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NBA Gambling Inefficiencies: A Second Look

U.S. Sports Academy2013-11-22T22:55:25-06:00January 4th, 2012|Contemporary Sports Issues, Sports Studies and Sports Psychology|

### Abstract

Our study used the log likelihood ratio methodology proposed by Even and Noble (2) to test the market efficiency of both point spread betting and totals betting for consecutive National Basketball Association (NBA) seasons from 2000–01 to 2007–08. It was motivated by recent contradictory evidence that both support and reject opportunities to exploit inefficiencies in NBA gambling by Paul and Weinbach (9, 11) as well as other evidence suggesting that these opportunities fade as the market responds to new information (12).

Based on the results of over 10,000 games in eight consecutive NBA seasons, betting the over on the total points per game is a fair bet, indicating an efficient market. For the higher totals (totals 211-220), the winning percentage on betting the over was above 52.38% (the percentage necessary to cover commissions) in eight of 10 cases, but the null hypothesis of a fair bet could not be rejected. The results for point spread betting also showed strong support for an efficient market in NBA gambling, with one exception: betting the home underdog was profitable for underdogs of 10 points or more. However, this was only true for a very small sub-sample and the inefficiency fades in the most recent sample period.

The few cases of big home underdogs beating the spread are consistent with the model of spread betting where bookmakers exploit the uninformed investor’s home favorite bias, shade the point-spread and maximize profits by betting on the underdog (7,6). Informed bettors may also bet the underdog but will not drive the point spread to the true value but only to the point where the probability of winning is no more than 52.38% (11). While bookmaker’s point shading activity is constrained by the action of informed bettors, the persistence of profit opportunities in a very small sub-sample can be explained by betting market constraints such as low limits on bets and the relative volume of bets placed by informed and uninformed bettors (9).

**Key Words:** point spreads, totals, National Basketball Association, NBA, gambling

### Introduction

Studies of market efficiency in sport betting are similar to those in the financial markets for good reason. Both markets involve many market participants and large sums of money, both involve informed and uninformed traders, market frictions, asymmetric information, and, as the weight of the evidence shows, both are heavily influenced by market psychology. In both markets, however, claims of abnormal returns and profitable strategies always raise a red flag. Like the anomalies literature in financial markets, claims of exploitable inefficiencies must be validated with out-of-sample tests to confirm that these inefficiencies are not confined to specific periods, or are driven by a few outliers in the data, or are simply artifacts of extensive data mining. Sport betting provides a unique test for market efficiency since the payoffs are known with certainty in advance of the outcome and the final outcome is determined when the game is played. This is not the case with equity investing (1).

The market for sports betting consists of a market maker, called a bookmaker or sports book, and a bettor. The bookmaker establishes the lines at which betting commences and then moves the line as bets are wagered on both sides of the line. Bettors typically pay the bookmaker $11 to win $10, providing the bookmaker a commission profit if money on both sides of the bet are balanced. Because of this commission, commonly called the “vig” or “juice”, bettors must win 52.38% of their bets to break even. A winning percentage greater than 52.38% insures a profit for the bettor. Recent evidence using data on dollars wagered has rejected the claim that bookmakers strive to balance the dollar on both sides of a wager and lends support to the argument that bookmakers attempt to set the line to accurately reflect actual game outcomes (6,7,11).

In the sports gambling world, an over/under or totals wager is a bet that is won or lost depending upon the combined score of both teams in a game. A bookmaker will predict the combined score of the two teams and bettors will bet that the actual number of points scored in the game will be higher or lower than that combined score. For example, in an NBA game of the Miami Heat versus the San Antonio Spurs the over/under for the score of the game was set at 195. A bet on the under wins the wager if the combined score at the end of the game is 194. If the combined score is 196 or more, then the over bet wins. If the combined score equals 195, then it is a tie and the bettor’s money is returned.

### Data And Methodology

This study was designed to test for the presence of exploitable inefficiencies in NBA sport gambling. Recent research in NBA gambling has produced evidence of over betting the over in totals betting, and over betting the favorite by uninformed bettors in point spread betting. The research also claims that there are profitable opportunities in betting the big underdog. This study tests those claims by examining both totals betting and point spread betting using updated data.

The data for studying the totals and point spread markets for National Basketball Association games was taken from the Gold Sheet, a well-known handicapping company, for eight NBA seasons 2000-01 through 2007-08. The data included all games from these years, both regular season and playoffs, except for games where totals or point spreads were not posted. Table 1 shows the summary statistics for the 10,325 games included in the sample. Five of the games had no line posted for the over/under and 175 games were ties. The average or mean actual total score for our sample of NBA games was 192.72 points and the average or mean over/under total for the sample was 192.27 total points per game.

The log likelihood ratio methodology proposed by Even and Noble (2) was used to test for market efficiency for the over/under betting market in the NBA. From the perspective of the over bettor, the value of the unrestricted log likelihood function (Lu) takes the form

> Lu = n[ln(q)] + (N – n)ln(1 – q) (1)

where N is the total number of NBA games where the over bettor or under bettor won the bet. The n is the number of games where the over covers the bet, and q is the proportion of games where the over covers the bet. If the betting market is efficient and a fair bet, then q = 0.5.

This creates the restricted log likelihood function (Lr), which was obtained by substituting 0.5 for q in Equation 2. The log likelihood ratio statistic for the null hypothesis that q = 0.5 is

> 2(Lu – Lr) = 2{n[ln(q) – ln(0.5)] + (N – n)[ln(1 – q) – ln(0.5)]} (2)

where q is the actual percentage of overs winning the over/under bet from our sample. To test for profitability, where the bettor must win enough to offset the commission or vigorish of the bookmaker, the test ratio changes into

> 2(Lu – Lr ) = 2{n[ln(q) – ln(0.524)] + (N – n)[ln(1 – q) – ln(0.476)]}. (3)

### Results And Discussion

#### Totals Betting

In a 2004 study, covering the seven NBA seasons from 1995-96 through 2001-2002, Paul et al.(8) found that, for all games, a bet on the underdog won about 50% of the time, as is expected in an efficient market. However, for the high scoring games (games above 200), they found a pattern of over betting the over, and this pattern increased as game totals increased. For every one point increase from 200 to 210, the winning percentage of the under bet was greater than 50%. In eight of those totals the winning percentage was greater than 52.38%, enough to cover the vigorish, and in five of those totals, the null hypothesis of a fair bet was rejected. However, none of the totals in their study produced a result that rejected the null of no profitability when accounting for commissions. Taking the contrarian bet, and betting against market sentiment, was not profitable. In a later study, using data on actual dollar amounts wagered, Paul and Weinbach (11) found that overs received a much higher percentage of bets compared to unders, but here again it was shown that informed bettors pushed the total to where it was not profitable to bet the under.

The results found the opposite of the 2004 study (8) for the high scoring games. For all games in the eight seasons from 2000-01 through 2007-08, a bet on the underdog still won about 50% of the time. However, a bet on the over won more often than a bet on the under for high scoring games. The game results, and the log likelihood test of efficiency, are reported in Table 2. For game totals between 200 and 210, the winning percentage of the over bets hover right around 50%, indicating an efficient market. When we extended the testing to higher totals (211-220) the percentage of over winners was more than the commission breakeven point (52.38%) for eight of the 10 totals. However, in no instance was the log likelihood ratio large enough to reject the null hypothesis of a fair bet.

Point Spread Betting and Betting the Underdog

When an NBA gambler bets the point spread of an NBA game he is not interested in who wins the game, only the final score. For example, if the point spread for a National Basketball Association game reads

> Heat -4 Pacers +4

The (-) before the 4 indicates that the Heat is the point spread favorite. The (+) indicates that the Pacers are the point spread underdog. If one bets on the Heat, the Heat would have to win by a total of five points for the bettor to win. If one bets on the Pacers, the Pacers would have to win outright or lose by no more than three points for the bettor to win. A four point victory by the Heat (four point loss by the Pacers) would equal a tie and the money bet by the NBA gambler is returned to him.

Prior evidence suggests that there are systemic bettor misperceptions in the NBA point spread gambling market. In a 2005 study Paul and Weinbach (9) presented evidence from the 1995-96 through 2001-2002 seasons that favorites are over bet by uninformed bettors. In that study, a strategy of betting big underdogs rejected the null hypothesis of a fair bet, and betting big home underdogs not only rejected a fair bet was also profitable. Levitt (7) provides us with a model where bookmakers do not attempt to balance the dollars wagered, but rather they shade the point spread to exploit uninformed bettor bias and then take positions on the opposite side, betting the big underdog. Informed bettors may attempt to exploit this inefficiency by also betting the big underdog but will only bet to the point where it is profitable to do so, meaning that they may bet on the underdog and push the point spread only to where there is no less than 52.38% chance of winning the bet. Other studies (6, 11), using data on actual dollars wagered, have found that a majority of dollars are wagered on the stronger or favorite team by uninformed bettors.

This study examined the NBA betting market on point spreads for the seasons 2000-01 through 2007-08 to see if this underdog anomaly persists. It used the closing line on point spreads for NBA games for the same seasons that we examined in the over/under analysis performed in the previous section of the paper. For the market to be efficient the actions of the informed bettors should offset any bias shown by uninformed bettors and the bookmakers closing line should equal the actual game score outcome. Recent studies have shown that the betting public removes biases in sport book’s opening lines in NBA betting by game time (3-5).

Table 3 is a summary of the data for point-spread betting. The sample contained 10,325 games with five of the games posting no closing line to bet on and 90 games posting a closing line of zero. This is called a push and these games were not included when betting favorites and underdogs. There were 141 ties which indicate that the difference in the score (underdog – favorite) was equal to the closing point spread. The average closing line based on the favorite score minus the underdog score was 5.89 and actual difference in score in the NBA games in the sample was 5.38. For the entire sample of games the underdog won 49.86% of the games, indicating that a strategy of betting the underdog was a fair bet, based on the log likelihood ratio test.

The results in Table 4 indicate that the betting public appears to over bet the heavy favorite by a slight margin, but, unlike the study by Paul and Weinbach (9), we found that the winning percentage of betting the big underdog (10 points or more) hovered around 50% and thus we failed to reject the null hypothesis of a fair bet. The same result occurred for the sub-sample of games for seasons 2000-01 through 2003-04 and for the sub-sample of games for seasons 2004-05 through 2007-08. In all of these cases the null hypothesis of a fair bet could not be rejected.

The results for the small sample of games involving the home underdog of 10 points or more had significant results for both a fair bet and profitability. For the entire sample of games (50 games over the entire seasons) the null hypothesis of a fair bet was rejected at a 10% significance level. For the small sample of games in the earlier sub-period (25 games) we found that a bet on the home underdog also rejected the null hypothesis of no profitability.

### Conclusion

This study found that gambling markets for both point spread betting and totals betting for NBA seasons spanning from 2000–01 to 2007–08 are efficient. Based on the results of over 10,000 games in eight consecutive NBA seasons, betting the over on the total points per game is a fair bet. Although for higher totals (211-220) the winning percentage on betting the over was above 52.38% (the percentage necessary to cover commissions), in eight of 10 cases the null hypothesis of a fair bet could not be rejected. The results for point spread betting also showed strong support for an efficient market in NBA gambling, with one exception: betting the home underdog was profitable for underdogs of 10 points or more. However, this was only true for a very small sub-sample and the inefficiency fades in the most recent sample period.

### Applications In Sports

Many fans enjoy wagering on their favorite sport whether it is NBA basketball or another sport. Gambling can be fun and can enhance the excitement of the game by adding a financial component. The evidence suggests that the average bettor is biased toward high scores and prefers betting on the favorite. However, utilizing this knowledge and betting on the underdog will probably not be a profitable strategy for a fan wagering on NBA games because of the actions of informed (professional) gamblers. The informed gambler will bet on the underdog until it is not profitable for him to do so. This activity drives the point spread to a level where a fan cannot make a profit on an underdog bet after accounting for commission. Therefore, the average gambler should focus on having fun and not count on making a profit when gambling on NBA games.

### Tables

#### Table 1
NBA Seasons 2000-01 Through 2007-08 Summary Statistics for Over/Under Betting for All NBA Games

#### Table 2
Winning Percentages for Betting the Overs

Note. The log likelihood test statistics have a chi-square distribution with one degree of freedom.

Critical values are 2.706 (for an α = 0.10), 3.841 (for an α = 0.05), 6.635 (for an α = 0.01).

* is significant at 10%.

** is significant at 5%.

*** is significant at 1%.

#### Table 3
Closing Line Betting Seasons 2000-01 Through 2007-08

#### Table 4
Betting the NBA Underdog Seasons 2000-01 Through 2007-08

Note. The log likelihood test statistics have a chi-square distribution with one degree of freedom.

Critical values are 2.706 (for an α = 0.10), 3.841 (for an α = 0.05), 6.635 (for an α = 0.01).

* is significant at 10%.

** is significant at 5%.

*** is significant at 1%.

NA – not applicable

### References

1. Brown, W., Sauer, R. (1993). Fundamentals or noise? Evidence from the professional basketball betting market. Journal of Finance, 48, 1193–1209.
2. Evan, W. E., & Noble, N. R. (1992). Testing efficiency in gambling markets. Applied Economics, 24, 85-88.
3. Gandar, J., Zuber, R., O’Brien, T., & Russo, B. (1988). Testing rationality in the point spread betting market. Journal of Finance, 43, 995-1007.
4. Gandar, J., Dare, W., Brown, C., Zuber, R. (1998). Informed traders and price variations in the betting market for professional basketball games. Journal of Finance, 53, 385–401.
5. Gandar, J, Zuber, R. & Lamb, R. (2000). The home field advantage revisited: a search for the bias in other sports betting markets. Journal of Economics and Business, (53) 4, 439-453.
6. Humphreys, B. (2010). Point spread shading and behavioral biases in NBA betting market. Rivista Di Diritto Economia Dello Sport, 13-26.
7. Levitt, S. (2004). Why are gambling markets organized so differently? The Economics Journal, 114, 223-246.
8. Paul, R., Weinbach, A., Wilson, M. (2004). Efficient markets, fair bets, and profitability in NBA totals 1995–1996 to 2001–2002. The Quarterly Review of Economics, 44, 624–632.
9. Paul, R. J. & Weinbach, A. P. (2005). Bettor misperceptions in the NBA, Journal of Sports Economics, (6) 4, 390-400.
10. Paul, R. J. & Weinbach, A. P. (2007). Does Sportsbook.com set pointspreads to maximize profits? Tests of the Levitt model of sportsbook behavior. Journal of Prediction Markets, (1) 3, 209-218.
11. Paul, R. J. & Weinbach, A. P. (2008). Price setting in the NBA gambling market: Tests of the Levitt model of sportsbook behavior. International Journal of Sports Finance, (3) 3, 2-18.
12. Wever, S., & Aadland, D. (2010). Herd Behavior and the Underdogs in the NFL. Applied Economics Letters, (forthcoming).

### Corresponding Author

Kevin Sigler, PhD
601 S. College Road
Cameron School of Business
University of North Carolina-Wilmington
Wilmington, NC 28403
<siglerk@uncw.edu>
910-962-3605

William Compton is Associate Professor of Finance in the Cameron School of Business, UNCW Kevin Sigler is Professor of Finance in the Cameron School of Business, UNCW

Coach Effectiveness and Personality Assessments: An Exploratory Analysis of Thin Slice Interpersonal Perceptions

U.S. Sports Academy2013-11-22T22:55:49-06:00January 4th, 2012|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|

### Abstract

Gordon Allport (3) suggested that people are able to form accurate perceptions of others from mere glimpses of their behavior. The concept of interpersonal perception accuracy based solely on thin slices has been brought to mainstream attention by the popular book Blink by Malcolm Gladwell (35). Gladwell (35) proclaims that “decisions made very quickly can be as good as decisions made consciously and deliberately” (p. 14). Research suggested that expressive behaviors (movement, speech, gesture, facial expressions, posture) contribute to impressions made about the target (8). With that said, coaching research has identified behaviors that elicit positive perceptions from athletes towards coaches (63, 78). This research examined accuracy, consensus, and self-other agreement of personality assessments and coaching effectiveness based on thin-slice judgments of 30-second video clips of 9 recreation level coaches. Naïve raters (N=206) viewed the clips and rated the targets on coaching effectiveness and personality attributes. Ratings of coaching effectiveness were correlated with expert ratings of effectiveness to measure accuracy. The ratings of attributes were correlated with expert ratings of the same attributes to measure consensus. Gender, race, and level of sport participation of naïve raters was subjected to independent samples t-tests and one-way analyses of variance (ANOVA) to determine if they moderated thin-slice judgments. Results indicated that naïve raters as a group were not accurate in assessment of coaching effectiveness, nor were there significant correlations on consensus or self-other agreement. There were significant differences between levels of sport participation groups on two of the fourteen attributes: competence and confidence.

**Key Words:** Thin-slicing, Coaching Effectiveness, Consensus, Accuracy

### Introduction

In 1937, Gordon Allport (3) introduced this idea that people are able to form accurate perceptions of others from mere glimpses of their behavior. Making judgments from so called “thin slices” of behavior has become very popular in contemporary social psychological research (6-9). Interpersonal perception accuracy is based on thin slices, which was brought to mainstream attention by the popular book Blink by Malcolm Gladwell (35). This concept suggests that most people can thin-slice with surprising success, so that “decisions made very quickly can be as good as decisions made consciously and deliberately (p. 14).” Gladwell provides examples from academic research to support his overall premise, including that of Ambady and Rosenthal (9). Thin-slices are brief excerpts of expressive behavior less than five minutes sampled from the behavioral stream (6).

Ambady and Rosenthal (8) suggested that expressive behaviors (movement, speech, gesture, facial expressions, posture) contribute to impressions made about the target. Early researchers were interested in the link between expressive behaviors as the indicators of personality (3,4). The cues that are projected by expressive behavior have been shown to be interpreted accurately in as little as a 2-second nonverbal clip of a target (9).

Ambady and Rosenthal (8) also suggested that the accuracy of thin-slice judgments have practical applications in fields that are interpersonally oriented. When thin slice ratings predict criterion variables, they can be used, for example, to target biased teachers or gauge expectancies of newscasters. They also suggest that thin slice judgments can be used in the selection, training, and evaluation of people in fields where interpersonal skills are important. Accuracy of thin-slice judgments of coaches could be very useful in selection, training, and evaluation of coaches.

Accuracy in personality and social psychology research can be defined in three ways: the degree of correspondence between a judgment and a criterion, interpersonal consensus, and a construct possessing pragmatic utility (49). These definitions fall into two approaches within the field. The pragmatic approach defines a judgment as accurate if it predicts behavior. This approach looks at personality judgments as necessary tools for social living and evaluates their accuracy in terms of their practical value (31). The constructivist approach focuses on consensus between raters. This approach looks at all judgments as perceptions and evaluates their accuracy in terms of agreement between judges (31). Kenny (45) further explained that target accuracy is broken into three categories: Perceiver, generalized, and dyadic. Generalized target accuracy is the correlation between how a person is generally seen by others and how that person generally behaves. Target accuracy can be defined in thin-slice research as the correspondence between participants’ judgments of a target individual and well-defined external criterion (6,8,9).

Thin-slice judgments have been shown to produce similar judgments to ecologically valid criterion. Ecologically valid criteria are characterized by pragmatic utility in that they are used in everyday decisions about people as an external outcome of observed behavior (9). Support for congruence in this relationship has been shown by significant positive correlations between naïve judgments and outcomes, such as predicting judgments of candidates in job interviews and effectiveness of teachers (7).

The target accuracy and consensus of naïve raters given thin-slices of information appears to be moderated by characteristics of the raters, traits assessed, and characteristics of the targets. Studies show that individual differences of raters can affect judgments based on thin-slices of information including gender and ethnicity (6,7,29,73). Previous research is equivocal regarding the accuracy of judgments based on gender. Some research suggests that females are more accurate judges of non-verbal behavior (40), while other research found no difference in judgments of non-verbal behavior based on gender (8). Researchers have found that raters judge targets of a different ethnicity more negatively than targets of the same ethnicity (73).

Another bias can involve the dimensions being rated. One study found accuracy at zero acquaintance for judgments of extraversion, but not conscientiousness (47). Another study found similar correlations for extraversion as well as a relationship between zero acquaintance ratings of conscientiousness, but not for agreeableness, emotional stability, and culture (14). John and Robins (42) suggest that differences in ratings on traits depend on evaluativeness and observability. Traits that are less evaluative (neutral) and more observable reach greater consensus and accuracy (42). They define observability by the degree to which behaviors are relevant to the trait can be easily observed. They define evaluativeness by the degree to which a trait is relatively neutral.

Limitations are also present on the persons being judged. Persons who possess extraversion and good mental health are simpler to judge at first glance than targets who possess introversion or poor mental health, as Flora (28) denotes “exterior behavior mimics their internal view of themselves. What you see is what you get” (p. 66). Social context can also play a role depending on personality types. Expressive behaviors were limited by individuals with a high self-monitoring in social situations, therefore making judgments on their mood more difficult.

Ambady and Rosenthal (9) researched intuitive judgments on teacher

effectiveness. It was determined that thin-slice evaluations by naive raters of 30 seconds, 5 seconds, and 2 seconds were congruent with evaluations by students and principals who observed the teacher for a semester. It is suggested that the accuracy of the thin-slice judgments can be attributed to raters’ years experience in classroom situations; therefore, within the coaching context, amount of sport experience may also be an individual difference that moderates interpersonal perception accuracy. Ambady and Rosenthal (8) measured judgments on fourteen personality attributes: Accepting, active, dominant, empathic, enthusiastic, honest, likable, optimistic, professional, supportive, and warm. Teaching is an interpersonal field, as well as coaching. Due to similarities in the fields the same attributes were chosen in this study.

The teaching and coaching environment may have parallels and crossover applications. Often cited in coaching and teaching lore is John Wooden, who was one of the most successful collegiate basketball coaches. Wooden pointed out that coaches are teachers first and profiled ten criteria needed for a successful teacher; Among them, knowledge and warm personality and genuine consideration of others (79).

Research in the teaching profession highlights attributes of successful teaching. The list includes a teacher’s enthusiasm and positive attitude, approachability, an environment that is positive, cooperative, and clear-cut, specific objectives, as well as appropriate feedback (20,52,62). Wooden’s (79) coaching philosophy includes all of the aforementioned in his pyramid of success. Bloom (13) explains that coaching, like teaching, can perhaps best be viewed as an interpersonal relations field, which rests primarily on effective communication and interaction among various participants.

Coaching research has identified behaviors that elicit positive perceptions from athletes towards coaches (63,78). Behaviors include positive reinforcement, technical instruction, encouragement, and structuring fun practices. It is theorized that coachs behaviors plays a significant role in the psychological development of young athletes (64). Youth sport research highlights the positive relationship between specific coaching behaviors and self-esteem, satisfaction, and enjoyment in children (64,67). This has led to a recent theoretical model (19) that emphasizes how coaching behaviors impact youth psychosocial outcomes which emphasizes the role of athletes’ perceptions.

A recent study explored the characteristics of expert university level coaches and found several personal attributes that these coaches possessed: Commitment to learning; learning from past mistakes; knowledgeable; open-minded; balanced; composed; caring; and genuinely interested in their athletes (72).

Previous research targets the importance of increasing self-awareness of coaches’ referencing personal behavior while coaching (63,65,74). In a study that coded coaches’ behaviors, the athletes were significantly more successful than the coaches’ in the recall of those behaviors (63). This same research determined that youth athletes’ interpretation of coaches’ behaviors are of even greater impact than the actual behaviors in psychosocial outcomes. At the recreation level, game outcomes bear little significance in psychosocial outcomes (reaction to coach, enjoyment, and self-esteem) for the athletes. The measurement of psychosocial outcomes showed a significant relationship between coaches’ behavior and aforementioned outcomes. Earlier research (13) indicated that the coach is central to the development of expertise in a sport.

Nonverbal behavior can be very significant in an environment where high levels of stress and decision-making are concerned. Perceptions can cause shifts in confidence.

Research supports that the self-efficacy of athletes who judged opponents non-verbal behavior was directly related to those perceptions (39). As outcome expectations may be influenced by perceptions of sporting opponents, and have been shown to influence performance levels (24,26,76).

The purpose of this study is to examine the relationship between naïve ratings of thin-slices of coaching and ecologically valid criterion measures, which are end of the season evaluations by supervisors, as well as self measures of coaching attributes and effectiveness. This research will also include the demographic background of the naïve raters and explore the differences among evaluations based on gender, race, and level of sport participation. The following nine research questions are explored: What is the naïve raters’ accuracy in their assessment of coaching effectiveness; What is the consensus between naïve raters and experts on each attribute; What is the self-other agreement between naïve raters and coaches on each attribute; Is there a significant difference in accuracy between male and female raters; Is there a significant difference in consensus between male and female raters; Is there a significant difference in accuracy between races of raters; Is there a significant difference in consensus between races of raters; Is there a significant difference in accuracy between raters’ level of sport participation; Is there a significant difference in consensus between raters’ level of sport participation?

### Methods

#### Participants

There were two samples of participants in this study. Sample A consisted of 206 naïve raters recruited from undergraduate healthful living classes. Raters ranged from 18 to 55 years old (M = 19.6; SD = 4.4) and included 115 men and 91 women. Raters included African-Americans (n = 47), Caucasians (n = 147), Hispanics (n = 6), and other races (n = 4). The naïve raters indicated the highest level of sport in which they participated: none (n = 26); recreation (n = 46); junior varsity (n = 16); varsity/elite (n = 91); and college (n = 20). Sample B consisted of nine coaching students (eight men, one woman) from an undergraduate level coaching course at a southeastern university. There were eight Caucasian coaches and one African-American coach. The average age of the coaches was 20.2 years old (SD = 1.4).

#### Instrumentation

Coach attributions. Naïve raters, coaches, and supervisors rated each coach using an attributional survey (9) which included the following subscales: accepting, active, dominant, empathic, enthusiastic, honest, likable, optimistic, professional, supportive, and warm. Each coach was rated three times for each attribute on a 9-point Likert scale ranging from not at all (1) to very (9). The reliability in previous research of the mean of the judges’ ratings of the sum of the mean ratings of the 14 nonverbal variables was .80, assessed by an intraclass correlation (9).

Coach effectiveness. In addition, overall effectiveness of the coach was rated on a 5-point Likert scale: “Overall, how would you rate this coach?” Respondents could answer from very poor (1) to very good (5). Coaches and supervisors completed evaluations with the attributional survey and overall effectiveness questions at the end of the evaluation tool.

#### Procedures

Permission was obtained to use videotapes of coaching sessions by nine students in an undergraduate coaching class, who, as part of their course, were filmed for a practice session to be evaluated by their professor. The students coached recreation level youth football (n = 5) and soccer (n = 4) teams which ranged in competition level from under six to under fourteen. Consistent with Ambady and Rosenthal’s (9) previous research, three 10 second silent video clips were used from each coach’s session from the beginning, middle, and end; the clips feature the coach alone, consistent with previous research to control for the effects of interaction effects in the environment of the target (9).

All of the coach’s clips were arranged in one videotape in a randomized Latin-square design (8). The final tape consisted of 27 clips: 3 clips for each of the 9 coaches.

Each coach rated him/herself on the attribution scale and effectiveness item.

Supervisors completed the attribution scale and overall effectiveness item on each coach as part of their formal evaluation of the coach. Evaluations were delivered by the supervisors to the professor and picked up by the researcher.

Raters completed a demographic questionnaire and observed the video of the twenty seven 10-second video clips. Following each clip, raters completed the attributional scale and overall effectiveness question. End-of-the season evaluations by the recreation department supervisors, as well as self-evaluations were used for comparison with the raters’ scores on each of the 14 attributes.

#### Data Analysis

Given that each naïve rater rated each of nine coaches on three occasions, a within-rater mean across three occasions was computed for each coach for each attribute as well as effectiveness. To create an individual difference variable representing target accuracy, 206 correlations between each rater’s mean effectiveness scores and supervisor effectiveness scores (df = 7) were calculated. To create an individual difference variable representing consensus, 206 correlations between each rater’s mean scores and supervisor scores (df = 7) were calculated for each attribute. To create an individual difference variable representing self-other agreement, 206 correlations between each rater’s mean scores and self scores (df = 7) were calculated for each attribute and for effectiveness.

Inferential statistics were utilized to examine moderators of target accuracy, consensus, and self-other agreement. Means were compared using independent sample t-tests for gender comparisons and one-way ANOVAs for comparisons between races and sport participation groups. Post hoc comparisons using Fisher’s LSD were conducted on any significant results ascertained from ANOVAs (p < .01).

Individual correlations between each naïve rater’s score on effectiveness and the supervisor’s score on effectiveness for each coach were calculated and a mean consensus score was obtained. This provided an individual difference variable representing accuracy accuracy.

Individual correlations between each naïve rater’s attributional ratings across nine

coaches observed and the supervisors’ attributional ratings of these coaches were calculated and a mean correlation was determined to provide an individual difference variable representing consensus. Individual correlations between each naive rater’s attributional ratings across nine coaches observed and the actual coach were calculated and a mean correlation was determined to provide an individual difference variable representing self-other agreement.

Means were compared using independent sample t-tests for gender comparisons and one-way ANOVAs for comparisons between races and sport participation groups.

Post hoc comparisons using Fisher’s LSD were conducted on any significant results ascertained from ANOVAs: (p < .01).

### Results

The mean correlations between the naïve raters’ effectiveness ratings and the supervisors’ effectiveness ratings were calculated to estimate target accuracy of the thin slice judgments by the naïve raters (see Table 1).

The mean correlations between the naïve raters’ ratings on each of the fourteen attributes with the supervisors’ ratings on each of the fourteen attributes were calculated to estimate consensus, as well as other results regarding self-other agreement (see table 1). Independent samples t-tests were run based off of means generated on male and female raters to determine differences between the two groups on accuracy. There were no differences found on accuracy between groups (see Table 2). Independent samples t-tests run on differences on consensus between genders found significant differences (p < .01) on one of the fourteen variables: likeability. Female raters were higher on means consensus than male raters on likeability (see Table 2).

Due to the small sample size of Hispanic, Asian, and Other, these categories were not included in analyses on race differences. Independent samples t-tests run on differences between Caucasian and African-American raters found no significant differences on accuracy or consensus (p > .01) (see Table 3).

In addition, a one-way ANOVA showed no significant differences between levels of sport participation on accuracy (p > .01) (see Table 4). However, there were significant differences (p < .01) between level of sport participation groups on consensus on two of the fourteen variables: Competence and confidence (see Table 4). Fisher’s LSD post hoc tests indicated that naïve raters who participated in collegiate athletics showed significantly more consensus with supervisor ratings on competence than all other categories of level of sport participation raters. College raters also showed significantly more consensus with supervisor ratings on confidence than two other sport participation groups: no participation and varsity/elite participation.

### Discussion

There were several constructs of accuracy measured in this study. The first research question examined the target accuracy of the naïve raters. Due to the lack of correlation between the naïve raters’ judgments and the supervisors’ evaluations, the naïve raters as a group were not accurate in their assessments of coaching effectiveness. There are several explanations why this may have occurred. The nine coaches varied across two sports and four age levels. They were not observed directly with the athletes so differences in coaching behaviors due to varying age and sport contexts may have caused some of the variability. Thin-slice judgments in the sport context may have more variables that need to be controlled for than thin-slicing in classroom settings or social settings that have been previously examined. Modeling the Ambady and Rosenthal (8) study, the coaches were presented on muted video clips without athletes present. Ambady and Rosenthal (8) presented teachers alone in the clips they showed to naïve raters to control for biases to the reactions from students being taught. The coaching context requires adaptations to lessons as well as more frequent feedback. There may be a need for more frequent transactions whereas teaching may include more directive communication. Observations of a coach may require this interaction to accurately assess coaching effectiveness. The design of this study did not allow naïve raters to observe direct interactions between the coach and players.

Another explanation to support the complexity of the sport context is the individual differences in perceptions of effective coaches. Previous research found a negative correlation between body size and perceptions of coaching effectiveness by female gymnasts, while no correlation was found for soccer players or basketball players (21). This study did not survey for particular sport participation so variation may be due mainly to perceptions of coaching effectiveness in a particular sport. Other research suggests that the personality of the athlete can effect coaching evaluations. Williams et al. (78) found that athletes with higher anxiety and lower self-confidence rated effectiveness of coaches more negatively. This study did not look at the personality makeup of the raters to determine if those attributes moderate accuracy.

Previous research also suggests that mood state can affect evaluations (6). Recent research shows that mood state of customers can effect evaluation of sales people (57). When customers were in a bad mood and the salesperson was perceived as happy the customer rated the salesperson negatively. Ambady and Gray (6) found that negative mood states affected accuracy of social perceptions.

Another possible explanation why there was not a relationship between naïve raters and coaches on coaching effectiveness is the lack of congruency between the present circumstances of the raters and the environment of the target. The targets were coaching at the recreation level and the raters were college students. If they had participated in recreation level athletics they were many years removed from the situation. Much of the previous research on thin-slicing has used blind raters who are within the context being evaluated. One example is Ambady and Rosenthal’s (8) study on teacher effectiveness. The naïve raters were college students and they were rating college instructors and their judgments were compared to other student evaluations. This current study used college aged naïve raters who evaluated other college student coaches in a youth sport context. Other studies look at social contexts that most people are familiar with on a current basis (7). It may be useful to preface the thin-slicing with the context being rated. The naïve raters were not aware they were judging recreation level coaches. It may have been more useful to use parents of children who are in the recreation level context.

Consensus between naïve raters and experts on attributes was not reached on thirteen of the fourteen attributes. Consensus was defined within this study as the agreement between the naïve raters and the expert on personality attributes. Overall significance was not reached on thirteen of the fourteen attributes. Overall consensus was not reached on thirteen of the fourteen attributes. Considering how many correlations were measured, it can be expected that one could reach significance solely by chance. Kenny (45) defines consensus as the agreement between two raters. This research treats the naïve raters as one and the expert as the second rater. Consensus operationalized this way shows if naïve raters view a target similarly to a person who has greater knowledge of the target.

This approach has limitations because the naïve raters are compared with only one knowledgeable rater. Previous research suggests that there is greater accuracy in judgments of a target when there are two are more evaluations from people who know the target (48). Consensus may have been higher if more than one judgment by knowledgeable others could have been averaged to determine consensus. Consensus in Ambady and Rosenthal’s (9) research was operationalized by intracorrelations of naïve raters’ judgments of attributes which were placed in a 15 X 15 matrix and subjected to a principle components analysis. It is possible that consensus between naïve raters was reached in this study, which means they could have viewed the target similarly. This is a research question that should be considered for future research.

In regards to consensus, there was a moderate relationship between naïve raters and supervisors on the attribute enthusiastic. Previous research on the Norman and

Goldberg’s (54) Big Five and zero acquaintance research found consensus on the extraversion factor of the Big Five (33,46,55). Characteristics suggested by the extraversion category include sociable and energetic. It is possible that enthusiastic may be very similar to, or an expression of, extraversion. It could be easier to observe than the other traits. Researchers (46) suggest that extraversion is processed very quickly. John and Robins (42) suggest that the observability and evaluativeness of the attributes can contribute to accuracy and agreement between raters. The more neutral (less evaluative) and observable an attribute is the greater the agreement between raters is about the target. For example talkativeness is observable and neutral, while arrogance could be viewed as negative and more difficult to observe. Most of the fourteen attributes in this study were positively charged and difficult to directly observe: Accepting, attentive, competent, confident, dominant, empathic, enthusiastic, honest, likable, optimistic, professional, supportive, and warm.

Little research has examined thin-slicing in the sport context. Potentially personal biases of raters could affect judgments of coaches’ attributes. Kenny (45) explains that “personal stereotypes”, such as whether a rater subscribes to a widely held view. An example would be “all professors are absent-minded”, which can be reflected in judgments, and does not necessarily change with increasing acquaintance. Current research shows that stereotypes are based on more than gender or race. Kenny (45) explains that appearance cues and nonverbal behaviors are associated with different personality traits.

There was not self-other agreement in this study between the naïve raters’ judgments and the coaches self judgments of personality attributes. Previous perception research found that self judgments were less accurate when assessing behavior than others (48,69). Robins and John (58) suggest that mood affects self judgments as well as the need to protect self-esteem. The coaches in this study were undergraduate college students with no previous coaching experience. Their own perceptions about their coaching may have entered into the answering of the survey questions. Coaching literature has found that coaches are unaware of how they present themselves and behave while coaching (63,65,74). It is possible that the coaches in this study are similar and unaware of their behaviors.

This study supports the research literature in which no significant differences were found between gender and target accuracy. This supports an earlier meta-analysis by Ambady and Rosenthal (8) that examined numerous studies and concluded that overall gender did not affect thin slicing or zero acquaintance judgments. It has been suggested that women are better judges of nonverbal behavior (40). Rosenthal and DePaulo (60) found that women are better judges when the information is presented in more controllable channels. Speech is considered the most controllable channel, while the voice is considered the least controllable (15). This study did not involve an auditory component so potential differences in gender may not have arisen because of the channels for the cues of nonverbal behavior.

There was a significant difference between male and female naïve raters on one of the fourteen attributes. The most only significant difference (p < .01) was for likeability attribute. Female raters were closer to consensus with supervisors than male raters. This may pertain to the different expectations by gender on participation in sport. Previous studies have shown that females emphasize friendship and social interaction over competition and achievement than males do (1,34,36,56). Dubois (22) found that the longer youth participate in sport the greater the divergence in values placed on the outcomes by gender, Experienced males place greater importance on outcomes, whereas females consistently place emphasis on social aspects of sport. Potentially female raters in this study may have been more attuned to characteristics that embody the outcomes they desire in a sport setting. The other two attributes in which females differed significantly from males were enthusiastic and optimistic. All three of the differences between variables could be explained by the greater emphasis females place on these attributes and potentially the greater awareness they have of these attributes.

Overall there were no differences between African-Americans and Caucasians on target accuracy or consensus. Little research has examined racial differences in perception of naïve raters. Previous research has found race of target to affect accuracy and consensus (17,37). This research shows that race of raters does not affect target accuracy or consensus. Perhaps the sport context is different due to the length of participation of different races in sport and public acceptance of different races in sport over other areas in society. Edwards (23) suggests that lack of opportunities in mainstream society due to discrimination has led a disproportionately high number of blacks to pursue sport. Bledsoe (12) highlighted the practice in which young blacks pursue sport because of the lack of successful black role models in other areas. Sport is an area that has provided opportunity for those lower on the socioeconomic ladder to gain recognition and money when other avenues were closed off to them. (18). This can be supported by statistics: Blacks make up 77% of the NBA, 64% of the WNBA, and 65% of the NFL, they are only 4.2% of our physicians, 2.7% of our lawyers and 2.2% of our civil engineers (16). In NCAA Division I athletics blacks comprise 23.5% of student athletes: black males = 29.5% of male athletes; black females = 14.2% of female athletes). Black males comprise 60% of basketball players and 51% of football players and 27% of track athletes, while black females constitute 35% of basketball players and 31% of track athletes (53).

Perceptions of the race of the coaches may have also played a role in the lack of significant differences between races. There were eight Caucasian coaches and one African American coach. Statistics show a disproportionate number of non-Latino white males in coaching positions in the professional leagues and NCAA (50). “Stacking” theories in sport studies suggest that blacks are placed in positions that require more speed and stamina but less cognitive processes. One result of this is less opportunity to coach for minorities because of the positions they played that required less understanding of the overall game (18). There is a pattern found in professional sports and college sports of a disproportionately high number of blacks playing on teams coached by whites (18).

Overall there were no differences among levels of sport participation of raters on consensus of effectiveness. There was no correlation with the criterion variable between sport participation groups. Eight of the nine coaches were rated by supervisors as a four or a five out of five on effectiveness. The ninth coach was rated a three. Naïve raters overall rated coaches less effective than the supervisors. This could be a function of expectations of effective coaches at different levels. These coaches are fulfilling a requirement of an undergraduate coaching course which meets 3 hours a week. These coaches may experience more instruction which affects their ratings by supervisors.

While there were not significant differences in most of the attributional categories, there were significant differences on two of the fourteen attributes among levels of sport participation of raters. The higher the level of sport participation the greater the consensus with the expert judge on the competence attribute: The raters with college participation were significantly different than raters with varsity/elite experience, junior varsity experience, recreation level experience, and no sport experience. The college level athletes had greater consensus than all the other groups. One explanation could be the greater participation of these raters in sport and their level of attunement to competence of coaches. These raters possibly had a greater exposure to a number of coaches and are more sensitive to competence. Millard (51) posits that the higher level an athlete pursues the greater the need for winning and the greater the need for technical instruction from a coach. She found that coaches who provided more instruction based feedback were perceived as more competent. High-experience coaches are noted to provide more technical feedback and less general encouragement than low-experience coaches (61). This difference could also account for the awareness of competence of the college level raters.

The college level raters were also significantly different than varsity/elite athletes and recreation level athletes on confidence. The college level raters showed more consensus with supervisors’ ratings. They could also be attuned to the confidence level of coaches. Research shows that male coaches are generally more confident in abilities than female coaches (51). This study used eight male coaches and one female coach. College level raters due to length involved in sport may be more attuned to the confidence level of a coach.

Researchers attempt to define the moderators surrounding the rater, the channel, the judgments, and the target that could affect accuracy. It is also valuable to learn in what scenarios judgments are not accurate. Evans (25) notes that it is more important to know in what contexts people do not make good decisions. Previous research suggests that the degree to which a judge cares about the judgment he or she is making can affect the accuracy and consensus (27,31). The environment observed may have also affected consensus on personality judgments. Previous research found that less structured situations yield greater correlations on personality (32,68). This research involved judgments of targets in a classroom setting observing video clips instead of directly observing the targets in the sport environment.

This research is promising because it is the first to examine thin-slicing in the sport environment. It suggests that the sport context may have more variables to control for when doing zero acquaintance research. Future research should attempt to control variables and look at particular sports and use naïve raters who have experienced that sport. Future research could also examine zero acquaintance situations at different levels, like the collegiate or elite level. Looking at moderators of consensus based on the demographics of the coach, like gender and race would be valuable. Qualitative studies could further understand personal biases that underscore perceivers’ views of effective coaches, whether gender, sport level and type, or race could affect that.

### Application in Sport

This thought of split second decision making about a coach could be very critical in developing the most cohesive team possible. With further research necessary based on the above suggestions, thin-slicing could potentially benefit the cohesion of the team. By reversing this idea, coaches might be able to more effectively choose players that fit their team when recruiting. Stats are very important, but if there were other intangible ways to ‘correctly’ choose athletes that fit the mold of their team, coaches might be able to more effectively choose a cohesive, talented team.

### Tables

#### Table 1
Descriptive Statistics

#### Table 2
Descriptive Statistics for Target Accuracy and Consensus Differentiated by Gender

* p < .01

#### Table 3
Descriptive Statistics for Target Accuracy and Consensus Differentiated by Race

#### Table 4
Analysis of Variance for Attributes between Levels of Sport Participation Groups

* p < .01

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### Corresponding Author

Dr. Daniel R. Czech, CC-AASP
Department of Health and Kinesiology
Box 8076
Georgia Southern University
Statesboro, Georgia 30460-8076
<drczech@georgiasouthern.edu>
(912) 478-5267

Qualitative Analysis of International Student-Athlete Perspectives on Recruitment and Transitioning into American College Sport

U.S. Sports Academy2013-11-22T22:56:03-06:00January 4th, 2012|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|

### Abstract

Recruiting international athletes is a growing trend in American intercollegiate sport, and international student-athletes play an increasingly prominent role in NCAA competition. This research answers the following questions regarding the recruitment of international student-athletes and their transition to college life: (1) what is the most difficult aspect of the international university experience?; (2) what do international athletes identify as the most important factor for a successful transition to American college?; (3) how did international athletes hear about athletic opportunities in the United States; (4) what advice would current international athletes give international athletes considering a move to the United States to participate in intercollegiate sport?; and (5) what would the athletes have done had they not played college sports in the United States? The researchers solicited the assistance of CHAMPS/Life Skills coordinators at 15 Division I schools who distributed surveys to student-athletes, who in turn completed the survey, sealed it in an envelope, and returned in to the coordinator. A total of 355 athletes completed the survey, including 192 international athletes. Homesickness and adjustment to the U.S. culture were identified as the most difficult aspects of the university experience for international athletes, while the most important elements to a successful transition for international athletes were a strong support system from teammates and coaches and also from friends and family in their native country. Only one-fourth of respondents learned about athletic opportunities from coaches in the U.S., while one-fourth of the respondents learned about these opportunities from friends, family, and other athletes. The top piece of advice given by respondents was to realize that playing sports in the U.S. will require important traits like focus, dedication, hard work, and persistence in order to succeed. The results of this study highlight the importance of transitioning international athletes into college life. Once international athletes are on campus, a member of the athletic department staff should oversee the athlete’s transition into college life, focused on combating the top three challenges identified in this research: homesickness, adjustment to U.S. culture, and language. This staff member should serve as a liaison between athletic department personnel and other campus resources to facilitate a smooth transition.

**Key Words:** international student-athletes, recruiting, transition to college

### Introduction

Recruiting athletes from outside of the United States is a growing trend in college athletics as international student-athletes play an increasingly prominent role in NCAA competition (6, 9, 22). For coaches, who must recruit talented athletes in order to be successful, “the pressures to win, and the penalties for losing, are exacting. Many Division I coaches’ jobs are predicated on the strength of their programs, causing them to recruit the best talent they can find, in many cases from the international pool” (19, p. 860). Evidence of a worldwide search for talent is found in the 17,653 international student-athletes that competed in NCAA competition during the 2009-10 school year, a large increase from the just under 6,000 that competed a decade prior (11). Among Division I schools, over one-third of the male and female athletes in both tennis and ice hockey, and over one-eighth of male and female golfers, were born outside of the United States (11). In addition to increasing participation numbers, international athletes have dominated in individual sports like tennis and golf, and led teams to championship performances (13, 22). However, international athletes face many challenges in adjusting to the language, coursework, dorm life, food, cultural expectations, coaching, paperwork, and the style of play in the United States. As international athletes continue to leave their mark on NCAA sports, coaches and administrators benefit from understanding what difficulties come with transitioning to life as a student-athlete in the U.S. and how international athletes learn about the recruitment process.

Previous research has examined the adjustment process for both international students and international athletes to college. While researchers have noted that a lack of groups with which to socialize is a problem for many international students (7, 10, 20), international athletes have the advantage of being immediately placed within a team structure (14). However, athletes may still face similar obstacles to a successful transition including culture shock, cultural differences, academic adjustment, homesickness, discrimination, and contentment (5). Ridinger and Pastore (17) were the first to create a model of adjustment for international student-athletes, which included four antecedent factors (personal, interpersonal, perceptual, and cultural distance), and five types of adjustment (academic, social, athletic, personal-emotional, and institutional attachment), resulting in two outcomes (satisfaction and performance) to define successful adjustment to college.

Researchers have also examined the recruitment of international athletes. Not only can coaches create winning programs through the recruitment of international athletes, but coaches can also maintain successful teams with international athletes through the establishment of talent pipelines (3-4, 21). Bale (3) identified talent pipelines in which concentrations of athletes from certain countries were found in particular NCAA institutions, with coaches hoping that friend-to-friend recruiting will result in attracting elite athletes from a particular foreign country. Bale (3) noted that institutions unable to compete for homegrown talent, due to lack of prestige or unattractive campus location, established talent pipelines with a foreign country. For example, a talent pipeline of elite track and field stars from Kenya was found at schools like University of Texas El Paso and Washington State University, and a pipeline of track talent from Nigeria was identified at the University of Missouri and Mississippi State University (3). Talent pipelines are an important recruiting strategy, particularly when coaches are unable to compete for local athletes or local talent is not available for certain sports (21).

This research seeks to provide answers the following questions regarding the recruitment of international student-athletes and their transition to college life: (1) what is the most difficult aspect of the international university experience?; (2) what do international athletes identify as the most important factor for a successful transition to college?; (3) how did international athletes hear about athletic opportunities in the United States; (4) what advice would current international athletes give international athletes considering a move to the United States to participate in intercollegiate sport?; and (5) what would the athletes have done had they not played college sports in the United States?

### Methods

The sample for this study included N = 355 athletes from 15 NCAA Division I institutions, including n = 192 international athletes. Schools selected for this study were based on a need to collect data from purposive clusters of Division I institutions, given certain factors may influence international student-athletes’ experiences at their United States institution such as school size, the size of the community within which the school is located, and the geographic location of a school (3). Seven schools were members of the Football Bowl Series (FBS) conferences, while eight were not. Eleven conferences were represented in the study. Eight schools were located in large metro areas with populations over 400,000, while seven were located in communities with populations under 170,000. Six schools were located in the eastern third of the U.S., six were located in the Midwest, and three were located in the western third of the country.

The researchers solicited the assistance of CHAMPS/Life Skills coordinators from the 15 schools via phone to see if they would agree to participate in the study. The researchers then collected the names of all international student-athletes listed on website rosters. The coordinators were instructed to distribute the surveys to the student-athletes, who in turn completed the survey, sealed it in an envelope, and returned in to the coordinator. Participation in the survey was voluntary and a letter indicating the participant’s rights were included, per the approval obtained by the university Human Subjects Review Committee.

A total of 192 athletes representing 57 countries responded to the survey for a response rate of 39.6%. The top three countries represented were: Canada, 24%; England, 8.3%; and Puerto Rico, 7.8%. Males accounted for 45% of the sample and females accounted for 55%. The responses from the open-ended questions in the International Student-Athlete Survey were content analyzed. Two raters independently examined the data and codes were developed to categorize written responses (18). To test intercoder reliability, the coders independently examined 20% of the sample. The codebook and coding protocol were clearly understood, as the correction for chance agreement (Scott’s Pi) exceeded .8 for all but one question, which yielded an acceptable .77 (23).

In addition to frequency counts for each question, chi square was utilized to examine differences between demographic variables, including: gender, native area of origin (Canada, Europe, South America), length of time in the United States (0-2 years, 2.5 to 3.5 years, 4+ years), type of sport (team or individual), class standing (freshman/sophomore and junior/senior), whether or not the athlete used a campus visit, number of schools considered (0-2, 3+), and whether or not the athletes had a full scholarship.

### Results

Ten variables were identified for the first question, “what is the most difficult aspect of the international university experience?” Homesickness was the most difficult aspect, accounting for 24.1% of all answers, followed by adjusting to the U.S. culture, 20.5%; and adjusting to the language, 14.7%. Table 1 displays all ten coded answers for question 1. In order to examine the difference between various demographic variables through chi square analysis, the ten answers in Table 1 were re-coded into four variables (language and cultural adjustments, homesickness, athletic and academic transitions, financial and logistical difficulties, and other). First, chi square analysis revealed that European athletes were more likely to note language and cultural adjustments as a difficult aspect of the international university experience than non-European athletes (χ² (4, N = 278) = 12.1, p = .017). Second, Canadian athletes were more likely to identify financial and logistical difficulties than non-Canadian athletes (χ² (4, N = 278) = 29.8, p = .001). Third, athletes participating in individual sports were more likely to identify language and cultural adjustments as a difficult aspect than athletes on team sports, while athletes participating on team sports were more likely to identify homesickness than athletes on individual sports (χ² (4, N = 278) = 11.4, p = .023). Finally, freshman/sophomore athletes were more likely to identify language and cultural adjustments than junior/senior athletes (χ² (4, N = 278) = 11.7, p = .020).

Seven variables were identified for the second question, “what were the most important factors in helping you transition to university life in the United States?” Over one-third of respondents indicated that a strong support system from teammates and coaches on their college team was important, and 20.2% indicated that a strong support system from friends and family in their native county was important. Table 2 displays all seven coded answers from question 2. The answers in Table 2 were re-coded into two variables (support system identified as important, support system not identified as important). First, chi square analysis revealed that athletes from the Carribean/South America were less likely to cite the need for a support system from coaches, family, and friends than athletes not from that area (χ² (4, N = 267) = 7.3, p = .006). Second, junior/senior athletes were more likely to identify the importance of a support system from coaches, family, or friends than freshman/sophomore athletes (χ² (4, N = 265) = 6.9, p = .006).

Eight variables were identified for the third question, “How did you first learn about opportunities to earn university sports scholarships in the United States?” One-fourth of the respondents learned about these opportunities from friends, family, or other athletes, while another one-fourth indicated they learned from individuals who had previously participated in U.S. sports. Only 23.9% learned from personnel related to U.S. college sports (i.e. coaches and administrators). Table 3 displays all 8 coded answers from question 3. Chi square analysis revealed that athletes playing team sports obtained information regarding U.S. college sports differently than athletes participating in individual sports. Team sport athletes were more likely to obtain recruiting information from those involved in U.S. college sports (i.e. coaches and recruiters) than individual sport athletes (χ² (1, N = 180) = 4.4, p = .030). Additionally, athletes participating in individual sports were more likely to learn about scholarship opportunities through personal relationships with family, friends, and athletes, while team sport athletes are more likely to learn about scholarship opportunities through those involved with the institutional sport structure (i.e. coaches, administrators, recruiting services) (χ² (1, N = 180) = 4.9, p = .02)

In a related question, international athletes were asked to compare the athletic facilities and athletic opportunities in the United States and their home country. The respondents overwhelmingly indicated that both the facilities and opportunities were better in the United States. Only ten percent of the international athletes believed that either the facilities or opportunities in their home country were better than what was available in the United States.

Fourteen variables were identified for the fourth question, “what advice would current international athletes give international athletes considering a move to the United States to participate in intercollegiate sport?” However, only four variables occurred in greater than 7% of the responses. The top piece of advice given by one-fifth of the respondents was to realize that playing sports in the U.S. will require important traits like focus, dedication, hard work, and persistence in order to overcome challenges. Second, 18.9% encouraged prospective international athletes to do adequate research on schools before deciding which school to attend, such as getting to know the coaches, athletes, and athletic facilities. Third, 14.2% recommended making the decision to play in the United States because it was such as an excellent opportunity. Fourth, 11.8% indicated it is important to consider academics and majors that can be used to obtain employment in their native country, meaning it is important to find the best overall fit between academics and athletics when deciding on a school.

Finally, international athletes were asked, “what would you be doing now if you had not had this opportunity to play for an NCAA university?” Responses were categorized by what the athlete would be doing (i.e. working, attending college, playing sports) and where they would be living (i.e. native country, United States), as presented in Table 4. Only seven athletes indicated they would be attending college in the United States, while 105 respondents indicated they would be attending college in their native country and only 33 would have continued to play sports in their native country.

### Discussion

American NCAA Division I universities provide opportunities for elite athletes from outside the U.S. to pursue their university degree while continuing to train and compete at a high athletic level, an opportunity not possible in many other countries. However, international athletes face challenges in adjusting to life as a student-athlete. It should come as little surprise that international athletes felt the most difficult aspects of playing university sport in the U.S. were dealing with homesickness, cultural differences, and language barriers. Many cross-cultural sojourners find themselves dealing with similar issues once the initial excitement of being submerged in a new culture wears off (1, 12). In fact, the greater the cultural distance between the sojourner’s native country and the host nation, the greater the adjustments international athletes would be expected to make (17). As was demonstrated in the results, Canadians, whose native country is culturally quite similar to the U.S., were much less likely to indicate a concern with homesickness, cultural differences, and language barriers (for many Canadians, the language barrier is non-existent). Canadian athletes were much more concerned with financial and travel logistics. The results also indicated that freshman and sophomores struggle with these issues more than experienced athletes in their junior and senior years.

The respondents to the survey revealed two key strategies to overcoming these difficulties and successfully transitioning into life as a student athlete during the first year on campus. First, international athletes indicated the high importance of understanding what international-student athletes are “getting themselves into” before committing to an NCAA school. Advice dispensed by the sample in this study focused on understanding the dedication and commitment required of an NCAA Division I athlete, knowing the differences between schools, coaches, and athletic programs at various universities, and learning which schools and academic programs could offer international athletes the best opportunities back in their home country after their college career is complete.

This strategy aligns with prior research. Craven (8) suggested the more athletes and coaching staffs are prepared and educated about the cultural differences they may experience while submerged in another culture, the easier their transition and adjustment to the new environment will be. In Bale’s work, several of his subjects suggested the U.S. college experience was not what they thought it would be, as over 30% encountered problems with U.S. coaches, nearly 25% had difficulties adjusting to the climate in their new location, and over 20% lacked motivation with academic work (2). When offered the chance to be a varsity athlete at an NCAA Division I school, many international athletes are initially so excited about the opportunity and chance to travel to the United States that the location and environment of the specific school they attend is not a key factor (15-16). As the results of this study indicate, however, current international athletes believe it is important for international student-athlete prospects to consider many issues beyond just an opportunity to compete in the U.S. college system before making the commitment to attend a U.S. university.

The second key factor in transitioning into life as a student-athlete is the development of a support system first built on teammates and coaches, but also built on family and friends back home. It is important for coaches and teammates to understand that international student-athletes identified developing a support system with them as the most important element of a successful transition. It is clear the relationships developed with the people international athletes spend the most time with are a key determinant to a successful transition. Coaches should also ensure international athletes have the technical support to maintain relationships with those at home through various video, chat, and online communication resources.

Another key finding in this study was that most of the respondents would not have moved to the U.S. or continued to participate in sports without the opportunities presented through American intercollegiate sport. One of the attractions of U.S. college sport is access to high quality facilities and abundant opportunities. Results indicated that the respondents felt the athletic facilities and athletic opportunities available to them as an NCAA Division I athlete were superior to their options in their native country. This finding could potentially be skewed as young athletes who did have access to better facilities and opportunities in their homeland may not have considered playing in the U.S. college system. However, this finding has key implications for sport managers outside of the U.S. Administrators of sport clubs in non-U.S. countries may lose elite athletes at the peak of their career as those athletes choose to accept an NCAA scholarship. If such club administrators hope to retain these athletes, they may need to examine the attraction of competing in the U.S. collegiate sport system (namely competitive opportunities and facilities) and attempt to replicate those factors in their native country. More research examining this specific issue is needed.

Finally, one surprising finding from this study is only a quarter of respondents indicated university athletic department staff, such as coaches and administrators, were the key source of information regarding the opportunity to compete in the United States college system. As illustrated in the introduction to this paper, recruiting is arguably the most important element in developing an elite college athletic program and many university athletic departments dedicate a relatively large percentage of their resources towards this endeavor. Yet the recruiting process does not seem to be overly efficient in reaching international prospects. Many of the respondents in this study indicated family, friends, and acquaintances that had competed in the U.S. college system were more important sources of information about playing opportunities at NCAA schools than were the coaches whose job it is to recruit these athletes. This study illustrates the need for coaches to more effectively and efficiently recruit the international landscape.

### Conclusions

American college sports provide an opportunity for athletes from countries outside the U.S. to continue their playing careers and educational training in the United States where high-level athletic facilities and strong competitive opportunities abound. International student-athletes must overcome many challenges and obstacles upon arrival on campus, including homesickness, adapting to the culture, and learning the language. Coaches and teammates play an important role in helping international athletes develop a support system that will assist in the successful transition to a student-athlete. Athletic administrators also play a key role, as discussed in the next section.

### Applications In Sport

Once international athletes are on campus, a member of the athletic department staff should oversee the athlete’s transition into college life, focused on combating the top three challenges identified in this research: homesickness, adjustment to U.S. culture, and language. This staff member should serve as a liaison between athletic department personnel (i.e. CHAMPS Life Skills coordinators, compliance, eligibility, coaches) and other campus resources (i.e. academic advising, international office) to facilitate a smooth transition. The liaison can coordinate paperwork deadlines, information updates, cultural sensitivity training in the athletic department, and any programming that might benefit the international athletes. Such programming could include a peer mentoring program, utilizing transition to college coursework, placing international athletes with experts in teaching the English language, offering open forums for athletes to socialize with athletes from other teams, developing information packets with multicultural resources in the community and university, and establishing relationships with host families in the community (under the supervision of the compliance office). Acquainting athletes with American college life should begin as soon as possible, either on an official visit or having international athletes arrive on campus as early as possible to adjust to the language, culture, food, teammates, and academic expectations. Finally, developing a strong relationship with the international office is important in order to ensure all government paperwork is completely in an accurate and timely fashion.

Finally, in contrast to domestic athletes who take official and unofficial visits and have many other opportunities to develop relationships with coaches who are recruiting them, international athletes rely on their personal support system (i.e. club coaches, former athletes, family, friends) to gather information on U.S. colleges. NCAA coaches must continue to improve their international recruiting connections with former athletes and club coaches because they are still the top source of information about competing in the U.S. college system. If NCAA coaches want to successfully recruit international athletes, they should focus on improving recruiting connections with key members of an athlete’s personal support system. Previous research by Bale (2-4) has established some institutions are able to develop talent pipelines where information about an institution is disseminated by athletes who competed for a particular school in the past.

### References

1. Adler, P. (1975). The transitional experience: An alternative view of culture shock. The Journal of Humanistic Psychology, 15, 13-23.
2. Bale, J. (1987). Alien student-athletes in American higher education: Locational decision making and sojourn abroad. Physical Education Review, 10(2), 81-93.
3. Bale, J. (1991). The brawn drain: Foreign student-athletes in American universities. Urbana, IL: University of Illinois Press.
4. Bale, J. (2003). Sports geography (2nd ed.). London: Routledge.
5. Berkowitz, K. (2006). From around the world. Athletic Management, 18(6). Available online at <http://www.athleticmanagement.com/2007/01/15/from_around_the_world/index.php>
6. Brown, G.T. (2004, Dec. 6). Foreign matter: Influx of internationals in college swimming tugs on bond between campus and country. The NCAA News, p. 5.
7. Chapdelaine, R., & Alextich, L. (2004). Social skills difficulty: Model of culture shock for international graduate students. Journal of College Student Development, 45, 167-184.
8. Craven, J. (1994). Cross-cultural impacts of effectiveness in sport. In R.C. Wilcox (Ed.) Sport in the global village, (pp. 433-448). Morgantown, WV: Fitness Information Technology, Inc.
9. Drape, J. (2006, Apr. 11). Foreign pros in college tennis: On top and under scrutiny. The New York Times, p. D1.
10. Furnham, A., & Bochner, S. (1986). Culture shock: Psychological reactions to unfamiliar environments. London: Methuen.
11. NCAA. (2010). 1999-00 – 2009-10 NCAA student-athlete race and ethnicity report. Available online at <http://www.ncaapublications.com/productdownloads/SAEREP11.pdf>
12. Oberg, K. (1960). Cultural shock: Adjustment to new cultural environments. Practical Anthropology, 7, 177-182.
13. Pierce, D., Kaburakis, A., & Fielding, L. (2010). The new amateurs: The National Collegiate Athletic Association’s application of amateurism in a global sports arena. International Journal of Sport Management, 11(2), 304-327.
14. Popp. (2006, September). International student-athlete adjustment to U.S. universities: Testing the Ridinger and Pastore model. Paper presented at the annual meeting of the European Association for Sport Management, Nicosia, Cyprus.
15. Popp, N., Love, A., Kim, S, & Hums, M.A. (2010). International student-athlete adjustment: Examining the antecedent factors of the Ridinger and Pastore theoretical framework model. Journal of Intercollegiate Sport, 3, 163-181.
16. Popp, N., Pierce, D., & Hums, M.A. (in press). A comparison of the college selection process for international and domestic student athletes at NCAA division I universities. Sport Management Review.
17. Ridinger, L. & Pastore, D. (2000). A proposed framework to identify factors associated with international student-athlete adjustment to college. International Journal of Sport Management, 1(1), 4-24.
18. Riffe, D., Lacy, S., & Fico, F. G. (2005). Analyzing media messages: Using quantitative content analysis in research. Mahwah, NJ: Lawrence Erlbaum.
19. Weston, M. A. (2006). Internationalization in college sports: Issues in recruiting, amateurism, and scope, 42 Williamette Law Review 829.
20. Westwood, M., & Barker, M. (1990). Academic achievement and social adaptation among international students: A comparison groups study of the peer-pairing program. International Journal of Intercultural relations, 14, 251-263.
21. Wilson, R. (2008). A Texas team loads up on All-American talent, with no Americans. Chronicle of Higher Education, 54(18), p. A30-A31.
22. Wilson, R., & Wolverton, B. (2008). The new face of college sports. Chronicle of Higher Education, 54(18), p. A27-A29.
23. Wimmer, R., & Dominick, J. (2006). Mass media research: An introduction. Belmont, CA: Thomson Wadsworth.

### Tables

#### Table 1
Most Difficult Aspects of International University Experience

Note: Respondents could have multiple answers in their written response

Intercoder Agreement: Scott’s Pi = .89

#### Table 2
Important Factors for Successful Transition to University Life

Note: Respondents could have multiple answers in their written response

Intercoder Agreement: Scott’s Pi = .82

#### Table 3
Source of Information Regarding Athletic Opportunity in the United States

Intercoder Agreement: Scott’s Pi = .87

#### Table 4
Life without American College Sports

Intercoder Agreement: Scott’s Pi = .85

### Corresponding Author

Dr. David Pierce
Ball State University
School of Physical Education, Sport, and Exercise Science
Muncie, IN 47306
765-285-2275
<dapierce@bsu.edu>

Effects of American Football on Height in High School Players

U.S. Sports Academy2013-11-22T22:56:36-06:00January 3rd, 2012|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Studies and Sports Psychology|

### Abstract

The aim of the present study was to investigate height change of high school football players during a single game. Ten high school football players served as participants. The participants were selected according to position and expected playing time. The chosen positions experience the repetitive longitudinal loading of the spine that may lead to a creep response in the vertebral disk. Height was measured using a standard physician beam scale with height rod. A practicing certified athletic trainer served as the tester for all measures (pre – post). A paired samples T-test was performed to determine significance between height before and after the game. A significant difference was shown in height magnitude (Mpre = 176.56±6.9cm, Mpost = 175.81±6.94cm, p = .032). The results indicate that high school football players’ height decreases during the course of a game. This process is likely due to the creep response caused by intermittent high impact compressive loading of the spinal column, as well as low impact continuous compressive forces from equipment weight.

**Key words:** American football, compression, spinal shrinkage, creep response

### Introduction

American football (football) places many physical demands on its participants due to the aggressive nature of the sport. External forces from running, blocking and tackling can cause much stress on the human body. Even with protective equipment such as helmets and pads, these forces are inevitable. During the course of a game, football players may experience substantial longitudinal loading of vertebral column from the compressive forces of running and tackling as well as the continuous load due to equipment mass. This loading of the spine may accelerate the creep response which could result in a decrease in height after a game.

Spinal creep is a process by which continual loading or compressive forces placed upon the spinal column cause a reduction in the vertical size of the intervertebral discs. This creep response is due to the viscoelastic properties of the intervertebral discs of the spinal column, and is also referred to as spinal shrinkage. When compressive loading of the spine exceeds the interstitial osmotic pressure of the discal tissue, water is expelled from the intervertebral discs. This results in a loss of disc height which is reflected as a loss in stature (11). Since the spinal column composes about 40% of total body length, and the intervertebral discs account for roughly one-third of the length of the spinal column (Reilly, 2002), fluid loss from the discs can potentially cause substantial change in stature.

Studies of the intervertebral discs have shown that by narrowing in response to compressive forces, the discs also stiffen, which alters the dynamic response characteristics of the intervertebral disc complex (7). Once the disc has been narrowed and stiffened, its ability to absorb sudden direct and indirect changes in force is reduced, and thus the disc is therefore more susceptible to injury (9), and is often suggested to be a major causal factor of back pain (8). Some of the sports that have the highest risk of these injuries are football, ice hockey, and rugby (1). Within the sport of football it is believed that there is an increase in risk factors associated with spinal creep that may cause many athletes to develop low back pain (5). Because specific spine injuries like fracture, disc herniation, and spondylolysis are more frequent in football players (5), the occurrence of spinal shrinkage during a football game may be greater than other activities.

Studies have investigated spinal shrinkage in various activities ranging from running (4), weight lifting (3) and circuit training (6), but currently there exists a gap in the literature surrounding spinal creep and American football. The compressive loads that can affect the vertebral column include gravity, changes in motion, truncal muscle activity, external forces and external work (13) all factors that can be involved in football. These factors may lead to an accelerated creep response which could result in a decrease in height after a game. In a sport such as football, any minute decrease in stature may mean the difference between blocking a last second field goal, or making a game winning catch. Chronic exposure to these factors may also lead to back pain or injuries to the spine or discs. Therefore, the purpose of this study was to investigate the amount of shrinkage due to spinal loading during a high school football game.

### Methods

#### Participants

Ten high school football players took part in the study. Mean values for height and weight were 176.6±6.9cm and 86.4± 9.5kg, respectively. All players were high school seniors aged 18 years and were selected according to position and expected playing time. The positions chosen were ones that experience the repetitive longitudinal loading of the spine that may lead to a creep response in the vertebral discs. This information was determined after interviewing the coach for the team and from observations made at other similar games. Based on these criteria, eligible (18yr old) players were recruited who started at the following positions: linebackers, running backs, and linemen. Players were also selected who would be likely to play the entire game with very few rest breaks.

#### Apparatus

A standard physician beam scale with height rod was used in this study for measuring changes in stature before and after participation in the game. All measurements were collected by a practicing certified athletic trainer. The apparatus was accurate to within 0.01 inches and all measurements were converted to millimeters.

#### Procedures

The football game used for this experiment was an evening high school football game, which took place after a regular day of school. An evening game was selected to ensure that any shrinkage occurring from normal daily activities would not affect the results of the study. Participants were measured barefoot while standing and wore t-shirt and shorts for both pre-game and post-game measurements. Pre-game measurements were taken prior to warm ups to ensure that starting heights reflected absolutely no football activity. Post-game measurements were taken immediately after completion of the game. Three consecutive measurements were taken each time by the certified athletic trainer to ensure that the apparatus was reliable.

#### Data Analysis

The effects of playing football on changes in stature were analyzed using a paired sample T-test. Post hoc power calculations were performed following any statistically significant finding. Comparisons were made between the pre- and post-game height measurements. All statistical analyses were performed with the use of a modern computer software package (SPSS 17.0 for Macintosh, G*Power 3). Statistical significance was set a priori at an alpha level > 0.05.

### Results

The mean and standard deviation for the pre-game height measurements was 176.6 ± 6.9 cm. Post-game measurements yielded a mean and standard deviation of 175.8 ± 6.9 cm. The results show that there was a significant increase in spinal shrinkage due to participation in a high school football game (p =0.032, power = 0.674). The average height loss for the ten participants was 7.62 (±SD = 9.25) mm.

### Discussion

The present study showed that participation in a high school football game causes measurable height differences before and after the game, the demonstrated mean loss of stature was 7.62mm. It can be assumed that the decrease in height is due to the increased external forces and equipment weight that are involved in the sport. These potentially lead to a rise in the intradiscal pressure and fluid to be expelled, resulting in a reduction in disc height. Though it is logical that loss of intervertebral disc height is responsible for all variations in height, it is also possible that the cartilage in joints and the soft tissue covering the scalp and soles of the feet may have been compressed. However, the total height of the intrajoint cartilage is small and the degree of compression is thought to be negligible (6). The soft tissue covering the scalp is also thin and the height rod of the scale used for measurement would compress the tissue to an insignificant level. The tissue covering the soles of the feet might also be compressed upon standing but it is likely that equilibrium was quickly reached (6). As a result, the measured changes in stature can be considered to reflect only the changes in disc height.

The spinal shrinkage recorded during a football game was greater than what was observed in previous research of other activities. The 7.62 mm decrease in stature in this study was greater than the 3.25 mm decrease during a 6 km run (6), 5.4 mm decrease during circuit-weight training (6), 3.6 mm decrease during weight training (3), and 1.81 mm during a drop jump regimen (2). Although shrinkage during participation in football was greater than other activities, it is not the greatest recorded occurrence of spinal shrinkage. The results of this study are comparable to the 7.8 mm loss in height during a 19 km run (6), and much less than the recorded loss of 11.2 mm during static loading with a 40 kg barbell (14).

A study that examined spinal recovery in pregnant women showed that women with lower back pain were unable to recover from spinal shrinkage to the same extent as women with no lower back pain (12). These findings suggest that lower back pain may be related to the diminished ability to recover, rather than the magnitude of the spinal shrinkage imposed during the task. Since there is believed to be a relationship between football and the development of lower back pain (5), this could suggest that football players may have a diminished ability to recover from spinal compression. This may be provoked by the magnitude and frequency of spinal loading that a football player is subjected to.

The inability of the spine to recover may also lead to serious acute and chronic injuries to the spine and discs. Football is considered to be one of the sports with the highest risks for the occurrence of spinal injuries (1). Many of the spinal injuries that are common in football include fractures, disc herniation, and spondylolysis (5). There may also be a positive correlation between the years of involvement in football and the chances of developing degenerative disc disease (5).

### Conclusions

Based on prior research, it can be assumed that more spinal shrinkage occurs during participation in a football game as compared to other less impactful activities because of a greater spinal load. Football players experience this load on the spine not only from running, but also from the static load from the weight of equipment and from direct impact forces caused by collisions with other players. Both these components, running (6) and static loading of the spine (14), have been found to cause accelerated loss in stature. This combination, along with the collisions during a football game, may be the reason for greater spinal shrinkage.

Although the present study was conducted on high school players, the results should be also consistent with higher levels of play. A previous study was conducted to compare the response to spinal loading between different age groups of males (10). When comparing younger males (18-25 years of age) and older males (47-60 years of age), it was found that regardless of age the pattern of spinal shrinkage between the two groups was similar. Based on this research, high school, college, and professional football players should experience a similar response to spinal loading during a game.

### Applications In Sport

In a game such as football, winning and losing can be a matter of inches. If a player decreases in height at the end of a game, the extra length could be the difference in catching a football, blocking a kick, or batting down a pass. Thus this height difference might be the difference between winning and losing. The degree of hydration may play a role in the extent of the creep effect and should not be overlooked. It may be beneficial to conduct future research on the effects of height decrease on athletic performance. Future research may also investigate if frequent practice of spinal unloading throughout a player’s career can prevent or reduce spinal injuries and back pain.

### References

1. Boden, B., Jarvis, C. (2009). Spinal injuries in sports. Physical Medicine and Rehabilitation Clinics of North America, 20(1), 55-68
2. Boocock, M. G., Garbutt, G., Linge, K., Reilly, T., Troup J. D. (1989). Changes in stature following drop jumping and post-exercise gravity inversion. Medicine and Science in Sports and Exercise, 22(3), 385-390
3. Bourne, N., Reilly, T. (1991). Effects of a weightlifting belt on spinal shrinkage. British Journal of Sports Medicine, 25(4), 209-212
4. Dowzer, C., Reilly, T., Cable, N. (1998). Effects of deep and shallow water running on spinal shrinkage. British Journal of Sports Medicine, 32, 44-48
5. Gerbino, P., d’Hemecourt, P. (2002). Does football cause an increase in degenerative disease of the lumbar spine? Current Sports Medicine Reports, 1(1), 47-51
6. Leatt, P., Reilly, T., Troup J. D. G. (1986). Spinal loading during circuit weight-training and running. British Journal of Sports Medicine, 20(3), 119-124
7. Markolf, K. (1972). Deformation of the thoracolumbar intervertebral joints in response to external loads. The Journal of Bone and Joint Surgery, A, 511-533
8. Nachemson, A. L. (1976). The lumbar spine: an orthopedic challenge. Spine, 1(1), 59-69
9. Perey, O. (1957). Fracture of the vertebral end plate in the lumbar spine: an experimental biomechanical investigation. Acta Orthop Surg Suppl, 25, 1-100
10. Reilly, T., Freeman, K. A. (2006). Effects of loading on spinal shrinkage in males Of different age groups. Applied Ergonomics, 37(3), 305-310
11. Reilly, T., Tyrrell, A., Troup, J. D. G. (1984). Circadian variation in human stature. Chronobiology International, 1, 121-126
12. Rodacki, C. L., Fowler, N. E., Rodacki, A. L., Birch, K. (2003). Stature loss and recovery in pregnant women with and without low back pain. Archives of Physical Medicine and Rehabilitation, 84(4), 507-512
13. Troup, J. D. G. (1979). Biomechanics of the vertebral column. Physiotherapy, 65(8), 238-244
14. Tyrrell, A., Reilly, T., Troup, J. D. G. (1984). Circadian variation in human stature and the effects of spinal loading. Spine, 10, 161-164

### Figures

#### Figure 1
Percent change in height pre- to post-game among high school athletes participating in American football.


### Corresponding Author

Brian J. Campbell, PhD, ATC
Department of Kinesiology
University of Louisiana at Lafayette
225 Cajundome Blvd.
Lafayette, LA 70506
<campbell@louisiana.edu>
(337) 501-0634

Brian J. Campbell is the Curriculum Coordinator of Exercise Science at the University of Louisiana at Lafayette. Dave Bellar, PhD is the Exercise Physiology Lab Director at the University of Louisiana at Lafayette. Kristina Estis is a Certified Athletic Trainer for Champion Sports Medicine at St. Vincent’s Birmingham. Tori Guidry is an undergraduate student of Exercise Science at the University of Louisiana at Lafayette. Matt Lopez is a DPT student at the University of South Alabama.

NCAA Division I Athletics: Amateurism and Exploitation

U.S. Sports Academy2016-04-01T09:31:59-05:00January 3rd, 2012|Contemporary Sports Issues, Sports Facilities, Sports Management|

### Abstract

In recent days, there has been increased dialogue concerning the topic of compensating college athletes above athletic scholarships. The purpose of this paper was to discuss the NCAA and its member institutions’ exploitation of student-athletes. Amateurism and exploitation were defined and discussed in relation to NCAA Division I athletics. The evolution of intercollegiate athletics and the student-athlete was reviewed in order to better understand the motives for today’s exploitive practices. Using Wertheimer’s two arguments for the exploitation of student-athletes, it was demonstrated some student-athletes are victims of exploitation. However, after examining mutually advantageous exploitation and consensual exploitation, it was determined not all student-athletes are exploited. The NCAA and those responsible for setting student-athlete policy should discuss the implications of these conclusions.

**Key words:** college athletics, NCAA, amateurism, exploitation, student-athletes, athletic scholarships

### Introduction

Last winter, National Collegiate Athletic Association (NCAA) president Mark Emmert was asked by a group of sports media members about the possibility of paying college athletes. Emmert responded, “We can never move to a place where we are paying players to play sports for us” (Garcia, 2010, para. 9). “No, it will not happen – not while I’m president of the NCAA,” he later stated (“NCAA president,” 2011, para. 17). These comments sparked the reoccurring ethical discussion concerning the topic of amateurism and exploitation in college athletics. While many believe as amateurs, college athletes are receiving more than their fair share through athletic scholarships, others argue universities are exploiting their own student-athletes. The questions remain unanswered. Should college athletes be compensated beyond their athletic scholarships, and specifically, are the NCAA and its institutions exploiting student-athletes?

The questions involved in this discussion are unable to be answered with a simple “yes” or “no.” In order to knowledgeably discuss the subject, there first needs to be a foundational understanding of the basic terms of amateurism and exploitation. In addition, the relationship between the two terms and intercollegiate athletics should be clearly defined. A history of the evolution of college sports and the role of student-athletes over the last two centuries must be examined also. The author will attempt to use all of this information to answer several key questions related to the topic of paying college athletes in order to determine if student-athletes are being exploited and, if exploited should they be compensated above their athletic scholarships?

Surprisingly, studies have not demonstrated an overwhelming support for paying student-athletes above their athletic scholarships. Schneider (2001) investigated college students’ perceptions of giving compensation to intercollegiate athletes in addition to the standard grant-in-aids. Of the 458 students (275 males and 183 females from 1 Division 1 athletic conference) surveyed, only a slight majority (54%) of the students believed athletes should receive additional compensation. Nevertheless, it is a subject that has again (even recently) become a hot topic in college athletics.

#### Amateurism and Exploitation in Collegiate Athletics

When it comes to debating whether or not college athletes should be paid, the two most often used terms are amateurism and exploitation. Neither term is new to intercollegiate athletics. Actually, both subjects have been topics of discussion for the NCAA since its inception in the early 1900s (“History,” 2010). Today, these two words drive both sides’ arguments concerning paying and exploiting student-athletes.

##### Amateurism Defined

Simply put, collegiate amateurism refers to the fact the athletes do not receive remuneration for their athletic services. College athletes today are referred to as student-athletes. The governing body of college athletics, the NCAA, views these individuals as students, not as professionals or employees of their member schools. Thus, student-athletes are not currently monetarily compensated (Murphy & Pace, 1994). According to the NCAA, student-athletes’ participation in athletics is just another part of their entire education, not the primary purpose for attending college (Meggyesy, 2000).

Late in the 19th century, college authorities conceived this idea of amateurism in an effort to maintain schools’ educational integrity and middle- and upper- class standing by not technically paying athletes (Flowers, 2009). “A Gentleman never competes for money,” once wrote author Walter Camp (Flowers, 2009, p. 354). As sports’ popularity and revenues increased over the next several years, athletes were given incentives such as free room, board, and tuition. In the middle of the 1900s, the NCAA instituted its key piece of legislation, the Sanity Code, in an attempt to preserve amateur sports while still allowing schools to compensate athletes (Kahn, 2007). By including room, board, and tuition in grant-in-aids (i.e. athletic scholarships), schools were able to reward student-athletes without paying them directly. After the Sanity Code’s establishment of athletic scholarships, the term “amateurism,” not “professionalism,” would be united officially with college athletics (Byers, 1997; Flowers, 2009).

In addition to assigning a fixed amount to athletic scholarships, there are additional ways the NCAA continues to preserve the “amateur” label in collegiate sports. Although the NCAA and the schools reserve the right to use a player’s images and names for commercial purposes, no athlete may be endorsed by or receive any payment from businesses or corporations (Suggs, 2009; Murphy & Pace, 1994). Student-athletes also may not receive financial assistant in addition to their grant-in-aids or be paid for any work with private sports camps related to their sport (Byers, 1997).

##### Exploitation Defined

The biggest issue in the subject of paying college athletes is the idea the NCAA and its member institutions are exploiting student-athletes. Throughout the years, exploitation has been defined countless ways by individuals discussing various topics such as economic, politics, and sports (Wertheimer, 2008). For the discussion involving college athletics, exploitation should be defined as an individual gaining something by taking an unfair advantage of another individual (Wertheimer, 2007).

There are generally two arguments used to demonstrate the exploitation of student-athletes. The first is student-athletes, many of whom are making large amounts of money for their schools, often are not receiving any kind of legitimate, quality education. The second is compensation student-athletes receive in the form of athletic scholarships is not comparable to the marginal revenue products they individually generate for colleges (Wertheimer, 2007; Brown & Jewell, 2004).

Before examining further these two claims, some distinctions must be made. Wertheimer (2008) maintains there are several specific types of exploitation that apply to this discussion. The first, called mutually advantageous exploitation, refers to a situation where both parties, both the one doing the exploiting and the one being exploited, gain from the agreement. The second is referred to as consensual exploitation and involves an instance where individuals who are exploited have given voluntarily consent to the situation prior to the transaction. In situations involving these types of exploitation, it can be argued nothing morally wrong has occurred.

In most circumstances involving exploitation, the issue is not whether exploited individuals are making any gains but rather they are not receiving what they ought to receive. In other words, those being exploited are not getting what is considered fair (Wertheimer, 2008). In the example of the exploitation of student-athletes, the specific issue is “they do not receive an appropriate return on the financial surplus” they create for their universities (Wertheimer, 2007, p. 366).

#### The Evolution of Intercollegiate Sports and the Student-Athlete

The face of intercollegiate athletics has changed drastically in the last two centuries. What started as nothing more than student-organized competitions has turned into what has been described as a “sports entertainment enterprise” (Flowers, 2009; Meggyesy, 2000, p. 25). Students who once went to school only for an education and participated in these kinds of competitions in their free time now often attend these same universities solely for the purpose of participating in sports. In most situations, they end up devoting hundreds of hours to sports-related activities and end up becoming athletes first and students second. The end result is a system that uses students to generate millions of dollars for both the NCAA and its universities.

##### The Origins of Intercollegiate Athletics and the Student-Athlete

Modern intercollegiate athletics have their foundations in intra-collegiate competitions. Sports were largely an unknown on most college campuses until the early 1800s when college students began organizing their own class (e.g. freshman, sophomore, etc.) teams to compete against other classes. The popularity of these different competitions grew over the next 50 years to the point that by the 1850s, universities were forming their own intercollegiate teams. At first, school authorities frowned upon these seemingly frivolous and sometimes violent competitions. But by the late 19th century, American colleges recognized the prestige that came from winning intercollegiate contests and the visibility sports teams provided for the school were too valuable to ignore. As the popularity of intercollegiate sports grew, schools realized they could manufacture additional income by charging spectators admission to events. Prestige, visibility, and money – intercollegiate athletics would now be a permanent fixture on college campuses (Flowers, 2009).

The next conclusion drawn by colleges was obvious, and it shaped intercollegiate athletics into what they are today. How can a school garner more prestige, visibility, and money? Win more games. How can a team win more games? Get the best players. So in an effort to field the best teams, schools began accepting students who never would have been admitted previously to these institutions. In order to lure athletes, colleges started in the 1870s to offer both graduates and undergraduates financial assistance in the form of room and board, jobs, and even small cash considerations in exchange for their athletic services (Flowers, 2009). In response to the “dangerous and exploitive athletics practices of the time,” college authorities joined together in 1906 to form the Intercollegiate Athletic Association of the United States, which would later change its name to the NCAA (“History,” 2010, para. 1). In actuality, this new organization was intended to officially legitimize athletics in higher education and control athlete admission to and distribution amongst colleges (thus hopefully eliminating some of the questionable practices of several schools) (Flowers, 2009; Kahn, 2007).

With sports’ popularity growing and athletic revenues increasing, by the 1940s several schools were unashamedly paying their athletes (Kahn, 2007). Realizing amateur intercollegiate athletics were turning into professional athletics, the NCAA modified its constitution in 1956 to allow schools to offer grant-in-aid to any undergraduate athlete. In addition, the NCAA coined the term “student-athlete” (instead of “employee”) to describe those receiving athletic scholarships (Byers, 1997). The amateur code was officially established, and the student-athlete was born.

##### Modern Intercollegiate Sports and Student-Athletes

The current NCAA Division I intercollegiate sports program has evolved into a multi-billion dollar industry where many of the schools’ annual revenues reach above $260 million (Meggyesy, 2000). In addition to fielding teams in the money-making sports of men’s basketball, football, and ice hockey, schools also run programs for sports such as baseball, lacrosse, softball, soccer, swimming, volleyball, and wrestling (Kahn, 2007). Because these programs are not self-supported, they rely on revenues from the men’s basketball and football programs and often some additional state funding (Suggs, 2009). It is not uncommon for the coaches of Division I teams to earn several hundred thousand to several million dollars every year (Wieberg, 2011).

Researchers and economists who have studied intercollegiate athletics have described today’s NCAA as a cartel (Deschriver & Stotlar, 1996; Zimbalist, 2001). A cartel is defined as a joint group of members who create policies in order to promote the mutual interests of the members (Kahn, 2007). Koch (1983) argued the NCAA’s cartel behavior is manifested when it regulates the means of acquiring athletes, puts a fixed value on the amount given to student-athletes, controls the rights to televising athletic events, periodically distributes its profits to members, and enforces policy on its members. According to the NCAA, all of this is done in an effort to create equal opportunity for monetary profit, athlete distribution, and athletic success (Kahn, 2007; Koch, 1983).

The NCAA itself, a non-profit educational organization with 270 employees, has an annual budget of $32 million (Meggyesy, 2000). Each year, it distributes over $500 million to its member schools (Suggs, 2009). Nearly all of the money is collected from revenue generated by men’s basketball and football, specifically the television rights to men’s college basketball’s March Madness and football’s Bowl Championship Series. Just this past year, the NCAA signed a 14-year, $10.8 billion contract with CBS and Turner Sports to have the exclusive rights to show the men’s college basketball tournament (Wieberg, 2011).

History has demonstrated today’s universities recruit student-athletes for the purpose of helping sports teams achieve success on the playing field and thereby increase the school’s prestige and overall revenue. Using financial records from NCAA Division I-A universities as well as NFL and NBA draft data from 1995-1998, Brown and Jewell (2004) estimated a draft-quality college football player earns $406,000 in revenue annually for his school, while a college basketball player earns $1.194 million. Schools today treat student-athletes as more than just typical students (Suggs, 2009). They are given academic assistance, game tickets, clothing and equipment, medical treatment, weight and conditioning training, and money towards room, board, and tuition. A recent analysis by USA TODAY determined the average NCAA Division I men’s basketball player receives at least $120,000 in goods and services each year (Weiner & Berkowitz, 2011). But while these athletes are not living in poverty, the question still remains. Are student-athletes being exploited?

#### Are Today’s Student-Athletes Truly Exploited?

The 2011-2012 NCAA Manual states the mission of the NCAA is to protect student-athletes “from exploitation by professionalism and commercial enterprises” (2011, p. 4). Many would contend the NCAA itself is responsible for exploiting student-athletes. Their proof would hinge on the two previously mentioned arguments that many of these students are receiving neither a legitimate education nor fair compensation for their athletic services (Wertheimer, 2007). In addition to considering Wertheimer’s two arguments, the terms mutually advantageous exploitation and consensual exploitation also factor into this discussion.

##### Wertheimer’s First Argument

Universities’ educational practices are quickly called into question when college players make comments similar to the one made by University of Connecticut men’s basketball’s Kemba Walker. While being questioned this past March about his schooling, the junior basketball star said, “[Forty Million Dollar Slaves: The Rise, Fall, and Redemption of the Black Athlete] is the first book I’ve ever read” (Layden, 2011, para. 26). Often, there are times when athletes are put into either easier courses or courses whose professors are known to like student-athletes so athletes are able to achieve and receive higher grades (Zimbalist, 2001). In these situations, the argument is student-athletes (B) are being exploited by schools (A) because A is profiting thousands, sometimes millions, from B’s efforts while B is receiving nothing of lasting significance (i.e. a quality education) (Wertheimer, 2007).

In response to this argument, the question is whether student-athletes are forced into these positions. It should be determined if student-athletes are required to attend educational institutions with weak or questionable academics. The best schools are not available to everyone. Some athletes are only recruited by schools with poor academic records. Although players are not forced to attend one of those schools, some are financially unable to attend college without the help of an athletic scholarship. A student-athlete under such circumstances would be considered a victim of exploitation. As for an athlete who has his choice of the best schools and still selects a poor academic institution, it has been argued that although he was not coerced into attending a particular school, a teenager should not be expected to choose a school based on whether or not that school will provide him with quality educational opportunities. In this situation, a case for exploitation could also be made (Wertheimer, 2007).

It also must be determined if student-athletes are forced into classes or majors which result in them not receiving a quality education. Of course there are always the “low-ability” level students who struggle academically and really have little chance of ever receiving a college education (Wertheimer, 2007, p.369). However, there are situations where some students do not achieve academic success or graduate simply because they fail to give enough effort in their academics. In these specific examples, an argument for the exploitation of the low-ability student-athletes could be made, but it would be harder for this same argument to apply to student-athletes who do not make an effort academically.

##### Wertheimer’s Second Argument

The second exploitation argument is universities (A) are exploiting student-athletes (B) due to the fact B is not receiving fair compensation in relation to B’s generated surplus. This argument is harder to make because of the difficulty in determining the surpluses of NCAA Division I schools. According to NCAA president Mark Emmert, only 14 out of over 1,150 schools finished the 2009-2010 school year with a financial surplus (Garcia, 2010). But any surplus generated by colleges’ football and basketball programs are used to pay for coaching salaries, academic counselor salaries, and athletic facility renovations. In most circumstances, a portion of the money subsidizes schools’ other intercollegiate sports programs (Wertheimer, 2007; Suggs, 2009). Subsequently, very few schools show a surplus in the end.

In addition to the difficulty in determining universities’ financial surpluses, it is equally difficult determining nonfinancial surpluses. Dating back to the beginnings of intercollegiate athletics, the primary purpose for having these types of sports programs was the prestige and visibility they provided for colleges. Today’s winning sports teams are given hundreds of hours of media attention and television coverage. It is impossible to put a monetary value on the advertisement which each intercollegiate team or each student-athlete is creating for colleges (Wertheimer, 2007).

The answer to this question lies in determining what fair compensation is. At first glance, a $10-40,000 a year education in return for generating $400,000-$1.2 million seems anything but fair (Zimbalist, 2001; Brown & Jewell, 2004). But a teenager with no prior professional experience who receives the equivalent of $120,000 a year is uncommon in other professions. When asked about fair compensation for college athletes, Butler University men’s basketball player Matt Howard replied, “Forty thousand dollars-plus a year to play, that’s a pretty good salary for an 18-year-old who has no college education” (Weiner & Berkowitz, 2011, para. 6).

Determining what is fair becomes even more difficult when considering other situations. First, if athletes are exploited only when they do not receive fair compensation for the surplus they themselves create, then this means only a portion of a school’s student-athletes (in most cases, only football and basketball players) are being exploited and should receive compensation. Is it fair for the volleyball, baseball, and soccer players not to be paid while their fellow schoolmates, the male football and basketball players, are paid? After all, athletes in nonsurplus sports put in the same amount of time and effort into competing for their schools as do athletes in surplus sports. It is no fault of the athletes whose programs are not as popular in American culture as other programs (Wertheimer, 2007). Murphy and Pace (1994) replied to this particular argument with an example from the professional world. In business, do all members of a company’s team receive the same compensation? Is a secretary who works the same number of hours and works just as hard as the boss paid a similar wage? Of course, the answer is no.

Second, if colleges were to pay athletes, any surplus created by those programs would be used to compensate the athletes. Consequentially, many of the non-revenue generating programs would not have adequate funding to continue. Is it fair to those athletes to deprive them of an opportunity to compete collegiately and, for those who would be unable to financially afford school, an opportunity for a college education? On the other hand, requiring universities to use revenues to pay athletes may force schools to cut down some of the exorbitant salaries paid to some Division I coaches and other athletic department employees.

##### A Case for Mutually Advantageous and Consensual Exploitation

In this discussion concerning the exploitation of student-athletes, a case can be made for both mutually advantageous exploitation and consensual exploitation. Mutually advantageous exploitation occurs when A gains from B and B gains from A, leaving both parties in a better position than before the transaction (Wertheimer, 2008). Take, for example, a star high school basketball player from a low-income family who is recruited and signed by a renowned academic institution. He competes four years for that school. Along the way, he helps his team win over 100 games, reach 2 Final Fours, and win a national championship. After 4 years of education (worth a total of approximately $160,000) and instruction from one of the best coaches in the nation, he graduates with a college degree, is named as a NCAA All-American, and one month later is selected in the NBA Draft. Over the next 7 years, the former student-athlete signs 3 NBA contracts worth over $28 million, thanks in large part to the coaching he received while in college. In this example, both parties made gains which left them better off. It could be argued, therefore, no wrongful exploitation took place.

In other examples, athletes have been known to become student-athletes for the sole purpose of receiving expert instruction, media exposure, and training. As a result of those benefits, their future earning power increased (Kahn, 2007). Many of these elite athletes stay in college for only the required amount of time and then leave to become professionals. Again in such situations, both the athletes and the schools have entered into agreements which benefit both groups. Nothing morally wrong has occurred.

When an individual volunteers or gives informed consent to a transaction, it is referred to as consensual exploitation (Wertheimer, 2008). Prior to the start of a student-athlete’s collegiate career, the individual must agree to sign several eligibility forms. One of those forms is the NCAA Student-Athlete Form 10-3a (2010) that reads, “You affirm that you meet the NCAA regulations for student-athletes regarding eligibility, recruitment, financial aid, amateur status and involvement in gambling activities” (p. 2). A separate read and sign section of the same document states:

> You authorize the NCAA [or a third party acting on behalf of the NCAA (e.g., host institution, conference, local organizing committee)] to use your name or picture in accordance with Bylaw 12.5 including to promote NCAA championships or other NCAA events, activities or programs. (p. 4)

The NCAA is not attempting to deceive individuals by having student-athletes sign confusing forms so then the schools can make money off the athletes. Instead, they are presenting a clear, understandable agreement that essentially says, “In order to participate in intercollegiate athletics, you must abide by these terms.” Players must sign the agreement to become student-athletes, but no athlete is forced to sign the NCAA Student-Athlete Form.

There is a common perception athletes are required to attend college in order to become eligible for the professional ranks. This is not the case. The current NBA Draft eligibility rules state a player must be 19 years of age, and 1 year must have elapsed since the player’s graduation from high school (“Article X,” 2009). In the NFL, a player must be out of school for three years before he is eligible for the draft (“NFL Collective Bargaining Agreement,” 2006). In baseball, Major League Baseball teams can draft any player who has graduated from high school, while anyone in hockey who is 19 or older is eligible for the NHL Draft (“First-year Player,” n.d.; “Hockey Operations,” n.d.). Neither athletes of surplus sports nor those participating in nonsurplus sports are required to attend college in order to be drafted into professional sports. In most circumstances, the visibility which comes from playing for prominent sports programs causes most athletes to choose to attend college.

### Conclusions

Even after knowing all the facts, the questions related to paying college athletes and the exploitation of student-athletes are difficult to answer. However, there is no doubt the current model for compensating college athletes is ethically questionable at best. If this were not the case, then President Emmert would not continue to make statements suggesting the necessity of exploring ways to increase the financial assistance given to student-athletes (Wieberg, 2011). Just last week, several NCAA conference commissioners began discussing ways to compensate their athletes above athletic scholarships. Conference USA commissioner Britton Banowsky said, “Unless the student-athletes in the revenue-producing sports get more of the pie, the model will eventually break down… [I]t is only a matter of time” (Schad, 2011, para. 3). When the current model does break down, the NCAA’s members will be forced to consider the topic of student-athletes’ exploitation prior to establishing a new model.
Going forward, the NCAA and its member institutions must address several ethical situations in order to avoid the continued exploitation of student-athletes. The first step is re-defining amateurism in college athletics. Currently, intercollegiate sports are amateur in name only (a practice continued today by colleges in an effort to avoid providing workers’ compensation and to continue eligibility for tax exemption status) (Haden, 2001; Murphy & Pace, 1994). The second step is deciding whom to pay. If it is determined only scholarship athletes in revenue-producing programs (i.e. basketball, football, and ice hockey) should be compensated, then the NCAA will have to be prepared to justify excluding some athletes, including the non-scholarship basketball, football, and hockey players (Murphy & Pace, 1994). Due to Title IX, which mandates equitable opportunities and benefits for women competitors, there is a possibility schools would be required eventually to extend remuneration to other student-athletes (Francis, 1993). The third step is determining what fair compensation is for student-athletes, a difficult task based on the information mentioned previously. The final step is choosing where to get the money to pay athletes.
Deciding where to get additional money opens the door to a vast array of ethical questions. Should the money made by men’s basketball and football be used to fund other athletic programs? Instead, should the money be used to pay the basketball and football players only? Will Title IX allow for only a portion of a school’s athletes to be paid? Are college coaches overpaid, or are their large paychecks justified by the prestige, visibility, and money they are helping to generate for their schools? If smaller schools are lacking the funds required to pay student-athletes, is it fair to raise regular students’ tuition prices to help cover costs (Schneider, 2001)? These are just a few of the questions which will have to be addressed.
Determining which student-athletes are being exploited is a difficult task. What is clear is both the NCAA’s current amateur rules and the questionable educational practices of some schools make it more likely for students-athletes to be exploited (Murphy & Pace, 1994). Deciding how to compensate student-athletes more fairly could potentially result in completely restructuring intercollegiate athletics. If the NCAA and its member schools truly desires to protect their student-athletes “from exploitation by professional and commercial enterprises,” then they will be forced to reexamine their own practices (2010-2011 NCAA Manual, 2010, p. 4).

### Applications In Sport

The topic of paying college athletes is one of, if not the most debated issues in collegiate athletics. Understanding the terms of amateurism and exploitation, a history of intercollegiate athletics, and how student-athletes are possibly being exploited may assist in helping to decide if NCAA student-athletes should be compensated above athletic scholarships.

### References

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### Corresponding Author

Anthony W. Miller, MEd
4 Amity Lane
Greenville, SC 29609
<awmiller@students.ussa.edu>
864-906-2548

Anthony W. Miller is a doctoral candidate at the United States Sports Academy. He is also a faculty member of Bob Jones University.