Athletic Directors – Page 2 – The Sport Journal

Black Women “DO” Workout!

Sherldine Tomlinson

### Abstract

Many studies cite that women of African descent have lower physical activity levels and/or are more sedentary, than White counterparts. The lack of exercise among Black women results in them experiencing compromised life quality and reduced life expectancy. To combat the striking rates of cardiovascular-related diseases and to increase habitual exercise, health promotion interventions have been initiated designed for Black populations. Female participants in Project Joy, a church-based cardiovascular education programme, reported weight loss and lower blood pressure. This paper reviews a similar initiative; Black Women “DO” Workout! (BWDW), which makes innovative use of social media to encourage physical activity (PA) among Black women.

**Key Words:** women of African descent; exercise; social media

### Introduction

Numerous studies indicate that women of African descent have lower physical activity levels, and/or are more sedentary, than their White counterparts. A 2006 national health survey on physical activity levels in Canada found that when compared to Caucasian Canadian females, both African Canadian and South Asian Canadian women less moderately active (Bryan, Tremblay, Pérez, Ardern & Katzmarzyk, 2004). In a similar American study looking at Black, White, Hispanic and Asian women, the data revealed that only 8.4% of African American women completed the recommended level of regular physical activity (Eyler, Matson-Koffman, Young, Wilcox, Wilbur, Thompson, Sanderson & Evenson, 2003). Unfortunately, this lack of exercise participation among Black women contributes to a significantly increased health risk of cardiovascular-related complications such as hypertension, type 2 diabetes and obesity (Flegal, Carroll, Ogden & Curtin, 2010). A lack of active activities also results in Black women experiencing compromised life quality and reduced life expectancy.

In an effort to combat these striking rates of cardiovascular-related diseases and complications among women of African descent, and to increase their habitual exercise involvement, a number of health promotion interventions have been initiated across North America. These include offerings of free exercise sessions especially designed for Black populations. Evaluative studies of these types of exercise programmes suggest they produce appreciably positive outcomes. The female participants in Project Joy, for instance, an African American church-based cardiovascular education programme, reported weight loss and improvement in blood pressure after participating in the included exercise sessions (Jakicic, Lang & Wing, 2010). This paper reviews a similar programme, Black Women “DO” Workout! (BWDW), which makes innovative use of social media to encourage exercise among women of African descent.

The BWDW initiative was created and founded by Crystal Adell, a fitness enthusiast and personal trainer. Adell uses Facebook as a tool to encourage regular exercise participation among African American women. She describes BWDW as a grassroots movement for championing weight loss and healthy living, a crusade she says is much needed to address the sobering statistics that show 49% of African American women are obese, while approximately 66% are overweight (US Dept of Health and Human Services 2000). Adell notes that using Facebook, which allows her to facilitate communication between Black women, is her “personal attempt to work with a collective who are more than willing to share their fitness goals, services and lifestyle changes towards healthier living”(personal communication, 2010). Information included on the site covers topics from exercising, body image, healthy eating habits and eating disorders to the importance of fitness and nutrition during pregnancy. Adell suggests that the success of BWDW is based on “information sharing and by showing praise, encouragement, inspiration and support in the way of sisterhood and by championing individuals for their fitness goals, which ultimately keep others motivated in to want to do the same”(C. Adell, personal communication, 2010).

There is little doubt that BWDW is a success. Thus far the site boasts more than 85,000 members, mainly women of African descent, many of whom regularly visit and post to the site. While African American women make up the largest block of BWDW users, the site also attracts international members from Canada, England, African and the Caribbean. Launching an online social media page as a means to promote exercise adherence and encourage healthy lifestyles among Black women is clearly a new, unique and successful approach. In addition to being innovative, the strategy is also in accordance with the American Healthy People 2010 mandate to (1) increase quality and years of healthy life and (2) eliminate health disparities that are associated with race, ethnicity and social economic status (US Dept of Health and Human Services 2000). One of Healthy 2010 physical activity and fitness objectives is to increase physical activity levels among Africa Americans as disparities in exercise and/or physical activity levels continue to exist with this group and other populations including Hispanics, the elders and people with disabilities (US Dept of Health and Human Services 2000). Indeed, the Black Women “Do” Workout social media campaign offers the opportunity for women of African descent to make regular exercise and a healthy lifestyle a part of their daily routine.

The BWDW web page is attractive, functional, and perhaps most importantly, interactive. Members are encouraged to participate through such means as submitting healthy recipes to the ‘Chef de Cuisine’ e-cookbook and posting images to the photo album which showcases before and after pictures. There are also announcements about the monthly BWDW ‘meet-ups’ held in locations across the United States for women who want to connect in person, as well as a service that informs members about personal trainers available in their area of the country. And the site has become a space of promotion for several members who now compete in fitness and body building competitions after experiencing significant body transformations via exercise and through healthy eating. In addition, a range of BWDW merchandise are available for sale on the site.

Health policy makers and promoters across North America have acknowledged the need for a better understanding of Black women’s exercise behaviour as a basis for improving their traditionally low physical activity rates. The BWDW programme offers an opportunity for those in the health field to learn from, and about, Black women and provides a potential avenue for the dissemination of health information. Adell herself notes these opportunities, commenting that she would like to see collaboration between BWDW and “organisations like the American Heart Association, Go Red For Women, the African American churches and corporate organisations” (C. Adell, personal communication, 2010). She believes these kinds of partnerships “will allow for an enhancement of services to local African American areas and communities that statistically have a high demand for wellness, health and fitness related support” (C. Adell, personal communication, 2010).

The BWDW programme presents a best practises model for building supportive and effective health networks within communities of African descent. The site has proven to be a powerful tool for increasing exercise rates and thus helping to address the troubling prevalence of cardiovascular-related and other diseases that continue to plague women of African descent. It is hoped the BWDW programme will inspire ongoing dialogue about finding other effective means of supporting Black women to become active, whether via other social media software, or in more traditional in-person venues.

### References

1. Adell, C. (November 2010). Telephone interview with author.
2. Bryan, S.N., Tremblay, M.S., Pérez ,C.E,, Ardern, C.I., Katzmarzyk, P.T. (2006, Jul/Aug). Physical Activity and Ethnicity: Evidence from the Canadian Community Health Survey. Can J Public Health. 2006 Jul-Aug; 97(4):271-6.
3. Eyler, A.A., Matson-Koffman, D., Young, D.R., Wilcox, S., Wilbur, J., Thompson, J.L., Sanderson, B., Evenson, K.R. Quantitative study of correlates of physical activity in women from diverse racial/ethnic groups: The Women’s Cardiovascular Health Network Project–summary and conclusions Am J Prev Med. 2003 Oct;25(3 Suppl 1):93-103.
4. Flegal, K.M., Carroll, M.D., Ogden, C.L., Curtin, L.R. Prevalence and Trends in Obesity Among US Adults, 1999–2008. JAMA. 2010 Jan 20; 303(3):235-41.
5. Jakicic, J.M., Lang, W., Wing, R.R. Do African-American and Caucasian overweight women differ in oxygen consumption during fixed periods of exercise? Int J Obes Relat Metab Disord. 2001 Jul; 25(7):949-53.
6. US Dept of Health and Human Services. Healthy People 2010: Understanding and Improving Health. 2000 Washington, DC: Government Printing Office.

### Corresponding Author

Sherldine Tomlinson, MSc.
2-440 Silverstone Drive,
Toronto, Ontario,
M9V 3K8,
<[email protected]>
416 749-7723

U.S. Sports Academy2013-11-22T22:58:08-06:00January 3rd, 2012|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology, Women and Sports|Comments Off

Effects of Augmented Visual Feedback and Stability Level on Standing Balance Performance using the Biodex Balance System

Steven F. Pugh; Robert J. Heitman, John E. Kovaleski; Chris M. Keshock; Shelley H. Bradford. University of South Alabama, Mobile, Alabama

### Abstract

This study compared the effects of visual feedback and stability level on standing balance performance using the Biodex Balance System. The analysis was performed on a 2 x 2 factorial design for the purpose of testing the main effects of the type of feedback (augmented visual feedback or none) and balance condition (less stable – Biodex level 2 or more stable – Biodex level 7). Four randomly assigned groups performed nine 20-second dynamic balance trials at stability level 2 or at level 7, depending on group assignment. The dependent variable was the mean stability index calculated as an average of the nine 20-seconds trials. A significant feedback by stability level interaction was found (P = .04). At stability level 7, augmented visual feedback mean stability index scores were better when compared to no augmented visual feedback (P < .001). No significant differences were found at stabilty level 2. Our data indicate that when balancing on a Biodex Balance System, as the degree of difficulty increases the effect of concurrent augmented visual-feedback is reduced.

**Key words:** balance, visual feedback, posture, augmented

### Introduction

Dynamic balance is critical for the acquisition and execution of motor skills. Balance training is used for injury rehabilitation, fall reduction, and sport and motor skill development. One commercial device used to quantify the degree of dynamic balance is the Biodex Balance System (4). The Biodex Balance System is an instrumented device that allows the tilting of a circular flat platform. The degrees of tilt from horizontal are measured and used to calculate an overall stability index (1). This index is a quantitative estimate used for the evaluation of an individual’s neuromuscular control as it pertains to the ability to maintain postural stability on an unstable surface (4).

One feature unique to the Biodex Balance System is that the stability of the balance platform can be increased or decreased, thus enabling control of the level of difficulty of the standing balance task. Biodex platform stability levels range from 1 to 8, with 8 being the most stable or least difficult to perform. Another feature of the Biodex Balance System is an attached LCD monitor that provides augmented visual feedback. The monitor provides information, via a screen tracing, concerning the subject’s ability to balance on the platform as the subject tries to maintain the cursor in the middle of the screen’s grid (4).

#### Theoretical Rationale

We were unable to find studies that compared the efficacy of augmented visual feedback at different levels of balance difficulty. As the stabilometer platform becomes less stable and thus more difficult, the ability to effectively process both intrinsic and augmented visual feedback may become increasingly difficult. This would be caused by a decrease in the amount of time available to process feedback information while balancing (11). The increase in time constraints as balance difficulty increases may also bring about a change in the type of motor control strategy used, i.e., open versus closed loop. During open looped motor control, the movement is executed entirely by the motor program without the use of sensory feedback (5,6). During closed looped motor control, an initial command is sent to the muscles which start the movement. The actual execution of closed loop movements, then, depends on sensory feedback which is used to monitor the movement (6). As the balance task becomes increasingly difficult, information processing demands may be increased because of the greater number and rate of balance adjustments that must be monitored. The less stable platform also brings about the rapid initiation of postural responses which limits the effectiveness of feedback mechanisms because of the inherent time delays (11). Horack and Nashner (1986) suggests that rapid postural actions are organized by a limited repertoire of open looped motor programs which do not require feedback for execution. Open looped strategies simplify the process of complex movement by incorporating knowledge of past experiences into motor programs enabling anticipation of events and reducing reliance on the slower feedback mediated responses associated with closed looped monitoring (6).
The purpose of this study was to determine the effects of concurrent augmented visual feedback and balance condition on standing balance performance using the Biodex Balance System. We postulate that concurrent augmented visual feedback will not be as effective at the less stable condition (Biodex stability level 2) when compared to the more stable condition (Biodex stability level 7). This hypothesized difference in the effects of visual feedback at the different levels of stability will be demonstrated in the form of a feedback by stability level statistical interaction.

### Methods

#### Participants

Forty healthy, male university students (age = 21.4 ± 3.6 years, mass = 70.3 ± 14.3 kg, height = 170 ± 3.1 cm) volunteered to participate in this study. No participants reported any sensory impairment or physical injury that hindered performance of the balance task, nor did any of the participants have previous experience with balance training on the Biodex Balance System. The study was approved by the university’s institutional review board, and informed consent was obtained from each individual before testing.

#### Instrumentation

The Biodex Stability System 945-300 (Biodex Medical Systems; Shirely, New York) was used to quantify bilateral standing balance (4). The system consists of a multi-axial tilting platform interfaced with a computer which records and calculates stability indices of standing balance. The platform stability can be varied by adjusting the resistance applied to the platform via one of 8 stability settings controlled by the system’s microprocessor-based actuator (14). Setting 1 represents the least stable platform and setting 8 the greatest platform stability. An 11.5 x 8.5 cm LCD display screen, located at eye level, provides visual feedback via a circular grid that visually shows a cursor tracing of the subject’s stability performance. The goal of dynamic balance testing on the Biodex Balance System during the augmented visual feedback condition is for the subject to maintain the cursor on the center of the circular grid for as long as possible during the test trial (8). During the no augmented visual feedback condition the goal was to keep the balance platform in a horizontal position while focusing straight ahead on a covered LCD screen. The Biodex Balance System has been shown to have high reliability.(8,14).

#### Procedures

Four randomly assigned groups with ten subjects in each group performed nine 20-second dynamic balance trials. The platform balance task required the subject to stand barefooted in a comfortable upright position with feet shoulder width apart with arms at sides. Groups 1 and 3 received augmented visual feedback during the balance task, while groups 2 and 4 received no augmented visual feedback. During the augmented visual feedback trials, the subject was instructed to keep the cursor directly in the middle of the screen while balancing on the platform. Group 1 performed the balance task at platform stability level 2 with augmented visual feedback. Group 2 performed at stability level 2 with no augmented visual feedback, which involved the subject performing the balance task while focusing straight ahead on a covered screen. Group 3 performed the balance task at stability level 7 with identical augmented visual feedback as used with group 1. Group 4 performed at stability level 7 with no augmented visual feedback which involved performing the balance task while focusing on a covered screen.

A familiarization session was conducted in which the participants were introduced to the testing protocol. Four 20-second practice trials were performed either at stability level 2 or at level 7, depending on group assignment. A 20-second rest period was allowed between trials. Participants assigned to the augmented vision condition practiced the balance task while being allowed to watch the balance tracing on the screen. Participants assigned to the no visual feedback condition practiced the balance task while viewing a covered screen.

Prior to the data acquisition trials, all subjects achieved a stable upright stance by positioning their feet shoulder width apart on the center of platform while looking straight ahead. The screen was either left uncovered or covered which was dependent on the assigned treatment group. The platform was then unlocked, requiring balance at the given stability level. Nine 20-second dynamic balance trials were performed. The same examiner (S.F.P.) administered the balance task in a non-distracting environment. If a participant lost control of balance that required grabbing the handrail, the trial was repeated. Three participants repeated one trial each. Two participants lost balance control more than once and were not included in the data analysis.

Platform stability levels 2 and 7 were chosen based on testing recommendations found in the literature and from pilot data (12). In addition, enough disparity between groups in terms of balance difficulty was necessary in order to ensure that statistical differences between feedback groups, if it in fact existed, could be found. A previous study reported that approximately nine 20-second trials could be safely performed in one practice session before participants reported fatigue (12). No participant was told their stability index scores or given any other information concerning their performance other than that given in the visual feedback conditions.

#### Statistical Analysis

A 2 x 2 factorial design was used to examine the effects of feedback and balance condition on dynamic balance performance using the Biodex Balance System. The first independent variable was type of feedback with two levels (augmented visual and no augmented visual). The second independent variable was balance condition with two levels (stability level 2 or stability level 7). The dependent variable was the mean stability index calculated as an average of the nine 20-seconds trials. The stability index is determined from the amount of platform tilt in degrees from a zero-centered balance-point (level). The index was calculated as the standard deviation of the platform displacement from horizontal obtained from each 20-second trial (4). A low stability index score indicates good dynamic stability or balance, whereas a high stability index scores indicates poor balance control.

A two-way univariate analysis of variance was conducted to examine the effects of the type of feedback and balance condition for the stability index score data. The α level was set a priori at .05. We used SPSS (version 18; SPSS Inc, Chicago, IL) to analyze the data.

### Results

Means and standard deviations for stability index scores are presented in Table 1. A significant main effect was found for balance condition (F_1,36 = 105.134, P = .001), which means participants assigned to the easier balance condition had better balance scores than those assigned to the more difficult balance task. No significant differences were found for the type of feedback groups (F_1,36 = 2.145, P = .152). More importantly, a significant feedback by balance condition statistical interaction was found (F_1,36 = 4.107, P = .04). At stability level 7, augmented visual feedback stability index scores were better when compared to no augmented visual feedback stability index scores (P < .001). However, for stability level 2, no difference was found between the feedback and no feedback conditions (P = .778).

### Discussion

We propounded the question of whether or not concurrent augmented visual feedback influences balance on the Biodex Balance System at different stability levels. The results supported our postulation that concurrent augmented visual feedback did not influence balance at the more unstable level (Biodex stability level 2). The importance of vision on postural control has long been known (2), however, the effect of concurrent augmented visual feedback on postural control while balancing on an unstable surface is equivocal. Most of the reported clinical studies that examined the effects of augmented visual feedback on postural control have involved stroke patients (7,11). Barclay-Goddard et al (2009) conducted a meta-analysis of the efficacy of concurrent augmented feedback using force platform standing balance in stroke patients. Their results showed no clear evidence that the use of force platform visual feedback improved standing balance. O’Connor et al (2008) compared the effects of different visual cues on postural sway in healthy older and younger adults. The older adults were able to habituate to repeated visual perturbations, however, it took more exposures compared to the younger adults. This finding suggests that aging impacts the ability to quickly modify augmented visual feedback for postural control. Hlavackova et al (2009) studied the effects of concurrent mirror feedback on upright stance control in elderly transfemoral amputees. Their results showed mirror feedback improved upright stance control.

Normal postural sway and equilibrium produced while standing on a flat stable surface may be controlled by lower level closed-looped feedback corrections. Standing balance on a stable surface primarily involves activating automatic postural reactions that are based on reflex actions rather than conscious control (12). The Biodex Balance System is unique in that it uses a moveable platform to create different levels of stability. Our rationale was that at the more difficult stability level 2 the influence of augmented visual feedback would be reduced as a result of change in motor control strategies. As platform stability decreased, open-looped strategies may have been used in an effort to maintain the platform in a horizontal position. Gutierrez et al (2009) in their clinical review state that during dynamic balance, open-looped mechanisms operate faster than closed looped mechanisms when perturbations to balance are imposed. This contention is supported by the study of Horak and Nasher (1986) who investigated the extent to which standing automatic postural reactions are controlled by motor programs. They adduce the theory that postural actions are organized by a limited repertoire of central programs selected in advance of movement. Organization of movements into motor programs simplifies the process of modifying movement by reducing reliance on concurrent sensory feedback. Our data suggest that the motor control strategies used when balancing on the Biodex Balance System may not be universal at all levels of difficulty.

### Conclusion

The learning/relearning of balance is a primary goal in many types of sport and wellness rehabilitation. Because of the importance of balance, there is a constant need for the identification of efficient and successful methods of balance testing and training as well as the delineation of variables that influence balance. We conclude that when balancing on the Biodex Stabilometer, the way feedback is administered is important because it significantly affects balance performance. Our study implies that, when balancing on a Biodex Balance System, as the degree of difficulty increases the influence of concurrent augmented visual-feedback is mitigated.

### Application in Sport

During the early stages of balance training, where the stabilometer tasks are performed at the more stable (less difficult) levels, augmented visual feedback may improve the performance of the balance task. However, as task difficulty increases the ability to use augmented visual feedback to guide postural reactions may decrease. These results infer that during Biodex stability training both open and closed looped motor control strategies are being used depending on the stability level being practiced. Under these conditions, previous research (12) has shown that variable practice, where several difficulty levels are practiced in a random order during any given training session, is a more efficient means of balance training when compared to constant practice where only one stability level is practiced during a training session. Variable practice has been shown to be more efficient in the development of open loop motor programs where rapid movements are required (6). Therefore, when doing Biodex balance training for sport a protocol that involves practicing several different levels of difficulty during one training session would be recommended. Future studies need to examine additional variables such as disability, injury and age in order to determine the most appropriate rehabilitation protocols.

### Tables

#### Table 1
Mean (± SD) Stability Index Scores Averaged Across Nine 20-Second Trials

Type of Feedback	Level 7	Level 2
Augmented Visual	1.62 ± .41	11.58 ± 4.54
No Augmented Visual	4.45 ± .83^a	11.12 ± 2.20

a. Difference between type of feedback at level 7 (P < .001).

### References

1. Arnold, B.L., Gansneder, B.M., & Perrin, D.H.(2005). Research Methods in Athletic Training. Philadelphia, PA: F.A. Davis.
2. Asakawa, K., Ishikawa H., Kawamorita T., Fuiyama Y., Shoji N., & Uozato H. (2007). Effects of ocular dominance and visual input on body sway. Jpn J Ophathalmol.,51:375-378.
3. Barclay-Goddard, R.E., Stevenson, T.J., Poluha, W., & Taback,S.P. (2009). Force platform feedback for standing balance training after stroke : The Cochrane Collaboration. New York, NY:Wiley.
4. Biodex Medical Systems. Balance System Operations and Service Manual. Shirley, NY: Biodex Medical Systems; 2003.
5. Davids K., Button C., & Bennett S. (2008). Dynamics of Skill Acquisition: A Constraints Approach. Champaign, IL: Human Kinetics.
6. Gutierrez, G.M., Kaminski, T.W., & Douex, A.T. (2009). Neuromuscular control and ankle instability: A clinical review. Phys Med Rehabil.,1(4):359-365.
7. Hartveld, A., & Hegarty, J.R. (1996). Augmented feedback and physiotherapy practice: Review report. Physiotherapy., 82(8):480-490.
8. Hinman, M. (2009). Factors affecting reliability of the biodex balance system: A summary of four studies. J Sport Rehabil., 9:240-252.
9. Hlavackova, P., Fristios, J., Cuisinier, R., Pinsault, N., Janura, M., & Vuillerme, N. (2009). Effect of mirror feedback on upright stance control in elderly transfemoral amputees. Arch Phys Med Rehabil., 90(11):1960-1963.
10. Horak, F.B., & Nashner, L.M. (1986). Central programming of postural movements: Adaptation to altered support-surface configurations. J Neurophysiol., 55(6):1369-1381.
11. Horak, F.B., Diener, H.C., & Nashner, L.M. (1989). Influence of central set on human postural responses. J Neurophysiol. ,62(4):841-853.
12. Kovaleski, J.E, Heitman, R.J, & Gurchiek L.R. (2009). Improved transfer effects on biodex balance system. Athletic Training & Health Care .,1(2):74-78.
13. O’Connor, K.W., Loughlin, P.J., Redfern, M.S., & Sparto,P.J. (2008). Posturaladaptations to repeated optic flow stimulation in older adults. Gait Posture., 28(3):385-391.
14. Schmitz R, Arnold B. (1998). (Intertester and intratester reliability of a dynamic balance protocol using the Biodex Stability System. J Sport Rehabil.,7:95-101.

### Corresponding Author

Dr. Steven Pugh, PhD.
HPELS Dept
University of South Alabama
HPE Building, RM 1016
171 Jaguar Drive
Mobile, Alabama 36688

<[email protected]>
(251) 461-8231

U.S. Sports Academy2013-11-22T22:58:23-06:00January 3rd, 2012|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off

The Effects of Conference Realignment on National Success and Competitive Balance: The Case of Conference USA Men’s Basketball

Martin M. Perline, G. Clayton Stoldt, Mark C. Vermillion

### Abstract

Collegiate athletic conferences serve multiple functions, including providing regular opportunities for members to compete in a relatively equitable environment and contributing to the financial well being of member institutions. Many conferences have undergone realignment in recent years, and the effects of those changes may impact the degree to which conferences realize those desired outcomes. The purpose of this paper is to assess how the churning of various institutions (i.e., changes in conference membership as institutions leave or are added) within Conference USA over a 10-year period affected the conference’s men’s basketball programs in regard to success at the national level and competitive balance within the conference. Both national success and competitive balance within the conference can significantly impact the financial well-being of the conference. Results of the study indicate decreases in both the competitive success of the men’s basketball programs at the national level and the in-conference competitive balance between the 2000-2001 through 2004-2005 and the 2005-2006 through 2009-1010 time periods.

**Key Words:** college athletics, competitive balance, conference realignment, basketball, conference USA

### Introduction

While amateur athletic conferences serve many functions for the individual member institutions, one important purpose is to attempt to enhance the financial status of their members. Although there are numerous ways this can be achieved, two important ways include (1) an attempt to accumulate a group of conference teams that are successful nationally against teams from rival conferences, and (2) an effort to insure teams are somewhat evenly matched within the conference—what is referred to as competitive balance.

Both winning against non-conference opponents and competitive balance are important as they tend to enhance the financial status of conference members. Indeed, “everyone loves a winner,” and is willing to attend games featuring successful teams more often and pay more to attend. Likewise, while people want their teams to win, fans like the games to be exciting and not a foregone conclusion as to the winner (5, 9, 12, 17, and 18).

Almost all major college athletic conferences have experienced changes in their membership within the last six years. These changes—commonly referred to as churning as members come and go—impact conferences in many ways. Competitive success at the national level and in-conference competitive balance are among the desired outcomes commonly impacted.

The purpose of this study was to assess how churning within Conference USA over a 10-year period has affected the conference’s men’s basketball programs in regard to success at the national level and competitive balance within the conference. The study is important because it assesses the impact of churning on two key but unrelated dimensions. A conference may be well balanced competitively but have negligible success at the national level. Conversely, a conference may be highly unbalanced, but the few teams who win consistently in-conference, may also enjoy considerable success at the national level. This can provide considerable financial rewards for the conference.

Competitive success at the national level and the financial well-being of conference members are inextricably linked because the number of teams a conference places in the NCAA national championship tournament and the number of victories those teams accrue determine the NCAA’s payout to participating conferences. Other studies have examined the effects of churning on competitive balance (see, for example, 13-15, 18) or the relationship between realignment and program revenue (8). This project is the first to combine both considerations, allowing for a more comprehensive assessment of churning outcomes.

### Related Literature

College conferences are comprised of college and universities that have established an association, one of the purposes of which is regular athletic competition (1). In 2011, Staurowsky and Abney (20) stated conferences “establish rules and regulations that support and sustain a level playing field for member institutions, while creating in-season and postseason competitive opportunities” (p. 149). And Rhoads (18) has observed that “(i)t is reasonable that conferences should be quite active in ensuring optimal levels of competitive balance” (p. 5).

Sustained competition among equitable teams is not the sole purpose of athletic conferences, however. Depken (4) observed:

> Sport leagues exist, in part, to insure profitability of their member franchises. Although the NCAA specializes in amateur sports, in which players do not receive direct salaries for their athletic performance, it is readily apparent that the schools that comprise the NCAA are often anxious to earn as much profit as possible from the sports programs (p. 4).

College athletic conferences contribute to their member institutions’ revenue by distributing rights fees from media agreements, corporate sponsorships, licensing and other forms of revenue received by the league (7). One source of revenue for NCAA Division I conferences are distributions from the annual Division I Men’s Basketball Championships. Payouts to conferences are based on financial values linked to units, which are accrued each time a conference member plays a game in the tournament (22). For example, a conference member advancing to the third round (i.e., “Sweet Sixteen”) is valued at three units. Payments to conferences are based on six-year averages of the financial values associated with units accrued (22).

#### Conference Churning

As illustrated in Table 1, 10 of the 11 conferences in the NCAA Division I’s Football Bowl Subdivision (FBS) experienced membership changes between 2005 and 2011. Additional changes at the FBS level are planned for 2012, and Quirk (16) has observed similar instability among non-FBS Division I conferences. Fort and Quirk (6) argued that football is the predominant consideration when institutions change conference affiliations. Competitive imbalance in existing conferences often results in churning because enhanced competitive balance is linked to desirable financial outcomes. Other scholars (5, 9, and 17) support that argument, observing that consumer uncertainty of a game’s outcome is linked to increased demand. Rhoads (18) specifically linked competitive balance with increased ticket sales and enhanced television rights fees.

Little scholarly attention has been devoted to effects of conference churning on competitive success against non-conference opponents. Minimal research has been devoted to evaluating conference realignment in terms of financial outcomes. One exception is Groza (8), who found FBS teams that changed conferences enjoyed an increase in attendance, even controlling for increased quality in competition. Of course, ticket sales (i.e., attendance) is only one of many financial factors that may be impacted by churning. Others include, but are not limited to, BCS and other bowl related revenue, NCAA tournament payouts; media rights fees, athletic donations, and corporate sponsorship fees.

Several studies have been conducted assessing the effects of conference churning on competitive balance within select sport programs. Rhoads (18) examined the Western Athletic and Mountain West conferences and found that membership changes in those conferences had resulted in enhanced competitive balance in football. The changes had no impact on competitive balance in men’s basketball however. Perline and Stoldt (13-14) conducted two studies focusing on competitive balance before and after the Big 8 Conference expanded to become the Big 12. Their first study focused on men’s basketball, for which they concluded that competitive balance within the sport decreased after the conference’s expansion (13).Their second study centered on football, for which they concluded that competitive balance improved after the merger (14). The same scholars also examined competitive balance in women’s basketball before and after the merger between the Gateway Collegiate Athletic Conference and Missouri Valley Conference (15). Multiple methods of assessing of competitive balance produced mixed results, with more measurements indicating more competitive balance after the merger.

#### Conference USA: History and evolution

Conference USA (C-USA) was formed in 1995 during a time of great upheaval in college athletics, which included the dissolution of the Southwest Conference and the formation of the Big XII in 1996 (21). C-USA is a Division I-A league that is divided into two competitive divisions: East and West. In the eastern division members include East Carolina University, Marshall University, the University of Memphis, Southern Mississippi University, University of Alabama- Birmingham, and the University of Central Florida. The western division includes the University of Houston, Rice University, Southern Methodist University, Tulane University, the University of Tulsa, and the University of Texas- El-Paso (2).

Since its inception in 1995, C-USA has endured much change. In the beginning the conference consisted of the University of North Carolina-Charlotte, the University of Cincinnati, DePaul University, the University of Houston (starting competition in 1996), Marquette University, the University of Memphis, Tulane University, St. Louis University, University of Alabama- Birmingham, and the University of Southern Florida. Mike Slive was appointed as the first commissioner, but left to become the commissioner of the Southeastern Conference in 2002 (19), leaving C-USA to appoint Britton Banowsky as its new commissioner. Additionally, in 2002, the C-USA headquarters moved from Chicago to Irving, Texas (2).

The major realignment of C-USA in 2005 was set in motion by larger conference realignment issues. The Atlantic Coast Conference’s (ACC) desire for football prestige triggered a mass reordering of conferences (23). Specifically, the ACC invited the University of Miami (FL), Virginia Polytechnic and State University, and Boston College to join their conference, thereby depleting the Big East Conference. In order to reestablish its conference, the Big East invited C-USA members the University of Cincinnati, DePaul University, Marquette University, the University of Louisville, and the University of South Florida (11). Additionally, four other institutions relinquished their C-USA memberships in 2005. Texas Christian University left to join the Mountain West Conference, the University of North Carolina-Charlotte and St. Louis University left to join the Atlantic 10 Conference, and the U.S. Military Academy (aka Army) became independent [11). Figure 1 lists the various institutions that have been members of C-USA, the dates of their memberships, and their current conference affiliations.

Crytzer (3) noted the unusual current geographical size of C-USA (over 1,500 miles separate the eastern most and western most schools) is a barrier for many of the member schools, which range in student population from 5,000 to 50,000. Additionally, conference defections over the past 15 years helped fuel speculation that future NCAA conference realignments could render C-USA obsolete.

### Methods

The purpose of this paper was to assess how churning within Conference USA over a 10-year period has affected the conference’s men’s basketball programs in regard to success at the national level and competitive balance within the conference. We employed two tactics each in evaluating winning success nationally and competitive balance.

#### Winning Success

In order to measure winning success, we measured the success of Conference USA teams against outside competition before the departure of teams in the 2004-05 season and after the addition of teams in the 2005-06 season. While the conference mean will always be .500, the non-conference mean could vary. We also measured the number of Conference USA teams that participated in the NCAA post-season tournament in both periods. The latter was a major source of revenue to the conference and ultimately to each team. The value of each appearance in the tournament varied from $94,086 in 2001 to $222,206 in 2010 and has continued to grow in magnitude over time. These values were paid annually for six years. Thus one appearance in 2001 would be worth $564,516 to the conference and one appearance in 2010 would be worth $1,333,236 to the conference over the six-year period. It is, therefore, readily apparent that the more appearances a conference makes in the tournament, the more revenue it receives.

#### Measuring Competitive Balance

There were several methods used in measuring competitive balance. The most appropriate of these methods depended on what the researcher was attempting to specifically measure (9). Methods most appropriate for measuring competitive balance within a given season may be different from those used to measure competitive balance between seasons (10). To measure competitive balance within a given year, we rely on the standard deviation of winning percentages and to measure competitive balance between seasons, we use the Hirfindahl-Hirschman Index (HHI).

##### Standard Deviation of Winning Percentages

Possibly the method most often used to measure competitive balance within a conference in a given season is the standard deviation of winning percentages. Since there will, outside of a tie, always be one winner and one loser for each game, the average winning percentage for the conference will always be .500.

In order to gain insight into competitive balance, we would need to measure the dispersion of winning percentages around this average. To do this we can measure the standard deviation. This statistic measures the average distance that observations lie from the mean of the observations in the data set. The formula for the standard deviation is:

![Formula 1](/files/volume-14/441/formula-1.jpg)

The larger the standard deviation, the greater is the dispersion of winning percentages around the mean, and thus the less competitive balance.

#### Championship Imbalance

While using the standard deviation as a measure of competitive balance provides a good picture of the variation within a given season, it does not indicate whether it is the same teams winning every season, or if there is considerable turnover among the winners, i.e., whether there is between season variation. Therefore, another method economists have used to measure imbalance is the Hirfindahl-Hirschman Index (HHI), which was originally used to measure concentration among firms within an industry ([10). We determine the HHI by counting the number of times a team won a championship during a given period, summing those values and then dividing by the number of years in the period considered.

![Formula 2](/files/volume-14/441/formula-2.jpg)

Using this method, the greater the number of teams that achieve championship status over a specific time period, the greater would be the competitive balance.

### Results

#### Winning Success

Table 3 gives the winning percentages for Conference USA teams against non-conference opponents in the two periods under consideration. For the earlier period the mean winning percentage was .606 and for the latter period it was .577—an approximate 5% differential favoring the earlier period. It should be noted that the highest winning percentage over this total period was .638 (2003-04) and the lowest was .539 (2005-06). The data suggest that Conference USA was more successful against outside competition in the earlier period.

Table 4 reflects the number of Conference USA members participating in the NCAA post-season tourney, the unit value of each appearance and the dollars received in each year from conference participation. The data in Table 4 indicates that in the 2001-05 period the conference received $30,722,250, and in the 2006-10 period the conference receipts were only $21,269,388. These numbers reflect a participation of 39 appearances in the earlier period and 19 in the latter period. Consequently, even though the dollars per unit were considerably higher in the latter period, the conference earned almost $10 million more in the earlier period.

#### Competitive Balance

##### Standard Deviation of Winning Percentages

Tables 5 and 6 display the winning percentage for men’s basketball for the years 2000-01 through 2004-05 and for 2005-06 through 2009-10. Table 7 displays the standard deviations for both time periods.

As shown in Table 7, the mean standard deviation was .208 for 2000-01 through 2004-05, and it was .250 for 2005-06 through 2009-10. As indicated above, the lower the standard deviation the greater the competitive balance. This is a 20.3% difference favoring competitive balance in the earlier period. It should also be pointed out that not only was the mean standard deviation lower for the earlier period, but the lowest standard deviation for the period, .173 (2000-01), was lower than the lowest standard deviation for the later period, .238 (2006-07). Likewise the highest standard deviation for the later period, .261 (2009-10) was higher than the highest standard deviation, .236 (2003-04) in the earlier period. As a matter of fact the standard deviation was lower every year of the earlier period than for the later period.

Why the standard deviation was lower for the earlier period can also be seen by the range of the means in the two periods. As indicated in Table 5 (the earlier period) the range was a high of .725 (Cincinnati) and a low of .266 (East Carolina). This was a range of .459 from top to bottom of the standings. On the other hand, and as indicated Table 6 (the latter period), the means ranged from a high of .948 (Memphis) to a low of .216 (East Carolina). This was a range of .732 from top to bottom. Indeed in this period Memphis had a perfect record of 16-0 in three of the five years investigated, while two teams, East Carolina and SMU, had losing records all five years.

##### Championship Imbalance

Using the data from Table 8 to construct the HHI to measure competitive balance between the two periods we find the results are consistent with the results found when using the standard deviation. Using the regular season standings we find that during the 2000-01 through 2004-05 period (see Table 8), three teams–Cincinnati, Marquette and Louisville–won the championship once each. Multiple teams shared the title for two seasons–2001-02 when Cincinnati and Southern Mississippi tied and 2003-04 when there was a five-team (DePaul, Memphis, Cincinnati, UAB and Charlotte) tie for first. If we give one point for each outright championship, .5 for a two-team tie, and .2 for a five-team tie, we find:

HHI = 1.7² + 1² + 1² + .5² + .2² + .2² + .2² + .2²= 2.89 + 1 + 1 + .25 + .04 + .04 + .04 + .04 = 5.3/5 = 1.06

When measuring the HHI over the 2005-06 through 2009-10 period (see Table 8), we find considerably less competitive balance. During this period one team, Memphis, won the regular season championship four times and another team, UTEP, won the championship the other year. Measuring these results we find:

HHI= 4² + 1² = 16 + 1 = 17/5 = 3.4

These calculations indicate less competitive balance during the 2005-06 through 2009-10 period.

### Conclusions

The results of this study offer strong evidence that the churning that occurred in C-USA over the 10-year period 2000-2001 through 2009-2010 had negative effects for men’s basketball in terms of both competitive success at the national level and competitive balance within the conference. Both of the indicators of national success—winning percentage against non-conference opponents and revenue derived from member appearances in the national championship tournament—were better during the earlier period than the latter. In addition both measures of competitive balance within the conference—standard deviation of winning percentages and the HHI—indicate more competitive balance in the earlier period.

It is also important to note that while this study examined the financial ramifications of C-USA’s success, or lack thereof, in the men’s basketball national championship tournament, that revenue stream was but one of several that determine the overall financial well-being of the conference and its members. However, Crytzer (3) has observed that as the financial benefits of the C-USA’s success in men’s basketball from 2003-2005 in particular run out, the conference’s long-term viability may be at risk. Clearly, multiple factors relating to a variety of sport programs will affect whether C-USA is susceptible to additional churning and/or will even survive. However, the findings of this study pertaining to one flagship sport, men’s basketball, indicate the conference faces significant challenges in the near future.

### Applications In Sport

While the results of this study are not to be generalized to other sports programs or other conferences, they do align with the findings of other studies that have examined the effects of conference churning on competitive balance in men’s basketball. While Rhoads (9) found realignment in the Western Athletic and Mountain West conferences had enhanced competitive balance in football, it did not have the same positive effect in men’s basketball. And two studies on the effects of churning in the Big 12 found improved competitive balance in football (14) but diminished competitive balance in men’s basketball (13). Since football is recognized as the primary factor in conference realignment (6), it may be that conference churning commonly results in desirable outcomes for that one sport program while others (i.e., men’s basketball) do not enjoy the same benefits. Given the potential for revenue generation in men’s basketball, and perhaps a few other sport programs aside from football (depending on the institution), the appeal of competitive success on a national level, and the importance of in-conference competitive balance, university and college leaders are well advised to consider likely ramifications for multiple sport programs when considering conference affiliation options.

### Tables

Conference	Last Change	Description
Atlantic Coast Conference	2005	Boston College joins
Big East Conference	2011	Texas Christian joins
Big Ten Conference	2011	Nebraska joins
Big 12 Conference	2011	Two institutions withdraw
Conference USA	2005	Five institutions join, four withdraw
Mid-American Conference	2007	Temple joins as football-only member
Mountain West Conference	2011	Two institutions withdraw, Boise State joins
Pac-10 Conference	2011	Two institutions join
Southeastern Conference	1990	Two institutions join
Sun Belt Conference	2010	New Orleans withdraws
Western Athletic Conference	2011	Boise State withdraws

#### Table 2
Evolution of C-USA, 1995-2011

Conference	Last Change	Description
UNC Charlotte	1995-2005	Atlantic 10
Cincinnati	1995-2005	Big East
DePaul	1995-2005	Big East
Houston	1995-Present	C-USA
Louisville	1996-Present	C-USA
St. Louis	1995-2005	Atlantic 10
Southern Miss	1995-Present	C-USA
Tulane	1995-Present	C-USA
Alabama, Birmingham	1999-Present	C-USA
Southern Florida	1995-2005	Big East
Central Florida	2005-Present	C-USA
Texas Christian	1999-2005	Mountain West¹
East Carolina	1996-Present	C-USA
Army	1997-2005	Independant
Marshall	2005-Present	C-USA
Rice	2005-Present	C-USA
Southern Methodist	2005-Present	C-USA
Tulsa	2005-Present	C-USA
Texas, El-Paso	2005-Present	C-USA

1. Moving to the Big East in 2011-2012 season

#### Table 3
Conference Winning Percentage in Games Against Non-Conference Opponents

Year	Winning Percentage
2000-01	.550
2001-02	.622
2002-03	.607
2003-04	.638
2004-05	.615
5-Year Mean	.606
2005-06	.539
2006-07	.590
2007-08	.585
2008-09	.589
2009-10	.583
5-Year Mean	.577

#### Table 4
NCAA Tournament Appearances and Related Revenue

Year	NCAA Appearances	Unit Volume ($)	Yearly Value ($)	6 Year Value ($)
2001	5	94,086	470,430	2,822,580
2002	4	100,672	402,688	2,416,128
2003	9	130,697	1,176,273	7,057,638
2004	11	140,964	1,550,604	9,303,624
2005	10	152,038	1,520,380	9,122,280
5-Year Totals	39	618,457	5,120,375	30,722,250
2006	5	163,981	819,905	4,919,430
2007	4	176,864	707,456	4,244,736
2008	5	191,013	955,065	5,730,390
2009	3	206,020	618,060	3,708,360
2010	2	222,206	444,412	2,666,472
5-Year Totals	19	960,084	3,544,898	21,269,388

#### Table 5
Winning Percentage for Men’s Basketball Teams, 2000-01 through 2004-05

Year	Cin	Char	Marq	StL	Lou	DeP	SouM	Mem	USF	UAB	Hou	Tul	ECar	TCU
2000-01	0.688	0.625	0.563	0.5	0.5	0.25	0.688	0.625	0.563	0.5	0.375	0.125
2001-02	0.875	0.688	0.813	0.563	0.5	0.125	0.25	0.75	0.5	0.375	0.563	0.313	0.313	0.375
2002-03	0.562	0.5	0.875	0.562	0.688	0.5	0.313	0.813	0.438	0.5	0.375	0.5	0.188	0.188
2002-04	0.75	0.75	0.5	0.563	0.563	0.75	0.375	0.75	0.063	0.75	0.188	0.25	0.313	0.438
2004-05	0.75	0.75	0.438	0.375	0.875	0.625	0.25	0.563	0.313	0.625	0.563	0.25	0.25	0.5
Mean	0.725	0.663	0.638	0.513	0.625	0.45	0.375	0.700	0.375	0.55	0.413	0.288	0.266	0.375

#### Table 6
Winning Percentage for Men’s Basketball Teams for 2005-06 through 2009-10

Year	Memphis	UAB	UTEP	Hou	UCF	Tulsa	Rice	Tulane	Marshall	SMU	So.Miss	E.Car.
2005-06	0.929	0.857	0.786	0.643	0.5	0.429	0.429	0.429	0.357	0.286	0.214	0.143
2006-07	1	0.438	0.375	0.625	0.688	0.563	0.5	0.563	0.438	0.188	0.563	0.063
2007-08	1	0.75	0.5	0.688	0.563	0.5	0	0.375	0.5	0.25	0.563	0.313
2008-09	1	0.688	0.625	0.625	0.438	0.75	0.25	0.438	0.438	0.188	0.25	0.313
2009-10	0.813	0.688	0.938	0.438	0.375	0.625	0.063	0.188	0.688	0.438	0.5	0.25
Mean	0.948	0.684	0.645	0.604	0.512	0.573	0.248	0.399	0.484	0.27	0.418	0.216

#### Table 7
Standard Deviation for Winning Percentages

Year	SD
2000-01	0.173
2001-02	0.223
2002-03	0.202
2003-04	0.236
2004-05	0.205
5-Year Mean SD	0.208
2005-06	0.253
2006-07	0.238
2007-08	0.256
2008-09	0.243
2009-10	0.261
5-Year Mean SD	0.250

#### Table 8
Regular Season Conference Champions, 2000-01 through 2004-05

Year	Champion(s)
2000-01	Cincinnati, Southern Mississippi
2001-02	Cincinnati
2002-03	Marquette
2003-04	DePaul, Memphis, Cincinnati, UAB, Charlotte
2004-05	Louisville
2004-05	Louisville
2005-06	Memphis
2006-07	Memphis
2007-08	Memphis
2008-09	Memphis
2009-10	UTEP

### References

1. Abbott, C. (1990). College athletic conferences and American regions. Journal of American Studies, 24, 220-221.
2. C-USA: Official site of Conference USA. (2011). About Conference USA. Retrieved March 21, 2011 from <http://conferenceusa.cstv.com/ot/about-c-usa.html>
3. Crytzer, J. (2009, August 30). The future of college football and the death of Conference USA 1995-2011 [Web log post]. Retrieved from <http://bleacherreport.com/articles/245204-the-future-of-college-football-and-the-death-of-conference-usa-1995-2011>
4. Depken II, C.A. (2011). Realignment and profitability in Division IA college football. Unpublished paper. Retrieved April 2, 2011 from <http://www.belkcollege.uncc.edu/cdepken/P/confsize.pdf>
5. Depken, C.A., & Wilson, D. (2005). The uncertainty outcome hypothesis in college football. Department of Economics, University of Texas-Arlington.Paper under review.
6. Fort, R., & Quirk, J. (1999). The college football industry. In J. Fizel, E. Gustafson and L. Hadley (Eds.) Sports economics: Current research (pp. 11-26). Westport, CT: Praeger.
7. Grant, R.R., Leadley, J., & Zygmont, Z. (2008). The economics of intercollegiate sports. Mountain View, CA: World Scientific.
8. Groza, M.D. (2010). NCAA conference realignment and college football attendance.Managerial and Decision Economics, 31, 517-529.
9. Humpreys, B. (2002). Alternative measures of competitive balance. Journal of Sports Economics, 3, (2), 133-148.
10. Leeds, M., & von Allmen, P. (2005).The Economics of Sports.Boston: Pearson-Addison Wesley.
11. Nunez, T. (2010, June 6). Conference realignment will have ripple effect on Conference USA. The Times-Picayune. Retrieved from <http://www.nola.com/tulane/index.ssf/2010/06/conference_realignment.html>
12. Paul, R.J., Wachsman, Y., & Weinbach, A. (2011). The role of uncertainty of outcome and scoring in the determination of satisfaction in the NFL. Journal of Sports Economics, 12, 213-221.
13. Perline, M.M., & Stoldt, G.C. (2007a). Competitive Balance and the Big 12. The SMART Journal, 4 (1), 47-58.
14. Perline, M.M., & Stoldt, G.C. (2007b). Competitive balance and conference realignment: The case of Big 12 football. The Sport Journal, 10 (2). <http://www.thesportjournal.org/2007Journal/Vol10-No2/Perline08.asp>.
15. Perline, M.M., & Stoldt, G.C. (2008). Competitive balance in women’s basketball: The Gateway Collegiate Athletic Conference and Missouri Valley Conference merger.Women in Sport and Physical Activity Journal, 17 (2), 42-49.
16. Quirk, J. (2004).College football conferences and competitive balance. Journal of Managerial and Decision Economics, 25, 63-75.
17. Rein, I., Kotler, P., & Shields, B. (2006). The elusive fan.New York: McGraw-Hill.
18. Rhoads, T.A. (2004). Competitive balance and conference realignment in the NCAA. Paper presented at the 74th Annual Meeting of Southern Economic Association, New Orleans, LA.
19. SECSports.com (2011). About the SEC. Retrieved March 21, 2011 from http://www.secdigitalnetwork.com/SECSports/Home.aspx
20. Staurowsky, E.J., & Abney, R. (2011). Intercollegiate athletics. In P.M. Pedersen, J.B. Parks, J. Quarterman, & L. Thibault (Eds.) Contemporary sport management (4th ed., pp. 142-163). Champaign, IL: Human Kinetics.
21. The State of Conference Realignment. (ND). The national championship issue: Perspectives on college football. [Web log post]. Retrieved March 22, 2011 from <http://thenationalchampionshipissue.blogspot.com/2008/01/state-of-conference-realignment.html
22. Where the money goes. (2010). Champion. Retrieved April 2, 2011 from http://www.ncaachampionmagazine.org/Exclusives/WhereTheMoneyGoes.pdf>
23. Wieberg, S. (2005, June 29). Conference shakeup continues as schools seek right fit. USA Today. Retrieved March 22, 2011 from <http://www.usatoday.com/sports/college/2005-06-28-conference-hopscotch_x.htm>

### Corresponding Author

G. Clayton Stoldt
Wichita State University
Department of Sport Management
1845 Fairmount
Wichita, KS 67260-0127
[email protected]
P: (316) 978-5441

Martin Perline is a professor and Bloomfield Foundation fellow in the Department of Economics at Wichita State University. G. Clayton Stoldt is chair and professor in the Department of Sport Management at Wichita State University. Mark Vermillion is an assistant professor in the Department of Sport Management at Wichita State University.

U.S. Sports Academy2015-11-08T07:40:19-06:00January 3rd, 2012|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off

The Effect of Music Listening on Running Performance and Rating of Perceived Exertion of College Students

Randy Bonnette, Texas A&M University-Corpus Christi; Morgan C. Smith III, Texas A&M University-Corpus Christi; Frank Spaniol, Texas A&M University-Corpus Christi; Liette Ocker, Texas A&M University-Corpus Christi; Don Melrose, Texas A&M University-Corpus Christi

### Abstract

The purpose of this study was to investigate how listening to music while running affects performance and perceived exertion of college students. Twenty-eight undergraduate kinesiology students (17 males, 11 females; age = 22.9 ± 5.9 yrs) were studied to determine if running performance and rating of perceived exertion were affected by listening to music. Running performance (RP) was measured by a 1.5-mile run. Two trials were performed, the first was a running performance without music listening (RPWOML = 12.94 ± 3.35 min) and the second trial was a running performance while music listening (RPWML = 12.50 ± 2.48 min). The second trial was measured five days post the initial trial. Listening to music (music listening) was defined as the subject’s self selection of music tracks and use of a personal digital audio player (e.g. IPod, MP3) during exercise. Perceived exertion without music listening (PEWOML = 14.7 ± 1.3) and perceived exertion with music listening (PEWML = 15.2 ± 2.4) was measured by the Borg 6 to 20 RPE scale. Data analysis was performed on the raw data by utilizing dependent t-tests to calculate and compare sample means. Statistical analyses determined a significant difference (p < .05) between running performance without music listening (RPWOML = 12.94 ± 3.35 min) and running performance with music listening (RPWML = 12.50 ± 2.48 min). However, no significant difference (p < .05) was determined between perceived exertion without music listening (PEWOML = 14.7 ± 1.3) and perceived exertion with music listening (PEWML = 15.2 ± 2.4) as measured by the Borg 6 to 20 RPE scale. In conclusion, the results of this study indicate that music listening has a significant effect on running performance during a maximal 1.5-mile run. However, music listening had no significant effect on rating of perceived exertion at this distance. Based on the results of this study it is recommended that coaches, athletes, and traditional exercisers consider listening to music during training to enhance performance.

**Key Words:** Music Listening, Aerobic, Performance, Rated Perceived Exertion (RPE)

### Introduction

In the past listening to music was relegated to travelling in automobiles, while in the home, while engaged in recreational activities and occasionally at work. Today, the portable music industry (e.g. cassettes, compact discs, and iPod/MP3 digital audio devices) has popularized music “on the go” and invaded just about every environment including training venues. These devices have made it easier for people to enjoy their music and create their own style of workouts with relative ease, regardless of the setting, and has transcended into a multi-million dollar industry (14). Similarly, the sports arena is an environment where music has flourished. Traditionally, music has been used to motivate and inspire people prior to an important event (e.g. pre-game of a critical contest) as well as when they engage in sports and training for competition. Thus, athletes and traditional exercisers alike have used music as an accompaniment to exercise to sustain motivation, resist mental and emotional fatigue, and potentially enhance their physical and athletic performance (10). Scientific inquiry has revealed three key ways in which music can ‘influence’ preparation and competitive performances through dissociation, arousal regulation, and synchronization (3, 4, 6, 8-10). More specifically, research indicates music to be particularly effective in distracting exercisers away from their perceived exertion.

#### Conceptual Framework

Conceptually the underlying framework of using motivational music in exercise and sport devised by Karageorghis et al. (7) indicated two main hypotheses regarding arousal regulation and fatigue dissociation. First, music can be used to alter emotional and physiological arousal and thus can act either as a stimulant or sedative prior to and during physical activity. Therefore, an athlete can use various music tempos as a ‘psych-up’ strategy in preparation for a competition or perhaps an aid to calming over anxiousness. Second, music diverts a performer’s attention from sensations of fatigue during exercise. This diversionary technique, known as dissociation, lowers perceptions of effort. Effective dissociation can promote a positive mood state, thus turning the attention away from thoughts of physiological sensations of fatigue (7).

#### Rated Perceived Exertion

Noble and Robertson (13) define perceived exertion as the subjective intensity of effort, strain discomfort and/or the fatigue that is experienced during an exercise. Currently, the most consistent findings suggest that perceived exertion will rate in lower values when participants exercise to music (12, 13, 22, & 24). The research data compiled from over the past two decades has found music particularly effective in distracting exercisers away from their perceived exertion during physical activity. A study by Nethery, Harmer, and Taaffe (12) found that perceived exertion while exercising to music was lower than for other attentional distracters and for the no distraction condition. Furthermore, Thornby et al. (22) tested exercising participants in the presence of music, no music and noise. They discovered that participants reported a lower perceived exertion while exercising in the presence of music in comparison to the no music and noise conditions.

These findings coupled with the popularity and substantial profits generated between the association of music and training (14) would seem to indicate a correlation between the use of music and performance. However, the effects of listening to music on performance and other physiological measures are less clear. Therefore, the purpose of this study was to investigate the effect listening to music has on running performance and rating of perceived exertion of college students.

### Methods

#### Experimental Approach to the Problem

Listening to music (music listening) was defined as the subject’s self selection of music tracks and use of a personal digital audio player (e.g. IPod, MP3) during exercise. Running performance was determined by a maximal 1.5 mile run to predict VO2 max. Subjects were asked to complete the distance run in the fastest time possible. Results were recorded in minutes and seconds. A common field test equation, V02 max (ml*kg-1*min-1) = 3.5 + 483 / (time in minutes), was selected to access cardio-respiratory fitness of the subjects utilizing their 1.5 mile running performance (1). Perceived exertion was determined by the Borg 6 to 20 RPE scale. Rating of perceived exertion summarizes the exertion levels between rest and maximum effort numerically from 6 to 20 (2).

#### Subjects

Twenty-eight undergraduate kinesiology students (17 males, 11 females; age = 22.9 ± 5.9 yrs) from a south Texas university were studied to determine if running performance and rating of perceived exertion were affected by listening to music. Institutional Review Board approval and subject informed consent were obtained prior to commencement of the research study.

#### Procedures

All participants were required to fill out an informed consent document two days prior to testing. Participants were then instructed to obtain sufficient sleep (6-8 hours) and avoid food, caffeine, tobacco products, or alcohol for 3 hours prior to testing the 1.5-mile run (1). Prior to testing, a 1.5-mile course was measured with a Rolatape® distance measuring wheel. The start/finish line and .75-mile line were marked off with two cones each on the large sidewalk course. Three testers were used to ensure subjects completed the 1.5-mile run, two researchers were stationed at the start/finish line to collect run times and RPE scores for each participant, while another tester was stationed at the .75-mile line or turn around portion of the course. To complete the 1.5-mile run each participant had to begin at the starting line, run to the .75-mile line, and then simply turn around and run back to the start/finish line. Stopwatches were used to measure 1.5-mile run times. Following the course explanation; the participants were encouraged to warm-up and stretch before starting the 1.5-mile run, as well as verbally read the following instructions for use of the Borg 6 to 20 RPE scale:

> During the exercise test we want you to pay close attention to how hard you feel the exercise work rate is. This feeling should be your total amount of exertion and fatigue, combining all sensations and feelings of physical stress, effort, and fatigue. Don’t concern yourself with any one factor such as leg pain, shortness of breath, or exercise intensity, but try to concentrate on your total, inner feeling of exertion. Try not to underestimate or overestimate your feeling of exertion, be as accurate as you can (20).

The participants completed two separate 1.5-mile runs as a group during their regularly scheduled class time on their campus. The first trial was performed in silence without any form of digital audio device (IPod, MP3) which would enable music listening. Five days post the initial trial, a second 1.5-mile run was administered during the regularly scheduled class meeting. However, in this 1.5-mile run test participants were required to use digital audio devices during the trial to enable music listening. Music selection was not controlled during this experiment; therefore the participants were able to select their favorite musical tracks to accompany them on their second trial run. All run times were recorded as the participants crossed the finish line, and RPE was obtained shortly thereafter when the subjects were asked to pick the number best reflecting their exertion from the Borg 6 to 20 scale poster board on site.

#### Statistical Analysis

An experimental one-group pretest-posttest design was utilized. The subjects completed two 1.5-mile run trials to test the effect of music listening on running performance and rating of perceived exertion. Dependent t-tests were utilized to compare mean data from the experimental conditions: music listening and without music listening. Significance was determined at the probability level of .05.

### Results
The results are divided into two sections: running performance and rating of perceived exertion. Data analysis was performed on the raw data by utilizing dependent t-tests to calculate and compare paired sample means. The mean and standard deviation values for these two measures, according to experimental conditions, are summarized in Table (1).

#### Running Performance

Dependent t-tests were conducted on the subjects running performance times in conditions without music listening and with music listening. Two trials were performed, the first was a running performance without music listening (RPWOML = 12.94 ± 3.35 min) and the second trial was a running performance while music listening (RPWML = 12.50 ± 2.48 min). Statistical analyses found music listening had a significant t (26) = 1.75, p = .0478 impact on running performance as shown in Figure 1. In addition, music listening was found to have a significant t (16) = 2.07, p = .0445 effect on running performance for male subjects, whereas female subject t (10) = 1.23, p = .12 indicated non significance.

#### Rating of perceived exertion
A paired two sample dependent t-test was conducted on the subjects rating of perceived exertion after completing a 1.5-mile running performance in conditions without music listening and with music listening. The result of the two trials found the subjects rated perceived exertion without music listening (PEWOML = 14.7 ± 1.3) to be lower than ratings of perceived exertion with music listening (PEWML = 15.2 ± 2.4). Statistical analysis found the effect of music listening on the groups rated perceived exertion to be non significant t (26) = -1.22, p = .11 as shown in Figure 2. However, music listening was found to have a significant t (10) = -2.96, p = .01 directional effect on reported female rating of perceived exertion scores while non significance t (16) = -.18, p = .4263 was found among male rating of perceived exertion scores.

### Discussion

The effects of listening to music on running performance and the rating of perceived exertion during maximal 1.5-mile runs were investigated. By comparing the recorded ratings of perceived exertion and running times of the two situations, it became clear when the subjects exercised to music their running performance improved collectively. Previous research by Thornby et al. (22) also found that the time spent exercising, the amount of work done, and heart rate were all significantly higher in the presence of music than in the other conditions. Similarly, Edworthy and Waring (4) make the suggestion, in regards to music’s effect on running performance, that the pace of music will influence the pace of exercise. Therefore, the assumption can be made that exercising to fast tempo music should produce faster running performance. However in this study’s case, music selection was not controlled; therefore some participant’s personal preferences might not have met the tempo or vigorous nature of the exercise conducted. Even so, the results of the two trials found the subjects running performance while listening to music (RPWML = 12.50 ± 2.48 min) to be substantially faster than running performance without music listening (RPWOML = 12.94 ± 3.35 min).

These results indicate that music listening has a significant effect (p < .0478) on running performance during a maximal 1.5-mile run. Therefore, the research null hypothesis in regards to music’s effect on running performance has been rejected. Furthermore, male subjects in particular were found to perform better while listening to music.

Additionally, music listening was found to have no significant effect on rating of perceived exertion during a maximal 1.5-mile run. The findings of the most recent research reported the effectiveness of music on the subjects’ perceived exertion rate during submaximal exercise, Copland and Franks (3), Szmedra and Bacharach (20), and Potteiger, et al. (15). These authors suggested that in the absence of external stimulation (e.g. music) participants may focus more strongly on their own efforts and perceive them to be higher. This reasoning provides an explanation as to why traditionally subjects experience decreased RPE, particularly in submaxial exercise where music has been shown to effectively dissociate sensations of fatigue and promote a more enjoyable exercise experience. However, this study evaluated music’s effectiveness on a maximal 1.5-mile run. The result of the two trials found the subjects rated perceived exertion without music listening (PEWOML = 14.7 ± 1.3) to be lower than ratings of perceived exertion with music listening (PEWML = 15.2 ± 2.4). Previous research by Yamishita and Iwai (22) suggest that music’s effect on RPE is limited by the intensity of the exercise. Schwartz et al. (17) experienced similar findings stating that at 75% V02max RPE values did not significantly differ for participants between music and control conditions. Accordingly, these findings share the similar reasoning of Rejeski (16) which suggest that when subjects work at maximal intensities beyond anaerobic threshold, physiological cues dominate the attentional processes leading to external cues, such as music, to become less effective on RPE. Additionally, the results indicate listening to music has no significant effect (p < .05) on rating of perceived exertion during a maximal 1.5-mile run. Therefore, the research null hypothesis regarding music’s effect on rating of perceived exertion has been accepted. Furthermore, female subjects were found to rate RPE more difficult while listening to music. This further supports that music’s dissociative properties exhibited in sub max exercise are not transferred into maximal exercise over 75% VO2 max.

It is important to note that although none of the trials were conducted in wet conditions, wind speed and wind direction could not be standardized between trials and this may have been an additional error source. Both performance trials were conducted outdoors at 75 degrees Fahrenheit. However, wind speeds differed between trials; trial one experienced wind speeds of 8 mph with gusts of 14 mph while trial two experienced wind speeds of 18 mph with gusts of 25 mph. Due to these confounding factors conducting the research indoors would have addressed this problem. Unfortunately, an indoor track was not yet available at the university where the research was conducted. Secondly, the participants completed the two running trials together as a group. A natural tendency to compete may have compromised the internal validity of the study. However, the threat to internal validity was preferred to the potential lack of motivation had participants been required to complete the task individually (18).

### Applications In Sport

Music has been found to be an ideal accompaniment for exercise. It has the ability to alter emotional and physiological arousal as well as dissociate a performer’s attention from sensations of fatigue during exercise (19). The tempo of the music can also be used to influence exercise performance as their arousal level will be heightened by the fast tempo (7). If music is applied to these types of situations, music’s impact may have the ability to change the context in which physical work or exercise is performed and become a viable way of positively influencing an individual’s disposition as well as performance (10).

Due to the aforementioned training benefits of listening to music coaches, trainers, as well as performers should be cognizant of this revelation when planning their training regimens. Obviously, this would be especially relevant when engaging in a training session that the athlete and/or coach/trainer identify as being particularly taxing on the performer’s physiological systems. This extra-musical association could very well promote thoughts that inspire physical activity or relaxation within the athlete. For example, an athlete may associate vigorous exercise with the theme from the popular “Rocky” movie series, or possibly dreams of Olympic glory from Vangelis’ “Chariots of Fire.” The resultant association can be attributed not only to the inherent musical characteristics, such as tempo or rhythm, but to the influence of elements of popular culture, such as cinema, television, and radio (6).

In general, the results of the research indicate that exercising to music makes training a more exciting and pleasant experience leading to improved performance. Accordingly, music used as a motivational aid can provide individuals an alternative to address the repetitiveness and mundane nature of many physical activities associated with aerobic performance training.

### Acknowledgements

The authors would like to acknowledge the efforts of Ms. Elizabeth Perez, administrative assistant, in the author’s department for her tireless efforts in support of this study. Her editorial prowess and knowledge of APA style was tremendously helpful in creating a quality manuscript.

### References

1. American College of Sports Medicine. ACSM’s guidelines for exercise testing and prescription (5th ed.). Baltimore, MD: Lippincott Williams & Wilkins, 2000.
2. Borg, E. and Kaijser, L. A comparison between three rating scales for perceived exertion and two different work tests. Scandinavian Journal of Medicine & Science in Sports, 16: 57-69, 2006.
3. Copland, B. and Franks, B. Effects of types and intensities of background music on treadmill endurance. The Journal of Sports Medicine and Physical Fitness, 31(1): 100-103, 1991.
4. Edworthy, J. and Waring, H. The effects of music tempo and loudness level on treadmill exercise. Ergonomics, 49: 1597-1610, 2006.
5. Gfeller, K. Musical components and styles preferred by young adults for aerobic fitness activities. Journal of Music Therapy, 25: 28-43, 1988.
6. Karageorghis, C. and Terry, P. The psychophysical effects of music in sport and exercise: a review. Journal of Sport Behavior, 20(1): 54-68, 1997.
7. Karageorghis, C., Terry, P., and Lane, A. 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 Sport Sciences, 17: 713-724, 1999.
8. Karageorghis, C., Jones, L., and Low, D. Relationship between exercise heart rate and music tempo preference. Research Quarterly for Exercise and Sport, 77(2): 240-251, 2006.
9. Karageorghis, C., and Priest, D. Music in Sport and Exercise: An update on research and application. The Sport Journal, 11(3): Retrieved October 25, 2008, from
<http://www.thesportjournal.org/article/music-sport-and-exercise-update-research-and-application>, 2008.
10. Mohammadzadeh, H., Tartibiyan, B., and Ahmadi, A. The effects of music on the perceived exertion rate and performance of trained and untrained individuals during progressive exercise. Physical Education and Sport, 6(1): 67-74, 2008.
11. Nethery, V. Competition between internal and external sources of information during mental exercise: influence on RPE and the impact of exercise load. Journal of Sports Medicine and Physical Fitness, 17: 172-178, 2002.
12. Nethery, V, Harmer, P, and Taaffe, D. Sensory mediation of perceived exertion during submaximal exercise. Journal of Human Movement Studies, 20: 201-211, 1991.
13. Noble, B. and Robertson, R. Perceived exertion. Champaign, IL: Human Kinetics, 1996.
14. O’Rourke, B.K. Email interview, March 5, 2011.
15. Potteiger, J., Schroeder, J., and Goff, K. Influence of music on rating of perceived exertion during 20 minutes of moderate intensity. Perceptual and Motor Skills, 91: 848-854, 2000.
16. Rejeski, W. Perceived exertion: An active or passive process. Journal of Sports Psychology, 75: 371-378, 1985.
17. Schwartz, S., Fernall, E., and Plowman, S. Effects of music on exercise performance. Journal of Cardiopulmonary Rehabilitation, 10: 312-316, 1990.
18. Simpson, S. and Karageorghis, C. The effects of synchronous music on 400-m sprint performance. Journal of Sport Sciences, 24(10): 1095-1102, 2006.
19. Smoll, F. and Schultz, R. Relationships among measures of preferred tempos and motor rhythm. Perceptual and Motor Skills, 8: 883-894, 1978.
20. Szmedra, L. and Bacharach, D. Effect of music on perceived exertion, plasma lactate, nor epinephrine, and cardiovascular homodynamic during treadmill running. Journal of Sports Medicine and Physical Fitness, 19(1): 32-37, 1998.
21. Thompson, D. and West, K. Ratings of perceived exertion to determine intensity during outdoor running. Canadian Journal of Applied Physiology, 23(1): 56-65, 1998.
22. Thornby, M., Haas, F., and Axen, K. Effect of distractive auditory-stimuli on exercise tolerance in patients with COPD. Chest, 107: 1213-1217, 1995.
23. Yamashita, S. and Iwa, K. Effects of music during exercise on RPE, heart rate and the autonomic nervous system. Journal of Sports Medicine and Physical Fitness, 46: 425-430, 2006.

### Tables

#### Table 1
Effects of Music Listening on Running Performance and RPE

Conditions	Running Performance				RPE
Conditions	No Music Listening		Music Listening		No Music Listening		Music Listening
Groups	M	SD	M	SD	M	SD	M	SD
Female (N=11)	14.51	3.81	13.74	1.98	14.73	1.35	15.82	1.60
Male (N=17)	11.94	2.69	11.70	2.49	14.65	1.37	14.76	2.77
Combined (N=28)	12.95	3.36	12.50	2.48	14.67	1.33	15.18	2.40

### Figures

#### Figure 1
Running performance mean comparison among groups

![Figure 1](/files/volume-14/440/figure-1.jpg)

#### Figure 2
RPE mean comparison among groups

![Figure 2](/files/volume-14/440/figure-2.jpg)

### Corresponding Author

Randy Bonnette, Ed.D.
Department of Kinesiology, Unit 5820
6300 Ocean Drive
Corpus Christi, TX 78412
<[email protected]>
(361)825-3317

Randy Bonnette is the chair of the Kinesiology Department in the College of Education at Texas A&M University – Corpus Christi.

U.S. Sports Academy2013-11-25T14:47:27-06:00January 3rd, 2012|Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

Implications of State Income Tax Policy on NBA Franchise Success: Tax Policy, Professional Sports, and Collective Bargaining

Timothy E. Zimmer

### Abstract

The paper examines the relationship between state income tax rates and the success of National Basketball Association (NBA) franchises. The model indicates that state income tax policy has an influence on team performance. The higher the rate for the top marginal tax bracket, the greater the negative bias on team performance. Team performance is dependent on the successful acquisition of quality resources which include players, coaches, and team management. The results infer that NBA franchises located in high tax states impose a burden on the ability of team ownership to attract the best resources in order to achieve success. The relationship could have broader implications on professional sports and their Collective Bargaining Agreements.

**Key words:** National Basketball Association (NBA), Professional Sports, Collective Bargaining Agreement (CBA), Salary Cap, Bias, Free Agency, State Income Tax Policy

### Introduction

The National Basketball Association (NBA) is a sports entertainment enterprise with yearly revenues surpassing $4 billion (5). The majority of these revenues are derived from ticket sales, merchandising and television revenues. The distribution of these revenues between franchises and players has been negotiated and is governed by the Collective Bargaining Agreement (CBA). The current version of the CBA was implemented before the 1984-85 season and was most recently re-negotiated prior to the 2005 season. The current CBA contract expires following the 2010-2011 NBA season, but league owners have the option to extend the agreement through the 2011-2012 NBA season (5).

A large component of the CBA is the provision of a salary cap. The salary cap dictates a fixed percentage of league revenues which are to be paid to players in terms of salaries and benefits. NBA teams are presented with a yearly salary cap number to be used as player compensation. This amount can only be exceeded utilizing certain exceptions as further defined by the CBA.

One justification for the salary cap is the concept that it is designed to benefit middle and small market teams. It is argued that larger market teams have significantly more ability to profit from ticket, merchandising and television revenues. This advantage could be used to enlist top talent by paying salaries far exceeding those of smaller markets. In using superior financial resources to lure and retain better talent (players, coaches and management), it is feared that larger market teams could dominate the league over a prolonged period.

The salary cap system, it is argued, should allow every NBA franchise an equal opportunity in acquiring and obtaining comparable resources. While not a perfect system, the CBA should work to distribute resources (player skill, coaching talent and management expertise) more evenly throughout the league. While the CBA only governs player salaries, the even distribution of quality players throughout the league should also dissuade quality coaches and management from concentrating and distribute them throughout the league.

League ownership believes that an equal chance of team success should promote larger game attendance and provide for a healthier competitive balance in the league. However, these goals have repeatedly been disputed in research (7, 3, 9) which have found increased disparity of play after the imposition of revenue sharing amongst teams and other results inconsistent with stated goals. This paper will extend this research by examining potential causes of the breakdown between the intended goals of the CBA and its results.

In assessing NBA franchise success, the incentive structure facing potential resources (players, coaches, management) should be examined. The different tax environment of NBA franchises is a potential variable which could disrupt league parity. It is argued in this paper that resources are influenced by the financial incentives created by varying state income tax rates applied to the differing NBA franchises based on location. The implications of these findings could have impacts on future CBA negotiations.

### Methodolgy

The study examines the potential for state tax income tax policy to influence NBA team success. The model employs data for eleven years (2000 through 2010) of previous NBA seasons. Also included are the rates (in percentage terms) for the individual states top marginal tax brackets for these eleven years.

The basketball data was assembled using information from a sports database website (6). The income tax bracket data was derived using information from the tax foundation website (10). For ease of computation and data gathering, only the top marginal tax bracket was used. As NBA salaries escalate, the importance of lower tax brackets becomes nominal.

The data from the Canadian-based team was removed. The examination is on the impact of income taxes on player decisions, which in the United States will be uniform at the federal level and vary only at the state level. Canadian players face differing income tax systems at both the federal and state/province level. Rather than trying to incorporate or properly account for these significant differences, the Canadian observations were removed.

A team’s success for a year is influenced by the players it has on the team from prior seasons. A proxy for the ability of players from prior seasons is created, which is the winning percentage for the team from prior years. Three years of winning percentages were lagged to account for anomalies in play in any one given year. While this proxy has shortcomings, it should provide a good baseline of team ability.

When a NBA team struggles to achieve success, the coaching position is often assessed. Coaching turnover in the NBA is prevalent and its impact on team performance must be considered. Teams will react differently to coaching change. Team ownership chooses a coach in the hopes that the new coach will develop player skills and enact schemes of play which will positively impact performance. For some teams, new coaching techniques might take some time to integrate into their play. For this reason, a lagged coaching change variable is created to account for this learning period.

A method for adding players is through the NBA draft. A variable is created to control for player acquisition through the player draft. A lagged variable is also created to account for maturation of these drafted players.

The use of financial incentives to pool resources in larger markets, has been somewhat muted by the CBA and the salary cap. However, larger metropolitan areas can provide amenities and lifestyle options not found in many smaller markets, which may still bias resources towards these markets. Additionally, salaries for coaches and management are not governed by the CBA and thus a larger market team could use financial resources to attract higher quality talent. To account for the potential residual bias in regard to market size, a control variable is created. The data incorporated is the 2000 Census Metropolitan Area found on the U.S. Census website (11).

NBA franchises attempt to achieve success by attracting the high-quality resources. These resources include players, both the addition and retention of high skilled free agents, coaches, and management personnel. While the significance of changes has been examined, particularly with regard to coaching and drafting of players, the issue of quality has not been addressed. The tax environment of each franchise may influence the potential of the team to attract the highest quality resources in order to achieve success. The tax environment of the team will bias the best resources (players, coaches, and management alike) towards certain franchises and away from others creating a performance bias. The decision process of these resources is contemplated the year prior to a given season. For this reason, the state income tax is lagged by a year.

The top marginal tax rate and metropolitan population for each team in 2010 is provided in Table 1. Tax information is time dependent and may vary in each year of the study. Also, in some instances teams switched host cities and states, such as the Sonics moving in 2008 from Seattle to Oklahoma. In these instances, not only would the tax information change, but so would the metropolitan population data. This type of movement increases data variation and helps to provide a more robust analysis. Descriptive statistics for all non-binary variables in the study are found in Table 2.

Using larger data sets can account for player injury. Player injury is frequent in the NBA and can have a substantial impact on team performance. By utilizing a large number of years, it can assume that player injury is random. Player injury is a risk every team takes when committing a large contract to a free agent. It is assumed that the risk of injury is normally distributed among the teams over a large sample.

The following equation is used to estimate the winning percentage of the team for the current season.

(Equation 1)

The dependent variable, WinPcti, t, represents the winning percentage of “i” team in the current “t” year. Yearly influences are captured through the use of a binary year variable Yeart, with the variable for 2005 dropped to prevent linear dependency. The regressor WinPcti,(t-1) is the winning percentage of the “i” team from one year prior. The variables WinPct(t-2) and WinPct(t-3), represent the winning percentage of the “i” team lagged two and three years respectively.

Coaching change is accommodated in the model through the Coachi,t variable. This is a binary variable and is positive if a new coach for the “i” team is in place at the start of current “t” year or if a coaching change is made during the year. To account for the potential of the learning period, this variable is further lagged one period and represented by the Coachi,(t-1) variable.

A NBA team can improve by changing its roster through the player draft.

The selection order of the NBA draft is the inverse of how the teams finished the season in terms of winning percentage. The draft is structured so that the worst teams, determined by winning percentage, have the best opportunity (in a lottery format) for high selections. In order to assess the influence of drafted players, a team is awarded points for the first pick in the first round of the NBA draft. Only the best pick was awarded points in the rare instance of a team having multiple first round picks. The first pick is awarded 30 points, scaled down one point for each pick down to the last pick of the first round which was awarded 1 point. The trading of draft picks is not considered as it is assumed the teams would require equal compensation for the traded draft pick.

The picking order of teams in the draft is scaled linearly. However, changes in player ability throughout the draft are likely non-linear. To account for the non-linear scaling of talent, draft points are squared to emphasis the ability of earlier picks to immediately influence team performance. The variable DraftSqri,t represents the value of the draft pick for team “i” going into the current “t” year. As these players mature and develop their skills, an additional lag variable DraftSqri,(t-1) is employed to capture these effects. The variable captures the lag effects for one period.

The variable MetroSizei represents the population of the metro or surrounding area to each NBA franchise. The variable is team dependent “i”, but is time invariant. The variable will detect larger market bias in the data.

Finally, the variable StateIncTaxi,(t-1) is the top marginal tax bracket for the state in which the “i” team competes lagged one period from the current “t” year. The variable will capture the effect of taxes on performance.

It is important to note that the dependent variable in “Equation 1” is integer-valued with a discrete distribution. To address this concern, it is possible to assume that the score differential Y is a manifestation of an underlying continuous variable Z. Where Y is determined by rounding Z to the nearest integer, and to assume Z follows the model (12):

(Equation 2)

Previous studies have indicated that for most purposes “Equation 1” provides an adequate approximation to the model determined by “Equation 2” (1). As a result, “Equation 1” can be estimated using ordinary least squares (OLS).

### RESULTS

The estimation results are presented in Table 3. The binary year variables are all shown to be insignificant, thus discounting the influence of yearly variation.

The prior two years’ season performances are an accurate predictor of a team’s level of play in the current season. The positive and significant coefficients two years lagged winning percentage implies commonality in team play over multiple seasons. The result also implies that it is difficult to altering a team success and may require several years of rebuilding a losing franchise. The third year being significant and negative suggests the cyclical nature of team success in a league governed by a salary cap restrictions and draft ordering process focused on parity.

The coaching variable is highly significant and negative. Rather than guiding a team towards success, a change in coaching personnel is associated with a negative response in team performance. There also does not appear to a learning curve with respect coaching change, as the lagged coaching variable is insignificant. Not only does the team suffer negatively in the short term from coaching change, the team does not improve in the longer term even after allowing time for the team to absorb the new coach’s playing philosophy.

The draft variables are shown to be insignificant. At least in the short term, drafted players do not have an impact on team performance in terms of winning percentage. The time required to develop these players may exceed the two years accounted for in this model. While accepting the potential for non-linear skill distribution of drafted players marginally increased the viability of the draft variable, it never appeared statistically significance.

Market size is shown not to bias team success. NBA franchises in larger markets are shown to be unable to significantly leverage their market size into acquiring superior skilled resources (players, coaches, and management) as reflected by team success.

Finally, of particular note is the significant and negative relationship between team success and state income tax policy. Teams which play in states with higher top marginal tax rates have less success and a lower winning percentage. The prolonged disparity in winning percentage is argued as an inherent bias of better resources avoiding teams in locations of high taxes. Teams in high tax states could have a more difficult time obtaining comparable talent compared to NBA franchises in low tax states.

### Conclusions

The study examined the potential incentive that state income taxes have on the ability of NBA teams to lure top talent and gain a competitive advantage. The results provide several insights which can be incorporated in the operation of the league and the collective bargaining agreements (CBA).

The results indicate commonality in team success over multiple periods. If a team wishes to alter its winning percentage, the data suggests that one season is insufficient to achieve this goal. Progress is only witnesses as a gradual process over several seasons.

The results also indicate that the window of opportunity for an established team having success is approximately two years before parity efforts begin to take affect and the team’s winning percentage begins to revert to the norm. Team management must understand that the opportunities for a successful team are fleeting and urgency is required for decisions during this period to maximize winning potential.

Once a decision is made to reconstitute a team, it is difficult to accomplish this task quickly. Fans are inherently impatient, which often manifests itself in team management making hasty decisions with regard to coaching. Coaching change is shown to have a negative impact on team performance. The data suggests that rebuilding progress can only be witnessed gradually.

The impact of the NBA draft is shown to have a negligible immediate impact term team performance in the short term. The benefits of the draft do not materialize in the model, even when considering the potential of a non-linear distribution of skill level in the draft. Also of negligible importance on team success is market size. The argument that larger markets can attract superior players due to lifestyle benefits and superior coaches/management through financial considerations is unsubstantiated by the data.

Finally, it is determined that a state’s top marginal tax bracket in which a particular team plays has a negative influence on the team’s success. The data suggests that NBA teams in states with high income taxes are negatively biased when attempting to lure superior talent in terms of player ability, coaching talent or management skill. The state tax influence on team success is indirect, suggesting that subsequent research can be done to detect the direct method of transmission of this influence through players, coaches, management, or some combination. Further research can also be conducted to determine if other professional sports exhibit a similar negative relationship between state tax policy and team success.

### Application In Sports

The Collective Bargaining Agreement (CBA) was recently renegotiated in the NBA. If owners and players in professional sports are interested in promoting league parity, thus ensuring an equal chance of team success and fan excitement, perhaps they should consider a scaled salary cap to benefit teams in high-taxed markets. A tax adjustment index could be applied to the salary cap thus ensuring equal opportunity to acquire equal resources and minimizing the negative bias.

### Tables

#### Table 1
State Income Tax Rate (Highest Marginal Bracket)

Team	State	Top Marginal Tax Bracket	Market Size (2000 Census) Metropolitan Areas (in millions)
Blazers	OR	11.00%	2.265223
Clippers	CA	10.55%	16.373645
Kings	CA	10.55%	1.796857
Lakers	CA	10.55%	16.373645
Warriors	CA	10.55%	7.039362
Knicks	NY	8.97%	21.199865
Nets	NJ	8.97%	21.199865
Wizards	Washington, DC	8.50%	7.608070
Wolves	MN	7.85%	2.968806
Bobcats	NC	7.75%	1.499293
Bucks	WI	7.75%	1.689572
Cavs	OH	5.9325%	2.945831
Grizzlies	TN	6.00%	1.135614
Hawks	GA	6.00%	4.112198
NO-Hornets	LA	6.00%	1.337726
Sonics	OK	5.50%	1.083346
Celtics	MA	5.30%	5.819100
Jazz	UT	5.00%	1.333914
Nuggets	CO	4.63%	2.581506
Suns	AZ	4.54%	3.251876
Pistons	MI	4.35%	5.456428
Pacers	IN	3.40%	1.607486
76ers	PA	3.07%	6.188463
Bulls	IL	3.00%	9.157540
Heat	FL	0.00%	3.876380
Magic	FL	0.00%	1.644561
Mavs	TX	0.00%	5.221801
Rockets	TX	0.00%	4.669571
Spurs	TX	0.00%	1.592383

Tax data from [Tax Foundation Website](http://www.taxfoundation.org)

Population data from [U.S. Census](http://www.factfinder.census.gov)

#### Table 2
Descriptive Statistics

Variable	Observations	Mean	Standard Deviation	Min	Max
Winning Pct	312	0.5030	0.1503	0.15	0.82
Tax Rate	312	0.0539	0.0342	0.00	0.11
Draft Position	312	13.3494	10.2441	0.00	30.00
Metro Size (in millions)	312	5.7900	5.7315	1.08	21.20

#### Table 3
Basketball Team Winning Percentage Estimation

Variable	Coefficient	Standard Error
Variable	Coefficient	Standard Error
Constant	0.2464
2001	-0.0175	0.0310
2002	-0.0162	0.0309
2003	-0.0290	0.0309
2004	-0.0050	0.0310
2006	-0.0105	0.0303
2007	-0.0379	0.0306
2008	-0.0279	0.0308
2009	-0.0094	0.0304
2010	-0.0195	0.0300
Win Pct Lagged 1 Year (WinPct_(t-1))	0.5656	0.0942***
Win Pct Lagged 2 Year (WinPct_(t-2))	0.1586	0.0963*
Win Pct Lagged 3 Year (WinPct_(t-3))	-0.1148	0.0587**
Coach (Coach_i,t)	-0.0808	0.0155***
Coach Lagged 1 Year (Coach_i,(t-1))	-0.0066	0.0160
Draft Squared (DraftSqr_i,t)	0.00005	0.00004
Draft Squared Lagged 1 Year (DraftSqr_i,(t-1))	0.00006	0.00004
Metro Size (MetroSize_i)	-0.0011	0.0012
State Income Tax Lagged 1 Year (StateIncTax_i,(t-1))	-0.4167	0.2167**
Observation	281
R-squared	0.4612
F(18,262), Prob>F	12.46	0.0000***

* Significant at the 10% level
** Significant at the 5% level
*** Significant at the 1% level

### References

1. Harville, D. (2003). The Selection or Seeding of College Basketball or Football Teams for Postseason Competition. Journal of the American Statistical Association. 98, 17-27.
2. Kahn, Lawrence M. (Summer, 2000). The Sports Business as a Labor Market Laboratory. The Journal of Economic Perspectives. 14 (3), 75-94.
3. Kaplan, R. A. (October, 2004). The NBA Luxury Tax Model: A Misguided Regulatory Regime. Columbia Law Review. 104 (6), 1615-1650.
4. Kendall, T.D. (October, 2003). Spillovers, Complementarities, and Sorting in Labor Markets with an Application to Professional Sports. Southern Economic Journal. 70 (2), 389-402.
5. NBA data website: www.insidehoops.com.
6. NBA data website: www.basketballreference.com.
7. Rosen, S. and Sanderson A. (February, 2001). Labour Markets in Professional Sports. The Economic Journal. 111 (469), F47-F68.
8. Scully, Gerald W. (March, 2004). Player Salary Share and the Distribution of Player Earnings. Managerial and Decision Economics. 25 (2), Sports Economics, 77-86.
9. Szymanski, S. and Kesenne, S. (March, 2004). Competitive Balance and Gate Revenue Sharing in Team Sports. The Journal of Industrial Economics. 52 (1), 165-177.
10. Marginal Tax Data: www.taxfoundation.org.
11. United States Census Data: <http://www.factfinder.census.gov>.
12. Zimmer, Timothy and Kuethe, Todd. (2008). Major Conference Bias and the NCAA Men’s Basketball Tournament. Economics Bulletin. 12 (17), 1-6.
13. Zodrow, G., and Mieszkowski, P. (1986). Pigou, tiebout, property taxation, and the underprovision of local public goods. Journal of Urban Economics. 19, 356-370.

### Corresponding Author

Timothy E. Zimmer, Ph.D.
6718 W. Stonegate Dr.
Zionsville, IN 46077
[email protected]
317-769-0336

### Author Bio

Tim Zimmer is an adjunct professor of economics at Butler University.

U.S. Sports Academy2016-05-16T11:16:23-05:00January 2nd, 2012|Sports Management, Sports Studies and Sports Psychology|Comments Off