Monitoring the Health of U.S. Professional Athletes : The Body Mass Index of NBA and WNBA Players, 2005-2006 9781909112438

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Monitoring the Health of U.S. Professional Athletes The Body Mass Index of NBA and WNBA Players, 2005-2006

Published by Adonis & Abbey Publishers Ltd United Kingdom St James House 13 Kensington Square London W8 5HD Tel: 0845 388 7248 Nigeria No.3 Akanu Ibiam Street, Aso-villa, Asokoro. P.O. Box 10546 Abuja Tel: +234 (0) 8165970458, 07066997765 Year of Publication 2014 Copyright© Amadu Jacky Kaba British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 9781909112438 The moral right of the author has been asserted All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted at any time or by any means without the prior permission of the publisher

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Monitoring the Health of U.S. Professional Athletes The Body Mass Index of NBA and WNBA Players, 2005-2006

by

Amadu Jacky Kaba

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Dedication This book is dedicated to all of my former basketball coaches and teammates at: (1) St. Patrick’s High School, Monrovia, Liberia (1991-1992) (2) St. John’s College High School, Washington, D.C. (1992-1994) (3) Seton Hall University, South Orange, New Jersey (1994-1998)

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TABLE OF CONTENTS Dedication………………………………………………………………....iv CHAPTER ONE Introduction….............................................................................................11 CHAPTER TWO Conceptualizing Health: What is Good Health?…..............................19 CHAPTER THREE Methodology, Data Availability and Limitations of Study…...........29 CHAPTER FOUR Findings/Results….....................................................................................31 Table 1A. Body-Mass-Index Categories………………………………………..32 Table 1B. Percent of healthy weight, overweight, and obese adults, ages 20 years and older, by sex and race-ethnic group: United States, 19881994….....................................................................................................33 Mean Age, Height, Weight and Body-Mass-Index of NBA and WNBA Players, 2005-2006…...............................................................34 Table 2. Mean Age, Height, Weight and Body-Mass-Index of WNBA Players, 2006 Season.............................................................................35 Table 3. Mean Age, Height, Weight, Body-Mass-Index and Salary of NBA Players, 2005-2006 Season…................................................................37 Breakdown of Body-Mass-Index Categories of NBA and WNBA Players, 2005-2006….............................................................................40

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Table 4. Breakdown of Body-Mass-Index Categories of WNBA Players, 2006 Season (N=172) ….....................................40 Table 5. Breakdown of Body-Mass-Index Categories of NBA Players, 2005-2006 Season …..............................................................................41 Median Age, Height, Weight and Body-Mass-Index of NBA and WNBA Players, 2005-2006 ….......................................41 Table 6. Median Age, Height, Weight and Body-Mass-Index of WNBA Players, 2006 Season….....................................................42 Table 7. Median Age, Height, Weight, Body-Mass-Index and Salary of NBA Players, 2005-2006 Season…................................................43 Pearson's Correlation (Age, Height, Weight, BMI and Salary) of NBA and WNBA Players, 2005-2006…........................................44 Table 8. Pearson's Correlations (Age, Height, Weight and BMI) of WNBA Players, 2006 Season….....................................................45 Table 9. Pearson's Correlations (Age, Height, Weight, BMI and Salary) of NBA Players, 2005-2006 Season…................................................47 CHAPTER FIVE Discussion and Conclusion…..................................................................49 Blacks as Majority in the NBA and WNBA…..................................50 High College Graduation Rates for WNBA Players…....................54 High Number of NBA and WNBA Players from the U.S. South…..........................................................................57

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Introduction

Salaries and Gender and Salaries and Race………………………..58 Implications of High BMI Rates……………………………………..68 Injuries…………………………………………………………………72 High BMI Rates and Death……………………………………..........74 References…………………………………………………………………77 Appendices………………………………………………………………..91 Table A 1 Mean Height (inches) for Females 20 Years and Over, 1999-2002: United States…………………………………………….91 Table A 2 Mean Weight (pounds) for Females 20 Years and Over, 1999-2002: United States……………………………………………..92 Table A 3 Age Groups of WNBA Players: 2006 season……………………….93 Table A4 Height Breakdown of WNBA Players, 2006 Season………………94 Table A 5 Number of Players Institutions (Colleges or Universities may send 1 or more players) in Sending States Sent: 2006 WNBA Season…………………………………………………..95 Table A 6 All 69 Sending Institutions and NCAA & NAIA Conferences: 2006 WNBA Season………………………………….........................97 Table A 7 Institutions and Regions sending players to the WNBA: 2006 Season…………………………………………………………..101 Table A 8 Players Coming Directly from Overseas to the WNBA, vii

2006 Season…………………………………………………….........102 Table A 9 Total # of Players of each sending Institution to the WNBA, 2006 season, and U.S. News & World Report Academic Ranking, 2006.......................................................................................................103 Table A 10 College or University Attendance and Graduation Rates of WNBA Players: 2006 Season……………………………………….106 Table A 11 Number of WNBA Players sent By NCAA & NAIA Conferences, 2006 Season…………………………………………………………..107 Table A 12 Number & Names of Institutions in States with Players in the WNBA: 2006 Season………………………………………………...109 Table B 1 Mean Height (inches) for Males 20 Years and Over, 1999-2002: United States………………………………………………………...110 Table B 2 Mean Weight (pounds) for Males 20 Years and Over, 1999-2002: United States………………………………………………………...111 Table B 3 Age Breakdowns of All NBA Players: 2005-2006 (Age figures as of March 31, 2006)……………………………………………………...112 Table B 4 Breakdown of Height of NBA Players: 2005-2006 Season………113 Table B 5 Salary Breakdown of NBA Players, 2005-2006 Season…………..114 Table B 6 Salary Breakdown of NBA Players, 2005-2006 Season…………..115

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Table B 7 Country or Territory and Region of Origin of Foreign-Born NBA Players: 2005-2006 Season (as of February 28, 2006)…………….116 Table B 8 Breakdown of Players Directly from Overseas: 2005-2006 Season…………………………………………………….117 Table B 9 Number of Players Institutions (a High School, College or University may send 1 or more players) in Sending States Sent: 2005-2006 NBA Season……………………………………………...118 Table B 10 All 166 Sending Institutions (High Schools, Colleges and Universities in U.S): 2005-2006 NBA Season (As of March 6, 2006)…………………………………………………………………..120 Table B 11 Institutions (a High School, College or University may send 1 or more players) and Regions sending players to the NBA: 2005-2006 Season………………………………………………………………...124 Table B 12 Number of NBA Players sent By NCAA/NAIA/NJCAA Conferences: 2005-2006 Season (As of March 6, 2006)…………..125 Table B 13 Numbers and Names of Institutions in States in the NBA: 20052006 Season………………………………………………………….126 Table B 14 Total Number of Players of Each Sending Institution to the NBA, and Their Academic Rank in the 2006 U.S. News and World Report College Rankings…………………………………………..129

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Appendix C Regional Breakdown of the United States (N=51)……………….134 Appendix D Composition of macro geographical (continental) regions, geographical sub-regions, and selected economic and other groupings…………………………………………………………….135 Endnotes……………………………………………………………….…139 Index……………………………………………………………………...140

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CHAPTER ONE Introduction The health condition of professional athletes in the United States is monitored consistently and thoroughly for a variety of reasons. Team doctors, coaches, strength coaches and other team managers do so to treat or prevent injuries, or to help understand how to treat potential injuries. Fans want to know the health status of players to determine how much time and money to invest in a particular team or teams. Family members, agents, business partners, money managers, and lawyers of professional athletes want to know how to prepare for unexpected financial problems and other emergencies. Government officials such as law makers and public health officials, public health researchers and medical doctors want to know the health condition of professional athletes to determine how to treat any serious medical problems during and after the careers of these athletes are over, or to determine what laws to enact that will prevent or protect severe injuries to these athletes. The players themselves on each team are very concerned about their own health status and that of their teammates because it directly affects them in important ways including number of games played and won, individual contract amounts and bonuses. This is especially the case as more examples of star players decide to play for a particular team (Yang and Shi, 2011, p.362). As a result, there is a substantial body of scholarly research on professional athletes (and amateur athletes) in the United States and in other parts of the world focusing on dozens of medical (injuries and other physical, emotional, mental or psychological) conditions (Abel and Kruger, 2006; Bloomfield et al., 2005; García and Guisado, 2011; Chalcarz et al., 2012; Cormery et al., 2008; Covassin and Elbin, 2011 ; Daneshvar et al., 2011; de Saá Guerra et al., 2011; Gocentas and Landor, 2012; Kalist and Peng, 2007; Korkmaz and Karahan, 2012; Kostopoulos and Dimitrios, 2010; Laios and Theodorakis, 2002; Levy et al., 2012; Matthew and Delextrat, 2009; McCarthy et al., 2013; McKee et al., 2009; Moreira et al., 2011; Munro et al., 2012; Nevill et

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al., 2009; Orchard and Hayes, 2001; Ostojic et al., 2006; Scanlan et al., 2012; Schröder et al., 2004; Shulman, 2012; Simenz et al., 2005; van der Worp et al., 2011; Ziv and Lidor, 2009). According to Gocentas and Landor (2012): “Because of the increased training loads and the growing number of matches accompanying modern team game sports, it is important to find simple, rigorous, and regular monitoring tools to prevent injury, to stimulate recovery, and to optimally train athletes” (p.73). Writing about professional basketball players, Ziv and Lidor (2009) point out that: Relevant information on the physical and physiological characteristics of elite basketball players should be obtained by those professionals basketball coaches, strength and conditioning coaches, athletic trainers, physiotherapists and sport physicians - who work regularly with the athletes throughout the different phases of the training programme. This information can be appropriately utilized when planning a daily practice, a weekly agenda, or a more long-term programme. It is assumed that such information will help coaches increase their control over the physical and physiological workloads in which the players are engaged, and in turn improve the quality of training (p.548).

In an article on U.S. professional Baseball players and their willingness to invest substantial sums of money to employ personal trainers, Kalist and Peng (2007) point out that: “… These expenditures of time and money on producing good health are a form of human capital investment—leading to increased performance, longer playing careers, and, ultimately, higher lifetime earnings. Moreover, the high salaries of players give owners and managers an incentive to ensure their players are in top physical condition to maintain peak performance” (p.654). Many scholarly publications have focused on the prevalence of concussions in many different sports. For example, according to Covassin and Elbin (2011), concussion continues to be a serious public health problem in the United States and that there are an estimated 1.6 million to 3 million sport-related and recreational traumatic and brain injuries that happen in the country annually. They continue by pointing out that male athletes (especially American football) have

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Introduction

been the subjects of most of the scholarly research on concussion, but that the rates have also been increasing for female athletes. “Recent studies suggest that the incidence of and recovery from sport-related concussion varies between male and female athletes, with women having a higher risk of sustaining a concussion and taking a longer time to recover than men” (p.125). Daneshvar et al. (2011) note that traumatic brain injuries that result from concussions where individuals experienced direct or indirect physical impact or force to the head, face or neck: “…may present with a wide range of clinical signs and symptoms, including physical signs (e.g., loss of consciousness, amnesia), behavioral changes (e.g., irritability), cognitive impairment (e.g., slowed reaction times), sleep disturbances (e.g., drowsiness), somatic symptoms (e.g., headaches), cognitive symptoms (e.g., feeling “in a fog”), and/or emotional symptoms (e.g., emotional liability)….” (p.3). According to Levy et al. (2012): “Traumatic brain injury (TBI) is a significant public health problem in the United States, with approximately 1.5–2 million TBIs occurring each year. …As many as 22% of all soccer injuries are concussions” (p.78). Daneshvar et al. (2011) add that the 1.7 million TBI sustained by individuals in the United States every year is associated with 1.365 million visits to emergency rooms and 275,000 hospitalization each year, costing the entire country an estimated $60 billion in 2000” (p.1 & 3). In the United States, a very important health concern for both athletes and non-athletes alike is the issue of overweight and obese members of society. Compared with other developed countries and China, a substantial proportion of people in the United States are overweight or obese. For example, according to the 2013 CIA World Factbook, as of 2006, the adult prevalence rate for obesity in the United States was 33.9%; 23.1% in Canada in 2004; 22.7% in the United Kingdom in 2002; 16.9% in France in 2007; 16.4% in Australia in 2005; 3.1% in Japan in 2000; and 2.9% in China in 2002.i Hergenroeder et al. (2011) point out that there are estimates of over 65% of overweight adults in the United States, with over 30% within that group categorized as obese. They point out that this is a very serious health and economic responsibility on the country because

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many serious diseases are due or strongly related to obesity. Some of these diseases are “hypertension, type 2 diabetes, osteoarthritis, and certain forms of cancer…obesity is an independent risk factor for cardiovascular disease and those with excess weight have higher risk for cardiac complications such as coronary heart disease, heart failure, and sudden death.” It is estimated that the total cost of obesity in the United States in 2000 was $117 billion (p.11). As a result, Hergenroeder et al. (2011) note that: “…it is imperative that all health care professionals, including physical therapists, are able to effectively evaluate and treat conditions related to overweight and obesity” (p.11). In mid-June 2013, delegates to the American Medical Association annual meeting in Chicago, Illinois: “Voted to recognize obesity as a disease state with multiple aspects requiring a range of interventions to advance obesity treatment and prevention” (“Delegates shape new policies for the future of medicine at AMA Annual Meeting,” 2013). In athletics in the United States, this has resulted in a substantial number of research studies focusing on the weight status of professional athletes, including basketball players. One area pertaining to the weight status of professional athletes, including basketball players that has received significant attention is their Body Mass Index (BMI) (Bloomfield et al., 2005; Daskalovski and ShukovaStojmanovska, 2012; Garrido-Chamorro et al., 2009; Hergenroeder et al., 2011; Kushner and Choi, 2010; Lovell et al., 2011; Mathews and Wagner, 2008; Nevill et al., 2010; Rexhepi and Brestovei, 2010). For example, Garrido-Chamorro et al., 2009 note that “… BMI is widely used in epidemiologic studies and is fairly well correlated with fat content, mainly in adults.” (p.283). Neville et al. (2010) note of “…the wide use of BMI in epidemiological research…” (p.1010). According to Hergenroeder et al. (2011): “The BMI is the most common method to quantify weight across a range of body sizes in adults… Body mass index has been shown to be highly correlated with body fat in adult women in…surveys…. Body mass index is calculated by dividing an individual's weight in kilograms by the height in meters squared (kg/m2)” (p.11).

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Introduction

This study serves many purposes. First, it provides the general public, including fans of professional basketball, with the breakdown of the various BMI categories of NBA and WNBA players in 20052006 to understand how similar or different their rates are with the general population. Another purpose of this study is to provide government officials such as law makers and public health officials and researchers such as those who work at the U.S. Centers for Disease Control and Prevention and medical doctors with this data to help them monitor the health status of not just NBA and WNBA players, but athletes as a whole. Another purpose of this study is to provide young people involved in sports, especially basketball to learn of the health status (in this case focusing on their BMI) of these athletes so that they can gain useful information from the data and analysis and improve their own health status. This study builds on the studies by Kaba (2011a and 2012) by examining the health status of players in the National Basketball Association (2005-2006 season) and Women’s National Basketball Association (2006 season) of the United States, focusing on their weight, especially their BMI. It is useful to point out that although BMI is regularly utilized in studies focusing on the health status of people from young to old and athletes and non-athletes in the United States and other parts of the world, researchers have continued to caution people that the BMI is not a perfect indicator of predicting or explaining the health status of individuals. For example, according to the U.S. Centers for Disease Control, “Although BMI is not a measure of body fatness, persons classified as obese, tend to have excess body fat. A BMI in the overweight range is less healthy for most people, but in some cases may be acceptable for people who are muscular and have less body fat. Similarly, people with a BMI in the healthy weight range may have excess body fat and little muscle. Therefore, the BMI ranges are not exact ranges of healthy and unhealthy weight. However, studies have shown that health risk increases as BMI increases.”ii According to Hergenroeder et al. (2011): While BMI is an important clinical tool that may be used in the initial assessment of overweight and obesity… BMI does not distinguish between fat and lean mass and thus, has several limitations. Body mass index may

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overestimate body fat in those with larger muscle mass, such as athletes, and may underestimate body fat in those that have lost muscle mass…. Furthermore, studies suggest there is a need for population-specific BMI classifications because adiposity associated with a given level of BMI varies by race, age, and gender (p.11-12).

According to Rexhepi and Brestovei (2010) the BMI, “…index is not a tool for diagnosis, but it can be used to estimate the healthy weight. BMI will change depending on gender, race, and age. A sportsman could have the same BMI as a non-sportsman, but his BMI will be due to increased muscle mass, than a rise in body fat” (p.1069; also see Garrido-Chamorro et al., 2009; Mathews and Wagner, 2008, p.33). Finally, Nevill et al. (2010) also point out that: Despite its convenience and popularity, some researchers still consider the BMI to be a crude index of adiposity, predominantly because it fails to quantify body composition. Indeed, healthy adults can be misdiagnosed by BMI as overweight or obese, if fat mass BMI is verified by a criterion method…. For instance, a slender-framed female with excess fat might register a false negative, and a muscular male a false positive. Furthermore, observations on different morphologies among various nationalities have led to ethnic-specific BMI cut-off points (p.1010).

The studies by Kaba (2011a and 2012) examined various characteristics of United States NBA players (2005-2006 season) and WNBA players (2006 season), including demographics, education (such as degree attainment, college or high school attended, state and region of the U.S.), salaries and country of origin data (Tables A3 to A12 and B3 to B14 in the Appendix section also present more indepth data on these variables for fans and individuals in the general public interested in them). This study begins with the methodology and its limitations. Next, the study presents a section that attempts to conceptualize Good Health or what is meant by Good Health especially as it pertains to the issue of weight and players in the NBA and WNBA. Next, the study presents the results or findings both for the NBA and the WNBA, beginning with descriptive statistics and tests (Pearson's Correlations) of a number of the variables, including BMI. Next, the

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Introduction

study presents a discussion and conclusion section. This section attempts to explain or analyze: (1) any similarities and differences in the data between NBA and WNBA players; (2) any similarities and differences in the data between Blacks and Whites in the WNBA; and (3) any similarities and differences in the data among Blacks, nonBlacks, and Whites in the NBA. This section also attempts to explain any number of implications as a result of the very substantial proportions of Black, non-Black, and White NBA players who are overweight and the few that are obese.

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CHAPTER TWO Conceptualizing Health: What is Good Health? One could present various types of definitions or conceptualizations of Good Health or Health. This topic of Good Health needs many types of definitions or conceptualizations. For example, there could be different definitions of Good Health or Health for different categories of people or groups, such as the general population, athletes, entertainers and political figures. Good Health could also be defined differently for children, adults and the elderly. The definition could also be different for males and females, or for various ethnic or cultural groups, or even nationalities or nations. Although Good Health could be defined based on whether one is overweight, obese or slim, could it be defined also based on height (relatively tall individuals versus those who are relatively short)? A substantial amount of time spent researching scholarly publications in various academic disciplines yielded a significant number of publications with various forms of definitions or conceptualizations of Good Health or Health. Furthermore, the scholarly publications found for this section of this study attempt to present definitions or conceptualization of Good Health or Health of people based on physical, emotional, psychological or mental, and spiritual perspectives. Others examine the concept of Good Health from an economic perspective, whether for individuals or nations. Some scholars examine the concept of Good Health as Well-being of a person or society. Others define Good Health or Health based on the lifestyles of individuals, including their level of physical activities, while others have conceptualized it from health knowledge or educational attainment. Some studies also point to a link between social capital and good health. Cultural identity has also been noted to contribute to Good Health. Some may conceptualize Good Health as longevity or the ability to live longer (Balog, 2005; Belloc, 1973; Blair et al., 1992; Brennon, 2005; Brownell, 1991; Buck and RyanWenger, 2003; Burström and Fredlund, 2001; Dunn, 1959; Gore and Kothari, 2012; Grossman, 1972; Hansen-Kyle, 2005; Hung et al., 2010;

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Kawachi et al., 1999; Kenkel, 1991; The Lancet, 2009; Manderbacka et al., 1998; McGee et al., 1999; Nordqvist, 2009; Poortinga, 2006; Post, 2005; Rahman and Barsky, 2003; Salovey et al., 2000; Schoenborn, 1986; Strawbridge et al., 2001; Taylor, 1970; Thomas and Irwin, 2009). According to Nordqvist (2009):“The word health means different things to different people, depending on the situation…. The words "health" or "healthy" can also be used in non-medical contexts. For example "A healthy economy needs an ideal GDP growth rate that is sustainable, one that remains in the expansion phase of the business cycle as long as possible”.iii Hansen-Kyle (2005) presents a number of definitions of Good Health or Health: The concept of “healthy” is even vaguer in its definitions…. “being in a state of good health and not suffering from disease, pain, or other defects.” … as “possessing good health so as to be able to discharge all functions efficiently” … that good health should be of the mind and the body, and involves freedom from infirmity or disease, and that good health indicates the state of finances and the “exercise of good judgment.” The focus of the definitions cited above is on physical and mental functioning, lack of impairment, and stable physical, psychological, and financial resources (p.45-46).

Buck and Ryan-Wenger (2003) provide the following definitions of Good Health: … "general condition of the body or mind with reference to soundness and vigor; freedom from disease or ailment"… as a "person's state of well being, which can range from high-level wellness to terminal illness"… "health is the actualization of inherent and acquired human potential through goaldirected behavior, competent self-care, and satisfying relationships with others while adjustments are made as needed to maintain structural integrity and harmony with relevant environments" …. Thus, health may refer to a state, a goal, a process, a set of behaviors or an endpoint, all of which underscore that health is a multidimensional concept… (p.50).

The World Health Organization is reported to have defined health in 1946 as, “… a state of complete physical, mental, and social wellbeing and not merely the absence of disease or infirmity (Balog, 2005, p.267). Balog (2005) points to an explanation of health by a scholar: 20

Conceptualizing Health….

“To be in good health is to be in a complete state of well-being. It is a state of complete physical, mental, and social prosperity. Health is multi-faceted. One can be in good mental and social health, but may be lacking in physical health. This does not make the person unhealthy, because he or she still has a sound mind. He or she may have an illness that affects just the physical aspect of his or her wellbeing. So one can be ill, but he or she is quite healthy at the same time.” (p.267). According to Manderbacka et al. (1998): A major conceptual issue of health/illness concerns its continuity from poor or bad to good health. …'bad' health refers to 'illness', whereas good health refers to absence of illness. This negative concept of health ranging from bad to 'not bad' or neutral states covers the most important area of health. Thus, it makes sense to say that one's health is good when there is nothing wrong with it. Additionally, 'bad' health is prior to good, as we usually think that illnesses have causes whereas 'normal' health does not have identifiable causes in a similar sense. From the negative concept of health a positive one can be distinguished. Positive health goes beyond 'not bad' and refers to enjoyment of good health and feelings of fitness. (p.208).

The Lancet (2009) presents this explanation of health: “… as the ability to adapt to one's environment. Health is not a fixed entity. It varies for every individual, depending on their circumstances. Health is defined not by the doctor, but by the person, according to his or her functional needs” (p.781). Nordqvist (2009) continues by focusing on physical and mental definitions and explanations of health: The state of the organism when it functions optimally without evidence of disease or abnormality" …. For humans, physical health means a good body health, which is healthy because of regular physical activity (exercise), good nutrition, and adequate rest….Another term for physical health is physical wellbeing. Physical wellbeing is defined as something a person can achieve by developing all health-related components of his/her lifestyle. Fitness reflects a person's cardiorespiratory endurance, muscular strength, flexibility, and body composition. Other contributors to physical wellbeing may include proper nutrition, bodyweight management, abstaining from drug abuse, avoiding alcohol abuse, responsible sexual behavior (sexual

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health), hygiene, and getting the right amount of sleep….Mental health refers to people's cognitive and emotional well-being. A person who enjoys good mental health does not have a mental disorder. According to WHO, mental health is "a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community.iv

According to Blair et al. (1992): “Research studies over the past several decades confirm the health benefits of regular physical activity, a concept with foundations in antiquity” (p.99). Writing about what constitute a good mental health, Brenon (2005) claims that: “There is a broad level of agreement of the main characteristics of good mental well-being, which include an ability to: develop selfesteem/sense of personal worth; communicate; face problems, resolve and learn from them; be confident and assertive; awareness of others and ability to empathise with them; be able to 'play' and have fun; be able to laugh, both at one's self and at the world around them” (p.18). Salovery et al. (2000) have linked one’s emotional state to good health: That the arousal of emotion might have consequences for physical health is not a new idea….Phychotherapists and practicing physicians similarly have recognized the comorbidity of psychological and physical disorders…Rates of mood and anxiety disorders are considerably higher among medical inpatients compared with the general population…Depressed individuals report somatic ailments in greater numbers than do nondepressed individuals…and appraised their health status less positively…. In general, negative emotional states are thought to be associated with unhealthy patterns of physiological functioning, whereas positive emotional states are thought to be associated with healthier patterns of responding in both cardiovascular activity and the immune system… (p.10-11).

According to Post (2005): “…a strong correlation exists between the well-being, happiness, health, and longevity of people who are emotionally and behaviorally compassionate…” (p.66). According to Kenkel (1991): “A report of the U.S. surgeon general concluded that as much as 50 percent of mortality in 1976 was due to unhealthy

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Conceptualizing Health….

behavior or lifestyles…” (p.287). Schoenborn (1986) claims that the health habits of individuals such as: …drinking, smoking, and exercise have gained increasing attention over the past 20 years because of their potential impact on health and well-beingand, indeed, even survival. In the mid-1960s a study in Alameda County, CA, found that seven specific health habits, commonly referred to as the "Alameda 7," were related to both concurrent and subsequent health status and to long-term survival…. The habits were having never smoked, drinking less than five drinks at one sitting, sleeping 7-8 hours a night, exercising, maintaining desirable weight for height, avoiding snacks, and eating breakfast regularly (p.571).

McGee et al. (1999) point to findings that show that in the United States: “…self-reported health status to be related to subsequent mortality among the various racial/ethnic and national groups. Those reporting poorer health have a higher mortality rate than do those reporting better health” (p.41). According to Rahman and Barsky (2003): “Multiple studies have demonstrated that SRH [Self-Reported Health] is a good predictor of mortality and functional ability, even after objective measurements of medical morbidity… (p.856). In an article examining health from an economic perspective, Taylor (1970) claims that: People seek medical care for many reasons: (1) Reduce pain and suffering (2) Reduce fear and anxiety about the seriousness of illness. (Most illness is minor, but until its minor nature is confirmed by a physician, uncertainty about this can cause more loss of well-being than the illness.) (3) Hasten recovery from illness (4) Improve appearance (5) Repair disabilities (6) Reduce risk of future disability (7) Extend life (p.51).

One must not underestimate the importance of knowledge or education when attempting to understand what Good Health is. According to Kalist and Peng (2007): “A number of studies document the positive effect of education on health for the general 23

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population…One theory suggests that the positive education–health link is causal, because education makes people more efficient producers of health … that is, people with more education better understand how to stay healthy by using and combining various resources, such as food, exercise, and medical inputs, and are more likely to recognize health problems and better articulate those problems to health care providers” (p.654). According to Kenkel (1991): Even a cursory examination of data reveals that people are not equally unhealthy: a striking pattern is that the better-educated are more likely to choose healthy life-styles” (p.287). Grossman’s (1972) study: “…argues that a person’s stock of health knowledge affects his market and nonmarket productivity, while his stock of health determines the total amount of time he can spend producing money earnings and commodities….most students of medical economics have long realized that what consumers demand when they purchase medical services are not these services per se, rather, “good’” (p.224). In this current study, the concept of Good Health or Health is examined through the weight or BMI scores of U.S. professional athletes, focusing on players of the NBA (for the 2005-2006 season) and the WNBA (for the 2006 season). Utilizing the weight or BMI of NBA and WNBA players, one could explain how it is connected to the many different definitions or conceptualizations of Good Health or Health presented above. Being a professional athlete or even an amateur athlete does not mean that one cannot be overweight or obese. Although it is explained that professional athletes in particular, including basketball players tend to be heavier because of their muscle build-up over years or decades, they can still be overweight or obese. The data in this study will support this claim. Indeed this is one of the primary reasons for conducting this study. During the NBA season and the playoffs in particular, it is not uncommon to hear the television announcers analyzing the on-court performance of certain players including how fast they are able to run up and down the basketball court, and then repeatedly saying the words “his conditioning” or “conditioning.” This usually means that the player is not just out of shape, but that he is out of shape because he is overweight and that “conditioning” can help him lose weight.

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Conceptualizing Health….

Scholars have examined the endurance or stamina of professional basketball players (and athletes in other professional sports) and, including their heart rate or breathing and stress during games and practices (Cormery et al., 2008; Korkmaz and Karahan, 2012; Mathew and Delextrat, 2009, p.813-814; ZIV and Lidor, 2009). In a study focusing on rule change in professional basketball in 2000, Cormery et al. (2008) note that: “In 2000, the time allowed for one team to carry the ball towards the opposite team’s basket was reduced from 30 s to 24 s by Fe´de´ration Internationale de Basketball Amateur (FIBA). Additionally, the total length of the game was separated in four quarters instead of the original two halves, while the total duration was kept at 40 min…” (p.25). Cormery et al. (2008) add that: “In 1995, before the regulation change, a professional basketball player performed an average of 105 (52) sprint bouts per game, with a resting period of 21 s between each sprint and with 95% of the sprints not exceeding 4 s” (p.28). Cormery et al. (2008) also claim that: “… the basketball physiological demand is mixed, as 15% of the game time is devoted to very intense activity (sprint), 65% is executed at moderate intensity (running at a pace greater than walking), and 20% is executed at low intensity (standing/walking)” (p.29). Korkmaz and Karahan (2012) point to research that claim: “…that during the game a basketball player, on average, performed 46 and 44 jumping acts, respectively. Sprint activities cover 8.8% of live time… and 39% of the sprint activities approximately occur in between 2 or 3 second during a basketball matches” (p.17). In the NBA and WNBA, while unlike in college or high school, players have already gained significant weight due to the natural process of aging and weight gain, the practices starting from the preseason are not as long, and once the season officially begins, the regular practices are fewer and shorter. The players are mostly now left with the responsibility to keep themselves in shape. A study by Laios and Theodorakis (2002) of the preseason training of professional basketball players in Greece shows that the proportions of time allocated for team physical conditioning were 90% in the 1st week; 50% second week; 40% each in 3rd and 4th weeks; 30% each in 5th and 6th weeks; and 20% each in 7th and 8th weeks (p.149). Kalist and

25

Amadu Jacky Kaba

Peng (2007) write of U.S. professional Baseball players that they “… have their own personal trainers in addition to being under the watchful eye of their team’s trainers, strength and conditioning coaches, and physicians” (p.654). In a league where the average NBA player is over 6’7” and the average WNBA player is over 6’0”, and with their substantial salaries, especially for the males, they are in a very tempting situation to consume large amounts of food. The physical and speed aspects of the game also make them consume large portions of food. The United States is known for the large portions of food served in its restaurants. This brings us to the emotional and mental or psychological perspectives of Good Health. The overwhelming pressure on professional athletes, whether to perform well in their sports, from fans and family members, etc. can also lead to some of them turning to food and alcohol beverages such as beer that can cause one to gain weight. In other instances, the pressure from coaches, team owners and managers can put these athletes in a position to have psychological or emotional problems that could lead to violence or cause them to stop making any real efforts and gaining weight as a result, as a way of fighting back, even though they are the ones who lose the most. However, in those instances with guaranteed contracts they get paid regardless. Pressure from his coach led an NBA player (Latrell Sprellwell) at the time, to choke his coach. According to Kim and Parlow (2009): “While playing for the Golden State Warriors, Sprewell attacked and choked his coach, P.J. Carlesimo, during practice on December 1, 1997” (p.581 also see Hoberman, 2000). The fact that a large number of scholarly publications have focused on the weight or BMI of professional athletes (and also amateur athletes), including men’s and women’s sports shows that this phenomenon is not limited to the general public or non-athletes. One can be an athlete and be overweight or obese, and this can have all types of implications or consequences at the time that they are actively playing professionally and during their retirement. In the discussion and conclusion section of this study, many of these implications are examined.

26

Conceptualizing Health….

The information presented in this conceptualization section of this study has helped to caution us that being an athlete especially professional athlete, does not mean one is healthy. In fact, professional athletes in the United States and elsewhere could even be more at risk of being unhealthy than non-athletes, and being overweight or obese is just one example.

27

CHAPTER THREE Methodology, Data Availability and Limitations of Study This methodology section merges and revises the methodology sections of Kaba (2011a and 2012). For the WNBA, the data are compiled from its official website (http://www.wnba.com as of May 20, 2006, the official opening day of the 2006 season). For the NBA, all of the data, with the exception of the 2005-2006 salary figures (which I compiled and computed from the website of the national newspaper called USA Today), were compiled and computed from the official website of the NBA (http://www.nba.com as of March 6, 2006, for the 2005-2006 season). On the websites of the NBA and WNBA, a profile page of each player in the league is presented in alphabetical order. I printed out the profile page of each player and first transferred her and his data into an excel spreadsheet in alphabetical order and later computed all of the data in SPSS. One large table was created for each league, and it contains the profiles of all the players. The variables include date of birth and age, racial background, height, weight, position played, college/university or institution attended, state in the U.S. where institution is located, region of the country (e.g. Northeast, Midwest, South and West, using U.S. Census or government classification) where institution is located, country of origin (or foreign born) for those directly from outside of the United States, and year of graduation for those players who attended colleges or universities in the United States. Data for year of graduation for the NBA players were not provided on its website and are not included in this study. The league only provided the name of the college or university where a player attended. For those NBA players who entered the league directly from U.S. high schools, the league also provided the name of the school. Data for salaries of WNBA players are not posted on their league’s website. Nor are they posted by the USA Today newspaper, which posts salary figures for the NBA players (salary figures for 423 out of a total of 430 players are available). For figures for age for the 29

Amadu Jacky Kaba

WNBA players are as of May 31, 2006. The figures for age for the NBA players are as of March 31, 2006. For the WNBA, the players are separated into two categories based on their pictures posted on the WNBA official website: (1) Players of African descent (but referred to as Black players in this study) and (2) White players. For the NBA, the players are also separated into two categories based on their pictures posted on the official website of the NBA: (1) Players of African descent (but also referred to as Black players in this study) and (2) Players of nonAfrican descent (who will also be referred to as non-Black players). For non-Black players, during different sections of the study, they are also separated into two categories: (1) White players and (2) East Asian players, who comprise only two. The racial and ethnic classifications of various groups in the United States is utilized to divide the players. For example, in the U.S., people who are of Turkish, Arab, Jewish, Iranian, or European ancestry, are classified as White, while anyone with Black African ancestry is classified as Black or African American. Individuals from East Asia and South Asia are classified under Asian/Pacific Islanders (Gans, 2012; Kaba, 2011a, 2012; Morning, 2000, 2005; “Standards for the Classification of Federal Data on Race and Ethnicity,” 1995). For example, according to the United States Office of Management and Budget, “The term "Black" in Directive No. 15 refers to a person having origins in any of the Black racial groups of Africa.” For who is White: “In Directive No. 15, the "White" category includes persons having origins in any of the original peoples of Europe, North Africa, or the Middle East” (“Standards for the Classification of Federal Data on Race and Ethnicity,” 1995).

30

CHAPTER FOUR Findings/Results Before presenting the findings for players in both the NBA and WNBA in 2005-2006, it is useful to first present similar data for the general U.S. population to put the research in a proper context. The mean height of women aged 20 or older in the United States from 1999- 2002 was 63.8 inches or almost 5’4” tall. When examined based on race and cultural background both non-Hispanic Whites and nonHispanic Blacks are taller on average than the national average: 64.2 inches each for those 20 years and over; and these two groups are also both at 64.6 inches tall among those 20-39 years (Table A 1 in Appendix). This is also the case for men aged 20 years and over: their average height was 69.2 inches; 69.7 inches for non-Hispanic White men; and 69.5 inches for non-Hispanic Black men. For those 20-39 years old, it was 70.2 inches for non-Hispanic White men and 70 inches for non-Hispanic Black men (Table B 1 in Appendix). For the average weight of women aged 20 and older in the general U.S. population from 1999-2002, it was 162.9 pounds: 161.7 pounds for non-Hispanic White women; and 182.4 pounds for non-Hispanic Black women. For those aged 20-39 years, it was 158.4 pounds for non-Hispanic White women; and 179.2 pounds for non-Hispanic Black women (Table A2 in Appendix). For men aged 20 years and older during that same period, their average weight was 189.8 pounds: 193.1 pounds for non-Hispanic White men; and 189.2 pounds for non-Hispanic Black men (Table B2 in Appendix). Table 1A shows the body-mass-index categories as presented by the U.S. Centers for Disease Control and Prevention. According to Table 1A, if an adult enters her or his height and weight into the BMI calculator on the website of the CDC and the number that appears is below 18.5, it indicates that person to be underweight; 18.5-24.9 indicates normal weight; 25-29.9 indicates overweight; and 30.0 and above indicates obese (Table 1A).

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Amadu Jacky Kaba

Table 1A. Body-Mass-Index Categories. BMI Weight Status Below 18.5 Underweight 18.5--24.9 Normal/Healthy 25--29.9 Overweight 30.0 & Above Obese Source: Extracted from “Healthy Weight, It’s not just a diet, it’s a lifestyle,” U.S. Centers for Disease Control and Prevention. Retrieved on March 30, 2013 from: http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/english_bmi_calculator/ results_overweight.html?pounds=260&inches=83

In the general U.S. population, “… the average adult man has a BMI of 26.6 and the average adult woman has a BMI of 26.5.”v According to Table 1B, in the United States, from 1988-1994, higher proportion of women (25.5%) aged 20 and over than their male counterparts (19.9%) are obese. However, higher proportion of women (45.9%) have normal or healthy weight than their male counterparts (39.3%). Also, a higher proportion of men (39.9%) than women (25.7%) are overweight. Examining Table 1B by race or cultural background, 37.2% of nonHispanic Black women were obese: 33.6% of Mexican American women; and 23.1% of non-Hispanic White women. A higher proportion of non-Hispanic White women (49.2%) have a normal or healthy weight; 32.2% for Mexican American women; and 31.1% for non-Hispanic Black women. Almost 1 out of every 4 (24.8%) of nonHispanic White women were overweight; 29.4% of non-Hispanic Black women; and 32.6% of Hispanic women. For men, the proportion of those obese was almost the same: 20.3% of non-Hispanic Whites; 21.1% of non-Hispanic Blacks; and 20.7% of Mexican Americans. A higher proportion of non-Hispanic Blacks (41.4%) have normal or healthy weight; 38.1% of non-Hispanic Whites; and 35.2% of Mexican Americans. Finally, a higher proportion of Mexican American men (43.3%) were overweight; 40.7% of non-Hispanic Whites; and 35.9% of non-Hispanic Blacks (Table 1B).

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Findings/Results

Table 1B. Percent of healthy weight, overweight, and obese adults, ages 20 years and older, by sex and race-ethnic group: United States, 1988-1994. Normal/Healthy Race-ethnic Group Weight Overweight Obese Men 39.3 39.9 19.9 Non-Hispanic White 38.1 40.7 20.3 Non-Hispanic Black 41.4 35.9 21.1 Mexican American 35.2 43.3 20.7 Women Non-Hispanic White Non-Hispanic Black Mexican American

45.9

25.7

25.5

49.2

24.8

23.1

31.1 32.2

29.4 32.6

37.2 33.6

Source: “Healthy weight, overweight, and obesity among U.S. adults,” U.S. Centers for Disease Control.Retrieved on April 8, 2013 from: http://www.cdc.gov/nchs/data/n hanes/databriefs/adultweight.pdf

According to Hergenroeder et al. (2011), “Among US adults, middle-aged women have the highest prevalence of obesity. It is reported that 38.2% of women aged 40 to 59 years were obese in 20072008 based on data from the National Health and Nutrition Examination Survey (NHANES)…. The substantial presence of obesity in the middle-aged is concerning given the association between obesity and future disability. Studies have demonstrated that obesity in young and middle age is associated with self-reported disability and poorer physical function later in life…. Analysis of recent trends has shown that obesity-related disability is on the rise… reinforcing the need for a better understanding of the impact of obesity on physical function in this group” (p.11).

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Amadu Jacky Kaba

Mean Age, Height, Weight and Body-Mass-Index of NBA and WNBA Players, 2005-2006 Tables 2 and 3 present the mean age, height, weight and BMI and their standard deviations of WNBA and NBA players for the 20052006 seasons. For the NBA, the mean salaries for the 2005-2006 season are also presented. According to Table 2, of the 175 players in the WNBA during the 2006 season, their mean ages was 26 years, with a standard deviation of + or - 3.8 years; mean height of 72.4 inches, with standard deviation of + or - 3.4 inches; mean weight of 168.6 pounds, with standard deviation of + or - 22 pounds; and mean BMI of 22.6 (172 players), with a standard deviation of + or - 1.96. For 118 Black players, their mean age was 26 years, with standard deviation of + or 4 years; mean height of 72.3 inches, with a standard deviation of + or 3.1 inches; mean weight of 168 pounds (116 players), with a standard deviation of + or - 21 pounds; and mean BMI of 22.6 (116 players), with standard deviation of + or - 2.07. For 57 White players, their mean age was 25.3 years, with standard deviation of + or - 3.2 years; mean height of 72.6 inches, with standard deviation of + or - 4.08 inches; mean weight of 169.7 pounds (56 players), with standard deviation of + or - 24 pounds; and mean BMI of 22.5 (56 players), with standard deviation of + or - 1.7 (Table 2). For comparative purposes, the study by McCarthy et al. (2013) of 496 WNBA players from 20002008 show that their mean age was 22.2 years; mean height was 70.6 inches; mean weight was 166.7 pounds; and mean BMI was 23.4 (p.646).

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Findings/Results

Table 2.Mean Age, Height, Weight and Body-Mass-Index of WNBA Players, 2006 Season Mean

Std.

Group

Number

Age

Deviation + or -

Black Players

118

26.2

4

White Players

57

25.3

3.2

All Players

175

25.94

3.79

Mean

Std. Deviation + or -

Group

Number

Height

Black Players

118

72.3

3.1

White Players

57

72.6

4.08

All Players

175

72.4

3.44

Mean

Std.

Group

Number

Weight

Deviation + or -

Black Players

116

168.1

20.9

White Players

56

169.7

23.9

All Players

172

168.6

21.9

Mean

Std.

Group

Number

BMI

Deviation + or -

Black Players

116

22.6

2.07

White Players

56

22.5

1.7

All Players

172

22.6

1.96

Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006; see also Tables A3 and A4 in the Appendix for more in depth breakdowns of data on the age and height of WNBA players, 2006 season.

According to Table 3, of the 430 players in the NBA as of March 6, 2006, their mean age was 26.5 years, with standard deviation of + or 4.3 years; mean height of 79.2 inches, with standard deviation of + or 3.75 inches; mean weight of 224 pounds, with standard deviation of + or - 29 pounds; mean BMI of 25, with standard deviation of + or - 1.78; and mean salary of $3.9 million (423 players), with a standard deviation of + or - $4 million. For 327 Black players, their mean age 35

Amadu Jacky Kaba

was 26.7 years, with standard deviation of + or - 4.5 years; mean height of 78.6 inches, with standard deviation of + or - 3.7 inches; mean weight of 220.3 pounds, with standard deviation of + or - 28.8 pounds; mean BMI of 24.96, with a standard deviation of + or - 1.83; mean salary of $ 4 million, with a standard deviation of + or - $4.2 million. For 103 non-Black players, their mean age was 26 years, with standard deviation of + or - 3.8 years; mean height of 81.1 inches, with a standard deviation of + or - 3.38 inches; mean weight of 235 pounds, with standard deviation of + or - 27 pounds; mean BMI of 25, with a standard deviation of + or - 1.6; and mean salary of $3.5 million (101 players), with standard deviation of + or - $3.2 million. For 101 White players, their mean age was 26.1 years, with standard deviation of + or - 3.8 years; mean height of 80.99 inches, with standard deviation of + or - 3.26 inches; mean weight of 234 pounds, with standard deviation of + or - 25.8 pounds; mean BMI of 25, with standard deviation of + or -1.58; and mean salary of $3.5 million (99 players), with standard deviation of + or - $3.2 million. Finally, for 35 players (34 Blacks and 1 White) directly from high schools in the United States, their mean age was 22.1 years, with standard deviation of + or - 3 years; mean height of 80.6 inches, with standard deviation of + or - 3 inches; mean weight of 230 pounds, with a standard deviation of + or - 34 pounds; mean BMI of 24.8, with a standard deviation of + or - 2.6; and mean salary of $4.6 million, with a standard deviation of + or - $5.1 million (Table 3). For comparative purposes, according to Kahn and Sherer (1988), “The National Basketball Association (NBA) appears to be an example of racial progress. Blacks comprise roughly 75% of the players and about 80% of starting players” (p.40). According to Groothuis and Hill (2004), of 4476 NBA players in their study, their mean height was 79.08 inches; 80.68 inches for Whites (918 total); and 78.48 inches for Blacks (3458 total). For weight, 221 pounds for all of them; 230 for Whites and 216 for Blacks (p.343). “In 2009-10, the NBA's 30 franchises only employed 442 players on the court” (Berri, 2012, p.159).

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Findings/Results

Table 3.Mean Age, Height, Weight, Body-Mass-Index and Salary of NBA Players, 2005-2006 Season Mean

Std.

Number

Age

Deviation + or -

Black Players

327

26.7

4.5

White Players

101

26.1

3.8

Non-Black Players High School Players

103

26

3.8

35

22.1

2.97

All Players

430

26.5

4.3

Mean

Std.

Number

Height

Deviation + or -

Black Players

327

78.6

3.7

White Players

101

80.99

3.26

Non-Black Players High School Players

103

81.1

3.38

35

80.6

2.99

All Players

430

79.2

3.75

Mean

Std.

Number

Weight

Deviation + or -

Black Players

327

220.3

28.8

White Players

101

233.9

25.8

Non-Black Players High School Players

103

235.1

27.1

35

230

34

All Players

430

223.8

29.1

Group

Group

Group

37

Amadu Jacky Kaba

Group Black Players

Mean

Std.

Number

BMI

Deviation + or -

327

24.96

1.83

White Players

101

25

1.58

Non-Black Players High School Players

103

25

1.6

35

24.8

2.6

All Players

430

24.98

1.78

Mean

Std.

Number

Salary

Deviation + or -

Black Players

322

4,040,633

4,244,396

White Players

99

3,473,882

3,193,404

Non-Black Players High School Players

101

3,466,792

3,181,009

34

4,560,993

5,101,548

All Players

423

3,903,616

4,020,086

Group

Source: Compiled and Computed by author based on Data on the NBA Website. www.nba.com, 2006; Salary data: Compiled and Computed by author based on Data on the website of USA Today Newspaper. www.usatoday.com, 2006; also see Tables B3 to B6 in Appendix for more in depth breakdowns of data on the age, height and salaries of NBA players, 2005-2006 season.

Pertaining to the salary data of NBA and WNBA players, there are no official figures of WNBA salaries. According to Staffo (1998a): “…WNBA salaries range from $15,000 to $50,000 excluding meal and travel money…” (p.193). Isaacson (2006) notes that, “The WNBA rookie minimum is $31,800…. The average WNBA salary is $50,000…” (p.1). Anthony et al. (2012) point out that by 2012, the average salary in the WNBA was $72,000, and that the minimum has increased to $36,570 and the maximum has increased to $105,000 (p.120). However, it is overseas where WNBA players go to play after their season ends in the United States and earn higher salaries: “Playing overseas allows the typical player to earn at least $40,000, and as much as $600,000, per season. Three WNBA players earned,

38

Findings/Results

including bonuses, $1 million from overseas play during the 20112012 season” (Anthony et al., 2012, p.120). In the NBA, although the average salary of Black players was higher than their non-Black counterparts during the 2005-2006 season, higher proportion of non-Black players earned $1 million or more. For example, during the 2005-2006 NBA season, 423 of the 430 players were paid a combined salary of just over $1.65 billion: The total salary for 322 Black players was just over $1.3 billion (78.8% of the $1.65 billion); and almost $350 million for 101 non-Black players (21.2% of the $1.65 billion) (Tables B5 to B6). Of the 423 players for whom salary data were available, 123 (29.1%) earned $5 million or more during the 2005-2006 season. For the 322 Black players for whom salary data were available, 101 (23.9% of all 423 players, but 31.4% of all 322 Black players) earned $5 million or more for the 2005-2006 season. Twenty-two non-Black players (5.2% of all 423 players, but 21.8% of all 101 non-Black players) earned $5 million or more for the 2005-2006 season. Twenty-one White players (5% of all 423 players, but 21.8% of all 99 White players) earned $5 million or more for the 2005-2006 season (Table B6). A total of 40 players (9.5% of all 423 players) earned $10 million or more during the 2005-2006 season. Thirty-five Black players (8.3% of all 423 players, but 10.9% of all 322 Black players) earned $10 million or more during the 2005-2006 season. Five non-Black players (1.2% of all 423 players, but 5% of all 101 non-Black players) earned $10 million or more during the 2005-2006 season. Four White players (0.95% of all 423 players, but 4% of all 99 White players) earned $10 million or more during the 2005-2006 season (Table B6). Finally, a total of 315 players (74.5% of all 423 players) earned $1 million or more. A total of 236 Black players (55.8% of the 423 players, but 73.3% of 322 Black players) earned $1 million or more. A total of 79 non-Black players (18.7% of the 423 players, but 78.2% of 101 NonBlack players) earned $1 million or more (Table B6).

39

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Breakdown of Body-Mass-Index Categories of NBA and WNBA Players, 2005-2006 Tables 4 and 5 present the BMI categories of WNBA and NBA players. According to Table 4, during the 2006 season, of the 172 WNBA players for whom data are available, 1 (0.6%) was underweight, 153 (89%) were categorized as normal, and 18 (10.5%) were categorized as overweight. For 116 Black players, 1 (0.9%) was categorized as underweight; 104 (89.7%) were categorized as normal; and 11 (9.5%) were categorized as overweight. For 56 White players, 49 (87.5%) were categorized as normal, and 7 (12.5%) were categorized as overweight. Table 4. Breakdown of Body-Mass-Index Categories of WNBA Players, 2006 Season (N=172) Group

Underweight

%

Normal

%

Overweight

%

Obese

%

Total

%

Black Players White Players

1

0.9

104

89.7

11

9.5

0

0

116

100.1

0

0

49

87.5

7

12.5

0

0

56

100

All Players

1

0.6

153

89

18

10.5

0

0

172

100.1

Source: Computed by author on the website of the U.S. Centers for Disease Control and Prevention: http://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/english _bmi_calculator/results_overweight.html? Pounds=260&inches=83

According to Table 5, of the 430 NBA players during the 20052006 season, none was in the underweight category. Of the 430 players, 213 (49.5%) were in the normal category; 214 (49.8%) players were in the overweight category; and 3 (0.7%) were in the obese category. For 327 Black players, 163 (49.8%) were in the normal category; 161 (49.2%) were in the overweight category; and 3 (0.9%) were in the obese category. For 103 non-Black players, 50 (48.5%) were in the normal category; and 53 (51.5%) were in the overweight category. For 101 White players, 50 (49.5%) were in the normal category; and 51 (50.5%) were in the overweight category. For all 35 players from U.S. high schools, 22 (62.9%) were in the normal

40

Findings/Results

category; 12 (34.3%) were in the overweight category; and 1 (2.9%) was in the obese category (Table 5). Table 5.Breakdown of Body-Mass-Index Categories of NBA Players, 2005-2006 Season Group

Underweight

%

Normal

%

Overweight

%

Obese

%

Total

%

0

0

163

49.8

161

49.2

3

0.9

327

100

0

0

50

49.5

51

50.5

0

0

101

100

0

0

50

48.5

53

51.5

0

0

103

100

0

0

22

62.9

12

34.3

1

2.9

35

100

All Players 0 0 213 49.5 214 49.8 3 0.7 430 Source: Compiled and Computed by author based on Data on the NBA Website. www.nba.com, 2006; -Salary data: Compiled and Computed by author based on Data on the website of USA Today Newspaper. www.usatoday.com, 2006

100

Black Players White Players Non-Black Players High School Players

Median Age, Height, Weight and Body-Mass-Index of NBA and WNBA Players, 2005-2006 Tables 6 and 7 present the median age, height weight and BMI of WNBA and NBA players for the 2005-2006 seasons. The median salaries of NBA players are also presented. According to Table 6, the median age of all 175 WNBA players during the 2006 season was 25 years; median height was 72 inches; median weight was 167 pounds; and their median BMI was 22.2. For 118 Black players, their median age was 25 years; median height was 72.5 inches; median weight was 165.5 pounds (116 players); and median BMI was 22.5 (116 players). For 57 White players, their median age was 24 years; median height was 72 inches; median weight was 168.5 pounds (56 players); and their median BMI was 22.2 (Table 6).

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Amadu Jacky Kaba

Table 6. Median Age, Height, Weight and Body-Mass-Index of WNBA Players, 2006 Season Median Group

Number

Age

Black Players

118

25

White Players

57

24

All Players

175

25

Median Group

Number

Height

Black Players

118

72.5

White Players

57

72

All Players

175

72

Median Group

Number

Weight

Black Players

116

165.5

White Players

56

168.5

All Players

172

167

Group

Number

BMI

Black Players

116

22.5

White Players

56

22.2

All Players

172

22.2

Median

Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

According to Table 7, the median age of all 430 NBA players in March 2006 was 26 years; median height was 80 inches; median weight was 221.5 pounds; median BMI was 25; and median salary was $2.42 million. For 327 Black players, their median age was 26 years; median height was 79 inches; median weight was 220 pounds;

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Findings/Results

median BMI was 24.9; and median salary was $2.45 million (322 players). For 103 non-Black players, their median age was 26 years; median height was 82 inches; median weight was 238 pounds; median BMI was 25; and median salary was $2.4 million (101 players). For 101 White players, their median age was 26 years; median height was 81 inches; median weight was 236 pounds; median BMI was 25; and median salary was $2.4 million (99 players). Finally, for 35 players directly from U.S. high schools, their median age was 21 years; median height was 81 inches; median weight was 220 pounds; median BMI was 24.3; and median salary was $2.3 million (34 players). Table 7. Median Age, Height, Weight, Body-Mass-Index and Salary of NBA Players, 2005-2006 Season Median Group

Number

Age

Black Players

327

26

White Players

101

26

Non-Black Players

103

26

High School Players All Players

35

21

430

26 Median

Group

Number

Height

Black Players

327

79

White Players

101

81

Non-Black Players

103

82

High School Players All Players

35

81

430

80 Median

Group

Number

Weight

Black Players

327

220

White Players

101

236

Non-Black Players

103

238

43

Amadu Jacky Kaba

High School Players All Players

35

220

430

221.5 Median

Group

Number

Salary $

Black Players

322

2,445,000

White Players

99

2,375,000

101

2,375,000

34

2,295,000

423

2,420,000

Non-Black Players High School Players All Players

Median Group

Number

BMI

Black Players

327

24.9

White Players

101

25

Non-Black Players

103

25

35

24.3

High School Players

All Players 430 25 Source: Compiled and Computed by author based on Data on the NBA Website. www.nba.com, 2006; Salary data: Compiled and Computed by author based on Data on the website of USA Today Newspaper. www.usatoday.com, 2006

Pearson's Correlations (Age, Height, Weight, BMI and Salary) of NBA and WNBA Players, 2005-2006 Using Pearson’s Correlation, Tables 8 and 9 test for the following variables of WNBA and NBA players for the 2005-2006 seasons: ageheight; age-weight; age-BMI; height-BMI; weight-height; weight-BMI; and for NBA players only, salary-age; salary-weight; salary-BMI; and salary-height. For all WNBA players in the 2006 season, Table 8 shows a strong height-weight correlation of .729 and significant at the 1% level (2tailed test). There is also a strong BMI-weight correlation of .685 and significant at the 1% level. For Black players, there is a strong heightweight correlation of .659 and significant at the 1% level. There is also a strong BMI-weight correlation of .727 and significant at the 1% level. Finally, for White players, there is a strong height-weight correlation

44

Findings/Results

of .831 and significant at the 1% level. There is also a strong BMIweight correlation of .624 and significant at the 1% level (Table 8). Table 8. Pearson's Correlations (Age, Height, Weight and BMI) of WNBA Players, 2006 Season Group

Age

Height

Weight

BMI

All Players Age

1

-0.009

-0.039

-0.04

All Players Height

-0.009

1

.729**

0.006

All Players Weight

-0.039

.729**

1

.685**

All Players BMI

-0.04

0.006

.685**

1

Black Players Age

1

-0.018

-0.028

-0.029

Black Players Height

-0.018

1

.659**

-0.034

Black Players Weight

-0.028

.659**

1

.727**

Black Players BMI

-0.029

-0.034

.727**

1

White Players Age

1

0.022

-0.054

-0.088

White Players Height

0.022

1

.831**

0.087

White Players Weight

-0.054

.831**

1

.624**

White Players BMI

-0.088

0.087

.624**

1

** Correlation is Significant at the 0.01 level (2-tailed) Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

For all NBA players in 2006, Table 9 shows 6 different positive correlations: a strong height-weight correlation of .840 and significant at the 1% level; a strong BMI-weight correlation of .716 and significant at the 1% level; a moderate age-salary correlation of .324 and significant at the 1% level; a moderate to low BMI-height correlation of .228 and significant at the 1% level; a low weight-salary correlation of .124 and significant at the 5% level; and a low BMIsalary correlation of .117 and significant at the 5% level. For Black players, Table 9 shows 8 different positive correlations: a strong height-weight correlation of .829 and significant at the 1%

45

Amadu Jacky Kaba

level; a strong BMI-weight correlation of .730 and significant at the 1% level; a moderate age-salary correlation of .309 and significant at the 1% level; a moderate to low BMI-height correlation of .229 and significant at the 1% level; a low weight-salary correlation of .162 and significant at the 1% level; a low BMI-salary correlation of .142 and significant at the 5% level; and a low height-salary correlation of .112 and significant at the 5% level. For non-Black players, Table 9 shows 4 different positive correlations: a strong height-weight correlation of .840 and significant at the 1% level; a strong BMI-weight correlation of .730 and significant at the 1% level; a moderate age-salary correlation of .383 and significant at the 1% level; and a moderate to low BMIheight correlation of .246 and significant at the 5% level. For White players, Table 9 shows 4 different positive correlations: a strong height-weight correlation of .826 and significant at the 1% level; a strong BMI-weight correlation of .720 and significant at the 1% level; a moderate age-salary correlation of .378 and significant at the 1% level; and a low BMI-height correlation of .206 and significant at the 5% level. Finally, for players directly from U.S. high schools, Table 9 shows 4 different positive correlations: a strong BMI-weight correlation of .904 and significant at the 1% level; a strong age-salary correlation of .833 and significant at the 1% level; a strong height-weight correlation of .752 and significant at the 1% level; and a moderate BMI-height correlation of .403 and significant at the 5% level (Table 9).

46

Findings/Results

Table 9. Pearson's Correlations (Age, Height, Weight, BMI and Salary) of NBA Players, 2005-2006 Season Groups All Players Age

Age

Height

Weight

BMI

Salary

1

-0.069

-0.021

0.049

.324**

All Players Height

-0.069

1

.840**

.228**

0.084

All Players Weight

-0.021

.840**

1

.716**

.124*

All Players BMI

0.049

.230**

.717**

1

.117*

All Players Salary

.324**

0.084

.124*

.115*

1

Black Players Age

1

-0.034

0.000

0.038

.309**

-0.034

1

.829**

.229**

.112*

Black Players Height Black Players Weight

0.000

.829**

1

.730**

.162**

Black Players BMI

0.038

.229**

.730**

1

.142*

Black Players Salary

.309**

.112*

.162**

.142*

1

1

-0.105

0.011

0.141

.378**

White Players Height

-0.105

1

.826**

.206*

0.084

White Players Weight

0.011

.826**

1

.724**

0.066

White Players Age

White Players BMI

0.141

.210*

.720**

1

0.016

White Players Salary

.378**

0.084

0.066

0.016

1

1

-0.134

0.031

0.102

.383**

Non-Black Players Height

-0.134

1

.840**

.246*

0.08

Non-Black Players Weight

-0.036

.840**

1

.730**

0.053

Non-Black Players BMI

0.102

.246*

.730**

1

0.003

Non-Black Players Salary

.383**

0.08

0.053

0.003

1

High School Players Age

1

0.221

0.135

0.057

.833**

High School Players Height

0.221

1

.752**

.403*

0.231

High School Players Weight

0.135

.752**

1

.904**

0.097

High School Players BMI

0.057

.403*

.904**

1

-0.012

High School Players Salary

.833**

0.231

0.097

-0.012

1

Non-Black Players Age

** Correlation is Significant at the 0.01 level (2-tailed) *Correlation is Significant at the 0.05 level (2-tailed) Source: Compiled and Computed by author based on Data on the NBA Website. www.nba.com, 2006; Salary data: Compiled and Computed by author based on Data on the website of USA Today Newspaper. www.usatoday.com, 2006

47

CHAPTER FIVE Discussion and Conclusion The data presented above and in the appendices include many important findings. For example, while the majority of players in both the NBA and WNBA are Black, the proportion of Black men (76%) is more substantial than that of Black women (67%). While the mean height of both WNBA Black players (72.3 inches) and White players (72.6 inches) are almost the same, non-Black players (81.1 inches) in the NBA are 2.5 inches taller than Black players (78.6 inches). The non-Black NBA players are almost 15 pounds heavier than their Black male counterparts, but almost the same for Black (168.1 pounds) and White (169.7 pounds) WNBA players. The mean BMI of WNBA players are almost the same, with 22.6 for Blacks and 22.5 for Whites, and it is also the same for Black (24.96), non-Black (25), and White (25) NBA players. In the WNBA, 9.5% of the Black players and 12.5% of the White players are categorized as overweight. For the NBA, 49.2% of Black players, 51.5% of non-Black players, but 34.3% of players directly from high school are categorized as overweight. All 3 NBA players categorized as obese are Black. Statistically, all NBA players, Black players, non-Black players and White players account for less than 50% in the normal BMI category, but 62.9% of players directly from high school are categorized as normal. The mean age of NBA players (22.1 years) directly from high school is over 4 years lower than the mean for the NBA (26.5 years). Players in the NBA directly from U.S. high schools have a higher mean salary ($4.56 million) than the mean salaries of all players ($3.9 million), Black players ($4 million), non-Black players ($3.47 million), and White players ($3.47 million). Higher proportion (78.2%) of non-Black players, however, earned $1 million or more than Black players (73.3%) (Tables 2, 3, 4, 5 and B6). The height-weight correlations are significantly higher for White (.831) WNBA players than their Black counterparts (.659), and the BMI-weight correlation is significantly higher for WNBA Black 49

Amadu Jacky Kaba

players (.727) than White players (.624). For NBA players, the heightweight correlation is higher for both Black players (.829) and nonBlack players (.840). Groothuis and Hill (2004) find 70% height-weight correlation rate of NBA players in their study (p.346). In the NBA, the BMI-weight correlation is significantly higher for players from high school (.904) than Black players (.730), non-Black players (.730), and White players (.724). The age-salary correlation is substantially higher for players from high school (.833) than Black players (.309), nonBlack players (.383), and White players (.378). It is interesting that a low weight-salary correlation only showed for all players (.124) and Black players (.162). Also, a low BMI-salary correlation only showed for all players (.117) and Black players (.142). A low height-salary correlation only shows for Black players (.112) (Tables 8 and 9). For this section of this study, I will focus on the following: (1) Blacks as the majority in the NBA and WNBA; (2) High college graduation rates for WNBA players; (3) High number of NBA and WNBA players from the U.S. South; (4) Salaries and gender and salaries and race; (5) and Implications of a high BMI among NBA players. Blacks as Majority in the NBA and WNBA One can present two interrelated factors responsible for Blacks comprising the majority of players in the NBA in 2005-2006 and the WNBA in 2006. Kaba (2011a and 2012) claims that the reason over 3 out of every 4 players in the NBA in 2005-2006 and almost 7 out of every 10 WNBA players (67.4%) in 2006 were Black is that both leagues are structured in a way that the majority of the players are drafted from colleges and universities in the United States where both Black female and Black male players account for very high proportions of players. Yang and Shi (2011) note that, “Most athletes enter the NBA through the draft system” (p.353). For example, looking at the WNBA, according to Kaba (2012), in a January 2005 NCAA report, there were an estimated 3,947 (27% of all female basketball players) non-Hispanic Black female basketball players and 9,373 (64.2% of all female basketball players) non-Hispanic White

50

Discussion and Conclusion

female basketball players in Divisions I, II & III combined in 20032004 in the United States. These figures did not include non-resident alien female basketball players, who comprised 364 during that same period. It is in Division I Women’s college basketball (where the majority of WNBA players are either drafted or come from), however, that has a higher proportion of Black female players. For example, in 2003-2004, there were 1,987 (41.6% of all Division I female basketball players) non-Hispanic Black female Division I basketball players, and there were 2,235 (46.8% of all Division I female basketball players) non-Hispanic White female basketball players (p.104). The situation is also similar for the NBA, which also drafts a very high majority of its players from colleges and universities in the United States. According to Kaba (2011a), in 2003-2004 there were 6,739 (42% of all male basketball players) non-Hispanic Black male basketball players and 7,952 (49.6% of all male basketball players) non-Hispanic White male basketball players in Divisions I, II & III combined in U.S. colleges and universities. These figures did not include non-resident alien male basketball players, who comprised 480 during that same period. Twenty-six (31.7%) of the 82 International NBA players in this study (Table B 7) attended colleges and universities as non-resident college basketball players in the United States. It is in Division I men’s college basketball (where the majority of NBA players are either drafted or come from), however, that has a majority of Black male players. For example, in 2003-2004, there were 2,905 (58.2% of all Division I male basketball players) nonHispanic Black male Division I basketball players, and there were 1,578 (31.6% of all Division I male basketball players) non-Hispanic White male basketball players. The colleges and universities with basketball programs in the United States continue to get their players from a very large poll of players coming out of high school, with one account putting the total number of high school basketball players (boys and girls) at 550,000 by 2006, with Blacks conservatively estimated to account for 200,000 (Kaba, 2011a, p.18). An important reason for the very high proportions of Black female and Black male basketball players in the WNBA and NBA is that they account for a significant proportion of students enrolled in

51

Amadu Jacky Kaba

colleges and universities in the United States. For example, in October 2004, there were 7,575,000 males enrolled in U.S. colleges and universities, with non-Hispanic Black males comprising 776,000 (10.2%), and White males accounting for 5,944,000 (78.5%) (Kaba, 2011a, p.18). In October 2004, there were 9,808,000 females enrolled in US colleges and universities, with non-Hispanic Black females comprising 1,525,000 (15.5%), and White females accounted for 7,438,000 (75.8%) (“School Enrollment”, 2005). Blacks also comprise tens of millions in the United States. As of March 2002, of the 282 million people in the US, males comprised 137.9 million (48.9%), and females comprised 144.2 million (51%). Non-Hispanic Black females comprised 19.3 million (13.4% of the total female population) and non-Hispanic White females comprised 99.4 million (68.9% of the total female population) (Kaba, 2012, p.104). In March 2002, nonHispanic Black males comprised 16.7 million (12.1% of the total male population), and non-Hispanic White males comprised 95.4 million (69.2% of the total male population) (Kaba, 2011a, p.18). It is important to discuss this ongoing trend of Blacks accounting for the majority of NBA and WNBA players and the demographic trends of the United States, Africa and the world. This is because if one were to do a DNA test of the Black players in the NBA and WNBA, it is very likely that a very high proportion of them may be traced to West Africa or Middle Africa. The physical characteristics of these Western and Middle Africans (with very long legs and arms), like those of Black Americans, tend to contribute to their success in sports such as basketball and track and field. Due to her or his very long legs and arms, a person of Western or Middle African descent or Black American can be the same height of 5’9” as a European or an Asian, but will look taller than them because of the long arms and legs (Bejan, et al., 2010; Nevill et al., 2009). According to Nevill et al. (2009), “…there is evidence that taller/more linear athletes, who have a higher RPI [Reciprocal Ponderal Index], are more successful at distance running” (p.425). An estimated 60% of all Africans brought to the New World as enslaved people were from West Africa (Bah, 2005, p.79). According to Kaba (2011b), from the 1400s to the 1900s, of

52

Discussion and Conclusion

the 10,308,213 Africans transported to the Western Hemisphere (Trans-Atlantic Slave Trade) to be enslaved: … 5,221,415 (50.6%) were from Western Africa; 4,558,117 (44%) were from Middle Africa; 525,931 (5%) were from Eastern Africa; 2,135 (0.02%) were from Southern Africa; and 615 (0.006%) were from Northern Africa….Within Western Africa, of the 6,634,714 Africans transported in all four slave trades [from 1400s to 1900s], 5,221,415 (78.7%) were through the Trans-Atlantic; zero (0%) through the Indian Ocean; 1,334,187 (20.1%) through the Trans-Saharan; and 79,116 (1.5%) through the Red Sea….Within Middle Africa, of the 5,093,203 Africans transported in all four slave trades, 4,558,117 (90.2%) were through the Trans-Atlantic; 7,047 (0.1%) through the Indian Ocean; 409,368 (8%) through the Trans-Saharan; and 118,673 (2.3%) through the Red Sea (p.94-95).

By the twenty-first century, these two regions (Middle and Western Africa) continue to grow in population at very rapid rates. There is the potential for these two regions to supply players to the NBA and the WNBA starting first through U.S. high schools or colleges and universities. In 2006, for example, of the 910.84 million people in Africa, Eastern Africa had the highest proportion, with 284 million (31.2%), followed by Western Africa with 260.9 million (28.6%), Northern Africa 202.6 million (22.2%), Middle Africa, 112.2 million (12.3%), and Southern Africa, 51 million (5.6%). West Africa and Middle Africa do not only have very high percentages and raw numbers of the youngest human beings on earth, but the women in those two regions give birth to very high numbers of children. The fertility rate in West and Middle Africa are significantly higher than those in the other three regions of the continent and the world. For example, as of 2006, Middle Africa and Western Africa had the two highest average total fertility rates, 5.43 and 5.22 children born per woman respectively; 4.87 children born per woman in Eastern Africa; 2.97 children born per woman in Southern Africa; and 2.86 children born per woman in Northern Africa. The average for all of Africa in 2006 was 4.68 children born per woman (Kaba, 2011a, p.19). As of 2006, the median age in Africa was 19.5 years (19.2 years for males and 19.8 years for females). The median age in the world in

53

Amadu Jacky Kaba

2006 was 27.6 years (27 years for males and 28.2 years for females). Of the five regions in Africa, Northern Africa has the highest average median age: 23.7 years (23.4 years for males and 23.9 years for females), followed by Southern Africa, 20.5 years (19.9 years for males and 21.06 years for females), Eastern Africa, 19.35 years (19.12 years for males and 19.59 for females), Western Africa, 19.1 years (18.8 years for males and 19.3 years for females), and Middle Africa, 17.5 years (17.2 for males and 17.9 years for females). This means that these young people in Africa have the potential to be significantly or substantially represented among Division I college basketball teams in the United States and teams in the NBA in the years and decades to come. In fact, in the past three decades, the majority of Black African International players in both the NBA and WNBA come from Western and Middle Africa combined (Kaba, 2011a, p.19) High College Graduation Rates for WNBA Players Another important finding which can be linked to the finding just mentioned above is that almost all of the WNBA players have made an extraordinary accomplishment by earning at least a bachelor’s degree from colleges and universities in the United States, including highly ranked or highly selective institutions such as Harvard University, Stanford University, Duke University, Georgetown University, University of California, Los Angeles, Vanderbilt University, and the University of Notre Dame (Kaba, 2012, p.102; Table A6). Of the 156 WNBA players who attended U.S. colleges and universities, 155 (99.4%) graduated. Of the 114 Black players who attended U.S. colleges and universities, 99.1% graduated; and all 42 (100%) of White players who attended U.S. colleges and universities graduated (Kaba, 2012, p.103; Table A 10). Lapchick (2011) points out that during the 2011 National Collegiate Athletic Association (NCAA) Men’s and Women’s tournaments in the United States: “95 percent (60) of the women’s teams compared to 63 percent (42) of the men’s teams graduated at least 60 percent of their players” (p. 1; also see Gaston-Gayles, 2004, p. 75). Hamilton (2003) writes of a talented African American University of Tennessee women’s basketball player 54

Discussion and Conclusion

named Kara Lawson, who despite being one of the top college basketball players in the country, managed to graduate “…as a finance major with 3.75 GPA” (p. 22). In the case of NBA players, although, the league did not provide their year of graduation data for the 2005-2006 season, a substantial number of them earned a bachelor’s degree, and others even went further to earn master’s or professional degrees or doctorates. For example, although Shaquille O'Neal entered the NBA early before graduating, he not only went on to graduate later while he was still in the league, he also went on to enroll in a doctoral program and finished by earning his doctorate a year after he retired from the league. Tables B 9 and B10 show that like their female counterparts, NBA players attended or graduated from highly ranked academic institutions (166 U.S. high schools, colleges and universities and from 41 U.S. states, with Washington, D.C. as a state equivalent), including Stanford University, Duke University, Georgetown University., University of California, Berkeley, University of California, Los Angeles, University of Michigan, Ann Arbor and the University of North Carolina, Chapel Hill. According to Table B 14, these institutions were ranked among the top 120 colleges and universities in the 2006 U.S. News and World Report College Rankings. There were166 institutions in the U.S. with players in the 2005-2006 NBA season (as of March 6, 2006), with the academic rankings of those institutions ranked in the 2006 U.S. News & World Report academic rankings of colleges and universities in the United States. The 2006 U.S. News & World Report college rankings were divided into three sections or categories: (1) National Universities, which ranks the top 120 institutions according to academic strength (Tier I and Tier II combined). This particular ranking had 124 institutions because some institutions are tied for certain positions. For example, Princeton University and Harvard University were tied for the top spot; (2) Tier 3 institutions, which are a group of 64 colleges and universities listed alphabetically; and (3) Tier 4 institutions, which are a group of 60 institutions listed alphabetically. The combined total number of all institutions in the three groups is 248. Let us examine which of the

55

Amadu Jacky Kaba

166 U.S. academic institutions with players in the 2005-2006 NBA season were ranked in the 2006 U.S. News & World Report college rankings. Two institutions (Duke University and Stanford University) were ranked in the Top 10, with both of them tied at number 5. A total of 8 institutions were ranked in the Top 25. They are: Duke University; Stanford University; Rice University (#17); University of Notre Dame (#18);University of California, Berkeley (#20); Georgetown University (#23); University of California, Los Angeles (#25); and the University of Michigan (#25). A total of 20 institutions were ranked in the Top 50. A total of 30 institutions were ranked in the Top 60. Fifty two institutions were ranked in the Top 100, and 62 institutions were ranked in the Top 120. For institutions ranked in Tier 3, a total of 27 of them with players in the NBA were ranked in that group. For Tier 4 institutions, a total of 10 institutions with players in the NBA were ranked in that category. For 67 institutions with players in the NBA, no data showed that they were ranked in any of those three separate 2006 U.S. News and World Report college rankings. The 62 institutions ranked in the Top 120 comprised 50% of the 124 institutions in that category. The 27 institutions ranked in the Tier 3 group comprised 42.2% of the total of 64 institutions listed. The 10 institutions ranked in Tier 4 accounted for 16.7% of the total 60 institutions on the list. Finally, combined 99 institutions with players in the NBA were ranked in any one of the three different 2006 U.S. News & World Report college rankings, accounting for 39.9% of the total 248 institutions (Table B 14; “America’s Best Colleges 2006,” 2006). This is a very important development because many of these players as already noted in the case of Shaquille O'Neal, tend to go back during or after their playing careers to finish their undergraduate education. Even in the instances, where others do not go back to finish, being on the campus and attending classes at such highly ranked institutions for two to three or four years can mean a lot because one comes into contact with various types of people and establish life-long networks. There are a number of wealthy and successful Americans who attended but did not graduate from

56

Discussion and Conclusion

college, including Bill Gates, Ted Turner, former owner of CNN television and Mark Zuckerberg, founder of Facebook. In addition to their love for the game of basketball, and their history of exclusion from colleges and universities in the United States, and their poverty rates which are higher than all other groups, both Black females and Black males, but Black females in particular, use the game of basketball to win scholarships to earn their bachelor’s or masters’ degrees, which can cost tens of thousands of dollars or more. It is noted that: “Proportionately, the black athlete has been more successful than any other group in any other endeavor in American life. And he and she did it despite legal and social discrimination that would have dampened the ardor of most participants” (quoted in Dey, 1997, p.84; also see Abney, 1999, p.35). It is also noted that: “The association between athletics and African American success is not surprising, given that sports is just about the only type of mainstream (nonfictional) media coverage in which one can see images of many successful African Americans” (quoted in Hughes, 2004, p.163). Videon (2002) points out that: “…participation in athletics is associated with an array of positive educational outcomes. Students who participate in sports have better attendance records, lower rates of discipline referrals, and higher academic selfesteem and are more likely to be in a college preparatory curriculum, earn higher grades, and aspire to, enroll in, and graduate from college” (p.415). High Number of NBA and WNBA Players from the U.S. South An important finding in this study that needs mentioning is that of the dominance of institutions from the Southern United States that sent players to both the NBA and WNBA. Of the 156 WNBA players who attended U.S. colleges and universities, 85 (54.5%) are from the South (Kaba, 2012, p.101; Table A 7). For the NBA, of the 375 players that attended U.S. high schools (35 of them), colleges and universities, 157 (41.9%) are from the South (Table B 11). This is a very interesting observation, because when it comes to athletes, especially basketball and American football, the South tends to dominate, while the

57

Amadu Jacky Kaba

Midwest, Northeast and West tend to dominate in other areas such as academic institutions, and having more intellectual, cultural and economic elites. The South, however, tends to have more people in poverty. For example, Kaba’s (2011c) study of political science professors at four Ivy League universities in the fall of 2005, finds that, “Of the 198 professors for whom data for university of graduation were available, 102 (51.5%) are from institutions in the Northeast and 39 (20%) are from the West…”vi Of the 471 contributors to all five issues of the American Economic Review (AER) in 2010, they held 473 employment positions (two of them held two positions each) and 145 (30.64%) were in the Northeast; 73 (15.4%) were in the West; 70 (14.97%) were in the Midwest; 65 (13.74%) were in the South; 96 (20.3%) were in Europe; 12 (2.54%) were in Canada; 5 (1.05%) in Asia; 4 (0.85%) in Oceania; 2 (0.42%) in South America; and 1 (0.21%) in Africa (Kaba, 2013, p.28-29). These 471 contributors to the AER in 2010 also earned 475 terminal or highest degrees. Of those 475 terminal or highest degrees, 186 (39.23%) were conferred by institutions in the Northeast; 80 (16.9%) in the West; 75 (15.8%) in the Midwest; 32 (6.8%) in the South; 91 (19.2%) in Europe; and 2 (0.42%) in Oceania (Australia) (Kaba, 2013, p.38-39). Looking at household incomes and poverty rates, according to DeNavas-Walt et al. (2012), the estimated median household income in 2011 in the Northeast was $53,864; $52,376 in the West; $48,722 in the Midwest; and $46,899 in the South (p.6). In 2011, 46.3 million (15%) people were below the poverty level in the United States; 16% in the South; 15.8% in the West; 14% in the Midwest; and 13.1% in the Northeast (p.14). Salaries and Gender and Salaries and Race The data in this study also reveal two interrelated findings pertaining to salaries of NBA and WNBA players. The first is that society tends to favor the male athletes than their female counterparts when it comes to compensating them for their athletic abilities. It is important to briefly discuss why society allows WNBA players to be paid a salary of $50,000 by 2006 while their brothers or male counterparts are paid an average of $3.9 million during the 2005-2006 season. This is

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Discussion and Conclusion

the case even with advertisement or endorsement opportunities. Fans appear to be willing to pay the males substantially more than their female counterparts. As a result, a substantial number of WNBA players have to go overseas to play professionally once the WNBA season ends because they are paid better in those nations than in the United States (James, 2002; McCabe, 2011; Spencer and McClung, 2001; Staffo, 1998ab; Ruihley et al., 2010; Wearden & Creedon, 2002). Staffo (1998a) notes that an estimated 500 women from the United States were playing overseas (p. 190). Staffo (1998a) also adds that: “Professional leagues outside the United States existed in Spain, Italy, Germany, Scandinavia and Japan. A few US stars, such as Teresa Edwards and Katrina McClain, made an estimated $200,000 for a sixmonth season” (p. 190). According to Anthony et al. (2012), “The void created by the fall of the WBL in 1981 again meant that American collegiate stars faced the prospect of relocating overseas after college in order to continue participating in their sport at the professional level. However, this prospect was viewed as inferior, by many players, to that of a professional basketball league in the United States due to the psychic and other migration costs associated with moving to another country to pursue one’s profession. Such costs were expressed by star player Lisa Leslie, when she stated simply, “[i]t’s hell being overseas”…” (p.108). Even though some WNBA players earn significantly more than the average and that some also get endorsements, those figures are not as high when compared to their male counterparts. Issacson (2006) points out that: “The highest-paid WNBA players earn about $90,000, and with endorsement deals, stars can push that to as much as $200,000. Overseas salaries for the best players approach $500,000” (p. 1). Staffo (1998a) also notes that “…superstars like Lisa Leslie, Rebecca Lobo and Sheryl Swoopes are said to be making up to $250,000 when promotional fees are added in...” (p. 193). According to Spencer and McClung (2001), former WNBA star Cynthia Cooper, signed endorsement contracts with both General Motors and Nike for an estimated $500,000 annually (p. 334). Ruihley et al. (2010) note that NBA player LeBron James, who entered the league directly from high school, signed a multi-year contract with Nike for $90 million; that in

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2009, golf player Tiger Woods’ endorsement income was $110 million; and that in 1997 former NBA player Michael Jordan earned $40 in endorsements (p. 133-135). According to Yang and Shi (2011): Star athletes such as David Beckham, Michael Jordan, and Tiger Woods are powerful global brands with fans all over the world. These star athletes profit greatly from their fame, as their fans watch their games, wear their jerseys, follow their news, and purchase the products they have endorsed. Tiger Woods ranked fifth in Forbes Magazine's Celebrity 100, earning $110 million in 2009 alone, whereas professional basketball player Kobe Bryant ranked tenth, earning $45 million in the same year…. Recently, when star basketball player LeBron James was deciding which team to join in June 2010, the prospective move stirred a massive discussion on the value of a star athlete to a team, to a city, and even to a state. …LeBron James added $100 million dollars in franchise value and other revenue to his former team, the Cleveland Cavaliers, from 2003 to 2010…. In addition, James's departure from the Cavaliers would drop the value of the franchise by as much as $250 million… the effect of LeBron James could add up to as much as $2.7 billion if he played in Chicago for the next six years (p.352).

Fans tend to show more support for male sports through their rate or level of attendance and also through ticket price they are willing to pay. As Yang and Shi (2011) note, “The value of star athletes and their huge earnings from salaries and endorsements are driven by their enormous popularity among fans” (p.352). According to McCabe (2011), “A critical outcome of understanding the nature of spectators’ involvement with competitive sports is its relevance in predicting consumption attitudes and purchasing behavior” (p. 107-108). Smith and Roy (2011) claim that: “Ticket sales represent the most important source of local revenues for most sport teams. Revenue from ticket sales makes up at least 50% of all local revenues for the four major professional sports leagues in the United States (NFL, MLB, NBA, and NHL)” (p. 93). According to Staffo (1998a): “During the first [WNBA] season average attendance was 9669 per game, with the single largest crowd being 18,937 when Houston played at Charlotte August 16, 1997… The first championship game was played August 30 at The Summit, with the Houston Comets defeating the New York Liberty. Attendance was 16,285” (p. 192). Cotes and Humphreys (2007) point out that the average attendance to NBA games from 1991 to 2001 was

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16,671 (p. 167). Jacobsen (2010) reports that in the WNBA: “[Ticket prices for] Most franchises start around $10 and go as high as $200 or more. Single-game tickets to the defending champ Phoenix Mercury begin at $10 and go as high as $195.25. The New York Liberty charges anywhere from $10 to $260, the latter for courtside seats” (p. B1). It is noted that the average NBA ticket price in 2010 was $48.08; $99.25 for the Los Angeles Lakers; and $88.66 for the New York Knicks (“NBA Sees Ticket Prices Slump,” 2010, p. C2). Staffo (1998b) claims that during the 1996-1997 NBA season, the price of front row seat at a New York Knicks home game at the Madison Square Garden was $1000 (p. 15). Voisin (2011) points out that the NBA’s annual revenue is $4 billion. According to Berri (2012), “…the NBA in 2009-10 earned $3.8 billion in revenue… … the NBA collected nearly $3.7 billion in revenue in 2005-06. Of this amount, $766.67 million came from the national TV contract…' Another $996.6 million came from regular season gate revenue.' The remainder of league revenue comes from such sources as local broadcast rights, playoff game receipts, exhibition game receipts, stadium concessions, parking, etc. (p.159160). How can one explain this human behavior of gender bias in sports? According to James (2002), “It has been proposed that women’s sports have a different appeal than men’s sports” (p. 141). Wearden and Creedon (2002) claim that: “Feminist scholars point to the huge disparity in endorsement revenue between male and female athletes as evidence of a male hierarchy in sport… The gender hierarchy argument holds that female athletes are both “other than” and “less than” their male counterparts” (p. 189). In addition, females involved in team sports may experience more discrimination in earnings than those in individual sports. For example, according to Wearden and Creedon (2002): “… researchers have found a sex-appropriate ranking scheme in sport that suggests individual sports (that is tennis, figure skating, golf and gymnastics) are more appropriate for women than team sports” (p. 189). Staffo (1998a) presents this philosophical explanation of this persistent gender bias in sports: “Finally one big difference between the development of men’s sports and women’s sports in the US is that

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women’s sports have always been based in the philosophy and are an outgrowth of the women’s physical education program and therefore have generally maintained a purer attitude in the pursuit of sports for sports sake. This philosophy has generally kept women’s sports free from the corruption that has frequently marred men's sports” (p. 195; also see Kaba, 2012, p.106). The second interrelated finding pertaining to salaries is that in the NBA, although over three out of every four players are Black, and Black players play more minutes, and almost always tend to be the most valuable players during the regular season and the playoffs, lower proportion of them earn $1million or more than non-Black players or White players in this study. This is an observation that has been made by many scholars (Berri, 1999, p.421-422; Bodvarsson and Brastow , 1998, p.154; Burdekin et al., 2005; Hoang and Rascher, 1999; Kahn, 2000 ; Kahn and Sherer, 1988; Mogull, 1974). Examining NBA players’ salaries for the 1985-1986 season, Kahn and Sherer (1988) point out that: “White and black players earn similar mean compensation; however, controlling for a variety of productivity and market-related variables and for the endogeneity of player draft position, we find a significant ceteris paribus black compensation shortfall of about 20%” (p.40; Hill, 2004, p.81; also see Groothuis and Hill, 2004, p.347). In a study of 28 NBA players (14 Blacks and 14 Whites), Mogull (1974) points out that: “The distribution of salaries [for 1970-1971) within the groups… is similar for both faces, but the range is much greater among blacks [$15,000 to $250,000] than whites [$22,000 to $200,000]. It is also relevant that although the mean salary of black players [$72,000] in this sample exceeds that of whites [$68,000], the larger standard deviation among black salaries [60 versus 55 for Whites] causes the difference in means to be insignificant” (pp.11-12). In this current study, higher numbers and proportions of Black players earned salaries of ten million dollars or more or five million dollars or more. According to Table B 6, within their groups, 101 (31.4%) Black players and 22 (21.8%) nonBlack players earned $5 million or more; and 35 (10.9%) Black players and 5 (5%) non-Black players earned $10 million or more. According to Kahn and Sherer (1988): “Many of the most highly paid players are

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black: the top three NBA salaries for the 1985-86 season went to Earvin Johnson ($2.5 million), Moses Malone ($2.145 million), and Kareem Abdul-Jabbar ($2.03 million), all black players.” Kahn and Sherer (1988) add that five more players earned $1 million or more during that same 1985-1986 season: Julius Erving, $1.485 million; Patrick Ewing, $1.25 million; Ralph Sampson, $1.165 million; Marques Johnson and Otis Birdsong, $1.1 million each. For White NBA players during that same season, they note that “The highest paid white players include Larry Bird ($1.8 million), Jack Sikma ($1.6 million), Mitch Kupchak ($1.15 million), and Kevin McHale ($1 million)” (p.4041; also see Bodvarsson and Brastow, 1999, p.248-249). In this current study, 35 Black players earned $10 million or more during the 2005-2006 NBA season. There are 13 (37.14%) of the 35 Black players who earned $15 million or more. The top 5 Black earners are: Shaquille O'Neal ($20 million); Chris Webber ($19.13 million); Michael Finley ($18.6); Kevin Garnett ($18 million); and the following 4 players are statistically tied at number 5; Stephon Marbury and Allen Iverson each ($16.45 million); Jason Kidd ($16.44 million); and Jermaine O'Neal ($16.43 million). There are 5 non-Black players (all White players) that earned $10 million or more during the 2005-2006 NBA season: Keith Van Horn ($15.7 million); Dirk Nowitzki ($13.84 million); Wally Szczerbiak ($11.02 million); Andrei Kirilenko and Pau Gasol each ($10.97 million). There is one White player (Raef LaFrentz) who earned $9.996 million, and another (Steve Nash) who earned $9.63 million. It is interesting to note that in the 1946-1947 NBA season the average salary was $4,300 (Lawler et al., 2012, p.410). Some of these scholars have claimed that racial discrimination against Blacks by team owners, team managers and fans in the United States is the primary reason for this phenomenon (Bodvarsson and Brastow, 1999; Burdekin et al., 2005; Dey, 1997, p.84; Kahn and Sherer, 1988; Kanazawa and Funk, 2001; Mogull, 1974). According to Fugazza (2003), “The International Convention on the Elimination of all Forms of Racial Discrimination adopted in 1965 defines racial discrimination broadly and in practical terms. Its definition of racial discrimination includes ‘any distinction, exclusion, restriction or preference based on

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race, colour, descent or national or ethnic origin which has the purpose or effect of nullifying or impairing the recognition, enjoyment or exercise, on an equal footing, of human rights and fundamental freedoms in the political, economic, social, cultural or any other field of public life’” (p.507). Bodvarsson and Brastow (1999) note that: “Racial discrimination by employers occurs if employers are prejudiced against workers of a certain skin color. Owners' preferences for skin color are assumed to apply to the managers they hire as well. For example, a white team owner who is prejudiced against blacks may prefer white managers over equally skilled nonwhite managers and will hire managers who will implement their discriminatory tastes” (p.245-246). According to Groothuis and Hill (2004): “Minority workers may be discriminated against by being paid a lower wage, having more difficulty finding a job, or having a lower probability of retention than those of the majority workers for comparable productivity” (p.342; also see p.341). Kahn and Sherer (1988) note that: “Economic theory predicts that customer discrimination can persist under competition, while employer or coworker discrimination is more likely to diminish…In customer discrimination, the customer is willing to pay a premium for white workers. In basketball, the white fan acts as if the white player is producing more entertainment value than a comparable black player” (p.41). According to Kanazawa and Funk (2001): “A number of economic studies have examined whether consumers of professional sporting events discriminate against black athletes…. Three main approaches have been pursued, yielding mixed results. One approach examines whether fans are willing to pay more for sports memorabilia of white players than of non-white players…A second approach has been to examine voting by fans for all-star teams…. A third approach examines whether the racial composition of a team influences fans' willingness to attend sporting events” (p.599-600). Dey (1997) points out that:” It has been suggested … that the cause of the salary differential [in the NBA] in the mid-1980's was the owners' perception of customer discrimination. Profit-minded owners, assuming fans would prefer to see a predominantly white team, would compensate

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equally productive black players less than white players” (p.88). According to Bodvarsson and Brastow (1999), “It is now well established in the literature on salary determination in the National Basketball Association (NBA) that during the 1980s, black players were paid less than comparably skilled white players” (p.243-244). Mogull (1974) points out that: “Thus, if discrimination is to be revealed, it will have to be demonstrated that black players are not being paid as much as they are worth in terms of performance, and in comparison to the earnings of white players” (p.11). Dey (1997) points out that in Professional sports in the United States, “The participation and success of blacks have not always been readily accepted and appreciated…” (p. 84). Burdekin et al. (2005) point out that: “Previous work found the racial composition of NBA teams to be positively correlated with the racial composition of their metropolitan markets in the 1980s.We find continued evidence of this relationship during the 1990s, with accompanying revenue gains from the inclusion of White players on teams located in whiter areas. And, as the number of White players declined significantly throughout the decade, the revenue product of a White player actually increased on the margin. The tendency for top-performing White players in the NBA to locate in cities with larger White populations also is consistent with their higher marginal value in such locations” (p.144). Kahn and Sherer (1988) note that: “…we find that all else equal, including team performance and market factors, replacing one black player with an identical white player raises home attendance by 8,000 to 13,000 fans per season. The compensation and attendance results together are consistent with the idea of customer discrimination” (p.40; also see Bodvarsson and Brastow, 1999, p.243). According to Bodvarsson and Brastow (1999): “While customer discrimination may have existed in the NBA in the 1980s (given attendance and team composition results of previous studies), this study presents strong evidence that prejudiced team owners and managers were a significant source of discrimination during that period” (p.254-255). According to Kanazawa and Funk (2001): “Using data on Nielsen ratings for locally televised NBA basketball games, we find strong evidence that viewership increases when there is greater participation by white

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players. This finding controls for a wide variety of other factors that could systematically affect Nielsen ratings, and signifies the presence of customer discrimination in the market for NBA players. We also find that higher Nielsen ratings allow NBA teams to realize greater advertising revenues, meaning that the marginal revenue product of white players exceeds that of comparable black players. This factor explains much of the race-based salary gap that exists in professional basketball” (p.599). According to Kanazawa and Funk (2001): “To calculate how much white players increase local commercial advertising revenues, recall that an extra white player increased viewing audiences by anywhere from 3,500 to 36,200 households, depending on the size of the local market. This implies that one white player will add about $2,600 per game in revenues in the smallest viewing markets, and about $27,200 per game in the largest markets” (p.606). However, scholars have also claimed that while salary discrimination in the NBA against Black players may have existed up to the 1980s, their research findings show that such discrimination has almost completely or completely disappeared starting in the 1990s. These scholars present a number of explanations for the disappearance of racial salary discrimination in the NBA (Bodvarsson and Brastow, 1999; Dey, 1997; Groothuis and Hill, 2004; Hill, 2004). According to Dey (1997): “The racial wage differential that existed in the NBA during the mid 1980's vanished over the past decade. The lifetime model employed in this study yielded a racial difference of slightly over fifteen percent for the 1984-85 season… but only a difference of one percent for the pooled sample between 1987 and 1993” (p.87). A number of mostly interrelated factors have been presented to explain salary discrimination against Black NBA players starting in the 1990s. According to Dey (1997), the disappearance of salary discrimination against Black NBA players starting in the 1990s “… may be due to changes in the structure of the league, the increasing relative salaries of black players, or changing societal norms” (p.86). Both Hill (2004) and Dey (1997) point to height as a contributing factor for Whites earning more, because as shown in this study non-

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Black NBA players and White players are on average to inches taller than their Black counterparts. Taller players on average tend to earn higher salaries as they also tend to have longer career length. According to findings by Hill (2004): “…in all cases these are the result of correlation between race and height. White players in the NBA are two inches taller on average than black players. The apparent pay premium for white players is a height premium” (p.9091). According to Dey (1997): “The second result of note is the huge payoff to NBA centers. It has long been argued that a dominating center is the key to any great team. These results show that owners and general managers agree. The bonus to centers, ceteris paribus, ranges from 29.6 percent to 34.2 percent in the two regressions “(p.86). In this current study, of the 5 non-Black players (all White) that earned $10 million or more during the 2005-2006 season, their mean height is 82 inches: 84 inches, 84 inches, 82 inches, 81 inches and 79 inches. There is also one non-Black player that earned $9.996 million and he is 82 inches tall. For the 35 Black NBA players that earned $10 million or more, their mean height is 79.2 inches. The range is 13 inches, with the tallest player at 85 inches and the shortest player at 72 inches. Of the 35 Black players, 17 (48.6 %) are 80 inches or taller. The change in attitude of team owners, which might have been the result of change in attitude of fans and advertisers is reported to have contributed to the disappearance of salary discrimination against Black NBA players starting in the 1990s. For example, according to Dey (1997): “Profit-maximizing owners now pay black and white players with the same characteristics the same salary because their customers no longer differentiate between the races. In other words, the marginal revenue created by equally productive black and white players is now identical” (p.88). According to Bodvarsson and Brastow (1999): “… this result is consistent with the decrease in team owners' monopsony power due to the 1988 NBA Collective Bargaining Agreement and the entry of four new teams to the league” (p.254-255). Age has also been cited as a contributing factor for the salary discrimination in the NBA. There are more Black players in the NBA

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and they are also older on average than non-Black players. Usually in the NBA when players are in their 30s, their salaries start to decrease because they are not in their prime anymore. Even if a player earns a very high salary in his 30s, it is because his contract was structured in a way that a very large sum was to be paid at the end of the last year or last two years of the contract. After that such a player would not earn that amount again because his body is not as active anymore due to age. According to Table B3, within their groups, 88 (26.9%) of Black players; 19 (18.4%) of non-Black players; and 19 (18.8%) of White players were 30 years or older during the 2005-2006 NBA season. According to Dey (1997): “The first result of interest is the estimate of the effect of NBA experience. In both regressions, the results support the hypothesis that salary increases with experience, but at a decreasing rate. For example, the return for an additional year for a second year player ranges from 7.6 to 8.4 percent. For a ten year veteran, this payoff ranges from 1.2 to 2.8 percent” (p.86). Finally, it has also been noted that Black players experience salary discrimination due to monitoring costs. According to Bodvarsson and Brastow (1998): “… for some workers it is more costly to monitor onthe-job productivity than for other workers. Because information gathered from monitoring is general human capital, workers pay for their monitoring through lower salaries. Consequently, monitoring costs and salaries will be inversely related….. Using a sample of players in the NBA, we found strong support for this monitoring costs hypothesis” (p.158). Implications of High BMI Rates Finally, let us now turn to the issue of health, especially for this study the implications of the high proportion of overweight BMI rates of NBA players than their female counterparts, and the implications or consequences of a high BMI rate for any player whether in the NBA or WNBA. Although it has been pointed out that one can expect to observe higher rates of BMI for athletes such as those in professional basketball, football and baseball players, the rates of 50% for both Black and non-Black NBA players and 3 obese in the 2005-2006 season

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due to muscle mass shows that muscles are not the only reason for such high rates but also high fat content. According to GarridoChamorro et al. (2009): “…BMI indirectly can correlate with body fat content in … well-trained athletes” (p.283). For the WNBA players on the other hand, there was no obese player, but 1 underweight player and 18 (10.5% of total) overweight players. One could claim this 10.5% figure for overweight WNBA players in the 2006 season is manageable. One can also claim that they may have made an extraordinary effort, in addition to their level of education, to maintain their 89% BMI normal weight category. In the case of the NBA and even American football, it has been known that the taller or bigger players are directly or indirectly encouraged to gain substantial amount of weight in order to be effective on the court or football field. In a January 28, 2011 article on the NFL in the New York Times entitled “N.F.L. Linemen Tip the Scales,” Jeré Longman discussed how society and the league encourage the taller or bigger players to get bigger: “In 1970, only one N.F.L. player weighed as much as 300 pounds, according to a survey conducted by The Associated Press. That number has expanded like players’ waistlines from three 300-pounders in 1980 to 94 in 1990, 301 in 2000, 394 in 2009 and 532 as training camps began in 2010.”vii (p.D1). Taller NBA players are claimed to have longer careers than shorter or smaller players. Fynn and Sonnenschein (2012) note that in the NBA: “Seven footers tend to have the longest career with a median duration of 6 years whereas 6 footers and 5 footers have medians of 4 years and 3 years respectively” (p.3). Groothuis and Hill (2004) find in their study of NBA players that “…heavier players have longer careers than smaller players do” (p.346). Groothuis and Hill (2004) continue by noting that: “…quickness and speed [which smaller or shorter players have] depreciate faster than size, showing that big men are predicted to have longer careers in the NBA than small men” (p.346). Yet, it is still possible that their heavier weight could result in a shorter career due to the many different negative implications of such weight on their bodies including injury, breathing problems, sleeping problems, and other physical and

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medical problems. Dong et al. (2012) point out that: “Research suggests that being overweight or obese is related to negative health consequences, because body weight influences health outcomes” (p.243). According to the study of Hyman et al. (2012) of 129 former NFL players, who retired from 1 to 32 years ago, 67% were categorized as obese (p.816-819). Longman (2011, January 29) explains that: On one hand, the largest players are celebrated for their strength, spry athleticism and beer-belly physiques that give them an Everyman quality. On the other hand, the enormousness of many players, and the recent deaths of one active lineman and several relatively young retired linemen, have raised questions — and brought conflicting answers — about potential health risks associated with size. Various studies indicate that current N.F.L. players are at a greater risk than the general population for high blood pressure and that retired players are more prone to obesity, sleep apnea and metabolic syndrome: conditions like elevated blood pressure, insulin and cholesterol levels and excessive body fat around the waist that together heighten the risk for heart disease, stroke and diabetes. Retired linemen have been linked to higher mortality rates than the general public. “I just can’t see how they can be healthy,” said Dr. Charles Yesalis, an epidemiologist and professor emeritus of health and human development at Penn State. “Yes, some may be 280 pounds of muscle, but then they carry 40 pounds of fat. It just overworks your heart. It puts a strain on your joints. You have the whole issue of concussive injuries (p.D1).

According to a study by Mathews and Wagner (2008) of 85 college football players: Using BMI as the assessment method, 81% of the collegiate football players in this study were classified as overweight and 35% were obese. Although these results are slightly less than the 97% and 56% of NFL players that… [are] reported as being overweight and obese, respectively, it is still an alarmingly high percentage assuming that BMI is a valid indicator of obesity in an athletic population… Not surprisingly, our finding that linemen are more likely to be significantly overweight and obese than are other positions is consistent with previous research…. In addition to having higher % BF, linemen also have higher levels of triglycerides and total cholesterol, lower levels of high-density lipoproteins, higher blood pressure, and lower aerobic capacity than do other players…. This may explain why NFL linemen have a 52% greater risk of CHD mortality and a 6-fold increase in cardiovascular disease compared with the general population…. Sleep-disordered

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breathing resulting in apnea and hypopnea, which is an additional risk factor for hypertension and cardiovascular disease, is another common ailment for overweight and obese individuals. Although this condition affects only about 4% of the general population, 14% to 34% of professional football players experience sleep-disordered breathing, with 85% of the affected players being linemen (p.35-37).

These substantial differences in BMI between the NBA and WNBA players in this study could be linked to how males and females are socialized or influenced in society. Females tend to have more pressure than their male counterparts to maintain their weight or seek to become thin. Brownell (1991) points out that: “There is tremendous pressure in American society to be thin and to have a specific body shape… The prevailing ideal is someone who is quite lean. Estimates are that popular models and actresses have 10% to 15% body fat, compared with 22% to 26% for healthy, normal-weight women…There is considerable pressure for people to control their personal behavior in order to control their weight and shape. People who do not meet the ideal and are overweight are thought to be indulgent, lazy, and lacking in willpower (p.307). Higher BMI rates could lead to a shorter career and retirement, which can cause all types of emotions for professional athletes. It can also lead to more weight gain because one is now likely to be less active. According to Abel and Kruger (2006), “Actively employed people tend to be healthier and live longer than the population at large, a phenomenon known in occupational epidemiology as the “healthy worker effect’” (p.239). According to Lawler et al. (2012), “A high level of physical activity has been shown to decrease mortality risks. … vigorous physical activity… revealed an increased life longevity and was associated with decreased incidence of coronary heart disease, hypertension, noninsulin-dependent diabetes, and colon cancer (p.411; also see Teramoto and Bungum, 2010, p.410-416). According to Díaz De Corcuera et al. (2013): “The retirement process involves emotionally charged… appearing feelings. For retired players, the time of retired is more complicated than expected…” (p.233). The study by McCarthy et al. (2013) of WNBA players show that for 220 of them their average career length in the league was 3.4

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years (p.648; also see Coates and Oguntimein, 2010). According to Berri (2011, December 6), the average career length of NBA players is 4.8 years, and for comparative purposes 5.6 years for professional Baseball players”viii Roberts (2010) notes that: “… the average length of an NFL career is only about 3.5 seasons” (p.99). Let us now examine various types of potential negative implications as a result of high BMI of NBA and WNBA players. Injuries Among the implications of high BMI rates (overweight and obese) in the NBA, WNBA and professional basketball players in general are the likelihood of various types of injuries on or off the court (Kostopoulos and Dimitrios, 2010; McCarthy et al., 2013; Munro et al., 2012; Orchard and Hayes, 2001; “Putting Shaq Together Again,” 2012; van der Worp et al., 2011). The study by Orchard and Hayes (2001) examined the prevalence of injuries among 311 NBA players for the 1999-2000 and 2000-2001 seasons, playing in 2,378 games. The players suffered 593 injuries and missed “5,819 player games”: “The true injury prevalence among NBA players in season 1999–2000 was probably at least 12%. Injuries caused more missed playing time in players 30 years or older… and players with a body mass index of 26 or higher…” (p.1). The specific injuries include: head/neck, shoulder, elbow/arms, hand/wrist, back, groin/hip, thigh, knee, lower leg, ankle and foot. The BMI injury prevalence were: 7.9% for those with BMI of 22 or less; 11.8% for those with BMI of 23; 14.8% with BMI of 24; 12.5% with BMI of 25; 16% with BMI of 26; 19.4% with BMI of 27; and 14% with BMI of 28 or more (p.1-13). In a study of 90 Greek professional basketball players from 8 teams for a single season, Kostopoulos and Dimitrios (2010) find that: “Forty-six injuries were recorded. Injury incidence was evaluated at 2.5 injuries per 1000 player-hours, with a significantly higher incidence in game injuries (14.3 injuries per 1000 game-hours) compared with practice injuries (0.6 injuries per 1000 practice-hours)….The upper extremity was involved in 37% of the injuries, and the lower extremity in 54%. The knee was the most

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commonly injured joint, followed by the finger, ankle, and shoulder. Knee injuries were the most severe injuries…and they were more frequent in high-level players” (p.48). A study by McCarthy et al. (2013) examines the demographic and injury data of female college basketball players entering the WNBA from 2000-2008, with injury data for 506 of them and full or complete demographic data for 496 of them. Their results are that: “Ankle sprain (47.8% of players), hand injury (20.8%), patellar tendinitis (17.0%), ACL [Anterior Cruciate Ligament] injury (15.0%), meniscus injury (10.5%), stress fracture (7.3%), and concussion (7.1%) were the most common injuries reported. Seventy-three athletes (14.4%) reported ACL reconstruction before entering the WNBA combine, and meniscus surgery was the next most common surgery (n = 50 players; 9.9%)…Excluding ACL and meniscus surgery, other reported surgical procedures were knee arthroscopic surgery (11.7%), ankle reconstruction (2.6%), and shoulder stabilization (2.0%)…. The ankle is the most common site of injury and ACL reconstruction is the most common surgery in elite female athletes participating in the WNBA combine” (p.645). The study of Munro et al. (2012) which evaluated the “…landing strategies” of “52 female football players and 41 female basketball players”, finds that: “Female basketball players display greater FPPA [Frontal plane projection angle] values during unilateral landing tasks than female football players which may reflect the greater ACL injury occurrence in this population. Injury prevention programs in these athletes should incorporate unilateral deceleration and landing tasks and should consider the specific injury mechanisms in each sport” (p.259). In a study examining the impact of Patellar tendinopathy (PT) injury on 1,505 basketball (421) and volleyball (1,084) players aged 18 to 35, van der Worp et al. (2011) find that: “The main finding of the present study is that heavy physically demanding work seems to be a risk factor for developing PT in people who play basketball or volleyball. Also, PT has a considerable impact on work limitations…. A high percentage of subjects, diagnosed with PT, was impaired in their work or less productive. Since the median duration of PT in the studied population was 24 months, this injury has considerable

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consequences for employers and employees…. “ (p.53-54). They note that “This finding has important clinical relevance in the treatment of this injury” (p.49). High BMI Rates and Death An important reason why public health officials and researchers, medical doctors, government officials are focused on reducing BMI rates whether for athletes or non-athletes is that high rates of BMI can contribute to one’s death. For professional athletes, there will be more concerns if they have high BMI rates or are overweight or obese while participating in sports because the chances of them remaining that way after their careers are very high. There have been studies that show the death rates of professional athletes during and after their careers, comparing them to the general population. Other studies have examined the death rate of Black professional athletes during and after their careers, including comparing them with athletes from other racial groups or Blacks in the general population (Abel and Kruger, 2006; Hergenroeder et al., 2011; Kalist and Peng, 2007; Kanda et al., 2009; Lawler and Lawler, 2011; Lawler et al., 2012; Teramoto and Bungum, 2010). Kalist and Peng (2007) point out that: “The literature on the longevity of athletes has spanned many sports (e.g., baseball, basketball, rugby, rowing, running, skiing, skating, and soccer). Some of these studies have controlled for race, body mass index (BMI), year of birth, length of playing career, and lifestyle factors (alcohol consumption, physical activity, and smoking)” (p.653). In a study of 430 professional Sumo wrestlers who were in a top division from 1926 to 1989, Kanda et al. (2009) find that : “…73 were deceased…. this study was a case-control study consisting of 73 deceased wrestlers born between the years 1908 to 1955 as cases, and 73 surviving wrestlers with matching birth years as controls….This study suggests that an higher BMI can be a predictive factor of death even amongst Sumo wrestlers, and that proper guidelines for taking care of their health are necessary” (p.711-712). The study by Kalist and Peng (2007) of U.S. Professional Baseball players found that: “The

74

Discussion and Conclusion

last important factor associated with a higher hazard rate of death was BMI. A 1-unit increase in BMI, which was measured based on the weight during a player’s career, increased the hazard rate of death by 16%. It is possible that the BMI is correlated with poor eating and health habits upon retirement, as well as poor initial health” (p.667). In a study comparing the death rates of Black and White male professional basketball players in the United States, Lawler and Lawler (2011) note that there were 2,981 of them. They note that 513 of them were confirmed dead (p.589-591). In another study comparing the death rates of Black and White NBA players, Lawler et al. (2012) note that: “The final dataset included 3366 individuals, of whom 56.0% were African American. Results suggest white players live 18 months longer than their African-American colleagues…. After controlling for covariates, we found that African-American players have a 75% increased risk of death compared with white players, a statistically significant gap” (p.406). This study begins with a discussion of the health condition of professional athletes in the United States. Government officials, public health researchers and other scholars, medical doctors, team owners, managers, fans, family members and friends are all said to be concerned about the health condition or status of professional athletes in the United States, including both female and male basketball players. The athletes themselves are noted to be concerned not only of their own health status or condition, but also that of their teammates. The study attempts to present various definitions or conceptualizations of what good health or health is and how it is connected to this study focusing on the BMI of NBA and WNBA players for the 2005-2006 season. In addition to presenting various types of BMI data on NBA and WNBA players during the 2005-2006 season, other descriptive and empirical data are also presented on their demographics and salaries. According to some of the findings, almost 9 out of every 10 WNBA players are in the normal BMI category, while less than half of the NBA players are in the normal BMI category. Racially, the WNBA players have almost the same height and weight, while the non-Black .

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NBA players are on average over two inches taller and 15 pounds heavier than their Black counterparts. Society continues to reward male athletes more than their female counterparts, including paying WNBA players, an average of $50,000 by 2006, while their male counterparts are paid an average of $3.9 million during that same year. While like their Black female counterparts, Black males account for the majority of NBA players, lower proportion of them earn $1 million or more than non-Black players in 2005-2006. However, higher numbers and proportions of Black NBA players than their non-Black counterparts earn $5 million or more and $10 million or more. The study attempts to explain the factors responsible for the gender bias in salaries and why higher proportions of non-Black NBA players earn $1 million or more in 2005-2006. One factor cited is discrimination against Black NBA players. Another factor cited is that non-Black players on average are over two inches taller than Black players and that the NBA tends to pay taller players higher salaries. The study presents explanations as to why Black NBA and WNBA players account for the majority of players in both leagues. One such factor is that Black players account for very high proportions of Division I college basketball players, where both leagues draft the majority of their players. There is also the finding that higher proportions of both NBA and WNBA (the majority) players come from academic institutions in the U.S. South. Finally, the study presents explanations for the potential negative implications of the fact that half of NBA players in 2005-2006 were categorized in the overweight BMI category, but also any player categorized as overweight or obese. These potential negative implications include various types of on and off the court injuries and death. Some of the evidence presented show that high BMI of athletes can lead to injuries and death.

76

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89

Appendices Table A 1 Mean Height (inches) for Females 20 Years and Over, 1999-2002: United States Females

Height

20 Years & Over

63.8

Non-Hispanic Black Females 20 Years & Over

64.2

20-39 Years

64.6

Non-Hispanic White Females 20 Years & Over

64.2

20-39 Years

64.6

Source: Kaba, 2012, p.97; Ogden et al., 2004, pp. 8-15.

91

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Table A 2 Mean Weight (pounds) for Females 20 Years and Over, 1999-2002: United States Females

Weight

20 Years & Over

162.9

Non-Hispanic Black Females 20 Years & Over

182.4

20-39 Years

179.2

Non-Hispanic White Females 20 Years & Over

161.7

20-39 Years

158.4

Source: Kaba, 2012, p.98; Ogden et al., 2004, pp. 8-15.

92

Appendices

Table A 3 Age Groups of WNBA Players: 2006 season % of all

% of all Whites

Item

Number

% of total (175)

Blacks

# of All Players 20 Years Old or Younger

0

0

0

# of All Players 21-25 Years Old

96

54.9

# of All Black Players 21-25 Years Old

61

34.9

# of All White Players 21-25 Years Old

35

20

# of All Players 26-29 Years Old

41

23.4

# of All Black Players 26-29 Years Old

28

16

# of All White Players 26-29 Years Old

13

7.4

# of All Players 30 Years Old or Older

37

21.1

# of All Black Players 30 Years Old or Older

28

16

# of All White Players 30 Years Old or Older

9

5.1

51.7 61.4

23.7 22.8

23.7

Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

93

0

15.8

Amadu Jacky Kaba

Table A4 Height Breakdown of WNBA Players, 2006 Season

Item

#

% of all Players

Total # of all players 6'0" and taller

107

61.1

Total # of all Black players 6'0" and taller

74

42.3

Total # of all White players 6'0" and taller

33

18.9

Total # of all players 6'1" to 6'2" tall

57

32.6

Total # of all Black players 6'1" to 6'2" tall

42

24

Total # of all White players 6'1" to 6'2" tall

15

8.6

Total # of all players 6'3" and taller

50

28.6

Total # of all Black players 6'3" and taller

32

18.3

Total # of all White players 6'3" and taller

18

10.3

Total # of all players 6'4" and taller

30

17.1

Total # of all Black players 6'4" and taller

17

9.7

Total # of all White players 6'4" and taller

13

7.4

Total # of all players 6'5" and taller

16

9.1

Total # of all Black players 6'5" and taller

6

3.4

Total # of all White players 6'5" and taller

10

5.7

Total # of all players 6'6" and taller

5

2.9

Total # of all Black players 6'6" and taller

1

0.6

% of Black Players only

% of White Players only

62.7 57.9

35.6 26.3

27.1 31.6

14.4 22.8

5.1 17.5

0.85

Total # of all White players 6'6" and taller 4 2.3 7 Total # of all players 5'3" to 5'11" tall 68 38.9 Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

94

Appendices

Table A 5 Number of Players Institutions (Colleges or Universities may send 1 or more players) in Sending States Sent: 2006 WNBA Season n=156 Total # of

# of Black

# of White

Players Sent

Players

Players

Tennessee

14

13

1

Texas

11

10

1

Connecticut

12

7

5

Louisiana

12

12

0

California

11

9

2

Florida

11

9

2

Georgia

9

7

2

North Carolina

9

8

1

Virginia

6

4

2

Kansas

5

1

4

Indiana

5

2

3

Pennsylvania

5

4

1

Illinois

4

3

1

Alabama

4

4

0

Iowa

4

3

1

Utah

4

1

3

Michigan

3

0

3

State

95

Amadu Jacky Kaba Total # of

# of Black

# of White

Players Sent

Players

Players

Missouri

3

3

0

New Jersey

3

3

0

Massachusetts

2

1

1

Minnesota

2

0

2

Mississippi

2

2

0

Ohio

2

1

1

Oklahoma

2

1

1

Oregon

2

1

1

South Carolina

2

2

0

Arkansas

1

1

0

Colorado

1

0

1

Nebraska

1

0

1

Nevada

1

0

1

Washington, D.C.

1

1

0

West Virginia

1

0

1

Wisconsin

1

1

0

156

114

42

State

Total

Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

96

Appendices

Table A 6 All 69 Sending Institutions and NCAA & NAIA Conferences: 2006 WNBA Season n=156 Players Total # of # of Black # of White Institution

Players

Players

Uni. of Connecticut

12

Uni. of Tennessee, Knoxville

NCAA or NAIA

Players

Conference

7

5

Big East Conference

11

10

1

Southeastern Conference

Uni. of Georgia

8

6

2

Southeastern Conference

Louisiana State Uni.

5

5

0

Southeastern Conference

Louisiana Tech Uni.

5

5

0

Western Athletic Conference

Duke Uni.

4

3

1

Atlantic Coast Conference

Kansas State Uni.

4

0

4

Big 12 Conference

Uni. Of Florida

4

4

0

Southeastern Conference

Uni. Of Southern California

4

4

0

Pacific-10 Conference

Michigan State Uni.

3

0

3

Big Ten Conference

Penn State Uni.

3

2

1

Big Ten Conference

Rutgers Uni.

3

3

0

Big East Conference

Texas Tech Uni.

3

3

0

Big 12 Conference

Uni. Of Iowa

3

3

0

Big Ten Conference

Uni. Of Missouri, Columbia

3

3

0

Big 12 Conference

3

3

0

Atlantic Coast Conference

Uni. Of North Carolina, Chapel Hill

97

Amadu Jacky Kaba Total # of # of Black # of White Institution

Players

Players

Uni. of Notre Dame

3

Uni. of Virginia

NCAA or NAIA

Players

Conference

1

2

Big East Conference

3

3

0

Atlantic Coast Conference

Auburn Uni.

2

2

0

Southeastern Conference

Baylor Uni.

2

2

0

Big 12 Conference

Brigham Young Uni.

2

1

1

Mountain West Conference

DePaul Uni.

2

2

0

Big East Conference

Florida International Uni.

2

1

1

Sun Belt Conference

Florida State Uni.

2

1

1

Atlantic Coast Conference

Mississippi State Uni.

2

2

0

Southeastern Conference

North Carolina State Uni.

2

2

0

Atlantic Coast Conference

Old Dominion Uni.

2

1

1

Colonial Athletic Association

Purdue Uni.

2

1

1

Big Ten Conference

Stanford Uni.

2

2

0

Pacific-10 Conference

Tulane Uni.

2

2

0

Conference USA

Angeles

2

2

0

Pacific-10 Conference

Uni. of Houston

2

2

0

Conference USA

Uni. of Kansas

2

2

0

Big 12 Conference

Uni. of Minnesota

2

0

2

Big Ten Conference

Uni. of Oregon

2

1

1

Pacific-10 Conference

Uni. of California, Los

98

Appendices Total # of # of Black # of White Institution

Players

Players

Columbia

2

Uni. of Texas, Austin

NCAA or NAIA

Players

Conference

2

0

Southeastern Conference

2

1

1

Big 12 Conference

Uni. of Utah

2

0

2

Mountain West Conference

Vanderbilt Uni.

2

2

0

Southeastern Conference

Boston College

1

0

1

Atlantic Coast Conference

Colorado State Uni.

1

0

1

Mountain West Conference

Florida Atlantic Uni.

1

1

0

Atlantic Sun Conference

Georgetown Uni.

1

1

0

Big East Conference

Technology

1

1

0

Atlantic Coast Conference

Harvard Uni.

1

1

0

Ivy League

Iowa State Uni.

1

0

1

Big 12 Conference

Liberty Uni.

1

0

1

Big South Conference

Uni. of South Carolina,

Georgia Institute of

Golden State Athletic The Master's College

1

0

1

Conference (NAIA)

Ohio State Uni.

1

0

1

Big Ten Conference

Pepperdine Uni.

1

1

0

West Coast Conference Heartland Conference

Saint Edwards Uni.

1

1

0

(Division II) Lone Star Conference

S.E. Oklahoma State Uni.

1

1

0

99

(Division II)

Amadu Jacky Kaba Total # of # of Black # of White Institution

Players

Players

Temple Uni.

1

Texas Christian Uni.

NCAA or NAIA

Players

Conference

1

0

Atlantic 10 Conference

1

1

0

Mountain West Conference

Uni. of Alabama, Birmingham

1

1

0

Conference USA

Uni. of Alabama, Tuscaloosa

1

1

0

Southeastern Conference

Uni. of Arkansas, Fayetteville

1

1

0

Southeastern Conference

Barbara

1

0

1

Big West Conference

Uni. of Central Florida

1

1

0

Conference USA

Uni. of Cincinnati

1

1

0

Big East Conference

Champaign

1

1

0

Big Ten Conference

Uni. of Memphis

1

1

0

Conference USA

Uni. of Miami

1

1

0

Atlantic Coast Conference

Uni. Of Nebraska, Lincoln

1

0

1

Big 12 Conference

Uni. of Nevada, Las Vegas

1

0

1

Mountain West Conference

Uni. of Oklahoma

1

0

1

Big 12 Conference

Uni. of Wisconsin

1

1

0

Big Ten Conference

Western Illinois Uni.

1

0

1

Mid Continent Conference

West Virginia Uni.

1

0

1

Big East Conference

156

114

42

73.1

26.9

Uni. of California, Santa

Uni. of Illinois, Urbana-

Total Percentages

100

Appendices NAIA=National Association of Intercollegiate Athletics NCAA=National Collegiate Athletic Association

Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

Table A 7 Institutions and Regions sending players to the WNBA: 2006 Season N=175 Total #

Total # of

Institutions

Black

% of

Total # of White

% of

Region

Sent

%

Players Sent

Blacks

Players Sent

Whites

Northeast

21

13.4

14

12.3

7

16.7

South

85

54.5

74

64.9

11

26.2

Midwest

31

19.9

15

13.2

16

38.1

West

19

12.2

11

9.6

8

19

Total/Ave.

156

100

114

100

42

100

Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

101

Amadu Jacky Kaba

Table A 8 Players Coming Directly from Overseas to the WNBA, 2006 Season Total #

Total # from

Total # from

Overseas

Overseas

Black

White

Directly from

Overseas

Players

%

Players

%

19

4

21.1

15

78.9

Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

102

Appendices

Table A 9 Total # of Players of each sending Institution to the WNBA, 2006 season, and U.S. News & World Report Academic Ranking, 2006 n=156 Players

Total # of

Rank # of Top

Tier 3

Tier 4

Players

120 Institutions

Institutions

Institutions

Uni. of Connecticut

12

68

Uni. of Tennessee, Knoxville

11

85

Uni. of Georgia

8

58

Louisiana State Uni.

5

Tier 3

Louisiana Tech University

5

Tier 3

Duke Uni.

4

Kansas State Uni.

4

Uni. of Florida

4

50

Uni. of Southern California

4

30

Michigan State Uni.

3

74

Penn State Uni.

3

48

Rutgers Uni., New Brunswick

3

60

Texas Tech Uni.

3

Uni. of Iowa

3

60

Uni. of Missouri, Columbia

3

85

Hill

3

27

Uni. of Notre Dame

3

18

Uni. of Virginia

3

23

Auburn Uni.

2

85

Baylor Uni.

2

78

Brigham Young Uni.

2

71

DePaul Uni.

2

Florida International Uni.

2

Florida State Uni.

2

Mississippi State Uni.

2

North Carolina State Uni.

2

Old Dominion Uni.

2

Institution

5 Tier 3

Tier 3

Uni. of North Carolina, Chapel

Tier 3 Tier 4 109 Tier 3 78 Tier 4

103

Amadu Jacky Kaba

n=156 Players

Total # of

Rank # of Top

Tier 3

Tier 4

Institution

Players

120 Institutions

Institutions

Institutions

Purdue Uni.

2

60

Stanford Uni.

2

5

Tulane Uni.

2

43

Uni. of California, Los Angeles

2

25

Uni. of Houston

2

Uni. Of Kansas

2

97

Uni. of Minnesota

2

74

Uni. of Oregon

2

115

Uni. of South Carolina, Columbia

2

109

Uni. of Texas, Austin

2

52

Uni. of Utah

2

120

Vanderbilt Uni.

2

18

Boston College

1

40

Colorado State Uni.

1

120

Florida Atlantic Uni.

1

Georgetown Uni.

1

23

Georgia Institute of Technology

1

37

Harvard Uni.

1

1

Iowa State Uni.

1

85

Liberty Uni.

1

na

The Master's College

1

na

Ohio State Uni., Columbus

1

60

Pepperdine Uni.

1

55

Saint Edwards Uni.

1

na

S.E. Oklahoma State Uni.

1

na

Temple Uni.

1

Texas Christian Uni.

1

Uni. of Alabama, Birmingham

1

Uni. of Alabama, Tuscaloosa

1

Uni. of Arkansas, Fayetteville

1

Tier 4

Tier 4

Tier 3 97 Tier 3 104 Tier 3

104

Appendices

n=156 Players

Total # of

Rank # of Top

Tier 3

Tier 4

Players

120 Institutions

Institutions

Institutions

Uni. of California, Santa Barbara

1

45

Uni. of Central Florida

1

Tier 3

Uni. of Cincinnati

1

Tier 3

Institution

Uni. of Illinois, UrbanaChampaign

1

Uni. of Memphis

1

Uni. of Miami

1

55

Uni. of Nebraska, Lincoln

1

97

Uni. of Nevada, Las Vegas

1

Uni. of Oklahoma

1

109

Uni. of Wisconsin

1

34

Western Illinois Uni.

1

na

West Virginia Uni.

1

Total

42 Tier 4

Tier 4

Tier 3

156

NAIA=National Association of Intercollegiate Athletics NCAA=National Collegiate Athletic Association na=Not Available

Source: “America’s Best Colleges,” U.S. News & World Report College Rankings. http://www.usnews.com/usnews/edu/college/rankings/. Retrieved on May 20, 2006.

105

Amadu Jacky Kaba

Table A 10 College or University Attendance and Graduation Rates of WNBA Players: 2006 Season N=175 Item Total # of all players enrolled in College/University in U.S. Total # of all players who graduated from College/University Total # of players Without College Attendance Data Available # of Black players who attended but no year of graduation data Shown # of White players who attended but no year of graduation data Shown % of Black players who graduated within 118 Black total (95.8%) % of White players who graduated within 57 White total (73.7%) % of Black players who graduated within 114 Blacks who attended (99.1%) % of White players who graduated within 42 Whites who attended (100%)

#

% of Total (N)

156

89.1

155

88.6

19

As % of those

# of

Enrolled

Blacks

%

Whites

%

113

72.9

42

27.1

4

21.1

15

78.9

99.4

# of

1 0

Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

106

Appendices

Table A 11 Number of WNBA Players sent By NCAA & NAIA Conferences, 2006 Season

n=156 Number of Name of Conference

Players Sent

%

American East Conference

0

0

Atlantic 10 Conference

1

0.6

Atlantic Coast Conference

17

10.9

Atlantic Sun Conference

1

0.6

Big 12 Conference

19

12.2

Big East Conference

23

14.7

Big Sky Conference

0

0

Big South Conference

1

0.6

Big Ten Conference

16

10.3

Big West Conference

1

0.6

Colonial Athletic Association

2

1.3

Conference USA

7

4.5

Division I Independents

0

0

Horizon League

0

0

Ivy Group

1

0.6

Metro Atlantic Athletic Conference

0

0

Mid Continent Conference

1

0.6

Mid-American Conference

0

0

Mid-Eastern Athletic Conference

0

0

Missouri Valley Conference

0

0

107

Amadu Jacky Kaba

Number of Name of Conference

Players Sent

%

Mountain West Conference

7

4.5

Northeast Conference

0

0

Ohio Valley Conference

0

0

Pacific-10 Conference

10

6.4

Patriot League

0

0

Southeastern Conference

38

24.4

Southern Conference

0

0

Southland Conference

0

0

Southwestern Athletic Conference

0

0

Sun Belt Conference

2

1.3

West Coast Conference

1

0.6

Western Athletic Conference

5

3.2

1

0.6

1

0.6

1

0.6

156

99.7

Heartland Conference (Division II, St. Edwards University) Lone Star Conference (Division II SE Oklahoma State Uni. Golden

State

Athletic

Conference

(NAIA), The Master's College Total NAIA=National

Association

of

Intercollegiate Athletics NCAA=National

Collegiate

Athletic

Association Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006.

108

Appendices

Table A 12 Number & Names of Institutions in States with Players in the WNBA: 2006 Season n=69 Total # of State Tennessee

Institutions 3

Names of Institutions Uni. of Tennessee, Uni. of Memphis, & Vanderbilt Uni. Uni. of Texas, Austin, Texas Christian, Uni. of Houston, St. Edwards

Texas

6

Uni., Texas Tech Uni. & Baylor Uni.

Connecticut

1

Uni. of Connecticut

Louisiana

3

Louisiana State Uni., Louisiana Tech Uni., & Tulane Uni. UCLA, USC, Pepperdine Uni., Stanford Uni., Uni. of California, Santa

California

6

Barbara, & The Master's College Florida International, Uni. of Florida, Florida Atlantic Uni., Florida

Florida

6

State Uni., Uni. of Miami, & Uni. of Central Florida

Georgia

2

Uni. of Georgia & Georgia Institute of Technology

Carolina

3

Duke Uni., N.C. State Uni., & Uni. of North Carolina

Virginia

3

Liberty University, Old Dominion Uni., & University of Virginia

Kansas

2

Kansas State Uni. & Uni. Of Kansas

Indiana

2

Uni. of Notre Dame & Purdue Uni.

Pennsylvania

2

Penn State Uni. & Temple Uni.

Illinois

3

DePaul Uni., Uni. Of Illinois, Champaign, & Western Illinois Uni.

North

Auburn Uni., Uni. Of Alabama, Birmingham, & Uni. of Alabama, Alabama

3

Tuscaloosa

Iowa

2

Iowa State Uni. & Uni. Of Iowa

Utah

2

Brigham Young Uni. & Uni. of Utah

Michigan

1

Michigan State Uni.

Missouri

1

Uni. of Missouri

New Jersey

1

Rutgers Uni., State Uni. of New Jersey, New Brunswick

109

Amadu Jacky Kaba

Total # of State

Institutions

Names of Institutions

Massachusetts

2

Boston College & Harvard Uni.

Minnesota

1

Uni. of Minnesota

Mississippi

1

Mississippi State Uni.

Ohio

2

Ohio State Uni. & Uni. of Cincinnati

Oklahoma

2

Southeastern Oklahoma State Uni. & Uni. Of Oklahoma

Oregon

1

Uni. of Oregon

Carolina

1

Uni. of South Carolina, Columbia

Arkansas

1

Uni. of Arkansas, Fayetteville

Colorado

1

Colorado State Uni.

Nebraska

1

Uni. of Nebraska

Nevada

1

Uni. of Nevada, Las Vegas

D.C.

1

Georgetown Uni.

West Virginia

1

Uni. of West Virginia

Wisconsin

1

Uni. of Wisconsin

Total

69

South

Washington,

Source: Compiled and Computed based on Data on the WNBA Website. www.wnba.com, 2006. Table B 1 Mean Height (inches) for Males 20 Years and Over, 1999-2002: United States Males 20 Years & Over

Height 69.2

Non-Hispanic Black Males 20 Years & Over 20-39 Years

69.5 70.0

Non-Hispanic White Males 20 Years & Over 69.7 20-39 Years 70.2 Source: Kaba, 2011, p.10;Ogden et al., National Center for Health Statistics, 2004: 8-15.

110

Appendices

Table B 2 Mean Weight (pounds) for Males 20 Years and Over, 1999-2002: United States Males 20 Years & Over

Weight 189.8

Non-Hispanic Black Males 20 Years & Over 20-39 Years

189.2 189.1

Non-Hispanic White Males 20 Years & Over 20-39 Years

193.1 189.7

Source: Kaba, 2011, p.11; Ogden et al., National Center for Health Statistics, 2004, pp. 8-13.

111

Amadu Jacky Kaba

Table B 3 Age Breakdowns of All NBA Players: 2005-2006 (Age figures as of March 31, 2006) % of all % of Blacks % of nonItem

#

Players

Total # of all players 19 years or younger

10

2.3

Total # of all Black players 19 years or younger

7

1.6

Total # of all players 19 years or younger

3

0.7

Total # of all players 21 years and younger

48

11.2

Total # of all Black players 21 years and younger

36

8.4

Total # of all non-Black players 21 years and younger

12

2.8

Total # of all White players 21 years and younger

11

2.6

Total # of all players 25 years and younger

201

46.7

Total # of all Black players 25 years and younger

151

35.1

Total # of all non-Black players 25 years and younger

50

11.6

Total # of all White players 25 years and younger

48

11.2

Total # of all players 30 years and older

107

24.9

Total # of all Black players 30 years and older

88

20.5

Total # of all non-Black players 30 years and older

19

4.4

Total # of all White players 30 years and older

19

4.4

only

Blacks only

% of Whites only

2.1 3

11 11.6 10.7

46.2 48.5 47.5

26.9 18.4 18.8

Source: Compiled and Computed based on Data on the NBA Website. www.nba.com

112

Appendices

Table B 4 Breakdown of Height of NBA Players: 2005-2006 Season N=430 Item Total # of all players 7'0" or taller

# 44

Total # of all Black players 7'0" or taller

20

4.6

Total # of all non-Black players 7'0" or taller

24

5.6

Total # of all White players 7'0" or taller

22

5.1

Total # of all players 6'10" or taller

129

30

Total # of all Black players 6'10" or taller

78

18.1

Total # of all non-Black players 6'10" or taller

51

11.9

Total # of all White players 6'10" or taller

49

11.4

Total # of all players 6'9" or taller

179

41.6

Total # of all Black players 6'9" or taller

114

26.5

Total # of all non-Black players 6'9" or taller

65

15.1

Total # of all White players 6'9" or taller

63

14.6

Total # of all players 6'6" or taller

301

70

Total # of all Black players 6'6" or taller

212

49.3

Total # of all non-Black players 6'6" or taller

89

20.7

113

% of all % of Blacks % of non% of Players only Blacks onlyWhites only 10.2 6.1

23.3 21.8

23.9

49.5 48.5

34.9

63.1 62.4

64.8

86.4

Amadu Jacky Kaba

N=430 Item

#

% of all % of Blacks % of non% of Players only Blacks only Whites only

Total # of all White players 6'6" or taller

87

20.2

Total # of all players 6'3" or shorter

83

19.3

Total # of all Black players 6'3" or shorter

74

17.2

Total # of all non-Black players 6'3" or shorter

9

2.1

Total # of all players 6'0" or shorter

16

3.7

Total # of all Black players 6'0" or shorter

15

3.5

86.1

22.6

8.7

4.6

Total # of all non-Black players 6'0" or shorter 1 0.9 Source: Compiled and Computed based on Data on the NBA Website. www.nba.com

Table B 5 Salary Breakdown of NBA Players, 2005-2006 Season N=423 Item Salary

# of

Salary

# of all Black

Salary

Black as %

# of nonBlack

Salary

Players

US$

Players

US$

of Total

Players

US$

423

1,651,299,693

322

1,301,083,667

78.8

101

# of % of White Players Total Only

350,146,026 21.2

99

Salary % of US$

343,914,27820.8

Source: Compiled and Computed by author based on Data on the website of USA Today Newspaper. www.usatoday.com, 2006.

114

Total

Appendices

Table B 6 Salary Breakdown of NBA Players, 2005-2006 Season # of all % of all # of Black Players Players

Players

Blacks as # of non- % of # of Whites % of Black non- % of all White % of as % all of all Blacks Players Players Blacks Players Players Whites Players % of

Players who earned $5 million or more 123 29.1 101 31.4 23.9 22 21.8 5.2 21 21.2 Players who earned $10 million or more 40 9.5 35 10.9 8.3 5 5 1.2 4 4 Players who earned $1 million (of 423) 315 74.5 236 73.3 55.8 79 78.2 18.7 78 78 Source: Compiled and Computed by author based on Data on the website of USA Today Newspaper. www.usatoday.com, 2006.

115

5

0.94

18.4

Amadu Jacky Kaba

Table B 7 Country or Territory and Region of Origin of Foreign-Born NBA Players: 2005-2006 Season (as of February 28, 2006). Geographic Region of the World Oceania

Country or Territory Australia New Zealand

Total Caribbean

Dominican Republic Haiti Martinique Puerto Rico St. Vincent & Grenadines U.S. Virgin Island

Total

Southwest Asia Total

Georgia

Europe (including Russia)

Croatia Czech Republic France Germany Greece Ireland Latvia Lithuania Netherlands Poland Russia Serbia & Montenegro Slovenia Spain

116

Total # of Players 1 1 2

% of all 82 Players

2.4

1 1 1 2 1 2 8

9.6

2 2

2.4

4 1 4 1 1 1 1 6 2 1 5 8 5 2

Appendices

Geographic Region of the World

Country or Territory Ukraine

Total Belize Brazil Mexico Uruguay Total Middle East Total

Turkey

Northeast Asia

China South Korea

Total North America Total

Canada

Sub-Saharan Africa

Congo, D.R. Nigeria Senegal Sudan

Total # of Players 2 44 1 4 1 1 11

% of all 82 Players 54

13.4

3 3

3.7

1 1 2

2.4

2 2

2.4

2 1 3 1 7

Total 8.5 Overall Total Source: Compiled and Computed based on Data on the NBA Website. ww.nba.com

Table B 8 Breakdown of Players Directly from Overseas: 2005-2006 Season n=55 Total # from Overseas 55

Total # from

Total # from

Overseas Black Players 10

Overseas Non-Black Players 45

% 18.2

% 81.8

Source: Compiled and Computed based on Data on the NBA Website. www.nba.com

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Table B 9 Number of Players Institutions (a High School, College or University may send 1 or more players) in Sending States Sent: 2005-2006 NBA Season. N=41 States and 375 players. State

Total # of Players

# of Institutions Sending

# of Institutions Sending

Sent by Institutions

Black Players

Non-Black Players

California

32

26

6

North Carolina

32

29

3

Texas

21

19

2

Ohio

19

16

3

Florida

17

12

5

Michigan

16

15

1

Pennsylvania

18

17

1

Kentucky

15

14

1

Arizona

13

12

1

Connecticut

13

12

1

Georgia

12

10

2

Illinois

12

12

0

Kansas

10

5

5

Indiana

10

5

5

New York

9

9

0

Mississippi

8

8

0

Alabama

8

7

1

Louisiana

8

8

0

Maryland

7

5

2

New Jersey

7

7

0

Utah

7

3

4

Iowa

6

4

2

Minnesota

6

4

2

Oklahoma

6

5

1

Virginia

6

6

0

Oregon

5

2

3

Rhode Island

5

4

1

Washington, D.C.

5

5

0

Washington

5

3

2

Arkansas

4

4

0

Missouri

4

3

1

118

Appendices

State Nebraska

Total # of Players

# of Institutions Sending

# of Institutions Sending

Sent by Institutions

Black Players

Non-Black Players

4

2

2

Nevada

4

4

0

South Carolina

4

4

0

Tennessee

4

4

0

Wisconsin

4

3

1

Colorado

3

3

0

Massachusetts

2

2

0

New Mexico

2

2

0

Hawaii

1

1

0

Wyoming

1

1

0

375

317

58

84.5

15.5

Total Percentages

Source: Compiled and Computed based on Data on the NBA Website. www.nba.com

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Amadu Jacky Kaba

Table B 10 All 166 Sending Institutions (High Schools, Colleges and Universities in U.S): 2005-2006 NBA Season (As of March 6, 2006) Institution Uni. of Kentucky Duke University Uni. of Arizona Uni. of Connecticut Uni. of Kansas Uni. of North Carolina Uni. of California, Los Angeles Uni. of Maryland Michigan State Uni. Uni. of California, Berkeley Uni. of Cincinnati Uni. of Florida Georgia Institute of Technology Georgetown Uni. Uni. of Michigan Uni. of Minnesota Stanford Uni. Uni. of Alabama Uni. of Arkansas Uni. of Georgia Indiana Uni. Louisiana State Uni. Oklahoma State Uni. Seton Hall Uni. Syracuse Uni. Uni. of Texas, Austin Villanova Uni. Uni. of Utah Temple Uni. Wake Forest Uni. Xavier University Arizona State Uni. DePaul Uni. Florida State Uni. Fresno State Uni.

# of Players Sent 12 11 10 10 10 10 8 7 6 6 6 6 5 5 6 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3

120

# of Black Players 11 9 9 9 5 10 7 5 6 5 6 2 3 5 6 3 4 4 4 4 4 4 4 4 4 3 3 1 4 3 4 3 3 2 3

# of White Players 1 2 1 1 5 0 1 2 0 1 0 4 2 0 0 2 1 0 0 0 0 0 0 0 0 1 1 3 0 1 0 0 0 1 0

Appendices

Institution Gonzaga Uni. Uni. of Houston Iowa State Uni. Uni. of Iowa Uni. of Missouri Uni. of Nebraska Oak Hill Academy Providence College Saint Joseph's Uni. Uni. of Memphis Uni. of New Mexico Uni. of Notre Dame Uni. of Oregon Uni. of Nevada, Las Vegas Baylor Uni. Bowling Green State Uni. Cal State uni., Fullerton Clemson Uni. Uni. of Colorado, Florida International Uni. Uni. of Illinois, Urbana-Champaign Uni. of Louisville Marquette Uni. Uni. of Miami Miami Uni. (Ohio) Mississippi State Uni. North Carolina State Uni. Oregon State Uni. Uni. of Pittsburgh Purdue Uni. Uni of Rhode Island South Kent Prep High School Uni. of Wisconsin Abraham Lincoln High School Alief Elsik High School Auburn Uni. Augsburg College Austin-Peay Uni.

# of Players Sent 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1

121

# of Black Players 1 3 2 2 2 2 3 2 3 3 2 0 1 3 2 2 2 2 2 2 3 2 1 2 1 2 2 1 2 0 2 2 2 1 1 1 1 1

# of White Players 2 0 1 1 1 1 0 1 0 0 0 3 2 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 2 0 0 0 0 0 1 0 0

Amadu Jacky Kaba

Institution Bakersfield High School Ball State Uni. Boston College Brigham Young Uni. Central Michigan Uni. Central Park Christian High School Clifton J. Ozen High School Creighton Uni. College of Charleston Coastal Christian Academy Colgate Uni. Colorado State Uni. Cypress Creek High School Uni. of Detroit Dominquez High School Drexel Uni. Duquesne Eastern Michigan Uni. East St. Louis High School Eau Claire High School Farragut Academy High School Fayetteville State Uni. Florida A&M Uni. Fordham Uni. Glynn Academy High School Gulf Shores Academy Hampton Uni. Uni. of Hartford Hofstra Uni. Jackson State Uni. Kent State Uni. Lanier High School, Jackson La Salle Uni. Louisiana-Lafayette Louisiana Tech Uni. Lower Merion High School Mount Zion Christian Academy Murray State Uni.

# of Players Sent 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

122

# of Black Players 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1

# of White Players 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

Appendices

Institution Uni. of Nevada, Reno Uni. of New Orleans North East Mississippi CC Oakland Uni. Ohio State Uni. Peoria Central High School Penn State Uni. Pepperdine Uni. Prentiss High School Rice Uni. Saint Benedict's Prep. Saint John's Uni. Saint Joseph's High School, NJ Saint Louis Santa Clara Uni. Saint Patrick's High School Saint. Vincent-St. Mary's H.S. Seattle Prop High School Shaw Uni. Skyline High School Southeastern Illinois College South Gwinnett High School South Florida Uni. Southern Illinois Uni. Southern Methodist Uni. South West Atlanta Christian Academy South West Texas State Uni. Starkville High School Texas A & M Uni. Texas Christian Uni. Texas Tech Uni. Texas San Antonio Uni. Thornwood High School Uni. of Tulsa Uni. of Alabama, Birmingham Uni. of Hawaii Uni. of Massachusetts Uni. of Mississippi

# of Players Sent 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

123

# of Black Players 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1

# of White Players 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

Amadu Jacky Kaba

# of # of Black # of White Institution Players Sent Players Players Uni. of North Carolina, Charlotte 1 1 0 Uni. of Oklahoma 1 0 1 Uni. of Pacific 1 1 0 Uni. of Southern California 1 0 1 University of Texas, El Paso 1 1 0 Uni. of Wyoming 1 1 0 Tulane Uni. 1 1 0 Utah Valley State Uni. 1 1 0 Virginia Union Uni. 1 1 0 Walsh Uni. 1 1 0 Washington Union High School 1 1 0 Uni. of Washington 1 1 0 Weber State Uni. 1 1 0 Westchester High School, LA 1 1 0 West Florida, Uni. 1 1 0 Western Carolina Uni. 1 1 0 Wright State Uni. 1 0 1 Total/Ave 375 317 58 Source: Compiled and Computed based on Data on the NBA Website. www.nba.com

Table B 11 Institutions (a High School, College or University may send 1 or more players) and Regions sending players to the NBA: 2005-2006 Season Total # of Institutions

Total # of White % of % of all all Region Sent % Players Sent Blacks Players Sent Whites North East 54 14.4 51 16.1 3 5.1 South 157 41.9 140 44.1 17 29.3 Midwest 91 24.3 69 21.8 22 38 West 73 19.5 57 18 16 27.6 Total/Ave. 375 100.1 317 100 58 100 Source: Compiled and Computed based on Data on the NBA Website, March 2006. www.nba.com n=375

Total # of Black

124

Appendices

Table B 12 Number of NBA Players sent By NCAA/NAIA/NJCAA Conferences: 2005-2006 Season (As of March 6, 2006) n=340 Players Name of Conference American East Conference Atlantic 10 Conference Atlantic Coast Conference Atlantic Sun Conference Big 12 Conference Big East Conference Big Sky Conference Big South Conference Big Ten Conference Big West Conference Colonial Athletic Association Conference USA Division I Independents Horizon League Ivy Group Metro Atlantic Athletic Conference Mid Continent Conference Mid-American Conference Mid-Eastern Athletic Conference Missouri Valley Conference Mountain West Conference Northeast Conference Ohio Valley Conference Pacific-10 Conference Patriot League Southeastern Conference Southern Conference Southland Conference Southwestern Athletic Conference Sun Belt Conference West Coast Conference Western Athletic Conference National Junior College Athletic Association (N. East Mississippi

125

# of Players 1 19 47 0 34 50 1 0 33 3 2 12 1 2 0 0 1 8 2 2 13 0 2 39 1 39 2 2 1 4 5 6

% 0.3 5.6 14 0 10 15 0.3 0 9.7 0.9 0.6 3.5 0.3 0.6 0 0 0.3 2.4 0.6 0.6 3.8 0 0.6 12 0.3 12 0.6 0.6 0.3 1.2 1.5 1.8

1

0.3

Amadu Jacky Kaba

Community College, DIV. I, Region 23) n=340 Players # of Name of Conference Players % Minnesota Intercollegiate Athletic Conference D III (Augsburg College) 1 0.3 American Mideast Conference, NAIA D II (Walsh Uni.) 1 0.3 Central Intercollegiate Athletic Association D II (Virginia Union Uni., Shaw Uni. & Fayetteville State) 3 0.9 Great Rivers Athletic Conference D II (Southeastern Illinois) 1 0.3 Gulf South Conference D II (Uni. of West Florida) 1 0.3 Total 340 100 Source: Compiled and Computed based on Data on the NBA Website. www.nba.com. NAIA=National Association of Intercollegiate Athletics.

Table B 13 Numbers and Names of Institutions in States in the NBA: 2005-2006 Season N=41 States and 166 Institutions # of Names of Inst. Institutions State U.C. Berkeley; Pepperdine Uni.; Fresno State Uni.; UCLA; Cal State Fullerton; Dominguez H. School; Stanford Uni.; Uni. of the Pacific; Wash. 13 Union H. School; Santa Clara; USC; Bakersfield H. School; California & Westchester H. School. Fayetteville State Uni.; UNC, Charlotte; Duke; UNC; NC State; 9 Mount Zion Christian Academy; Western Carolina Uni.; Wake Forest; & North Carolina Shaw Uni. Texas Tech; Texas San Antonio; UTEP; Texas, Austin; Gulf Shore Academy; Houston; Alief Elsik High School; Skyline H. School; Clifton J. Ozen High 15 School; SMU; Baylor; TCU; Rice; Texas A&M; & Texas South West Texas Uni. Bowling Green; Cincinnati; Xavier; Ohio State; St. Vincent-St. Mary's H.S.; 9 Ohio Walsh Uni.; Kent State; Wright State; & Miami (OH). Florida Intl; South Florida; Florida State; Florida; Florida A&M; Miami; 8 Florida West Florida; & Cypress Creek H.S.,

126

Appendices

State Kentucky Arizona Connecticut Georgia Illinois Kansas Indiana Mississippi New York Maryland Louisiana Alabama Utah Iowa New Jersey Oklahoma Virginia Washington, D.C. Minnesota Nevada Oregon Rhode Island Washington Arkansas South Carolina Tennessee Wisconsin Colorado Missouri

# of Names of Inst. Institutions 3 Kentucky; Louisville; &, Murray State Uni. 2 Uni. of Arizona; & Arizona State Uni. 3 Connecticut; Uni. of Hartford; & South Kent Prep (H.S) Georgia; Georgia Tech; Glynn Academy; SW Atlanta Christ H.S.; & South 5 Gwinnett H.S., Illinois; Farragut Academy H.S.; Thornwood H.S.; Southern Illinois; 8 DePaul; Peoria Central H.S.; East St. Louis H.S.; & Southeastern Illinois. 1 Uni. of Kansas 4 Indiana; Ball State; Purdue; & Notre Dame Mississippi State; Lanier High School; Jackson State; Prentiss High School; 7 Starkville High School; Uni. of Mississippi; & Northeast Mississippi CC St. John's Uni.; Hofstra Uni.; Fordham Uni.; Syracuse Uni.; Colgate Uni.; & 6 Abraham Lincoln High School. 1 Uni. of Maryland, College Park 5 LSU; Louisiana Tech; Louisiana Lafayette; New Orleans; & Tulane 4 Alabama; UAB; Auburn; & Central Park Christian High School 4 Utah; Utah Valley State; Brigham Young University; Weber State Uni. 2 Iowa; & Iowa State 4 St. Joseph's H.S.; Seton Hall; St. Benedict's H.S.; & St. Patrick's Prep. 3 Oklahoma State; Tulsa; & Oklahoma Coastal Christian Academy; Oak Hill Academy; Hampton Uni.; & Virginia 4 Union Uni. 1 2 2 2 2 3 1 3 2 2 2 2

Georgetown Uni. Uni. of Minnesota; & Augsburg College UNLV; & Uni. of Nevada, Reno Uni. of Oregon; & Oregon State Providence College; & Uni. of Rhode Island Gonzaga Uni.; Uni. of Washington; & Seattle Prep. H.S. Uni. of Arkansas Clemson Uni.; College of Charleston; & Eau Claire H.S. Uni. of Memphis; & Austin Peay State Uni. Uni. of Wisconsin; & Marquette Uni. Uni. of Colorado; & Colorado State Uni. Uni. of Missouri; & St. Louis Uni.

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# of Names of Inst. Institutions State 1 Uni. of Hawaii, Manoa Hawaii 2 Boston College; & Uni. of Massachusetts Massachusetts 1 Uni. of Wyoming Wyoming 166 Total Source: Compiled and Computed based on Data on the NBA Website, March 2006. www.nba.com

128

Appendices

Table B 14 Total Number of Players of Each Sending Institution to the NBA, and Their Academic Rank in the 2006 U.S. News and World Report College Rankings n=375 Players n=166 institutions

Total # of

Rank # of Top

Institution

Players

120 Institutions Institutions Institutions

Uni. of Kentucky

12

120

Duke University

11

5

Uni. of Arizona

10

97

Uni. of Connecticut

10

68

Uni. of Kansas

10

97

Uni. of North Carolina

10

27

Uni. of California, Los Angeles

8

25

Uni. of Maryland

7

55

Michigan State Uni.

6

74

Uni. of California, Berkeley

6

20

Uni. of Cincinnati

6

Uni. of Florida

6

Tier 3

Tier 3 50

Georgia Institute of Technology

5

37

Georgetown Uni.

5

23

Uni. of Michigan

6

25

Uni. of Minnesota

5

74

Stanford Uni.

5

5

Uni. of Alabama

4

104

Uni. of Arkansas

4

Tier 3

Uni. of Georgia

4

58

Indiana Uni.

4

74

Louisiana State Uni.

4

Oklahoma State Uni.

4

Syracuse Uni.

4

50

Uni. of Texas, Austin

4

52

Villanova Uni.

4

na

Wake Forest Uni.

4

27

Uni. of Utah

4

120

Xavier University

4

na

Tier 3 Tier 3

Arizona State Uni.

3

T3

DePaul Uni.

3

Tier 3

Florida State Uni.

3

109

Gonzaga Uni.

3

na

Uni. of Houston

3

Uni. of Nebraska

3

Tier 3 97

129

Tier 4

Amadu Jacky Kaba

n=166 institutions

Total # of

Rank # of Top

Institution

Players

120 Institutions Institutions Institutions

Tier 3

Uni. of Missouri, Columbia

3

85

Uni. of Notre Dame

3

18

Oak Hill Academy

3

na

Providence College

3

na

Saint Joseph's Uni.

3

na

Uni. of Memphis

3

Tier 3

Uni. New Mexico

2

Tier 3

Uni. of Oregon

3

115

Seton Hall Uni.

4

Tier 3

Temple Uni.

4

Tier 3

Uni. Of Nevada, Las Vegas

3

Baylor Uni.

2

Bowling Green State Uni.

2

Cal State Uni. Fullerton

2

Tier 4

Tier 4 78 Tier 3 na

Clemson Uni.

2

78

Uni. of Colorado

2

78

Florida International Uni.

2

Tier 4

Fresno State Uni.

3

na

Uni. of Illinois, Urbana Champaign

3

42

Uni. of Louisville

2

Marquette Uni.

2

85

Uni. of Miami

2

55

Miami Uni. (Ohio)

2

66

Mississippi State Uni.

2

North Carolina State Uni.

2

78

Ohio State Uni.

1

60

Oregon State Uni.

2

Uni. of Pittsburgh

2

58

Purdue Uni.

2

60

Uni. of Rhode Island

2

South Kent Prep High School

2

na

Uni. of Wisconsin

2

34

Abraham Lincoln High School

1

na

Alief Elsik High School

1

na

Auburn Uni.

2

85

Augsburg College

1

na

Austin-Peay State Uni.

1

na

Bakersfield High School

1

na

Tier 4

Tier 3

Tier 3

Tier 3

130

Appendices

n=166 institutions

Total # of

Rank # of Top

Institution

Players

120 Institutions Institutions Institutions

Brigham Young Uni.

1

71

Central Michigan Uni.

1

na

Central Park Christian H. S.

1

na

Clifton J. Ozen High School

1

na

Creighton Uni.

1

na

College of Charleston

1

na

Coastal Christian Academy

1

na

Colgate Uni.

1

na

Colorado State Uni.

1

120

Cypress Creek High School

1

na

Detroit Uni.

1

na

Dominquez High School

1

na

Drexel Uni.

1

109

Duquesne Uni.

1

Tier 3

Tier 3

Eastern Michigan Uni.

1

na

East St. Louis High School

1

na

Eau Claire High School

1

na

Farragut Academy High School

1

na

Fayetteville State University

1

na

Florida A&M Uni.

1

na

Fordham Uni.

1

68

Glynn Academy High School

1

na

Gulf Shores Academy

1

na

Hampton Uni.

1

na

Hartford, Uni. of

1

Tier 3

Hofstra Uni.

1

Tier 3

Jackson State Uni.

1

Tier 4

Kent State Uni.

1

Lanier High School, Jackson

1

na

La Salle Uni.

1

na

Uni. of Louisiana-Lafayette

1

na

Louisiana Tech Uni.

1

Lower Merion High School

1

Tier 4

Tier 3 na

Mount Zion Christian Academy

1

na

Murray State Uni.

1

na

Uni. of Nevada

1

Uni. of New Orleans

1

North East Mississippi CC

1

Tier 4

Tier 3 Tier 4 na

131

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n=166 institutions

Total # of

Rank # of Top

Institution

Players

120 Institutions Institutions Institutions

Penn State Uni.

1

48

Pepperdine Uni.

1

55

Prentiss High School

1

na

Rice Uni.

1

17

Saint Benedict's Prep.

1

na

Saint John's Uni.

1

Saint Joseph's High School

1

na

Saint Louis Uni.

1

78

Tier 3

Tier 4

Tier 3

Santa Clara Uni.

1

na

Saint Patrick's High School

1

na

Saint. Vincent-St. Mary's H.S.

1

na

Seattle Prop High School

1

na

Shaw Uni.

1

na

Skyline High School

1

na

Southeastern Illinois Uni.

1

na

South Gwinnett High School

1

na

South Florida Uni.

1

na

Southern Illinois Uni.

1

Southern Methodist University

1

Tier 4 71

South West Atlanta Christian Academy

1

na

South West Texas State Uni.

1

na

Starkville High School

1

na

Texas A & M Uni.

1

60

Texas Christian Uni.

1

97

Texas Tech Uni.

1

Texas San Antonio Uni.

1

na

Thornwood High School

1

na

Uni. Of Tulsa

1

93

Uni. Of Alabama, Birmingham

1

Tier 3

Uni. of Hawaii, Manoa

1

Tier 3

Uni. of Massachusetts

1

Uni. Of Mississippi

1

Uni. Of North Carolina, Charlotte

1

na

Uni. Of Oklahoma

1

109

Uni. Of Pacific

1

104

Uni. Of Southern California

1

30

University of Texas, El Paso

1

Uni. of Wyoming

1

Tier 3

104 Tier 3

Tier 4 Tier 3

132

Appendices

n=166 institutions

Total # of

Rank # of Top

Institution

Players

120 Institutions Institutions Institutions

Virginia Union Uni.

1

na

Walsh Uni.

1

na

Washington Union High School

1

na

Washington Uni.

1

45

Weber State Uni.

1

na

Westchester High School, LA

1

na

West Florida, Uni.

1

na

Western Carolina Uni.

1

na

Wright State Uni.

1

Total

375

Tier 3

Tier 4

Tier 4

na=Not Available or Not Listed

Source: Compiled and Computed based on Data on the NBA Website, March 2006. www.nba.com; and U.S. News & World Report.www.usnews.com/usnews/edu/college rankings. na=Not Available

133

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Appendix C Regional Breakdown of the United States (N=51) Northeast (n=9) Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont. Midwest (n=12) Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North, Dakota, Ohio, South Dakota, Wisconsin. South (n=17) Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia. West (n=13) Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming. Source: “Summary Social, Economic, and Housing Characteristics: 2000 Census of Population and Housing,” (2003, June). Selected Appendixes: 2000. PHC-2-A. Washington, D.C.: U.S. Census Bureau.

134

Appendices

Appendix D Composition of macro geographical (continental) regions, geographical sub-regions, and selected economic and other groupings Nations, Territories and Entities plus Taiwan (N=238) Africa (n=57) Eastern Africa (n=19) Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Reunion, Rwanda, Seychelles, Somalia, Tanzania, Uganda, Zambia, Zimbabwe and Mayotte. Middle Africa (n=9) Angola, Cameroon, Central African Republic, Chad, Republic of Congo, Democratic Republic of Congo, Equatorial Guinea, Gabon and Sao Tome & Principe Northern Africa (n=7) Algeria, Egypt, Libya, Morocco, Sudan, Tunisia and Western Sahara Southern Africa (n=5) Botswana, Lesotho, Namibia, South Africa and Swaziland Western Africa (n=17) Benin, Burkina Faso, Cape Verde, Cote d’Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo and Saint Helena.

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Americas N=53 Latin America and the Caribbean (n=48) Caribbean (n=26) Anguilla, Antigua and Barbuda, Aruba, Bahamas, Barbados, British Virgin Islands, Cayman Islands, Cuba, Dominica, Dominican Republic, Grenada, Guadeloupe, Haiti, Jamaica, Martinique, Montserrat, Netherlands Antilles, Puerto Rico, Saint-Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Trinidad and Tobago, Turks and Caicos Islands, United States Virgin Islands, Central America (n=8) Belize, Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama South America (n=14) Argentina, Bolivia (Plurinational State of), Brazil, Chile, Colombia, Ecuador, Falkland Islands (Malvinas), French Guiana, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela (Bolivarian Republic of). Northern America (n=5) Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America Asia (N=51) Central Asia (n=5) Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan

136

Appendices

Eastern Asia (n=8) China, Hong Kong Special Administrative Region of China, Macao Special Administrative Region of China, Democratic People's Republic of Korea, Japan, Mongolia, Republic of Korea, Taiwan* (As noted in the methodology, I added Taiwan to Eastern Asia) Southern Asia (n=9) Afghanistan, Bangladesh, Bhutan, India, Iran (Islamic Republic of), Maldives, Nepal, Pakistan, Sri Lanka South-Eastern Asia (n=11) Brunei Darussalam, Cambodia, Indonesia, Lao People's Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste, Viet Nam Western Asia (n=18) Armenia, Azerbaijan, Bahrain, Cyprus, Georgia, Iraq, Israel, Jordan, Kuwait, Lebanon, Occupied Palestinian Territory (Gaza and the West Bank), Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Turkey, United Arab Emirates, Yemen. Europe (N=52) Eastern Europe (n=10) Belarus, Bulgaria, Czech Republic, Hungary, Poland, Republic of Moldova, Romania, Russian Federation, Slovakia, Ukraine. Northern Europe (n=17) Åland Islands, Channel Islands, Denmark, Estonia, Faeroe Islands, Finland, Guernsey, Iceland, Ireland, Isle of Man, Jersey, Latvia, Lithuania, Norway, Svalbard and Jan Mayen Islands, Sweden, United Kingdom of Great Britain and Northern Ireland

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Southern Europe (n=16) Albania, Andorra, Bosnia and Herzegovina, Croatia, Gibraltar, Greece, Holy See, Italy, Malta, Montenegro, Portugal, San Marino, Serbia, Slovenia, Spain, The former Yugoslav Republic of Macedonia. Western Europe (n=9) Austria, Belgium, France, Germany, Liechtenstein, Luxembourg, Monaco, Netherlands, Switzerland. Oceania (N= 25) Australia and New Zealand (n=3) Australia, New Zealand, Norfolk Island. Melanesia (n=5) Fiji, New Caledonia, Papua New Guinea, Solomon Islands, Vanuatu. Micronesia (n=7) Guam, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, Northern Mariana Islands, Palau Polynesia (10) American Samoa, Cook Islands, French Polynesia, Niue, Pitcairn, Samoa, Tokelau, Tonga, Tuvalu, Wallis and Futuna Islands Source: “Composition of macro geographical (continental) regions, geographical subregions, and selected economic and other groupings” Retrieved on November 15, 2009 from: http://unstats.un.org/unsd/methods/m49/m49regin.htm.

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Endnotes Retrieved on April 16, 2013 from: https://www.cia.gov/library/publications/theworld-factbook/).

i

“Healthy weight, overweight, and obesity among U.S. adults,” National Health and Nutrition Examination Survey. U.S. Centers for Disease Control. National Center for Healt Statistics. Retrieved on April 8, 2013 from: http://www.cdc.gov/nchs/data/nhan es/databriefs/adultweight.pdf)

ii

Nordqvist, Christian. 2009, May 21. “What Is Health? What Does Good Health Mean?” Medicalnewstoday.com. Retrieved on April 12, 2013 from: http://www.medic alnewstoday.com/printerfriendlynews.php?newsid=150999. iii

Nordqvist, Christian. 2009, May 21. “What Is Health? What Does Good Health Mean?” Medicalnewstoday.com. Retrieved on April 12, 2013 from: http://www.medic alnewstoday.com/printerfriendlynews.php?newsid=150999. iv

v “Healthy weight, overweight, and obesity among U.S. adults,” U.S. Centers for Disease Control. Retrieved on April 8, 2013 from: http://www.cdc.gov/nchs/data/nha nes/databriefs/adultweight.pdf vi . Kaba, Amadu Jacky. 20011c, June 29. “Demographics and Publication Productivity of Ivy League Political Science Professors: Harvard, Princeton, University of Pennsylvania and Yale,” (3 pages double space) Holler Africa Magazine. http://www.h ollerafrica.com/index.php. vii Longman, Jeré. 2011, January 29. “N.F.L. Linemen Tip the Scales,” New York Times, Page D1.

Berri, Dave. 2011, December 11. “Why the NBA Players Keep Losing to the Owners,” Freakonomics. Retrieved on May 27, 2013 from: http://www.freakonomics. com/2011/12/06/why-the-nba-players-keep-losing-to-the-owners/. viii

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INDEX Duke University, 54, 55, 120, 129

A Abdul-Jabbar, Kareem, 63 Adibe, Jideofor, i Akpan, Otoabasi, ii American football, 12, 57, 69 American life, 57 American Medical Association, 14, 79

F Finley, Michael, 63 Forbes Magazine, 60 Frontal plane projection angle, 73 G

B

Garnett, Kevin, 63 Georgetown University, 54, 55 Golden State Warriors, 26 Good Health, v, 16, 19, 20, 23, 24, 26, 86, 88, 139 Greece, 25, 83, 116, 138

Beckham, David, 60 Bird, Larry, 63 BMI-salary correlation, 45, 46, 50 BMI-weight correlation, 44, 45, 46, 49

H C

Healthy economy, 20 Hispanic Black women, 31, 32 Houston Comets, 60 Hypertension, 14, 71

cardiorespiratory endurance, 21 Cleveland Cavaliers, 60 CNN television, 57 College Rankings, ix, 55, 105, 129 Cooper, Cynthia, 59, 85

I Injuries, 11, 12, 13, 70, 72, 73, 76 Iranian, 30 Iverson, Allen, 63

D

J

Death rates, 74, 75 DNA test, 52

James, LeBron, 59, 82

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Index

O

Jewish, 30 Johnson, Earvin, 63 Jordan, Michael, 60, 137

Obesity, 13, 14, 15, 33, 70, 139 O'Neal, Shaquille, 56 Overweight, v, 13, 14, 15, 16, 17, 19, 24, 26, 27, 31, 32, 33, 40, 41, 49, 68, 69, 70, 71, 72, 74, 76, 139

K Kanayo, Kanayo, 30 Kidd, Jason, 63 L

P

LaFrentz, Raef, 63

Patellar tendinopathy, 73 Physical activity, 21, 22, 71, 74

M

R

Madison Square Garden, 61 Marbury, Stephon, 63 McHale, Kevin, 63 Mexican American men, 32 Middle Africa, 52, 53, 135 Moguluwa, Shedrack, v

Reciprocal Ponderal Index, 52 Red Sea, 53 S Salary data of NBA and WNBA players, 38 Scandinavia, 59 Sikma, Jack, 63 South Asia, 30 Southern Africa, 53, 135 Stanford University, 54, 55 Sumo wrestlers, 74 Szczerbiak, Wally, 63

N National Basketball Association, 15, 36, 65, 77, 80, 82, 84, 86, 87 National Collegiate Athletic Association, 54, 101, 105, 108 National Health and Nutrition Examination Survey, 33, 139 New York Liberty, 60 Nike, 59 North Africa, 30 Nowitzki, Dirk, 63

T The Lancet, 20, 21, 83 Training loads, 12 Trans-Atlantic Slave Trade, 53 Traumatic brain injury, 13

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Turkish,, 30 Type 2 diabetes, 14

V Van Horn, Keith, 63

U

W

U.S. Census, 29, 79, 87, 134 U.S. Centers for Disease Control and Prevention, 15, 31, 32, 40 United States Office of Management and Budget, 30 University of Michigan, 55 University of Notre Dame, 54, 56

Webber, Chris, 63 Western Africa, 53, 135 Woods, Tiger, 60 Y Yesalis, Charles, 70 Z Zuckerberg, Mark, 57

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