ISSN: 1543-9518|Vol. 24

An Examination of the Moneyball Theory: A Baseball Statistical Analysis

Submitted by: Ehren Wassermann, Daniel R. Czech, Matthew J. Wilson & A Barry Joyner

INTRODUCTION

Money is a very important aspect in almost every professional sport. In professional baseball, there are large (New York Yankees) and small (Oakland Athletics) market organizations that make important decisions based on their economic status. For example, many smaller city market teams, must spend their money wisely to ensure the best outcome; whereas, a larger city market team has more income that is expendable (Lewis, 2003). This money spending process originates during the Major League Baseball player draft held each June. The draft process involves fifty rounds of selections by all thirty teams. Each team gathers their general managers, scouts, and professional consultants to decide which players should be drafted. The higher the draftee the more valuable he is believed to the team. Therefore, the procedure to decide which players should be selected earliest is very important (Lewis, 2003). According to Lewis (2003) there are two main theories that are being used to narrow the selection process.

The first theory is generally considered the “old” scouting theory. Scouts venture out and evaluate players all over the country. They do not pay particular attention to statistics, but rather base decisions on the five tools: speed, quickness, arm strength, hitting ability and mental toughness (Lewis, 2003). Each scout goes through “scout school” and is given a pamphlet on what should be looked for in certain aspects of baseball, such as arm strength, fielding, running, and the most important hitting. For arm, strength evaluation, scouts are instructed to look for players exhibiting a “fluid arm action and easy release” (Major League Baseball, 2001 p. 10). Furthermore arm strength evaluation is conducted with the assistance of a radar gun. In the fielding category, “a strong arm and defensive skills can and do carry a player to the major leagues” (MLB, 2001 p. 10). Also, “a live, active lower body, quick feet, agility, instinct, . . . alertness, are some of the qualities that go into the rankings of a major league infielder” (MLB, 2001 p. 10). Running is commonly judged through a timed 60 yard sprint (Baechle & Earle, 2003). Hitting is the “most difficult of all scouting categories of judgment” (MLB, 2001 p. 11). A general list of guidelines that scouts look for is: (1). Strength, (2). Starting the bat, generating bat speed, (3). Full arm extension and follow through after making contact, (4). Head stays on ball, (5). Lack of fear, butt stays up at plate, (6). Short stride, (7). Top hand is evident upon making contact and follow through, (8). Head of bat does not lag, (9). Aggressive, hits first good pitch, (10). Short strokes, yet ball jumps off bat, (11). Bat goes to ball (Not a swing through a certain arc area and the ball happens to be in that zone) (MLB, 2001 p. 11). Scouts are instructed not to scout performance but to “watch for things that are done mechanically that will eventually bring results and success” (MLB, 2001 p. 13). When a scout sees a player he then gives the player a certain grade. “The evaluated grade of five (5) in any respective category portrays the player as having, or will have, an average skill of major league standards, currently or once he reaches major league competition” (MLB, 2001 p. 14)

The second theory is based on the Oakland A’s general manager Billy Beane and is illustrated in a novel by Micheal Lewis entitled Moneyball. The Moneyball theory places no emphasis on the body of the athlete or the physical tools that the athlete possess’ (Lewis, 2003). This theory illustrates the simplicity of baseball by asking two questions: Does this player get on base? and Can he hit? According to Lewis (2003), Billy Beane (the inspiration of Moneyball) decided to base his drafting of position players/hitters on certain statistics. His main two statistics included on-base percentage (OBP) and slugging percentage. These two stats combined to form a new statistic called on-base plus slugging (OPS). Another differing aspect in Beane’s approach was his lack of emphasis on power (Lewis, 2003). Therefore, Beane believed that power could be developed, but patience at the plate and the ability to get on base could not. Moreover, Beane believed in the notion to select college players who are experienced on a different level than the high school “phenom” who needs to be developed into a player. Beane’s theory was created based on the works of a sabermetrician named Bill James. “Sabermetrics is the mathematical and statistical analysis of baseball records” (James, 1982 p. 3). James spent years trying to decipher numbers via the Bill James Baseball Abstract, which in turn, resulted in a specific philosophy on hitters.

James’ idea on hitters differs from the draft process of Billy Beane, but Beane adopted his views from James’ ideology. When putting together a lineup, managers must decide the best order in which the team has the best chance of winning. To win the game one must score more runs than the opposing team. This thought provokes the question as to why such great importance is placed on batting averages? “People are in the habit of listing their teams offensive statistics according to batting averages rather than in order of runs scored” (James, 1984 p.10). James believes that “a hitter’s job is not to compile a high batting average, maintain a high on-base percentage, create a high slugging percentage, get 200 hits, or hit home runs” (James, 2001 p. 329). However, part of a hitter’s job from a coach’s perspective, is to hit homeruns, singles, doubles, get on base, drive in runs, and steal bases (James, 2001). James believes the job of a hitter is to create runs. “The essential measure of a hitter’s success is how many runs he has created” (James, 2001 p. 330). James then developed a formula that allows one to establish created runs:

(Hits + Walks) x Total Bases
At-bats + Walks

This formula works 90 % of the time and gives a total of the team’s actual scored runs within 5 % (James, 2001). From this philosophy, Beane developed his theory. The only way to score runs is to get on base and since walks are such a vital part of the created runs formula, on-base percentage should be closely monitored. Even though this formula is very accurate, additional steps can be taken to improve the accuracy. This new formula accounts for the more minute aspects of meaningful baseball statistics. It works off the simple formula:
(A x B)/ C
The A variable adjusts the “on-base” aspect of baseball.

A = hits + walks + hit batsmen – caught stealing – ground into double play (H + W + HBP – CS – GIDP)
The B variable takes into account the advancement of the player.
B = total bases plus .26 times hit batsmen and non-intentional walks, plus .52 times stolen bases, sacrifice hits, and flies (TB + .26(TBB – IBB + HBP) + .52(SB + SH + SF)

The C variable accounts for opportunity.

C = at-bats + total walks + sacrifice hits and flies + hit batsmen (AB + TBB + SF + HBP) (James, 1984 p. 14)

James believed that “figuring the number of runs created is a great tool to evaluate hitters since a hitter’s job is to create runs” (James, 1983 p. 5). Therefore, Beane also placed a major emphasis on what had to be done to create runs and drafted players accordingly.

The difference between these two theories leads to the following questions, what are the optimal attributes of the ideal draft pick? Are young high school prospects with the ideal 5 physical tools more advantageous to draft than the seasoned college player with high offensive Moneyball statistics?

The purpose of this investigation was to answer the question of whether there is a significant difference in on base percentage, slugging percentage and on base + slugging percentage (OPS) between high school and college drafted position players performing at the professional level? It is hypothesized that because of more experience, more rich statistical data, and better competition at the college level, the college baseball players will have better offensive Moneyball statistics than the high school players.

METHODS

Participants

The participants in this study were 60 professional baseball players. More specifically, thirty high school and thirty college players from the 1997 major league professional amateur draft were selected for participation in this study. The age range of the participants was 18 to 23 years of age. The mean age of the high school players was x=18.3 and the mean age of the college players is x=20.9. The mean age for the entire participant sample is 19.6 years of age.

Procedure

A comprehensive internet search was conducted to locate the high school and college players from the 1997 amateur draft. The authors felt that four years was enough time to examine a drafted player’s moneyball statistics, as four years is the time when many players move to their highest level of play. By use of the following website (www.sports-wired.com), draft information i.e. the top thirty drafted position players from high school and college Moneyball statistics were obtained. Each player’s professional (Major and Minor League) Moneyball statistics (slugging percentage, on-base percentage, and on-base plus slugging) from their rookie year to their 4th year of playing professionally were utilized. Slugging percentage was calculated as (Total Bases divided by At Bats). On base Percentage was calculated as (Hits + Base on Ball + Hit By Pitch) divided by (At Bats + Base on Balls + Hit by Pitch + Sacrifice Flies)

Results

Descriptive statistics included the means and standard deviation ranges overall and as a function of both major league and minor league slugging percentage, on base percentage, and OPS. A score was calculated, comparing college and high school players, for each variable using the SPSS 12.0 statistical package. An independent samples T-test was utilized to compare differences between collegiate and high school players. An alpha level of .05 was used for all statistical tests.

The mean and standard deviation for the college and high school player’s performances in the major and minor leagues is illustrated in Table 1. An independent T-test revealed a significant difference between college and high school minor league slugging percentage. No significant differences were found when comparing college and high school on base percentage and OPS.

DISCUSSION

The purpose of this study was to compare the top collegiate and high school drafted baseball player’s professional offensive Moneyball statistics- slugging percentage, on base percentage, and on base plus slugging (OPS) over a four year period. It was hypothesized that college drafted players would have significantly higher Moneyball related offensive statistics than the high school players. The results did not support the hypothesis in that the only significant difference was between college and high school minor league slugging percentage. These results may contradict some of Beane’s Moneyball theory (Lewis, 2003).

Beane postulated in Lewis’ (2003) that college players would perform better than high school players. This hypothesis is due to several factors. First, college players are more mature physically, mentally, and emotionally than high school players. This maturity would enable them to handle the stresses that are involved in minor league baseball such as, long bus rides, the occasional slump, and unfamiliarity with surroundings. Secondly, college players play against stronger and more advanced competition more often than high school players. This allows for more experience which may provide a better preparation for professional play. Finally, college players play a longer schedule and usually practice year round. This consistent playing allows for skills to be refined and mastered. Using these facts, Beane decided that college players are a better investment than high school players (Lewis, 2003).

The results may not have supported the hypothesis because both groups of athletes had to make adjustments to professional baseball. The high school players may adapt more easily to new changes because they are younger and may have had less influence from other less experienced coaches; however, college players may have developed a certain approach to hitting from college that contradicts a new approach at the professional level. Therefore, the college players may take a longer time to alter their approach to hitting and thus hindering their productivity at the plate. Another factor may be due to the notion that high school players are usually placed in lower levels of professional baseball than college players, which in turn may even the offensive statistics. Lastly, college baseball players may have the opportunity to gain more experience with the wooden bat when competing in collegiate summer leagues.

The rest of baseball has seemed to take notice of the Billy Beane philosophy of drafting. In the 2003 First-Year Player Draft, more than 70 % of the players drafted through the first twenty rounds were from a four-year college or a junior college (Mayo/MLB.com, 2003). This percentage was “a marked increase compared to the last three years” (Mayo/MLB.com, 2003, p.1). Even though this significant increase in drafting college players seems to be the trend, “there [has been] little statistical data to support doing that” (Newman/MLB.com, 2003, p. 2). Baseball America researched the 1990s draft and announced that 2,115 players signed in the first ten rounds between 1990-97 (Newman/MLB.com, 2003). “The group includes 1,024 collegians, 398 of whom (38.9 %) reached the Majors” and “920 prepsters, 259 (28.2 %) did the same” (Newman/Mlb.com, 2003, p 2-3). It was noted that most of the differences amounts to only limited time in “The Show”. However, “further research noted that 90 college players (8.8 %) and 77 high school players (8.4 %) became Major League regulars for at least a few seasons” (Newman/MLB.com, 2003, p. 3). These last numbers correlate with the findings of this study illustrating little difference between the productivity of college players versus high school players.

It is important to note that there were limitations to this study. For example, one relevant limitation was the number of participants used in the study. A more significant result could have been established utilizing the entire draft. With more participants and more statistical data, a better idea of the purpose could have been allocated. Another limitation that needs to be noted is the speed at which certain players are promoted. Some high draft picks (top ten rounds) are quickly promoted to a higher level, regardless of their success at the current level. This is due to the amount of money invested in the athlete. For example, a fourth round shortstop may get a signing bonus of 450,000 dollars while the 38th round shortstop may only get 1,000 dollars.

Consequently, the organization has a tremendous amount of money invested in the fourth rounder and they need him to develop faster (Lewis, 2003). Hence, even though this player may not be physically and mentally ready, the organization wants to see a quick return on its investment. Finally, a major limitation is the amount of playing the athlete does. Each year when the regular season ends, many players face the decision of playing winter ball (Lewis, 2003). Many believe that rest is needed to help the body recover from a long, strenuous season; however, others believe that winter ball allows them to gain an extra advantage over their competition. No matter the limitations there is significant evidence against the Billy Beane philosophy.

What this study attempted to illustrate was how an organization with a low budget produces quality baseball players using a new philosophy unorthodox to the norm of baseball (Lewis, 2003). From a financial standpoint, the authors believe there are two mindsets regarding the lack of significance. Because of the minimal significant differences between college and high school players’ “moneyball” statistics, many MLB teams might want to disregard the notion that cheaper “moneyball” college drafted players are better investments because they do not do as well as their high school drafted counterparts. However, even though the comparison is not significant statistically, the statistics may be significant to an organization/coach, which is playing the Moneyball way of baseball. A small market organization may want to pay less for college players who average .432 (slugging percentage), .344 (on base percentage) and .776 (OPS) than pay more for high school players who average .396 (slugging percentage), .332 (on base percentage), .728 (OPS) over a four year time period. Even though slugging percentage is the only significant difference, the college players have better statistics from a baseball playing perspective. This difference may be the rationale as to draft cheaper players based on the Moneyball statistics and play the Moneyball way of baseball, especially for small market teams. More research, both qualitative and quantitative needs to be completed before making a conclusion regarding the Moneyball way of drafting and playing professional baseball. If the Moneyball method is proven as significant, it could revolutionize the baseball industry. The importance of this theory is not only relevant monetarily, but it could institute a new theory to the selection of baseball players. Future research should examine if other organizations are using Beane’s philosophy and if they are how this will affect the Oakland organization. Moreover, future research should analyze OPS and Runs Created.

REFERENCES

1. Baechle, T.R., & Earle, R.W. (2000). Essentials of Strength Training and
Conditioning. Human Kinetics: Champaign, Il.
2. James, B. (1982). The Bill James Baseball Abstract 1982. New York: Ballantine
Books.
3. James, B. (1983). The Bill James Baseball Abstract 1983. New York: Ballantine
Books.
4. James, B. (1984). The Bill James Baseball Abstract 1984. New York: Ballantine
Books.
5. James, B. (2001). The New Bill James Historical Baseball Abstract. New York:
The Free Press.
6. Lewis, M. (2003). Moneyball: The Art of Winning the Unfair Game. New York:
W.W. Norton and Company.
7. Major League Baseball. (2001). Major League Baseball Scouting Pamphlet.
8. Mayo, J. (2003). A Strong Lean Toward Collegians: Trend Away from High
Schoolers Continues in Draft. November 24, 2003, http://mlb.mlb.com/NASApp/mlb/mlb/news/mlb_news.jsp?ymd=20030603&content_id=353523&vkey=draft2003&fext=.jsp&c_id=mlb.
9. Mayo, J. (2003). High School Players Fall in Draft. November 24, 2003,
http://mlb.mlb.com/NASApp/mlb/mlb/news/mlb_news.jsp?ymd=20030604&content_id=355074&vkey=draft2003&fext=.jsp.
10. Newman, M. (2003). High School vs. College: Does Either Provide a Better
Shot at a “Sure Thing?”. November 24, 2003,
http://mlb.mlb.com/NASApp/mlb/mlb/news/mlb_news.jsp?ymd=20030520&content_id=328934&vkey=news_mlb&fext=.jsp&c_id=mlb

2015-03-20T10:41:26-05:00January 2nd, 2005|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on An Examination of the Moneyball Theory: A Baseball Statistical Analysis
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Predicting Fund Raising Revenues in NCAA Division I-A Intercollegiate Athletics

Submitted by: Chad McEvoy

Introduction

According to the NCAA (Fulks, 2001), contributions from alumni and others, or fund raising, is the second-largest revenue source for Division I-A athletic programs, trailing only ticket sales. Fund raising accounts for nearly five million dollars of the typical Division I-A athletic programs’ $25 million of total revenue, and, as such, is clearly a vital source of funding for intercollegiate athletic programs. Therefore, the ability to forecast fund raising revenues is crucial for college athletic departments. This study will create a model to predict annual fund raising revenues in NCAA Division I-A intercollegiate athletics, thus aiding practitioners in predicting these revenues on their own respective campuses.

Related Literature

Numerous authors have examined the relationships between intercollegiate athletic programs and higher education. These studies have focused on the relationship between college sports performance and variables such as applicants to universities (Allen & Peters, 1982; Chressanthis & Grimes, 1993; Murphy & Trandel, 1994; Toma & Cross, 1998; Zimbalist, 2001), SAT scores of incoming students (Bremmer & Kesserling, 1993; McCormick & Tinsley, 1987; Mixon, 1995; Tucker & Amato, 1993), and university fund raising (Baade & Sundberg, 1996; Brooker & Klastorin, 1982; Budig, 1976; Gaski & Etzel, 1984; Grimes & Chressanthis, 1994; McCormick & Tinsley, 1990; Sack & Watkins, 1985; Sigelman & Carter, 1979). Few studies, however, have investigated athletic fund raising in this regard.

Sigelman and Brookheimer (1983) examined the relationship between 11 predictor independent variables and contributions to both athletics and university fund raising programs at 57 NCAA Division I-A institutions in major athletic conferences using a multiple regression analysis. Football success (r = .335) and traditionalism (r = .242), a scaled measurement of the social and political culture towards civic responsibility and philanthropy, were determined to be significant predictors of giving to intercollegiate athletics annual fund raising programs, albeit not overly strong predictors given their Pearson’s coefficient values.

Coughlin and Erekson (1984, 1985) utilized multiple linear regression analysis to model contributions to athletic fund raising program using 16 independent variables. Coughlin and Erekson utilized 1980-1981 athletics fund raising data published in the Omaha World-Herald as their measurement of the contributions dependent variable, as Sigelman and Brookheimer (1983) had done previously. Coughlin and Erekson’s final regression model accounted for 58% of the variance in predicting athletic contributions. The authors identified football attendance, conference affiliation, bowl participation, state population, men’s basketball winning percentage, and professional competition to be significant determinants of athletic contributions.

Daughtrey and Stotlar (2000) investigated the relationship between contributions to both athletics and the university and winning a national championship in football at the Division I-AA, Division II, and Division III levels. Daughtrey and Stotlar found significant relationships between football championships and increased contributions to athletics with Division II and Division III schools and between football championships and increased contributions to the university with Division III institutions. The authors’ delimitations of studying only national champions and not examining Division I-A institutions prevent useful comparisons between their work and the study conducted here.

No other published studies were identified that attempted to use a variety of variables to predict fund raising contributions to NCAA Division I-A intercollegiate athletics programs. As such, the only published works investigating the ability to predict athletic fund raising contributions are currently 20 years old and each relies upon contributions data collected in 1980-1981. Obviously, much has changed in intercollegiate athletics since then. If an athletic fund raising practitioner today tried to understand and predict fund raising contributions based upon the existing body of literature, they would be relying upon considerably outdated research. Clearly then, there is a need to re-examine the prediction of athletic fund raising contributions, as is the purpose of this study.

Methods

Subjects

The population for this study was defined as all 119 NCAA Division I-A athletic programs and their athletic fund raising contributions for each of the five-year span from 1998-1999 to 2002-2003. Questionnaires were sent to the athletic fund raising director at each of the 119 institutions in performing a census of the population. Thirty-five questionnaires were returned, representing 171 usable subjects, for a usable response rate of 28.7%.

Variables

Based on the work of Coughlin and Erekson (1984, 1985) and Sigelman and Brookheimer (1983), 13 predictor variables were selected to use in explaining the variation in annual athletic fund raising contributions: football and men’s basketball winning percentages for the year examined, the change in football and men’s basketball winning percentages from the previous year, average home attendance for football and men’s basketball in the year examined, whether the school is a member of a “major” athletic conference, whether the school is a public or private institution, state population, and four categorical variables to control for fixed-effects in the time-series regression analysis. Each of these variables is described further in Table 1.

Procedures

Questionnaires were sent to athletic fund raising directors at all 119 NCAA Division I-A athletic programs to collect dependent variable data. Data collection on each of the predictor variables was performed as discussed in Table 1. A fixed-effects ordinary least squares (OLS) multiple regression equation was developed to empirically explain annual athletic fund raising contributions. The fixed-effects model is used to control for changes over time due to the use of panel data. Four indicator variables were used to represent the five years of data from 1998-1999 to 2002-2003. A significance level of .01 was established a priori to reduce the risk of Type 1 error common with time-series regression analyses and large sample sizes.

Results

Table 2 provides descriptive data for the continuous variables included in the regression equation. The results show that the average annual athletic fund raising contributions total was $4,065,616. Additionally, the average home football game attendance was 43,119 and the average men’s basketball game attendance was 8,749.

Five of the 13 independent variables were found to be significantly related to athletic fund raising contributions at the .01 level, including football home attendance (r=.721), conference affiliation (r=-.621), football winning percentage (r=.322), type of institution (r=-.302), and men’s basketball home attendance (r=.237). In examining the correlation coefficients between the independent variables, only the relationship between football attendance and conference affiliation was above .500 or below -.500 (r=-.651), thus providing evidence that multicollinearity was not problematic.

Table 3 summarizes the multiple regression results. The model was a statistically significant estimator of annual athletic fund raising contributions. The model F-statistic equaled 18.647 and was significant at the .01 level. In addition, the model explained 60.7% of the variation in spectator attendance and the adjusted R2 was .574. The R2 and adjusted R2 findings were similar to those found in Coughlin and Erekson (1984, 1985). Additionally, this type of regression analysis allows for an estimation of the magnitude of change in annual contributions based upon a change in values of the independent variables. For example, the results suggest that membership in one of the six conferences with automatic bids to the Bowl Championship Series in football is worth more than $2.5 million per year in athletic fund raising contributions to conference members. Also, the data suggests that annual athletic fund raising contributions would increase by $70 for each average attendee increase at home football games.

Discussion

The purpose of this study was to predict annual athletic fund raising contributions in NCAA Division I-A intercollegiate athletics, providing a needed re-examination of this issue given the dated works in this area of the literature. Despite the passing of two decades and major changes in intercollegiate athletics since the studies of Sigelman and Brookheimer (1983) and Coughlin and Erekson (1984, 1985), this study supports the findings of those previous works, particularly Coughlin and Erekson. As with their work, this investigation found both home football attendance and conference affiliation to be statistically significant predictors of annual athletic fund raising contributions. Additionally, the amount of variance explained in annual athletic fund raising contributions in this study (R2=.607) was extremely close to that of Coughlin and Erekson (R2=.58). None of the similarities between the findings of these studies are, in and of themselves, overly surprising; however, these similarities are somewhat surprising given the radical changes in intercollegiate athletics since the early 1980’s. These changes include a dramatic increase in media/television coverage, rapid increases in revenues and expenses among athletic programs, the creation of the Bowl Championship Series, conference realignment, and progress towards gender equity. It is in the context of all of these major changes that the similarities between this study and previous dated works are noteworthy.

These results indicate that, assuming conference affiliation does not change, an athletic fund raising practitioner should carefully track home football attendance as an indicator of fund raising contributions. A fairly strong positive relationship (r=.721) was found between these two variables. Other changeable variables of interest to practitioners in this regard are football winning percentage (r=.322) and men’s basketball home attendance (r=.237), however, neither approaches home football attendance in the ability to predict athletic fund raising contributions.

References

  1. Allen, B. H., & Peters, J.I. (1982). The influence of a winning basketball program upon undergraduate student enrollment decisions at DePaul University. In M.J. Etzel & J.F. Gaski (Eds.), Applying marketing technology to spectator sports (pp. 136-148). Notre Dame, IN: University of Notre Dame.
  2. Baade, R. A., & Sundberg, J. O. (1996). Fourth down and gold to go? Assessing the link between athletics and alumni giving. Social Science Quarterly, 77, 789-803.
  3. Bremmer, D. S., & Kesserling, R. G. (1993). Advertising effects of university athletic success. Quarterly Review of Economics and Business, 33, 409-421.
  4. Brooker, G., & Klastorin, T. D. (1981). To the victors belong the spoils? College athletics and alumni giving. Social Science Quarterly, 62, 744-750.
  5. Budig, J. E. (1976). The relationships among intercollegiate athletics, enrollment, and voluntary support for public higher education (Doctoral dissertation, Illinois State University, 1976). Dissertation Abstracts International, 37, 4006A.
  6. Chressanthis, G. A., & Grimes, P. W. (1993). Intercollegiate sports success and first-year student enrollment demand. Sociology of Sport Journal, 10, 286-300.
  7. College Football Data Warehouse (2004). Retrieved from cfbdatawarehouse.com.
  8. Coughlin, C. C., & Erekson, H.O. (1984). An examination of contributions to support intercollegiate athletics. Southern Economic Journal, 66, 180-195.
  9. Coughlin, C. C., & Erekson, H.O. (1985). Contributions to intercollegiate athletic programs: Further evidence. Social Science Quarterly, 66, 194-202.
  10. Fulks, D. L. (2002). Revenues and expenses of Division I and II intercollegiate athletics programs: Financial trends and relationships-2001. Indianapolis: National Collegiate Athletic Association. Retrieved from http://www.ncaa.org.
  11. Gaski, J. F., & Etzel, M. J. (1984). Collegiate athletic success and alumni generosity: Dispelling the myth. Social Behavior and Personality, 12(1), 29-38.
  12. Grimes, P. W., & Chressanthis, G. A. (1994). Alumni contributions to academics: The role of intercollegiate sports and NCAA sanctions. American Journal of Economics and Sociology, 53, 27-40.
  13. McCormick, R. E., & Tinsley, M. (1987). Athletics versus academics: Evidence from SAT scores. Journal of Political Economy, 95, 1103-1116.
  14. McCormick, R. E., & Tinsley, M. (1990). Athletics and academics: A model of university contributions. In B. L. Goff & R. D. Tollison (Eds.), Sportometrics (pp. 193-204). College Station, TX: Texas A&M University Press.
  15. Mixon, F. G. (1995). Athletics v. academics: Rejoining evidence from SAT scores. Education Economics, 3, 277-283.
  16. Murphy, R. G., & Trandel, G. A. (1994). The relation between a university’s football record and the size of its applicant pool. Economics of Education Review, 13, 265-270.
  17. National Collegiate Athletic Association. (2004). Attendance data. Retrieved from http://www.ncaa.org.
  18. Sack, A. L., & Watkins, C. (1985). Winning and giving. In D. Chu, J. O. Segrave, & B. J. Becker (Eds.), Sport and Higher Education (pp. 299-306). Champaign, IL: Human Kinetics.
  19. Sigelman, L., & Bookheimer, S. (1985). Is it whether you win or lose? Monetary contributions to big-time college athletic programs. Social Science Quarterly, 64, 347-359.
  20. Sigelman, L., & Carter, R., (September, 1979). Win one for the giver? Alumni giving and big-time college sports. Social Science Quarterly, 60, 284-293.
  21. Toma, J. D., & Cross, M. E. (1998). Intercollegiate athletics and student college choice: Exploring the impact of championship seasons on undergraduate applications. Research in Higher Education, 39, 633-661.
  22. Tucker, I. B., & Amato, L. (1993). Does big-time success in football or basketball affect SAT scores? Economics of Education Review, 12, 177-181.
  23. Zimbalist, A. (2001). Unpaid professionals: Commercialism and conflict in big-time sport. Princeton, NJ: Princeton University Press.

Table 1

<th”>Variable <th”>Description and Sources

Variable Descriptions and Data Sources
CONTRIB Dependent variable representing one year’s annual athletic fund raising contributions, not including capital campaigns. Collected via questionnaire self-reporting from each institution’s athletic fund raising director.
FBWINPCT Represents a school’s football winning percentage for that respective year. Collected from ncaa.org.
BBWINPCT Represents a school’s men’s basketball winning percentage for that respective year. Collected from ncaa.org.
FBWINCH Represents the change in a school’s football winning percentage from the previous year. Collected from ncaa.org.
BBWINCH Represents the change in a school’s football winning percentage from the previous year. Collected from ncaa.org.
FBATTEN Represents a school’s average home football attendance for that respective year. Collected from ncaa.org.
BBATTEN Represents a school’s average home men’s basketball attendance for that respective year. Collected from ncaa.org.
CONFERNC Represents whether or not a school is a member of one of the six major Division I-A conferences that receives an automatic bid to the Bowl Championship Series in football. Coded as a “0” for BCS conference, “1” for non-BCS conference.
INSTTYPE Represents whether the school is a public or private institution. Coded as a “0” for public institution, “1” for private institution.

Table 2

<th”>Variable <th”>Mean <th”>Standard Deviation <th”>Minimum <th”>Maximum

Descriptive Statistics
CONTRIB $4,065,615.92 $3,502,701.71 $201,791 $14,363,913
FBWINPCT .513 .212 .000 1.000
BBWINPCT .575 .155 .222 .892
FBWINCH -.007 .206 -.727 .492
BBWINCH -.005 .151 -.419 .398
FBATTEN 43,118.94 25,737.64 6,595 111,175
BBATTEN 8,748.54 4,975.17 935 22,248
CONFERNC .40 .491 .00 1.00
INSTTYPE .16 .371 .00 1.00
POPULATE 8,596,946.04 8,142,659.46 1,808,344 33,871,648

Note: N=171

Table 3

<th”>Variable <th”>Unstandardized Beta Coefficient <th”>Standard Error <th”>T-statistic <th”>P-value

Regression Results
FBWINPCT 934.813 1,259.942 .742 .459
BBWINPCT 1,474.402 1,631.419 .904 .368
FBWINCH -658.047 1,064.320 -.618 .537
BBWINCH 434.665 1,386.728 .313 .754
FBATTEN 70.567 12.129 5.818 .000
BBATTEN -137.200 53.892 -2.546 .012
CONFERNC -2,587,336.224 529,184.751 -4.889 .000
INSTTYPE -636,637.927 530,003.528 -1.201 .231
POPULATE -.0253 .023 -1.105 .271
TIME0203 1,417,825.114 567,331.928 2.499 .013
TIME0102 1,207,864.865 568,200.487 2.126 .035
TIME0001 916,870.951 560,734.574 1.635 .104
TIME9900 392,457.586 565,992.692 .693 .489
Constant 1,443,097.013 1,260,510.634 1.145 .254

Note: R2=.607, Adjusted R2=.574, F-statistic=18.647, P-value=.000

2015-03-20T10:32:05-05:00January 1st, 2005|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on Predicting Fund Raising Revenues in NCAA Division I-A Intercollegiate Athletics
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The Usage of the Sports Image in Advertising Sector in Selected Turkish Television Channels

Submitted by: Hakan Sunay, PhD. & Velittin Balci, PhD.

ABSTRACT

This research was made to assess the relationship between advertisements
on marketing sports products on Turkish television channels and sports
images on the basis of products, and to get information on the tendencies
of onlookers of sports products through strengthening onlookers’
sports images and remembering them later by analyzing advertisements.
This research was made to determine how often sports images are used in
the advertisement sector and the impact of sports in advertisements.

The universe of the research was the first four most watched television
channels according to the reports of AGB (Research Improvement and Information)
in June, 2001. These channels were Show TV, Channel D, Star TV, TRT 1
(Turkish Radio and Television). In this research, the books and researches
on sports management and advertising, the total durations of advertisements
on related channels and the ratios of sports images used in advertisements
were studied. All the advertisements broadcast between 09.00-21.00 hours
for one week on each of these four most watched channels were studied,
and the results were shown in graphics and tables.

Observation method was used to determine the results of the research.
‘SPSS’ statistics programmer was used for the statistical
analysis of this research. Frequency and percentage techniques were used
to determine the results.

In conclusion, this research showed that the advertisements including
sports image were broadcast more than the others, and advertisement producers
showed a great deal of interest in sports images. Sports concept is used
as on important tool in advertisement, marketing and image in advertisements
broadcast on Turkish television channels studied in this research.

INTRODUCTION

Sport is one of the most important social concepts. Many companies use
sports as tool to its popularity. The companies producing sports equipment
and private sports clubs are the examples using sports. They turn the
Professional sportsmen into stars and give the image that equipment used
by such sportsmen supports their success. As a result, the equipment produced
by them sells very easily. Today, many companies like banks, construction
companies not related to sports use sports as images (Zeki,1998). For
instance, a private bank can use marathon runners as an image to emphasize
that this bank is forward.

In our century, sports is in a process that interfered a lot in marketing
and its industrialization. Advertisements make sports more popular. All
the organizations hoping for profit use concepts like arts and sports
to introduce themselves (Ünsal,1994). This is the basic factor in sport
image. Sports image can be used in various types in society. The basic
objective of advertisements is to become part of success, and to be remembered
with this image (Zeki,1998). A winner in the Olympic Games in remembered
by the name of the sponsor company tries to become part of sportsmen’s
success. The objective is to remembered and to be well known.

The reason for the popularity of football and basketball is the popular
sport images. Companies can be popular using star sportsmen and sport
image. Sports equipment producers work with famous sportsmen. Sport images
often used in textile, food, transportation and popular sportsmen are
remembered with such companies(Kocabaş, 2001).

Advertisement etiquettes used in sports and product introduction are
(Bir,1988);

  • Advertisement is a guide for consumers. It gives information on new
    products.
  • Advertisement decreases distribution costs and helps retail sellers.
  • Advertisement encourage competition, and increases the amount and
    quality of production.
  • Increases in production and sale amounts helps prices go down.
  • Advertisement makes communication tools independent.

In this study, the visage type, frequency, and effect of sports image
in product presentations in advertisements on Turkish Television is analyzed.

MATERIAL AND METHOD

The universe of this research is the Turkish Television channels. These
channels are Show TV, Channel D, Star TV, TRT 1, the most watched 4 channels
according to the reports of AGB (Research, improvement and information:
AGB Group,2001). All the advertisements on these channels were watched
and studied for one week between 09:00-21:00 hrs. And the results are
shown in table as 1. Pre-program watching 2. During program watching.

Pre-program advertisement are 7-10 minutes and this variation is due
to Prime Time programs. During programs advertisements last 3-4 minutes.
The intervals of programs can change 2 or 3 times. The duration of advertisement
within the programs is 6-9 minutes. According to AGB reports the total
advertisement duration is 28 hrs. a week and 4 hrs a day in Show TV, Channel
D, Star TV, TRT 1. the first for channels in june 2001 and their percentages
are as shown:

Show TV 21%
Channel D 20%
Star TV 19%
TRT 1 17%
Others 23%

While collecting data, necessary information were found in documents.
Each of the four most watched channels were studied for one week and the
results were sown in graphics.

The reason to watch every channel separately is that advertisements start
and finish at the same hours.

The ratio and distribution of advertisements containing sports image
were shown in percentages. SPPS statistics program was used for the statistical
analysis of this research.

FINDING AND COMMENTS

The results and comments on the relationship between advertisement and
sports image in the most watched channels in June according to AGB reports
are shown below.

Table-1: Daily and weekly amount of all the advertisements broadcast in
all the channels.

Duration Total broadcast Time (hrs) Total broadcast Time (hrs) Advertisement Time (hrs) Total advertisement percentage
Daily 48 100 4 12
Weekly 336 100 28 12

These tables show total weekly and daily broadcasts of television channels
and the amount of advertisement in these broadcasts. The total amount
of hours is very high. However, this amount shows that companies made
big investments in advertisements.

Graphic-1: The frequency of subjects in all the
advertisements broadcast in all channels
Graphic 1
The reason for the high ratio of food, textile and bank advertisements
is that they are the basic necessities sports image is used in all the
advertisements for them to be remembered.

Table -2 : Daily and weekly advertisement ratio of sports image used in advertisement in all the channels.

Duration Total broadcast minutes Total advertisement percentage Advertisement broadcast minutes Sports İmage percentage used
Daily 175 100 65 37
Weekly 1225 100 455 37

According to these graphics and tables, the ratio of advertisements containing
sports image is very high among the other advertisements. In this table,
the high amount of advertisements containing sports image in other advertisements
are shown. The high frequency of the usage of sports image shows that
advertisement companies prefer it to affect people.

DISCUSSION AND RESULTS

According to our research, the advertisements containing sports image
are very high. Sport concepts affects a lot of people and sports ımage
can be used everywhere. The high number of products are variety in advertisement
sector lead to usage of sports image . advertisement companies combine
every event and concept with sport.

Advertisement producers prefer sports image according to this research.
Sport image is the highest among the other images. Advertisements makers
use sports image because it is a concept affecting people. Sports image
has become a tool to affect people. Advertisements makers use Professional
sportsmen to affect young people so that they can sell their products
easily.

Sport image can be used frequently in advertisements because sport affect
people in various directions. Sports can be the symbol of many subjects
(Ünsal, 1994).

People may like sports more through sport image. Production companies
use sports image to sell their products, however, they let people like
sport more. Companies should employ people who enough information on sports.

Sports influences many people. It is observed that advertisements containing
sports image has been increasing. Company owners prefer using sports image
more (Zeki,1998).

In this research, popular sports branches and popular sportsmen are
preferred in advertisements containing sport image. For instance, football
image is used many advertisements. Because football is a very popular
branch of sport. Either a famous footballer is used or popular brand is
used with football image in advertisements. If other branches become popular,
advertisement subjects can be more various.

The images of less popular sports branches can be used so people can
be interested in various sport branches.

REFERENCES

  1. AGB Group, (2001),(TAM- Television Audience Measurement) Haziran,
    2001 kayıtları.
  2. Bir, A.; (1988), Reklamın Gücü, Bilgi Basım, İstanbul,
  3. Kocabaş, F; Elden, M., (2001), Reklamlar, Kavramlar, Kararlar,
    Kurumlar, İletişim Yayınları, İstanbul,
  4. Ünsal; Y. ; (1984), Bilimsel Reklam ve Pazarlamadaki Yeri,
    TİVİ; Basımevi, İstanbul,
  5. Zeki, A.; (1998), Reklam ve İmajları, Bilişim
    Yayınları, Ankara,
2016-10-14T11:42:27-05:00March 9th, 2004|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on The Usage of the Sports Image in Advertising Sector in Selected Turkish Television Channels
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What Type of Character Do Athletes Possess?

Submitted by: Andy Rudd, PhD. & Sharon Stoll, PhD.

Abstract

The purpose of this study was two-fold: (1) to develop a paper and pencil
instrument that measures two types of character: moral versus social;
(2) to determine if college athletes, particularly team sport athletes
support social character over moral character as a result of the way character
may be defined and fostered by many coaches, parents, and general society.
To test our hypothesis that athletes support the practice of social character
over moral character we developed a paper and pencil instrument called
the RSBH Value Judgment Inventory. Participants in the study were N=595
college students from a variety of colleges/universities (National Collegiate
Athletic Association Division I, II, III and National Association for
Intercollegiate Athletics). More specifically, there were n=293 team sport
athletes, n= 76 individual sport athletes and n=225 non athletes (and
1 subject that did not indicate their status). College athletes were compared
to college non athletes in order to understand the effects of sport participation
on moral and social character.

Saliently, results showed that on average, team sport athletes’ social
character index scores were higher than their moral character index scores.
Also of salience, non-athletes scored significantly higher than team sport
athletes on the moral character index whereas team sport athletes scored
significantly higher than non-athletes on the social character index.
Reasons for why there were differences between team sport athletes and
non-athletes on the RSBH Value Judgment Inventory are discussed as well
as other findings.

Introduction

Since the early part of the 20th century, participation in American sport
has been widely and strongly viewed as a vehicle for developing character
(Armstrong, 1984; Ogilvie & Tutko, 1971; Sage, 1988, 1998; Shields
& Bredemeier, 1995). In response to this claim, researchers from a
variety of disciplines have empirically tested the popular notion that
sport builds character (see for example, Beller & Stoll, 1992, 1995;
Hodge, 1989; Kleiber & Roberts, 1981; Ogilvie & Tutko, 1971; Penny
& Priest, 1990; Rudd, Stoll & Beller, 1997; Shields & Bredemeier,
1995). Contrary to what many may believe, results from these studies have
suggested that sport does not build character.

From the numerous studies, character development research that has used
an instrument called the Hahm-Beller Values Choice Inventory (HBVCI) may
be the most profound because of the sizeable, accumulative database and
replication (Belier & Stoll, 1992, 1995; Hahm, Beller, & Stoll,
1989; Penny & Priest, 1990; Rudd, Stoll, & Beller, 1997). With
a database of over 60,000 athletes and non-athletes and over 250 university
studies, the HBVCI has consistently found that the majority of athletes
will not support the moral ideal in competition, i.e., moral character.
However, despite the well-published and disseminated research, we have
continued to hear from coaches, parents, and the media that sport builds
character or that athletes frequently display character (Browit, 1999;
Docheff, 1997; Herman, 2000; Zimmerman, 2001). As a result, we began to
wonder if there is another way to define character, which might explain
why athletes do not support the notion of moral character.

From the character development literature, newspapers, media, and personal
communications with coaches, parents, and the general populace we discovered
that many individuals appear to define character from a social perspective
rather than a moral perspective. Thus, many define character in terms
of social values such as teamwork, loyalty, self-sacrifice, work ethic,
and perseverance which may be considered as “social character” as
opposed to “moral character” which has been denoted by moral values
such as honesty, fairness, and responsibility (see for example, Arnold,
1999; Shields & Bredemeier, 1995). The purpose of this study then
was two-fold: (1) to develop a paper and pencil instrument that measures
two types of character in the sport context: moral versus social; (2)
to determine if college athletes, particularly team sport athletes support
social character over moral character as a result of the way character
may be defined and fostered by many coaches, parents, and general society.
To test our hypothesis that athletes support the practice of social character
over moral character we developed a paper and pencil instrument called
the Rudd-Stoll-Beller-Hahm Value Judgment Inventory. This article will
present the findings from our instrument and general study.

Method

Participants

A sample of N=595 college students from a variety of colleges/universities
(National Collegiate Athletic Association Division I, II, III and National
Association for Intercollegiate Athletics) participated in the study.
More specifically, there were n=223 non-athletes, n=290 team sport athletes,
and n=76 individual sport athletes that responded to all of the questions
on the social character index (first 10 questions of RSBH Value Judgment
Inventory). There were also n=296 males and n=293 females that responded
to all of the questions on the social character index. The number of subjects
that responded to all of the questions on the moral character index (last
10 questions of RSBH Value Judgment Inventory) were n=221 non-athletes,
n=289 team sport athletes, and n=76 individual sport athletes. Lastly,
there were n=294 females and n=292 males that responded to all of the
questions on the moral character index.

Definition of the Non Athlete

For the purpose of this study, non-athletes were defined as any student
who was not currently participating in college athletics at the time of
the administration of the RSBH Value Judgment Inventory. In most cases
this means that a non-athlete was someone who had never been involved
in athletics or someone who had been involved in athletics but not at
the college level. There was also the possibility that there could have
been non-athletes in the sample who were collegiate competitors at one
time.

Although there may be non athletes in the sample that in the past competed
at one level or another, because they no longer compete at a high level,
we hypothesized that their competitive values that relate to character
in the sport context would not be the same as the sample of athletes that
currently compete. Thus, we would have some indication of how sport participation
affects athletes who compete versus those that do not in terms of moral
and social character.

Procedure

College non-athletes were administered the RSBH Value Judgment Inventory
while in their respective academic classes. College team sport athletes
and college individual sport athletes were administered the RSBH Value
Judgment Inventory also in academic classes or at practice or in an athletic
training room. With every administration of the RSBH Value Judgment Inventory,
both college athletes and college non-athletes were told that their participation
in the study was anonymous and that there participation was voluntary.

Design

A retrospective causal-comparative design in which college athletes were
compared to college non-athletes was used to understand the effects of
sport participation on moral and social character (see Gay & Airasian,
2002 for causal-comparative designs).

Instrumentation

In 1998, the Rudd-Stoll-Beller-Hahm (RSBH) Value Judgment Inventory was
developed to measure moral and social character (Rudd, 1998). To do so,
the RSBH Value Judgment Inventory is comprised of two indices: a social
character index and a moral character index. The social character index
consists of ten sport context scenarios that mostly take place outside
the lines of competition. Concomitantly, these scenarios are infused with
the social values of teamwork, loyalty, and self-sacrifice. Subjects are
asked to respond to each scenario via a 5-point Likert scale (Strongly
Agree, Agree, Neutral, Disagree, and Strongly Disagree).

The moral character index is comprised of ten gamesmanship scenarios
that were selected from the Hahm-Beller Values Choice Inventory (HBVCI).
These 10 questions were selected based on their high internal reliability
ranging from .81 to .88 over six different studies (see for example, Beller
& Stoll, 1992, 1995; Beller, Stoll, Bunnell, & Cole, 1996; Hahm,
Beller, & Stoll, 1989). In sum, subjects receive two scores: a social
character index score and a moral character index score.

The more frequently subjects agree with the social character scenarios,
the higher one scores on the social character index. The higher the score,
the more it is suggested that individuals are believed to support social
values and more generally social character in the sport milieu. Concurrently,
for the moral character index, the more frequently subjects “disagree”
with the various gamesmanship practices, the higher one’s score and the
more one is believed to support moral character in sport.

Four pilot studies were conducted to establish the reliability and validity
of the RSBH Value Judgment Inventory. Specifically, for the fourth pilot
study, the sample contained n=149 non-athletes, n=169 team sport athletes
and n=36 individual sport athletes. There were also n=182 males and n=172
females. An internal reliability analysis indicated a Cronbach alpha of
.72 for the social character index and a Cronbach alpha of .86 for the
moral character index. The internal reliability for the current sample
used in this study showed a Cronbach Alpha of .87 for the moral character
index and a Cronbach Alpha of .73 for the social character index.

As part of establishing the validity of the RSBH Value Judgment Inventory,
an exploratory factor analysis was conducted during the fourth pilot study
to seek evidence of construct validity. Results from the factor analysis
are somewhat difficult to interpret, however, the first factor does suggest
that there is a distinct contrast between social character (questions
1-10) and moral character (questions 11-20). Thus, there is evidence to
suggest that our instrument is measuring two distinct constructs; moral
character versus social character (see Tables 1 and 2).

Table 1: Total Variance Explained

Total Variance Explained
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation
Factor Total % Variance Cumulative % Total % Variance Cumulative % Total
1 6.28 31.40 31.40 5.76 28.84 28.84 4.08
2 1.57 7.86 39.27 1.04 5.22 34.05 2.70
3 1.30 6.52 45.79 0.69 3.46 37.52 3.25
4 1.09 5.43 51.22 0.46 2.32 39.83 2.57
5 1.06 5.32 56.53 0.36 1.82 41.65 1.38
6 0.96 4.78 61.33
7 0.81 4.05 65.37
8 0.79 3.93 69.30
9 0.77 3.86 73.16
10 0.66 3.28 76.44
11 0.62 3.09 79.52
12 0.60 3.01 82.53
13 0.58 2.89 85.41
14 0.52 2.59 88.00
15 0.47 2.37 90.37
16 0.43 2.15 92.52
17 0.43 2.13 94.65
18 0.40 2.00 96.64
19 0.36 1.81 98.45
20 0.31 1.55 100.00

 

Table 2: Factor Matrix

Factors
1
2
3
4
5
Quest. 1 -0.23 0.15 0.19 -5.04E-02 0.26
Quest. 2 -0.55 0.31 0.19 -5.71 2.01E-03
Quest. 3 -0.53 0.39 -8.95E-02 -7.61E-02 -0.12
Quest. 4 -0.15 2.55E-02 0.17 0.15 -6.51E-02
Quest. 5 -0.49 0.35 0.22 0.10 0.20
Quest. 6 -0.46 0.24 0.17 6.74E-02 -0.28
Quest. 7 -0.38 0.28 -2.00 9.03E-02 7.40E-02
Quest. 8 -0.64 0.22 -0.16 0.15 -4.85E-02
Quest. 9 -0.24 8.21E-02 -0.13 0.14 0.24
Quest. 10 -0.34 1.56E-02 0.22 0.13 -0.09
Quest. 11 0.61 -7.83E-02 .6.14E-02 9.52E-02 0.14
Quest. 12 0.65 0.31 -9.39E-02 -0.25 2.19E-02
Quest. 13 0.61 9.18 0.31 9.12E-02 -1.19E-02
Quest. 14 0.61 0.15 0.27 -0.14 -0.18
Quest. 15 0.60 1.79E-02 0.31 0.15 8.08E-02
Quest. 16 0.62 0.41 -9.56E-02 -0.21 9.48E-02
Quest. 17 0.72 0.20 6.90E-02 2.10E-02 -0.10
Quest. 18 0.55 0.23 -0.38 0.28 -7.11
Quest. 19 0.67 0.26 -8.50E-02 0.14 3.07E-02
Quest. 20 0.61 1.19E-02 1.89E-02 0.30 8.04

 

Analysis

A 3×2 (teams sport athletes, individual sport athletes, and college non
athletes) x (males and females) univariate factorial analysis of variance
was used to compare differences between college team sport athletes, college
individual sport athletes, and college non athletes and to also compare
males and females on the moral and social character index. A Tukey Post
hoc test was used to detect specific group differences after a significant
F test was found.

For clarification, although comparing differences between gender on the
RSBH Value Judgment Inventory was not the focus of this study, gender
was introduced into the analysis as a result of previous studies with
the HBVCI that have shown that overall females score significantly higher
than males (Belier & Stoll, 1992, 1995., Penny & Priest, 1990;
Rudd, Stoll, & Beller, 1997). As well, a previous study by Rudd (1998)
showed that overall, males scored significantly higher than females on
the social character index part of the RSBH Value Judgment Inventory.
Thus, we were concerned with interaction effects.

Results

Results from the univariate factorial analysis of variance revealed that
there was a significant difference between team sport athletes, individual
sport athletes, and non athletes on the moral character index F (2, 580)
= 31.04, p<. 05. There was also a significant difference between team
sport athletes, individual sport athletes, and non athletes on the social
character index F (2, 583) = 22.86, p<. 05.

A significant difference between males and females on the moral character
index F (1, 580) = 87.23, p<. 05 was also found. There was also a significant
difference between males and females on the social character index F (1,
583) = 68.33, p<. 05 There was no gender interaction for either of
the two univariate analyses.

More specifically, a Tukey’s post hoc indicated that non-athletes scored
significantly higher (M=27.51, SD=7.13) than team sport athletes (M=20.75,
SD=6.41) on the moral character index. Further, individual sport athletes
scored significantly higher (M=26.02,

SD=6.87) than team sport athletes (M=20.75, SD=6.41) on the moral character
index. And non-athletes (M=27.51, SD=7.13) scored only slightly higher
than individual sport athletes (M=26.02, SD=6.87) on the moral character
index. Finally, overall there was a significant difference between males
and females in which females scored significantly higher (M=27.56, SD=6.81)
than males (M=20.42, SD=6.36) on the moral character index.

Dissimilarly, a Tukey’s post hoc test indicated that team sport athletes
scored significantly higher (M=28.47, SD=5.92) than non-athletes (M=23.30,
SD=5.35) on the social character index. Team sport athletes (M=28.47,
SD=5.92) also scored significantly higher than individual sport athletes
(M=25.46, SD=5.59) on the social character index.

And individual sport athletes (M=25.46, SD=5.59) scored significantly
higher than non-athletes (M=23.30, SD=5.35). Also on the social character
index, males scored significantly higher (M=28.87, SD=6.18) than females
(M=23.35, SD=4.72).

Discussion

The purpose of this study was to develop an instrument that could measure
two types of character: moral versus social and to then determine if college
athletes, team sport athletes in particular, support social character
over moral character. Concurrently, this study was aimed towards ascertaining
the effect of sport participation on moral and social character and therefore
we compared college athletes (team sport athletes and individual sport
athletes) to college non-athletes.

Team sport athletes scored significantly higher than non-athletes and
individual sport athletes on the social character index. And individual
sport athletes scored significantly higher than non-athletes on the social
character index. In contrast, team sport athletes scored significantly
lower than non-athletes and individual sport athletes on the moral character
index. Lastly, non-athletes scored only slightly higher than individual
sport athletes on the moral character index. All group differences on
moral and social character are consistent with previous studies using
the HBVCI to measure moral character (for example, Beller & Stoll,
1995, Beller, Stoll & Rudd, 1997; Rudd, Stoll & Beller, 1997)
or a previous study using the RSBH Value Judgment Inventory to measure
moral and social character (Rudd, Stoll & Beller, 1999).

As for explanations of the group differences, page limitations do not
allow for a full explication of all the various differences. Instead,
we will briefly address differences between team sport athletes and non-athletes
given those were the comparisons of most interest.

The reason why team sport athletes scored significantly higher than non
athletes on the social character index may be as a result of the emphasis
that coaches, parents, and general society place on values such as teamwork,
loyalty, self-sacrifice, perseverance, and work ethic in team sports.
Why such values are emphasized may be related to our American ideology
that emphasizes capitalism and corporation. Those such as (Berlage, 1982;
Coakley, 1998; O’Hanlon, 1980; Sage, 1988, 1998) have maintained that
sport is used as a vehicle to instill the types of values among sport
participants that will allow them to go out into society and contribute
to corporate America.

As for why team sport athletes scored significantly lower than non athletes
on the moral character index, the reason may relate to the socialization
process in the sport milieu in which many team sport athletes learn that
winning takes precedence over the moral ideal (see for example, Dreyfuss,
2001; Eitzen, 1999; Hawes, 1998; “A Purpose,” 1999). Therefore,
many athletes have not been taught to appreciate moral idealism or the
notion of moral character in competition.

In conclusion, there is evidence from our study to suggest that sport
may build social character, e.g., teamwork, loyalty, and self-sacrifice
as a possible result of the emphasis that is placed on social character.
In opposition, there is little evidence to suggest sport builds moral
character when defining character from a moral idealistic standpoint.

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Footnotes

1. A study by Stoll, Beller, Cole, and Burwell (1995) revealed that there
was not a significant difference between Division I and Division III athletes
on the HBVCI.

Results suggest that athletes have similar competitive values regardless
of the competitive level of the university. Therefore, researchers felt
it was acceptable to use a sample of athletes from various university
levels and to then compare an aggregation of the athletes to the sample
of non-athletes.

2015-03-20T08:37:41-05:00March 8th, 2004|Contemporary Sports Issues, Sports Studies and Sports Psychology|Comments Off on What Type of Character Do Athletes Possess?
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Evaluation of Motivation in Patients with Coronary Heart Disease Who Participate in Different Rehabilitation Programs

Submitted by: Panagiota Papageorgiou, Panagiotis Fotinakis, Efstratia Tsitskari & Vasiliki Giasoglou

ABSTRACT

The purpose of this study was to evaluate “motivation in patients
with coronary heart disease, who participated in different rehabilitation
programs and those who did not participate.” Fifty-one (n=51) male patients
suffering from coronary heart disease participated in the present study.
Fifteen participated in a rehabilitation program in a gym; eighteen participated
in a swimming program and eighteen consisted of the control group. The
mean age of the participants was 60.83 (SD=±3.3). Participants completed
the Sport Motivation Scale (SMS). According to the results, patients who
participated in the gym program had statistically higher levels in IM
to knowledge, to stimulation, to accomplishment and EM to interjected
regulation. On the contrary, the control group had statistically higher
levels in EM to external regulation and motivation.

INTRODUCTION

Atherosclerotic cardiovascular diseases are the major cause of death
in middle-aged and older-adults in Europe and United States (BC Ministry
of Health and Ministry Responsible for Seniors, 1996; Giannuzzi et al.,
2003; Sarafino, 1990).

Cardiac Rehabilitation programs were first developed in the 1960s when
the benefits of ambulation during prolonged hospitalization for coronary
events had been documented. Exercise was the primary component of these
programs (Giannuzzi et al., 2003). Over the past 4 decades, numerous scientific
reports have examined the relationships between physical activity, physical
fitness and cardiovascular health (Cerubini, Lowenthal, Williams &
Aging Clinical and Experimental Research, 1997; Fletcher, Balady &
Amsterdam, 2001; Oldridge, et al., 1993; Pate et al., 1995). Randomized
clinical trials of exercise training showed improvement in coronary risk
factors such as blood pressure, body composition, fitness, lipid and lipoprotein
profiles (Dunn et al., 1997; European Hear Failure Training Group, 1998;
EUROASPIRE II Study Group, 2001; Myers, 2003; Rockhill, Willet & Manson,
2001). Swimming and exercise in a gym are included in the so-called coronary
sport groups; as endurance sports with training effects suitable for rehabilitation
(Lins et al., 2003).

Although exercise is considered to be the easiest type of rehabilitation
for patients with coronary heart disease (CHD), their maintenance into
exercise programs is difficult most of the times (Harlan et al., 1995).
Reported rates of uptake of cardiac rehabilitation range from 15% to 59%
(Gattiker, Goins & Dennis, 1992; Pell, Pell & Morrison, 1996).
Approximately 20-25% of patients dropout of exercise programs within the
first three months and about 40-50% within 6 to 12 months (Song et al.,
2000; Oldridge, 1998; Oldridge, 1982).

Psychosocial variables that were found to influence the entrance and
completion of a CR program include motivation, mood states, and social
support (Myers, 2003). Motivation consistently has been shown to be a
strong indicator of initiation and maintenance of participation in a CR
program. It was found that the people that seem to have lower levels of
motivation perceive more barriers or problems associated with their exercise
programs. (Dishman & Ickes, 1981; Evenson & Fleury, 2000). The
literature on physical rehabilitation frequently refers to patient motivation
in explaining differences in outcome among patient groups with similar
pathologies (King, Taylor & Haskel, 1993; Maclean, & Pound, 2000).
Several studies have lent empirical support to the hypothesis that patient
motivation is a determinant of rehabilitation outcome (Clark & Smith,
1997; King & Barrowclough, 1989; Oldridge & Stoedefalke, 1984;
Wolf, 1969).

In general, motivation expresses the needs and the wishes that regulate
the direction, the intensity and the continuation of a specific behavior
(Deci & Ryan, 1985). Deci and Ryan (1985) explained intrinsic and
extrinsic motivators and their influence on self-determination in their
theory of self-determination. Self-determination is a quality of human
functioning that involves the experience of a choice. An important distinction
concerning motivation in exercise and sports is the one between intrinsic
and extrinsic motivated behavior for participation (Ryan et al., 1984).
Intrinsic motivation (IM) refers to an individual who participates in
an activity simply for the satisfaction of doing so (Fortier, et al.,
1995). Intrinsic motivation has been postulated to have three separate
categories: IM to know, to accomplish things and to stimulation (Vallerand
& Losier, 1999; Vallerand, et al., 1989; Vallerand &Bissonnette,
1992).

Extrinsic Motivation (EM), on the other hand, is related to external
factors, such as rewards and punishment (Vallerand & Perrault, 1999;
Ryan & Deci, 2000). The three types of extrinsic motivation, from
the least self-determined to the most self-determined, are external regulation,
interjected regulation and identification (Ryan et al., 1990).

The third type of motivation, amotivation, is characterized by
the thought that actions have no control over outcomes (Deci & Ryan,
1985). In other words, amotivated individuals believe that forces out
of their control determine behaviors.

The specific purpose of this study was to examine the differences in
motivation between patients, who participated in different cardiac rehabilitation
programs and patients who did not participated.

METHOD

Sample

A sample of 51 male patients suffering from coronary heart disease was
selected and divided into 3 groups. Fifteen (n=15) participated in a rehabilitation
program in a gym, eighteen (n=18) participated in a swimming program and
eighteen (n=18) patients consisted of the control group. The participants
couldn’t choose the type of activity and all of them followed a
phase III cardiac rehabilitation program. The mean age of patients was
(mean±S.D. 60.83 ± 3.3).

Procedures

The sampling procedure required that the prospective subjects met the
following criteria: (1) having undergone cardiac-related procedures such
as coronary artery bypass graft surgery (CABG) or percutaneous transluminal
coronary angioplasty (PTCA); (2) able to participate in the cardiac rehabilitation
programs for more than 15 weeks (for the exercise groups) with an attendance
rate of more than 70%. Exclusion criteria were clinically unstable heart
failure, unstable arrhythmias and other exercise limiting concurrent condition
as skeletal or muscular disorders. All exercise patients followed the
routine 3 times per week for 45-90 minutes per session at an intensity
of 60-85% of the maximum heart rate (MHR).

The duration of the rehabilitation programs was 20 weeks. During the
20-week period, the type and intensity of exercise and heart rate and
blood pressure before, during and after exercise were recorded for all
subjects in the exercise groups. Exercise patients did not participate
in any other physical training.

Each training-session in the gym rehabilitation program consisted of
walking, cycling or running on an ergometer. It consisted of 10 minutes
warm–up, 10 minutes stretching and flexibility exercises, of 25
minutes endurance training with heart rate (HR) maintained on 60% – 85%
of the maximum heart rate (MHR) and 10 minutes cool–down.

The swimming exercise program included 10 minutes warm-up, 10 minutes
stretching and flexibility exercises in the pool, 12 minutes walking in
the pool with kickboards and barbell and 12 minutes running or walking
in the pool with alternative intensity in a distance of about 200-250m,
with heart rate (HR) maintained on 60% – 85% of the maximum heart rate
(MHR) and 10 minutes cool-down.

Permission to conduct the investigation was received from the local athletic
association and the individual coaches. Each participant took 10-15 minutes
to complete the questionnaire and responses to the instrument were kept
anonymous. The participants were advised to ask for help if confused about
either the instructions or the clarity of any particular item. No problems
were encountered in completing either of the inventories or understanding
the nature of the questions.

Questionnaire

Patients completed the Sport Motivation Scale (SMS) developed by Pelletier,
Fortier, Vallerand and Tuson (1995). The SMS consists of seven sub-scales
that measure the three types of motivation: intrinsic, extrinsic, and
amotivation. There are four items per sub-scale, thus there are a total
of 28 items being assessed. Each item represents a possible reason why
patients with coronary heart disease participated in an exercise rehabilitation
program. Subjects must rate the extent to which each item corresponds
to one of their participation motives on a seven-point Likert scale, ranging
from “not at all” (1) to “exactly” (7). The English
questionnaire is valid, consistent, and reliable. Pelletier et al. (1995)
found that the English translation of the questionnaire had a satisfactory
level of internal consistency. Additionally, correlations between the
subscales and confirmatory factor analysis have confirmed the determination
continuum and the construct validity of the scale (Pelletier, et al. 1995).

Statistical Analysis

The data was analyzed in two steps. First, internal consistency of subscales
was assessed using Cronbach alphas (Cronbach, 1951). Secondly, a one –way
MANOVA was used to determine if significant differences existed among
patients exercise groups and control group across the seven SMS subscales.
When the results of the one –way MANOVA were statistically significant,
Post hoc Scheffe analysis were conducted to determine which specific patient-group
means were significantly different from one another. The level of significance
was 0.5.

RESULTS

The internal consistency of the Sport Motivation subscales was determined
by calculating Cronbach’s Coefficient Alpha. The seven subscales
of SMS demonstrated acceptable internal reliability (IM to know =. 70,
IM to stimulation =. 80, IM to accomplishment =. 75, EM to external regulation
=. 69, EM to interjected regulation =. 66, EM to identified regulation
=. 75 and amotivation =. 70). These findings are supported by previous
study (Papageorgiou, 2001).

A one – way MANOVA indicated significant differences between the three
patients groups across the seven SMS subscale, Wilk’s Lambda=. 113,
(F7,14=9.892, P<0.05, eta squared=0.664).

Univariate ANOVA results indicated a significant difference only for
the six dependent variables. Statistically significant differences were
found for IM to know (F2,41=13.485, P<0.05, eta squared=0.397),
IM to stimulation (F2,41=43.581, P<0.05, eta squared=0.680),
IM to accomplishment (F2,41=6.581, P<0.05, eta squared=0,243),
EM to external regulation (F2,41=6.548, P<0.05, eta squared=0.242),
EM to interjected regulation (F2,41=22.913, P<0.05, eta
squared=0.528) and amotivation (F2,41=5.707, P<0.05, eta
squared=0.218). Scheffe post hock analysis indicated that patients who
participated in the gym rehabilitation program had statistically higher
levels in IM to know, to stimulation. to accomplishment and EM to interjected
regulation. Additionally, the control group had statistically higher levels
in EM to external regulation and Amotivation. Table 1 provides the means
and standard deviations for these dependent variables.

Table 1 Means and Standard Deviations of Motivation Variables by Group

Variables Gym Group Swimming Group Control Group
M±SD M±SD M±SD
IM to know 4.56±0.798 3.73±0.504 3.44±0.455
IM to stimulation 4.64±0.432 4.18±0.175 3.39±0.433
IM to accomplishment 4.41±0.701 3.75±0.365 3.98±0.358
EM to external regulation 4.10±0.991 3.76±0.240 4.5±0.342
EM to introjected regulation 3.79±0.729 3.46±0.311 2.69±0.286
Amotivation 1.47±0.588 1.63±0.208 2.0±0.450

DISCUSSION AND CONCLUSION

This study explored the influence of two specific types, frequency and
duration of exercise cardiac rehabilitation programs in-patient motivations.

Findings from this study indicated that patients who participated in
the gym rehabilitation program had statistically higher levels in IM to
know, to stimulation, to accomplishment and EM to interjected regulation,
than patients who participated in the swimming rehabilitation program
and patients who did not participate in any program (control group). One
of the possible reasons for the differences between the two exercise patient
groups may be due to the fact that swimming is not very much allowed for
cardiac patients, despite the valuable advantages as an overall physical
conditioning and leisure avocation (Kawahatsu et al., 1986). According
Ebbeck, Gibbons and Loken-Dahle (1995) the differences in reasons for
participating depend on the type of physical activity in which the individual
is involved.

Specifically, patients who participated in a gym program to fulfill intimacy
or acceptance needs were motivated intrinsically to participate in order
to gain knowledge, to experience stimulation and accomplishment (Stults,
2001). According to previous studies, personal satisfaction, knowledge
and pleasure (IM) constitute the main reasons of adult’s participation
in exercise programs (Ebeck et al., 1995; Eix, 2001; Brodkin & Weiss,
1990). These findings are consistent with the findings of previous studies
that suggest effects of the type of rehabilitation in-patients motivation
(Papageorgiou, 2001).

However, the gym exercise group differs significantly from the swimming
and control group in EM to introjected regulation. Introjection is related
to the internal pressures that the patient may put on himself. The guilt
that they feel when they fail to complete a health task or a training
session, will motivate them so as to make it up (Vlachopoulos, Karageorghis
& Terry, 2000). According to Brodkin & Weiss (1990) health reasons
were rated highest by older adults for participating in exercise programs.
Additionally control group had statistically higher levels in EM to external
regulation and Amotivation.

Given the study findings, further research is suggested. A research design
for assessing long-term adherence is recommended. Previous studies indicated
that the dropout rate for an exercise program remains high until 12 months,
with an average attrition rate of 50% (Comoss, 1988; Oldridge, 1979; Song
et al., 2001). It is imperative to assess adherence changes over a long-term
period, focusing on the motivation related variables influencing participation
in rehabilitation programs.

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