Latest Articles
Acclimatization in High-Altitude Sport: Predictive Modeling of Oxygen Saturation as an Expedition Management Tool
Abstract:
A management perspective is taken in developing a predictive model to forecast blood oxygen saturation levels for trekkers and mountaineers ascending to high altitudes. Blood oxygen saturation is an important indicator of risk of acute mountain sickness and other potentially lethal health risks for high-altitude athletes. This model is based on data collected from a seventeen-person expedition to Mt. Everest. The results of the model are compared to actual saturation levels and the model is found to be a good predictor. The practical implication is that an oximeter and the results it produces are useful tools for expedition managers and base camp managers charged with the safety of a multi-person expedition.
Introducing a Risk Assessment Model for Sport Venues
Abstract:
With the ‘unknown certainty’ of terrorist actions and fan behavior, it is impossible to ensure a risk-free environment at America’s sporting venues. Incidents will happen and emergencies will arise. It is a matter of how one prepares, responds, and recovers to mitigate the consequences of emergencies at a sporting venue. Sport venue managers need to be aware of risk assessment methodologies to detect threats, identify vulnerabilities, and reduce consequences. Information gathered through this process is extremely valuable for enhancing security measures. This article discusses risk assessment and analysis, addresses the need for risk assessments at sporting venues, and describes the sport-specific risk assessment model developed while conducting research through a Homeland Security grant.
Introduction:
Sport lost its innocence on September 5, 1972, at the Olympic Games in Munich, Germany (CNN.com, 2002). A Palestinian group known as Black September crept into the Olympic Village and took nine members of the Israeli team hostage. The captors demanded a safe exit out of Germany and the release of Palestinian prisoners held in Israeli jails (2002). Unfortunately, a failed rescue attempt led to the death of all nine Israeli hostages, five terrorists, and one German policeman (2002). “For the first time, world sport had become a victim of terrorism, bringing with it a brutal reminder of the world’s harsher realities” (2). Terrorism struck again in 1996. A ‘domestic terrorist’ was responsible for the Centennial Olympic Park bombing at the Atlanta Games. This incident killed one person and injured more than 100 (CNN.com, 1996). Regardless of the motives for these attacks, terrorists chose to act on a world stage that offered global exposure for their cause. The incident in which an Oklahoma student prematurely detonated a bomb strapped to his body outside a football stadium packed with 84,000 in October 2005 (Hagmann, 2005), and the most recent threat of a dirty bomb attack on several NFL stadiums in October 2006 (CNN.com, 2006), emphasize the fact that sport venues are an attractive target for potentially catastrophic consequences. Besides terrorism, sport venue managers must plan for other incidents or unexpected disasters, such as fan/player violence or natural hazards.
One problem that sports venue manager’s face is determining the potential threat level, “causing leagues, teams and venues to prepare for a range of possible incidents at their facilities and to maintain close contact with federal, state and local law enforcement representatives regarding possible threats” (Hurst, Zoubek, Pratsinakis, n.d., p. 4). The risk assessment process is a way to determine risk and threat levels and identify vulnerabilities. “A good risk management approach includes three primary elements: a threat assessment, a vulnerability assessment, and a criticality assessment.” (Decker, 2001, p. 1). These assessments provide vital information for the protection of critical assets against terrorist attacks and other threats. Sport venue managers are able to identify vulnerabilities and thus harden the facility and improve physical protection systems. This may include implementing access controls, using CCTV security cameras, adding lighting, encouraging background checks, credentialing, checking backpacks, enhancing communication networks, and developing or updating emergency response and evacuation plans.
Understanding Risk
“Risk is the possibility of loss resulting from a threat, security incident, or event” (General Security Risk Assessment Guideline, 2003, p. 5). Risk is inherent in almost all aspects of life. Sport venue managers must continually attempt to minimize risk at their facilities. Risk cannot be totally eliminated from the environment, but with careful planning it can be managed. “Risk management is a systematic and analytical process to consider the likelihood that a threat will endanger an asset, individual, or function and to identify actions to reduce the risk and mitigate the consequences of an attack” (Decker, 2001, p. 1).
Risk is best understood as the product of the consequence of an event and the probability of the event occurring: Risk = Consequence x Probability (“Risk 101”, n.d). Risk increases as the consequences and probability of occurrence increases (n.d.). “In order to manage risk, it must first be identified, measured, and evaluated” (4). The Vulnerability Methodologies Report (2003) issued by the Office for Domestic Preparedness, Department of Homeland Security, identified three types of risk: mission or function risks, asset risks, and security risks. Mission risks prevent an organization from accomplishing a mission. Asset risks may harm an organization’s physical assets. Security risks have the potential to cripple actual data and people (2003).
Sport facility managers identify risks through various means. They can conduct surveys of attendees, conduct inspections of the facility, interview present employees, or ask experts in the field (Ammon, Southall, & Blair, 2004). Sport facility managers must address primary and secondary factors in order to reduce risk (2004). Primary factors are identified in the standard operating procedures. Facility staff is included among these factors (2004). An unsupervised or improperly trained ticket taker, usher, or cashier can become a risk for the facility manager (2004). “A well-trained staff, educated about proper risk management procedures, can help the risk manager to identify potential risks” (p. 108). Secondary factors of risk faced by most sport facilities include weather, type of event, patron demographics, and facility location (2004).
The essence of risk is dependent on the potential of threats. “A threat is a product of intention and capability of an adversary, both manmade and natural, to undertake an action which would be detrimental to an asset” (Vulnerability Assessment Report, 2003, p. 11). Vulnerabilities expose the asset to a threat and eventual loss. The General Security Risk Assessment Guideline (2003) defines vulnerability as “an exploitable capability; an exploitable security weakness or deficiency at a facility, entity, venue, or of a person” (p. 5). A risk analysis evaluating the potential of loss from a threat will determine whether risk should be reduced, re-assigned, transferred, or accepted (Vulnerability Assessment Report, 2003). “An acceptable risk is the risk level that an individual or group considers reasonable for the perceived benefit of an activity” (“Risk 101”, n.d., Acceptable Risk 1). An acceptable level of risk is usually determined by the asset manager or owner (2003). Severe risks that cause a high degree of loss and occur frequently should be avoided (Ammon, Southall, & Blair, 2004). Average frequency and moderate severity risks can be transferred to someone who’s willing to assume the risk. The facility manager may decide to pay an insurance company to cover physical and financial damages (2004). Some facility mangers may decide to keep or retain the risk. In so doing, they become financially responsible (2004). Facility managers can reduce risk through staff training, preventative maintenance, and development of a risk management plan to be included in the standard operating procedure (SOP) (2004). “The SOP is a set of instructions giving detailed directions and appropriate courses of action for given situations. SOP’s should be developed for all risks,” (Farmer, Mulrooney, & Ammon, 1996, p. 81).
In order to determine threats and vulnerabilities, an organization must undergo a risk assessment. The Department of Homeland Security issued a ten-step risk assessment methodology criterion (Vulnerability Assessment Report, 2003):
- Clearly identify the infrastructure sector being assessed.
- Specify the type of security discipline addressed, e.g. physical, information, operations.
- Collect specific data pertaining to each asset.
- Identify critical/key assets to be protected.
- Determine the mission impact of the loss or damage of that asset.
- Conduct a threat analysis and perform assessment for specific assets.
- Perform a vulnerability analysis and assessment to specific threats.
- Conduct analytical risk assessment and determine priorities for each asset.
- Be relatively low cost to train and conduct.
- Make specific, concrete recommendations concerning countermeasures.
This is general in nature and may be adapted to meet the needs of a specific organization. Several other risk assessment models exist today. For example, Sandia National Laboratories developed the RAM-Chemical to assess chemical facilities in the United States. Sports facilities in the U.S. must embrace risk management processes. Identifying the greatest threats and eliminating or reducing vulnerabilities will help minimize risk at sports events. “A sports arena is always critical as a high value terrorist target because of the potentially high casualty rate” (Durling, Price, & Spero, 2005, p. 8). Whether facing a terrorist attack, natural disaster, or unruly fan behavior, sport venue managers must pursue an effective risk management approach to protect the facility and human lives.
The Sports Event Security Assessment Model (SESAM)
In May, 2005, the Department of Homeland Security, in conjunction with the Mississippi Emergency Management agency, awarded the University of Southern Mississippi a $568,000 research grant to create a research-based model for the security management of university sport venues. Several risk assessment methodologies were reviewed and the DHS risk assessment criterion was customized for the assessment of sport venues. The Sport Event Security Assessment Model (SESAM) was developed through the collaboration of academic and security professionals in a six-hour brainstorming session. Academic professionals with experience in the sport event security area and training in DHS threat/risk assessment participated. Security professionals included former employees of the FBI, CIA, and Secret Service with extensive background in risk assessment methods and vulnerability assessment experience in the security and sport security field. This collaborative group supported the development and field testing of the model. A seven step procedure was created to evaluate sport security operations. An overview of the SESAM is presented in figure 1.
A risk assessment was conducted of the sport operations at seven public universities in Mississippi between May 2005 and February 2006. The following highlights the critical points during each stage of the seven-step process:
1. Step 1 of the process involves the identification of a SESAT team, including all key personnel responsible for game day security. These may include the athletic facility manager, campus police chief, emergency management director, local sheriff, and/or campus physical plant facility manager. Once the SESAT is established, meetings and interviews are scheduled to provide assessment objectives and define the assessed area based on a one mile radius of the sport venue.
2. Characterization of assets and target identification are achieved through in-depth surveys and interviews at each sport facility. Campus and community assets are identified and prioritized. Critical infrastructure and existing physical protection countermeasures are also identified. The target attractiveness is finally evaluated.
3. The threat assessment focuses on potential threat elements on campus and in the surrounding community. Specific factors are taken into consideration, including the existence of a group/individual operating close to the venue, history or past activity of the group/individual, intentions of the potential threat to act, their capability to act, and the ultimate targeting of the sport venue. A threat level is assigned to each critical asset, which is identified during step 2 of the risk assessment process.
Figure 1: Sport Event Security Assessment Model (SESAM). Adapted by Robert Rolen, Walter Cooper, Lou Marciani, and Stacey Hall. The Center for Spectator Sports Security Management.
4. The vulnerability assessment is a key component of the risk assessment model involving the analysis of several key factors about the venue, including:
- Level of Visibility: assess the awareness of existence and visibility of the sport venue to the general public.
- Criticality of Sport Venue to the Jurisdiction: assess the usefulness of the sport venue to the local population, economy, or government.
- Potential Sport Venue Population Capacity: assess the maximum number of people at a site at any given time.
- Potential for Collateral Mass Casualties: assess potential mass casualties within a one-mile radius of the sport venue.
- Impact Outside of the Venue: assess the loss outside of the sport venue.
- Existence of CBRNE Elements: assess the presence of a legal WMD on the site.
- Potential Threat Element Access to Sport Venue: assess the availability of the sport venue for ingress and egress by a PTE.
5. The consequence evaluation component analyzes the number of potentially injured people at the sport venue who might require transportation/hospitalization. It also assesses the loss of life, loss of infrastructure, economic and environmental impact, and the potential social trauma.
6. The overall risk level of a sport venue is calculated during this step. The risk assessment evaluates the threat potential (produced during step 3), likelihood of adversary success (produced during step 4), and severity of the consequences of an attack (produced during step 5). A final risk level is determined for the sport venue based on a scale of 0 to 5, with 0 being low and 5 being the greatest. It is the sport manager’s responsibility to determine what level is acceptable for the venue.
7. The final step involves the proposal of
consequence reduction
measures. These recommendations will help sport managers develop and/or enhance security policies and procedures, emergency response capabilities, and physical protection systems and capabilities at the venue. Also, suggestions for appropriate training in security awareness for staff and the sporting public are recommended.
The SESAM is a cyclical model, as assessments must be continuously completed to ensure that adequate plans and security measures are in place and maintained over a period of time. A sport venue’s threat or vulnerability level may change regarding circumstances in the country or even in the surrounding community. Evaluations of potential threats and existing vulnerabilities “are not only used to determine what dangers to prepare for and how to meet them, but also to prioritize preparedness efforts.” (Sauter & Carafano, 2005, p. 338). By determining which threats are the most dangerous, managers are able to decide where they should invest their time and effort in preparing to deal with the consequences of a potential incident (2005). The risk assessment process is also considered by most specialists “to be the most vital task establishing an effective business continuity/disaster recovery plan” (p. 338). Contingency planning will aid sport businesses in recovery efforts and continuation of operations during incidents.
Conclusion:
“On September 11th, it became abundantly clear that stadium and arena operators needed to incorporate security safeguards at America’s sporting venues.” (Pantera et. al, 2003, 1). It is critical that all sport organizations complete a risk assessment of their sport venues in order to identify vulnerabilities and improve security measures. The sport organization should not become complacent or content with their current security practices. Sport programs in America are faced with an ongoing battle to stay alert and be prepared for the ‘unthinkable.’
References:
Ammon, R., Southall, R. & Blair, D. (2004). Sport facility management: Organizing events and mitigating risks. Morgantown, WV: Fitness Information Technology, Inc.
CNN.com. (1996, July 27). Sources: arrest in Olympic bombing could occur within days. Retrieved September 15, 2005, from http://www.cnn.com/US/9607/27/blast.am/index.html
CNN.com. (2002, September 5). When sport lost its innocence. Retrieved September 26, 2005, from http://archives.cnn.com/2002/WORLD/europe/09/05/munich.72/
CNN.com (2006, October 18). Threats against NFL stadiums not credible. Retrieved October 18, 2006, from http://www.cnn.com/2006/US/10/18/football.threats/index.html
Decker, R.J. (2001). Key elements of a risk management approach. United States General Accounting Office. [On-line]. Available: http://www.gao.gov/new.items/d02150t.pdf
Durling, R.L., Price, D.E., & Spero, K.K. (2005). Vulnerability and risk assessment using the Homeland-Defense operational planning system (HOPS). Retrieved October 4, 2005, from http://www.llnl.gov/tid/lof/documents/pdf/315115.pdf
Farmer, P.J., Mulrooney, A.L., & Ammon, R. (1996). Sport facility planning and management. Morgantown, WV: Fitness Information Technology, Inc.
General Security Risk Assessment Guideline. (2003). ASIS International. [On-line]. Available: http://www.asisonline.org/guidelines/guidelinesgsra.pdf
Hagmann, D.J. (2005, October 30). Black hole in America’s heartland. Northeast Intelligence Network. Retrieved July 20, 2006, from http://www.homelandsecurityus.com/site/modules/news/article.php?storyid=16
Hurst, R., Zoubek, P., & Pratsinakis, C. (n.d.). American sports as a target of terrorism: The duty of care after September 11th. [On-Line]. Available: www.mmwr.com/_uploads/UploadDocs/publications/American%20Sports%20As%20A%20Target%20Of%20Terrorism.pdf
Pantera, M.J., et. al. (2003). Best practices for game day security at athletic & sport venues. The Sport Journal, 6 (4). [On-Line]. Available: http://www.thesportjournal.org/2003Journal/Vol6-No4/security.asp
Risk 101. (n.d.). U.S. Coast Guard. Retrieved October 4, 2005, from http://www.uscg.mil/hq/gm/risk/background.htm
Sauter, M. A. & Carafano, J.J. (2005). Homeland Security: A complete guide to understanding, preventing, and surviving terrorism. New York, NY: McGraw Hill.
Vulnerability Assessment Report. (July, 2003). Office of Domestic Preparedness, U.S. Department of Homeland Security. Retrieved May 31, 2005, from http://www.ojp.usdoj.gov/odp/docs/vamreport.pdf
Gender-specific Aspects of Football Expertise: Implications of Two Prospective Observation Studies
Abstract:
Women and men differ in many aspects of life; among these, their view of sport activities differ considerably. Thus, football (soccer) and the prediction of football results are recurrent sources of stress. Despite this, until now no study has investigated the parameters affecting football expertise in detail. We performed two prospective observation studies in health care employees to investigate whether football expertise, as a parameter combining behavioural, social, and physical aspects of life, is related to gender or anthropometric parameters.
The first study was performed in 2004 during the UEFA European Cup in Portugal. In order to confirm the results of the initial study, a second study was performed during the FIFA World Cup 2006 in Germany. A total of 307 persons were included in the studies. All volunteers had to predict the results of the preliminary round of the respective tournament. An evaluation of the results was done by scores, which were given for correct tendency and correct numbers of goals for each team.
In the first study, a significant difference between male and female participants was found (46.7 ± 1.3 pts, n=41 f: 42.7 ± 1.4 pts, n=42; p = 0.03). This was confirmed in the second study, which had a total of 224 participants. Here, male participants scored significantly higher than female participants (m: 113.9 ± 1.0 pts; f: 108.7 ± 1.3pts; p = 0.004). This difference remained significant in both studies after adjustment for age, profession, and BMI. Despite the fact that the majority of “couch potatoes” are supposed to be outstanding football experts, no relation between BMI and the ability to predict football results was found.
We demonstrated that men perform better in predicting football results than women. This finding was confirmed in a second independent cohort. The consequences of this apparent discrepancy between these gender specific realities on men’s health and the question of whether advertisement and television increasingly favour promoting women as football experts remain to be determined.
Introduction:
Football (soccer) expertise depends on psychological, social, and physiological factors. Despite the apparent impact of this topic on daily life, no study has investigated the parameters affecting football expertise in detail until now. In particular, the question of whether gender is important for individual football expertise is recurrent, due to a lack of valid studies and often irrational debate. Initially, football was dominated on and beside the football field by males. The classical roles were described; the male was the football expert, who rarely played football himself, watched football on TV, and liked to analyse previous games. On the other hand, women tried to avoid watching football games if possible and judged it simply as a sport with twenty-two men running for one ball. Therefore, discussions between males and females about this topic have been often dominated by males.
In recent years, this picture has changed remarkably. Apart from a considerable number of female football players and increasing interest by the media for professional female football, an increasing number of female football supporters have been registered (Member Statistics 2005 German Football Association). This has resulted in changes in the typical behavioural roles in relation to football. Indeed, football discussions often result in quarrels. These discussions are often passionate and lack rational bases. Taking all this together, there is certainly a considerable chauvinism in terms of supposed football expertise. Whether this is justified is completely unclear.
Therefore, we performed the first study to investigate whether football expertise, as a parameter combining behavioural, social, and physical aspects of life, is related to gender. Since men and women are apparently different in aspects potentially influencing football expertise, among them anthropometry and social status, we included these parameters in our multivariate analysis.
Methods:
The first study was performed in 2004 during the UEFA European Cup in Portugal. Participants for this study were recruited by e-mail and personal communication at the Charité-University Medical School Berlin and the German Institute of Human Nutrition. A total of eighty-three volunteers were recruited. Apart from personal information, all participants had to predict the results of the preliminary round. In total, there were twenty-four games.
To confirm the results of the initial study, a second study (study II) was performed during the 2006 FIFA World Cup in Germany. Participants of this study were recruited by Internet and intranet from the Charité-Medical School Berlin. Two hundred and forty-one persons agreed to participate in this study. However, due to missing data, seventeen individuals had to be excluded from final analysis so that a total of 224 persons were ultimately included in this study. All volunteers had to predict the results of the preliminary round of the FIFA World Cup 2006. In total, there were forty-eight games. Baseline characteristics of the volunteers of both studies are presented in Table 1. Additional questions about profession and occupational localization were asked.
Table 1: Baseline characteristics of volunteers in Study I and Study II. P-values for reached points were adjusted for BMI, age, profession, and workplace.
A) Study I: UEFA EC 2004
| Males | Females | p-value | |
| Participants | 41 | 42 | – |
| Points | 46.7 ± 1.3 | 42.7 ± 1.4 | 0.03 |
| BMI (kg/m2) | 23.3 ± 0.7 | 22.5 ± 0.5 | 0.34 |
| Age (y) | 32.2 ± 1.2 | 34.5 ± 1.6 | 0.25 |
B) Study II: FIFA WC 2006
| Males | Females | p-value | |
| Participants | 132 | 92 | – |
| Points | 113.9 ± 1.0 | 108.7 ± 1.3 | 0.004 |
| BMI (kg/m2) | 23.7 ± 0.2 | 22.2 ± 0.4 | 0.001 |
| Age (y) | 35.0 ± 0.7 | 36.6 ± 1.0 | 0.18 |
A total of 307 persons were included. An evaluation of the results was done by scores given for correct tendency and correct numbers of goals for each team. For correct tendency, three points were given and for correct number of goals for one team, one point was given. In one game, a maximum of five points could be achieved.
Statistics:
Statistical calculations were performed with SPSS 12.0 (SPSS Inc., Chicago, IL, USA). All values are given as mean ± standard error. Unpaired T-test was applied if parameters were normally distributed, otherwise Mann-Whitney-U test was used. Multivariate analysis was performed by General Linear Model procedure. Correlations between variables were investigated by Pearsons coefficient of correlation. An alpha-error below 5% was considered to be statistically significant.
Results:
In Study I during the 2004 EC in Portugal, a significant difference between males and females was found in eighty-three individuals (m: 46.7 ± 1.3 pts, f: 42.7 ± 1.4 pts; p = 0.03). This result was confirmed in Study II, which had a total of 224 participants. Here, male participants scored significantly higher than female participants (m: 113.9 ± 1.0 pts; f: 108.7 ± 1.3 pts; p = 0.004).
We next speculated that differences in anthropometry might affect these results, given that “couch-potatoes” might score differently than lean and fit individuals. However, no significant correlation was found between BMI (2006: r = 0.061; p = 0.391; 2004: r = 0.001; p = 0.991) and football expertise (Figure 1) in either study. Correspondingly, the gender specific difference of football expertise remained significant in both studies after adjustment for age and BMI.
Figure 1: Males show higher football expertise compared to female participants in the studies. Results were adjusted for age, profession, and BMI.
Figure 1:
| a) FIFA WC 2006 | b) UEFA EC 2004 | |
 |
 |
While no significant differences between physicians and non-physicians could be observed in Study I, physicians had significantly more points than non-physicians in Study II (P: 114.3 ± 1.3 pts, N.P.: 110 ± 1.1 pts; p = 0.007, figure 2). In the WC 2006 study, a more detailed analysis on the influence of profession was performed. The analysis of working areas showed that neurologists and psychiatrists had the highest levels of football expertise, while the lowest results were achieved by the members of the departments of pediatrics (internal medicine: 111.1 ± 1.3 pts, neurology/psychiatry: 115.2 ± 2.6 pts, pediatrics: 109.8 ± 4.4 pts, surgical departments: 112.1 ± 3.2 pts, radiology: 112.1 ± 5.2 pts, others: 109.7 ± 2.3 pts, administration: 111.3 ± 3.2 pts). Although apparently of considerable interest, none of these differences reached statistical significance. We additionally tested whether profession or workplace affected the relation between gender and football expertise. Although profession had a significant influence in Study II (p = 0.03), the gender-specific difference remained significant in both cohorts.
Subsequently, the relation between professional experience and football experience was tested in physicians. Although senior registrars had significantly more points than all other groups, especially the directors of the clinics (directors: 111.5 ± 9.7 pts, senior registrars: 118.6 ± 2.6 pts, SHO: 113.0 ± 1.5 pts, care staff: 109.3 ± 2.3 pts, scientist: 110.6 ± 1.6 pts, administration: 108.8 ± 3.2 pts, technicians: 108.9 ± 3.3 pts, students: 113.8 ± 2.8 pts), these differences were not statistically significant.
Discussion:
We demonstrate that men predict football (soccer) results more accurately than women. Thus, the widespread chauvinism in terms of football expertise appears to be partially justified. However, it is important to note that gender accounts for only about 5% of the variability of football expertise. Thus, additional, not-yet identified factors are apparently predominantly responsible for the individual football expertise.
Differences in health care between genders were recently acknowledged as important neglected points; these are part of the ongoing competition between men and women (1). The gender confrontation can also be found in the field of sports, which is not exclusive to the sport itself but includes parasportive activities (2). Football is among the most discussed topics, especially during globally communicated events like the recent FIFA World Cup (3;4). The classical role, which is also often presented by the media, characterises men as football experts, while women are neglected in that context. In recent years, this picture has changed considerably. Women are increasingly recognised as a potential focus of advertisement in the environment of sport events. Consequently, more and more women are presented as experts, i.e. in television broadcasts, which clearly challanges the classical role of the man being the football expert.
Our results indicate that in the general population, men are still better qualified to predict football results than women. Thus, any overemphasis with respect to women in that context is in contrast to the existing reality. The health consequences of such undeserved discrimination are unclear, but may finally result in inferiority complexes or aggression in men, which remains to be determined. Some points of the study design should be mentioned. The presented data are based on healthcare workers and it is unclear whether they can be transferred to the general population. In addition, the ability to predict football results is unlikely to represent the whole spectrum of football expertise. Another important topic addressed here was the relation between anthropometry and football expertise. Although no direct association with BMI was found, a relation to abdominal obesity cannot be excluded. However, “couch potatoes,” who are likely to perform pretty well in football results prediction, are characterised by abdominal obesity, rather than simply elevated BMI. In addition, only about 20% of the cohorts had a BMI higher than 25 kg/m2. Thus, the study may have been underpowered to address this question sufficiently.
Figure 2: Physicians (P) in Study II (n=224) show a significantly higher football expertise than non-physicians (N.P.). Results are after adjustment for age, sex, and BMI.
Figure 2:
In summary, we demonstrated that men perform better in predicting football results than women. This finding was confirmed in a second independent cohort. The consequences on men’s health due to the apparent discrepancy between gender specific realities and the fact that advertisement and television increasingly favour women as football experts remain to be determined.
References:
Carroll D., S. Ebrahim, K. Tilling, J. Macleod, G.D. Smith. Admissions for myocardial infarction and World Cup football: Database survey. BMJ 2002; 325(7378):1439-1442.
Collin J., R. MacKenzie. The World Cup, sport sponsorship, and health. Lancet 2006; 367(9527):1964-1966.
Doyal L. Sex, gender, and health: The need for a new approach. BMJ 2001; 323(7320):1061-1063.
Tanaka H. The battle of the sexes in sports. Lancet 2002; 360(9326):92.
African-Americans in College Baseball
Abstract:
The under-representation of African-Americans in college baseball is evident. African-American athletes make up only 4.5% of all National Collegiate Athletic Association (NCAA) baseball players. They are a shrinking percentage of Major League Baseball players. A focus group was established to identify specific sociological issues which were perceived to influence the under-representation of African-Americans in collegiate baseball. Additionally, information from the observation of SEC baseball games during the 2006 season was used to quantify the social pattern. Data from the “traditionally black” Southwestern Athletic Conference (SWAC) and the Mid-Eastern Athletic Conference (MEAC) were also collected during the 2006 season. For the Southeastern Conference (SEC), fan attendance was less than 1% African-American and the player participation rate was 1.91 per team during the 2006 season. Additionally, none of the SEC head or assistant baseball coaches were African-American. The focus group determined that the reasons for the decline in numbers were related to (1) lifestyle factors, (2) competition from other sports and social opportunities, and (3) the absence of African-American role models in baseball. The authors propose that Title IX legislation and the influence of sports media were primary factors in the change.
African-Americans in College Baseball
The under-representation of African-Americans in college baseball is an obvious yet perplexing picture in athletics today. African-American athletes are more than equitably represented among many of the most popular collegiate spectator sports; however, their near absence in college baseball appears to be more than coincidental. Questions arise as to whether the educational system, the social system of athletics, and/or federal legislation have been responsible for the reduction in the number of African-American baseball players in America.
Only 4.5% of all National Collegiate Athletic Association (NCAA) baseball players were African-American during the 2004 season. This includes all divisions, in addition to the historically African-American colleges and universities. On the contrary, 42.0% and 32.3% of NCAA basketball and football players, respectively, were African-American in the 2003-2004 academic year (Bray, 2005).
When specifically examining one of the perennial collegiate conference baseball powers, the Southeastern Conference (SEC), only 4.2% of 2006 roster players were African-American, as noted in Table 1. The twelve universities that make up the SEC represent states with an average African-American population of 20.8%.
Ironically, when examining the historically black Mid-Eastern Athletic Conference (MEAC) and the Southwest Athletic Conference (SWAC), findings surface which again support the difficulty of finding African-Americans in collegiate baseball. African-Americans are the minority on many of the rosters of these teams, as seen in Table 1.
Table 1: African-American Participation and Attendance at SEC Baseball Games
| University | Number of African American Players | State Population (African American) | Number of African American Fans | Average Attendance | Number of African American Coaches |
|---|---|---|---|---|---|
| Alabama | 1 | 26.0% | 15 | 4172 | 0 |
| Auburn | 3 | 26.0% | 7 | 3021 | 0 |
| Arkansas | 1 | 15.7% | 0 | 7156 | 0 |
| Florida | 3 | 14.6% | 8 | 2607 | 0 |
| Georgia | 1 | 28.7% | 10 | 1958 | 0 |
| Kentucky | 1 | 7.3% | 6 | 1250 | 0 |
| Louisiana State | 3 | 32.5% | 8 | 7508 | 0 |
| Mississippi | 0 | 36.3% | 2 | 4363 | 0 |
| Mississippi St. | 0 | 36.3% | 3 | 6160 | 0 |
| South Carolina | 3 | 29.5% | 22 | 3424 | 0 |
| Tennessee | 4 | 16.4% | 5 | 1378 | 0 |
| Vanderbilt | 3 | 16.4% | 3 | 1484 | 0 |
| Alabama | 1 | 26.0% | 15 | 4172 | 0 |
| Source: Attendance statistics from SEC member schools 2006. All observations of fan counts were from weekend games in spring 2005 and spring 2006. State African-American percentages were obtained from the United States Census Bureau. |
With approximately 12.8% of the United States population reported to be African-American (United States Census Bureau, 2006), it would appear that African-American collegiate baseball players are under-represented. This is the case in both college and professional baseball.
Ken Williams of the Chicago White Sox, Major League Baseball’s (MLB) only African-American general manager, blamed the small number of collegiate scholarships designated for baseball on the small number of African-American players (Nightengale, 2006). Logan White, the Los Angeles Dodger’s amateur scouting director, noted that in his trips to colleges across the United States, he rarely encounters an African-American baseball player. Not only is the absence of the African-American player obvious at the collegiate level, the population has gone from 27% of Major League Baseball (MLB) players in 1975 to 8% today (Nightengale, 2006). Sociologists have recognized this trend and have proposed several theories to explain it.
Theories
A possible explanation for the diminishment of African-Americans in collegiate and professional baseball could be explained by Giddens’ (1979) “structuration” theory. This theory assumes that certain behaviors are shaped by an array of interconnected structures. These interconnected structures can include norms, accessibility, and facilitators. Norms are the expected behaviors that govern a culture. Facilitators can be individuals or events that increase the likelihood of engaging in a behavior. (The behavior in this case would be baseball.) Accessibility refers to the degree of availability a population has to baseball.
The Negro Leagues of the early part of the 20th century, in particular, provided African-Americans with access to a culture aligned with baseball. Prior to the integration of African-Americans into Major League Baseball (MLB) in 1947 (“African Americans in Sports,” n.d.), an estimated 2,300 African-Americans participated in professional baseball through the Negro Leagues (Lynn, 2006). In the 1920’s, even small African-American communities, such as the town of Buxton, Iowa, touted semiprofessional teams like the Wonders (Beran, 1990). African-American fans often traveled to surrounding states to watch the Wonders play. These games became a routine part of daily life for this community. Beran (1990) further noted that the games served as a gathering place for members of the community. As a result, the Wonders became a major part of the cultural identity of Buxton. Since the retirement of former Negro-League stars who went on to stellar careers in MLB, such as Henry Aaron, the number of both African-American baseball players and spectators has steadily declined in MLB (Early, 2000; Flanagan 1999).
Research by Odgen (2003a) suggested that television images may perpetuate the stereotype that African-Americans are not welcome in baseball venues. This is the basis for Odgen’s ‘Welcome Theory’, which states that certain groups feel a sense of belonging in some leisure activities, but not in others. Odgen found that African-Americans felt most welcome playing basketball and least welcome at country clubs. Feeling unwelcome in some leisure activities is not restricted to the African-American race. All races share a common attitude that activities are suited to some ethnicities more than others (Philipp, 1999). For example, of the 137 crowd shots at a particular televised baseball game, only one of them displayed a group of African-Americans (Odgen, 2003a). Furthermore, Ogden reported that only 3% of the attendance at a game dubbed “African-American Heritage Night” consisted of African Americans.
The Welcome Theory may be partially created by the extensive mass media edification of professional African-American basketball players (Hall, 2002). African-American youth are frequently shown that basketball is the most efficient route to fame and fortune. As a result, almost 80% of basketball players in the National Basketball Association (NBA) are African-American (Boyd, 1997).
Another factor that might explain the absence of African-Americans from baseball is a lack of social support for the game. A primary reason that children select extracurricular activity is for interaction with peers (Watson and Collis, 1982). Children naturally gravitate towards activities endorsed by peers within their social groups. The peers of African-American youth frequently endorse basketball instead of baseball by donning the apparel of their favorite NBA stars (Philipp, 1998; Wilson & Sparks, 1996).
Gravitation towards participation in sports other than baseball may begin at the youth level. Of the 2,000 youth players in select tournaments from 1998-2000, only 3% were African American (Odgen, 2001). Select leagues, also known as traveling teams, are the highest level of play in age-group baseball. These teams may be compiled from competitive tryouts and/or selecting players from other “all star” teams. Select team baseball is characterized by long and arduous seasons that may contain as many as 150 games for youth players (Odgen, 2003b). These teams often play games all across the country, which requires considerable travel expenses. This external demand may validate limited access as an explanation, if one assumes that African-Americans have less access to baseball leagues, select-travel teams, and fields. Baseball diamonds are documented more frequently in the suburbs than in the urban core, where the population of African-Americans is more heavily populated, further supporting the theory of a reduced access that African-American youth have to baseball (Odgen 2003b).
Efforts to Curb Disparity
Recently, several MLB celebrities and players have attempted to curb the lack of interest of African-Americans in the sport of baseball. Initiatives such as Reviving Baseball in Inner Cities (RBI), founded by John Young, a former major league player, have received funding from Major League Baseball (“Major League Baseball,” n.d.). RBI was created to enable inner city youth with reduced accessibility and funds to enjoy baseball. Since its inception in 1989, the RBI program has provided opportunities for youth baseball in more than 200 cities. Major League Baseball also sponsors a program known as the Urban Youth Baseball Academy (“Major League Baseball,” n.d.). Some former participants in this program have remained in baseball and are now professional baseball players. Another project, known as the Urban Initiative for Little League Baseball, plans to expand existing facilities and baseball programs in the inner cities (“Little League Online,” n.d. ). Professional players, such as Torii Hunter, have even spearheaded efforts to raise funds for the creation and maintenance of baseball facilities in low income areas (“The Torii Hunter Project,” n.d.).
After an examination of the literature, it appears that those who are associated with and who study baseball have taken note of the declining African-American population in the sport. The authors of this study attempted to quantify the number of African-Americans playing college baseball in several of the most visible collegiate conferences in America in an attempt to measure the magnitude of the social change.
Methods:
A focus group was established to assist in identifying specific sociological issues perceived to influence the under-representation of African-Americans in collegiate baseball. The focus group consisted of twelve college age, African-American males who were either currently on a NCAA Division II baseball roster or who had played baseball in high school but were no longer playing in college. The group met during the fall of 2006 in three, one-hour sessions over a one month period. The first meeting consisted of an introduction to the topic, followed by the distribution of the outline of this study. This was followed by a period of general brainstorming. The group was asked to investigate the literature related to this study topic before the next meeting. In the second meeting, the group continued brainstorming. Members were allowed to present findings from the previous week of research and to begin extrapolating reasons for the social change in baseball. Common themes among the focus group were identified. In meeting three, the focus group began the process of assembling and ranking its theories for the reduction of African-Americans in collegiate baseball.
In addition to the qualitative, focus group study, the authors gathered data from NCAA data bases and from observation of SEC baseball games during the 2006 season. The authors personally attended and collected SEC baseball attendance data by conducting visual counts of African-American fans and players at select SEC games during the 2006 season.
For point of interest purposes, data from the Southwestern Athletic Conference (SWAC) and the Mid-Eastern Athletic Conference (MEAC) were collected by examining the media publications of each member institution’s athletic website for the 2006 season. The schools in these two conferences are known as historical black colleges and universities (HBCU) with predominantly African-American populations. The authors attempted to secure the numbers of African-American baseball players and coaches from these conferences.
Results:
Examinations of the findings in Table 2 depict an SEC baseball fan attendance base that was 0.2% African-American during the 2006 season. There was an average of seven African-American fans at each weekend SEC baseball game in 2006, out of the average crowd of 3,707. In all cases, the African-American fan count was less than 1% of the attendance.
Table 2: African-American Participation in the Mid-Eastern Atlantic Conference (MEAC) and the Southwest Atlantic Conference (SWAC)
<tr”>MEAC% of African American PlayersSWAC% of African American Players
| Bethune-Cook | 21 | Alabama A&M | 90 |
| Coppin State | 30 | Alabama State | 84 |
| Delaware State | no baseball | Alcorn State | 50 |
| FAMU | 39 | Arkansas PB | no data |
| Hampton | no baseball | Grambling | 80 |
| Howard | no baseball | Jackson State | 72 |
| Maryland Eastern | 16 | Miss Valley St | 100 |
| Morgan State | no baseball | Prairie View | 84 |
| Norfolk | 25 | Southern | no data |
| N.C. A&T | 60 | ||
| Average among those reporting | 31 | 68 | |
| Sources, http://www.meacsports.com/, http://www.swac.org |
Additionally, none of the SEC head or assistant baseball coaches were African-American during the 2006 season. The average SEC team had 1.91 African-American players on the forty-man roster with the range from one to four players. The two SEC schools representing states with the highest African-American population, Mississippi State and Mississippi, from a state with a 36.3% African-American population, had zero African-American players.
As presented in Table 3, the focus group identified four categorical areas as reasons for the limited number of African-Americans in college baseball. The reasons noted by the focus group, in order of their perceived importance, were: (1) lifestyle factors, (2) competition for the African-American athlete from other sports and social opportunities, (3) the absence of African-American role models (either active players or coaches), and (4) a limitation resulting from the perception that the African-American athlete is more difficult to coach.
Table 3: Focus Group Conclusions for the Scarcity of African-American Baseball Players
| Themes | Description |
| Lifestyle Factors | African Americans are more interested in fast-paced sports. |
| Competition | College baseball is out-recruited by more visible sports. |
| The popularity of AAU basketball draws interest. | |
| College baseball has less recruiting money. | |
| Minority scholarships take away opportunities for African-Americans in historically black colleges and universities. | |
| Role Models | There is a small number of African-American baseball icons. |
| There are not many visible African-American GMs and Managers. | |
| Limitations | The African-American athlete is viewed as less able to be coached and is, therefore, less likely to be recruited. |
Discussion:
There have been many theories presented as to why African-Americans are rapidly disappearing from college baseball. One possible explanation could be the relationship between the onset of Title IX, which led to many NCAA I schools reducing the number of baseball scholarships to 11.7 and to 10.0 in NCAA Division II, which may have contributed to the loss of interest in a college sport where full-scholarships are rare. In both NCAA Division I and II, partial scholarships are the rule, not the exception. There is the possibility that baseball has been socially architected out of the mainstream of African-American culture by means of well-intended legislation, such as Title IX. Results of this legislation have been to reduce access for the less affluent to college baseball and to influence athletes with the ability to play multiple sports to select a sport that can lead to a full scholarship.
Well-documented theories, such as Gidden’s structuration theory (1979), Ogden’s Welcome Theory, and limited access proposals (2003) may have credibility; however, they are difficult to prove quantitatively. It is likely that more than one specific theory or variable has been key in this social shift in baseball.
Several questions must be addressed. Have high-school and college baseball priced themselves out of the African-American athlete’s market by requiring participation in select teams for high schools or by limiting scholarship money for the college bound? Has the eagerness of the Central-American baseball player to sign for small bonuses become more appealing to MLB than going after the African-American player? Has the fact that MLB is now an international game influenced the reluctance of the high-school athlete to pursue baseball in college because other sports appear to be less competitive in the athlete’s quest for stardom? Is baseball too slow for the fast-paced lifestyle of the inner-city African-American youth? Are white athletes replacing African-Americans in baseball or is the international growth of the game naturally reducing the influence of any one racial group?
Perhaps the most perplexing rationale for the reduction in participation rates among African-Americans arises from the focus group in this study, which stated that the perceived slower pace of baseball has become a deterrent to participation among African-Americans. Baseball has many strategic games within it that are, in reality, constantly changing and fast paced. Therefore, the pace issue may have evolved out of a false perception which has been capitalized upon by those marketing other sports. People may not understand or see these elements of baseball. This issue itself merits further study.
No doubt, the evolution of sport participation is well documented. However, much study is needed before the theories behind the change can be scientifically proven. This author believes the change is primarily the result of a combination of the ramifications of Title IX legislation and the mass media marketing of the perception that other sports are faster paced and more entertaining.
References:
African Americans in Sports (n.d.). Retrieved September 15, 2006, from http://www.jimcrowhistory.org/scripts/jimcrow/sports.cgi?sport=Baseball
Beran, J. (1990). Diamonds in Iowa: Blacks, Buxton, and baseball. Journal of Negro History, 75(3-4), 81-95.
Boyd, T. (1997). “The day the Niggaz took over: Basketball, Commodity, Culture, and Black Masculinity in Out of Bounds: Sports, Media, and the Politics of Identity, ed. Aaron Baker and Todd Boyd. Bloomington: Indiana University Press, p. 140.
Bray, Corey. (2005). 1999-2000-2003-2004 Student-Athlete ethnicity report. The National Collegiate Athletic Association. January 2005, Retrieved September 1, 2006, from http://www.ncaa.org/library/research/ethnicity_report/2003-04/2003-04_ethnicity_report.pdf
Early, G. (2000). Why baseball was the Black national pastime. In T. Boyd & K.L. Shropshire (Eds.), Basketball Jones (pp.27-50). New York: New York University Press.
Flanagan, J. (1999, June 29). Baseball continues to ponder how to attract black fans. Kansas City Star, p. C2.
Giddens, A. (1979). Central Problems in Social Theory. Berkeley: University of California.
Hall, R. (2002). The bell curve: Implications for the performance of black/white athletes. Social Science Journal, 39(1), 113-118.
Little League Online. (n.d.). Retrieved September 22, 2006, from http://www.littleleague.org/programs/urban.asp
Lynn, A. (2006, February 21). Research on minority stars for Baseball Hall of Fame a revelatory process. Illinois News Bureau. Retrieved September 15, 2006, from http://www.news.uiuc.edu/news/06/0221hallfame.html
Major League Baseball: Community: Reviving Baseball in Inner Cities (RBI). (n.d.). Retrieved September 22, 2006, from http://mlb.mlb.com/NASApp/mlb/mlb/official_info/community/rbi
Nightengale, B. (2006, June 2). Where are black ballplayers? USA Today, pp. 1C-2C.
Odgen, D. (2001). African Americans and pick-up ball: A loss of diversity and recreational diversion in midwest youth baseball. NINE: A Journal of Baseball History & Culture 9: pp. 200-207.
Odgen, D. (2003a, April 12). Baseball Crowd Shots and the Social Construction of Spectators: An Exploratory Analysis. Paper presented at the Central State Communication Association meeting.
Odgen, D. (2003b, March 20-23). The Welcome Theory: An approach to studying African American Youth Interest and Involvement in Baseball. Paper presented at the Tenth Annual NINE Spring Training Conference. Retrieved September 1, 2006 from http://muse.jhu.edu, from the Project Muse Database.
Philipp, S. (1998). Race and gender differences in adolescent peer group approval of leisure activities. Journal of Leisure Research, 30(2), 214-232.
Philipp, S. (1999). Are we welcome? African American racial acceptance in leisure activities and the importance given to children’s leisure. Journal of Leisure Research 31: pp.385-403.
The Torii Hunter Project. (n.d.). Retrieved September 22, 2006, from http://www.toriihunter48.com/
United States Census Bureau. (Revised 2006, June 23). USA QuickFacts from the U.S. Census Bureau. Retrieved September 1, 2006, from http://quickfacts.census.gov/qfd/states/00000.html
Watson, G. & Collis, R. (1982). Adolescent values in sport: A case of conflicting interests. International Review of Sport Sociology 17 (1982): pp. 73-90.
Wilson, B. & Sparks, R. (1996). It’s gotta be the shoes: Youth, race, and sneaker commercials. Sociology of Sport Journal, 13(4), 398-427.
Competitive Balance and Conference Realignment: The Case of Big 12 Football
Abstract:
Past research has indicated that an effort to achieve greater competitive balance has been one factor in conference realignments within college athletics. The purpose of this study was to determine if greater levels of competitive balance in football were realized after the Big 8 conference merged with four members of the Southwest Conference. Specifically, comparisons were made between competitive balance levels for the last ten years of the Big 8 with the first ten years of the Big 12. Three measures of competitive balance were employed; in general, the findings indicated that competitive balance in football has improved in the ten years after the merger.
Introduction:
In the area of competitive sports, it is mandatory that the outcome of any game or match contain some degree of uncertainty. If this was not the case, it is believed fans would lose interest (Depken & Wilson, 2006; El Hodiri & Quirk, 1971; Kesenne, 2006; Quirk & Fort, 1992; Sanderson & Siegfried, 2003) and thus there would be significantly lower revenues for the organizations involved, particularly media revenue. Stated somewhat differently, it is of vital importance that for any sports league or conference, there needs to be some degree of competitive balance among the various teams.
Competitive imbalance is often linked to disparate revenues among competing organizations (Kaplan, 2004; Sanderson, 2002). At the professional level, these disparities are commonly linked to variables such as the size of a particular team’s media market or home facility. Organizations serving larger markets and/or having newer facilities may be able to generate more revenue than competitors, and thus secure the most talented teams. Likewise, at the collegiate level institutions may enjoy competitive advantages as a result of revenues generated from larger fan bases and better facilities. While those monies may not be passed to student-athletes in the form of salaries, high-revenue programs arguably enjoy significant recruiting advantages because they can invest in new or improved facilities and other program enhancements.
At the professional level, a variety of tactics are commonly employed to enhance competitive balance (Sanderson & Siegfried, 2003). They include salary caps, luxury taxes, revenue sharing, and draft orders favoring those teams that enjoyed the least success the previous season. At the college level, measures such as scholarship limits and prohibitions against extra benefits for student-athletes have attempted to promote competitive balance (Rhoads, 2004). These regulations are commonly enforced by a national governing body (e.g., NCAA, NAIA). However, college athletic conferences also play roles in promoting competitive balance. In particular, Rhoads (2004) has argued the conference realignments are at least partially driven by competitive disparity among institutions. Because of the large gate and television revenues that are often at stake, particularly in football and men’s basketball, efforts to maintain a certain degree of competitive balance in these sports would serve as an incentive to bring about churning within, and mergers between, conferences (Rhoads, 2004).
The purpose of this paper is to attempt to measure the change in competitive balance as a conference changes its membership. Does this bring about the desired increase in competitive balance? In order to shed light on this question, we surveyed the changes in competitive balance as the Big 8 Conference merged with four members of the Southwest Conference to become the Big 12.
Since previous research has suggested no increase in competitive balance in men’s basketball as conferences have gone through change (Rhoads, 2004; Perline & Stoldt, in press), we have chosen to test the hypothesis that attempts to increase competitive balance in football are major reasons for conference realignment (Rhoads, 2004; Fort & Quirk, 1999; Quirk, 2004). More specifically, we compared levels of competitive balance in football in the ten years before the merger with the ten years after. Although the Big 12 separated football into two divisions, we chose to use the overall conference standings for our analysis. This seemed most appropriate, since the Big 8 was not so divided, and teams in each division of the Big 12 played three of their eight conference games with teams in the opposite division.
The Big 12 Conference
The Big 12 is a NCAA Division I-A level conference founded in 1995 (Big 12, 2006). Its membership includes Baylor University, the University of Colorado, Iowa State University, the University of Kansas, Kansas State University, the University of Missouri, the University of Nebraska, the University of Oklahoma, Oklahoma State University, the University of Texas, Texas A&M University, and Texas Tech University.
Each institution in the conference was formerly a member of either the now-defunct Big 8 or Southwest conferences. Changing dynamics in the collegiate athletics marketplace, such as other conferences churning members and new agreements for television coverage, provided an impetus for the formation of the Big 12 (Michaelis, 1996; Thompson, 2000). The new conference included each member of the Big 8 and four institutions from the Southwest Conference. Texas and Texas A&M were, arguably, the flagship programs in the Southwest Conference, so their selection was not surprising. The decision to include Baylor and Texas
Tech was more controversial because those institutions were from smaller markets than the four other members of the Southwest Conference that were not selected. However, both institutions had alumni in key offices within the Texas state government at the time of the merger, and the political influence of those state officials impacted Baylor’s and Texas Tech’s selection (Thompson, 2000; Waldman, 1995). The resultant geographic market of the new conference includes 42 million people and 18 million households with television — roughly 16% of the nation’s total (Big 12, 2006; Michaelis, 1996; Thompson, 2000; Waldman, 1995).
The conference is separated into two divisions for football. The North Division features Colorado, Iowa State, Kansas, Kansas State, Missouri, and Nebraska. The South Division is comprised of Baylor, Oklahoma, Oklahoma State, Texas, Texas A&M, and Texas Tech. Each year, each school plays one game against its divisional opponents and three games against teams from the other division. A rotation system is used to select which three “other division” opponents a team will face in a given system. Over four years, each team will play every team from the other division twice — once at home, once away. All conference games count toward the division standings, and the two division winners meet in a conference championship game each year. The winner of that championship game receives an automatic bid to participate in the Bowl Championship Series (BCS).
Big 12 football teams have enjoyed considerable success at the national level. The conference has placed a team in the BCS national championship game five times, more than any other conference (Big 12, 2006). Further, three teams have won national championships since the conference was founded: Nebraska in 1997, Oklahoma in 2000, and Texas in 2005.
Measuring Competitive Balance:
Several methods have commonly been used to measure competitive balance. The most appropriate of these methods often depends on what the researcher is attempting to measure. Methods most appropriate for measuring competitive balance within a given season may be different from those used to measure competitive balance between seasons (Leeds & VonAllmen, 2005). Since different concepts are being measured, there is no reason to assume the various methods will reach the same conclusions about degree of competitive balance. Indeed, if it is argued that competitive balance is necessary to keep fans interested and thus revenues maximized, it could be argued that no particular method can address theoretical optimal balance, i.e., what the fans who buy tickets and watch television believe is most appropriate. One could even argue that overall conference revenue could be maximized if the teams in the largest markets, with the largest fan base, won most often. Given these caveats, efforts have been made to measure competitive balance. In addressing our task, we rely on such methods.
Three of the most commonly employed measures are:
- the standard deviations of winning percentages of the various teams in the conference or league
- the Hirfindahl-Hirschman Index to measure the number of teams that achieve championship status over a given period of time
- the range of winning percentages
Standard Deviation of Winning Percentages
The method probably used most often to measure competitive balance within a conference in a given season is the standard deviation of winning percentages. Since there will, outside of a tie, always be one winner and one loser for each game, the average winning percentage for the conference will always be .500.
In order to gain insight into competitive balance, we need to measure the dispersion of winning percentages around this average. To do this, we can measure the standard deviation. This statistic measures the average distance that observations lie from the mean of the observations in the data set. The formula for the standard deviation is:
σ = (√(Σ(WPCT – .500)2)) / N
The larger the standard deviation, the greater the dispersion of winning percentages around the mean, thus the less the competitive balance. (If all teams have winning percentages of .500, there would be a standard deviation of zero and there would be perfect competitive balance.)
Using the actual standard deviation in our case presents a potential problem. This occurs because, all things being equal, there is a likelihood that the larger the number of conference games played, the more likely there will be less deviation of winning percentages, since various lucky breaks, injuries, etc. will, over time, even out. Since the number of league games played in the Big 8 was seven and the number of league games played in the Big 12 was eight, there is a need to adjust for these differences. This adjustment entails finding the ideal competitive balance in which each team has a 50% chance of winning each game. This ideal can be measured as:
σ = 0.5 /√ N
where .5 indicates the .5 probability of winning, and n is the number of games played by each team in the season.
In the Big 8, the ideal standard deviation ratio would be 0.5 / √ 7 = 0.1890 and for the Big 12 would be 0.5 / √ 8 = 0.1768.
To measure the competitive balance within a given season, we find the ratio of the actual standard deviation to the ideal standard deviation.
R = σA / σI
The closer the measure is to one, the more competitive balance there is.
Championship Imbalance
While using the standard deviation as a measure of competitive balance provides a good picture of the variation within a given season, it does not indicate whether the same teams win every season, or if there is considerable turnover among the winners i.e., whether there is between-season variation.
Therefore, another method economists have used to measure imbalance is the Hirfindahl-Hirschman Index (HHI), which was originally used to measure concentration among firms within an industry (Leeds & von Allmen, 2005). Whereas the standard deviation was used to measure percentage winning imbalance, the HHI is used to measure championship imbalance — how the championship is spread amongst the various teams. Using this method, the greater the number of teams which achieve championship status over a specific time period, the greater the competitive balance. The HHI can be calculated by measuring the number of times each team won the championship, squaring that number, adding the numbers together, and dividing by the number of years under consideration. Using this measure, it can be concluded that the lower the HHI, the more competitive balance among the teams.
Range of Winning Percentage Imbalance
Although the standard deviation of winning percentages can tell us about variation around the mean, it does not specifically reveal if the same teams win or lose from season to season. Likewise, whereas the HHI gives us some perspective on the number of teams who win the championship over a period of time, it does not tell us what is happening to the other teams in the conference. It is quite possible that a few teams could always finish first, but that the other teams could be moving up or down in the standings from one year to another.
One way of gaining insight into the movement in the standings of all teams over time is to get the mean percentage wins for each team over a specific period. The closer each team is to .500, the greater the competitive balance over this period. If several teams had very high winning percentages and others had very low winning percentages, it would suggest that there was not strong competitive balance over time, because the same teams would be winning losing, year after year.
Results:
We employed each of the three measures of competitive balance in our analysis of football results for the Big 8 and Big 12 Conferences. Findings are offered in the following sections.
Standard Deviation of Winning Percentages
Source: Information provided by Big 12 Conference office.
| Year | MO | KU | OU | KSU | NU | ISU | OSU | CU | |
|---|---|---|---|---|---|---|---|---|---|
| 1986 | .286 | .000 | 1.000 | .143 | .714 | .428 | .571 | .857 | |
| 1987 | .428 | .071 | 1.000 | .071 | .857 | .286 | .714 | .571 | |
| 1988 | .286 | .143 | .857 | .000 | 1.000 | .428 | .714 | .571 | |
| 1989 | .143 | .286 | .714 | .000 | .857 | .571 | .428 | 1.000 | |
| 1990 | .286 | .357 | .714 | .286 | .714 | .357 | .286 | 1.000 | |
| 1991 | .143 | .428 | .714 | .571 | .928 | .214 | .071 | .928 | |
| 1992 | .286 | .571 | .571 | .286 | .857 | .286 | .357 | .786 | |
| 1993 | .286 | .428 | .571 | .643 | 1.000 | .286 | .000 | .786 | |
| 1994 | .286 | .428 | .571 | .714 | 1.000 | .071 | .071 | .857 | |
| 1995 | .143 | .714 | .286 | .714 | 1.000 | .143 | .286 | .714 | |
| Mean | .257 | .343 | .700 | .343 | .893 | .307 | .350 | .807 | .500 |
Source: 2005 Big 12 Football Media Guide contained data for 1996-2004. Big 12 Website contained data for 2005.
| Year | KU | CU | UT | ISU | TTU | OU | NU | OSU | BU | MU | TAMU | KSU | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1996 | .250 | .875 | .750 | .125 | .625 | .375 | 1.000 | .250 | .125 | .375 | .500 | .750 | |
| 1997 | .375 | .375 | .250 | .125 | .625 | .250 | 1.000 | .625 | .125 | .625 | .750 | .875 | |
| 1998 | .125 | .500 | .750 | .125 | .500 | .375 | .625 | .375 | .125 | .625 | .875 | 1.000 | |
| 1999 | .375 | .625 | .750 | .125 | .625 | .625 | .875 | .375 | .000 | .125 | .625 | .875 | |
| 2000 | .250 | .375 | .875 | .625 | .375 | 1.000 | .750 | .125 | .000 | .250 | .625 | .750 | |
| 2001 | .125 | .875 | .875 | .500 | .500 | .750 | .875 | .250 | .000 | .375 | .500 | .375 | |
| 2002 | .000 | .875 | .750 | .500 | .625 | .750 | .375 | .625 | .125 | .250 | .375 | .750 | |
| 2003 | .375 | .375 | .875 | .000 | .500 | 1.000 | .625 | .625 | .125 | .500 | .250 | .750 | |
| 2004 | .250 | .500 | .875 | .500 | .625 | 1.000 | .375 | .500 | .125 | .375 | .625 | .250 | |
| 2005 | .375 | .625 | 1.000 | .500 | .750 | .750 | .500 | .125 | .250 | .500 | .375 | .250 | |
| Mean | .250 | .600 | .775 | .313 | .575 | .688 | .700 | .388 | .100 | .400 | .550 | .663 | .500 |
Tables 1 and 2 display the annual winning percentages for the football teams in the Big 8 and Big 12 Conferences, respectively. Tables 3 and 4 display the annual standard deviations, the standard deviation ratios, and the means for the ten years of data in the Big 8 and Big 12 conferences.
Source: Authors’ calculations according to formulas in text from data in Table 1.
| Year | Standard Deviation |
Standard Deviation Ratio |
|---|---|---|
| 1986 | .3498 | 1.851 |
| 1987 | .3498 | 1.851 |
| 1988 | .3498 | 1.851 |
| 1989 | .3498 | 1.851 |
| 1990 | .2725 | 1.442 |
| 1991 | .3415 | 1.807 |
| 1992 | .2321 | 1.228 |
| 1993 | .3171 | 1.678 |
| 1994 | .3479 | 1.841 |
| 1995 | .3238 | 1.731 |
| Mean | .3234 | 1.711 |
Source: Authors’ calculations according to formulas in text from data in Table 2.
| Year | Standard Deviation |
Standard Deviation Ratio |
|---|---|---|
| 1996 | .2968 | 1.679 |
| 1997 | .2919 | 1.651 |
| 1998 | .2919 | 1.651 |
| 1999 | .2968 | 1.679 |
| 2000 | .3153 | 1.783 |
| 2001 | .2968 | 1.679 |
| 2002 | .2770 | 1.567 |
| 2003 | .2968 | 1.679 |
| 2004 | .2556 | 1.446 |
| 2005 | .2500 | 1.414 |
| Mean | .2869 | 1.623 |
The data indicate that overall competitive balance increased with the merger of the Big 8 into the Big 12. After adjusting, the mean of the standard deviation ratio was 1.711 for the Big 8 (see Table 3 – mean standard deviation ratio) and 1.623 (see Table 4 – mean standard deviation ratio) for the Big 12. This was a difference of 5.4%.
If we eliminate the lowest standard deviation ratio for the Big 8 – 1.228 – which would appear to be an outlier as it was well below the mean, the mean for the Big 8 would rise to 1.767, which would raise the percentage differential between the Big 8 and Big 12 to 8.9%.
Championship Imbalance
Using the HHI to measure competitive balance in the Big 8, we find that over the ten-year period, three teams achieved a first place finish: Nebraska 6, Oklahoma 2, and Colorado 2. If we give one point for each first place finish squared, we find:
HHI= 62 + 22 + 22 = 44/10= 4.4
Since the Big 12 is divided into two divisions, the first place finishers in each division play each other to determine the championship. We find that over the ten-year period, six different teams won the championship: Oklahoma 3, Texas 2, Nebraska 2, Colorado 1, Texas A&M 1, and Kansas State 1. Applying the HHI to this data, we find:
HHI= 32 + 22 + 22 + 12 + 12 + 12 = 20/10= 2
Here, the numbers are particularly revealing. We see twice as many institutions won the championship in the first ten years of the Big 12 than had won in the previous ten years with the Big 8. These results, though, need be mitigated by the fact that one would expect there to be more difference in teams achieving the championship with twelve competitors than with eight. Nevertheless, in the case of the Big 8, three teams out of a possible eight (37.5%) won the championship, whereas in the case of the Big 12, six out of a possible twelve teams won the championship (50%). While this does lessen the difference, the Big 12 still remains considerably more competitively balanced.
Range of Winning Percentage Imbalance
If we arbitrarily set .500 plus or minus .100 as a range, which would suggest a high degree of competitive balance over the ten-year period, we find significantly more competitive balance in the Big 12 than in the Big 8.
The mean winning percentages displayed for each team in Table 1 (Big 8) suggest that when using such an approach, no teams fell within this .400-.600 range. There were obvious winners and losers, but not many in the middle. (Nebraska, Colorado, and Oklahoma were the winners, and the remaining five institutions were the losers.)
On the other hand, the mean winning percentages displayed for each team in Table 2 (Big 12) indicates that four institutions (33% of the league total) – Colorado, Missouri, Texas A&M, and Texas Tech – fell within the specified range. Texas, Nebraska, Oklahoma, and Kansas State exceeded the range. Oklahoma State, Iowa State, Kansas, and Baylor fell below the range, with the latter two institutions never having a winning season.
When looking at the range between the top and bottom winning percentages, we find that in the Big 8 the range is .636 (Nebraska .893 and Missouri .257), whereas it is actually larger for the Big 12 at .675 (Texas .775 and Baylor .100). Baylor has not had a winning season since joining the Big 12, and only once in the ten-year period has it won as many as two conference games. Therefore, if we were to exclude Baylor at an outlier, we find the range drops to .525 (Texas .775, and Kansas .250). This would make the range approximately 20% lower in the Big 12 than in the Big 8.
Conclusions:
Previous research had suggested that one reason for conference realignment was to achieve greater competitive balance in sports among the various member institutions (Rhoads, 2004). This appeared to be particularly true in football, one of the very high revenue sports in major athletic conferences. With this in mind, we investigated whether there was an increase in competitive balance in the sport of football after the Big 8 Conference merged with four members of the Southwest Conference to form the Big 12 Conference. The data for this study came from the conference standings in football for the Big 8 for the ten years prior to the merger and the standings for the Big 12 ten years subsequent to the merger.
Using the standard deviation to measure the winning percentage imbalance, and the Hirfindahl-Hirschman Index to measure championship imbalance, we concluded that each of the above measures indicated an increase in competitive balance after the merger. In the case of the range of winning percentages, the results suggested a slightly greater competitive balance for the Big 8, although once the least successful team in the Big 12 was dropped as an outlier, there was considerably more competitive balance in the Big 12. Given the fact that conferences often realign in an attempt to achieve greater competitive balance (Rhoads, 2004), these findings would support the decision to realign.
Achieving greater levels of competitive balance in a single sport is not the only justification for conference realignment. There are numerous ways in which the Big 8-Southwest Conference merger has impacted its member institutions and the overall landscape of college athletics. However, since competitive balance is recognized as being generally appealing to consumers and football is among the conference’s most marketable sports, the implications of these findings must be deemed important if not surprising.
References:
Barfknect, L. (2005, August 28). The Big 12 Conference turns 10: Still lookin’ good. Omaha World-Herald, 12fb.
Big 12. (2006). The Big 12 Conference-Celebrating its 10th season. Carlsbad, CA: CSTV Networks, Inc. Retrieved June 27, 2006 from http://www.big12sports.com/aboutbig12/big12-aboutbig12.html
Big 12. (2006). The Big 12 Conference. Carlsbad, CA: CSTV Networks, Inc. Retrieved June 27, 2006 from http://www.big12sports.com/sports/m-baskbl/big12-m-baskbl-body.html
Depken, C.A. (1999). Free-Agency and the competitiveness of Major League Baseball. Review of Industrial Organization, 14, 205-217.
Depken, C.A., & Wilson, D. (2006). The uncertainty of outcome hypothesis in Division I-A college football. Manuscript submitted for publication.
El Hodiri, M. & Quirk, J. (1971). An economic model of a professional sports league. Journal of Political Economy, 79, 1302-19.
Fort, R., & Quirk, J. (1999). The college football industry (pp. 11-25). In J. Fizel, E. Gustafson, & L. Hadley (Eds.). Sport economics: Current research. Westport, CT: Praeger.
Humpreys, B. (2002). Alternative measures of competitive balance. Journal of Sports Economics, 3, (2), 133-148.
Kaplan, D. (2004, March 29). “Our work just started” on revenue sharing, group says. Street & Smith’s SportsBusiness Journal, 40.
Kesenne, S. (2006). Competitive balance in team sports and the impact of revenue sharing. Journal of Sport Management, 20, 39-51.
Leeds, M. & vonAllmen, P. (2005). The Economics of Sports. Boston: Pearson-Addison Wesley.
Michaelis, V. (1996, June 30). Big time: Big Eight, SWC get the message, The Denver Post, 2D.
Quirk, J. & Fort, R.D. (1992). Pay dirt: The business of professional team sports. Princeton, NJ: Princeton University Press.
Rhoads, T.A. (2004). Competitive balance and conference realignment in the NCAA. Paper presented at the 74th Annual Meeting of Southern Economic Association, New Orleans, LA.
Sanderson, A. (2002). The many dimensions of competitive balance. Journal of Sport Economics, 3, 204-228.
Sanderson, A.R., & Siegfried, J.J. (2003). Thinking about competitive balance. Unpublished manuscript. Vanderbilt University
Thompson, A. (2000, July 1). No piece of cake: ‘Pandemonium’ prevailed during Big 12’s creation; Planning paid off in the end. The Denver Post, D09.
Waldman, A. (1995). A death in the family. Sport, 86 (6), 78-83.