaders involved only in extracurricular activities. A 2006–07 NFHS survey on participation counted participants on “competitive spirit squads”: cheerleading, pom, kick, dance, and drill teams. In terms of number of participants, such squads rank among the top 10 sports in high schools nationwide (National Federation of State High School Associations, 2007, p. 47). The squads comprise competitive athletes who over the past several years have qualified and been recognized as athletic teams by OCR under Title IX.

Collegiate cheerleaders operate within their institutions’ athletic departments, but are not always deemed to represent a sport. When a squad’s central purpose is to support and promote athletes in other sports, then it does not qualify as a sports team. To qualify, a squad must meet five OCR criteria for varsity sports, as follows:

  1. Selection of squad members must be based largely on factors related to athletic ability.
  2. The squad’s activity must have as a primary purpose the preparation for and participation in athletic competition against other, similar teams.
  3. The squad must prepare for and participate in competition in the same way other teams in the athletic program do, for example by conducting tryouts, being coached, practicing regularly, and being scheduled regularly for competitions.
  4. National-, state-, and conference-level championship competitions must exist for the squad’s activity.
  5. The squad’s activity must be administered by an athletics department.

While the specification of what makes a sport a sport has no doubt been beneficial to cheerleading, cheerleaders are like other female athletes nationwide in facing challenges to the advancement of their sports. Typifying these challenges is the case of McCormic and Geldwert v. School District of Mamaroneck and School District of Pelham (2004). Hoping to be observed by college recruiters, two female athletes wanted to compete in high school soccer during the fall. They attended schools where, by tradition, boys used the athletic facilities for their fall and winter sports, relegating girls’ soccer season to spring. Spring soccer not only potentially deprived them of collegiate opportunities, it interfered with their participation in state and regional championships, the girls claimed. In affirming the trial court’s finding for the girls in this case, the U.S. District Court of Appeals said,

Title IX was enacted in order to remedy discrimination that results from stereotyped notions of women’s interest and abilities, and to allow a numbers-based lack of interest defense to become the instrument of further discrimination against the underrepresented gender would pervert the remedial purpose of Title IX. (McCormic and Geldwert v. School District)

Unlike the two New York school districts of McCormic, the school district for Lacey, Washington, where there are three public high schools, made a willing effort to increase girls’ participation in sports (which had significantly trailed boys’). The district surveyed girls about the sports they would enjoy that were not already available to them. As a group they chose gymnastics over lacrosse, water polo, and power lifting (Wochnick, 2007). It may seem a small step, but the survey is nevertheless an encouraging sign that the fundamentals of Title IX are being implemented and organizations are acting on the Title IX tenet that girls’ purported lesser interest in sports does not justify boys’ greater access to sport.

Title IX also mandates that athletics funding for girls must be on a scale with that for boys. When Florida’s high school athletic association recognized cheerleading as a sport in 2007, cheerleaders looked forward to smaller personal expenditures for coaching, facilities, insurance, transportation, and uniforms. Cheerleading teams in states whose high school athletic associations do not sanction cheerleading cover their own costs, often running to hundreds of dollars monthly. During the 2006–07 school year only 26 states were represented in cheerleading competitions; yet how many states actually recognize cheerleading as a sport is hard to determine, because qualifying is difficult and registration and entry fees are high (Peters, 2003). Despite the sport of cheerleading’s recent recognition in Florida, squads have encountered roadblocks to financial support, in the form of school district decisions to delay an inaugural season until the 2008–09 school year. Public budgets are tight and, despite Florida high school cheerleaders’ new status, few school districts’ allocations covered spirit squads. Even when allocations do come, competitive cheerleaders will pay for extras (e.g., choreographers, camps) out of pocket and with the old standby, the fundraiser. Non-school-based all star teams are on their own, of course, financially.

A byproduct of nearly 30 years without sport status is competitive cheerleading’s reliance on private enterprise and independent, often certified professionals to supply training, coaching, and mentoring. All star gyms or clubs exist that have been tailored to the demands of cheerleader preparation, featuring for example spring-mat floors that meet competitive “specs.” Not surprisingly, most public high schools lack such ideal facilities. Their floor mats tend to be of foam. Nor are public school teachers reliably equipped with the background and certification ideal for leadership in so-called “adapted” sports. Further, as within the coaching profession generally after Title IX, ever fewer of the coaches overseeing high school competitive cheerleaders may be female. This does not bode well for a 90%-female competitive spirit sports constituency.

Health and Well-Being of Cheerleading Athletes

Cheerleading presentations are judged on originality of choreography, athleticism of athletes, showmanship—and degree of risk, as well. Cheerleading injuries have led to their fair share of law suits. [In at least one recent case, however, an injured plaintiff lost her bid for compensation from an allegedly negligent school (Krathen v. School Board of Monroe County, Florida). The Florida cheerleader and her parent had signed a certification of consent and release from liability, and an appellate court confirmed the trial court’s decision that the signed certification released the school board from liability (Herbert, 2008).] Injuries aside, the many benefits of sports participation to girls have been noted by Title IX supporters (and others). Research shows girls who play sports to be relatively more likely to graduate, to have greater confidence and better self-esteem and body image, to avoid teenage pregnancy, and to avoid drug use. Athletics is, furthermore, the kind of physical activity that builds muscle, reduces fat, reduces risk of heart disease, and prevents osteoporosis. A teen who exercises for only two hours weekly still reduces her lifetime risk of breast cancer. Of course, competitive cheerleaders can become injured, and there are as well two further health issues commonly associated with the sport: eating disorders and sexual harassment.

Sport-Related Injuries

Quantifying the risk of injury to cheerleaders is a challenge, given the lack of a national database tracking such incidents (Lin, Huang, & Esposito, 2007). What does exist are frequent alarming reports decrying near-epidemic levels of injury in the sport. The National Center for Catastrophic Sports Injury Research, for instance, reported that out of all athletes, cheerleaders are most likely to suffer catastrophic injury. According to Pennington (2007),

Emergency room visits for cheerleading injuries nationwide have more than doubled since the early 1990s, far outpacing the growth in the number of cheerleaders, and the rate of life-threatening injuries has startled researchers. Of 104 catastrophic injuries sustained by female high school and college athletes from 1982 to 2005—head and spinal trauma that occasionally led to death—more than half resulted from cheerleading. . . . All sports combined did not surpass cheerleading.

But such reports may well need to be questioned. The executive director of the Association of Cheerleading Coaches and Advisors, Jim Lord (2007), has argued with the publishers of the statistics, making the following points to refute many of the dramatic claims about cheerleading:

  1. The numbers being used are the same as those over the past two years, yet they are given as if this is continuing information and that injury rates continue to rise. No participation figures, relevant background information or corresponding data for other athletic activities are presented in these articles. In fact, no actual injury rate is ever given.
  2. Cheerleading does not have more serious injuries than football, hockey or all other sports combined. Over 350,000 people were treated in emergency rooms for football related injuries in 2004. That number is often ignored because there are probably more football players than cheerleaders, and it is primarily males that participate. It may be more realistic to compare cheerleading numbers to women’s basketball as the participation numbers are likely similar. In the same year in which 26,000 cheerleaders were treated in emergency rooms, they were sitting next to over 100,000 female basketball players. Nearly quadruple the amount of emergency room visits for women’s basketball, who a) have more access to an athletic trainer to filter out minor injuries and b) who do not participate in a year-round activity.
  3. The numbers being given also do not account for the fact that cheerleading is nearly a year round activity that takes place across sports seasons. Any comparison to other activities must account for the shorter participation time for those sports. Consider an athlete that participates in football and in basketball and is injured once in each season. Now consider another athlete participating on a cheerleading squad that cheers for football and basketball and is injured once during each of those seasons. The two injury rates are statistically equal, yet cheerleading will be shown to have twice the number of injuries. Without injury rate information, the statistics show what the author intends to show.
  4. Using emergency room visits is also inflammatory in that the vast majority, over 98% of those visits, were classified as “Treated & Released, Or Examined & Released Without Treatment.” The average person reading “emergency room visit” envisions 26,000 cheerleaders going into the emergency room on a stretcher. There are obviously serious injuries that need emergency procedures including hospitalization, but to include the percentage that were treated and released or even released without treatment needed only adds to the misrepresentation of cheerleading injuries and obscures the strides that have been made with regard to safety.

The report from the National Center for Catastrophic Injury incorporated statistics dating to 1982. Lord makes the valid point that cheerleading today enforces safety rules and stunt standards not enforced in the past. Numbers and types of flips and tosses are regulated and strict guidelines govern equipment from clothing to mats. It should also be remembered that through the 1980s, cheerleading was one of very few athletic outlets deemed acceptable for girls, so an injured girl athlete stood a relatively good chance of being a cheerleader.

Eating Disorders

Like young females in the population generally, cheerleaders in recent years are at a rising risk of developing eating disorders, which Pirtle (2002) contends are conservatively estimated to affect 5–10 million young American women and kill 50,000 directly. Doctors do not understand why women develop eating disorders, according to Pirtle, but do know that these disorders have death rates higher than any other psychiatric illness. Cheerleaders are at risk of eating disorders because (a) they share the same risk factors as young females who are not cheerleaders, (b) their sport has an “aesthetic” standard as well as an athletic one and performs before large audiences and even on television (which proverbially adds 10 lb), (c) they wear revealing costumes that may increase body consciousness as they highlight thinness, (d) the stunts they perform, as in the sport of gymnastics as well, are easier at low body weights, and (e) coaches have historically claimed small size produces more proficient tumbling and stunting (it is not uncommon for cheerleading teams to enforce weight standards with group weigh-ins) (Thompson, 2003). Scholarly research on cheerleaders is rare, but one study found high school cheerleaders to exhibit more body dissatisfaction and more eating disorders than college cheerleaders (Thompson, 2004).

Eating disorders in women and girls can lead to three interrelated health problems referred to at times as the female triad: lack of energy, menstrual disorders (amenorrhea), and weak bones (osteoporosis) (“The Female Triad,” 2006). Adulthood infertility and bone fractures (females accumulate about 50% of bone mass as teens) are potential effects. At greatest risk from the triad are those who participate in “aesthetic sports” (cheerleading, diving, gymnastics), weight-class sports (rowing, judo, karate, boxing, body building), and long-distance running. According to one study, primary menarche at 15 years occurs in 1% of girls in the general population and in 22% of girls competing in aesthetic sports. Cessation of periods might occur in 2%–5% of young women in general, whereas a study of one women’s track team found 65% of the athletes to be amenorrheic (“The Female Triad,” 2006).

Sexual Harassment

According to at least one study, students who experience some form of sexual harassment far outnumber students who get through school with no such incident. The research included 8th- through 11th-graders, 80% of whom, the results indicated, had been sexually harassed at school in some fashion—girls and boys alike. In colleges and universities the rate among female students, according to a survey, was 62%; harassment was occasionally the cause of dropping a course or avoiding a particular location (National Coalition for Women and Girls in Education, 2008, p. II).

Cheerleaders are young women who literally stand out in the crowd, making them prime targets for sexual harassment. Sexual harassment lawsuits are common in education: When a cheerleader sued Marshall University over alleged sexual harassment, counsel at the Higher Education Policy Commission (the board that coordinates public colleges in West Virginia) said “lawsuits against the commission ‘happen about every day’” (Leubsdorf, 2006). Perhaps not surprisingly, a Westlaw search shows the vast majority of lawsuits involving cheerleaders to fall into the category of sexual harassment.

The National Coalition for Women and Girls in Education’s report Title IX at 35: Beyond the Headlines reiterates the legal power stemming from Title IX as it relates to sexual harassment in schools at any level:

Sexual harassment is sex discrimination that is prohibited by Title IX, whether the student is harassed by employees such as teachers or coaches, or by other students. Students who have suffered sexual harassment may sue for damages in court under Title IX, but schools have an obligation to end harassment that goes well beyond their monetary liability. OCR issued a Sexual Harassment Guidance in 1997, which was revised in 2001, that requires all schools subject to Title IX to maintain an environment that is free of sexual harassment and to remedy the effects of harassment on the victim. (National Coalition for Women and Girls in Education, 2008, p. II)

Title IX may protect cheerleaders (to some extent) from sexual harassment in public schools, but all star competitive cheerleaders function in environments not necessarily subject to Title IX. Harassment could go unpunished there, but in at least one case crime did not. In Vancouver, Washington, a cheerleading coach was sentenced to a nine-month jail stay and 10 years’ registration as a sex offender for having sex with a 16-year-old athlete he was training. As many other states do, Washington prohibits teachers and coaches from having sex with students, whatever the student’s age (Rice, 2008).

Hegemonic Implications

Title IX has greatly increased opportunities for girls and women to participate in sports. Since the statute’s enactment in June 1972, the number of female high school athletes has increased over 900%, from some 290,000 to 2.9 million. Women’s participation in intercollegiate sports has soared from 16,000 to 180,000, or more than 450% (Rhode, 2007). While the express purpose of Title IX was to prohibit sex discrimination in institutions receiving federal funding, the regulation also explicitly permits organizers of school-based contact sport programs to segregate competitors by sex. (The primary contact sports are boxing, wrestling, rugby, ice hockey, football, and basketball; other sports whose purpose or major activity involves bodily contact may also be included.) As McDonagh and Pappano explain (2008),

A recipient may operate or sponsor separate teams for members of each sex where selection for such teams is based upon competitive skill or the activity involved is a contact sport. However, where a recipient operates or sponsors a team in a particular sport for members of one sex but operates or sponsors no such team for members of the other sex, and athletic opportunities for members of that sex have previously been limited, members of the excluded sex must be allowed to try-out for the team offered unless the sport is a contact sport.

McDonagh and Pappano illuminate, moreover, the reality that the use of Title IX to sanction segregation within the contact sports has been a signal to non-contact sports (and some other activities) to follow suit. Title IX does not seem to suggest that billiards, chess, or bridge should be sex-segregated, but they have been. Title IX would seem to encourage, not discourage, mixed-sex teams for tennis, skiing, bowling, and even discus throwing, with men and women competing against each other. Title IX’s language separating male from female within the realm of contact sports perpetuates the male model of sport more than it promotes the equality of men and women. Not accepted on those teams, women have been distanced, connoting inferiority of their athletic ability and perhaps their will to compete.

Women stepping aside is an old theme, not confined to sports but reappearing readily even in cheerleading—90% female or not. For example, Thompson (2003) examines how it is assumed that, where cheerleader physical standards are concerned, it is the female gender that must hold to the mark. Thompson cites a 1996 study by Reel and Gill that found that cheerleaders were pressured from many sides to lose weight. The weight-watching college coaches who were interviewed claimed that below-average-sized females (and males) better demonstrated the required flexibility, in the same way that above-average-sized basketball players better demonstrate the ability to get to the basket. The argument as it figures in cheerleading, apparently, is that the females must be small so that males can lift them.

A case in the Superior Court of Connecticut involved a cheerleader dismissed for failing to satisfy her squad’s weight restriction (Connecticut Commission on Human Rights and Michelle Budnik v. State of Connecticut, University of Connecticut, et al., 1996). Her suit was dismissed on appeal, with the following remarks:

As for the weight restriction, the hearing officer found that the defendant, UConn, had a legitimate, nondiscriminatory reason for imposing the weight standard. . . . [I]n order for the entire squad to perform advanced partner stunts, which included lifting and tossing, it was necessary for general limits on how much the female cheerleaders, the “tosses,” could weigh. [The cheerleading coach] did not arbitrarily pick a number, but reviewed comparable programs at different schools to set the maximum at 125 pounds. The cheerleading coach testified that tossing was involved in 85 to 90 percent of the cheers the squad performed. (Budnick v. Connecticut, 1996)

It is not likely that any mention was made before the Court of male cheerleaders’ ability to bench press the 130 lb required by team rules. Thompson (2003) asks why coaches can’t as easily require male cheerleaders to be stronger as requiring female cheerleaders to be lighter. Another option would be the elimination of stunts, something the University of Nebraska’s athletic director suggested, concerned about safety (Thompson, 2003). But we need to remember the sensationalism Lord (2007) pointed out as coloring reports on cheerleading injuries. Male-dominated sports do not seem subject to censure simply because they involve risk. No one suggests removing tackling or the kickoff from the playbook. What is advised is increased safety awareness, proper supervision, and training in proper techniques—the same things that are recommended by organizations governing cheerleading.

Conclusion

The sport of cheerleading is here to stay. If it can perhaps be accused of perpetuating stereotypes with its sexualized performance—“aesthetic” standards, revealing costumes, pom-poms—competitive cheerleading also provides an example of American women turning the tables. Cheerleading was originated by men and handed to women in a sort of afterthought. But once it became women’s, it was developed with courage, perseverance, and creativity into something different, competitive cheerleading: women’s own sport for which they struggled and which they are establishing in more and more states as well as internationally.

Volumes have been written on the benefits of participating in sports, and women have proven their love for sports’ competitive demands: skill, strength, precision, courage, excellence. The sport of cheerleading incorporates all of these, truly earning respect and support for its athletes as they work to manifest their own view of the world through sports. Title IX rightly works to afford them equal opportunity: After all, the greater the success of women in any sport, in any endeavor, the greater the success of the society as a whole.

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