Generic Alcoholism: Are College Athletes at Risk?

 

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

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

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

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

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

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

Method

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

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

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

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

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

Results

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

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

Athletic Status

110.15610.15617.488.000

Gender

 

14.7174.7178.122.005

2-Wat Interactions16.8016.80111.711.001

Athletic Status X

Gender

16.0816.08111.711.001      Within12170.274.581        Total12481.888.660

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

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

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

Discussion

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

 

Factors Associated with Success Among NBA Teams

 

Abstract

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

Introduction

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

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

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

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

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

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

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

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

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

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

Table 1
Pearson Product-Moment Correlations of Winning Percentage and Selected Variables for the 1997-1998 Regular NBA Season
Variable   Winning
Percentage 
three-point conversion percentage .38  
field goal conversion percentage .78* 
free-throw conversion percentage .03  
average number of offensive rebounds per game -.31 
average number of defensive rebounds per game .47  
number of total rebounds .19  
average number of assists per game .61*  
average number of steals per game .08 
average number of blocks per game   -.13 
number of points scored per game .57* 
field goal conversion percentage of the opposing teams -.68* 
average three-point conversion percentage of the opposing teams -.50  
average free-throw conversion percentage of the opposing teams .18  
average number of offensive rebounds per game of the opposing teams -.49  
average number of defensive rebounds per game of the opposing teams   -.71* 
average number of total rebounds of the opposing teams -.69*  
average number of assists per game of the opposing teams -.70*  
average number of steals per game of the opposing teams -.45  
average number of blocks per game of the opposing teams -.58*   
average number of points scored per game of the opposing teams -.70*  
* statistically significant after the Bonferroni adjustment

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

Surgical Reconstruction of the Anterior Cruciate Ligament: The Central Quadriceps Tendon as an Alternative Graft Source

*Red numbers
indicate references

 

INTRODUCTION
Significant advances in surgical reconstruction of the anterior
cruciate ligament (ACL) have been made since Jones’ described
open reconstruction with the central one-third patellar tendon
in 1963.29 Advancements in technology, arthroscopic instrumentation,
and surgical skills have decreased surgical morbidity while improving
functional outcome.5,
23
Continued technological
and surgical improvements in the 1990’s eventually enabled surgeons
to perform ACL reconstructions endoscopically. SIZE=”-2″>3, 4, 19, 23, 24, 37

During the evolution of ACL reconstruction surgery, numerous
graft sources have been described. Currently, the most commonly
utilized tissues for ACL reconstruction are autologous semitendinous/gracilis
tendons (ST-G), central one-third patellar tendon (B-PT-B), and
allograft patellar tendon. >13, 14, 21, 22, 31, 32, 46, 49, 50
Each of these grafts has been touted
to reliably restore knee stability, thereby enabling many patients
to return to pre-injury activity levels. Despite these reports,
complications have been noted with all three types of tendons,
the most frequent being anterior knee pain. SIZE=”-2″>1, 6, 7, 9, 10, 12, 17, 18

Based upon its proven efficacy,
the central 1/3 autologous patellar tendon is considered by a
large number of orthopaedic surgeons to be the graft of choice
in the symptomatic ACL-deficient patient. However, the incidence
of anterior knee pain with the use of this graft has been reported
from 13% to 47%, which diminishes the functional outcome in a
large percentage of individuals. SIZE=”-2″>1, 35, 43-45, 51 Moreover, due to technical factors such as tunnel
angle and graft length, many B-PT-B grafts cannot be secured
at the joint level, resulting in non-anatomic graft fixation.
In an effort to eliminate these problems, other graft sources
have been explored. The ideal ACL graft should result in minimal
or no damage to the patient’s tissues after harvest. It should
enable immediate rigid fixation and reproduce the normal anatomy
of the native ACL. It should also restore normal proprioception
and kinematics to the knee. Although numerous graft sources have
been reported, currently, no graft material, autograft or allograft,
can meet all these requirements.

In an effort to minimize post-surgical
anterior knee pain after ACL reconstruction, the use of semitendinosus/gracilis
tendons has been reported. Advocates of ST-G (hamstrings) point
out that post-operative patellar pain is diminished by virtue
of the patellar mechanism not being violated during graft harvest.
Various authors have reported the incidence of anterior pain
to vary from 3% to 21% following hamstrings ACL reconstruction.<FONT
COLOR=”#ff0000″ SIZE=”-2″>2, 11, 15, 26, 30,
33, 42 However, in the
author’s experience, the use of hamstrings for ACL reconstruction
does not eliminate anterior knee pain in many patients. In addition,
some authors have reported increased tibial translation in females
after ST-G ACL reconstruction. >16

 

Allograft B-PT-B has been advocated
as an alternative graft source due to the lack of harvest morbidity
and decreased operative time required. SIZE=”-2″>25, 32, 36, 41, 46 However, despite the diminished risk of disease
transmission, opponents of allografts cite reports of prolonged
graft-tunnel healing and intraarticular reactions to some allografts.<FONT
COLOR=”#ff0000″ SIZE=”-2″>28, 40

As a result of the continued
controversy regarding the correct ACL graft source, an alternative
graft has emerged, the central quadriceps tendon (CQT). The central
quadriceps tendon was reported as a graft source as early as
1979 by Marshall et al, however, it did not gain popularity among
surgeons until the 1980’s and 1990’s. SIZE=”-2″> 8, 21, 34, 47 Proponents of the CQT cite it’s greater cross-sectional
area, lower strain at failure, and lower modulus of elasticity
when compared to patellar tendon. SIZE=”-2″>48
Advocates have also cited the lower incidence of patellofemoral
symptoms after CQT graft usage. >20, 31
Our experience at the University of South Alabama Medical Center
is similar, with less then 5% of patients demonstrating postoperative
anterior knee pain symptoms after CQT ACL reconstruction.

The CQT consists of a central
portion of the quadriceps tendon approximately 10-11mm wide.
The graft depth extends 7mm with an average length of approximately
80-90mm. When the graft is harvested as the initial portion of
the surgical procedure, the central portion is obtained without
violation of the suprapatellar pouch or transection of the quadriceps
tendon. This eliminates the need to repair the pouch or tendon
before proceeding with the arthroscopic portion of the procedure.

Initial descriptions of the use of the CQT described harvest
of the tendon without a bone block from the patella.31 Prior
to the development of bioabsorable screws for soft tissue fixation,
such a graft would have required the tendon ends had to be secured
by sutures tied over a post, such a staple, button, or screw.
Several biomechanical studies have demonstrated that such fixation
does not reconstitute the normal isometricity of the ACL, with
increased instability noted as the tibial side is fixed further
away from the articular surface. SIZE=”-2″>27, 38
Consequently, many authors now advocate graft fixation near the
articular surface insertions of the native ACL. SIZE=”-2″>39
When the CQT is being contemplated as a graft source, articular
fixation can be accomplished by harvesting a patellar bone block
and fixing both ends of the graft with bioabsorbable screws near
the surface of the tibia and femur.

SURGICAL TECHNIQUE
After previous studies (radiographs, MRI) and clinical examination
confirm that the ACL is disrupted and causing symptomatic instability,
the patient is brought to the operating room for reconstruction.
The CQT is harvested through a short 2-3 inch incision obliquely
along the lines of the quadriceps mechanism. After delineation
of the quadriceps tendon, a 10mm x 7mm x 85mm graft is harvested,
being careful to not violate the suprapatellar pouch. At the
distal end of the quadriceps tendon a 10mm x 25mm x 8mm bone
plug is harvested from the proximal end of the patella. The patellar
defect can be later filled with cancelleous bone from the tibial
reamings or with allograft chips. The CQT is sized on a back
table to fit through the smallest tunnel that the tendon (not
the bone) will glide through easily; the bone plug is trimmed
to fit accordingly. This usually represents 9-10mm. Two #2 Ethibond
sutures are placed in the patellar bone plug, while two #1 Ethibond
sutures are whipped stitched in the tendon end.

After the stitches are in place
and CQT has been sized, appropriate markings are made to aid
the surgeon during arthroscopic reconstruction. In our use of
the CQT, we place the tendon side in the femoral tunnel, with
the patellar bone block in the tibia. First, a pen mark is made
at the bone-tendon junction. Next, a distance of 35mm is measured
from the CQT-bone junction. This marked area represents the approximate
intraarticular distance of the native ACL. This distance is approximately
30mm in most individuals, however, we allocate an additional
5mm in case the graft slides more proximally in the femoral tunnel.
The distance of the remaining tendon represents the portion of
the tendon that will be pulled into the femoral tunnel (~25mm).
The following calculation is what we use in preparing the CQT
graft for implantation:
85mm {total graft} – 60mm {bone plug + intraarticular
tendon} = 25mm {femoral tunnel}
The graft is set in a moist sponge until later implantation.

After harvest of the CQT, a
routine knee arthroscopy is performed. We routinely perform a
5-7mm lateral notchplasty, along with debridement or repair of
any meniscal lesions. Using a standard tibial guide set at an
angle of 55o, we drill a 9mm tibial tunnel centered 5mm anterior
to the PCL within the footprint of the native ACL. After the
posterior portion of the tibial tunnel has been debrided of all
soft tissue and rasped posteriorly, a 7mm offset endoscopic guide
is placed through the tibial tunnel at the “10:30”
or “1:30” positions on the posterior femoral notch.
A 9-10mm femoral tunnel is reamed to the depth of the previous
calculations (~ 25mm).

After reaming, an eyed Beath
pin (Arthrex, Naples, FL) is placed in the femoral tunnel. Sequential
impaction dilatation of the tibial and femoral bone tunnels is
performed to increase the bone density of the tunnels for bioabsorable
screw placement. After the tunnels are dilated to the size of
the graft (9-10mm), the tendon side of the CQT is brought into
the femoral tunnel. Through the anteromedial or an accessory
anterior portal, a bioabsorable screw of the same diameter as
the femoral tunnel is placed anterior to the graft. Fixation
of the graft is assessed by pulling upon the tibial sutures,while
the knee is put through a range of motion. Tensioning the graft
through several motion cycles diminishes creep within the graft
prior to tibial fixation. After the graft is assessed in extension
for signs of impingement, the knee is placed in 10o-20o of flexion
with 5kg of tension place upon the tibial sutures. The graft
is fixed adjacent to the tibial articular surface with a 10mm
bioabsorable interference screw. The knee is assessed for anterior
tibial translation and the wounds closed with absorbable sutures.
Prior to waking the patient, an intraarticular pain pump is placed
within the knee.

CLINICAL EXPERIENCE
At the University of South Alabama Medical Center, our experience
with CQT spans over 2 years, with nearly 20 cases. To date we
are gathering 2 year follow-up data. 1 patient ruptured his graft
during athletics (collegiate athlete) at 9 months postoperatively.
No patient has reported significant patellofemoral pain and there
have been no ruptures of the quadriceps tendon. All patients
have been happy with their results, indicating that they believe
the procedure improved their quality of life.

Other authors have reported
good results with the CQT. Fulkerson reported excellent results
with the use of the CQT with either endobutton or bioabsorbable
screw fixation.20,
31
Leitman et al reported
on 65 CQT graft cases with a KT-1000 side to side differences
of 2.1mm at 1-2 year follow-up. The authors noted that no patient
had patellofemoral pain and all subjects had returned to their
previous level of activity with no instances of quadriceps tendon
rupture.31

CONCLUSIONS
Individuals with a disrupted anterior cruciate ligament and symptomatic
knee instability often require surgical reconstruction of the
ACL. Over the last several decades, tremendous technological
advances have enabled surgeons to reconstruct the ACL with a
more anatomic and durable graft, while minimizing postoperative
morbidity. As the evolution of ACL graft material continues,
numerous graft choices are available. The central quadriceps
tendon (CQT) is an alternative graft source with biomechanical
properties comparable to or better than a bone-patellar tendon-bone
or double-looped hamstrings graft. In early follow-up studies,
patients undergoing CQT ACL reconstruction have demonstrated
minimal patellofemoral symptoms and excellent clinical function.
The CQT provides another weapon in the orthopaedic surgeon’s
repertoire of surgical graft alternatives to reconstruct the
symptomatic ACL-deficient knee.

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Address correspondence to:
Albert W. Pearsall, IV, MD
Department of Orthopaedic Surgery
University of South Alabama Medical Center
2451 Fillingim Street
Mobile, Alabama 36617
Email: apearsal@usamail.usouthal.edu

Sports Equipment and Technology

Introduction

In this Olympic year it is appropriate to consider the roles that sport play in our societies. While the natural focus of attention associated with an Olympiad is on “the elite” of sport, we as administrators in the profession cannot lose sight of the fact that sport is truly an activity for everyone in society. Thus it is also appropriate that the focus of this conference is on sport and social inclusion which is really what SPORT FOR ALL should be all about.

I have been asked to address the topic of the application of technology to sports equipment. This is difficult to do in 30 minutes but I will attempt to provide an overview of how technology is changing the nature of sport. The discussion of the application of technology in the world of sport can be done in two broad areas:

1st Sport specific applications of technology most notably in the area of equipment.
2nd How today’s “technological revolution” can be applied to sport. This will be addressed in terms of how technology can impact broad based participation and promote social inclusion.

When looking at how technology can enhance social inclusion and to expand the base of participation in sport throughout society, it is the second case that is most significant. First, however, I will provide an overview of technology in sport specific applications.

Sport Specific Applications

First of all, when we say technology, what exactly do we mean? There are several definitions from the dictionary, but I picked only two to illustrate the scope and impact of technology on the human race. These are:

1st The application of science or a technical method of achieving an intended purpose.
An even broader definition is:
2nd Technology is the totality of the means employed to provide the objects necessary for human sustenance and comfort.

As you can see, there is a lot of latitude when we start talking about how technology affects sport or even more narrowly, sports equipment.

There was a time when technology and equipment had very little impact on sport, even in the Olympics. As you may recall, the athletes who participated in ancient Olympic Games did so in the nude using implements such as discii that were both “off the rack” and shared among the competitors. So it is safe to say in this instance, there was no real advantage that accrued to any participant as a result of the application of “technology” in-so-far as equipment or personal gear was concerned.

But it is equally safe to say that in the Olympics of the modern era, technology applied to sport has played an important role both in training and in competition. This has manifested itself in a variety of ways that range from the creation of new sports, to facilities used to accommodate them, to the equipment used by the athletes in competition to the training support used by teams to prepare the athletes for competition. Moreover, the processes employed in the adoption of technology and technological methods to enhance sport and recreation have accelerated with each successive Olympiad. These advances in technology, as with all other walks of life, have had a marked impact in most aspects of sports. Examples of this impact include:

1st The development of new sports both recreational and competitive.

These changes reflect a natural evolution in sports as well as generational shifts that are more pronounced. In the latter instance, there are new multi-sport competitions such as the “X-Games,” which include events such as mountain biking, in-line roller skating, roller boarding, boogie boarding and snow boarding. The X-Games are, incidently, named after the 15 to 30 year old demographic group called “Generation X” who make up the largest participant group for these sports which have sprung up to compete for sponsorship and media space with older, more established events.

A very good example of this process is the introduction of snow boarding in the Nagano Olympiad. At one time, snow boarding was banned from most ski resorts because of a perceived conflict on the part of resort managers between the snow board enthusiasts and the more traditional skier. This antipathy stemmed from both the free-wheeling way in which the snow boards are used on the slopes and a perceived cultural clash between the two sets of resort patrons. Now most ski resorts could not survive or remain economically viable without the revenue generated by snow boarders.

2nd Which brings us to facility design. The application of technology in sport facility design has yielded real changes in terms of athlete use, spectator comfort and usable life span. Example of these changes include:

Equipment which makes competition judging and compiling results more accurate. Further, technological applications such as photo-finish timing devices tied in with communications technology for in-stadium displays such as scoreboards and broadcasting make the events more enjoyable for the spectators.

Technological changes have resulted in facilities that are more cost efficient to operate resulting in the freeing up of scarce financial resources for programs which would have otherwise been expended for operating costs such as utilities. Among these advances are lighting options that extends the useable hours of facility operation or computerized HVAC controls that gain operating efficiency as well as increasing both athlete and spectator comfort.

And lastly, technological change frequently results in better building finishes that extend the life of the facility, are safer for the participants and are less costly to maintain. Most notable in this area are finishes such as sports flooring and playing surfaces.

There exists, however, a real irony with respect to the application of technology in sport facility design, at least in North America. While on the one hand technological advances allow for a greater life span of the facility through the development of components such as better finishes and surfaces, on the other hand technological changes also drive obsolescence causing facilities to be replaced for economic reasons long before they are worn out. Thus stadia and auditoriums which should last more than 50 years are now being razed and replaced at great cost after only 30 years of service in many cities.

3rd Technology has affected equipment design at all levels; from low level recreational activities to high level competitive sports. We’ve already seen that the application of technology to sports serves a role in creating whole new sports events. The use of technological tools such as Computer Assisted Design (CAD) can also play a role in the enhancement of sport equipment. A good example of this and well known in sailing circles is the story of how America³ won the 1992 Americas Cup, which is probably the most venerable race in among the oldest of sports. Through the application of technology (and a large amount of money) a rookie skipper surprised the veterans and took the prize.

A better example of applied technology in sports is the use of “smart” equipment that incorporates sensors and computers as a part of their function. Most international caliber athletes typically undergo some form of human performance evaluation as a part of their training regimen. This can range from exercise stress testing and cardiovascular assessment to the use of very sophisticated biomechanical analysis using equipment such as the APAS system. Computer technology can even be found in equipment used for the evaluation of strength and conditioning.

4th Equipment construction also benefits from the application of composite materials which reduces weight while yielding increased strength and extended life spans. Composite materials can certainly be found in high level competitive equipment such as bikes, skis, racquets, and other types of gear such kayaks which almost by definition are targeted toward the elite competitor.

But it is also important to note that advances in materials have made sport participation safer and has penetrated down the sports hierarchy to the recreational user, for example, in items such as bike helmets.

5th And lastly, technology is also applied to personal sports gear such as clothing and shoes. A recent, albeit controversial example, is the full body suits used in swimming which streamline the competitor reducing times in a sport where winning or losing is measured in hundredths of a second.

Unfortunately, the use of technology in enhancing sports facilities and equipment is generally an expensive proposition. And because of the expense involved in these applications, the benefits derived at least initially tend to be limited to the upper end of the sports hierarchy. For example, because of cost, changes brought about in sport through the application of technology tend to be available first to elite level athletes and teams. By definition, elite level athletes and sports are exclusive and thus omit the broader base of participants further down the sports hierarchy.

Nonetheless, there is a “trickle down” effect. As more people seek out the “best and newest”, market economies come into play and the cost for equipment brought about or improved through technological innovation declines as distribution is expanded. Unfortunately, this trickle down effect is frequently a process that is measured in years. In the meantime, technological development as applied to sports equipment continues with the next generation of the “newest and the best” being developed. And as before, this new generation of equipment is out of the reach of many sports participants. And so the cycle continues.

This division between the “haves” and the “have nots” is a problem for the profession and it manifests itself in many ways. Consider the following example.

In the United States those professional teams that can afford better facilities and equipment, frequently developed through the application of technology, typically earn more revenue. With a better revenue stream, the team owners can obtain better performers. Better performers yield better team results, which creates more interest by the fans. Increased interest on the part of the fans means an increase in ticket sales and more revenue generated for the team. More revenue means better facilities and players and so too does this cycle continue.

Striking a competitive balance is a real challenge facing the professional leagues in North America. But North America is not alone with this problem. Relative cost related to the application of technology is also a challenge with respect to international sports as well. Wealthier nations, such as those found in Western Europe or the United States, can better afford the training facilities, expensive composite equipment and personal gear required for elite level competition. Thus the benefits of technological advances applied to sports accrue most greatly to those who can afford the price.

The point is that the application of technology to sports equipment is by no means universal and is, at best, unevenly applied. And solving this problem is very difficult because of the inherent conflicts of interests between the various stake-holders or constituencies in sports. Among the stake holders are the fans who are the consumers of the sport product. The people who pay to see sport competitions, whether it be the individual fan or media companies, want the excitement of high scoring contests or record setting performances. Athletes and participants want the recognition that accompanies victory and the setting of records. Regulators such as the national and international sport governing bodies similarly are motivated to gain the best possible competitive advantage for their teams and athletes. Equipment manufacturers want to recoup their investment in research and product development.

Thus, at least at the upper end of the sports hierarchy, there is a natural pressure among all of these constituencies toward the ever more efficient and costly facilities and equipment. This process continues to increase the gap between those who can afford to acquire the “latest and the best” and those who cannot. It is very important, however, this natural tendency toward a division between the “haves” and “have nots” be controlled in the best interests of the sports profession.

This is simply because central to the philosophy of sport is the concept that on any given day, every athlete participating in a competition has a chance to emerge victorious. It is this idea of competitiveness that keeps fans returning to the stadium and buying tickets, the revenue from which supports the athletes and the teams. Should the fans believe that too great an advantage has accrued to one competitor or another, their interest will diminished to the detriment of all. This is why drug abuse by athletes brings such severe penalties from sports regulators and why fans tend to lose interest in a given sport when the “best team that money can buy” consistently wins the championship.

The challenge then for sports administrators is to insure that too great an advantage does not accrue too greatly to one team or another through the application of technology for better equipment or facilities. Those who are in a position to develop rules with respect to the use of sports equipment or to fund equipment or facility acquisitions for economically disadvantaged teams through the administration of grants need to bear in mind the fundamental principle of parity in competition.

So the question arises, what technology is available and can be applied toward leveling the playing field for all? What equipment can promote social inclusion with respect to sport? Interestingly enough, the answer to that question lies within what most people think of in terms of “technology” itself.

The Technological Revolution

We are living in the midst of one of those very unusual occurrences that come along every few generations. What has been occurring over the past few decades is a fundamental paradigm shift that is moving society as a whole from the age of industry to the age of information. The currency in this new society that is being formed is called IT – information technology. IT is simply the tools and methods used for the identification, organization and manipulation of facts that we call data. IT has become the engine that is driving all sectors of today’s economy be it industry, government, education or indeed, sports.

The most important piece of equipment that lies at the heart of the whole IT process is the computer. The computer and the software that it runs is an essential element in the new societal paradigm and it is a key to success for the modern sports administrator. It is THE piece of equipment that allows the sports administrator to maximize the return on scarce resources whether this is people, facilities and equipment or finances. In turn, it is also perhaps the single most important tool to insure the extended reach of sport and recreational programming and with it, the whole idea of inclusion in these activities of the greatest number of participants.

Just as money has been the currency and a source of power in the old paradigm, information is the currency and a source of power in the new paradigm. No where is the old saying “that knowledge is power” more true than in a society where information or data is what drives the economy. The secret to managing knowledge and information is in the development and maintenance of databases.

A data base is nothing more than an organized collection of common records that can be searched, accessed and modified. Database software is very widespread as most standard office computer software packages will typically have a simple database program in addition to word processing, spreadsheet and presentation applications.

There is, however, a far more powerful and useful kind of database for sport managers than the one that comes in the standard software suite. This most powerful of data management tools is called a relational database. Very simply, a relational database is a data management system that stores information in a series of tables e.g. rows and columns of data. When the operator conducts a search, a relational database allows the individual to match data from one table with data from a second to produce a third table or a report. So for example, if you are a manager conducting a complex sports competition, the details of which have been entered into a relational database, the event manager can retrieve the time for a scheduled event from one table, a roster that has the names of qualified referees who can officiate the event from another table, their availability from a third table and produce a report that lists all of the personnel who can undertake the officiating task. The same type of event management software can assist the sports manager with a myriad of other tasks ranging from facility scheduling, equipment set up and knock-down, or even ordering soft drinks for the concession stand.

If one of the goals of the sports manager is social inclusion, and I submit to you that it should be, then the computer and the data base software that it contains within it are one of the key tools available to them to help them to reach this goal. That this is the case can be seen by the kinds of sport program information that can be contained within these databases aside from the event management sample just cited:

  1. Athlete or team rosters to include demographic information such as name, sex, age, contact information such as addresses and phone numbers and so on. Other parts of the database can contain details on medical conditions, performance history, or other participation characteristics of the athletes.
  2. There are databases that can assist coaches organize scouting reports of the opposition as well as conditioning and preparation activities for their own athletes.
  3. Rosters of volunteers such as officials, drivers, timekeepers, or medical staff. Aside from the similar demographic details, a database of this type might also contain information about availability and reliability, e.g. do they actually show up when they volunteer?
  4. Donors or potential donors whether this be for money or in-kind services. Here too will be demographic information but also other kinds of details as to the source of their motivation or affiliation, frequency of participation and so on.
  5. While the foregoing are examples of useful data with respect to programming, databases are also essential for administrative information. Examples here include equipment and inventory lists, facility maintenance software packages, marketing information such as ticket sales, accounting and business records, employee directories and the list could go on and on.

Once entered, data can be easily manipulated. More importantly, databases can and should be updated to record changes. Bear in mind that the passage of time presents a more comprehensive picture of most activities and the ability of record change and make sense of them is essential for long term survival. Further, there is nothing so constant as change and a well thought out and maintained data base is a great way to record those changes and their implications to the organization.

There are a couple of keys to remember when considering the acquisition of this kind of software:

1st System capacity which addresses the hardware questions. Bear in mind that a relational data base can consume huge amounts of memory capacity.
2nd The degree of accuracy required with the application is an issue. Standard vendor prepared packages typically operate on the basis of the lowest common denominator which usually means that only about 80% of most organization’s needs are met with an off-the-shelf product. So the sport administrator is left with the choice of adapting organizational operating procedures to the software to some degree or writing their own programs. The latter can be hugely time consuming and very expensive. Generally, the more specific modification required for a software product, the more expensive the product becomes and the more difficult it will be to accommodate software upgrades from the vendor.
3rd Lastly, one needs to consider user capabilities. The adoption of information technology and procedures usually means extensive training of the staff.

As great as databases are for effective sport program management, the real power of technology comes when we tie the individual machines together through the medium of a network. This is truly a case where there are synergies created; where the sum is greater the individual parts e.g. 2 + 2 = 6. A computer network simply is the hardware required to connect two or more machines together in such a manner as to allow the sharing of data and other resources.

When I speak of resources, for example, a network can have any number of computers sharing a very good quality printer instead of a using a bunch of stand alone mediocre printers. Proper presentation of the organization’s message is very important for effective marketing and image. Another example of a shared resource is that the network can be driven by a powerful server which can substantially increase computing speed and effectiveness throughout an organization. Most importantly, particularly where relational databases are employed, a network allows the users to share information which is typically stored on a server.

Most larger enterprises, either commercial or sports, use computer networks to link together their operatives. All of the permeations and configurations available to the sports administrator are clearly beyond the scope of this presentation except to note that the most common configuration of these kinds of networks are of the client -server variety. This type of network is where a main server houses most of the information and data base files and the individual operatives access the server through their desktop terminals or workstations which are called clients.

It is important to note that computer networks need not be limited to a single site or facility. Wide Area Networks (WANs) can link together sports administrators located throughout a country. For example, all of the local offices of a national sports body such as the Football Association can be linked whether they be located in Recife or Rio, Brasilia or Belem. All of the administrators so linked can share rosters, performance data, programming information and communicate with each other cheaply and efficiently through the medium of e-mail.

The computer network most familiar to the public is the internet. And the most familiar part of the internet to most people is the World – Wide – Web also known simply as “the Web”.

While the internet has been around for decades going all the way back to ARPAnet in the 1960s, the Web is a comparatively new innovation first introduced in the mid 1990s. It is a medium which presents information in text, audio and graphics, with the latter being both still or animated, in a simple hyper-text computer language readable by a browser. This medium has simply exploded and today there are more than 3.6 million web addresses called Uniform Resource Locators (URLs), many with hundreds of individual pages on their sites. Additional applications for thousands of URLs are received virtually every week. Of these millions of Websites, more than 50% of them are located in the United States but this will eventually change with time. I should note that while much of the data used in this presentation originates from the U.S. Much of what I say is applicable to other nations to a greater or lesser degree already and if not now, then it will certainly be applicable to them at some point in the future. The changes being wrought in society by IT or its brother CT – communications technology – are not confined to any one country and will eventually effect us all.

All the ways in which the Web has changed society are almost too numerous to mention. Suffice to say it has become an extremely important medium of communication and its importance will only continue to grow in the future. For example, USA Today which is the closest thing a national newspaper in America, gets more than three million visits per day. Some 60% of these visits are to its sports pages. Further, there are virtually no professional sports teams in the United States that do not have a Website and most are linked together through networks of Websites coordinated through the various league offices. Just how tight are these linkages is driven in part by agreements between the league teams on activities such as revenue sharing for media broadcasting and merchandise sales among others.

The Web is currently used by professional sports teams in ways that the developers of this technology never envisioned. For example, there are no English language radio broadcasts in Montreal for the Montreal Expos professional baseball team. Fans wanting hear the play-by-play in English can only do so by calling up the team’s Website and listen to it coming across as an audio feed. Another example of how deeply the internet has penetrated professional sports is how some pro hockey teams now require their players to have e-mail addresses as a means to interact with both the team administration and their fans.

These examples lie at the heart of how the internet will affect sports in the future: through the changing of the way that the sports fan will consume the product. Where once sport marketing did not extend much beyond putting out a sign saying “Game Today”, now sports teams must have well developed and extensive Websites to market effectively to their consumers. The trend in this regard is also clear. What will emerge is networks of teams and users bound together by a common interest and driven in part by advances in communications technology.

A good example of this is trend is that of Worldsport.com. This internet presence has succeeded in tying together all 88 members of the General Association of International Sport Federations which represent all of the sports played in the Olympics. Worldsport.com not only hosts the federations individual Webpages, but also provides general technological support through activities such as promotional information and marketing, administrative information for athletes and administrators in secure areas of their sites, educational programming such as certification, logistical support such as a global e – mail communication system and the list could go on and on.

These developments are not limited to the upper end of the sports hierarchy. Compared to the extremely high cost of traditional television broadcast, the comparatively low cost of this technology will bring to sports fans events that could never before be seen broadcast on traditional media. A simple example of how this can occur is an annual sailboat race across the Gulf of Mexico from Mobile to Tampico. Last summer the skipper of a local boat participating in the event took photos with a digital camera of the race activities and the participants every four hours and uplinked them by a satellite phone to his own Website. Thus friends in the community, or anyone else in the world who stumbled into the Web address, could participate in this event as they never could before. Sports events of a distinctly local flavor without the mass appeal that make them economical for television broadcast can so be distributed to anyone with an interest. The Web is not constrained by the limited availability of broadcast channels and high production costs. And while bandwidth is currently an issue for the web, this will resolve itself in the near future with the introduction of broadband technologies.

I’d like to start bringing this discussion to a close with the topic on which we started: sports equipment. I think it appropriate to discuss how the Web will change the sale and distribution of sporting goods. We have already noted that the relative cost for sports equipment can be an issue for the profession, particularly in terms of inclusion by a great number of participants. E-commerce through the internet can play a key role in containing costs for sports equipment as illustrated by the following example.

In the traditional model of manufacture and distribution through a sporting goods store, a tennis racquet which cost $40 to manufacture could be marked up as much as 134% to $94 as it moves through various wholesalers and retailers in the distribution chain to a tennis player. With an e-commerce arrangement whereby the manufacturer can reach the player directly, the mark-up in distribution can be reduced to as little as 20% resulting in a sale price to the end user of $48. The more middle men in a distribution chain, the greater the benefit derived to the end user from using e-commerce distribution. Japan is a classic case in the application of this scenario. Japanese consumers have historically suffered extremely high prices for most consumer products because of a distribution system that has had many layers of middle men in the distribution channel. In Japan today this is changing such that even individual farmers are going on-line with their own websites to sell their produce directly to the consumer, an activity that is empowering both the farmer and the consumer.

E-commerce is well on its way to becoming a force in the world economy as it serves to remove barriers both natural and artificial. The barriers that will vanish include those of time and space as well as national borders both physical and ideological. That this will occur is underscored by the fact that this year e-commerce will employ more than 2 million people and create a turnover in excess of $500 billion. By next year, the turn over is expected to pass $1 trillion.

While this is all well and good, in closing I would like to note a problem similar to that mentioned earlier with respect to the application of technology to sports equipment. Very simply, technological tools are expensive which has resulted in what we call in the United States the Digital Divide. In the U.S., 56% of American adults, 106 million users representing 47% of American households, are connected to the internet and are on-line. These users are largely from the upper and middle class and have the financial wherewithal to purchase computers and internet services. It is a matter of great concern that the very people who stand to benefit the most from economies to be realized through communications technology as outlined earlier in my discussion on e-commerce are the ones least able to afford it. It is the economically disadvantaged that are currently being left out of the IT revolution.

This Digital Divide also transcends national borders. While 56% of American adults are connected to the internet, only 4% of the global population can make that claim. Some areas, Africa for example, are totally disconnected and can only be considered disadvantaged as a result. Herein lies the challenge for the future.

In conclusion, the application of IT to sports management has dramatically changed the way that we do business. Thinking through how we can use this kind of equipment and these tools greatly enhances outcomes. The bottom line is that these IT tools are rapidly becoming a necessity for the sports administrator at whatever level in the sports heirarchy they are working. They are a powerful force for social inclusion in sport and recreational activity and for the profession as a whole.

Thank you.

 


Paper Presented at the
International Seminar for Sport and Social Inclusion
Sao Paulo, Brasil
26-29 June 2000

 

Sponsored by
SESCO
and
COB Brasil, UNESCO, F.I.S.p.T, FICTS, Sport For All and
University of Sao Paulo – School of Physical Education and Sport


 

Address correspondence to:
Dr. T.J. Rosandich, VP of Development
United States Sports Academy
One Academy Drive
Daphne, AL 36526
(334) 626-3303
Email: tjrosand@ussa-sport.ussa.edu