Sports Studies and Sports Psychology – Page 59

Olympism for the 21st Century: New Life to a Timeless Philosophy

Submitted by: Deborah J. Hunter

Introduction

The Olympic Movement, sometimes referred to as Olympism, is a universal concept that is not defined simply. It is a philosophical ardor for life and the uncompromising pursuit of excellence. Just as individuals operate with a personal philosophy that guides their decision-making, Olympism, too, is philosophically directed through the elevated dimension of quality in how an individual conducts his/her life.

Olympism is an inner faith of a man in himself, a constant effort of physical and intellectual enhancement (Filaretos, 1993 p. 61). It is a general concept which emphasizes not only development of bodily strength, but generally healthier individuals with a happier attitude and a more peaceful vision of the world (The International Olympic Academy, 1997, p. 10). Olympism recognizes and extols individual effort and accepts no discrimination among nations, races, political systems, classes, etc. (The International Olympic Academy, p. 9). As we build awareness and highlight our commonality as human beings, we must realize we are all interconnected in this world. Though these connections are sometimes complex, elusive and difficult to recognize, examining our own patterns of behavior as world citizens will reduce our distance and allow us to find our common ground. All become part of the whole when members of nations learn about global perspectives and become familiar with national issues — this has been a long and historical pattern of the dynamics of relationships among many different people. The fact remains, Olympism involves not only active participants of the sport movement, but also the general public (The International Olympic Academy, p. 9). All people are relevant and interconnected among the diverse cultures of the world.

A Viewpoint on Olympism

The good intentions of Olympism are indeed well-established, but not necessarily well known. A prevailing challenge in today’s world is how to capture people’s attention long enough to convey important and life enhancing messages. Being the difficult job it is, merely sharing information only illustrates the size of the challenge it is to effectively educate people. Education takes quality time and the perception, too often, is that simply receiving information is the same as education. Education is the process of learning conceptual ideas that leads to behavioral awareness or change. A clear distinction needs to be understood on this matter; learning occurs only through practice. Our desire to educate young people regarding the values within the Olympic Movement runs deep and has long existed within a few select people all around the world. Accomplishing the goal of educating others about the Olympic Movement requires recognition of the major reorganization that must occur: 1) there must be an open willingness for revision of the Movement’s principles/values to be better understood in today’s reality; 2) we must package the valuable principles/values in numerous effective ways for appealing delivery; 3) advocates must first educate the deliverers (e.g., teachers, coaches, administrators, etc.) on the importance of the values within Olympism; 4) we must interrupt long existing educational patterns by convincing these systems to provide a window of opportunity for educational time to be devoted to the teaching of Olympism.; and 5) we should provide simulated, lifelike environments in which to apply the practice of the principles/values.

What is Valuable about Olympism Today?

Olympism encourages exploration of self and how self relates to community in a local sense. The smallest local actions accumulate and make an important global contribution. Also, Olympism is a tool that can better unify the people of the world. As experience is gained, the ability to see and think about the global picture becomes a natural outcome. Finally, everyone could be a role model to someone. If we have more people living with the concepts of Olympism in their daily lives, the philosophy will permeate our world at an exponential rate.The evolution of the principles of the Olympic Philosophy is essential. More importantly, there are necessary changes to be made in the moral standards and the values of people, their mentality and sentiments. The inherent values of Olympism that seem to have lost their meaning in our changing society must be identified and revised so that they match the continuous advancement of today’s world. People gain experience and perspective as they advance along the continuum of life. The birth of the Modem Olympic Games spawned a formal sporting event and the growth and change that has occurred from 1896 until today is almost immeasurable. As philosophy directs individual lives and the spirit of Olympism affects those lives around the globe, the common thread the two has is embedded in founding principles. These principles are anchoring and timeless values that have endured. From where or whom we are born, the principles of life that parents teach affect their children throughout their future. The Olympic Movement is much more than just the parent of the Modem Olympic Games, it is a choice that people can undertake by which to conduct their lives.

Gain More Widespread Respect For Olympism

To gain placement within an educational curriculum, the Olympic values must be progressive and command widespread public support and respect. For all of the positive stories that exist within the Olympic Movement, unfortunately, those stories told most frequently and with greatest sensationalism are the negative ones. Often this is said to sell more magazines, newspapers, to keep more television viewers, radio listeners, internet browsers, etc. Modem man is easily influenced by the somewhat contradictory information coming from a myriad of sources. This makes the individual lose his/her intellectual and spiritual independence and lowers the level of healthy self-analysis, which is imperative for self-improvement. Such an individual does not concentrate on the personal spiritual world; rather, he/she develops a tendency to suppress the thoughts and ideas that do not coincide with the interests of other people and society in general. The negative stories and constant reliance on other sources is in conflict with the development of a self-determined individual with unwavering moral standards. By the time an athlete becomes an Olympic-level performer, his/her character and value system has long been formed. In turn, these values are the reflection of the moral standards of society where the athlete has been raised. Reality shows that violence in sport and the use by top athletes of prohibited means of increasing their physical capacity are contradictory to the Olympic concepts of excellence and achievement. Contemporary competitive sport, with its emphasis on the materialistic benefits for individuals and societies, can create elite athletes with an individualistic, egocentric mentality and an excessively self-sufficient attitude (Dellamary, 1994, p. 210). So many adjunct sources contribute to the “win-at-all cost” acceptance of the Olympic Games, that the values of Olympism are often overlooked by the participants, spectators and organizers. It seems we espouse philosophical statements and then act contradictorily toward them. We most naturally reward the outcome rather than the process. Life’s journey is a process and cannot be ignored. The values of Olympism can be taught only through constant practice. Theory without practice is utopian. In Olympism, the principles and values that do not have a connection with an application to real life will not live long in people’s minds. When this connection is established, then Olympism will become not just a philosophy, but a beneficial lifestyle.

Improved Ways To Package The Message Of Olympism

Incorporating the values of Olympism into current curriculums and practices that develop athletes is better than to develop something entirely separate. This enhances the already existing curriculums and athletic practices and can contribute throughout the participation phase. Individuals must be practical and conceptual in the process of learning, understanding and most importantly, experiencing Olympic values. The worth of values is determined by their practice. That is why the education of Olympism should not be a promotion of statements; rather, it should teach the implementation of the values in life situations. Create ways to practice and reinforce these values; extend and apply them to today’s real life. Coaches are the instrumental and influential figures in the promotion of Olympism among young athletes. To develop a uniform and global reinforcement procedure has limited feasibility. It is best if the nations contribute within their cultural means and understanding of how to reach and reward their people in the best possible manner. An excellent program that is successfully operating to this end is the United States Olympic Committee’s Champions in Life program, which is targeted to include the disadvantaged children through youth recreational organizations. The program addresses the benefits of staying in school, staying drug-free, avoiding gangs and violence and being good citizens by being the best one can be. The concept of being a productive member of a society should be promoted as the prerequisite to being a good citizen of the world with global awareness. Sports, therefore, offer us a great opportunity to promote Olympic principles and values, but this opportunity is often under-utilized. Constant reminders of what we believe in are needed. Even simple things (T-shirts, pins, posters, banners, etc.) could have messages written in a simple but thought provoking and heart warming way. We should make a point to devote a few minutes at sporting events to recognizing our belief in the importance of Olympic values (messages in game programs, banners in the gym, public address announcements, athlete or coach comments at the end of the competition, etc.). It could be a valuable contribution if famous athletes and coaches, in their interviews, would sincerely include their support of the Olympic Movement in their commentary.

Improved Ways To Deliver The Message Of Olympism

There are three necessary steps in promoting new concepts and values:

Delivery of the message: the message must be clear, simple to understand and deliver the intended values through sport activities at different levels;
Education and reinforcement of the message: the application process should have reinforcement so the message is taken seriously and the learner comprehends the merits of the message and accepts them as desirable guidelines; and
Consistency which promotes the philosophy in all activities: continuous emphasis is a key to show how much the promoters care about their message.

This will require sincerity regarding why one is teaching/coaching and careful rationale as to what one is teaching/coaching. Concentrated educational experiences such as the International Olympic Academy are an effective model for delivery of Olympism as a valuable curriculum to study. The atmosphere and revered respect that the Olympic Movement is afforded changes lives and perspectives in a short amount of time. Undoubtedly, each and every individual who studies at Olympia becomes a lifetime activist for the movement. Disseminate teacher lesson plan guides beyond the formal educational system. Include children’s museums, national chain daycare centers, Girl and Boy Scouts and other youth organizations where quality children’s activities are valued and sought after. Incorporate the teaching of Olympism in the educational background of coaches.

These teachings must educate coaches how purposefully teaching about Olympic values will contribute to more balanced individual athletes and ignite their personal desire to find their own personal excellence and how Olympic values will strengthen and improve team interaction and success. Administrators, teachers and coaches should show personal interest and reward the adherence to the principles and make the experiences personal and valuable. There is a fine line between competition and cooperation — both are essential and the fine line must be identified and honored for sport to be optimized successfully as an asset to society. Today’s Olympic Movement must be challenged to assist with the removal of all barriers in allowing competitive excellence to be available to all. Sport within the Olympic Movement changes lives positively when performance excellence is sharply focused upon and established as a founding principle in life. When the fine line is blurred and disrespected to the point of allowing competition to be used only for personal gain (as in the pursuit of money or recognition), those driving pursuits are shallow and short-lived. They offer no lasting substance for a quality life from which our new generations will be born.

Suggestions For Gaining Educational Time

Traditions are a base for the formation of values. When people forget their traditions, they interrupt the connection between the past and the present and, as a result, lose the values. The revival of the traditions of Olympism will help to return the essence of the true values to our world.

Transcendence

Contemporary Olympism is influenced by the interaction of many factors that may cause its progressive decline (Dellamary, p. 209). There are two major threats that may prevent the progress of Olympism. They are excessive commercialism and the active involvement of governmental politics in sports (Filaretos, p. 61). The Olympic Movement will always be able to be improved. Implications for our Future Will teaching and thus interweaving these values into society uplift us and provide an eagle-eye view so that we may bring solutions to our varied world problems, which include: Asian economic instability, hyper-urbanization in Brazil, environmental degradation in China, civil war in Rawanda, starvation in North Korea, violence and drugs in the schools of the USA? Is Olympism powerful enough to make a difference to the even bigger issues in our world? Adherents of Olympism cannot influence the human tendency for violence, war, destruction and aggression among nations and groups. Advocates cannot stop economical and political changes of nations. They are helpless in the face of the commercialization of sports and the gigantism and luxury of the Olympic Gaines. But with all of their limits, they have a powerful instrument in their hands that can revive Olympism with its unique philosophy of ideal social coexistence. Only through the education of our youth and the establishment of high moral standards that unite the human race and disregard grounds for discrimination can the dissemination of a true universalization of Olympism become possible.

U.S. Sports Academy2013-11-27T15:02:50-06:00February 13th, 2008|Contemporary Sports Issues, Sports Coaching, Sports History, Sports Management, Sports Studies and Sports Psychology|Comments Off

It’s Time to Work Together to Stop Doping in Sports

Submitted by: Richard Bell, Ed.D.

The greatest threat to international sport isn’t the pay offs in Salt Lake City, but the use of dangerous performance-enhancing drugs. Their use threatens the very foundation of sport. The integrity, the image and even the existence of elite-level international competition is in jeopardy. Every world-class event is somehow tainted by “doping”, the use of illicit performance-enhancing drugs.

Charges continue to fly among world governing bodies as they try to shift blame and responsibility for the sullied reputation of international competition. In the aftermath of what is arguably the most successful Olympic Games ever, accusations persist that the International Olympic Committee (IOC) routinely turns a blind eye to evidence of doping and that its drug-detection methods are ineffectual. Perhaps overly sensitive to continued criticism about drugs in the Olympics, IOC leaders ripped the United States Track and Field Federation. They accused them of turning a blind eye and being in a “state of denial” about the use of performance-enhancing drugs by C.J. Hunter, the world shot-put champion and husband of Olympian Marian Jones. The IOC has even been accused of “covering-up” the drug epidemic in sport, but critics contend that the organization has sometimes discarded positive drug test results in fear that the image of the Games would suffer.

At the Atlanta Games in 1996 several athletes tested positive for Probenecid, the banned masking agent, but the IOC took no action. Only two Olympians were reported for testing positive for steroids although there were several other unreported positive samples. In spite of its new one million-dollar mass spectrometer, the IOC, afraid of legal challenges from athletes, discarded the results. The motivation, say critics, for the IOC’s soft policy on doping is the fear of loss of sponsorships resulting from a tarnished image. How much more tarnished does the image need to be before they put their considerable weight and influence behind an international independent drug testing and enforcement agency? At a meeting in 1999 at the IOC’s World Conference on Doping in Sport in Lausanne, Switzerland, the representatives of different sports federations from around the world failed to reach agreement for instituting a meaningful policy in drug enforcement which included a mandatory 2-year ban for doping. The delegates didn’t agree on a policy, ostensibly because the IOC insisted on control.

Historically, the IOC would appear to be a leader in the war against doping. It was the IOC that first defined and banned doping in 1967. The first drug tests in international sport were administered for research purposes by the IOC at the 1968 Olympic Games in Mexico City. No athletes were punished for positive tests. The motivation for drug testing was based upon suspicions about drug related deaths among cyclists and soccer players and rumors about widespread drug use among Eastern European athletes. Rumors subsequently were verified after the fall of the Berlin Wall and world access to secret records detailing significant and long-term performance-enhancing drug use among East German athletes.

In 1982 the IOC added testosterone to its list of banned substances. More and more athletes tested positive for drug use in a wide range of amateur and professional sports as the investigations into drug use in sports came under closer public scrutiny.

As testing methods and the detection of banned substances became more familiar to athletes and the professionals that helped them with doping, the better the methods to avoid detection became. In the mid 1980’s steroid users turned to unbanned masking drugs or switched to harder to detect substances such as human growth hormone, hGH. The most recent substance to be added to the “doper’s pharmacy” is Erythropoietin (EPO), a form of protein that stimulates the formation of red blood cells and therefore boosts the oxygen-carrying capacity of an athlete’s blood.

There may be solutions out there to stem the wholesale use of performance-enhancing drugs in sport besides detection and punishment. Indeed, education and public condemnation is the best long-term idea, but something drastic needs to be done to stop doping now. Drastic measures are called for. The IOC and other international governing bodies in sport need to set their self-interests aside and form a united front to fight doping at every level.

The development of drug tests and administering the tests is expensive. On some scales it is cost prohibitive. Purchasing and developing equipment to administer effective testing is also costly.

Why not pool the drug testing financial resources from the different organizations around the world? Set aside self-interest, pride and arrogance and develop an independent body financed by a consortium of the IOC and world sports organizations. Solicit sponsors to raise money. Give the independent body worldwide jurisdiction to test randomly anywhere in the world. Vest it with enforcement authority at all levels and provide for serious, mandatory penalties. Then constitute a tribunal to hear and decide appeals. There has been enough fingerpointing, accusations and shifting blame and responsibility. Let’s unite to send a message that the world sport community is finally serious and will do whatever it takes to get the “doping” problem under control.

This solution would lead to state-of-the-art technology, remove politics and bribery from the equation and allow for swift and sure punishment. Let’s put a “zero tolerance” policy in effect for doping and then have guts enough to enforce it!

Correspondence concerning this article should be addressed to Dr. Richard Bell, Chair of Sport Management, One Academy Drive, Daphne, AL 35626-7055, (334) 626-3303, email: rbell@ussa.edu.

U.S. Sports Academy2017-12-11T11:27:58-06:00February 13th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off

A Coach’s Guide to Recognizing Alcohol/Drug Problems Among Athletes

Submitted by: University of Massachusetts Athletic Health Enhancement Program

How do I know if one of my athletes is abusing alcohol, drugs, or both?

Assessing a potential alcohol or drug problem is a difficult and often frustrating process. Your influential role as a coach and a confidant, however, places you in a unique position to successfully reach a troubled student.

What should coaches look for?

There are many reasons why students may show the following signs and symptoms. The behavior may or may not be alcohol or drug related. When these behavior patterns occur with some regularity and are interfering with the student’s performance, it’s time to intervene.

Behavioral Patterns

Actions

Physically assaultive or threatening
Exaggerated self-importance
Rigid, inflexible, unable to change plans with ease
Incoherent, irrelevant statements
Excessive attention to routine procedure, almost making it a ritual
Frequent arguments
Frequent outbursts of temper
Frequent episodes of crying
Excessive amount of breaks at practice
Reports from peers who are worried about the person in question
Complaints from community regarding debts, rude behavior
Minor scrapes with campus or municipal authorities
Depressed
Withdrawn
Suspicious
Mood swings: high and low
Oversensitive
Frequent irritability with teammates and other students

Performance

Accidents

Frequent minor injuries due to carelessness, lack of conditioning
Mishaps not related to sports
Frequent physical complaintsAthletic Performance and Patterns:
Assignments take more effort and time to complete
Difficulty in recalling instructions
Difficulty in handling complex procedures
Lack of interest in one’s game
Difficulty in recalling previous mistakes
Absent-mindedness, general forgetfulness
Coming to practices or games in an intoxicated or impaired state
Fluctuating periods of high or low productivity
Mistakes due to poor judgement
Complaints from others concerning her work or habits
Improbable excuses for deteriorating performance
Overall carelessnessAcademic
Poor reports from instructor or academic advisors
Lateness or failure to complete assignments
Listlessness or sleeping in class
Sharp fluctuations in classroom work
Evidence of cheating or using someone else’s work
Frequent cutting of classes
Excessive time spent sick
Misuse of excused absences
Unreasonable resentment to discipline or mistakes of others
Excessive lateness for practices, meeting, or after breaks
Increasingly improbable and peculiar reasons for absence
Absences after weekend, holidays, or other time off given to team

Strategies for Approaching and Helping a Student

If the student comes to your for help

Commend the student’s initiative and courage for coming to you for help. This first step is one of the toughest.
Listen. Listen. Listen. Allow the student to tell you why she thinks there’s a problem.
Discuss options available for ongoing help. The student may want to continue talking to you, in which case you may need to set a limit on how long you can be put in this position. Encourage the student to seek professional help.
Know the resources on campus: Alcohol and Drug Awareness Project x2616, Health Education x2466, Health Services x2121, Counseling Center x2307
If you have reason to suspect a drug/alcohol problem
Arrange a private meeting with the student.
Develop a highly specific list of facts which substantiates your reasons for believing the student may have a problem.
During your initial meeting with the student, express your concern based on the list of facts you have documented about behavioral changes.
If the student denies there is a problem, continue monitoring his or her behavior. Approach the again in a couple of weeks. If there’s no change in behavior or if denial persists, you may need to consider stronger action.
If the student acknowledges there is a problem, be prepared to suggest where she can go for help.

Key Points to Remember

Remember that the message you want to convey is: “There is a problem and I care.” (Note: Anticipate your own anger fear and/or disappointment, so that it can be controlled.)
Policies and procedures you follow must be consistent with all of your students.
Privacy and confidentiality are necessary to ensure trust.
Speak in terms of behavioral fact: weed out your personal judgements on personality, performance, etc. (Note: Avoid “labeling” the individual as an alcoholic or drug addict.)
Anticipate the student’s reactions; learn to expect:
- Defensive reactions: denial, rationalization, blaming
- Emotional reactions: anger, shame, embarrassment, hopelessness, despair, disappointment in self and support system

Correspondence concerning this article should be addressed to Robin Harris, Mount Holyoke Health Educator, UMASS Athletic Health Enhancement Program, (414) 545-4588.

U.S. Sports Academy2013-11-27T16:26:14-06:00February 13th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off

Generic Alcoholism: Are College Athletes at Risk?

Submitted by: Michael Moulton, Ed.D

Alcohol and other drug use by college athletes have received increased attention in recent years. The purpose of this study was to explore the relationship of collegiate athletes and non-athletes drinking patterns to those of generic alcoholism. The findings revealed a large portion of the college sample, both athlete and non-athlete, reported alcohol dependency as indicated by the scores of the Michigan Alcoholism Screening Test (MAST). Additionally, a significant difference was found to exist between males and females with respect to their scores on the MAST.

In recent years alcohol and other drug use by college athletes has received increased attention by the media. The drug-related deaths and arrests of several professional athletes have fueled the public interest in examining the role which alcohol and other drugs play in the lives of athletes. Despite the general perception that athletes are more health-conscious than their non-athlete counterparts, studies indicate that athletes abuse drugs regularly with alcohol as the most widely abused drug of all (Evans, Weinberg, & Jackson, 1992; Anderson, Albrecht, McKeag, Hough, & McGrew, 1991).

Over the past two decades very few studies have investigated alcohol use among college athletes and compared their use to student non-athletes. However, the findings of the studies which have been conducted (Overman & Terry, 1991; Anderson et al., 1991; Vance, 1982) indicate that minimal differences in alcohol use exist between these two groups. In a large national survey Anderson et al. (1991) found that nearly 89 percent of collegiate athletes reported alcohol use during the previous 12 months compared to 91.5 percent of the general population of college students. Similar findings were observed in a study comparing alcohol use and attitudes among college athletes and non-athletes (Overman & Terry, 1991). In this study, the researchers found no evidence that alcohol and other drug use is higher among college athletes than the rest of the student population. Furthermore, Vance (1982) reported NCAA survey findings indicated that athletes and non-athletes do not differ with respect to alcohol use.

In comparison, numerous studies have been conducted investigating alcohol use among high school athletes and non-athletes. The findings in these studies have been somewhat conflicting. Shields (1995) and Forman, Dekker, Javors, and Davison (1995) found a lower prevalence of alcohol use by student-athletes as compared to non-athletes. In contrast, a comprehensive study conducted by Rainey, McKeown, Sargent, and Valois (1996) found that adolescent athletes reported more drinking and binge drinking than did non-athletes. Similarly, in a study comparing alcohol use and intoxication in high school athletes and non-athletes, researchers found that athletes drank more frequently and reported less abstinence from alcohol consumption than student non-athletes (Carr, Kennedy, & Dimick, 1990).

Reviewing the literature for both the college and high school athlete populations in respect to alcohol use is important. Recent research indicates unhealthy drinking patterns in college may begin in high school (Anderson et al., 1991). Specifically, Anderson et al. (1991) found that 63 percent of the college athlete sample who reported using alcohol and drugs had their first experiences while in high school and 22 percent in junior high school.

Based on the findings reported, research is indicating that when studying substance use at the high school level, athletes are reporting drinking more alcohol more frequently that non-athletes. In addition, it appears that college athletes are not more health conscious, with regard to substance use, that their non-athletic counterparts. These types of findings lead to questions regarding the long-term effects of alcohol use by athletes. Are collegiate athletes at risk for developing generic alcoholism? So far, there have been no studies conducted examining and comparing college athletes and non-athletes and their tendency toward generic alcoholism using an alcoholism screening questionnaire. The purpose of the current study was to explore the relationship of collegiate athletes and non-athletes drinking patterns to those of generic alcoholism. Specifically, the study was designed to determine if significant differences existed between college athletes and non-athletes with regard to scores on the Michigan Alcoholism Screening Test (MAST) (Selzer, 1971). The secondary purpose of this study was to determine if gender differences existed between and within the two groups.

Method

Participants
A sample of 367 undergraduate students attending psychology and health courses at a small Southern university volunteered to participate in this study for extra credit points. Approximately 34 percent were male (n = 123) and 66 percent were female (n = 244) with approximately 74 percent between the ages of 18 and 21. There were 327 non-athletes and 38 athletes; Data from two of the participants were not included in the subject pool due to missing information about athletic status.

For the purpose of this study, only the data from the subjects who scored between 5 and 9 on the Michigan Alcoholism Screening Test (Selzer, 1971) were used. Thirty-four percent of the participants scored in this range: 110 non-athletes and 15 athletes; 44 males and 81 females.

Materials
The Michigan Alcoholism Screening Test (MAST) (Selzer, 1971) and a demographic information sheet were used to collect data. The MAST is used to predict alcohol dependence. For this study’s purposes, only data from the subjects scoring between 5 and 9 on the MAST were used in the analysis. Scores in this range indicate an 80 percent association with generic alcoholism (Selzer, 1971). The demographic information sheet asked questions about age, gender, and athletic status. Athletic status was determined by participation in a college varsity sport.

Procedures
Students from selected courses in the Psychology and Health and Human Performance Departments were asked to participate in the study. Recruitment occurred during the subjects’ regularly scheduled class times using sign-up sheets for testing sessions. During this time the subjects were told the amount of extra credit they would receive for their participation. Testing occurred at various class times within one week. Each testing session lasted approximately 45 minutes. Prior to the distribution of the surveys, the subjects received a description of the study and an informed consent form, and were allowed to withdraw at any time without penalty. They were also advised that their answers would remain anonymous. After returning the informed consent forms, subjects received instructions and the questionnaires, which included the MAST and demographics sheet.

The subject’s responses from the questionnaires were entered on a general scantron sheet without their names to ensure confidentiality.

Results

Thirty-four percent (44 males and 81 females) of the total sample scored in the
5 – 9 category of the MAST. A two-way analysis of variance (ANOVA) for unequal sample sizes was computed to find if the differences in scores on the MAST were significant between and within the sample of athletes and non-athletes. Table II reports the findings of this analysis.

Table 1 Analysis of Variance – Michigan Alcoholism Screening Test
Source of Variation	df	Sums of Squares	Mean Square	F	P
Main Effects	2	10.175	5.088	8.760	.000
Athletic Status

110.15610.15617.488.000

Gender

14.7174.7178.122.005

2-Wat Interactions16.8016.80111.711.001

Athletic Status X

Gender

16.0816.08111.711.001 Within12170.274.581 Total12481.888.660

The Analysis of Variance Summary Table indicated that there was a significant difference between athletes and non-athletes with respect to their scores in the 5 – 9 category of the MAST, F.01 = (1,121) = 17.488, p < .001. The mean score (M = 6.87) for athletes was significantly higher than the mean score (M = 6.26) for non-athletes. (See Table II) There were also significant differences between males and females with respect to their scores in the 5 – 9 category of the MAST, F.01= (1, 121) = 8.122, p < .005. The mean score (M = 6.45) for males was significantly higher than the mean score (M = 6.27) for females. (See Table II) It is notable that while males (N = 44) scored significantly higher on the MAST, the frequencies of females (F = 81) reporting a 5 – 9 generic range was higher.

Table 2 Group Means of the Michigan Alcoholism Screening Test
	M	SD
Athlete	6.8667	1.187
Non-Athlete	6.2636	.725
Males	6.4545	.901
Females	6.2716	.758

Finally, the test for the interaction of athletic status and gender was significant,
F.01(1,121) = 11.71, p < .001. However, due to the relatively low number of female athletes in the sample, further investigation into the interaction was not conducted.

Discussion

The findings revealed that a large proportion of the college sample used in this study reported alcohol dependence as indicated by their scores on the MAST. These findings correspond very closely to the large percentage of college student binge drinkers found in a large-scale study by Weschler, Davenport, Dowdall, Moeykens, and Castillo (1994). The results from this study indicated that 44 percent of the nation’s college students engaged in binge drinking behaviors. While it is acknowledged that binge drinking is a separate construct from generic alcoholism, binge-drinking behaviors are considered as primary indicators of alcoholism (Diagnostic and Statistical Manual of Mental Disorders, 1994).

The findings of the current study are in direct contrast with earlier studies (Overman & Terry, 1991; Anderson et al., 1991; Vance, 1982) indicating minimal differences in alcohol use between athletes and non-athletes. The present study revealed that there were significant differences between athletes and non-athletes with respect to their scores on the MAST. Athletes scored higher on the MAST than did non-athletes, suggesting that alcohol dependency is greater among athletes than for the general student body. Several possibilities have been suggested as to why athletes might abuse alcohol more than non-athletes. Falk (1990) investigated the various sociological and psychological factors associated with the chemically dependent athlete. Obsessive compulsive personality features, difficulty in maintaining interpersonal relationships, preoccupation with body image and physical appearance, and inability to cope with high expectations are a few of the factors identified by Falk. It appears that athletes have specific pressures and concerns directly related to athletic participation. Additionally, there may be a lack of awareness, information and/or support for many athletes in developing positive coping skills to address the pressure surrounding athletics.

The findings also indicated that significant differences exist between males and females with respect to their scores on the MAST. Males scored higher on the MAST than did females indicating that males have a greater dependency for alcohol than females. These results are supported by several other studies that found alcohol frequency and consumption rates to be higher among males than females (Weschler et al., 1994; Overman & Terry, 1991; Flynn & Shoemaker, 1989).

Based upon the results of this study, two factors that are associated with alcohol dependency in college are participation in athletics and being male. However, the number of females scoring in the 5 – 9 category in this study indicate that females (athlete or non-athlete) are at risk for developing alcohol dependency similarly to their male counterparts. This is evident in several studies that found minimal differences between females and males (athlete or non-athlete) in regards to their drinking behaviors (Anderson et al., 1991; Center on Addiction and Substance Abuse, 1994; Anderson & McKeag, 1985).

The 5 – 9 category of the MAST scores was chosen to meet specific purposes in the present study. This 5 – 9 scoring is considered to be a conservative estimate when aiding in the clinical diagnosis of alcohol dependence. It is considered to eliminate false positives in the adult population. This means that a higher incidence of high-risk behavior is needed to categorize an individual as dependent. This category of scoring (5 – 9) was deemed the most appropriate for the present study due to its conservative nature, the progressiveness of the disease of alcoholism, the peer culture, and the developmental stage of the college population.

Findings such as these indicate a strong need for further research in this area beyond the preliminary study. Future research needs to address design issues such as sample and cell size. In addition, focus may be placed on the effects of various sports on alcohol behaviors, specific indicators of athletes at risk, early prevention, and positive coping skills. Continued research and application is needed to aid young individuals, both athletes and non-athletes, in meeting their full potential.

References

American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. (1994) Washington, DC, American Psychiatric Association.

Anderson, W. A., Albrecht, R. R., McKeag, D. B., Hough, D. O., & McGrew, C. A. (1991). A national survey of alcohol and drug use by college athletes. The Physician and Sportsmedicine, 19(2), 91-104.

Anderson, W. A., & McKeag, D. B. (1985). The substance use and abuse habits of college student-athletes (Report No. 2). Mission, KS: The National Collegiate Athletic Association.

Carr, C. N., Kennedy, S. R., & Dimick, K. M. (1990). Alcohol use among high school athletes: A comparison of alcohol use and intoxication in male and female high school athletes and non-athletes. The Journal of School Health, 66(1), 27-32.

Center on Addiction and Substance Abuse (CASA). (1994). Commission reports on substance abuse on american campuses. The Alcoholism Report [On-line], 22(5), 4-5. Available: http://pogo.edc.org/hec/pubs/catalst4.txt

Evans, M., Weinberg, R., & Jackson, A. (1992). Psychological factors related to drug use in college athletes. The Sport Psychologist, 6, 24-41.

Falk, M. A. (1990). Chemical dependency and the athlete: Treatment implications. Alcoholism Treatment Quarterly, 7(3), 1-16.

Flynn, C. A, & Shoemaker, T. A. (1989). Alcohol and college athletes: Frequency of use versus perceptions of others. NASPA Journal, 27(2), 172-176.

Forman, E. S., Dekker, A. H., Javors, J. R., & Davison, D. T. (1995). High-risk behaviors in teenage male athletes. Clinical Journal of Sports Medicine, 5, 36-42.

Overman, S. J., & Terry, T. (1991). Alcohol use and athletes: A comparison of college athletes and nonathletes. Journal of Drug Education, 21(2), 107-117.

Rainey, C. J., McKeown, R. E., Sargent, R. G., & Valois, F. (1996). Patterns of tobacco and alcohol use among sedentary, exercising, non-athletes and athletic youth. Journal of School Health, 66(1), 27-32.

Selzer, M. L. (1971). The michigan alcoholism screening test: The quest for a new diagnostic instrument. American Journal of Psychiatry, 127, 1653-1658.

Shields, E. W. (1995). Sociodemographic analysis of drug use among adolescent athletes: Observations-perceptions of athletic directors-coaches. Adolescence, 30(120), 839-860.

Vance, N. S. (1982, September 1). Colleges urged to teach athletes the dangers of drug abuse and “doping”. Chronicle of Higher Education, pp. 25, 28.

Wechsler, H., Davenport, A., Dowdell, G., Moeykens, B., & Castillo, S. (1994). Health and behavioral consequences of binge drinking in college: A national survey of students at 140 campuses. The Journal of the American Medical Association, 272(21), 1672-1677.

Correspondence concerning this article should be addressed to Michael Moulton, moultonm@nsula.edu, (318) 357-5142.

U.S. Sports Academy2016-10-12T11:42:46-05:00February 13th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off

Factors Associated with Success Among NBA Teams

Submitted by: Anthony J. Onwuegbuzie

Abstract

Data from the 1997-1998 National Basketball Association (NBA) regular season were analyzed to determine factors that best predicted success, as measured by winning percentage. A total of 20 variables were examined. A multiple regression analysis revealed that field goal conversion percentage was the best predictor of success, explaining 61.4% of the variance in winning percentage. The average three-point conversion percentage of the opposing teams explained a further 18.9% of the variance. These two variables combined explained 80.3% of the variance in winning percentage. The finding pertaining to field goal conversion percentage suggest that the attainments of the offense are more important than are the defensive attainments in predicting the success levels of NBA teams. These and other implications are discussed.

Introduction

The game of basketball was invented in December 1891 by Dr. James A. Naismith while an instructor in the physical training department of the International Young Men’s Christian Association (YMCA) Training School in Springfield, Massachussets (Fox, 1974). Naismith’s goal was to answer the challenge of Dr. Luther H. Gulick, his department head, who wanted an indoor game to be invented that (1) would attract young men during the winter, when baseball and football were out of season, and (2) would replace gymnastics and calisthenics, which provoked little interest (Fox, 1974). Naismith, known as “the father of basketball,” incorporated features of soccer, U.S. football, rugby football, field hockey, and other outdoor sports in developing the game of basketball.

By 1946, professional basketball had acquired a large and faithful following among U.S. sports fans, who wanted to watch their former collegians in action. During this period, there was the American Basketball League (ABL) on the East Coast and the National Basketball League (NBL) in the Midwest. In June, 1946, the Basketball Association of America was formed, which effectively replaced the ABL and competed directly with the NBL (Fox, 1974). The BAA and the NBL merged in 1950 as the National Basketball Association (NBA), comprising 17 teams. The NBA was reduced to 10 teams in 1951, as 7 NBL teams with marginal franchises dropped out (Fox, 1974). However, in the 1970s, the NBA expanded to 22 teams. Presently, the NBA contains 29 teams, with 15 teams in the Eastern Conference (with 7 teams representing the Atlantic division and 8 teams representing the Central division) and 14 teams in the Western Conference (with 7 teams representing the Midwest division and 7 teams representing the Pacific division). Basketball is now one of the most popular sports in the United States. Indeed, in the 1997-1998 season (the last time a full 82-game season was played), a total of 8,877,309 people attended an NBA game (The Sports Network, 1998), with an average attendance of 17,135 people per game (USATODAY, 1999).

Currently, at the end of the regular season, that is, when each team has played 82 matches, the top eight teams in each conference qualify for the playoffs. These eight teams then participate in a knockout tournament with the eventual winners of this stage within each conference advancing to the NBA finals. Because the teams which advance to the playoffs are those that have the highest winning percentages in their respective divisions during the regular season, knowledge of factors which predict success during this period would be of educational value for NBA coaches and analysts. Indeed, the former group could use this information to target coaching interventions.

Basketball is abound with empirical facts. Surprisingly, however, only descriptive statistics (e.g., averages, totals, percentages) tend to be utilized. Conversely, few inferential statistical analyses are undertaken on NBA data. Yet, such analyses provide consumers with information regarding the relationships among variables. As such, inferential statistics can yield very detailed and important information to consumers of professional basketball. Moreover, inferential statistics can be used to determine factors that predict the performance levels of teams.

To date, only a few studies have investigated correlates of basketball-related performance. Of those that have, the majority have involved an examination of psychological antecedents of basketball performance. For example, Whitehead, Butz, Vaughn, and Kozar (1996) found that increased stress (assumed to be present in games as opposed to practices) among members of an NCAA Division I men’s varsity team was associated with longer pre-shot preparations and a greater incidence of overthrown shots.

Newby and Simpson (1994) reported (1) a statistically significant negative relationship between minutes played by a sample of men and women college basketball players and mood, (2) a statistically significant negative relationship between the number of assists and depression, (3) a statistically significant negative relationship between the number of turnovers committed and mood, and (4) a statistically significant positive relationship between the number of turnovers committed and degree of tension. The researchers concluded that success in basketball is negatively related to psychopathology.

Both Pargman, Bender, and Deshaires (1975) and Browne (1995) found no relationship between free-throw and field goal shooting and field independency/field dependency. Additionally, Shick (1971) found no relationship between hand-eye dominance and depth perception and free-throw shooting ability in college women. Hall and Erffmeyer (1983) examined the effect of imagery combined with modeling on free-throw shooting performance among female college basketball students. These researchers noted that players who shot free throws under the conditions of videotaped modeling combined with relaxation and imagery were significantly more accurate than were those who shot in the relaxation and imagery condition only.

All the above studies investigated correlates of specific basketball skills (e.g., free-throw shooting), and, with a few exceptions (e.g., Butz et al., 1996), these skills typically were examined under simulated conditions. Such studies, although interesting, have limited utility for basketball coaches, in particular, because they does not provide any information as to why or how a team wins a basketball game. Indeed, the only inquiry found determining factors associated with success among basketball players was that of Steenland and Deddens (1997). These researchers studied the effects of travel and rest on performance, utilizing the results for 8,495 regular season NBA games over eight seasons (1987-1988 through 1994-1995). Findings revealed a statistically significant positive relationship between the amount of the time that elapsed between games and performance level. Specifically, more than 1 day between games was associated with a mean increase of 1.1 points for the home team and 1.6 points for the visitors. Peak performance occurred with 3 days between games. The researchers theorized that the negative effects of little time between games may be due more to insufficient time for physical recovery than to the effects of circadian rhythm (i.e., jet lag). However, although not statistically significant, they also found that visiting teams performed four points better, on average, when they traveled from the west coast to the east coast than when they traveled form east to west.

Surprisingly, no other study has investigated predictors of success among NBA teams. Even more surprising is the fact that no research appears to have examined what factors directly associated with skill level (e.g., field goal conversion percentage) best predict a team’s winning percentage. This was the purpose of the present inquiry. A secondary goal was to determine whether offensive or defensive factors would have more predictive power. It was expected that knowledge of these factors could help coaches to decide where to focus their attention, as well as assist analysts and fans in predicting a team’s performance.

Method
The data comprised all 21 unique team-level variables (when both team averages and totals were presented, only the averages were utilized, since they rendered totals redundant) that were presented on the official NBA website (i.e., http://www.nba.com) for the 1997-1998 regular professional basketball season. (The 1997-1998 NBA season was chosen because it represented the last time a full 82-game season was played.) These variables comprised winning percentage, which was treated as the dependent measure and 20 other variables which were utilized as independent variables. All variables are presented in Table 1. Scores pertaining to each variable for each team were analyzed using the Statistical Package for the Social Sciences (SPSS; SPSS Inc., 1999).

Table 1 Pearson Product-Moment Correlations of Winning Percentage and Selected Variables for the 1997-1998 Regular NBA Season

Variable	Winning Percentage
three-point conversion percentage	.38
field goal conversion percentage	.78*
free-throw conversion percentage	.03
average number of offensive rebounds per game	-.31
average number of defensive rebounds per game	.47
number of total rebounds	.19
average number of assists per game	.61*
average number of steals per game	.08
average number of blocks per game	-.13
number of points scored per game	.57*
field goal conversion percentage of the opposing teams	-.68*
average three-point conversion percentage of the opposing teams	-.50
average free-throw conversion percentage of the opposing teams	.18
average number of offensive rebounds per game of the opposing teams	-.49
average number of defensive rebounds per game of the opposing teams	-.71*
average number of total rebounds of the opposing teams	-.69*
average number of assists per game of the opposing teams	-.70*
average number of steals per game of the opposing teams	-.45
average number of blocks per game of the opposing teams	-.58*
average number of points scored per game of the opposing teams	-.70*
* statistically significant after the Bonferroni adjustment

Results and Discussion
Table 1 presents the correlations between winning percentage and each of the selected variables. It can be seen that, after adjusting for Type I error (i.e., the Bonferroni adjustment), winning percentages increased with field goal conversion percentage, number of assists per game, and number of points scored per game, and decreased with field goal conversion percentage of the opposing teams, average number of defensive rebounds per game of the opposing teams, average number of total rebounds per game of the opposing teams, average number of assists per game of the opposing teams, average number of blocks per game of the opposing teams, and average number of points per game of the opposing teams.

An all possible subsets (APS) multiple regression (Thompson, 1995) was used to identify which combination of independent variables best predicted NBA teams’ success. Again, success was measured by NBA teams’ regular season winning percentages. For this study, the criterion used to determine adequacy of the model was the maximum proportion of variance explained (i.e., R2), which provides an important measure of effect size (Cohen, 1988). Specifically, all variables were included except for those that represented (1) the total number of points scored or the total number of rebounds (use of the number of defensive rebounds and offensive rebounds rendered use of the total number of rebounds redundant). Consequently, a total of 16 independent variables were analyzed.

The multiple regression analysis revealed that the following two variables made a statistically significant contribution (F [2, 26] = 53.12, p < .0001) to the model: field goal conversion percentage and average three-point conversion percentage of the opposing teams. The regression equation was as follows:

winning percentage =
-159.53 + {(7.90) X field goal conversion percentage} – {(4.24) X average three-point conversion percentage of the opposing teams}

The regression equation indicates that every 1 percentage increase in field goal conversion rate is associated with a 7.90% increase in winning percentage. The confidence interval corresponding to this variable suggests that we are 95% certain that every 1 percentage increase in field goal conversion rate is associated with an average increase in winning percentage of between 6.00% and 9.80%. Additionally, every 1 percentage increase in the three-point conversion rate of the opposing teams is associated with a 4.24% decrease in winning percentage (95% confidence interval is 2.49% to 5.99%).

With respect to predictive power of the model, field goal conversion percentage explained 61.4% of the variance in winning percentages, whereas average three-point conversion percentage of the opposing teams explained 18.9%. These two variables combined to explain 80.3% of the total variance in winning percentage (adjusted R2 = 78.8%). In the study of human behavior, this percentage is extremely large, suggesting that an NBA team’s success can be predicted with an excellent degree of accuracy.

Conclusions
The purpose of this study was to determine which variables best predict whether an NBA team’s success rate. The finding that field goal conversion percentage explains more than three times the variance in success than does the average three-point conversion percentage of the opposing teams suggests that the attainments of the offense are more important than are the defensive attainments in predicting whether an NBA team will be successful. Thus, the present finding is in contrast to Onwuegbuzie (1999a), who identified four multiple regression models which adequately predicted the winning percentages of National Football League (NFL) teams for the 1997-1998 regular football season–the most notable being a two-variable model comprising turnover differential (which explained 43.4% of the variance in success) and total number of rushing yards gained by the offense (which explained a further 9.3% of the variance). Based on these models, Onwuegbuzie concluded that, outside the 20-yard zone, the attainments of the defense are more important than are the offensive attainments in predicting whether an NFL team is successful.

The present result pertaining to NBA teams also is in contrast to Onwuegbuzie’s (1999b) replication study of NFL teams for the 1998-1999 football season in which a model was identified containing the following five variables: (1) turnover differential (which explained 54.4% of the variance); (2) total number of rushing yards conceded by the defense (which explained 21.3% of the variance); (3) total number of passing first downs attained by the offense (which explained 9.4% of the variance), (4) percentage of third-down plays that produce a first down (which explained 4.1% of the variance), and (5) total number of penalties conceded by the opponents’ defense resulting in a first down (which explained 4.1% of the variance). Onwuegbuzie concluded that defensive gains are better predictors of success than are offensive gains because the first two variables, which explained more than 75% of the variance, were characteristics of the defense.

The finding that field goal percentage rate explained a very large proportion of the variance in success (i.e., 61.4%) highlights the importance of offensive efficiency not only of the starting players but also of the “bench” players, since the latter group also contribute to the field goal percentage rate. Nevertheless, the fact that three-point conversion percentage also made a contribution to the regression model, albeit a smaller one, suggests the importance of teams forcing the opposition to hurry their three-point shots and to take these shots from non-optimal parts of the basketball court.

Although a significant proportion of the variance in winning percentage was explained by the selected variables, this study also should be replicated using data from other seasons. Furthermore, regression models should be fitted using college basketball data. Information from such analyses should help coaches and analysts alike to obtain objective data which can be used to monitor the performance of NBA teams.

References

Browne, G.S. (1995). Cognitive style and free throw shooting ability of female college athletes. Unpublished master’s thesis, Valdosta State University, Valdosta, Georgia.

Cohen, J. (1988) Statistical power analysis for the behavioral sciences. New York: Wiley.

Fox, L. (1974). Illustrated history of basketball. New York, NY: Grosset & Dunlap.

Hall, E.G., & Erffmeyer, E.S. (1983). The effect of visuo-motor behavior rehearsal with video taped modeling of free-throw shooting accuracy of intercollegiate female basketball players. Journal of Sport Psychology, 5, 343-346.

Newby, R.W., & Simpson, S. (1994). Basketball performance as a function of scores on profile of mood states. Perceptual and Motor Skills, 78, 1142.

Onwuegbuzie, A.J. (1999a). Defense or Offense? Which is the better predictor of success for professional football teams? Perceptual and Motor Skills, 89, 151-159.

Onwuegbuzie, A.J. (1999b, November). Is defense or offense more important for professional football teams? A replication study using data from the 1998-1999 regular football season. Paper presented at the annual meeting of the Midsouth Educational Research Association, Point Clear, AL.

Pargman, D., Bender, P., & Deshaires, P. (1975). Correlation between visual disembedding and basketball shooting by male and female varsity athletes. Perceptual and Motor Skills, 41, 956.

Shick, J. (1971). Relationships between depth perception and hand-eye dominance and free-throw shooting in college women. Perceptual and Motor Skills, 33, 539-542.

SPSS Inc. (1999) SPSS 9.0 for Windows. [Computer software]. Chicago, IL: SPSS Inc.

Steenland, K., & Deddens, J.A. (1997). Effect of travel and rest on performance of professional basketball players. Sleep, 20(5), 366-369.

The Sports Network. (1998). Statistics: 1997-1998 NBA attendance. The Sports Network, 21(21).

Thompson, B. (1995). Stepwise regression and stepwise discriminant analysis need not apply here: A guidelines editorial. Educational and Psychological Measurement, 55, 525-534.

USATODAY. (December 28, 1999). Inside the numbers. Retrieved January 28, 2000 from the World Wide Web: http://www.usatoday.com/sports/basketba/skn/numbers.htm.

Whitehead, R., Butz, J.W., Vaughn, R.E., & Kozar, B. (1996). Stress and performance: An application of Gray’s three-factor arousal theory to basketball free-throw shooting. Journal of Sport Behavior, 19(4), 354-364.

Footnote
1 Due to space constraints, the intercorrelations among all the variables is not presented. However, this can be obtained by contacting the author.

Address correspondence to Anthony Onwuegbuzie, Department of Educational Leadership, College of Education, Valdosta State University, Valdosta, Georgia, 31698 or e-mail (TONWUEGB@VALDOSTA.EDU).

U.S. Sports Academy2013-11-27T16:29:09-06:00February 13th, 2008|Sports Coaching, Sports History, Sports Management, Sports Studies and Sports Psychology|Comments Off