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Advantages of Diversifying the Funds of Sports Organizations
Advantages of Diversifying the Funds of Sports Organizations
Diversification of funds is a hot topic in the business world. The sports world must adapt and apply diversification principles in order to maximize the stability of sports organizations. Diversification is a generic term that can be applied to assets, funds, and investors. Making sure these financial factors are well maintained is critical to the overall fiscal success of a sports organization. This paper seeks to establish the importance of diversifying funds within sports organizations. It also investigates the financial advantages of diversification, which include percentage return, stability, and investors. The paper is an exploration tailored to individuals who want to discover the importance of diversification, for example coaches, administrators, and the general public.
One definition of diversification is “an investment fund that contains a wide array of securities to reduce the amount of risk in the fund” (Diversified Fund, n.d.). The logic beneath advocating diversification is that “Actively maintaining diversification prevents events that affect one sector from affecting an entire portfolio, making large losses less likely” (Diversified Fund, n.d.). Diversification, then, is a key component of achieving long-term financial stability and success.
In discussing diversification of funds, one acknowledges two general categories of funds, the diversifiable and undiversifiable. Diversifiable funds are those that can be controlled by the company, for example choosing where to invest monies, choosing the amount of money to be diversified, the business and financial risk associated with types of investments, and the current status of diversification options. Undiversifiable funds involve matters beyond an organization’s control, such as rates of currency exchange, political developments, fluctuations in investment fields, and interest rates. To become and remain financially sound, a sports business must take all of these under consideration as it identifies the method of diversification that best serves its needs (Chamberlain, 2003).
Diversifying funds is a serious endeavor for financial organizations. There are many factors to consider before reaching a final decision. An organization must decide if diversification will benefit its financial needs, including determining any long-term benefits to its fiscal well-being (Brooks & Kat, 2002). Every sports organization has unique needs; every team in a league has its own unique needs, as well, given that different team-owning corporations emphasize different areas of finance. Deciding on the proper amount of diversification can be an arduous task, but it is worth the time and effort, because the economic stability of the organization is at stake.
Because diversification has become increasingly popular, diversification options have expanded. Many banks, other financial outfits, and sports establishments now offer guidance or services aimed at funds diversification. Understanding one’s organization’s particular needs is essential in determining the most effective diversification method. Its monetary structure must be thoroughly scrutinized. Investing without fully understanding the organization’s status could prove disastrous.
Importance of Liquid Cash
Liquidity describes “how fast something can be turned into cold hard cash” (Kennon, n.d.). Keeping much liquid cash is considered by many to be a waste of investment potential; however, it has proved beneficial in some instances. For example, when catastrophe strikes some area (or areas) of investment, those portions of a financial portfolio which are liquid would suffer minimal damage. The September 11, 2001, events, for example, had a tremendous negative effect on financial stability. In the wake of the attacks, many of the United States’ financial structures suffered from a dramatic decrease in consumer confidence. Stock market investors even had to endure a 4-day freeze on their holdings. This had an incredible impact on America’s confidence in the market. Because catastrophe in inherently unpredictable, there is a sense of security in keeping some part of an organization’s finances liquid (Kennon, n.d.).
Traditionally, bank accounts have been regarded as a safe alternative to investments. Although savings accounts generally offer less return on one’s money, the money in such accounts is generally secure even in the event of an emergency. Having quick access to money can be the difference between a business’s success and its failure. When companies or sports organizations have most of their money tied up in nonliquid investments or holdings and an immediate need for funds arises, it can be difficult to fulfill that need. If a sports organization is going through a downturn in terms of ticket sales or sponsorships, the team may find itself unable to make payroll, which requires liquid cash. Such instability can have far-reaching effects on consumer support, fan support, and the general appeal of the organization within the sports community. Keeping adequate supplies of liquid cash allows sports organizations to continue to function throughout trying economic periods, and persisting in rocky times will allow an organization to develop and achieve success.
Diversification of finances helps to eliminate the effects of sudden changes in particular areas of investment. These specific changes are generally referred to as unsystematic risks, and they strike when least expected, by their very nature producing devastating effects on those invested too heavily in the stricken area. In this instance, the term unsystematic means that the investment did not meet expectations, but was drastically affected by an unforeseen circumstance like fire, strike, or scandal. Unpredictable, unsystematic risks frequently influence the liquidity of an investment.
Liquidity, again, is the capacity of a financial holding to be turned into cash without being affected by the present economy. The liquidity of diversification can prove to be extremely beneficial to sports organizations, because of their constant need for liquid cash. Furthermore, the more liquid the diversified investment, the safer is the money that was invested. However, liquidity’s price is often a lower return. Common forms of liquid assets are certificates of deposit, stocks, and bonds. While most experts consider certificates of deposit to be relatively liquid, there is a penalty for withdrawing funds froms certificates prior to a date set as the certificates’ date of maturity. Stocks and bonds are regarded as slightly less liquid than certificates of deposit, although both can typically be converted into cash within a couple days.
Liquid cash is viewed as an organization’s heart and soul. This is particularly relevant to sports organizations because of their direct need for cash. Operational cash flow must be sustained by the organization if the organization is to be deemed successful. Banks are the safest course for organizations aiming to maintain operational cash flow, because banks often act as a mediator between investments and ready cash. The basic rule of liquidity is simple and finite within a sports organization: If the organization cannot meet its financial needs, it will fail.
Savings Accounts
Savings accounts are commonplace in today’s economic structure. They are a safe alternative to risking money in investments which may never produce a return. Savings accounts are a way to save money that does not immediately need to be spent; the money is considered liquid because it is easily accessed—although it is not as liquid as funds in a checking account. Checking accounts are the most fluid form of investment, because banks administering the accounts assume the money will be promptly retrieved. For this reason, checking accounts tend to offer lower interest rates than savings accounts. Banks offer many types of savings accounts, but it is important to research associated fees and interest rates before determining which savings account is right for one’s business. Because savings accounts are not accessed as frequently as checking accounts, banks pay a higher interest rate on savings. Savings account interest rates are often between 1% and 3%, depending on the institution and type of account.
Savings accounts have changed over the past 20 years. Online banking services have led to a transitioning of the traditional savings account, one in which deposited savings slowly built interest. Owners of savings accounts in the past had to wait for earned interest, which was applied to deposits only on a few preset dates throughout the year. With online banking, interest is applied minute by minute, making an account’s maturity instantaneous.
Banks can pay interest on savings deposits because they use the money to fund loans, on which they charge interest greater than the interest paid to savers. In short, the banks are selling the money (Pritchard, n.d.). Savings accounts are viewed as safe because the money is insured. If something were to happen to the bank, the money would always be recoverable, thanks to the Federal Deposit Insurance Corporation, a government bank-insurance program established after the Great Depression.
Sports organizations often take advantage of savings accounts to keep their resources in a safe place, out of the hands of individuals, since there is a history of individuals mishandling teams’ funds. Keeping money in a secured savings account eliminates part of that problem, because the money is easily traced to and from the bank. Professional sports teams that have substantial sums to keep in savings accounts can benefit greatly from interest earned. In short, it has become a necessity for businesses to possess savings accounts. Savings accounts allow businesses to store money and yet, simultaneously, gain interest. One of the prime advantages of a savings account is that the money stays separate from the normal circulation of funds.
Investors
Obtaining investors is one of the most challenging, and also most potentially profitable, aspects of stabilizing a business’s finances. When businesses begin operations, money is often hard to come by; money from investors can mean the difference between success and failure. Investors are individuals or businesses that provide money to an organization, in hopes of receiving a financial return on their investment. Investors can invest money in various facets of organizations, ranging from advertisement to stocks and bonds. The bottom line is that organizations have a great need for investors’ money during the start-up phase, and investors expect financial progress on an organization’s part, in order to maximize return on their investments. It is a situation not free of stress: The start-up business and its investors are concerned that the business become financially stable and able to turn a profit (Arnold, n.d.). And investor confidence is time sensitive; the longer it takes the business to become established, the greater the impact on investor confidence.
Small sports companies and entrepreneurs may be able to fund their business opportunities helped by friends and family only. Or, they may look to “angel investors.” Corporations must be cautious about involving angel investors (Advani, 2006). They typically do not have access to large amounts of money, and a corporation will need to ensure that the funds offered are actually available.
Information is the key to attracting investors. Investors need to know the current status of the organization and the rate of growth within its market. Having information about an organization’s competition can also enhance investors’ confidence. Many investors require the signing of an investment agreement establishing a framework, rules, and guidelines for the investment, outlining the steps to be followed to achieve success. An investment agreement makes clear that a new business is serious about its long-term profitability. Companies often use two forms of communication to maintain healthy investor relationships, the record of growth statement and the financial statement. The information in these statements provides investors the bottom line on their investments’ current status. If used properly, the statements also show investors that the business is able to set and meet (or at least approach) goals, which can be a great advantage in maintaining investor confidence.
Stock and Bonds
Professional sports organizations that own sufficient resources have an opportunity to make investments of their own—in stocks and bonds. If the investments are sound, the organization’s worth can grow markedly. Purchasing stocks and bonds is a common means of diversifying funds. Stocks can be purchased by the general public and represent ownership in companies. The price of stock issued by companies that are experiencing success goes up, allowing the investor to receive more money than he or she invested. The price of stock issued by companies that struggle goes down, and an investor may not only see no return on investment, he or she may lose the money initially paid for the stock. Unlike stocks, bonds do not represent ownership, being more akin to a loan. A company issues a bond in order to raise money to fund its daily operations. The purchaser of a bond is essentially loaning the purchase price to the company for a specified time, having been promised by the company that when the time elapses, the loan and interest will be returned. Both stocks and bonds are means of diversification that are considered lucrative and are readily accessible in today’s financial structure.
In the stock market, where stocks are bought and sold, the basic strategy is, of course, buy low and sell high. But investors regularly fall short of this aim, since the market can be unpredictable. In such instances, diversification can play a major role in investors’ overall success in the stock market. To use the old cliché, not putting all the eggs in one basket—in other words, investing in more than one kind of stock—can protect an investor, since falling stock prices in one area of the market may not affect prices in other areas. Diversifying the categories of stocks in which one invests is also a protective measure. If all of one’s eggs are transportation-related—if one purchases only automotive and aerospace and railroad stocks—the development of adverse economic conditions affecting transportation could wipe out the investment. Bonds are viewed as more stable than stocks, since they comprise a financial agreement between investors and bond issuers. Because they entail fewer risks, bonds generally yield smaller returns than stocks.
Advantages and Disadvantages of Diversification
The basic reasoning behind diversification is to make a financial portfolio less volatile. Diversification lowers the possibility of one bad investment affecting the stability of a financial portfolio. In order to take full advantage of diversifying a portfolio, one must invest in different types of assets that move in different directions and to different rhythms. The following are some key advantages of diversification, according to Klein and Saidenberg (1998):
- Money is invested in various assets, not consolidated in one
- Investors may benefit financially from many markets
- Investors are not wholly financially affected by one poor investment
- Return on diversified investments is higher, traditionally, than return on money kept in cash holdings
- Diversification adds stability to and reduces volatility within a financial portfolio
While diversification tends to serve investors well, overdiversification is not advisable. Overdiversification means spreading investments over too many opportunities too thinly. Overdiversified investments lack opportunity to prosper. While essential in today’s economy, diversification of funds does have a limit, and most experts set it at 20 investments or assets at one time (Dangers of Over-Diversification, n.d.). An organization that invests in more than 20 entities may well find that its funds begin to plateau rather than increase, because the amount of money available for each investment does not carry its own weight within the investments (Dangers of Over-Diversification, n.d.).
Summary and Conclusions
Anything a sports organization possesses or controls is considered an asset. How its assets are managed determines the financial steadiness of an organization. Today, sports organizations’ assets typically extend far beyond bank accounts. For example, corporate funds are invested in stocks and bonds. In order to achieve financial success, professional sports organizations must acquire funding. When it is not forthcoming from a league, it may be obtained from other corporations, from private investors, or in the form of secured bank loans.
Diversification of assets may have become a buzzword, fun to discuss, but it should be remembered that diversification requires understanding and serious preparation, including trial and error that frequently comes with a price tag. To truly diversify one’s funds requires constant monitoring of one’s investments. Investments are never foolproof; unavoidable risk is the nature of investment. Businesses that seek to diversify their funds do so to reduce the volatility of their financial portfolio. Many sports organizations benefit from diversification, in light of the financial fluctuations which occur from month to month and, especially, season to season. In the course of a competitive season, a sports organization may be responsible to pay out millions of dollars. It is thus vital that the organization maintain a sound financial structure providing ready access to needed funds. If a sports organization is unable to fund its own team, the consequences reach beyond angry players; they can shake the foundation of the organization: fan support, community support, ticket sales, consumer sales, advertising sales, consumer confidence. Diversification acts against such a situation by sustaining investments in various investment fields, increasing the chances of having accessible money.
Ready availability of the professional sports organization’s money can mean success; the lack of it can mean failure. Professional sports businesses are always in need of cash—not just assets, but cash. But keeping assets in cash means the money is not earning much interest or otherwise making the organization money. Money will always earn more when it is placed in the hands of financial institutions that loan or invest it. Because professional sports organizations require a frequent turnover of funds, a smaller portion of their overall worth is available to be invested. Thus the recommended ratio of liquid funds to other funds is different for professional sports organizations than for many other corporations: 60:40. Professional sports organizations are unlike many other businesses in needing substantial amounts of cash to sustain day-to-day operations. Of course, from team to team, unique and varying needs and financial statuses mean that the 60:40 rule is actually a rule of thumb.
Diversification today is looked on as a prerequisite for securing financial permanence. The world has embraced the concept, and many options are offered for taking full advantage of diversification’s benefits. Professional sports corporations operate within a highly volatile system that nevertheless demands financial stability. Diversification of funds helps sports organizations develop a solid base able to withstand bumps in the road. The telephone, when it was newly available, seemed a luxury for the few; today it seems a necessity. The same can be said of funds diversification; once a strategy for investment adepts to consider, it has become a necessity for operating corporations. Knowledge of diversification is key, and the sports organization that does its homework is likely to benefit greatly from diversification, minimizing the strategy’s risks.
References
Advani, A. (2006, October 12). Raising money from informal investors: The devil’s in the details when taking money from—and structuring a deal with—friends, family and angel investors. Retrieved September 26, 2008, from http://www.entrepreneur.com/money/financing/startupfinancingcolumnistasheeshadvani/article168860.html
Arnold, R. (n.d.). Finding investors and hard money lenders. Retrieved September 26, 2008, from the Creative Real Estate Online.com website: http://www.creonline.com/money-ideas/mm-038.html
Brooks, C., & Kat, H. M. (2002). The statistical properties of hedge fund index returns and their implications for investors. The Journal of Alternative Investments, 26-44.
Chamberlain, G. (2003). Funds, factors, and diversification in arbitrage pricing models. Econometrica, 51, 1305-1323.
The Dangers of Over-Diversification. (n.d.). Retrieved September 26, 2008, from http://www.investopedia.com/articles/01/051601.asp
Diversified fund: What does it mean? (n.d.). Retrieved October 7, 2008, from the Investopedia website: http://www.investopedia.com/terms/d/diversifiedfund.asp
Kennon, J. (n.d.). The importance of liquidity and liquid assets: A lesson from September 11th. Retrieved September 26, 2008, from the About.com website: http://beginnersinvest.about.com/cs/banking/a/091102a.htm
Klein, P. G., & Saidenberg, M. R. (1998). Diversification, organization, and efficiency: Evidence from bank holding companies (Working Paper No. 97-27). Philadelphia: University of Pennsylvania, Wharton School, Center for Financial Institutions.
Pritchard, J. (n.d.). Bank savings accounts: Best uses for bank savings accounts. Retrieved September 26, 2008, from the About.com website: http://banking.about.com/od/savings/a/savingsaccount.htm
An Examination of Preservice Routines of Elite Tennis Players
Abstract
A preperformance routine may support consistent optimal performance. Preperformance routines’ benefits for closed skills are largely accepted, but effects of time and situational factors are little understood, nor have results of altering movements of preperformance routines been much studied. This observational study investigated preservice routines of 4 elite tennis players. Inconsistent with much prior research, the presence of a routine did not enhance performance in this study: Mean serving percentage measured 66% for players with routines, 69% for others. The findings do support Jackson (2003) and Jackson and Baker (2001), studies of rugby players forced to alter routines during competition. Observation of preservice routines and performance over several months at various tournaments may advance the research on this topic.
An Examination of Preservice Routines of Elite Tennis Players
The development and administration of a preperformance routine has been linked to optimal and consistent performances in many activities. Past research has shown the positive effects of preperformance routines in various sports, including tennis (Moore, 1986), golf (Cohn, Rotella, & Lloyd, 1990), bowling (Kirschenbaum, Ordman, Tomarken, & Holtzbauer, 1982), basketball (Czech & Burke, 2003; Lobmeyer & Wasserman, 1986; Wrisberg & Pein, 1992), and skiing (Orlick, 1986). Preperformance routines seem most beneficial within closed skill and self-paced tasks found in these sports, for example free-throw shooting in basketball, serving in tennis, kicking in football, and putting in golf.
Previous research has shown that preperformance routines can help athletes focus attention, enhance confidence, eliminate distractions, and reduce anxiety (Weinberg & Gould, 1995). Eliminating distractions and focusing attention creates an ideal state of concentration prior to performance; consistently replicating this state of concentration can create consistent performances (Schmidt & Peper, 1998). Focus and concentration allow for other psychological skills (i.e., visualization and relaxation) to be implemented during the preperformance routine, which helps block any external stressors and unwanted environmental stimuli (Schmidt & Peper, 1998). The ability to eliminate distractions before a performance may be the difference between a good athlete and a great one (Orlick, 1997).
Another benefit of preperformance routines is that they structure and organize the time leading up to a desired task, mentally preparing the athlete for the performance (Weinberg & Gould, 1995). For example, Crews and Boutcher (1987) observed the preperformance routines of golfers in the Ladies Professional Golf Association (LPGA), measuring the time taken for routines. Results indicated that all the golfers were automatic in their routines, starting and finishing with consistent and purposeful actions and completing their routines in a consistent amount of time. Purposeful behavior is key to consistent and effective performance during a preperformance routine. Foster, Weigand, and Baines (2006) studied free-throw shooters found to have superstitious behaviors and attempted to implement a preperformance routine among the athletes. Surprisingly, the effect of preperformance routines and of superstitious behavior differed little (performance worsened when neither was conducted before shooting). Purposeful behavior, whether based on superstition or on a structured preperformance routine, resulted in consistent and effective performances.
A preperformance routine can also help athletes reactivate appropriate physiological and mental processes before each shot, hit, service, or putt, increasing the chance of a successful performance (Schmidt, 1982). Boutcher and Zinsser (1990) studied the cardiac, respiratory behavior patterns of elite and nonelite golfers during a putting task. Elite golfers’ consistent preperformance routines resulted in slower breathing and heartbeats, indicating relaxation and focus on the task. Nonelite golfers lacked consistent preperformance routines and had higher heart rates. Physiologically, preperformance routines prepare the body for competition and sync mind and body for better control.
Some research argues that consistency of performance as a result of using a preperformance routine involves more than simply keeping the routine to a consistent time period. For example, Southard and Miracle (1993) conducted a study of female basketball players that manipulated how fast their free-throw routines occurred (time for the routine was doubled, was cut in half, etc.). Despite the manipulations of time, the results showed that the relative time to complete the routine did not vary, and that the rhythm of the routine was most important to successful performance.
While there is little argument about the positive effects of preperformance routines on closed skills, external variables make each skill unique, making necessary the investigation of various skills. Lobermeyer and Wasserman (1986), Gayton, Cielinski, Francis-Keniston, and Hearns (1989), and Wrisberg and Pein (1992) have investigated the effects of preshooting routines in basketball extensively, but the literature reflects little research on preservice routines in tennis (Moore, 1986). Additionally, little research is found on the effects of time and situation (i.e., winning or losing) on preperformance routines. Research shows that altering the movements of a preperformance routine can lead to poor or inconsistent performances (Gayton et al., 1989).
The purpose of this study was to investigate the preservice routines of 4 elite tennis players. Observation of the players was expected, ultimately, to yield visual identification of the presence of a preservice routine. Then, correlation would be sought between use of a preservice routine and successful service attempts.
Method
Participants
The participants in this study were 4 professional tennis players (2 men and 2 women) who were competitors belonging to the United States Tennis Association or the Women’s Tennis Association.
Procedure
Videotapes were viewed of 2 male participants playing in the Australian Open and 2 female participants playing in the Olympic Games. Data were collected during several matches in each tournament. To control for external variables, only first services were examined. The Preservice Routine Index (appendix) was developed to record data. Each researcher recorded data independently; then the data were compared and combined to produce a single set of final data to be used in analysis. Data discrepancies were discussed by the researchers until all felt comfortable with the data.
Collection of the data was initiated when a server placed his or her feet in their final ready position. For each service the following information was collected: server’s gender, server’s score in the game or match (i.e., leading or trailing the opponent), success of the service, and preservice routine. After several practice trials, racket position (low/high, horizontal/vertical) as well as number of times the player bounced the ball prior to serving were identified as the predominant variables in a preservice routine. These two elements were the most consistent and measurable preservice actions the players displayed. A minimum of 30 services were needed to determine the presence or absence of a preservice routine. A service was counted only if the server was fully visible from the time he or she set his or her feet until the final service motion was initiated.
Data Analysis
The first step of data analysis was identification of a preservice routine. Each player’s preservice data was examined to determine whether or not a consistent routine was present. Looking at the data, the researchers established for each player his or her most common racket position and the typical number of bounces used prior to a service. These comprised the player’s preservice routine, which in this study had to precede at least 80% of a player’s services in ordered to be considered a consistent preservice routine. Players who did not follow the identified routine at least 80% of the time were assigned to one of two groups in the study, the nonroutine group.
For each player, the researchers calculated the percentage of services (over all 30 trials) featuring the identified preservice routine. Restricting the calculation to first services only, they also computed the serving percentages for the entire tournament, in an effort to balance effects of emotion across “good” and “bad” matches. The serving percentages were compared by gender and by group (routine, RG, or nonroutine, NRG). Use or omission of a routine by players in the routine group was evaluated in the context of the player’s score (game and match) at the time of each service.
Results
Only one study participant, Player 1, could be assigned to the routine group; the remaining players showed limited consistency of preservice actions. Player 1 used a preservice routine 83% of the time and had an overall serving percentage of 66% over the entire tournament. Player 2’s use of a preservice routine had the second-highest rate of consistency, 67%. Players in the nonroutine group had an overall successful serving percentage of 69%: Player 2 was successful 65% of the time; Player 3, 78% of the time; and Player 4, 63% of the time. Overall for the tournament, the routine group had a mean serving percentage of 66%, while the nonroutine group had a mean serving percentage of 69%.
One of the two men in the sample had a detectable preservice routine, while neither of the women had a detectable routine. Mean serving percentage for the men was 65%; for the women, mean serving percentage was 70%.
Finally, the participant who used a detectable preservice routine seemed to do so more frequently when he trailed, rather than led, his opponent in either the game or match. Player 1 followed his routine 100% of the time when he was losing a game and 82% of the time when he was losing a match. He followed his routine 72% of the time when he was winning a game and 68% of the time when he was winning a match.
Discussion
In light of the performance benefits research shows to derive from a preperformance routine, the studied elite tennis players’ lack of consistency in using such routines was unexpected. The findings could be explained in three ways. First of all, research suggests that preperformance routines can be cognitive in nature. Imagery and self-talk are two examples of mental skills that could play a very important role in the preperformance routine (Wrisberg & Anshel, 1989). Cognitive preperformance strategies may or may not have constituted significant elements of our participants’ preservice rituals. With videotape observation the sole means of data collection, cognitive routines were undetectable. It is quite possible that the presence or absence of a cognitive strategy influenced effects of the psychomotor aspects of positioning the racket and bouncing the ball. For example, Player 3’s high percentage of successful services could have been supported by focused, consistent use of imagery before each service. Future research on preservice routines definitely should include interviews aimed at understanding players’ preservice cognitive strategies.
A second explanation of our study’s findings is the limited time span of the play we observed. Relatively unchanged situational factors from match to match may have positively or negatively affected the data. For instance, perhaps one of the study participants had an injury affecting performance. In addition, it was unknown whether any players used a specific number of preservice bounces, a number that might have changed during the rest of the tournament. Our findings are interesting, given that traditional research focuses on consistency of the preperformance routine, and they support previous findings reported in Jackson (2003) and Jackson and Baker (2001). Jackson and Baker (2001) studied professional rugby players in a highly competitive environment and found that preperformance routines were often altered during competition due to factors beyond players’ control (i.e., time running out, players out of position, speeding or delaying in response to others, and the like). Future research should include several months of observation of the participants’ services, involving a variety of different tournaments. An expanded time frame would help control such external variables as injuries, skill constraints, and development of the preservice routine.
A final explanation for the discrepancy between participants’ behavior reflected in this study and in earlier research findings on preperformance routines is that in tennis, serving may not be positively affected by a preperformance routine. While earlier researchers have found a positive correlation between free-throw success and preshooting routines, it is entirely possible that environmental, physiological, and psychological aspects of closed skills from basketball and closed skills from tennis differ enough to drastically affect the results of a preperformance routine. But this is unlikely. In our study Player 1, whom we observed to employ a consistent preservice routine, is consistently ranked (by the United States Tennis Association) among the best tennis players and servers in the world. Individual differences must also be taken into account. Preservice routines are as unique as the players who use them, meaning each routine’s benefits are likely to be unique as well.
One strength of our study is that the services were observed in the most elite competitive venues. Player 1 and Player 2 were videotaped competing in a grand slam competition; Player 3 and Player 4 were videotaped contending for Olympic gold. Environmental distractions during each of these tournaments were significant. In addition, it can be assumed that each player tried his or her best to serve successfully in each observed trial. Many consider first-service success an important component of elite competitive tennis. Such anecdotal evidence can certainly be challenged, but it is useful to consider. Future research might also explore the benefits of a preservice routine to second services.
References
Boutcher, S. H., & Zinsser, N. (1990). Cardiac deceleration of elite and beginning golfers during putting. Journal of Sport and Exercise Psychology, 12, 37-47.
Cohn, P. J., Rotella, R. J., & Lloyd, J. W. (1990). Effects of a cognitive-behavioral intervention on the preshot routine and performance in golf. The Sport Psychologist, 4, 33-47.
Crews, D. J., & Boutcher, S. H. (1987). An exploratory observational behavior analysis of professional golfers during competition. Journal of Sport Behavior, 9, 51-58.
Czech, D. R. & Burke, K. L. (2003). An examination of the maintenance of pre-shot routines in basketball free throw shooting. Journal of Sport Behavior, 3, 23-32.
Foster, D. J., Weigand, D. A., & Baines, D. (2006). The effect of removing superstitious behavior and introducing a preperformance routine on basketball free-throw performance. Journal of Applied Sport Psychology, 18, 167-171.
Gayton, W. F., Cielinski, K. L., Francis-Keniston, W. J., & Hearns, J. E. (1989). Effects of pre-shot routine on free-throw accuracy of intercollegiate female basketball players. Journal of Sport Psychology, 5, 343-346.
Jackson, R. C. (2003). Preperformance routine consistency: Temporal analysis of goal kicking in the Rugby Union World Cup. Journal of Sport Sciences, 21, 803-814.
Jackson, R. C., & Baker, J. S. (2001). Routines, rituals, and rugby: Case study of a world class goal kicker. Sport Psychologist, 15, 48-65.
Kirschenbaum, D. S., Tomarken, A. J., & Ordman, A. M. (1982). Specificity of planning and choice in adult self-control. Journal of Personality and Social Psychology, 41, 576-585.
Lobmeyer, D. L., & Wasserman, E. A. (1986). Preliminaries to free-throw shooting: Superstitious behavior? Journal of Sport Behavior, 9, 70-78.
Moore, W. E. (1986). Covert-overt service routines: The effects of a service routine training program on elite tennis players. Unpublished doctoral dissertation, University of Virginia.
Orlick, T. (1986). Psyching for sport: Mental training for athletes. Champaign, IL: Leisure Press.
Orlick, T. (1997). In pursuit of excellence (3rd ed.). Champaign, IL: Leisure Press.
Schmidt, A., & Peper, E. (1998). Strategies for training. In J. Williams (Ed.), Applied sport psychology: Personal growth to peak performance (pp. 316-328). Mountain View, CA: Mayfield.
Schmidt, R. A. (1982). Motor control and learning. Champaign, IL: Human Kinetics.
Southard, D., & Miracle, A. (1993). Rythmicity, ritual, and motor performance: A study of free throw shooting in basketball. Research Quarterly for Exercise and Sport, 3, 284-290.
Weinberg, R. S., & Gould, D. (1995). Foundations of Sport and Exercise Psychology, Champaign, IL: Human Kinetics.
Wrisberg, C. A., & Anshel, M. H. (1989). The effect of cognitive strategies on free throw shooting performance of young athletes. The Sport Psychologist, 3, 95-104.
Wrisberg, C. A., & Pein, R. L. (1992). The pre-shot interval and free throw shooting accuracy: An exploratory investigation. The Sport Psychologist, 6, 14-23.
Compliance by Hong Kong’s National Sport Organizations With the World Anti-Doping Program
Abstract
The present study aimed to assess current anti-doping efforts among Hong
Kong’s national sport organizations (NSOs), for example
organizations’ readiness to change and to initiate or strengthen
anti-doping measures. The points of view of administrators, coaches,
and committee members were considered. A great majority of NSOs in Hong Kong appeared to be at the
contemplation stage, concerning anti-doping actions. The major
constraints they faced were limited funds and manpower.
The World Anti-Doping Program, developed by the World Anti-Doping
Agency (WADA), is structured in three levels: a World Anti-Doping
Code, international standards, and models of and guidelines for best
practices. WADA officials state that one purpose of the World
Anti-Doping Program and code is “to ensure harmonized, coordinated,
and effective anti-doping programs at the international and national
level with regard to detection, deterrence, and prevention of doping”
(World Anti-Doping Agency, 2003). We would like to suggest that the
program actually can serve two purposes. On the macro level, it can
provide various international federations and national anti-doping
organizations (NADOs) with a framework for developing anti-doping
policies, rules, and regulations. On a micro level, it can guide
national sport organizations (NSOs) in carrying out anti-doping
functions like educational programming and in adopting appropriate
practices to demonstrate compliance with various anti-doping
regulations.
The World Anti-Doping Code has been in place for over 5 years, so the
roles of international federations and NADOs in promoting and
monitoring athletes’ anti-doping behaviors should be clear to sport
organizations and professionals involved in high-level competition
(e.g., World Games, Olympics). Those not involved at that level may
be less familiar with arrangements, for instance coaches and
administrators of NSOs that have not produced athletes qualifying for
high-level competitions. Even NSOs with experience in high-level
competition may have second- or third-tier athletes lacking the
exposure their elite counterparts have had. Given that NSOs play a
significant role in communicating anti-doping information to athletes
and explaining their role in anti-doping regulations, the evaluation
of NSOs’ current practices is important. The present study provided
such an evaluation, using a case-study approach to determine the
extent of Hong Kong NSOs’ compliance with the anti-doping program.
Specifically, we aimed to assess whether Hong Kong’s NSOs were
implementing anti-doping functions, as well as to identify
constraints on their full compliance. Although the study involved
only Hong Kong organizations, knowledge gained should be applicable
in countries with similar anti-doping experience, and the study
should thus prove useful to international federations, NADOs, and
WADA as they direct resources and efforts.
Since to an extent NSOs are organizations whose anti-doping
compliance or noncompliance can be treated as the adoption of one
management practice over another, their anti-doping compliance can be
modeled as organizational change. We therefore reviewed such models
and chose Prochaska’s transtheoretical model (TTM) (Prochaska,
2000) to analyze NSO anti-doping functions. The popular TTM was
originally developed to explain behavioral change in individuals
(Prochaska, Prochaska, & Levesque, 2001).
Central to the TTM are three theoretical constructs related to
change: (a) stages of change, (b) decisional balance, and (c) process
of change. Intentional change—whether by an individual or an
organization—can occur in stages and so can be seen as a series of
movements along a continuum. There are six such movements or stages:
pre-contemplation, contemplation, preparation, action,
maintenance, and termination. The terminology process
of change, in contrast, connotes the belief that change is
influenced by both overt and covert activities that comprise
experiential processes and behavioral processes.
Experiential processes characterize the early-stage transition and
include consciousness raising, dramatic relief, environmental
reevaluation, social liberation, and self-reevaluation.
Behavioral processes characterize later-stage transition and include
stimulus control, helping relationship, counter conditioning,
reinforcement management, and self-liberation.
In sum, the TTM provides an opportunity to understand the temporal
ordering of events as an established pattern is changed, which is
what we intended to do in terms of the NSOs’ implementation of
anti-doping functions. It also provides opportunity to explore
mechanisms mediating intentional change (e.g., constraints on
implementation of anti-doping functions). An additional rationale for
adopting the model was its prior successful application in an
analysis of family-service agencies (Prochaska, 2000), a study of the
implementation of a system of “time-limited therapy” that has
notable parallels to the implementation of anti-doping functions.
Method
Design of Questionnaire
The three versions of the self-report instrument used in the present
study were developed with input from three NSOs of different sizes,
whose staffs were invited to participate in face-to-face interviews
with a member of the research team experienced in anti-doping works.
During these interviews, the purpose and procedures of the study were
clarified for the NSOs, and items for inclusion in the questionnaire,
as well as in a structured interview, were identified. NSOs
participating in these preliminary interviews did not participate in
the study itself.
Collection of Survey Data
A letter of invitation to participate in the research project and
three copies of the final questionnaire were delivered to each NSO in
Hong Kong (except the three involved in instrument development).
Follow-up telephone calls were made to confirm the organizations’
interest in participating. NSOs that volunteered to participate were
scheduled for interviews with research team members. Completed
questionnaires were collected during or after an interview session.
The three versions of the study questionnaire included one for NSO
administrators, one for NSO coaches, and one for NSO committee
members. All versions included Part 1 and Part 2; the version for
administrators contained an additional three parts. Part 1 of the
questionnaire represented a modification of the Readiness to Change
Questionnaire (RTCQ) (Rollnick, Heather, Gold, & Hall, 1992). The
original RTCQ, designed to study drinking behavior, is a 12-item
questionnaire that assigns excessive drinkers to either the
precontemplation, contemplation, or action stages
(Heather, Gold, & Rollnick, 1991). For the present study, the
modified questionnaire assessed each NSO’s readiness to increase
its anti-doping efforts. Part 2 of the questionnaire was based on the
early interviews with the three NSOs not generating study data. From
these interviews, a list of pros and cons of increased anti-doping
efforts was developed. Part 2 asked respondents to rate the
importance of these pros and cons as influences on the NSO’s
decisions about increasing or not increasing anti-doping work.
Finally, Parts 3, 4, and 5 of the questionnaire were directed to NSO
administrators only and collected information about (a) spending on
anti-doping works, (b) opinions about anti-doping education programs,
and (c) an NSO’s demographic information.
Collection of Interview Data
Two members of the research team conducted structured face-to-face
interviews with representatives of NSOs who were either
administrators, committee members, or senior coaches. All were
familiar with their NSO’s anti-doping works. Standard questions
were posed initially, with a respondent’s answers guiding a series
of appropriate follow-up questions.
Results
A total of 62 invitations were sent to NSOs in Hong Kong to
participate in the research project, and 44 NSOs returned completed
questionnaires, a response rate of 71%. Interviews were completed
with 42 NSOs’ representatives, a response rate of 67.7%.
National
Sport Organization Demographics
The participating NSOs’ demographics provide a rough idea of the
scope of Hong Kong’s locally organized sport. Tables 1–4 present
the numbers of athletes, of coaches, and of competitions organized by
or participated in by our respondents. Most of the NSOs had fewer
than 5 full-time and 5 part-time employees. A majority (77.1%) had
fewer than 50 athletes active in international events that were
endorsed by an international federation. Over half of the surveyed
NSOs (60.6%) had 50–200 Level-1 coaches, while about half (57.6%
and 51.5%, respectively) had fewer than 31 Level-2 coaches and fewer
than 6 Level-3 coaches. About half of the NSOs organized fewer than
10 local competitions per year, and 65% organized 0–1 international
event annually. About 63% of the NSOs sent athletes to 1–5
international competitions each year.
Table
1
Numbers
of Employees at Hong Kong’s National Sport Organizations, With
Percentage of All Surveyed NSOs Having Similar Numbers
| Full-time | Part-time | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Count | % | Count | % | ||||||||||||||||
| 0 | 2 | 4.8 | 20 | 48.8 | 1–5 | 28 | 66.7 | 20 | 48.8 | >5 | 12 | 28.6 | 1 | 2.4 | Total | 42 | 100 | 41 | 100 |
Table 2
Numbers
of Athletes Within Hong Kong’s National Sport Organizations, By
Competitive Event Type, With Percentage of All Surveyed NSOs Having
Similar Numbers
100
26
100
| International Eventa | Other Event | |||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Count | % | Count | % | |||||||||||||||||||||||
| 0–10 | 7 | 20.0 | 1 | 3.8 | 11–50 | 20 | 57.1 | 5 | 19.2 | 51–100 | 4 | 11.4 | 9 | 34.6 | 101–200 | 3 | 8.6 | 2 | 7.7 | > 200 | 1 | 2.9 | 9 | 34.6 | Total | 35 |
aFor
purposes of this study, an international event is a competition
endorsed by an appropriate international federation.
Table 3
Numbers
of Coaches Within Hong Kong’s National Sport Organizations (By
Level), With Percentage of All Surveyed NSOs Having Similar Numbers
| Level 1 | Level 2 | Level 3 | ||||||
|---|---|---|---|---|---|---|---|---|
| Count | % | Count | % | Count | % | |||
| 0–50 | 8 | 24.2 | 0–10 | 13 | 39.4 | 0 | 7 | 21.2 |
| 51–100 | 9 | 27.3 | 11–30 | 6 | 18.2 | 1–5 | 10 | 30.3 |
| 101–200 | 11 | 33.3 | 31–50 | 3 | 9.1 | 6–10 | 7 | 21.2 |
| 201–300 | 4 | 12.1 | 51–100 | 5 | 15.2 | 11–20 | 4 | 12.1 |
| >300 | 1 | 3.03 | >100 | 6 | 18.2 | >20 | 5 | 15.2 |
| Total | 33 | 100 | Total | 33 | 100 | Total | 33 | 100 |
Table 4
Annual
Average Number of Competitions Organized By and Participated in By
NSOs, With Percentage of All Surveyed NSOs Having Similar Numbers
17
42.5
3–5
13
31.7
| Average # of Local Competitions Organized |
Average # of International Competitions Organized |
Average # of International Competitions Participated In |
|||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Count | % | Count | % | Count | % | ||||||||||||||||||||||||||||||
| 0–5 | 14 | 34.1 | 0 | 9 | 22.5 | 1–2 | 13 | 31.7 | |||||||||||||||||||||||||||
| 6–10 | 10 | 24.4 | 1 | ||||||||||||||||||||||||||||||||
| 11–20 | 8 | 19.5 | 2 | 6 | 15 | 6–10 | 6 | 14.6 | 21–30 | 1 | 2.4 | 3 | 1 | 2.5 | 11–20 | 6 | 14.6 | >30 | 8 | 19.5 | >3 | 7 | 17.5 | >20 | 3 | 7.3 | Total | 41 | 100 | Total | 40 | 100 | Total | 41 | 100 |
Resources
Used for Anti-Doping Efforts
Our data suggest that Hong Kong’s national sport organizations have
not invested much, either in terms of finances or manpower, in
anti-doping efforts (Table 5). A majority of our respondents—close
to 88%—had expended no funds for anti-doping efforts within the 3
years preceding the study and anticipated no such spending throughout
the current year. Moreover, 80%–90% of the NSOs had neither any
staff members nor honorary consultants assigned to anti-doping work.
Table 5
Average
Annual Spending on Anti-Doping Efforts by Hong Kong NSOs, Over 4-Year
Period, in United States Dollars, With Percentage of All Surveyed
NSOs Spending Similar Amounts
| Average Annual Spending in 3 Years Preceding Study |
Anticipated Spending During Current Year |
|
|---|---|---|
| 0 USD | 36 (87.8%) | 37 (88.1%) |
| 1–1,000 USD | 3 (7.3%) | 2 (4.8%) |
| 1,001–2,000 USD | 1 (2.4%) | 2 (4.8%) |
| > 2,000 USD | 1 (2.4%) | 1 (2.4%) |
Tables 6
NSOs’
Staffing for Anti-Doping Efforts, By Paid Status and Position, With
Percentage of All Surveyed NSOs Providing Similar Numbers of Staff
Paid Staff
| Count | % | |
|---|---|---|
| 0 | 35 | 85.4 |
| 1 | 5 | 12.2 |
| 2 | 1 | 2.4 |
Honorary
Consultant from Medical Profession
| Count | % | |||||||
|---|---|---|---|---|---|---|---|---|
| 0 | 32 | 80 | ||||||
| 1 | 3 | 7.5 | 2 | 2 | 5 | >2 | 3 | 7.5 |
Honorary
Consultant from Legal Profession
| Count | % | |
|---|---|---|
| 0 | 36 | 90 |
| 1 | 2 | 5 |
| 2 | 2 | 5 |
Honorary
Consultant from Technical Field (e.g., Doping Control Officer)
| Count | % | |
|---|---|---|
| 0 | 33 | 82.5 |
| 1 | 2 | 5 |
| 2 | 3 | 7.5 |
| >2 | 2 | 5 |
Honorary
Consultant (Unspecified)
| Count | % | |
|---|---|---|
| 0 | 38 | 95 |
| 4 | 1 | 2.5 |
| 6 | 1 | 2.5 |
Opinions
About Anti-Doping Education Programs
The NSO respondents were asked their opinions or perceptions
concerning appropriate content for inclusion in anti-doping
educational programs or informational materials (Table 7). The three
most important content areas, according to our respondents, were
“ways to avoid inadvertent doping,” “rights and
responsibilities of athletes in doping control,” and “anti-doping
rules and regulations.”
Table 7
NSO
Respondents’ Rank Ordering of Importance of Content Areas in
Anti-Doping Educational Programs, From Most to Least Important
| Content | Score | ||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | SD | ||||||||||||||||||||||||||||
| Ways to avoid inadvertent doping | .97 | 1.09 |
Rights and responsibilities of athletes in doping control |
.95 | 1.17 | Anti-doping rules and regulations | .77 | 1.02 | Responsibilities of NSO in doping control | .56 | .93 | Competitive sports and ethics | .47 | .69 | Therapeutic use exemption for prohibited drugs | .45 | .92 | Drug testing procedures | .40 | .80 | Current international anti-doping practices | .39 | .84 | Whereabouts information of athletes | .35 | .87 | Current Hong Kong anti-doping practices | .34 | .72 |
As shown in Table 8, the surveyed respondents indicated that the most
suitable medium to deliver anti-doping educational programs was a web
page. Workshops, pamphlets, and video presentations were also
considered suitable modes of delivery.
Table 8
NSO
Respondents’ Rank Ordering of Suitability of Anti-Doping
Educational Program Delivery, From Most to Least Suitable
| Mean | SD |
|---|
Web page
2.77
2.02
Workshop
2.58
2.12
Pamphlet
2.15
1.79
VCD
2.13
1.73
Other
.35
1.03
Surveyed
NSO associates suggested other suitable media for providing
anti-doping education (Table 9), as well.
Table 9
Other Modes of Anti-Doping Education Suggested by Respondents
| Mode | Number of Respondents Making This Suggestion |
|---|---|
| TV Commercial/Program |
3 |
| Seminar | 1 |
| Newspaper Article | 1 |
| Commercial Media | 1 |
| Exhibition | 1 |
Respondents
were asked what they thought would be a suitable time to conduct an
anti-doping workshop; opinions varied from NSO to NSO. As shown in
Table 10, while 45% preferred weekday evenings, other times also had
support (i.e., weekday “office hours,” 30%; weekends, 25%).
Table 10
Anti-Doping Workshop Times Preferred By Respondents
| Frequency | % | |
|---|---|---|
| Monday–Friday “Office Hours” |
12 | 30 |
| Monday–Friday Evenings |
18 | 45 |
| Saturday–Sunday | 10 | 25 |
| Total | 40 | 100 |
Asked if they would recommend that their NSO staff attend a 6–8-hr
anti-doping workshop costing $300 HKD (about $40 U. S.) per
participant, 68.3% of our respondents said yes (Table 11).
Table 11
Number/Percentage of Respondents Who Would/Would Not Recommend NSO
Staff Attendance at 6–8-Hr, 300 HKD Anti-Doping Workshop
| Frequency | % | |
|---|---|---|
| Yes | 28 | 68.3 |
| No | 13 | 31.7 |
| Total | 41 | 100 |
Readiness
for change
Data from the modified RTCQ completed by NSO administrators, coaches,
and committee members are presented in Table 12. A majority of
respondents of all three types were in the contemplation stage (54.5%
of administrators, 51.1% of coaches, and 47.7% of committee members).
Being in the contemplation stage meant actively considering whether
to initiate or strengthen an NSO’s anti-doping effort.
Table 12
Indicated
Readiness to Initiate or Strengthen NSO’s Anti-Doping Efforts, In
Terms of RTCQ “Stage,” With Percentage of All Respondents at Same
“Stage”
| Precontemplation | Contemplation | Action | |
|---|---|---|---|
| Administrators | 8 (18.2%) | 24 (54.5%) | 14 (27.3%) |
| Coaches | 8 (17.8%) | 23 (51.1%) | 14 (31.1%) |
| Committee Members | 10 (22.7%) | 21 (47.7%) | 13 (29.5%) |
Factors in
Decision Making About Anti-Doping Efforts
Administrators, coaches, and committee members were asked to rate the
importance of a list of pros and cons of initiating or strengthening
anti-doping efforts within their NSO (Tables 13 and 14).
Table
13
NSO
Respondents’ Rank Ordering of Importance of “Pro” Factors in
Anti-Doping Decisions, From Most to Least Important
| Pros | Score |
|---|---|
| Average | SD |
Administrators
| It will directly or indirectly improve professional knowledge of the NSO staff. |
5.1 | 1.17 |
It will help us to
avoid being penalized by an international federation.
3.85
1.61
It will affect the
professional image of the NSO.
3.69
1.49
It will help to
preserve the health of our athletes.
3.17
1.38
There is a need to
comply with the rules and regulations set forth by the
international sporting community.
2.06
1.17
It will help to
maintain fair play.
2.06
1.21
Coaches
| It will directly or indirectly improve professional knowledge of the NSO staff. |
4.11 | 1.41 |
| It will help us to avoid being penalized by an international federation. |
3.93 | 1.67 |
| It will affect the professional image of the NSO. |
3.7 | 1.66 |
| There is a need to comply with the rules and regulations set forth by the international sporting community. |
2.93 | 1.6 |
| It will help to preserve the health of our athletes. |
2.7 | 1.6 |
| It will help to maintain fair play. |
2.41 | 1.54 |
Committee
members
| It will directly or indirectly improve professional knowledge of the NSO staff. |
4.85 | 1.24 |
| It will help us to avoid being penalized by an international federation. |
4.1 | 1.62 |
It will affect the
professional image of the NSO.
3.94
1.6
It will help to
preserve the health of our athletes.
2.73
1.58
There is a need to
comply with the rules and regulations set forth by the
international sporting community
2.45
1.11
It will help to
maintain fair play.
2.24
1.28
Table
14
NSO
Respondents’ Rank Ordering of Importance of “Con” Factors in
Anti-Doping Decisions, From Most to Least Important
| Cons | Score |
| Average | SD |
Administrators
| It will create unnecessary hassle for our athletes. |
4.98 | 1.23 |
It will pose additional
financial pressure on our NSO.
3.81
1.46
Anti-doping work is not
essential to the development of our NSO.
3.36
1.55
Athletes in our sport
do not use prohibited substances to enhance performance.
3.12
1.66
There is a lack of
professional knowledge to implement such works.
3.07
1.51
There is a lack of
manpower to implement such works.
2.44
1.38
Coaches
| It will create unnecessary hassle for our athletes. |
4.56 | 1.28 |
| Anti-doping work is not essential to the development of our NSO. |
3.78 | 1.41 |
| It will pose additional financial pressure on our NSO. |
3.6 | 1.55 |
| Athletes in our sport do not use prohibited substances to enhance performance. |
3.58 | 1.76 |
| There is a lack of professional knowledge to implement such works. |
3.06 | 1.63 |
| There is a lack of manpower to implement such works. |
2.76 | 1.21 |
Committee
Members
| It will create unnecessary hassle for our athletes. |
4.92 | 1.41 |
Anti-doping work is not
essential to the development of our NSO.
3.92
1.68
It will pose additional
financial pressure on our NSO.
3.85
Athletes in our sport
do not use prohibited substances to enhance performance.
3.27
1.71
There is a lack of
professional knowledge to implement such works.
3.52
1.69
There is a lack of
manpower to implement such works.
2.85
1.66
For
the list of “pros” associated with initiating or strengthening an
anti-doping effort, administrators, coaches, and committee members
alike said the three most important considerations were, in
descending order of importance, “It will directly or indirectly
improve professional knowledge of the NSO staff,” “It will help
us to avoid being penalized by an international federation,” and
“It will affect the professional image of the NSO.” Similarly,
for the list of “cons,” they agreed that the most important
consideration was “It will create unnecessary hassle for our
athletes,” and that the second and third most important factors
were “Anti-doping work is not essential to the development of our
NSO” and “It will pose additional financial pressure on our NSO,”
respectively. However, administrators said financial pressure was a
more important consideration than coaches and committee members said
it was, while the latter groups felt more influenced than
administrators did by anti-doping’s perceived nonessential role in
the development of an NSO.
NSOs’
Present and Upcoming Anti-Doping Efforts
The interviews we conducted with representatives of Hong Kong’s
NSOs allowed for collection of information about their current and
upcoming anti-doping activities, including work in education,
capacity building, drug testing, cooperation with international
federations and anti-doping organizations, and policy. Results
obtained are presented in Table 15.
Table
15
NSOs’
Present and Upcoming Anti-Doping Work, By Activity, With Percentage
of All Surveyed NSOs Pursuing Same
| Activity | Statusa | Count | % |
Education
| To remind athletes and athlete support personnel that they are bound by the anti-doping rules |
1 | 7 | 16.3 |
| 2 | 1 | 2.3 | |
| 4 | 35 | 81.4 |
Total
43
100
To distribute
information on doping control from third parties to your athletes
and athlete support personnel
1
14
32.6
2
1
2.3
4
28
65.1
Total
43
100
To distribute
information about education programs on doping control to
athletes/coaches/sport administrators
1
18
41.9
4
25
58.1
Total
43
100
To include
information on doping control in newsletter, web page, or
correspondence with NSO members
1
30
69.8
2
5
11.6
4
8
18.6
Total
43
100
To seek assistance from
relevant parties to organize education or information sessions for
your athletes and athlete support personnel, on matters related to
doping control
1
28
65.1
2
8
18.6
3
2
4.7
4
5
11.6
Total
43
100
To organize educational
talk or seminar for your athletes/coaches/sport administrators on
anti-doping
1
35
81.4
2
5
11.6
4
3
7
Total
43
100
Capacity Building
| To upgrade the existing staff on doping issues, through information/education program |
1 | 32 | 74.4 |
| 2 | 5 | 11.6 | |
| 4 | 6 | 14 |
Total
43
100
To train a doping
control officer for your NSO
1
38
88.4
2
3
7
4
2
4.7
Total
43
100
Drug Testing (and Related Functions)
| To conduct drug tests for locally held international event |
1 | 23 | 53.5 |
| 2 | 4 | 9.3 | |
| 4 | 16 | 37.2 |
Total
43
100
To conduct drug
tests for local competition
1
39
90.7
2
1
2.3
4
3
7
Total
43
100
To conduct
out-of-competition drug tests on your athletes
1
41
95.3
2
1
2.3
4
1
2.3
Total
43
100
To keep record of all
drug tests conducted on your athletes (for international
competition and out-of-competition)
1
26
60.5
2
3
7
3
1
2.3
4
13
30.2
Total
43
100
To regularly update
your international federation(s) and anti-doping organizations on
the drug test record and results of your athletes
1
36
83.7
2
1
2.3
4
6
14
Total
43
100
To collect or
coordinate the whereabouts information of your athletes
1
24
55.8
4
19
44.2
Total
43
100
To regularly update
your international federation(s) and anti-doping organizations on
the whereabouts information of your athletes
1
30
69.8
4
13
30.2
Total
43
100
To assist athletes in
the application of the therapeutic use exemption (TUE)
1
34
79.1
2
1
2.3
4
8
18.6
Total
43
100
To keep records of TUE
for your athletes
1
35
81.4
2
1
2.3
4
7
16.3
Total
43
100
To regularly update
your international federation(s) and anti-doping organizations on
the TUE status of your athletes
1
39
90.7
2
1
2.3
4
3
7
Total
43
100
Cooperation
with International Federations and Anti-Doping Organizations
| To assist international federation(s) and anti-doping organizations in conducting drug tests |
1 | 35 | 81.4 |
| 4 | 8 | 18.6 | |
| Total | 43 | 100 |
Policy
| To discuss doping issues in meetings of your NSO |
1 | 25 | 58.1 |
| 2 | 1 | 2.3 | |
| 4 | 17 | 39.5 |
Total
43
100
To include a clause
forbidding use of prohibited substances by athletes in the
constitution of your NSO
1
26
60.5
2
5
11.6
4
12
27.9
Total
43
100
To prepare a procedural
guideline to handle anti-doping duties (If such a guideline
exists, please provide details on the target group and contents.)
1
33
76.7
2
7
16.3
4
3
7
Total
43
100
aA
numeral 1 in this column indicates an NSO does not intend to pursue
the activity in the foreseeable future; a 2 indicates that an NSO is
seriously considering action within 6 months (i.e., in the
foreseeable future); a 3 indicates that an NSO has developed a plan
to act; and a 4 indicates that the NSO has a system in place and
pursues the activity.
In terms of education, most NSOs (81.4%) had reminded their athletes
and athlete support personnel that they are bound by anti-doping
rules. Answers to our follow-up questions suggested that most of the
reminders were sent prior to major competitions. The majority of Hong
Kong NSOs would distribute to relevant persons information on doping
control obtained from third parties (65.1%) and related educational
programs (58.1%). However, only 18.6% of the NSOs had included
anti-doping information in a newsletter, a web page, or
correspondence with its members. To organize educational programs,
with or without assistance from third parties, was uncommon among the
local NSOs. Programs to enhance an NSO staff’s anti-doping
knowledge were also relatively undeveloped. Only 14% of NSOs had
organized educational programs to upgrade such knowledge, and only
4.7% had a trained doping control officer of their own.
On issues of drug testing and related functions, 37.2% of the NSOs
reported they had experience conducting drug tests at locally held
international events. However, only 7% had conducted drug tests for
local competitions and 2.3% had conducted out-of-competition tests on
athletes. It seems that in Hong Kong only athletes competing at the
international level are monitored via drug testing. Athletes in local
competitions have minimal exposure to drug testing.
In terms of record keeping, about 30.2% of NSOs had records of drug
tests conducted on their athletes, but only 14% reported this
information to an international federation (most federations made no
requests for the information). About half of the NSOs (44.2%) had
experience collecting or coordinating whereabouts information for
athletes. Only 30.2 %, however, updated an international federation
regularly about such information (follow-up questions suggested that
international federations did not request regular updates, especially
from NSOs without athletes competing internationally). Only 18.6% of
NSOs had experience applying the therapeutic use exemption with their
athletes; 16.3% kept records on TUE and 7% regularly updated an
international federation concerning athletes’ TUE status.
Only 8% of NSOs had assisted an international federation or
anti-doping agency in conducting drug testing. Responses to follow-up
questions suggested that both in-competition testing and
out-of-competition testing were involved. In terms of policy, 39.5%
of NSOs had discussed doping issues in their meetings. About one
third (27.9%) had included a clause prohibiting the use of specified
substances by athletes affiliated with them. Response to follow-up
questions indicated that most NSOs addressed the issue only
indirectly, asking individuals to refer to rules and regulations set
forth by international federations. Among the respondents, only 7%
had a procedural guideline for handling anti-doping duties.
Discussion and Recommendations
The main purpose of the survey was to evaluate the anti-doping
functions of Hong Kong’s NSOs. Data from a questionnaire and
interview suggest that the majority of NSOs in Hong Kong were at the
contemplation stage in terms of the implementation of anti-doping
functions. According to Prochaska’s transtheoretical model,
individuals at the contemplation stage have started to acknowledge a
target behavior, but they may not be ready to make any change
(Prochaska, 2000). Moreover, if pressured about the behavior,
individuals in the contemplation stage can be very resistant to
change. In the case of Hong Kong’s NSOs in the contemplation stage,
educational workshops and realistic support with resources are
essential to moving them to the next stage, which is the action
stage.
Studies of TTM suggest that “stage-matched interventions”
outperform “action-oriented interventions” (Prochaska et al.,
2001); the former can increase the likelihood of progress to the next
stage, action. For organizational change, TTM dictates that
interventions should be individualized and matched to employees’
readiness to change. This would be a necessary consideration during
development of anti-doping workshops’ content.
According to Prochaska et al. (2001), dramatic relief,
self-reevaluation, and thinking about commitment are processes of
changes that should be emphasized with those in the pre-contemplation
and contemplation stages. The Hong Kong NSOs can, then, be moved to
change their anti-doping functions through the use of emotional
arousal components, for example discussion of fears of sanctioning by
an international federation if noncompliance persists, or discussion
of advantages of successfully implementing the anti-doping code. A
reevaluation of the NSO’s strengths and weaknesses pertaining to
implementation can be helpful. NSOs should also be encouraged to
discuss the possibility of implementing anti-doping programs and to
make a commitment to further anti-doping efforts.
The present study found that resources are the major constraint on
implementation of anti-doping functions by the Hong Kong NSOs. To
provide the needed additional funds and manpower most
cost-effectively, a centralized body could be established to
coordinate anti-doping functions, rather than providing funds to
underwrite various NSOs’ individual efforts.
The present study is the first to study the status of anti-doping
efforts among Hong Kong’s national sport organizations. Apart from
investigating what anti-doping functions the NSOs are currently
fulfilling, we also measured their—the administrators’, coaches’,
and committee members’—readiness to change by starting or
strengthening anti-doping efforts. It appears that a majority of NSOs
in Hong Kong are in the contemplation stage of implementing
anti-doping functions and facing the constraints of limited funding
and manpower. These data provide a starting point for the design of
assistance to the NSOs as they initiate or strengthen anti-doping
efforts to comply with the World Anti-Doping Code. Results are likely
relevant, as well, in countries with similar anti-doping experience.
They should thus be of use to international federations, national
anti-doping organizations, and the World Anti-Doping Agency, in terms
of directing effort and resources.
References
Heather, N., Gold, R., & Rollnick, S. (1991). Readiness to
Change Questionnaire: User’s manual. (Tech. Rep. No. 15).
Kensington, New South Wales: University of New South Wales, National
Drug and Alcohol Research Centre.
Prochaska, J. M. (2000). A transtheoretical model for assessing
organizational change: A study of family service agencies’ movement
to time-limited therapy. Family in Society, 81, 76–84.
Prochaska, J. M., Prochaska, J. O., & Levesque, D. A. (2001). A
transtheoretical approach to changing organizations. Administration
and Policy in Mental Health, 28(4), 247–261.
Rollnick, J. O., Heather, N., Gold, R., & Hall, W. (1992).
Development of a short “readiness to change” questionnaire for
use in brief, opportunistic intervention among excessive drinkers.
British Journal of Addiction, 87, 743–754.
World Anti-Doping Agency. (2003). World Anti-Doping Code.
Retrieved August 28, 2006, from http://www.wada-ama.org/en/
Author Note
Lena Fung, Hong Kong Baptist University; Yvonne Yuan, Hong Kong
Sports Institute Limited.
This research was supported by a social science research grant from
the World Anti-Doping Agency.
Music in Sport and Exercise : An Update on Research and Application
Abstract
In spring 1999, almost a decade ago, the first author published in The Sport Journal an article titled “Music in Sport and Exercise: Theory and Practice.” The present article’s origins are in that earlier work and the first author’s research while a master’s student at the United States Sports Academy in 1991–92. To a greater degree than in the original 1999 article, this article focuses on the applied aspects of music in sport and exercise. Moreover, it highlights some new research trends emanating not only from our own publications, but also from the work of other prominent researchers in the field. The content is oriented primarily towards the needs of athletes and coaches.
Factors Affecting Attendance at Bowl Games During the BCS Era
Abstract
Six independent variables combined in a formula that explains 82.2 percent of the variance in attendance (r2 = .822) in all 271 college football bowl games that have been played in the past 10 years. This is despite the fact that during a recent explosion in new bowl games and the creation of the Bowl Championship Series (BCS), attendance to these traditional post-season football exhibitions has varied from 5,494 for the 2004 Silicon Valley Football Classic to 94,392 for the 2001 Rose Bowl. These six variables, out of 11 that were tested, each showed a relationship to attendance that was statistically significant at the 0.01 alpha level (p > 0.01). They include the seating capacity of the stadium (Stadium), the average home game attendance of the participating teams (AHAtt), the age of each bowl game (Age), the winning percentages of the participating teams (Wpct.), the travel distance between the representative institutions and the sites of the bowl games (Distance) and the number of days that elapsed between the participating teams’ final regular season or conference championship game and the bowl game itself (Notice). When the researcher studied only the bowls that were at least six years of age (n = 194), where the attendance track record of the individual bowls could be used as an independent variable, a formula of five independent variables that explain 91 percent of the variance (r2d = .910) was developed. All five variables had a relationship with attendance that was statistically significant at the 0.01 alpha level (p > 0.01). The formula included the attendance average of each bowl game over the previous five years (FiveAtt), Stadium, Distance, Wpct and AHAtt.
Introduction
It would be difficult to find a decade in the 106-year history of college football bowl games in which more dramatic changes have occurred in the major college football postseason. The number of bowl games has increased more than 50 percent from 22 in 1998 to 34 in 2008. The Bowl Championship Series (BCS) has dramatically increased revenues for the elite-level bowl games, and payouts for their participants. The growth of conference championship games and other games being played on the first Saturday in December has decreased the amount of notice academic institutions and fans have in finding out which bowl games their teams will be participating.
Attendance has also taken on an added importance because the growth in number of non-BCS bowl games has created a “clutter” of bowl games on television, creating a potential for a saturated market.
While the National Collegiate Athletic Association (NCAA) does not have an official national championship event for its Division One Football Bowl Subdivision (D1-FBS), the fact that there has been only one bowl game a year that has had an effect on the national championship (in eight out of the past 10 years) makes new marketing approaches all the more necessary for even more bowl games. Bowls such as the Cotton Bowl, which helped determine the national champion in 1978 and hosted the No. 2-ranked team in 1984; the Holiday Bowl, which hosted the No. 1-ranked team in 1984; and the Citrus Bowl (now the Capital One Bowl), which hosted the United Press International’s (UPI) recognized national champion in 1991; were left out of the BCS when it formed in 1998.
The surge in bowl certification has also led to an increasing number of non-traditional bowl teams participating in bowl games. These teams have little or no track record of bowl attendance and fans that are not accustomed to making postseason bowl travel plans. In some cases, football programs that either did not even exist or play at the NCAA Division 1-A level 10 years before the BCS started have participated in non-BCS bowl games during the BCS era.
These facts all indicate a need to research bowl attendance data. The increase in the number of bowls means that 271 such games have been played during the BCS era, enough to create a valid sample for research purposes.
Review of Literature
Many studies have been conducted regarding spectator attitudes and preferences in sporting events, although most have been based on survey data as opposed to fan behavior. A need to study spectator attitudes and preferences based on behavior instead of surveys arises not only from the practicality of obtaining this data (the NCAA lists attendance figures for all bowl games in a record book stored in a PDF file on its website) but also because a ticket to a sporting event differs from other products and the decision to attend an athletic event or support its participants differs from other types of consumer decisions. These decisions are emotionally-based (Hardy et al, 2003), so depending on the rational mind in a survey and the limited number of responses that can be obtained through survey data creates some disadvantages compared to data based on spectator behavior.
Definition of Terms
- Bowl Championship Series (BCS)
- A partnership involving the Rose Bowl, FedEx Orange Bowl, Tostitos Fiesta Bowl, Allstate Sugar Bowl, Notre Dame and six collegiate athletic conferences (ACC, Big East, Big Ten, Big 12, PAC 10 and Southeastern) to produce an unofficial national championship game for D1-FBS college football and provide the best possible matchups in four major bowl games.
- Bowl Games
- Special exhibition, All-Star or championship games played at the end of the regular football season. For the purpose of this study, the term will be used in reference to games played after the regular season by D1-FBS teams. As of the end of the 2007 season, there are 32 such games played each year.
- National Collegiate Athletic Association (NCAA)
- The largest governing body of intercollegiate athletics in the United States.
- NCAA Division One
- The most competitive level of athletics in the NCAA, with a varying number of schools that usually exceeds 300.
- NCAA Division One Football Bowl Subdivision (D1-FBS)
- A body of Division One college football teams that play a season culminating in bowl games for 64 of its members. The membership number varies, but usually exceeds 100.
Methodology
Using bowl game attendance as the dependent variable, the researcher analyzed 11 independent variables that theoretically would have an effect on the number of people who attended a bowl game. For the second formula, the researcher analyzed the same 11 independent variables plus one additional independent variable. The researcher used stepwise linear regression analysis as a research method and Statistical Package of Social Science (SPSS) 16.0 as the instrument.
Selection of Subjects
The researcher collected data from all 271 bowls that took place during the BCS era, from December of 1998 to January of 2008. For the second formula, the researcher used data from only those bowl games that were at least six years old at the time they were played (n = 199).
Variables
Dependent
Attendance: The number of spectators who attended each individual bowl game, as reported in the NCAA Football Record Book (NCAA.com, see Table 1).
Table 1
Bowl Attendance
| Bowl | Attendance | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1998/99 | 1999/00 | 2000/01 | 2001/02 | 2002/03 | 2003/04 | 2004/05 | 2005/06 | 2006/07 | 2007/08 | |
| Alamo | 60780 | 65380 | 60028 | 65232 | 50690 | 56226 | 65265 | 62000 | 65875 | 66166 |
| Aloha | 46451 | 40974 | 24397 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Armed Forces | N/A | N/A | N/A | N/A | N/A | 38028 | 27902 | 33505 | 32412 | 40975 |
| BCS | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 74628 | 79651 |
| Capital One | 63584 | 62011 | 66928 | 59693 | 66334 | 64565 | 70229 | 57221 | 60774 | 69748 |
| Champs Sports | N/A | N/A | N/A | 28562 | 21689 | 26482 | 28237 | 31470 | 40168 | 46554 |
| Chick-fil-A | 72876 | 73315 | 73614 | 71827 | 68330 | 75125 | 69332 | 65620 | 75406 | 74413 |
| Cotton | 72611 | 72723 | 63465 | 72955 | 70817 | 73928 | 75704 | 74222 | 66777 | 73114 |
| Emerald | N/A | N/A | N/A | N/A | 25966 | 25621 | 30653 | 25742 | 40331 | 32517 |
| Fiesta | 80470 | 71526 | 75428 | 74118 | 77502 | 73425 | 73519 | 76196 | 73719 | 70016 |
| Gator | 70791 | 43416 | 68741 | 72202 | 73491 | 78892 | 70112 | 63780 | 67704 | 60243 |
| GMAC | N/A | 34200 | 40300 | 40139 | 40646 | 40620 | 40160 | 35422 | 38751 | 36932 |
| Hawaii | N/A | N/A | N/A | N/A | 31535 | 29005 | 39662 | 26254 | 43435 | 30467 |
| Holiday | 65354 | 57118 | 63278 | 60548 | 58717 | 61102 | 66222 | 65416 | 62395 | 64020 |
| Houston | N/A | N/A | 33899 | 53480 | 44687 | 51068 | 27235 | 37286 | N/A | N/A |
| Humanitarian | 19664 | 29283 | 26203 | 23472 | 30446 | 23118 | 28516 | 30493 | 28652 | 27062 |
| Independence | 46862 | 49873 | 36974 | 45627 | 46096 | 49625 | 43000 | 41332 | 45054 | 47043 |
| Insight | 36147 | 35762 | 41813 | 40028 | 40533 | 42364 | 45917 | 43536 | 48391 | 48892 |
| International | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 26717 | 31455 |
| Las Vegas | 21429 | 28227 | 29113 | 30894 | 30324 | 25437 | 29062 | 40053 | 44615 | 40712 |
| Liberty | 52192 | 54866 | 58302 | 58968 | 55207 | 55989 | 58355 | 54894 | 56103 | 63816 |
| Meinecke | N/A | N/A | N/A | N/A | 73535 | 51236 | 73238 | 57937 | 52303 | 53126 |
| MicronPC.com | 44387 | 31089 | 28359 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Motor City | 32206 | 44863 | 52911 | 44164 | 51872 | 51826 | 52552 | 50616 | 54113 | 60624 |
| Music City | 41248 | 59221 | 47119 | 46125 | 39183 | 55109 | 66089 | 40519 | 68024 | 68681 |
| New Mexico | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 34111 | 30467 |
| New Orleans | N/A | N/A | N/A | 27004 | 19024 | 25184 | 27253 | 18338 | 24791 | 25146 |
| Oahu | 46451 | 40974 | 24187 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Orange | 67919 | 70461 | 76835 | 73640 | 75971 | 76739 | 77912 | 77773 | 74470 | 74111 |
| Outback | 66005 | 54059 | 65229 | 66249 | 65101 | 65372 | 62414 | 65881 | 65601 | 65601 |
| Papajohns.com | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 32023 | 35258 |
| Poinsettia | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 36842 | 29709 | 39129 |
| Rose | 93872 | 93731 | 94392 | 93781 | 86848 | 93849 | 93468 | 93986 | 93852 | 93923 |
| Seattle | N/A | N/A | N/A | 30144 | 38241 | N/A | N/A | N/A | N/A | N/A |
| Silicon Valley | N/A | N/A | 26542 | 30456 | 10132 | 20126 | 5494 | N/A | N/A | N/A |
| Sugar | 76503 | 79280 | 64407 | 77688 | 74269 | 79342 | 77349 | 74458 | 77781 | 74383 |
| Sun | 46612 | 48757 | 49093 | 47812 | 48917 | 49894 | 51288 | 50426 | 48732 | 49867 |
| Texas | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 52210 | 62097 |
Source: NCAA
N/A (not applicable) means the bowl game did not exist during the indicated season.
Independent
For the whole group (n = 271)
Age of the bowl (Age): The number of times the bowl game has been played, including the year in question (Table 2). In 2006, the BCS stopped rotating its four bowls as being designated championship games and instead added a fifth bowl, the BCS Championship, on Jan. 8 each year to be hosted by one of the four BCS bowl committees. Two such games have been played during the period of this study, but the age of the BCS title game (1 and 2) would not be reflected in attendance. So in these particular games, the age of the host committee’s bowl game is also used as the age of the BCS Championship game. For example, the 2007 BCS title game was the first one of its kind, but since it was hosted by the Fiesta Bowl Committee, it is listed in this study as being the same age as the Fiesta Bowl (35).
Table 2
Age of the Bowls
| Current Bowls | Age (in 2007-’08) | Discontinued Bowls | Final Year | Age (in final year) |
|---|---|---|---|---|
| Rose | 106 | Aloha | 2000-’01 | 19 |
| Orange | 73 | Houston | 2005-’06 | 6 |
| Sugar | 73 | Micron PC.com | 2000-’01 | 10 |
| Cotton | 71 | Silicon Valley | 2004-’05 | 5 |
| Sun | 71 | Oahu | 2000-’01 | 3 |
| Gator | 61 | Seattle | 2002-’03 | 2 |
| Capital One | 60 | |||
| Liberty | 48 | |||
| Chick-fil-A | 39 | |||
| Fiesta | 36 | |||
| Indy | 31 | |||
| Holiday | 29 | |||
| Outback | 21 | |||
| Insight | 18 | |||
| Champs Sports | 17 | |||
| Vegas | 15 | |||
| Alamo | 14 | |||
| Human | 11 | |||
| Motor | 11 | |||
| Music | 10 | |||
| GMAC | 9 | |||
| New O | 7 | |||
| Emerald | 6 | |||
| Hawaii | 6 | |||
| Meinecke Car Care | 6 | |||
| Armed | 5 | |||
| Poinsettia | 3 | |||
| BCS Championship* | 2 (35 and 73 in the study) | |||
| International | 2 | |||
| New Mexico | 2 | |||
| Papajohns.com | 2 | |||
| Texas | 2 | |||
*For the purpose of this study, the age of each BCS Championship game will be recorded as the same age as the BCS bowl hosting the event.
Average Home Attendance (AHAtt): The average number of spectators that attended the regular-season home games of the participating teams. The average home attendance for each team is averaged together for this variable (Table 3).
Table 3
Average Home Attendance
| Bowl | Average Home Attendance (averaged between two participating teams) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1998/99 | 1999/00 | 2000/01 | 2001/02 | 2002/03 | 2003/04 | 2004/05 | 2005/06 | 2006/07 | 2007/08 | |
| Alamo | 47643.0 | 84813.0 | 55688.0 | 55383.0 | 63621.0 | 75292.0 | 75840.5 | 94200.0 | 79545.0 | 95562.0 |
| Aloha | 45336.0 | 38311.5 | 44969.5 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Armed Forces | N/A | N/A | N/A | N/A | N/A | 32126.5 | 23594.0 | 29364.5 | 32321.5 | 63136.0 |
| BCS | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 97752.5 | 98864.5 |
| Capital One | 81782.5 | 61890.5 | 96364.0 | 108375.5 | 95091.0 | 75327.5 | 80803.0 | 83356.0 | 77631.5 | 100326.0 |
| Champs Sports | N/A | N/A | N/A | 49084.5 | 60325.5 | 46012.0 | 41942.5 | 64320.5 | 52425.0 | 56265.0 |
| Chick-fil-A | 64689.0 | 58061.5 | 65243.0 | 69016.0 | 76478.0 | 90558.5 | 73771.5 | 68445.0 | 79489.5 | 82717.0 |
| Cotton | 57412.5 | 67272.5 | 78927.5 | 68044.5 | 84680.0 | 50690.5 | 90571.0 | 65995.0 | 85053.5 | 64969.5 |
| Emerald | N/A | N/A | N/A | N/A | 51317.5 | 36617.5 | 34149.5 | 46571.5 | 72743.5 | 46318.5 |
| Fiesta | 93702.0 | 92284.0 | 56975.5 | 46700.5 | 86513.5 | 75990.0 | 42856.0 | 92906.0 | 57507.0 | 72629.0 |
| Gator | 60641.5 | 42517.0 | 65618.5 | 66101.5 | 65147.5 | 51720.5 | 69693.0 | 53058.0 | 54695.0 | 55867.5 |
| GMAC | N/A | 34163.0 | 30571.5 | 31976.5 | 32131.0 | 32589.5 | 29299.0 | 34858.0 | 22859.0 | 20309.5 |
| Hawaii | N/A | N/A | N/A | N/A | 35655.5 | 31299.0 | 28703.5 | 21769.0 | 45575.5 | 35937.5 |
| Holiday | 62051.0 | 59820.5 | 63654.5 | 77765.5 | 47513.0 | 58335.0 | 58421.0 | 71382.5 | 70151.5 | 74009.5 |
| Houston | N/A | N/A | 39252.5 | 55841.5 | 35099.5 | 39772.0 | 44552.0 | 38979.5 | N/A | N/A |
| Humanitarian | 18925.0 | 30684.5 | 35604.0 | 50292.5 | 35955.0 | 37497.0 | 50635.5 | 34770.5 | 29318.0 | 43285.5 |
| Independence | 44674.0 | 60746.5 | 60268.5 | 63742.0 | 67509.5 | 57652.5 | 28630.0 | 66615.5 | 66636.0 | 71413.5 |
| Insight | 55735.5 | 46736.0 | 41444.0 | 44840.0 | 40431.5 | 50168.0 | 58564.5 | 47170.5 | 51540.5 | 38514.0 |
| International | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 19499.0 | 28374.0 |
| Las Vegas | 15165.8 | 16350.0 | 18483.0 | 22080.2 | 26235.4 | 27997.4 | 28799.0 | 28966.0 | 31154.0 | 33898.2 |
| Liberty | 45323.0 | 26705.5 | 33633.0 | 50013.5 | 29137.0 | 31889.5 | 35515.0 | 31066.5 | 48770.0 | 46657.0 |
| Meinecke | N/A | N/A | N/A | N/A | 54330.5 | 59810.5 | 47824.0 | 45895.0 | 33275.0 | 35400.0 |
| MicronPC.com | 51571.5 | 45804.0 | 47050.0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Motor City | 32233.5 | 46929.5 | 24725.0 | 26924.0 | 31865.9 | 24823.5 | 31664.5 | 25442.0 | 21800.0 | 39048.5 |
| Music City | 60857.5 | 57248.5 | 50019.5 | 64580.0 | 51268.5 | 81844.5 | 64721.5 | 54909.0 | 70161.0 | 73645.5 |
| New Mexico | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 23743.5 | 23380.0 |
| New Orleans | N/A | N/A | N/A | 21434.5 | 21665.5 | 28681.0 | 22074.0 | 23121.5 | 17785.0 | 22705.5 |
| Oahu | 58505.0 | 35576.0 | 70379.0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Orange | 66644.0 | 97115.5 | 77953.0 | 64498.5 | 64300.0 | 70642.0 | 84880.5 | 93791.5 | 36998.5 | 56508.5 |
| Outback | 77134.5 | 74794.0 | 89830.5 | 93073.0 | 97880.5 | 77987.5 | 87557.0 | 80495.5 | 106678.0 | 92832.5 |
| Papajohns.com | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 33695.0 | 28483.5 |
| Poinsettia | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 31180.0 | 26348.5 | 38605.0 |
| Rose | 75568.5 | 63909.5 | 67897.0 | 62468.5 | 56617.5 | 94361.0 | 97059.5 | 87072.5 | 100753.0 | 71174.0 |
| Seattle | N/A | N/A | N/A | 46609.5 | 39716.5 | N/A | N/A | N/A | N/A | N/A |
| Silicon Valley | N/A | N/A | 41500.5 | 58259.0 | 41144.7 | 47619.5 | 24140.0 | N/A | N/A | N/A |
| Sugar | 75897.5 | 65622.0 | 71841.5 | 71843.5 | 84143.0 | 87088.0 | 74100.0 | 74494.0 | 86503.5 | 68130.0 |
| Sun | 44040.0 | 44689.0 | 72995.5 | 44611.5 | 63932.5 | 50924.5 | 63095.0 | 48372.5 | 48374.0 | 56007.5 |
| Texas | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 43903.0 | 25486.5 |
Attendance Per Mile (APM): The Average Home Attendance of each team, divided by the number of miles between the bowl’s host city and the city where each academic institution is located, averaged together. An example is in Figure A.
2007 Insight Bowl: Oklahoma State vs. Indiana
Distance from Stillwater, Okla. (home of Oklahoma State University.) to Phoenix, Ariz. (site of the bowl) = 1085.64 miles
Oklahoma State’s Average Home Attendance = 40,024
Oklahoma State’s APM = 40,024/1085.65 = 36.87
Distance from Bloomington, Ind. (home of Indiana University) to Phoenix = 1,747 miles
Indiana’s Average Home Attendance = 37,004
Indiana’s APM = 21.18
2007 Insight Bowl’s APM = (36.87+21.18)/2 = 29.03
Figure A. Example of Attendance Per Mile (APM) Variable.
BCS Status (BCS): The variable that separates BCS bowls from non-BCS bowls, under the hypothesis that a BCS bowl will normally draw higher attendance. The value of “1” is assigned to BCS bowls while “0” is assigned to non-BCS bowls.
Championship Status (CStatus) — The status of a game as it pertains to the unofficial national championship of D1-FBS football. For this study, the BCS-designated national championship game is given a value of “1” and all other bowl games a value of “0” (Table 4).
Table 4
BCS Bowls
| Bowl | Year | Winning Team in Bold, Championship Games in Italics | |
|---|---|---|---|
| BCS | 2006-’07 | Florida | Ohio State |
| BCS | 2007-’08 | LSU | Ohio State |
| Fiesta | 1998-’99 | Tennessee | Florida State |
| Fiesta | 1999-’00 | Nebraska | Tennessee |
| Fiesta | 2000-’01 | Oregon State | Notre Dame |
| Fiesta | 2001-’02 | Oregon | Colorado |
| Fiesta | 2002-’03 | Ohio State | Miami |
| Fiesta | 2003-’04 | Ohio State | Kansas State |
| Fiesta | 2004-’05 | Utah | Pittsburgh |
| Fiesta | 2005-’06 | Ohio State | Notre Dame |
| Fiesta | 2006-’07 | Boise State | Oklahoma |
| Fiesta | 2007-’08 | Oklahoma | West Virginia |
| Orange | 1998-’99 | Florida | Syracuse |
| Orange | 1999-’00 | Michigan | Alabama |
| Orange | 2000-’01 | Oklahoma | Florida State |
| Orange | 2001-’02 | Florida | Maryland |
| Orange | 2002-’03 | USC | Iowa |
| Orange | 2003-’04 | Miami | Florida State |
| Orange | 2004-’05 | USC | Oklahoma |
| Orange | 2005-’06 | Penn State | Florida State |
| Orange | 2006-’07 | Louisville | Wake Forest |
| Orange | 2007-’08 | Virginia Tech | Kansas |
| Rose | 1998-’99 | Wisconsin | UCLA |
| Rose | 1999-’00 | Wisconsin | Stanford |
| Rose | 2000-’01 | Washington | Purdue |
| Rose | 2001-’02 | Miami | Nebraska |
| Rose | 2002-’03 | Oklahoma | Washington State |
| Rose | 2003-’04 | USC | Michigan |
| Rose | 2004-’05 | Texas | Michigan |
| Rose | 2005-’06 | Texas | USC |
| Rose | 2006-’07 | USC | Michigan |
| Rose | 2007-’08 | USC | Illinois |
| Sugar | 1998-’99 | Ohio State | Texas A&M |
| Sugar | 1999-’00 | Florida State | Virginia Tech |
| Sugar | 2000-’01 | Miami | Florida |
| Sugar | 2001-’02 | LSU | Illinois |
| Sugar | 2002-’03 | Georgia | Florida State |
| Sugar | 2003-’04 | LSU | Oklahoma |
| Sugar | 2004-’05 | Auburn | Virginia Tech |
| Sugar | 2005-’06 | West Virginia | Georgia |
| Sugar | 2006-’07 | LSU | Notre Dame |
| Sugar | 2007-’08 | Georgia | Hawaii |
Distance (Distance): The number of miles between the bowl’s host city and the city where each participating team is located (Table 5). The travel distance of each team is averaged out for this variable. The distances were found through the internet using Mapquest.com for mainland bowls and Ask.com for the Aloha, O’ahu and Hawaii bowls.
Table 5
Distance
| Bowl | Mean of Two Teams’ Travel Distances in Miles | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1998/99 | 1999/00 | 2000/01 | 2001/02 | 2002/03 | 2003/04 | 2004/05 | 2005/06 | 2006/07 | 2007/08 | |
| Alamo | 982.46 | 912.34 | 1,081.26 | 765.05 | 1,147.07 | 1,167.58 | 921.44 | 1,177.24 | 600.90 | 912.34 |
| Aloha | 2,930.00 | 3,807.00 | 4,000.50 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Armed Forces | N/A | N/A | N/A | N/A | N/A | 849.07 | 1,003.34 | 388.53 | 774.47 | 1,205.70 |
| BCS Title Game | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 1,982.42 | 495.28 |
| Capital One | 1,117.34 | 631.92 | 823.29 | 901.23 | 732.99 | 746.85 | 975.12 | 863.40 | 1,196.24 | 631.92 |
| Champs Sports | N/A | N/A | N/A | 783.90 | 985.59 | 937.58 | 828.90 | 1,198.63 | 952.28 | 1,264.36 |
| Chick-fil-A | 292.31 | 205.01 | 263.38 | 244.75 | 433.94 | 169.08 | 501.81 | 596.95 | 243.41 | 116.10 |
| Cotton | 375.48 | 264.93 | 669.21 | 261.56 | 319.94 | 401.79 | 513.02 | 466.01 | 671.07 | 468.80 |
| Emerald | N/A | N/A | N/A | N/A | 2,020.38 | 2,157.90 | 1,964.73 | 1,609.41 | 1,510.00 | 1,694.69 |
| Fiesta | 1,880.06 | 1,646.11 | 1,582.88 | 1,240.15 | 2,151.54 | 1,556.28 | 1,648.40 | 1,910.65 | 1,022.55 | 1,590.78 |
| Gator | 683.30 | 351.37 | 489.32 | 359.76 | 736.91 | 741.77 | 461.25 | 661.00 | 552.40 | 1,005.90 |
| GMAC | N/A | 717.02 | 362.91 | 794.15 | 700.53 | 689.82 | 649.35 | 1,068.07 | 489.05 | 811.33 |
| Hawaii | N/A | N/A | N/A | N/A | 2,105.00 | 1,949.00 | 2,181.00 | 3,654.50 | 1,459.00 | 3,848.50 |
| Holiday | 989.72 | 1,415.66 | 1,141.56 | 1,280.59 | 970.32 | 1,257.22 | 769.09 | 1,175.80 | 952.31 | 835.25 |
| Houston | N/A | N/A | 923.12 | 179.86 | 470.37 | 1,011.63 | 943.21 | 626.77 | N/A | N/A |
| Humanitarian | 1,232.12 | 940.64 | 612.92 | 2,106.12 | 698.79 | 1,845.16 | 1,547.59 | 1,331.75 | 1,633.05 | 1,448.64 |
| Independence | 460.90 | 381.80 | 309.81 | 584.59 | 564.92 | 445.68 | 818.13 | 696.13 | 414.47 | 735.66 |
| Insight | 1,738.43 | 1,801.57 | 1,798.01 | 1,777.19 | 1,690.28 | 1,423.95 | 1,571.16 | 1,225.81 | 1,226.59 | 1,416.23 |
| International | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 439.50 | 502.67 |
| Las Vegas | 1,318.14 | 408.33 | 668.78 | 345.28 | 421.74 | 766.57 | 527.43 | 472.05 | 647.41 | 323.58 |
| Liberty | 1,003.56 | 723.58 | 766.93 | 997.44 | 817.06 | 944.98 | 1,137.04 | 1,161.31 | 588.30 | 471.04 |
| Meinecke | N/A | N/A | N/A | N/A | 324.03 | 359.91 | 504.73 | 377.72 | 651.57 | 437.13 |
| Micron PC.com | 406.00 | 1,151.93 | 1,293.62 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Motor City | 349.84 | 1,010.91 | 297.73 | 157.79 | 385.69 | 191.29 | 368.26 | 466.09 | 359.43 | 246.14 |
| Music City | 331.14 | 531.73 | 416.63 | 709.39 | 706.60 | 462.30 | 565.14 | 714.33 | 287.00 | 353.53 |
| New Mexico | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 524.47 | 511.44 |
| New Orleans | N/A | N/A | N/A | 1,026.25 | 684.29 | 477.72 | 334.72 | 287.86 | 330.37 | 609.61 |
| Oahu | 3,012.50 | 1,270.50 | 4,658.50 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Orange | 883.07 | 1,086.26 | 991.30 | 707.28 | 2,106.81 | 240.85 | 2,119.11 | 868.32 | 948.15 | 1,205.45 |
| Outback | 971.59 | 764.38 | 756.62 | 756.62 | 649.45 | 701.57 | 899.16 | 701.57 | 887.37 | 997.65 |
| Papajohns.com | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 587.35 | 352.64 |
| Poinsettia | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 1,924.81 | 1,675.89 | 1,730.69 |
| Rose | 990.34 | 1,163.52 | 1,617.19 | 2,117.25 | 1,231.63 | 1,122.54 | 1,806.68 | 695.08 | 1,122.54 | 1,004.08 |
| Seattle | N/A | N/A | N/A | 1,757.49 | 1,574.78 | N/A | N/A | N/A | N/A | N/A |
| Silicon Valley | N/A | N/A | 759.95 | 1,260.29 | 1,296.04 | 245.97 | 2,267.72 | N/A | N/A | N/A |
| Sugar | 676.85 | 610.31 | 701.90 | 438.10 | 463.21 | 395.50 | 599.06 | 344.90 | 535.07 | 2,375.18 |
| Sun | 706.00 | 1,521.90 | 1,117.54 | 1,509.80 | 1,598.51 | 1,521.90 | 949.15 | 1,151.98 | 1,384.94 | 1,693.86 |
| Texas | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 1,172.12 | 131.63 |
Source of data: Mapquest.com and ask.com. N/A ( not applicable) indicates the game was not played in that particular year.
Improved Winning Percentage (Impct): The regular season winning percentage (including a conference championship game, when applicable) of each team minus its winning percentage from the previous season (including a bowl game, when applicable). The two scores are averaged for this variable (Figure B). It is designed to measure how a team performed compared to expectation, something that would theoretically affect the enthusiasm of the fans and influence their decision to travel.
Improved Winning Percentage:
Regular Season Winning Percentage – Previous year’s total winning percentage
2007-’08 BCS Championship Game: Ohio State vs. LSU
Ohio State
2007 Regular Season = 11-1 = .917
2006-’07 Season = 12-1 = .923
Improved Winning Percentage = .917-.923 = -.006
LSU
2007 Regular Season = 11-2 = .846
2006-’07 Season = 11-2 = .846
Improved Winning Percentage = .846 – .846 = 0
2007-’08 BCS Championship Improved Winning Percentage:
(-.006+0)/2 = -.003
Figure B. Example of Improved Winning Percentage
Market Strength (Market): A measurement of the support for college football in a participating team’s area based on the number of D1-FBS institutions are located in the same state as the participating team. For example, a team from Texas would have a market strength rating of 10 because there are 10 D1-FBS teams in the state of Texas (Table 6). A team from Oklahoma would have a market strength rating of three for the same reason. Therefore, a bowl game played between the University of Tulsa and Texas A&M would have a market strength rating of 6.5.
Table 6
Market Strength
| Bowl | Market Strength (averaged between the two participating teams) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1998/99 | 1999/00 | 2000/01 | 2001/02 | 2002/03 | 2003/04 | 2004/05 | 2005/06 | 2006/07 | 2007/08 | |
| Alamo | 3.0 | 6.5 | 2.0 | 6.0 | 2.0 | 3.0 | 5.5 | 3.0 | 6.0 | 6.5 |
| Aloha | 2.5 | 3.0 | 1.5 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Armed Forces | N/A | N/A | N/A | N/A | N/A | 6.0 | 5.0 | 6.0 | 3.0 | 5.0 |
| BCS | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 7.5 | 6.5 |
| Capital One | 3.5 | 6.0 | 4.5 | 4.5 | 3.5 | 3.0 | 3.5 | 2.5 | 1.5 | 6.0 |
| Champs Sports | N/A | N/A | N/A | 4.0 | 26.0 | 5.0 | 2.5 | 2.5 | 3.0 | 3.0 |
| Chick-fil-A | 2.0 | 2.5 | 3.5 | 4.5 | 3.0 | 3.0 | 7.0 | 6.0 | 2.0 | 4.0 |
| Cotton | 6.5 | 6.0 | 3.0 | 2.5 | 7.5 | 2.5 | 7.0 | 7.0 | 3.0 | 1.5 |
| Emerald | N/A | N/A | N/A | N/A | 2.5 | 2.0 | 2.0 | 2.5 | 7.0 | 2.0 |
| Fiesta | 5.5 | 2.5 | 3.0 | 2.5 | 7.5 | 5.0 | 3.0 | 6.0 | 2.5 | 2.5 |
| Gator | 3.0 | 4.5 | 2.0 | 4.5 | 4.5 | 2.0 | 4.5 | 2.5 | 2.0 | 6.0 |
| GMAC | N/A | 7.5 | 6.5 | 3.5 | 2.0 | 5.0 | 6.0 | 9.0 | 5.5 | 5.5 |
| Hawaii | N/A | N/A | N/A | N/A | 7.5 | 5.5 | 5.5 | 4.5 | 1.5 | 3.5 |
| Holiday | 1.5 | 2.0 | 6.0 | 6.0 | 2.0 | 6.0 | 8.5 | 2.5 | 8.5 | 6.0 |
| Houston | N/A | N/A | 7.5 | 10.0 | 3.0 | 6.0 | 6.5 | 6.0 | N/A | N/A |
| Humanitarian | 2.5 | 2.0 | 6.0 | 3.5 | 2.0 | 2.5 | 4.5 | 1.5 | 4.5 | 4.5 |
| Independence | 6.5 | 3.0 | 6.5 | 3.0 | 2.0 | 1.5 | 5.0 | 1.5 | 3.5 | 3.5 |
| Insight | 1.5 | 2.0 | 2.5 | 2.5 | 2.5 | 4.5 | 3.0 | 1.5 | 5.5 | 3.5 |
| International | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 6.5 | 2.5 |
| Las Vegas | 6.0 | 5.0 | 2.0 | 5.0 | 4.5 | 2.0 | 4.0 | 5.0 | 2.5 | 5.0 |
| Liberty | 4.0 | 3.0 | 3.0 | 3.0 | 6.5 | 3.0 | 2.5 | 5.0 | 6.0 | 5.0 |
| Meinecke | N/A | N/A | N/A | N/A | 2.0 | 2.5 | 3.0 | 6.0 | 1.5 | 3.0 |
| MicronPC.com | 6.0 | 2.5 | 3.0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Motor City | 2.0 | 2.5 | 5.0 | 8.0 | 4.5 | 5.5 | 4.5 | 6.0 | 4.5 | 4.5 |
| Music City | 3.0 | 2.5 | 2.5 | 1.5 | 1.5 | 2.5 | 2.0 | 1.5 | 2.0 | 4.5 |
| New Mexico | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 4.5 | 2.0 |
| New Orleans | N/A | N/A | N/A | 6.5 | 9.0 | 7.0 | 6.5 | 2.5 | 7.0 | 5.5 |
| Oahu | 2.5 | 1.5 | 2.0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Orange | 5.0 | 4.5 | 5.0 | 4.5 | 4.5 | 7.0 | 5.0 | 5.0 | 4.0 | 2.0 |
| Outback | 3.0 | 3.0 | 5.0 | 5.0 | 6.0 | 4.5 | 1.5 | 4.5 | 3.5 | 2.5 |
| Papajohns.com | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 6.0 | 5.0 |
| Poinsettia | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 2.5 | 6.5 | 2.5 |
| Rose | 4.0 | 4.0 | 3.0 | 7.0 | 2.5 | 6.0 | 7.5 | 8.5 | 6.0 | 5.0 |
| Seattle | N/A | N/A | N/A | 4.5 | 3.5 | N/A | N/A | N/A | N/A | N/A |
| Silicon Valley | N/A | N/A | 5.0 | 6.0 | 4.5 | 7.0 | 3.5 | N/A | N/A | N/A |
| Sugar | 9.0 | 4.5 | 7.0 | 4.0 | 4.5 | 4.0 | 3.0 | 2.0 | 4.5 | 1.5 |
| Sun | 8.5 | 1.5 | 4.0 | 3.0 | 3.0 | 1.5 | 3.0 | 5.0 | 1.5 | 4.5 |
| Texas | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 1.5 | 10.0 |
Sources: U.S. Department of Education N/A ( not applicable) indicates the game was not played in that particular year.
Notice (Notice): The number of days between the date of a team’s last regular season or conference championship game and the date of its bowl game. The notice for the two teams is averaged together for this variable. The theory is that the longer notice fans have, the more likely they are to travel to a bowl game (Table 7).
Table 7
Notice
| Bowl | Notice (averaged between the two participating teams) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1998/99 | 1999/00 | 2000/01 | 2001/02 | 2002/03 | 2003/04 | 2004/05 | 2005/06 | 2006/07 | 2007/08 | |
| Alamo | 31.0 | 35.0 | 33.5 | 35.0 | 28.0 | 34.0 | 35.5 | 36.0 | 39.0 | 39.0 |
| Aloha | 31.0 | 31.5 | 30.5 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Armed Forces | N/A | N/A | N/A | N/A | N/A | 20.5 | 30.0 | 27.0 | 28.5 | 37.5 |
| BCS | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 44 | 44.0 |
| Capital One | 33.5 | 35.0 | 37.0 | 31.0 | 39.0 | 33.0 | 39.0 | 41.0 | 37.0 | 42.0 |
| Champs Sports | N/A | N/A | N/A | 22.5 | 30.0 | 30.0 | 24.0 | 31.0 | 34.0 | 34.0 |
| Chick-fil-A | 33.0 | 37.5 | 34.5 | 30.0 | 31.0 | 37.5 | 34.0 | 30.5 | 35.0 | 37.0 |
| Cotton | 31.0 | 27.0 | 33.5 | 38.5 | 33.0 | 38.5 | 32.0 | 44.0 | 37.0 | 35.0 |
| Emerald | N/A | N/A | N/A | N/A | 31.0 | 39.5 | 33.0 | 36.5 | 28.5 | 30.5 |
| Fiesta | 37.0 | 42.0 | 40.5 | 31.0 | 34.0 | 34.0 | 35.0 | 40.5 | 33.5 | 32.0 |
| Gator | 33.5 | 31.5 | 40.5 | 31.0 | 35.5 | 33.0 | 39.5 | 30.0 | 30.0 | 41.5 |
| GMAC | N/A | 29.0 | 26.0 | 22.5 | 14.5 | 17.0 | 27.0 | 27.0 | 37.5 | 39.5 |
| Hawaii | N/A | N/A | N/A | N/A | 24.5 | 22.5 | 23.5 | 25.0 | 25.5 | 29.5 |
| Holiday | 33.0 | 39.0 | 38.0 | 30.5 | 31.0 | 35.0 | 29.5 | 36.5 | 35.0 | 31.0 |
| Houston | N/A | N/A | 36.0 | 28.0 | 27.0 | 31.0 | 28.5 | 42.0 | N/A | N/A |
| Humanitarian | 34.0 | 40.0 | 40.0 | 34.0 | 38.0 | 38.5 | 38.0 | 36.5 | 37.0 | 34.5 |
| Independence | 37.5 | 35.0 | 37.5 | 30.5 | 28.5 | 32.5 | 26.0 | 36.0 | 36.5 | 36.5 |
| Insight | 32.0 | 35.0 | 37.0 | 35.0 | 29.5 | 30.5 | 33.5 | 31.5 | 41.0 | 41.0 |
| International | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 43.0 | 39.5 |
| Las Vegas | 24.5 | 28.0 | 27.0 | 31.0 | 21.5 | 25.0 | 26.0 | 33.0 | 27.0 | 21.0 |
| Liberty | 30.5 | 37.5 | 42.0 | 30.5 | 31.0 | 36.0 | 30.5 | 28.5 | 31.0 | 32.0 |
| Meinecke | N/A | N/A | N/A | N/A | 28.0 | 28.0 | 36.5 | 31.5 | 32.5 | 35.0 |
| MicronPC.com | 27.5 | 40.0 | 36.5 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Motor City | 25.5 | 30.5 | 32.0 | 28.5 | 22.5 | 28.0 | 28.5 | 27.5 | 28.5 | 32.0 |
| Music City | 34.5 | 35.5 | 34.5 | 30.5 | 30.0 | 39.0 | 44.5 | 37.5 | 34.0 | 37.0 |
| New Mexico | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 24.5 | 24.5 |
| New Orleans | N/A | N/A | N/A | 24.0 | 17.5 | 19.0 | 18.0 | 24.0 | 23.5 | 23.5 |
| Oahu | 27.0 | 31.5 | 29.0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Orange | 38.5 | 35.0 | 39.0 | 39.0 | 40.0 | 33.0 | 31.0 | 38.0 | 31.0 | 36.5 |
| Outback | 37.5 | 38.0 | 44.0 | 41.5 | 36.0 | 36.5 | 38.5 | 40.0 | 41.0 | 38.0 |
| Papajohns.com | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 28.0 | 28.0 |
| Poinsettia | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 26.0 | 21.0 | 22.5 |
| Rose | 34.0 | 42.0 | 44.0 | 37.0 | 25.0 | 33.0 | 39.0 | 32.5 | 37.0 | 38.0 |
| Seattle | N/A | N/A | N/A | 26.0 | 33.5 | N/A | N/A | N/A | N/A | N/A |
| Silicon Valley | N/A | N/A | 39.5 | 30.0 | 28.5 | 34.5 | 40.0 | N/A | N/A | N/A |
| Sugar | 34.5 | 42.0 | 34.5 | 32.0 | 28.5 | 29.0 | 30.0 | 30.0 | 39.5 | 34.5 |
| Sun | 36.5 | 41.0 | 37.5 | 37.0 | 38.0 | 42.5 | 38.0 | 34.0 | 30.5 | 33.5 |
| Texas | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 33.0 | 34.0 |
Source of data: Division 1A Historical Scores, jhowell.net. N/A ( not applicable) indicates the game was not played in that particular year.
November Winning Percentage (Novpct): The winning percentage of a team in games played during November or the first week of December (Table 8).
Table 8
November Winning Percentage
| Bowl | November Winning Percentage (averaged between the two participating teams) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1998/99 | 1999/00 | 2000/01 | 2001/02 | 2002/03 | 2003/04 | 2004/05 | 2005/06 | 2006/07 | 2007/08 | |
| Alamo | 0.875 | 0.333 | 0.500 | 0.625 | 0.425 | 0.375 | 0.500 | 0.583 | 0.167 | 0.500 |
| Aloha | 0.500 | 0.666 | 0.333 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Armed Forces | N/A | N/A | N/A | N/A | N/A | 0.875 | 0.500 | 0.625 | 0.458 | 0.625 |
| BCS | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 1.000 | 0.733 |
| Capital One | 0.625 | 0.750 | 0.666 | 0.667 | 0.875 | 0.675 | 1.000 | 0.667 | 0.800 | 0.667 |
| Champs Sports | N/A | N/A | N/A | 0.875 | 0.750 | 0.292 | 0.583 | 0.625 | 0.625 | 0.533 |
| Chick-fil-A | 0.583 | 0.583 | 0.833 | 0.576 | 0.800 | 0.875 | 0.750 | 0.775 | 0.833 | 0.666 |
| Cotton | 0.633 | 0.500 | 0.875 | 0.750 | 0.625 | 0.625 | 0.417 | 0.500 | 0.708 | 0.775 |
| Emerald | N/A | N/A | N/A | N/A | 0.433 | 0.500 | 0.833 | 0.583 | 0.625 | 0.625 |
| Fiesta | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.875 | 0.875 | 1.000 | 1.000 | 0.775 |
| Gator | 0.875 | 0.625 | 0.583 | 0.500 | 0.375 | 1.000 | 0.500 | 0.750 | 0.600 | 0.666 |
| GMAC | N/A | 0.833 | 0.500 | 0.567 | 0.650 | 0.750 | 0.708 | 0.583 | 0.775 | 0.900 |
| Hawaii | N/A | N/A | N/A | N/A | 0.567 | 0.550 | 0.650 | 0.800 | 0.650 | 0.750 |
| Holiday | 0.833 | 0.666 | 0.833 | 0.625 | 0.875 | 0.750 | 0.833 | 0.875 | 0.417 | 0.583 |
| Houston | N/A | N/A | 0.500 | 0.333 | 0.625 | 0.625 | 0.708 | 0.833 | N/A | N/A |
| Humanitarian | 1.000 | 0.750 | 0.833 | 0.500 | 0.625 | 0.625 | 0.750 | 0.708 | 0.500 | 0.625 |
| Independence | 0.375 | 0.542 | 0.576 | 0.625 | 0.250 | 0.650 | 0.708 | 0.500 | 0.125 | 0.167 |
| Insight | 0.667 | 0.500 | 0.642 | 0.708 | 0.625 | 0.575 | 0.500 | 0.500 | 0.833 | 0.458 |
| International | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.583 | 0.583 |
| Las Vegas | 0.875 | 0.666 | 0.625 | 0.750 | 0.625 | 0.625 | 0.333 | 0.500 | 0.625 | 0.625 |
| Liberty | 0.875 | 1.000 | 0.833 | 0.775 | 0.666 | 1.000 | 1.000 | 0.576 | 0.750 | 0.833 |
| Meinecke | N/A | N/A | N/A | N/A | 0.750 | 0.550 | 0.708 | 0.675 | 0.750 | 0.500 |
| MicronPC.com | 0.708 | 1.000 | 0.500 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Motor City | 1.000 | 0.667 | 0.875 | 0.750 | 0.800 | 0.550 | 0.708 | 0.625 | 0.625 | 0.300 |
| Music City | 0.500 | 0.333 | 0.500 | 0.542 | 0.833 | 0.417 | 0.167 | 0.500 | 0.542 | 0.417 |
| New Mexico | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.467 | 0.500 |
| New Orleans | N/A | N/A | N/A | 0.708 | 0.917 | 0.875 | 0.600 | 0.500 | 0.900 | 0.750 |
| Oahu | 0.667 | 0.708 | 0.333 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Orange | 0.708 | 1.000 | 1.000 | 0.868 | 1.000 | 0.675 | 1.000 | 0.625 | 0.800 | 0.875 |
| Outback | 0.583 | 0.417 | 0.333 | 0.708 | 0.750 | 0.750 | 0.500 | 0.666 | 0.583 | 0.733 |
| Papajohns.com | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.750 | 0.750 |
| Poinsettia | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.542 | 0.833 | 0.833 |
| Rose | 0.708 | 1.000 | 0.750 | 0.833 | 0.708 | 1.000 | 0.917 | 1.000 | 0.750 | 1.000 |
| Seattle | N/A | N/A | N/A | 0.600 | 0.292 | N/A | N/A | N/A | N/A | N/A |
| Silicon Valley | N/A | N/A | 0.875 | 0.700 | 0.750 | 0.400 | 0.750 | N/A | N/A | N/A |
| Sugar | 0.708 | 1.000 | 0.875 | 1.000 | 0.800 | 0.900 | 1.000 | 0.875 | 0.875 | 1.000 |
| Sun | 0.666 | 1.000 | 0.667 | 0.433 | 0.708 | 0.708 | 0.666 | 0.583 | 0.567 | 0.425 |
| Texas | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.583 | 0.750 |
Stadium Size (Stadium): The seating capacity of the stadium when used for football games (Table 9).
Table 9
Stadiums
| Bowl | Years | Stadium | Capacity |
|---|---|---|---|
| Alamo | 1993-Present | Alamo Dome | 65000 |
| Aloha | 1982-2000 | Aloha Stadium | 50000 |
| Armed Forces | 2003-Present | Amon G. Carter Stadium | 43000 |
| BCS | 2006-’07 | University of Phoenix Stadium | 73000 |
| BCS | 2007-’08 | Louisiana Superdome | 72500 |
| Capital One | 1986-Present | Florida Citrus Bowl | 65438 |
| Champs Sports | 2002-Present | Florida Citrus Bowl | 65438 |
| Chick-fil-A | 1993-Present | Georgia Dome | 71990 |
| Cotton | 1938-Present | Cotton Bowl | 71252 |
| Emerald | 2002-Present | AT&T Park | 38437 |
| Fiesta | 1971-’06 | Sun Devil Stadium | 73397 |
| Fiesta | 2007-Present | University of Phoenix Stadium | 73000 |
| Gator | 1997-Present | Jacksonville Municipal Stadium | 77000 |
| GMAC | 1999-Present | Ladd-Peebles Stadium | 40048 |
| Hawaii | 2002-Present | Aloha Stadium | 50000 |
| Holiday | 1978-Present | Qualcomm Stadium | 66000 |
| Houston | 2000-2005 | Reliant Stadium | 69500 |
| Humanitarian | 1997-Present | Bronco Stadium | 30000 |
| Independence | 1976-Present | Independence Stadium | 48947 |
| Insight | 1989-’99 | Arizona Stadium | 57803 |
| Insight | 2000-’05 | Bank One Ballpark | 42915 |
| Insight | 2006-Present | Sun Devil Stadium | 73397 |
| International | 2007-Present | Rogers Center | 53506 |
| Las Vegas | 1992-Present | Sam Boyd Stadium | 40000 |
| Liberty | 1965-Present | Liberty Bowl Memorial Stadium | 62598 |
| Meinecke | 2002-Present | Bank of America Stadium | 73298 |
| MicronPC.com | 1996-2001 | Joe Robbie Stadium | 77823 |
| Motor City | 1997-’01 | Pontiac Silverdome | 80368 |
| Motor City | 2002-Present | Ford Field | 65000 |
| Music City | 1998-Present | LP Field | 68000 |
| New Mexico | 2006-Present | University Stadium | 38634 |
| New Orleans | 2001-Present | Louisiana Superdome | 72500 |
| Oahu | 1998-’00 | Aloha Stadium | 50000 |
| Orange | 1996-Present | Dolphin Stadium | 77823 |
| Outback | 1998-Present | Raymond James Stadium | 65500 |
| Papajohns.com | 2006-Present | Legion Field | 72000 |
| Poinsettia | 2005-Present | Qualcomm Stadium | 66000 |
| Rose | 1943-Present | Rose Bowl | 91887 |
| Seattle | 2001-’02 | Seahawks Stadium | 67000 |
| Silicon Valley | 2000-’04 | Spartan Stadium | 30000 |
| Sugar | 1975-’05, 2007-Present | Louisiana Superdome | 72500 |
| Sugar | 2006 | Georgia Dome | 71990 |
| Sun | 1963-Present | Sun Bowl Stadium | 50426 |
| Texas | 2006-Present | Reliant Stadium | 69500 |
Data is from NCAA.com. N/A ( not applicable) indicates the game was not played in that particular year.
Winning Percentage (Wpct) — The percentage of games won by each team in the regular season (including conference championship games, when applicable), averaged together (Table 10).
Table 10
Winning Percentage (averaged between the two participating teams)
| Bowl | 1998/99 | 1999/00 | 2000/01 | 2001/02 | 2002/03 | 2003/04 | 2004/05 | 2005/06 | 2006/07 | 2007/08 |
|---|---|---|---|---|---|---|---|---|---|---|
| Alamo | 0.792 | 0.739 | 0.773 | 0.591 | 0.615 | 0.708 | 0.636 | 0.636 | 0.625 | 0.625 |
| Aloha | 0.682 | 0.545 | 0.545 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Armed Forces | N/A | N/A | N/A | N/A | N/A | 0.920 | 0.545 | 0.591 | 0.625 | 0.625 |
| BCS | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.962 | 0.881 |
| Capital One | 0.784 | 0.784 | 0.739 | 0.780 | 0.708 | 0.760 | 0.818 | 0.826 | 0.843 | 0.708 |
| Champs Sports | N/A | N/A | N/A | 0.591 | 0.576 | 0.542 | 0.545 | 0.610 | 0.671 | 0.676 |
| Chick-fil-A | 0.773 | 0.682 | 0.727 | 0.576 | 0.718 | 0.750 | 0.682 | 0.826 | 0.718 | 0.667 |
| Cotton | 0.697 | 0.626 | 0.748 | 0.735 | 0.833 | 0.750 | 0.693 | 0.576 | 0.763 | 0.756 |
| Emerald | N/A | N/A | N/A | N/A | 0.679 | 0.583 | 0.727 | 0.591 | 0.542 | 0.583 |
| Fiesta | 0.958 | 1.000 | 0.864 | 0.871 | 1.000 | 0.810 | 0.864 | 0.826 | 0.923 | 0.833 |
| Gator | 0.818 | 0.697 | 0.864 | 0.682 | 0.769 | 0.708 | 0.818 | 0.576 | 0.763 | 0.708 |
| GMAC | N/A | 0.727 | 0.773 | 0.689 | 0.708 | 0.837 | 0.727 | 0.727 | 0.654 | 0.679 |
| Hawaii | N/A | N/A | N/A | N/A | 0.676 | 0.599 | 0.610 | 0.697 | 0.676 | 0.708 |
| Holiday | 0.833 | 0.773 | 0.818 | 0.780 | 0.724 | 0.792 | 0.682 | 0.746 | 0.750 | 0.792 |
| Houston | N/A | N/A | 0.610 | 0.591 | 0.583 | 0.625 | 0.655 | 0.773 | N/A | N/A |
| Humanitarian | 0.682 | 0.693 | 0.773 | 0.591 | 0.728 | 0.583 | 0.727 | 0.739 | 0.583 | 0.625 |
| Independence | 0.545 | 0.636 | 0.576 | 0.591 | 0.542 | 0.666 | 0.606 | 0.591 | 0.500 | 0.500 |
| Insight | 0.682 | 0.636 | 0.682 | 0.648 | 0.667 | 0.641 | 0.545 | 0.591 | 0.542 | 0.542 |
| International | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.625 | 0.583 |
| Las Vegas | 0.591 | 0.697 | 0.564 | 0.591 | 0.561 | 0.625 | 0.545 | 0.591 | 0.708 | 0.667 |
| Liberty | 0.846 | 0.727 | 0.818 | 0.875 | 0.818 | 0.784 | 0.955 | 0.576 | 0.676 | 0.676 |
| Meinecke | N/A | N/A | N/A | N/A | 0.683 | 0.625 | 0.636 | 0.591 | 0.750 | 0.667 |
| MicronPC.com | 0.682 | 0.636 | 0.591 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Motor City | 0.777 | 0.864 | 0.610 | 0.727 | 0.679 | 0.635 | 0.652 | 0.564 | 0.638 | 0.599 |
| Music City | 0.682 | 0.545 | 0.591 | 0.682 | 0.638 | 0.583 | 0.545 | 0.591 | 0.667 | 0.583 |
| New Mexico | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.583 | 0.583 |
| New Orleans | N/A | N/A | N/A | 0.500 | 0.561 | 0.708 | 0.591 | 0.545 | 0.583 | 0.583 |
| Oahu | 0.731 | 0.652 | 0.591 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Orange | 0.773 | 0.826 | 0.958 | 0.864 | 0.875 | 0.826 | 1.000 | 0.788 | 0.881 | 0.881 |
| Outback | 0.682 | 0.636 | 0.682 | 0.682 | 0.576 | 0.708 | 0.818 | 0.682 | 0.739 | 0.760 |
| Papajohns.com | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.625 | 0.667 |
| Poinsettia | 0.591 | 0.667 | 0.708 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Rose | 0.909 | 0.773 | 0.818 | 0.958 | 0.840 | 0.875 | 0.864 | 1.000 | 0.875 | 0.792 |
| Seattle | N/A | N/A | N/A | 0.701 | 0.542 | N/A | N/A | N/A | N/A | N/A |
| Silicon Valley | N/A | N/A | 0.682 | 0.696 | 0.599 | 0.558 | 0.682 | N/A | N/A | N/A |
| Sugar | 0.878 | 1.000 | 0.839 | 0.830 | 0.808 | 0.923 | 0.917 | 0.871 | 0.833 | 0.917 |
| Sun | 0.606 | 0.727 | 0.606 | 0.682 | 0.500 | 0.708 | 0.682 | 0.576 | 0.679 | 0.708 |
| Texas | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 0.708 | 0.583 |
For Bowl games that are more than five years old (n = 194)
Five-year Average Attendance (FiveAtt) — The average attendance of a bowl game for the past five years (Table 11).
Table 11
Five-year Average Attendance
| Bowl | 1998/99 | 1999/00 | 2000/01 | 2001/02 | 2002/03 | 2003/04 | 2004/05 | 2005/06 | 2006/07 | 2007/08 |
| Alamo | 53129.60 | 56142.40 | 60397.20 | 59483.40 | 61394.40 | 60422.00 | 59511.20 | 59488.20 | 59882.60 | 60011.20 |
| Aloha | 43592.00 | 44080.40 | 43302.80 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Armed Forces | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| BCS* | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 74872.20 | 76662.60 |
| Capital One | 70171.00 | 68396.60 | 66559.80 | 65786.00 | 65031.20 | 63710.00 | 63906.20 | 65549.80 | 63608.40 | 63824.60 |
| Champs Sports | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 62519.00 | 65549.80 |
| Chick-fil-A | 66795.40 | 68687.40 | 70370.00 | 70927.80 | 72568.80 | 71992.40 | 72442.20 | 71645.60 | 70046.80 | 70762.60 |
| Cotton | 65886.00 | 66437.20 | 66938.20 | 67988.40 | 68193.80 | 70514.20 | 70777.60 | 71373.80 | 73525.20 | 72289.60 |
| Emerald | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 29662.60 |
| Fiesta | 72114.80 | 73755.00 | 73266.60 | 72379.40 | 74181.80 | 75808.80 | 74399.80 | 74798.40 | 74952.00 | 74872.20 |
| Gator | 56165.20 | 56882.40 | 53125.60 | 57833.40 | 61853.20 | 65728.20 | 67348.40 | 72687.60 | 71695.40 | 70795.80 |
| GMAC | N/A | N/A | N/A | N/A | N/A | N/A | 39181.00 | 40373.00 | 39397.40 | 39119.80 |
| Hawaii | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 33978.20 |
| Holiday | 53624.40 | 56273.60 | 55806.60 | 58252.00 | 59411.80 | 61003.00 | 60152.60 | 61973.40 | 62401.00 | 62770.40 |
| Houston | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 42073.80 | N/A | N/A |
| Humanitarian | N/A | N/A | N/A | N/A | 24655.50 | 25813.60 | 26504.40 | 26351.00 | 27209.00 | 28245.00 |
| Independence | 40344.20 | 42952.80 | 47479.00 | 45106.80 | 45959.00 | 45086.40 | 45639.00 | 44264.40 | 45136.00 | 45021.40 |
| Insight | 45341.60 | 42756.00 | 40884.00 | 41045.80 | 40627.00 | 38856.60 | 40100.00 | 42131.00 | 42475.60 | 44148.20 |
| International | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Las Vegas | 15165.80 | 16350.00 | 18483.00 | 22080.20 | 26235.40 | 27997.40 | 28799.00 | 28966.00 | 31154.00 | 33898.20 |
| Liberty | 40229.40 | 46448.40 | 50765.60 | 52946.40 | 54907.40 | 55907.00 | 56666.40 | 57364.20 | 56682.60 | 56109.60 |
| Meinecke | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 61649.80 |
| MicronPC.com | 39691.40 | 40865.60 | 36916.80 | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Motor City | N/A | N/A | N/A | N/A | 43496.80 | 45203.20 | 49127.20 | 50665.00 | 50206.00 | 52195.80 |
| Music City | N/A | N/A | N/A | N/A | N/A | 46579.20 | 49351.40 | 50725.00 | 49405.00 | 53784.80 |
| New Mexico | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| New Orleans | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | 23360.60 | 22918.00 |
| Oahu | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Orange | 74557.20 | 71833.80 | 69575.40 | 70502.80 | 72571.40 | 72965.20 | 74729.20 | 76219.40 | 76407.00 | 76573.00 |
| Outback | 57864.60 | 60535.80 | 59070.80 | 59054.00 | 61671.60 | 63328.60 | 63202.00 | 64873.00 | 65003.40 | 64873.80 |
| Papajohns.com | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Poinsettia | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Rose | 101088.00 | 99615.00 | 97912.00 | 96770.00 | 95399.00 | 92525.00 | 92520.00 | 92468.00 | 92386.00 | 92401.00 |
| Seattle | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Silicon Valley | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Sugar | 73515.40 | 73728.60 | 74339.80 | 73164.60 | 73033.40 | 74429.40 | 74997.20 | 74611.00 | 76621.20 | 76639.80 |
| Sun | 47080.20 | 47633.00 | 47262.00 | 47257.40 | 48275.60 | 48238.20 | 48894.60 | 49400.80 | 49667.40 | 49851.40 |
| Texas | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
*Figures are based on last five bowls hosted by the local committee
Data source: NCAA.com
N/A (not applicable) indicates the game was not played in that particular year.
Results
When all bowl games (n=271) were counted, six variables combined to explain 82.2 percent of the variance in attendance (r2d = .822). The variables were: Seating Capacity (Stadium), Age of the bowl (Age), Average Home Attendance of the participants (AHAtt), number of miles in travel between the location of the institutions and the bowl games (Distance), and the number of days elapsed from the end of the regular season or conference championship game to the bowl game itself (Notice), as shown in Table 12.
Table 12
Model Summary
| Model | R | r2 | Adjusted r2 | Std. Error of the Estimate |
|---|---|---|---|---|
| 1 | .743a | .552 | .551 | 12822.102 |
| 2 | .841b | .707 | .704 | 10399.231 |
| 3 | .884c | .782 | .779 | 8983.648 |
| 4 | .897d | .804 | .801 | 8524.124 |
| 5 | .903e | .816 | .813 | 8279.997 |
| 6 | .907f | .822 | .818 | 8156.106 |
| a. Predictors: (Constant), Stadium | ||||
| b. Predictors: (Constant), Stadium, Age | ||||
| c. Predictors: (Constant), Stadium, Age, AHAtt | ||||
| d. Predictors: (Constant), Stadium, Age, AHAtt, Wpct | ||||
| e. Predictors: (Constant), Stadium, Age, AHAtt, Wpct, Distance | ||||
| f. Predictors: (Constant), Stadium, Age, AHAtt, Wpct, Distance, Notice | ||||
Each variable had a relationship to attendance that was statistically significant at the 0.01 alpha level (p > 0.01), as noted in Table 13.
Table 13
Coefficients and Relationships
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
|---|---|---|---|---|---|---|
| B | Std. Error | Beta | ||||
| 1 | (Constant) | -5233.704 | 3280.305 | -1.595 | .112 | |
| Stadium | .944 | .052 | .743 | 18.214 | .000 | |
| 2 | (Constant) | 4910.494 | 2794.302 | 1.757 | .080 | |
| Stadium | .636 | .049 | .501 | 12.887 | .000 | |
| Age | 318.709 | 26.845 | .461 | 11.872 | .000 | |
| 3 | (Constant) | -2534.098 | 2535.494 | -.999 | .318 | |
| Stadium | .527 | .044 | .415 | 11.947 | .000 | |
| Age | 237.622 | 24.682 | .344 | 9.627 | .000 | |
| AHAtt | .294 | .031 | .327 | 9.598 | .000 | |
| 4 | (Constant) | -18954.895 | 3822.348 | -4.959 | .000 | |
| Stadium | .485 | .043 | .382 | 11.401 | .000 | |
| Age | 190.531 | 24.920 | .276 | 7.646 | .000 | |
| AHAtt | .270 | .029 | .301 | 9.216 | .000 | |
| Wpct | 30760.395 | 5564.072 | .184 | 5.528 | .000 | |
| 5 | (Constant) | -16058.380 | 3779.075 | -4.249 | .000 | |
| Stadium | .448 | .042 | .353 | 10.583 | .000 | |
| Age | 189.036 | 24.209 | .274 | 7.808 | .000 | |
| AHAtt | .271 | .029 | .302 | 9.515 | .000 | |
| Wpct | 34305.556 | 5473.019 | .205 | 6.268 | .000 | |
| Distance | -3.142 | .764 | -.112 | -4.113 | .000 | |
| 6 | (Constant) | -24353.011 | 4626.876 | -5.263 | .000 | |
| Stadium | .455 | .042 | .358 | 10.885 | .000 | |
| Age | 178.672 | 24.093 | .259 | 7.416 | .000 | |
| AHAtt | .238 | .030 | .265 | 7.904 | .000 | |
| Wpct | 34631.879 | 5392.212 | .207 | 6.423 | .000 | |
| Distance | -3.240 | .753 | -.115 | -4.303 | .000 | |
| Notice | 299.769 | 99.308 | .089 | 3.019 | .003 | |
Dependent Variable: Attendance
So a formula that would explain the variance in bowl attendance would look something like this:
S = Seating Capacity
A = Age of the bowl
T = Average Home Attendance of the Participating teams (The sum Average Home Attendance of each team divided by two)
W = Winning Percentage (the sum of the regular season winning percentages of the two participating teams, including conference championship games when applicable, divided by two).
D = Travel distance (the sum of the travel distance between each participating institution’s home city and the city hosting the bowl game, divided by two)
N = Notice (the sum of the number of days between the last regular season game or conference championship game of each team and the bowl game, divided by two)
Bowl Attendance = .455S + 172.672A + 34631.879W – 3.24D + 299.769N – 24353.011.
On bowls that were at least six years old (n = 194), the five-year attendance average was included as an independent variable. Five variables; Five Year Average Attendance (FiveAtt, Travel Distance (Distance), Winning Percentage (Wpct), Average Home Attendance (AHAtt), and Seating Capacity (Stadium); combined to explain 91 percent of the variance in attendance (r2d = .910), as can be observed in Table 14.
Table 14
Model Summary Bowls that are at least Six Years Old
| Model | R | |||
|---|---|---|---|---|
| VAR00021 > 5 (Selected) | r2 | Adjusted r2 | Std. Error of the Estimate | |
| 1 | .939a | .883 | .882 | 5893.063 |
| 2 | .946b | .894 | .893 | 5604.919 |
| 3 | .949c | .901 | .899 | 5451.683 |
| 4 | .952d | .906 | .904 | 5301.487 |
| 5 | .954e | .910 | .908 | 5202.987 |
| a. Predictors: (Constant), FiveAtt | ||||
| b. Predictors: (Constant), FiveAtt, Distance | ||||
| c. Predictors: (Constant), FiveAtt, Distance, Wpct | ||||
| d. Predictors: (Constant), FiveAtt, Distance, Wpct, AHAtt | ||||
| e. Predictors: (Constant), FiveAtt, Distance, Wpct, AHAtt, Stadium | ||||
As in the previous formula, all variables had a relationship with attendance that was statistically significant (Table 1) at the 0.01 alpha level (p < 0.01).
Table 15
Coefficients and Relationships for bowls at least six years old.
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||
| B | Std. Error | Beta | |||||
|---|---|---|---|---|---|---|---|
| 1 | (Constant) | 6456.892 | 1446.943 | 4.462 | .000 | ||
| FiveAtt | .917 | .024 | .939 | 37.981 | .000 | ||
| 2 | (Constant) | 9579.045 | 1533.840 | 6.245 | .000 | ||
| FiveAtt | .913 | .023 | .935 | 39.696 | .000 | ||
| Distance | -2.996 | .650 | -.109 | -4.610 | .000 | ||
| 3 | (Constant) | 2699.321 | 2491.405 | 1.083 | .280 | ||
| FiveAtt | .846 | .029 | .867 | 28.691 | .000 | ||
| Distance | -3.401 | .643 | -.123 | -5.290 | .000 | ||
| Wpct | 15344.128 | 4450.253 | .105 | 3.448 | .001 | ||
| 4 | (Constant) | 537.570 | 2502.378 | .215 | .830 | ||
| FiveAtt | .788 | .033 | .807 | 23.629 | .000 | ||
| Distance | -3.253 | .627 | -.118 | -5.190 | .000 | ||
| Wpct | 15823.608 | 4329.875 | .108 | 3.655 | .000 | ||
| AHAtt | .082 | .024 | .097 | 3.452 | .001 | ||
| 5 | (Constant) | -3309.442 | 2798.385 | -1.183 | .238 | ||
| FiveAtt | .690 | .047 | .707 | 14.603 | .000 | ||
| Distance | -2.909 | .627 | -.105 | -4.641 | .000 | ||
| Wpct | 15173.014 | 4255.479 | .104 | 3.566 | .000 | ||
| AHAtt | .090 | .023 | .106 | 3.847 | .000 | ||
| Stadium | .142 | .050 | .117 | 2.868 | .005 | ||
The formula for bowls that are at least six years old would include:
S = Seating Capacity
T = Average Home Attendance of the Participating teams (The sum Average Home Attendance of each team divided by two)
W = Winning Percentage (the sum of the regular season winning percentages of the two participating teams, including conference championship games when applicable, divided by two).
D = Travel distance (the sum of the travel distance between each participating institution’s home city and the city hosting the bowl game, divided by two)
F = Average attendance over the past five years.
Attendance = 0.690F – 2.909D + 15173.014W + 0.09T + .142S – 3309.442
Discussion
Bowl committees will publicly state that they invite the most deserving teams more so than those that will bring the highest attendance. It is easy to see why the latter option would be more tempting. On table 12, where all bowls are included, it shows that average home attendance accounts for 7.5 percent of the variance in bowl attendance, while winning percentage only accounts for 2.2 percent. This can be figured from the r2d numbers on step 2, a formula that does not include Average Home Attendance but explains 70.7 percent of the variance, but step 3, which adds Average Home Attendance explains 78.2 percent of the variance, a difference of 7.5 percent. Step 4, which adds winning percentage, explains 80.4 percent of the variance, a difference of 2.2 percent. In Table 14, where only bowls that are at least six years old are studied, winning percentage accounts for 0.7 percent of the variance while Average Home Attendance accounts for 0.5. Notice, which theoretically would become an issue with the increasing number of bowl games played before Christmas and the later invitation dates brought about by the BCS and conference championship games, was not a factor in the bowls that were six years old or more and only explained .6 % of the variance in bowls overall.
Future Studies
Since the adjusted r2 for the first formula is .818, this means the average accuracy of any prediction on bowl attendance would be 81.8 percent (.908/90.8 percent on the second formula). Future studies could focus on bowls that exceed their expected attendance with a qualitative look at the marketing methods used by these bowls compared to other bowls that do not fare as well. Conferences and teams whose bowl appearances draw larger crowds than expected could also be studied.
The second formula, that takes five-year attendance averages into account, could be used by bowl committees to set goals for attendance each year. Since the bowls do not know until December who their participants will be, setting a goal based on this formula’s expectation and using it to measure improvement would be more reasonable.
References
Hardy, Stephen, Bernard J. Mullin and William A. Sutton (2003). Sports Marketing (pp. 55-75). Champaign, Ill: Human Kinetics.
Official Website of the NCAA. Retrieved May 13, 2008 from Ncaa.com.
Ask.com. Retrieved May16, 2008 from Ask.com.
Mapquest. Retrieved May 23, 2008 from Mapquest.com.