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

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  • Withdrawn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

  1. Commend the student’s initiative and courage for coming to you for help. This first step is one of the toughest.
  2. Listen. Listen. Listen. Allow the student to tell you why she thinks there’s a problem.
  3. 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.
  4. 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

  5. Arrange a private meeting with the student.
  6. Develop a highly specific list of facts which substantiates your reasons for believing the student may have a problem.
  7. During your initial meeting with the student, express your concern based on the list of facts you have documented about behavioral changes.
  8. 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.
  9. 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.

2013-11-27T16:26:14-06:00February 13th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on A Coach’s Guide to Recognizing Alcohol/Drug Problems Among Athletes

Student Athlete Drug Testing

Since the June 1995 U. S. Supreme Court ruling in support of random interscholastic student athlete drug testing, more schools then ever before have begun either mandatory, reasonable suspicion or voluntary types of drug testing as they battle drug abuse by their students. By far most student drug testing programs consist of mandatory testing of only student athletes since the U.S. Supreme Court upheld this type of testing. Some schools have begun drug testing all co-curricular students or students wishing to drive to school. This latter action was challenged in Rush County, Indiana, and upheld by the District Court. When appealed to the U. S. Supreme Court they allowed the District Court ruling to stand.

Schools contemplating a drug testing program must first document their student athletes are using drugs to comply with the U.S. Supreme Court ruling. Likewise it is imperative that they rally community support for such a program in order for it to be a helpful tool used both my school officials and parents. Urine drug testing is the industry standard and recommended over hair or saliva testing which may not be defendable in court.

Student Drug Testing Options

Drug testing programs can be mandatory, as with interscholastic student athletes, voluntary as part of a student assistance program, or based on reasonable suspicion only. Random urine drug testing by far is the most deterrent to drug use by students since the students may be selected at any time for testing. This type program gives the students another reason to say “No!” However such a program requires more testing be done which elevates the total cost of the program. Voluntary programs do help those students caught breaking the rules by using drugs or alcohol, but has little impact on students using and not getting caught. Reasonable Suspicion programs are very effective at keeping drugs out of schools but have little deterrence to use by students in general.

Drugs to Screen for

A student drug testing program must screen for the appropriate illicit drugs and banned substances. Most schools have student codes of conduct and/or athletic codes of conduct that state illicit drugs are not to be used and most include tobacco as a banned substance. The substance abuse coordinator from your school should be able to tell you what drugs or substances your students are using and abusing. This list will probably include Pre-Game Drugs like tobacco (smoked and chewed), marijuana, pain medications (often used with parent knowledge) and anabolic steroids. After games, the Post-Game Drugs will include tobacco, marijuana, alcohol, LSD, and inhalants. There are several “club drugs” that students are starting to use including ecstacy, a methamphetamine known as MDMA, and ketamine, a veterinary anesthetic. Ecstacy has stimulation and hallucinogenic effects and often used at parties called Raves where students dance wildly and often dehydrate from elevated body temperatures. Unfortunately one dose of ecstacy causes permanent brain damage. During a Rave a student may take as many as 15 hits of Ecstacy. Current urine drug testing can discover Ecstacy but only when ordered as a special test and at considerable expense. There is not a screening test yet available for Ecstacy or Ketamine.

Currently most certified laboratories offer a standard Substance Abuse Panel -10 (SAP-10) which screen for the following ten drugs:

  • Amphetamines
  • Barbiturates
  • Benzodiazepines (Valium®)
  • Cocaine
  • Marijuana
  • Methadone
  • Methaqualone
  • Opiates (Codeine)
  • Phencyclidine
  • Propoxyphene (Darvon®)

Many other chemical substances can be detected in the urine, and considering the drug use patterns of today’s youth, the following additional drug screens can be ordered:

  • Alcohol
  • Anabolic Steroids
  • LSD
  • Nicotine (Tobacco)
  • MDMA (Ecstacy)

In the laboratory, the SAP-10 is automated and therefore less expensive to do. The other drugs must be tested for by different methods or at special locations, resulting in higher prices. The typical school will pay from $25 to $50 for each SAP-10 ordered, with urine alcohol costing $6 – $10 each, LSD $22-25 each, urine nicotine costs $10-12 each, and anabolic steroids costing $80-95 each. For this price, you should get MRO services as well (see Medical Review Officer section).

Since the drugs abused mostly by teenagers today consist of tobacco, marijuana, alcohol and LSD, a drug abuse intervention program must screen for these chemicals. Simply doing the industry standard of a SAP-10 is not enough. However, detection of alcohol in the urine is not very reliable since it is eliminated from the body very quickly. A drug screen for alcohol done on Tuesday is not likely to find alcohol that was consumed on Saturday night. LSD also leaves the body very quickly and is hard to catch. Therefore, schools often do weekend collection to deter the use of alcohol by their athletes.

A Certified Laboratory is a Must

There are many laboratories, both local and national, who advertise the ability to do urine drug testing. However, not every lab uses the same methods nor are they all certified by the Government. Therefore, it is imperative that only Government certified laboratories are used for any student drug testing program. This is the only way you can be assured that your results are accurate. If your policy will deny the privilege to participate in sports or other co-curricular activities when a positive test is found, your school will be in really hot water if your results are wrong. Any lab your medical vendor uses must be certified by the Substance Abuse and Mental Health Services Administration(SAMHSA) and should have a minimum of ten years of experience in toxicology testing and chair-of-custody procedures. Sport Safe Testing Service uses Quest Diagnostics, Inc. exclusive for testing due to their many years experience with athlete drug testing.

Analysis of Specimens

There are two levels of analysis that occur routinely with urine drug abuse screens. The sample is first subjected to an automated screening test that quickly looks for the presence of specified drugs or their metabolites. This initial testing uses a highly accurate immunoassay technique commonly called an EMIT®. All presumptive positive results should be confirmed by a Gas Chromatography/Mass Spectroscopy (GC/MS) confirmatory tests. This confirmation method provides a “molecular fingerprint” of the drug and/or metabolite, providing a high level of accuracy and specificity. The quantitative results, in nanograms per milliliter, are usually reported as well. Currently the GC/MS confirmatory test is the only acceptable industry standard for drug abuse screen confirmations. Thin layer chromatography, as sometimes offered by non-certified labs, is not acceptable.

Second-Hand Exposure

The first words out of a teen’s mouth when told their urine showed marijuana is that they were in a car with someone who was smoking and therefore that is why their test came up positive. It is very true that detectable levels of both THC (active ingredient of marijuana) and nicotine can be found in those individuals having close exposure to the smoke of the burning tobacco or marijuana. The urine of young children whose parents smoke will have detectable nicotine. For this reason, a series of cutoff levels has been determined and proven scientifically so when a urine drug screen is positive, we know it is from use and not second-hand exposure. For THC the standard is 20-50 nanograms (ng) per milliliter in the screening test and 15 ng/ml by GC/MS. Nicotine has to be above 300 ng/ml to be called positive. When a drug screen is reported as positive, the actual quantitative levels are reported to the Medical Review Officer. This data is becoming more important in determining recent use verses natural decay of levels in the body. The levels for nicotine are less standardized and often take careful interpretation by the MRO.

Medical Review Officer

A Medical Review Officer (MRO) is a licensed physician who has additional training and certification in the area of drug testing. Specifically they have learned how drug testing is done, what affects the results, specifically medications and foods, and how individuals will try and adulterate the specimens to give false negative results. A physician can be certified by the Medical Review Officer Certification Council (MROCC) or the American Association of Medical Review Officers.

Any program of drug testing involving students should have a certified MRO to review all results and make a final certification as to being positive or negative. The MRO must be willing to phone parents when a positive result is found to verify if any medication has been prescribed. This is very important since medications like Tylenol® with codeine could be legally prescribed for a student following a tooth extraction and that student having a drug test would be positive for opiates. The MRO’s job is to verify if any medication has been prescribed for the student which could have resulted in the positive result. If the MRO receives from the prescribing physician or dentist documentation that a codeine containing medication was prescribed, the MRO will rule the test negative. However, if the parent happened to give the student one of his or her pills, and the student has no legal prescription for the medication, then the MRO must rule this test positive since a controlled drug was given and taken without the order of a licensed physician. (Believe it or not, athletes, with full knowledge of their parents, have taken pain medications prior to athletic competition to make their overused joints hurt less during that day’s events). Having an MRO adds significant credibility to any program and shares the burden of liability the school is placed under. For more information and contact Sport SafeTesting Service, Inc., 18 Grace Drive, Powell OH 43065 or call (614) 847-0847. Sample policies are available on our web site at www.sportsafe.com.


Correspondence concerning this article should be addressed to Dr. Joseph C. Franz, Medical Director, SPORT SAFE Testing Service, Inc., 18 Grace Drive, Powell, OH 43065. Phone/Fax:(614) 847-0874. Sample policies are available at www.sportsafe.com.

2016-04-01T09:57:44-05:00February 13th, 2008|Contemporary Sports Issues, Sports Management|Comments Off on Student Athlete Drug Testing

United States Anti-Doping Agency Protocol For Olympic Movement Testing

    1. USADA’s Relationship with the United States Olympic committee (“USOC”)USADA is an independent legal entity not subject to the control of the USOC. The USOC has contracted with USADA to conduct drug testing and results management for participants in the Olympic movement within the United States and to provide educational information to those participants. For the purposes of transmittal of information by USADA, the USOC is USADA’s client/ However, the USOC has authorized USADA to transmit information simultaneously to the relevant National governing Body (“NGB”), International Federation (“IF”) the World Anti-Doping Agency (“WADA”) and involved athlete.

    1. Athletes Subject to Testing USADAThe USOC and NGBs have authorized USADA to test the following athletes:
      1. Any athlete who is a member of a NGB;
      2. Any athlete participating at a competition sanctioned by the USOC or a NGB;
      3. Any foreign athlete who would otherwise be subject to testing by USADA, the USOC or NGB; or
      4. Any other athlete who has given his/her consent to testing by USADA.
      5. Any athlete who has been named by the USOC or an NGB or is competing in a qualifying event to represent the USOC or NGB is in international competition.

USADA will not allow the testing process to be used to harass any athlete. In selecting athletes for testing, USADA will focus primarily on athletes who are participating or have the potential to participate, in international competition.

    1. Choice of Rules In conducting drug testing and results management under this protocol, USADA will look to the following sources of rules:

 

      1. The selection and collection procedures set forth in paragraphs 4, 5, & 6

herein shall apply to all testing done by USADA unless different procedures are agreed to between USADA and the party requesting the test for a particular event.

  • All test performed by USADA shall be analyzed by IOC-accredited laboratories. In analyzing samples for USADA, those laboratories shall follow the standards established by the IOC.
  • Tests performed by USADA shall be analyzed for the categories of prohibited and restricted substances set forth in the rules of the applicable IF unless agreed otherwise between USADA and the party ordering the test.
  • USADA shall be responsible for results management of all tests performed by it and all other tests for which the applicable IF rules require the initial adjudication forth in paragraph 9 herein, unless otherwise referred by USADA to a foreign sports organization having jurisdiction over the athlete.

 

 

    1. Selection of Athletes to be Tested In-Competition
      USADA shall have the authority to determine which athlete will be selected for testing in all competitions tested by USADA. In making this determination, USADA will normally follow NGB or IF selection procedures and will include at a minimum the selection formulas or requests for target selection on particular athletes which are proposed by the USOC or a particular NGB. USADA retains the right to test any athlete that it chooses, with or without cause or explanation.

 

    1. Selection of Athletes to be Tested Out-of-Competition USADA shall have the authority to determine which athlete will be selected for testing out-of-competition testing by USADA. In making this determination, USADA will carefully consider selection formulas or requests for target selection on particular athletes which are proposed by the USOC or a particular NGB. USADA retains the right to test any athlete that it chooses, with or without cause or explanation.
      Each NGB will provide USADA with a regularly updated list of athletes to have included in No Advance Notice or other out-of competition testing. With respect to each athlete on such list and such additional athletes as may be designated by USADA, the NGB will provide USADA with the information as set forth on the athlete location form attached as Annex A. Thereafter it shall be the responsibility of each individual athlete to provide USADA with updated information as to his or her whereabouts.

 

    1. Sample Collection Sample collection by USADA will substantially conform to the standards set forth by the IOC and the World Anti-Doping Agency.

 

    1. Laboratory Analysis All samples collected by USADA will be sent for analysis only to IOC-accredited laboratories.

 

    1. Notification USADA will provide the following notification will respect to each laboratory report received by USADA:

 

    1. Upon receipt of a negative laboratory report, USADA will promptly forward that result to the athlete, the USOC and the applicable NGB.
    2. Upon receipt of a positive laboratory A report or a report indicating an elevated testosterone, epitestosterone ratio or epitestosterone concentration, USADA will promptly notify the applicable NGB and athlete at the address on the Doping Control Notifications/Signature Form and shall advise the athlete of the date on which the laboratory will conduct the B sample analysis. The athlete may attend the B sample analysis accompanied by a representative at his or her own expense. Prior to the B sample opening, USADA shall provide to the athlete the A sample laboratory documentation set forth on Annex B. A sample shall not be considered positive until after the B sample analysis confirms the A sample analysis.
    3. Upon receipt of the laboratory’s B sample report, USADA shall promptly notify the USOC, the applicable NGB and the athlete. USADA shall then provide to the athlete the B sample documentation package set forth on Annex C. The laboratory shall not be required to produce any documentation in addition to Annexes B and C unless ordered to do so by an arbitrator(s) during adjudication, in which case it shall be produced at the athlete’s expense.
    4. In special circumstances where USADA is conducting testing for an IF, regional or continental sports organization or other Olympic movement sporting body, other than the USOC or an NGB, the notification described in this section shall be made exclusively to that sporting body, the athlete, and , if applicable, to the USOC and NGB.
  1. Results Management Whenever USADA receives a laboratory report confirming positive test, elevated testosterone or epitestosterone ration or epitestosterone concentration, or when USADA has other reason to believe that a doping violation has occurred, such as admitted doping, address that case through the following results management procedures:
      1. USADA ANTI-DOPING REVIEW BOARD
        The USADA Anti-Doping Review Board (“Review Board”) is a group of experts independent of USADA with medical, technical and legal knowledge of anti-doping matters. The Review Board members shall be appointed for two year terms by the USADA Board of Directors. The Review Board shall review all B sample test results reported by the laborator7y as analytically positive or elevated in accordance with i below. Such review shall be undertaken by between three and five Review Board members appointed in each case by USADA’s Chief Executive Officer and composed of at least one technical, one medical and one legal expert.

    Upon USADA’s receipt of a laboratory report identifying an analytically positive or elevated B test result, the following steps shall be taken:

      1. USADA’s Chief Executive Officer shall appoint a Review Board as provided in Section (a) above.
      2. The athlete shall be promptly notified of the date by which the athlete shall submit any written materials, through USADA, to the Review Board for its consideration. The athlete shall also be provided the name and telephone number of the Athlete Ombudsman.
      3. The Review Board shall be provided the laboratory documentation and any additional information which USADA deems appropriate. Copies of this information shall be provided simultaneously to the athlete and the athlete shall be entitled to file a response with the Review Board.
      4. The Review Board shall be entitled to request additional information from either USADA or the athlete.
      5. Notwithstanding the forgoing, the process before the Review Board shall not be considered a “hearing.” The Review Board shall only consider written submittals. Submittals to the Review Board shall not be used in any further hearing or preceding without the consent of the party making the submittal. The Review Board’s recommendations shall not be admissible in any further hearing or proceeding.
      6. The Review Board shall consider the written information submitted to it and shall, by majority vote, make a recommendation to USADA with a copy to the athlete whether
        or not there is sufficient evidence of doping to proceed to a hearing.
      7. USADA shall also forward the Review Board’s recommendation to the USOC, the applicable NGB and IF and WADA.
    1. ADJUDICATION
      1. Following receipt of the Review Board Recommendations, USADA shall notify the athlete in writing whether USADA considers the matter closed or alternatively what specific charges or alleged violations will be adjudicated and what sanction, consistent with IF rules, USADA is adjudicated and what sanction, consistent with IF rules, USADA is seeking the have imposed (an other possible sanctions which could be imposed under the applicable IF rules). The notice shall also include a copy of the USADA Protocol for Olympics Sport Testing and the modifications to AAA Commercial Rules. Within ten (10) days following such notice, the athlete must notify USADA if he or she desires a hearing to contest the sanction sought by USADA. If the sanction is to contested, then it shall be communicated by USADA to the USOC, the applicable NGB and If and WADA and thereafter imposed by the NGB. If the sanction is contested by the athlete, then a hearing shall be conducted pursuant to the procedure set forth below.
      2. The hearing will take place before the American Arbitration Association (“AAA”) using a single arbitrator (or a three arbitrator panel if demanded by either of the parties) selected from a pool of the North American Court of Arbitration for Sports (“CAS”) Arbitrators who shall also be AAA Arbitrators. The hearing will take place in the U.S., be administered by Decentralized Office of CAS in the Americas (the “Administrator”), and the conducted under modified AAA Commercial Rules attached as Annex D. The parties will be USADA and the athlete. USADA shall also invite the applicable IF to participate either as a party or as an observer. For their information only, notice of the hearing date shall also be sent to the USOC, the applicable NGB and WADA.
      3. Either the athlete or the IF(whether a party or not) shall be entitled to appeal the AAA arbitrator(s) decision to CAS. A CAS appeal shall be filed with the Administrator and the CAS hearing will automatically take place in the U.S. Otherwise the regular CAS appellate rules apply. The decision of CAS shall be final and binding on all parties and shall to be subject to further review or appeal.
      4. The athlete, within ten (10) days following the Notice described in section (i) above, shall be entitled, at his or her option, to elect to bypass the hearing described in section (ii) above and proceed directly to a single final hearing before CAS conducted in the United States. The CAS decision shall be final and binding on all parties and shall not be subject to further review or appeal.
      5. In all hearings conducted pursuant to this procedure the applicable IF’s categories of prohibited substance, definition of doping and sanctions shall be applied. In the event an IF’s rules are silent on a issue, the rules set for the in the Olympic Movement Anti-Doping Code shall apply. Notwithstanding the foregoing; (a) The IOC laboratories used by USADA shall be presumed to have conducted testing and custodial procedures in accordance to prevailing and acceptable standards a of scientific practice. This presumption can be rebutted by evidence to the contrary, but the accredited laboratory shall have no onus in the first instance to show that it conducted the procedures other than in accordance with its standard practices conforming to any applicable IOC requirements; (b) minor irregularities in sample collection, sample testing or other procedures set forth herein which cannot reasonablely be considered to have effected the results of an otherwise valid test or collection shall have no effect on such results; and (c) if contested, USADA shall have the burden of establishing the integrity of the sample collection process, the chain of custody of the sample, the accuracy of laboratory test results by clear and convincing evidence unless the rules of the applicable IF set a higher standard.
      6. All administrative costs of the USADA review and adjudication process will be borne by USADA except the CAS appeal fee which will be refunded to eh athlete by USADA should the athlete prevail on appeal.
      7. The results of all hearings shall be communicated by USADA to the athlete, the USOC, the applicable NGB and If and WADA. The NGB shall impose any sanction resulting from the adjudication process. The NGB shall not impose any sanctions until after the athlete has had the opportunity for a hearing pursuant to section 9(b)ii or 9(b)iv.
  2. Ownership and Use of Samples All samples collected by USADA shall be the property of USADA. USADA may authorize the use of negative samples for research; however, in such event all markings on the sample which identify the ample as coming from a particular athlete shall be obliterated.
  3. Confidentially Except for the notifications to the USOC, NGB, IF, WADA (or other sporting body ordering the test) as otherwise provided in this protocol, USADA shall not publicly disclose an athlete’s positive test result or other alleged doping violation until after the athlete has been found to have committed a doping violation in a hearing conducted under either article 9(b)(ii) or 9(b)(iv) above. USADA may release aggregate statistics of testing and adjudication results.
  4. Expedited Procedures USADA may shorten any time period set fourth in these procedures where doing so is reasonable necessary to resolve an athlete’s eligibility before a protected competition.
2013-11-27T16:27:27-06:00February 13th, 2008|Contemporary Sports Issues, Sports Management|Comments Off on United States Anti-Doping Agency Protocol For Olympic Movement Testing

Generic Alcoholism: Are College Athletes at Risk?

 

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.

 

2016-10-12T11:42:46-05:00February 13th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on Generic Alcoholism: Are College Athletes at Risk?

Factors Associated with Success Among NBA Teams

 

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).

2013-11-27T16:29:09-06:00February 13th, 2008|Sports Coaching, Sports History, Sports Management, Sports Studies and Sports Psychology|Comments Off on Factors Associated with Success Among NBA Teams
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