ISSN: 1543-9518|Vol. 24

Can Academic Progress Help Collegiate Football Teams Win?

Submitted by: Jeffrey W. Lucas & Michael J. Lovaglia

INTRODUCTION

Recently, the National Collegiate Athletic Association (NCAA) released its first Academic Progress Rate (APR) scores for its football and basketball programs. The APR measures how well athletic programs educate student athletes and will be used to sanction programs that do not perform well academically. With these new academic reforms, the NCAA has taken the groundbreaking step of linking athletic success to academic success.

Proposed NCAA sanctions for collegiate athletic programs that fail to adequately educate student-athletes highlight the prevailing view that athletic success comes at the expense of academic progress. Some research, including research sponsored by the NCAA, has found that high-visibility athletic programs do not help to financially support the academic missions of universities (Litan, Orszag and Orszag 2003, Shulman and Bowen 2001). Research also has found no link between money spent on athletic programs and academic quality (Litan, Orszag and Orszag 2003). Yet, some clear links have been identified between athletic and academic success. Athletic success increases student applications to universities (Murphy and Trandel 1994, Zimbalist 1999). Theoretically at least, increased applications lead to more selective admissions and thus better students. Moreover, research by Lovaglia and Lucas (2005) suggested that high-visibility athletic programs increase the prestige of a public university’s academic degrees. The APR may be useful in promoting a positive association between academics and athletics in another way: Might providing better education for collegiate athletes now help athletic programs win?

The purpose of the proposed NCAA sanctions for programs with low APR scores is to motivate collegiate athletic programs to do a better job educating student athletes. In addition, the APR has the potential to motivate coaches in more powerful ways. First, it allows a direct test of the hypothesis that the athletic success of collegiate sports programs is negatively correlated with the academic success of their student athletes. If it can be demonstrated that no strong negative correlation exists between athletic and academic success, then coaches might be less ambivalent about insisting that athletes progress academically. Second, and most importantly, athletic recruits can use the APR to decide among competing athletic programs. While young athletes recruited to high profile athletic programs may be most concerned with pursuing a successful athletic career, they (and their parents) nonetheless realize the value of a college education. When deciding between two equally successful athletic programs, it would be in a student’s interest to pick the one with a higher APR. If student athletes begin to favor programs with higher APR scores, then the best athletes will go to schools that promote the academic progress of their athletes. Coaches would then have a powerful reason to promote the academic progress of their athletes. It would help them recruit better athletes and win. The perceived relationship between athletic and academic success would shift from negative to positive.

Comparing the academic and athletic success of collegiate programs, however, is not a simple calculation. If an accessible indicator existed that gave equal weight to academic and athletic success, then the best student athletes might well gravitate toward those programs that offered not only the best chance of athletic stardom but also the best opportunity for a solid education.

We develop a combined measure of athletic and academic success, the Student-Athlete Performance Rate (SAPR). The SAPR assigns programs a score based equally on athletic and academic success. To demonstrate its use, we compute SAPR scores for football programs in major conferences (ACC, Big East, Big 10, Big 12, PAC-10, and SEC plus Notre Dame).

THE APR

On January 10th, 2005, the NCAA Division I Board of Directors approved measures to link athletic scholarships to academic success. In the words of Robert Hemenway, the Chair of the Board of Directors, “This action today is a critical step in our journey to establishing much stronger and significant academic standards for NCAA student-athletes. The ultimate goal is for our student-athletes to stay on track academically and graduate” (NCAA, 1/10/05).

Seven weeks later, on February 28th, the NCAA released its first APR numbers. The APR is based on the eligibility and retention of student-athletes (Brown 2005). Rates of eligibility and retention are exactly the indicators that recruits to a collegiate program would find important in deciding which program to join. Recruits would want to know whether a program is likely to keep them academically eligible to compete and retain them through to graduation.

Each Division I sports program received an APR score on a 1000 point scale. The NCAA set a score of 925, roughly equivalent to an expected 50% graduation rate, as a minimum acceptable standard. About 21% of all athletic teams have APR’s below the 925 cutoff. Perhaps by 2006, programs with subpar APR’s face losing up to 10% of their athletic scholarship allotments.

The number of high-visibility athletic programs that face potential sanctions is substantial. Although 21% of all athletic teams have APR’s below the 925 standard, the percentage is much higher for football and men’s basketball programs. For example, among the 63 football programs in the power conferences representing the Bowl Championship Series, 30 have APR’s below 925 (NCAA, 2/28/05).

THE APR AND ATHLETIC RECRUITS

Aside from its use as a punitive tool, the APR can provide student-athletes recruited to universities a tool to use when deciding among various programs. Talented young athletes recruited by major collegiate sports programs must weigh a dizzying array of information before deciding on a school. Sometimes that information can be contradictory. To make an informed decision, a recruit should be able to answer at least two questions. First, which program will provide the best athletic experience, including the most visibility and the best opportunity for a professional career? Second, which program will provide the best education and opportunities if a pro career doesn’t materialize?

The APR gives student-athletes a way to measure the academic success of athletic programs. From the standpoint of recruits, however, the APR neglects the athletic half of the equation to focus exclusively on the academic side. The most successful sports programs in athletics may not be the ones that do a good job of educating their student athletes. Similarly, the programs that provide the best educational opportunities for student athletes may not provide the best athletic opportunities. There is no clear way to judge how well a program both educates its players and gives them a chance for success in athletics.

We propose an indicator that combines academic and athletic success. The Student-Athlete Performance Rate (SAPR) described below gives equal weight to the athletic and academic success of sports programs.

COMPUTING THE SAPR

We constructed a method for computing SAPR scores and applied it to Division I-A football programs. The SAPR is calculated on a 2000 point scale, half reflecting athletic success and half academic success. 1000 possible points of each program’s SAPR score is its Academic Progress Rate (APR). The other 1000 points of the SAPR is determined by a program’s Athletic Success Rate (ASR). Table 1 displays the factors used to calculate the ASR and their weightings.

Table 1: Factors in ASR (and weightings)

All-time winning % (.10)

Conference championships in last 5 years (.10)

Attendance average (2003) (.15)

Bowl games in last 5 years (.15)

National rankings in last 5 years (.15)

Players in the National Football League (.15)

Wins in the last 5 years (.20)

A number of factors reflect the current status of a football program, including conference championships in the last 5 years, bowl games in the last 5 years, national rankings in the last 5 years, and wins in the last 5 years. All-time winning percentage is included to reflect the tradition of a program. Attendance and professional players from a program are included because we believe they are factors that reflect the potential visibility and chance for professional success of athletes associated with a collegiate program. Similarly, the weightings reflect the factors that we believe recruits would consider most seriously. For example, an important athletic factor for new recruits would be how much a program wins.

For each of the seven factors in the ASR, we gave each program a score reflecting its percentage of the highest possible value for that factor. For example, the University of Michigan had the highest attendance average at about 111,000 fans per game and received a 1.0 for the attendance factor. A program with an average attendance of 55,500 fans per game would receive a score of .5 for the attendance factor. In the same way, a program that has participated in 3 bowl games in the past 5 years receives a score of .6 for the bowl game factor.

We multiplied each school’s score for each factor by its weighting. We then added the weighted factor scores. The factor weightings add to 1.0 and thus adding each school’s weighted scores for each factor produced a total score with a maximum possible value of 1.0. We then multiplied these values by 1000 to put ASR scores on the same scale as the APR.

Our initial ASR calculations produced a range of scores among football programs in power conferences between 148 and 856. We then standardized the scores to produce a range comparable to that of the APR. We then added ASR and APR scores to produce for each program an SAPR score with a maximum possible value of 2000. Table 2 displays SAPR scores for football programs in conferences represented in the Bowl Championship Series.

Table 2: SAPR scores for football programs in conferences represented in the Bowl Championship Series-ACC, Big East, Big 10, Big 12, PAC-10, and SEC (as well as Notre Dame)

School SAPR School SAPR
1) Michigan 1920 33) Iowa State 1822
2) Miami 1917 t34) Ohio State 1820
3) Florida State 1911 t34) Rutgers 1820
4) Auburn 1903 t34) Washington St. 1820
5) Oklahoma 1897 t37) Arkansas 1818
6) Georgia 1894 t37) Illinois 1818
7) Florida 1891 t39) South Carolina 1817
8) Boston College 1890 t39) Wake Forest 1817
9) Texas 1882 t41) Duke 1816
10) LSU 1880 t41) Northwestern 1816
11) Virginia Tech 1879 t41) Texas Tech 1816
12) Iowa 1876 44) Minnesota 1812
13) Virginia 1870 45) Cal 1808
14) Mississippi 1867 46) Purdue 1806
15) Stanford 1865 t47) Oregon State 1800
16) Maryland 1864 t47) Washington 1800
17) Nebraska 1863 49) Baylor 1798
18) USC 1860 50) Vanderbilt 1792
19) Notre Dame 1854 t51) Kentucky 1790
20) Tennessee 1853 t51) Michigan St. 1790
21) Clemson 1848 53) Oklahoma St. 1789
22) Georgia Tech 1847 54) Indiana 1788
23) North Carolina 1846 t55) Oregon 1787
24) West Virginia 1845 t55) Texas A&M 1787
25) Pittsburgh 1845 57) Alabama 1785
26) Colorado 1841 58) Arizona St. 1784
27) Kansas State 1838 59) Mississippi St. 1768
28) Syracuse 1833 60) Missouri 1767
29) N. Carolina St. 1828 61) UCLA 1765
t30) Penn State 1826 62) Kansas 1749
t30) Wisconsin 1826 63) Arizona 1722
32) Connecticut 1824 64) Temple 1697

ANALYSIS

Comparing the APR and ASR components of the SAPR allow a test of the hypothesis that athletic success is negatively correlated with academic success of major collegiate football programs. If athletic success is antithetical to academic success, then we would expect a strong negative correlation between scores on our ASR scale and on the APR scale. Instead, we found only a slight (Pearson’s r = -..122, two-tailed p = .335) and non-significant negative correlation between the ASR and the APR. Statistically, major collegiate football programs whose athletes make good academic progress are just as successful as those programs whose athletes make little progress.

DISCUSSION

The SAPR has a number of potential uses. One is to give student-athlete recruits a measure of combined athletic and academic success to consider when choosing among various collegiate programs. Some football programs that have been very successful on the football field-Michigan, Miami, and Florida State, for example-also have very high SAPR scores. Others fare less well. Recruits considering alternative programs can use the SAPR as a tool when making their decisions. If use of the SAPR for this purpose becomes widespread, then we can expect the correlation between the athletic and academic success of collegiate programs to shift from neutral to positive. If coaches are able to use high SAPR scores to recruit better athletes, then their success in promoting the academic progress of their student athletes will lead to greater athletic success as well.

Another potential use of the SAPR is to determine the likelihood of programs changing coaches. 10 of the schools with the lowest 15 rankings in our SAPR scores for football programs from major conferences have changed coaches since the end of the 2002 football season. Only 3 of the top 15 programs did so. Some of the changes at both ends of the spectrum reflected coaches being fired, and some reflected coaches moving on to new positions. In a logistic regression analysis with any coaching change as the dependent variable, the coefficient for SAPR approaches significance (B = -.012, SE = .006, two-tailed p = .056) in the direction of schools higher in SAPR scores being less likely to change coaches. More research and a larger sample are necessary to determine the relationship between SAPR scores and coaching changes.

A question for future research is whether the coach or the institutional climate is the primary determining factor in a program’s SAPR score. We can gather more data to test this prediction. We will compute SAPR scores for men’s and women’s basketball programs (which will entail using some different factors in the ASR formula) in power conferences. We will then compare SAPR scores for football and basketball programs at the same institution. If scores for football and basketball are highly positively correlated, then the institution is likely the more important factor. If the correlation is weak or negative, then the coach is probably the driving force.

REFERENCES

  1. Brown, G. T. (2005). “APR 101.” NCAA News Online, February 14.
  2. Litan, R. E., J. M. Orszag and P. R. Orszag (2003). The Empirical effects of collegiate athletics: An interim report. National Collegiate Athletic Association.
  3. Lovaglia, M. J. and J. W. Lucas (2005). “High visibility athletic programs and the prestige of public universities.” The Sport Journal 8(2):1-5.
  4. Murphy, R. G. and G. T. Trandel (1994). “The relation between a university’s football record and the size of its applicant pool.” Economics of Education Review, 13, 383-387.
  5. NCAA. 1/10/2005. “NCAA Division I Board of Directors sets cutlines for academic reform standards.” NCAA Press release.
  6. NCAA. 2/28/05. “Academic Progress Rate data for NCAA schools.” http://www2.ncaa.org/academics_and_athletes/education_and_research/academic_reform/school_apr_data.html
  7. Shulman, J. L. and W. G. Bowen (2001). The Game of Life: College Sports and Educational Values. Princeton, NJ: Princeton University Press.
  8. Zimbalist, A. (1999). Unpaid Professionals: Commercialism and Conflict in Big-Time College Sports. Princeton, NJ: Princeton University Press.
2015-03-24T09:48:32-05:00June 3rd, 2005|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off on Can Academic Progress Help Collegiate Football Teams Win?
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The Analysis of the Opinions of Supporters of a Football Team in the Turkish Super League; Before and After the Same Game

Submitted by: Dr. Ertan Kilcigil, PhD.

ABSTRACT

This study has been conducted in Turkey by asking a 15-question-lichert type of survey in order to obtain the before and after opinions of 45 Besiktas Gymnastics Sports Club’s football fans from Ankara who went to Besiktas Gymnastics Sports Club’s ( BJK) UEFA second semi-final match versus S.S. Lazio Club that took place in Istanbul on the 20th of March 2003 and returned from the match together on the same bus. Besiktas lost this game. The score was 2-0.

The survey questions the effects of players’, referee’s, spectators’, coach’s individual success and errors, the players’ being unable to play because of injury or penalty and weather conditions on the final score of the game. The survey was prepared by consulting experts’ opinions.

At the end of the research, the below results on the differences of opinion before and after the game were obtained in order of importance:

Before the game, it was thought that the game was to be won by Besiktas (most likely 82%, least likely 82%). The players are to blame for losing the game (most likely 60%, likely 40%).

The coach is unsuccessful, he couldn’t direct the game well and he couldn’t interfere at the right time (most likely 58%, very unlikely 51%). The host team did not have any advantages or could not use this advantage (most likely 56%).

The negative weather conditions did not affect the team’s failure or in other words there were no negative weather conditions during the game (not likely 53%). The players had individual failures (least likely 51%). The goal and problem we aimed to achieve at the end of this research have been achieved. Except for the sub-problem that is the player’s being unable to play because of injury or penalty affecting the game, all the other sub problem’s statistics have been defined as important. Supporters think that their team will definitely win before the game without accepting any excuses but after the defeat, they list all the causes of defeat one by one. Before the game, these causes are not even thought as a probability.

INTRODUCTION

Today supporting a football team is in such a position that it eliminates cultural differences. Intellectual, educated, uneducated, employed, unemployed people are all supporters of their team in the grandstand. What separates these people from each other is not the dosage of fanatics but their response to it. The supporters in the grandstand always want their team to win. The colors in the grandstand have a meaning only when they belong to their team. The supporters can give up everything for the sake of their team. When they have intensive worry or reaction, supporters even commit suicide in Turkey. Although supporters give more than they should for their team, they might receive the least in return. Supporters can change their love in moments of desperation but not their team; they would never go to another team. For the supporter, supporting his team and defending it is as natural a passion as eating or drinking. The game football is not simply a symbol of colors that reflect the social system but it is a social action that unites all the colors. In Turkey, the supporters are all actively involved in this action. The emotional responsibility or reaction towards one’s team sometimes obstructs being objective and thus supporters always want their team to win. Below are the short headlines of the explanation of some of the factors that affect the result of football match in Turkey.

Supporter and Spectator:

“Spectator is the person who watches the game, show, performance or sports competitions in its exact place. According to a study of social psychology, spectators are considered as a group. The approach that defines spectators as “A group made up of individuals that come together in order to meet certain needs” is in accordance with football spectators (Acet, 2001).On the other hand, “A football supporter/fan is a person who is emotionally devoted to a sports event”. As it is understood from these definitions, a football supporter and a football spectator are different concepts. Being a spectator is a superior state that includes football fanaticism; every spectator may not be a football supporter (Kayaoglu, 2000). Most of the spectators are not just spectators. Moreover, just like religious fanatics participating in religious ceremonies, these spectators are real fanatics that can remember previous games very well and plan for future games very well and are extremely devoted to their football team giving it more importance than their colleagues, their friends, their family or important days for them (Sloan, 1979). According to Meri (1999), football supporters are a kind of group that represents the popular Turkish football culture in a micro economic social standpoint and its revival. “There are four elements of football which engrosses millions of people’s attention. These are: the sportsman (footballer), technical staff and director (football coach), spectators and media. Among these, the most honest and sincere is the crowd of spectators. A supporter of a football club is a part of the team whether it wins or loses” (Talimciler, 2003).

Social Identity and Supporter:

The emotional responsibility that comes with supporting a team consciously or unconsciously becomes a part of person’s life. “People find the support they have been looking for at times in religion at times in the team they support. This means by supporting a team, a lot of people change their status from crowd that has unlimited opportunities to a group that has a lot in common” (Imamoglu, 1991). “Football is the most collective among all the social sense of belongings and cultural forms” (Meri, 1999). The widespread of shared fanaticism makes an individual feel stronger. In other words, an individual feels stronger by relying on the protection of a strong and crowded group of people. In Turkey, “people in the society feel themselves under pressure when they can’t fulfill their economic and social needs. By identifying themselves with their team, they try to satisfy their own feelings of pride and confidence when their team succeeds” (nlcan, 1998). Whatever the conditions or circumstances are, the supporter always feels that he can contribute to his team physically and spiritually and that his team needs his support. According to Fin (1994), supporters see themselves as the morale guards of their team even though they don’t participate in the decision making process and they perceive themselves as deflated or diminished. Supporters’ belief that the team belongs to them seems to mislead the financial truth. But the claim that the team belongs to them should not be taken as a financial one, it should be seen as a manifest of the belief that the team is a part of them because of the intensive devotion they feel towards their team.

Supporter and the Referee:

Generally and briefly, a referee is responsible for directing the team. In other words, “a referee is the most designated person of the game; he is the symbol of the rules, limitations and honesty” (Ycel, 1998). As referees draw the line between the rights of one team and the other, it is a difficult job. To finish this job with the least number of errors is only possible with the harmony of experience, knowledge and wisdom. A referee is “out of sight and out of mind as much as he accomplishes to put forward these qualifications properly. A word is enough to describe him and his job. However, he is in the foreground as much as he deviates from the rules (Ycel, 1998). “There is no such thing as defeat for supporters. Therefore, most often referees are not appreciated by either the winning or the losing team” (Kilcigil, 2002).

Supporter and the Footballer:

Some footballers are remembered by some names. Nicknames such as “Brain” and “Professor” describe their styles “in the football field”. Just like everything that addresses to the big masses, footballers’ behaviors in and out of the field may affect some people. For example, a footballer with a high excitement level has the opportunity to direct the society that are there for the same purpose, shares similar feelings and gets their power from their unity. To be in front of the societies naturally brings some responsibilities. The first responsibility of a footballer is to his club, but there is an important point here to consider; “the club’s supporters”. Because they themselves are the team’s spiritual owners and they watch every step of the footballer very carefully. They want a share of the footballer, that is, when they go to a football game, the footballer should play very well and win. At this point, the footballer’s responsibility is conveyed to the supporters; the masses. Today, we can say that what makes football so important is “the supporter”. Therefore, the footballer’s most important duty, according to the supporters, is to make them happy. According to the supporters that say “we created you; we made you who you are”, the footballer should get on well with the supporters and should be able to live up to their expectations. Otherwise, he will be unwanted and the supporters will cheer against him in every game. The cheer “The best in Turkey are the spectators, footballers are impostors” were made up after a game that was expected to be won was lost.

Supporters and Technical Director:

“As he is experienced in football, as there are a lot of people training the team; and as there can be more than one coach or trainers in a team; the person with the most authority is called the “technical director” (Ycel, 1998). Technical Directors, along with their responsibility to train the footballers and the team the best way for the games, also have individual social, cultural duties and responsibilities. With his responsibility towards the supporters of his team or to public opinion of the sports society, his speech before and after the game, his behavior, and his reactions, he should be able to set example and not do these for the sake of winning the game. Reactions that might lead to violence in the football supporters who hide all sorts of their identity and sense of belonging in their fanaticism in Turkey should not be given. The supporter although wanting his expectations to be met firstly by the footballer conveys this expectation indirectly to the technical director. The technical director is responsible to the supporter for all the team members whereas the footballer is only responsible for himself. “Therefore a technical director is often likened to a “commander” or an “orchestra conductor”; he is said to direct the game well or bad; use his baton well or bad” (Ycel, 1998). As it can be understood from above, the technical director of a team is not only the person that directs the game, the team technically but he is also the person that directs the pulse of the supporters’ and lays the groundwork for the positive and negative events with his behavior towards the footballers and the referee.

MATERIAL AND METHODS

This study has been conducted in Turkey by asking a 15-question-lichert type of survey in order to obtain the before and after opinions of 45 Besiktas Sports Club’s football fans from Ankara who went to Besiktas Gymnastics Sports Club’s ( BJK) UEFA second semi-final match versus S.S. Lazio Club that took place in Istanbul on the 20th of March 2003 and returned from the match together on the same bus. It is difficult to ask the same questions after the game that had been answered by the same people before the game in terms of research technique (just like gathering the same group whose upset after the game that was lost 2-0 and asking them to answer the survey questions). This means that although the study group consists of 45 people; there are 90 answer sheets. It is assumed that football players, referees, spectators, coach’s individual success and errors, the players’ being unable to play because of injury or penalty, the team’s being the host team or not and weather conditions are all factors that can affect the final score of the game. The first stage of the study was conducted before the game on the bus from Ankara to Istanbul. The study group was asked to put a code or sign on the survey they have answered so that the same survey could be given to them after the game. After the game, the same people answered the questions at the back of the survey whose first page they had already answered before the game. The data that was obtained after the game has been analyzed according to Z test method by comparing the ratio and percentage distribution. The difference of the views has been evaluated in the range between p < 0.01 and p < 0.05. That there is a difference between the views of football supporters before and after their team’s game, that this difference is an important one and that this research is the first on its subject in Turkey are all factors that contribute to the growing importance of this research.

FINDINGS

Table 1

The data concerning the views of the football supporters before and after the game in terms of the effect of winning this game on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 37 82.22 -0.82 Important (p<0.01)
Least likely 2 0.00 5 11.11 -0.11 Important (p<0.01)
Likely 7 15.56 2 4.44 0.11 Not important
Most likely 38 84.44 1 2.22 0.82 Important (p<0.01)

Table 2

The data concerning the views of the football supporters before and after the game in terms of the effect of losing this game on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 34 75.56 1 2.22 0.73 Important (p<0.01)
Least likely 9 20.00 0 0.00 0.20 Important (p<0.01)
Likely 2 4.44 7 15.56 -0.11 Not important
Most likely 0 0.00 37 82.22 -0.82 Important (p<0.01)

Table 3

The data concerning the views of football supporters before and after the game in terms of the effects of individual errors of the footballers on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 0 0.00 0.00 Not important
Least likely 9 20 4 8.89 0.11 Not important
Likely 33 73.33 11 24.44 0.49 Important (p<0.01)
Most likely 3 6.67 30 66.67 -0.60 Important(p<0.01)

Table 4

The data concerning the views of the football supporters before and after the game in terms of the effects of the tactical success of the technical director(directing the game well, interfering at the right time) on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 14 31.11 -0.31 Important (p<0.01)
Least likely 0 0.00 23 51.11 -0.51 Important (p<0.01)
Likely 16 35.56 5 11.11 0.24 Important (p<0.01
Most likely 29 64.44 3 6.67 0.58 Important (p<0.01)

Table 5

The data concerning the views of the football supporters before and after the game in terms of the effect of being the host team on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 15 33.33 -0.33 Important (p<0.01)
Least likely 1 2.22 19 42.22 -0.40 Important (p<0.01)
Likely 14 31.11 6 13.33 0.18 Important (p<0.01)
Most likely 30 66.67 5 11.11 0.56 Important (p<0.01)

Table 6

The data concernng the views of the football supporters before and after the game in terms of the effects of negative weather conditions or the field’s having a bad ground on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 11 24.4 35 77.78 -0.53 Important (p<0.01)
Least likely 17 37.78 7 15.56 0.22 Important (p<0.01)
Likely 15 33.33 1 2.22 0.31 Important (p<0.01
Most likely 2 4.44 2 4.44 0.00 Not important

Table 7

The data concerning the views of the football supporters before and after the game in terms of the effects of individual success of the footballers on the final score of the game

Alternatives Before the game After the game The results of the Z Test in comparing the ratios Interpretation level
n % n % Difference
Not likely 0 0.00 15 33.33 -0.33 Important (p<0.01)
Least likely 1 2.22 24 53.33 -0.51 Important (p<0.01)
Likely 24 53.33 4 8.89 0.44 Important (p<0.01)
Most likely 20 44.44 2 4.44 0.40 Important(p<0.01)

CONCLUSION AND DISCUSSION

During this study conducted on the March 20, 2003 Besiktas Gymnastics Sports Club’s football team (BJK) lost the football match against S.S Lazio Club’s football team 2-0. The data findings refer to the answers of the views’ of the supporters before and after the game. The evaluation is based on the statistical level of interpretation of the difference in the views before and after the game.

Table 1: As it was not considered that the team would lose before the game, the alternatives “not likely” and “least likely” (both zero %), these alternatives increased as the game was lost in the end: “not likely” (82.22% and “least likely” (11.11%). The difference between before and after the game for the alternative “not likely” (82%) is at a statistically important level p < 0.01and the alternative “least likely” (difference is 11%) is at a statistically important level p< 0.05.

The probability of winning the game seen as “likely” (15.56%) before the game decreased to 4.44% after the game. But the difference is not important.

Before the game the team was thought as “most likely” to win before the game (84.44%). But as the game was lost, this percentage declined to 2.22%; the difference being 82% is at a statistically important level p< 0.01.

Table 2: As the team was considered “not likely” to lose the game before the match (75.56%), this percentage decreased to 2.22% with the loss of the game. The difference is 73% and is at a statistically important level p<0.01.

As the team was thought “least likely” to lose the game before the match (20%); this alternative was not ticked at all after the game (zero %). The reason is that with the loss of the game, most of the people answered the alternative “most likely”. The difference is 20% and is at a statistically important level p< 0.01.

When we look at the alternative “likely” before and after the game (4.44%, 15.56%); the difference between them (11%) is statistically not important.

The alternative “most likely to lose” is given no chance (0%) as the team was thought to win; with the loss of the game this percentage increased greatly (82.22%). The difference is 82% and is at a statistically important level p<0.01.

Table 3: The study group considered the loss of the game due to individual errors of the footballers as “not likely” and “least likely” (not important). But although the same group that said the individual errors of the footballers were “likely” (73.33%) to affect the game before the game changed their views to “likely” (24.44%) (The difference is 49%); from the statistical point (p< 0.01), it was observed that the footballer’s individual errors were “likely” to affect the loss of the game. The supporters that said that the footballers were not going to make individual errors (6.67%) before the game revealed after the game the footballers made mistakes during the game (66.67%) and that these “most likely” (66.67%) affected the loss of the game (difference 60%) (Important: p< 0.01).>

Table 4: Although before the game the technical director’s success was seen as “likely” and “most likely” ( 35.56% and 64.44%) before the game; no chance was given to the alternatives “not likely” and “least likely” (0%).

After the game, “not likely” and “least likely”( 31.11% and 51.11%) differed than those before the game (0%) creating a 31% and 51% difference and became important p< 0.01.

When we combine this result we obtained from table 7 that technical director’s errors were different looking at the percentages of the alternatives “least likely” “most likely” before and after the game and that this was at a statistically important level, with the result from table 8; the expectation that the technical director was to succeed can be interpreted as certifying his failure in the end.

There has been a decrease in the percentages which reflected that technical director’s success would “likely” affect the final score of the game before the game (35.56) and after the game (11.11%). The difference is 24% and is important p< 0.01.

The percentages of those who expected a “most likely” success from the technical director before the game (64.44%) decreased (6.67%) making the difference between these (58%) important p< 0.01.

Table 5: As being the host team was seen as an advantageous thing before the game: the alternatives “not likely” (zero %) and “least likely” (2.22%); these opinions changed after the game; “not likely” (33.33%) and “least likely” (42.22%) and increased (the difference 33% and 40%). These are at a statistically important level p< 0.01.

Before the game as being the host team was thought to be advantageous the alternative “likely” was 31.11% before the game; this is seen as not advantageous after the game. The difference between the percentages of after and before the game are 18% and are at a statistically important level p< 0.05.

The most important alternative that being the host team “most likely” affects the outcome of the game before the game (66.67%) changed their opinions completely making this alternative have the least percentage (11.11%) after the game. The difference is 56% and is at a statistically important level p< 0.01.

As this is seen as one of the most important reasons of losing the game; it is thought that the advantage of being the host team was not used by the team itself.

Table 6: Although before the game the percentages reflecting that it was “not likely” “least likely” and “likely” that the weather conditions might affect the success of the team were high before the game, the percentage stating it was “least likely” before the game was statistically not important. Although the alternative “not likely” was kept in mind before the game (24.44%), it increased greatly after the game (77.78%). This most important difference for this question made it to be more likely than the other alternatives and it made this statistically important p< 0.01. The negative weather conditions did not affect the team’s failure. During the game, the weather conditions were not unfavorable. The “least likely” probability before the game (37.78%) declined after the game (15.56%). The difference between these (22%) is at a statistically important level p< 0.01.

The answer “likely” which was an important alternative before the game (33.33%) became unimportant after the game (2.22%). The difference is 22% and at a statistically important level p< 0.01. There were no unfavorable or negative weather conditions during the game and the weather conditions during the game did not affect the team’s failure.

The alternative “most likely” before and after the game is more or less the same (4.44%); it is statistically not important as well as it shows that no unfavorable weather condition took place during the game and the weather conditions did not affect the team negatively and did not contribute to the team’s failure.

Table 7: The alternative: “not likely” was not chosen before the game; this proved that the team’s success was expected rather than the individual success of the footballers before the game. But after the game the same alternative increased to (33.33%) and the footballers were considered as unsuccessful (33.33 %) (Important: p< 0.01).

By choosing the “least likely” alternative again team success was expected rather than individual success of the footballers’ (2.22%), but after the game it was said that the game was lost due to the footballer’s individual failures (53.33%). (the difference is 51%; important: p< 0.01).

It was observed that there was a decrease in the alternative “Likely” (53.33%) referring to supporters that expected individual success of the footballers after the game (difference 0.44%; important: p< 0.01). Those who viewed success as certain by choosing “most likely” (44.44%) before the game evaluated the footballers as unsuccessful after the game (difference 40%, important: p< 0.01).

In conclusion, when all the tables are considered in view of their importance (The important percentages of the Z test results in the tables are bold and underlined):

  1. The thought of winning the game is first (least likely 82%; most likely 82%; Tables 1-2).
  2. The players are to blame for losing the game (most likely 60%, likely 49%; Table 3).
  3. Technical director is unsuccessful, he could not direct the game well and he did not interfere well-timed and appropriately (most likely 58%, least likely 51%; Table 4).
  4. Being the host team had no advantages or this advantage has not been used (most likely 56%; Table 5).
  5. The unfavorable weather conditions did not have any effect in the game’s failure or in other words there has not been any unfavorable weather condition during the game (not likely 53%; Table 6).
  6. The footballers have individual errors and failures (least likely 51%; Table 7).

At the end of the research, the aim and problem has been achieved; all the problems except for the sub-problem “the players’ being unable to play due to injury or penalty” proved themselves statistically important. In Turkey, supporters believe that their team will definitely win no matter what happens before the game but start listing reasons for losing the game after the defeat. These reasons are not viewed even as likelihood before the game or considered little as they have unimportant percentages

REFERENCES

1. Acet, M., (2001), “Factors that Steer Football Spectators Towards Fanaticism and Violence”, Marmara University Institute of Medical Sciences Department of Physical Education and Sports PhD. Thesis, pages 16,20-21,23,29-30,36-37,115-117,119-122,126, Istanbul

2. Finn, G., (1994), Football Violence: A Social Psychological Perspective, in “Football Violance and Social Identity”, (ed. R, Gulianotti, N. Bonney, and H., Hepwort), London: Routledge

3. Imamoglu, O., (1991), “Sportsmen’s and Spectators’ Health”, Marmara University Institute of Medical Sciences Department of Physical Education and Sports, PhD Thesis, page 333, Istanbul

4. Kayaoglu, A. G.(2000), “Football Fanaticism, Social Identity and Violence, A study conducted on football supporter”, Ankara University Institute of Social Sciences Department of (Social) Psychology. PhD Thesis, pages 12-15,54-57, Ankara

5. Kilcigil, E., (2002), “Preferring to go to the stadiums instead of watching the matches on television on a soccer team fans in super league” Performance, volume:8, Number 1-2, 10-29

6. Meri, ., ( 1999) “Towards a Conscious Society” Ayyildiz Magazine, page 28

7. Sloan, L.R., (1979), The Function and Impact of Sport for Fans: A Review of Theory and Contemporary Research in H.J., Goldstein, (ed), Sports, Games and Play, (Ed: H., J., Goldstein) Social and Psychological Viewpoints, Hillsdale, Laurence, Erlbaum Associates, New Jersey

8. Talimciler, A., (2003), Football Fanaticism in Turkey and its relation with Media, Baglam Publishing, pages 21,29,33, Istanbul

9. nlcan, ., (1998), “Types of Violence in Turkish Football Spectators”, Marmara University Institute of Medical Sciences Department of Physical Education and Sports, M.A Thesis, pages 14,18, Istanbul

10. Ycel, T., (1998), From the Discourses, Yapi Kredi Publishing, pages 36-37, 43, Istanbul

2015-03-24T09:32:30-05:00June 1st, 2005|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on The Analysis of the Opinions of Supporters of a Football Team in the Turkish Super League; Before and After the Same Game
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A Look at Women’s Participation in Sports in Maryland Two-Year Colleges

ABSTRACT

Much research has been conducted on college athletics.  The populations studied most often are four-year, NCAA member institutions.  In higher education, 40 percent of the institutions in the United States are two-year colleges.  These two-year colleges enroll more than ten million students annually (IPEDS, 2002).  Although 56 percent of the students enrolled in these institutions are women, little research exists that examines the participation in two-year college athletic programs.  The purpose of this study was to examine the degree of participation and opportunity for female students and coaches at two-year colleges within the state of Maryland.  With 18 institutions reporting participation data, results of this study showed that female students participate in far fewer numbers in Maryland than do male students.  Results of this study also showed that relatively few women hold administrative or coaching positions within existing sport programs.

INTRODUCTION

Over the last thirty-two years, female students have seen substantial gains in sports participation opportunities.  These gains came as a result of the federally mandated legislation know as Title IX of the Education Amendments of 1972.  Since the passage of this legislation, opportunities for girls and women to compete in sports have increased dramatically.  According to a longitudinal study by Acosta and Carpenter (1996), participation opportunities for women athletes by the late 1990’s hit an all-time high.  Increased female athletic participation is evident at all levels of sport, including high schools, colleges, and universities (NFSHSA, 2001; NCAA, 2000).

Much research (Acosta & Carpenter, 1996; Carpenter, 2003; Fitzgerald, 2003; Kramer & Marinelli, 1993) has been conducted with regards to college athletics, opportunity, and participation.  The populations studied most often are four-year, National Collegiate Athletic Association (NCAA) member institutions.  Within higher education, the two-year (also referred to as community or junior) college is taking on a greater significance.  According to a study by the U.S. Department of Education (2002), 40% of the institutions of higher education in the United States are now two-year colleges.  These two-year colleges enroll more than 10 million students annually.  Many of the athletes at these two-year colleges go on to star in major four-year athletic programs (Douchant, 2002). Although 56% of the students enrolled in these institutions are women, little research exists that examines the two-year college athletic program (Smith, 1997).  Thus, the specific purpose of this study was to examine the degree of participation and opportunity for female students and coaches at two-year colleges within the state of Maryland.

Overview of Title IX

The impetus for the change in opportunity and participation for females can be attributed to the passage of the Education Amendments Act of 1972 and its Title IX provision.  Title IX was enacted to help remedy past discriminatory practices. Title IX of the Educational Amendments Act of 1972 states that: “No person in the United States shall, on the basis of sex be excluded from participation in, or denied the benefits of, or be subjected to discrimination under any educational program or activity receiving federal aid” (Title IX, n.d., para. 1).

The passage of Title IX and the threat of litigation have resulted in the vast improvement in opportunities for girls and women in sport.  With regard to intercollegiate athletics, three primary areas determine if an institution is in compliance: athletic financial assistance, accommodation of interest and abilities, and equity in other specified program areas.

A three-part test for compliance is used in determining whether the required number of participation opportunities is being provided.
An institution must show:

  • that the intercollegiate participation opportunities for its students of each sex are substantially proportionate to its male and female undergraduate enrollments; or
  • a history and continuing practice of program expansion responsive to developing interests and abilities of members of the “underrepresented sex”; or
  • that the interests and abilities of the “underrepresented sex” are fully and effectively accommodated by the existing program (Carpenter, 2003).

Compliance is established when an institution can demonstrate that it has satisfied any one of these three tests.

Title IX requires that, for an institution to be in compliance, the interest and abilities of both sexes must be accommodated.  This includes the institution’s obligation to provide a sufficient number of participation opportunities for male and female athletes.  “Participation opportunities” are defined as the number of slots on teams as determined by the number of athletes on each team.  This definition is important because athletic directors at two-year institutions often define participation by the number of teams offered and not by the number of participants (Mumford, 1998).  According to Title IX policy interpretations and recent judicial decisions, participation in the intercollegiate sports program by women should be substantially proportionate to the number of women enrolled at the given institution.  For example, if 70 percent of the students enrolled at an institution are women, then approximately 70 percent of the students participating in intercollegiate athletics should be women (Lichtman, 1997).

The impact of Title IX policy has been felt a great deal more at the four-year level than at the two-year level of college athletics (Mumford, 1998).  Although many students have benefited from this federal policy, the consequences of this policy have also been unpleasant to many institutions.  Institutions have been subjected to expensive court battles as a result of lawsuits filed by female student-athletes and coaches.  Litigation from lawsuits has risen dramatically.  The costs and consequences of these lawsuits have had a negative impact on institutions.  Institutions found in violation of Title IX have been forced to pay expensive monetary damages, attorney fees, and program support funding.  These awards have been reported as high as $1 million (Fitzgerald, 2003).

Courts have also taken more control of athletic decision making.  They have ordered specific actions, such as hiring coaches and providing practice and other facilities.  In some instances, the litigation of one Title IX claim has generated even more claims (Kramer & Marinelli, 1993).

Research Questions

With the goal of exploring women’s participation in collegiate sports in mind, the purpose of the study was to determine the degree of participation and opportunity at two-year colleges within the state of Maryland for female student athletes and coaches.
Specific research questions which guided the study were:

  • What does the leadership, in terms of the gender of administrators, and coaches, look like at these institutions?
  • At what rates do women and men participate in two-year collegiate athletic programs?  Is their participation in proportion to that of the general student body population or are women underrepresented?
  • Are Maryland two-year colleges in compliance with Title IX?  If so, how?

Methodology

Respondents

Respondents for this study were athletic directors of all two-year colleges with membership in the Maryland Junior College Athletic Conference (MD JUCO).  The MD JUCO is comprised of 18 two-year colleges in the state of Maryland.

Instrumentation

A survey instrument was used in this study to gather demographic data on the leaders (athletic directors and coaches) of two-year colleges in the state of Maryland.  The survey instrument consisted of 33 items containing both closed-ended and open-ended questions.  The survey instrument was also designed to collect institutional programmatic information about coaching and intercollegiate sport opportunities.  Data was gathered for comparative purposes only.  Confidentiality of responses was guaranteed to all respondents.  The overall return rate of the survey was 83 %, which included responses from 15 subjects.

Procedure

Athletic directors (n=18) employed at degree-granting two-year colleges in the state of Maryland (MD JUCO) were mailed a cover letter, consent form, questionnaire, and a stamped self-return envelope.  Three weeks following the initial mailing, a reminder letter, survey, and stamped self-return envelope was sent to all subjects who had not responded (non-respondents).

Another method of gathering data was the review of related documents and archival records.  Documents used to gather data included the MD JUCO website, college catalogs, minutes from MD JUCO meetings, National Junior College Athletic Association (NJCAA) Student Eligibility Forms, the NJCAA 2000-2001 Handbook & Casebook, and the NJCAA website. This method of data gathering provided complementary information to that obtained in the surveys.  In this manner, the researcher could triangulate and cross-check data provided by the survey (Wolcott, 1994).

RESULTS

Administration

The gender of athletic directors in Maryland two-year colleges included 16 men (89%) and two women (11%).  The ethnic background of the athletic directors included 17 Caucasian (94%) and one African-American (6%).

Participation

Respondents were asked to identify the number of teams offered at their institution for men and women.  They were also asked to indicate the total number of student-athletes that participated on those teams.  On average, two-year colleges in Maryland sponsored seven teams per institution (four teams for men and three teams for women).  On average, 96 student-athletes participate across those seven teams (65 male and 31 female). Respondents stated that 134 teams were offered by their institutions.  Of the 134 total teams, 69 teams (51%) were offered for men and 65 teams (49%) were offered for women.  A total of 1,719 student athletes participated on those 134 teams.  Of that number, 1166 participants (68%) were male and 553 participants (32%) were female.

Coaches

Respondents were asked to identify the number of coaches at their institution.  They were also asked to specify whether these coaches were employed on a full or part-time basis.  On average, colleges employed seven coaches per institution. Respondents stated that 117 coaches were employed at Maryland institutions.  Of the 117 total coaches, 22 coaches (19%) were employed full-time at the institutions and 97 coaches (81%) were employed on a part-time basis.

DISCUSSION

This study examined the participation opportunities for female students and coaches in Maryland two-year colleges. The criteria used to measure participation opportunities were based on Title IX guidelines. With regards to Title IX guidelines, the first test (Proportionate Athletic Opportunity) is referred to as a “safe harbor.” The safe harbor test is the measuring stick most often used by institutions to show Title IX compliance (Davis, 2003).

To demonstrate compliance, Maryland two-year institutions must show that the numbers of male and female participants in its intercollegiate sports program are substantially proportionate to its male and female enrollments. If this is the case, no further inquiry needs to be made.

Maryland JUCO institutions do not meet the requirements for compliance based on this first test.  Women comprise 61% of the total enrollment in the Maryland Community College institutions. Men comprise 39% of the total enrollment (see Figure 1 – Appendix A). Women comprise 32% of the total student-athlete population. Men comprise 68% of the total student-athlete population (see Figure 2 – Appendix B). All of the two-year colleges, all 18 institutions, had more male than female participants.

Title IX obligates institutions to provide a sufficient number of participation opportunities for individuals of each sex.  Looking at the number of teams offered gives the appearance of near compliance.  Of the teams offered for students, 49% of the teams (n=65) are for women and 51% of the teams (n=69) are for men.  Looking at the number of participants on each team shows a much different picture. Looking at the number of participants shows that Maryland two-year colleges are not in compliance.  Of the number of participants on the teams, 32% of the participants (n=553) are female and 68% of the participants (n=1166) are male.

One aspect that stands out in this data is that the institutions have relatively small athletic programs.  As a result, they offer very limited opportunities for men or women to participate in sports.  The number of sport offerings was small in comparison to four-year institutions and high schools in the state.

A second important observation from the data is that most of the two-year colleges in Maryland employ their coaches on a part-time basis, as these coaches often hold other full-time jobs outside of the college.  Of the head coaches at two-year colleges in the state, 81% are part-time.  Given the limited resources of many two-year colleges, it is economically advantageous to hire coaches in this manner.  Coaches in two-year colleges are often paid by stipend or released time from teaching or administrative duties.  In some cases, the amount of the stipend is set for a specific coaching position with no relationship to the coach’s background or experience (Bichy, 1997).

The majority of the women’s teams in Maryland two-year colleges are coached by men.  According to the Equity in Athletics Disclosure Act of 1998 (n.d.), women comprise only 23 % of the coaches in the Maryland JUCO. This is significant because the majority of the female student-athletes in the state never get the opportunity to be coached by a woman.  The exclusion of women from the coaching ranks can provide fuel and support for the myth that male coaches are more capable than female coaches (Mumford, 1998).

Concluding Comments

The purpose of this study was to examine the participation of women in sports in Maryland two-year colleges.  Current national participation trends at the high school and college level show that women’s sports participation has increased dramatically and women are participating in sports in record numbers.  However, women remain underrepresented.  In Maryland two-year colleges, that is the case as well.  Female students participate in far fewer numbers in Maryland than do men.  In this area, Maryland’s two-year colleges are not in compliance with Title IX.

More concerns may arise as further examination is made in the areas of administration and coaching.  In these two areas of leadership, the two-year colleges in Maryland have maintained the status quo.  The athletic directors and coaches of these two-year colleges remain mostly Caucasian and mostly male.  Although women have made adequate gains on the playing field, they continue to be left behind in a dramatic fashion, when it comes to coaching or leadership opportunities.  In these areas, Maryland’s two-year colleges are not performing well at all.

References

Acosta, R.  & Carpenter, L.  (1996). Women in intercollegiate sport: A longitudinal study – nineteen year update, 1977-1996.  Unpublished manuscript, Brooklyn College: Brooklyn, NY.

Bichy, T.  (1997). Athletic/gender equity.  Unpublished manuscript, Montgomery College: Rockville, MD.

Carpenter, L.  (2003). Gender equity: Opportunities to participate.  In D. Cotton & J. Wolohan (Eds.), Law for Recreation and Sport Managers (pp. 548-558).  Dubuque, IA: Kendall/Hunt Publishing Company.

Davis, M.  (2003, March 5). Title IX review concludes with competing reports. Retrieved October 1, 2004, from the Education Week website: http://www.edweek.org

Douchant, M.  (2002, March 25).  Junior college jewels. Retrieved October 6, 2004, form the College Sporting News website: http://www.collegesportingnews.com

Equity in Athletics Disclosure Act of 1998 (n.d.).  Retrieved November 1, 2004, from U.S. Department Education, Office of Postsecondary Education website: http://ope.ed.gov/Athletics/index.asp

Fitzgerald, M.  (2003). Gender equity: Coaching and administration.  In D. Cotton & J. Wolohan (Eds.), Law for Recreation and Sport Managers (pp. 548-558).  Dubuque, IA: Kendall/Hunt Publishing Company.

Higher Education General Information Survey.  (2002, November).  Retrieved February 21, 2004, from the U. S. Department of Education, National Center for Educational Statistics website: http://www.nces.ed.gov/programs/digest/d02/tables/dt243.asp

Integrated Postsecondary Education Data Systems.  (2002, December).  Retrieved February 21, 2004, from the U.S. Department of Education, National Center for Educational Statistics website: http://www.nces.ed.gov/programs/digest/d02/tables/dt243.asp.

Kramer, W. & Marinelli, M.  (1993, September).  Title IX in intercollegiate athletics: Litigation risks facing colleges and universities.  Washington, DC: Baker & Botts L. L. P.

Lichtman, B.  (1997). Playing fair: What school leaders need to know about title ix and gender discrimination in athletic programs.  The American School Board Journal, 184 (8), 27.

Mumford, V.  (1998). Teams on paper: Title IX compliance in the Maryland junior college athletic conference.  Ann Arbor, MI: UMI

National Collegiate Athletic Association.  (2000, June 7).  NCAA sports participation numbers show largest increase in fourteen years [On-line].  Available: http://www.ncaa.org/releases/makemenu.cgi?research.

National Federation of State High School Associations.  (2001). Sports participation survey [On-line].  Available: http://www.nfhs.org.

Smith, H.  (1997, November). Association report: 2YC3 a federal perspective on community colleges.  Journal of Chemical Education, 74 (11), 1264.

Title IX of the Education Amendments of 1972. (n.d.).  Retrieved February 19, 2004, from U.S. Department of Labor, Office of the Assistant Secretary for Administration and Management website: http://www.dol.gov/oasam/regs/statutes/titleix.htm

Wolcott, H.  (1994). Transforming qualitative data: Description, analysis, and interpretation.  Thousand Oaks, CA: Sage.

APPENDIX A
General Enrollment by Gender in Maryland Two-Year Colleges
Figure 1. Enrollment by Gender
Figure One

APPENDIX B
Total Athletes on Teams by Gender in Maryland Two-Year Colleges
Figure 2. Total Athletes on Teams
Figure 2

2016-10-12T14:44:24-05:00January 10th, 2005|Contemporary Sports Issues, Sports Coaching, Sports Studies and Sports Psychology, Women and Sports|Comments Off on A Look at Women’s Participation in Sports in Maryland Two-Year Colleges
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The Implementation of Ethical and Social Standards in Youth High-Performance Sport on the Basis of Olympic Ideals

Submitted by: Kristina Bohnstedt & Norbert Müller

Introduction:

Ethical issues are coming to the fore in our society as a result of numerous problematic developments.

In addition to doping and profit orientation, child and teenage high-performance sport is a recurrent focus of discussions about ethical responsibility. The person’s responsible, particularly leading members of sports associations and sport scientists, must make their position clear and help parents to shed their fears.

In this connection, a specific ethic for trainers is increasingly being demanded. The German Sport Association responded to this demand in 1997 by publishing the “Code of Conduct for Trainers”.

As early as 1984, the sport educationalist MEINBERG (Cologne) formulated ethical principles for child and teenage high-performance sport, which have lost nothing of their importance. In fact, they are more important than ever in 2004, as a result of professionalization and media attention.

According to MEINBERG, an optimum approach to training children and teenagers in high-performance sport depends on the following ethical principles:

  1. using another person as an end in themselves rather than a means to an end,
  2. the principle of respect,
  3. the principle of equality,
  4. the principle of solidarity,
  5. the principle of fairness,
  6. the principle of suitability for children/teenagers,
  7. the principle of reasonableness,
  8. the principle of helping,
  9. the principle of trust,
  10. the principle of participation,
  11. the principle of responsibility,
  12. the principle of achievement – making no fetish of achievement,
  13. a body ethic suitable for children/teenagers – not exploiting the body.

Aim of survey and methods

The paper investigates whether child and teenage high-performance athletes think that their trainers observe MEINBERG’s 13 ethical principles for a humane high-performance sport. The young high-performance athletes were asked to evaluate the implementation of ethical standards during daily training situations.

Research data were collected through a survey using a standardized questionnaire, which was filled in under supervision. The interviewees, aged between 14 and 19, attend sport-orientated schools in the German cities of Berlin, Erfurt, Kaiserslautern and Koblenz, and the House of Athletes at the Olympic Centre in Frankfurt/Main.

Results and interpretation

The sample survey

Altogether 181 young high-performance athletes participated in the survey, 80 of them female (44.2%) and 101 male (55.8%). 15.5% of the athletes belong to sports with a young high-performance age (e.g. gymnastics, figure skating, swimming), 30.9% to team sports (volleyball, handball, basketball, football) and 53.6% to various other sports (athletics, cycling, table tennis, rowing etc.).

Training in their main sport starts at the average age of 8.7, and 11.2 years is the average age for the start of high-performance training (daily training, regular competitions and training camps). The young athletes train at different training centers. Approximately 40% train at two centers. About half of the athletes are members of training groups at their sports club, at the Olympic training center and at their sport-orientated school. Thus, 52% of the athletes work with at least two different trainers, and 20.7% even with three. 27.6% of the athletes live in a full-time-boarding school, 11% attend a part-time-boarding school, and 61.3% live with their families. Irrespective of their living arrangements, 90.1% of the young athletes say that they have become more independent through high-performance training.

Before the survey, 65.0% of the athletes had already discussed practice-oriented ethical questions at school.

Given the age of the interviewees, it can be assumed that under normal circumstances they possess a sufficient capacity for ethical judgement. This would not be the case with children, but teenagers can be considered mature enough. Their involvement with high-performance sport may in the course of time affect their evaluation of trainer behavior and of their own situation.

The teenagers’ answers show, however, that they are able to critically judge both their own situation and the behavior of their trainers. The fact that the majority of interviewees pursue sports with a late high-performance age positively affects the starting age for high-performance training.

Olympic values in youth high-performance sport

The analysis focusses on the attitudes of young high-performance athletes. The questionnaire was intended to show whether the teenagers’ values, including their evaluation of trainer behavior, had already been influenced by their involvement with high-performance sport and the concomitant concentration on achievement. In addition, the teenagers’ general value systems were to be compared with the values conveyed by their trainers, and the values perceived in competitions.
The majority of interviewees above all want their main sport to teach them achievement-oriented values. At the top of the hierarchy of desirable values are

– ambition (93.4%),
– competitiveness (90.1%) and
– assertiveness (75.7%).

While fairness (68.5%),
– independence (65.7%),
– friendship (64.6%),
– health (47%) and
– willingness to take risks (44.2%) still play a fairly important role for the young athletes, they consider values like

– happiness (35.9%),
– honesty (27.6%),
– justice (27.1%) and
– equal opportunities (22.7%) to be less important.

It can be seen from the athletes’ answers that trainers tend to teach the achievement-oriented values much more frequently than the others.

An examination of correlations shows that – except for the values of ambition and justice – there is a statistically significant correlation between the athletes’ desire to learn these values through their main sport, and the frequency with which these values are taught by the trainer.

The assessment of values in competitions reveals an even stronger emphasis on achievement-oriented values. Thus, 98.8% of the athletes hold competitiveness and 95% hold ambition to be very important in competitions, while values such as justice and friendship play a secondary role

Since the young high-performance athletes prioritize achievement-oriented values, which they expect their main sport to teach them, and which they consider even more important in competitions, we must conclude that trainer behavior may likewise be assessed on the basis of the principle of achievement. This tendency seems to be further strengthened by the fact that the athletes consider those values to be particularly important which are taught most often by their trainers. Accordingly, in their moral assessment of trainer behavior, the athletes may be following the ethical standards of their trainers. The close connection between the values of trainers and athletes casts some doubt on the young high-performance athletes’ ability to judge their trainers’ behavior objectively and critically. However, it is not clear whether the values held by the athletes are shaped by the trainers or by parents or other factors such as personality. The data suggest that the interviewees judge the behavior of their trainers as well as their own situation from an achievement-oriented perspective.

The implementation of MEINBERG’s ethical principles using the example of trainer behavior

Questions concerning trainer behavior were intended to show whether, in dealing with the young athletes, the main trainer (i.e. the trainer with whom the athlete works most of the time) follows MEINBERG’s 13 ethical principles in order to ensure a humane high-performance sport. At the same time, the analysis examined possible connections between the implementation of these principles and factors such as the athletes’ gender, their particular school, their age or their main sport.

The most important results of the survey are shown in the following table:

Using another person as an end in themselves rather than a means to an end My TRAINER wants me to win so that I can enjoy success. 61.3 %
My TRAINER accepts that my body cannot always achieve perfect results. 53.0 %
The principle of respect My TRAINER is very often ready to listen to my wishes and fears. 52.5 %
My TRAINER always respects my opinion. 48.1 %
My TRAINER is sometimes ready to listen to my wishes and fears. 42.5 %
The principle of equality My TRAINER sometimes asks me what I think. 54.7 %
My TRAINER listens to my criticism and responds to it. 53.3 %
My TRAINER very often asks me what I think. 30.9 %
The principle of solidarity My TRAINER only reproaches me when there is a good reason. 82.3 %
My TRAINER never reproaches me when I am not successful. 9.4 %
My TRAINER always reproaches me when I am not successful. 8.3 %
My TRAINER critically examines both his/her own work and mine when I am not successful. 79.0 %
My TRAINER lays the entire blame on me when I am not successful. 14.9 %
My TRAINER also blames himself/herself when I am not successful. 6.1 %
The principle of fairness My TRAINER is equally happy for all when they do well. 81.8 %
My TRAINER supports us all equally. 68.0 %
The principle of suitability for children/teenagers My TRAINER knows how old I am and treats me accordingly. 84.0 %
My TRAINER involves me in more decisions as I grow older. 54.1 %
The principle of reasonableness I never feel afraid during training. 76.8 %
I sometimes feel overtaxed during training. 68.0 %
The principle of helping My TRAINER immediately tries to help me when I say I have a problem. 82.2 %
My TRAINER always listens to me when I have a problem. 66.7 %
The principle of trust I very often trust my TRAINER. 70.7 %
My TRAINER makes all decisions together with me. 65.7 %
I sometimes discuss personal problems with my TRAINER. 50.8 %
I sometimes have a say in which competitions I enter. 40.3 %
The principle of responsibility My TRAINER sometimes talks with my parents. 60.8 %
I take part in both team and individual competitions. 59.6 %
My TRAINER sometimes organizes activities outside training for us. 58.6 %
My TRAINER reduces the training load when there are many exams and I have problems at school. 54.1 %
My TRAINER sometimes helps me to make plans for school and for my career. 47.0 %
My TRAINER never helps me to make plans for school and for my career. 44.2 %
My TRAINER sometimes talks to my teachers in order to coordinate the demands of school and training. 41.7 %
My TRAINER never talks to my teachers in order to coordinate the demands of school and training. 38.3 %
My TRAINER never reduces the training load. 29.8 %
The principle of achievement – making no fetish of achievement My main TRAINER is happy in competitions when I do well. 94.5 %
Our training is achievement-oriented and fun-oriented. 59.7 %
Our training is purely achievement-oriented. 40.3 %
A body ethic suitable for children/teenagers – not exploiting the body When I am physically exhausted in training, I still have to carry on until the end of the training session. 44.2 %
When I am physically exhausted in training, I am allowed a short break during training. 43.6 %

In general, while the majority of trainers are largely guided by MEINBERG’s ethical principles in their work with the young high-performance athletes, a critical examination of individual principles also reveals some transgressions. The athletes noted incidences of behavior on the part of trainers which fall short of their ethical expectations and have to be considered problematic or irresponsible.

Correlations between the kind of sport and the degree of conformity with individual principles are rare. Sport-specific differences only occur with the implementation of the principle of equality, the principle of suitability for children/teenagers, the principle of participation and a body ethic suitable for children/teenagers.

As far as these principles are concerned, ethical boundaries are transgressed most frequently by trainers in team games. But a disregard of ethical principles by trainers working with young high-performance athletes can be found in all kinds of sport. This indicates that adherence to ethical principles depends on the individual personality of the trainer rather than on other factors.

More detailed results could be obtained by examining the implementation of these ethical principles in all kinds of youth high-performance sport on an even broader basis.

Conclusion

It has become clear that MEINBERG’s principles, while very theoretical and general, have a practical application and can be used for critically examining the behavior of trainers in different kinds of sport.

The teenagers’ assessment of their own situation and of individual problems shows that MEINBERG’s demands are realistic. The athletes most frequently criticize the disregard for individual principles on the part of trainers and coaching assistants.

The Olympic Movement must become more aware of its responsibility for the ethical and social conditions under which young athletes live and train. Surveys like the present one should therefore be conducted on a large international scale.

References

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  2. BETTE, K.-H.: Die Trainerrolle im Hochleistungssport. System- und rollentheoretische Überlegungen zur Sozialfigur des Trainers. St. Augustin 1984.
  3. BETTE, K.-H.: Körperspuren: Zur Semantik und Paradoxie moderner Körperlichkeit. Berlin – New York 1989.
  4. BETTE, K.-H./NEIDHARD, F.: Förderungseinrichtungen im Hochleistungssport – Strukturen und Probleme. Schorndorf 1985.
  5. BRETTSCHNEIDER, W.-D./HEIM, R.: Heranwachsende im Hochleistungssport. In: Leistungssport 31 (2001) 4, 34-38.
  6. BRETTSCHNEIDER, W.-D./RICHARTZ, A.: Jugendliche im Leistungssport – Entwicklung im Spannungsfeld von Schule und Sport. In: Leistungssport 26 (1996) 5, 6-10.
  7. CACHAY, K./THIEL, A.: Sozialkompetenz für Trainerinnen und Trainer im Hochleistungssport. In: ANDERS, G./HARTMANN, W. (eds.): Sozialkompetenz von Trainerinnen und Trainern: Dokumentation des Workshops vom 28. September 1996. Cologne 1997, 11-28.
  8. DEUTSCHER SPORTBUND (ed.): Kinder im Leistungssport. Grundsatzerklärung. In: Deutscher Sportbund 1982-1986. Bericht des Präsidiums. Frankfurt am Main 1986, 225-230.
  9. DEUTSCHER SPORTBUND: Ehrenkodex für Trainerinnen und Trainer im Sport. In: ANDERS, G./HARTMANN, W. (eds.): Sozialkompetenz von Trainerinnen und Trainern: Dokumentation des Workshops vom 28. September 1996. Cologne 1997, 88-90.
  10. DIGEL, H.: Ist der Hochleistungssport verantwortbar? In: Leistungssport 32 (2002) 1, 9-13.
  11. FREI, P./LÜSEBRINK, I./ ROTTLÄNDER, D./THIELE, I.: Belastungen und Risiken im weiblichen Kunstturnen. Part 2: Innensichten, pädagogische Deutungen und Konsequenzen. Schorndorf 2000.
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  13. HACKFORT, D.: Karriere im Sport – psychosoziale Aspekte eines Karrieremanagements. In: Leistungssport 31 (2001) 4, 55-58.
  14. HEINEMANN, K.: Einführung in die Soziologie des Sports. Vol. 1. Schorndorf 19984.
  15. HERMS, E.: Ist Sportethik möglich? In: DEUTSCHER SPORTBUND (ed.): Die Zukunft des Sports. Schorndorf 1996, 84-110.
  16. HOTZ, A.: Klippen für ein Nachwuchstalent auf dem Weg zur Weltspitze. In: Leistungssport 28 (1998) 1, 29-30.
  17. HOTZ, A.: “Doping und Ethik” – ein Thema mit Tradition und offenbar ohne Ende! In: Leistungssport 33 (2003) 1, 17-22.
  18. KAMINSKI, G./MAYER, R./RUOFF, B. A.: Kinder und Jugendliche im Hochleistungssport. Schorndorf 1984.
  19. KURZ, D. (with BINZ, C./KUHLMANN, D.): Pädagogische Grundlagen des Trainings. Schorndorf 1988.
  20. LENK, H.: Aspekte einer Pragmatisierung der Ethik – auch für die Sportethik. In: CACHAY, K. et al. (eds.): Sport und Ethik. Clausthal-Zellerfeld 1985, 1-20.
  21. LÜSEBRINK, I.: Lebenswelten von Kunstturnerinnen. Sankt Augustin 1997.
  22. MEINBERG, E.: Kinderhochleistungssport: Fremdbestimmung oder Selbstentfaltung? Cologne 1984.
  23. MEINBERG, E.: Sportethik/Moral des Sports. In: GRUPE, O./MIETH, D. (eds.): Lexikon der Ethik im Sport. Schorndorf 1998, 498-504.
  24. MEINBERG, E.: Trainerethos und Trainerethik. Cologne 2001.
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  26. MÜLLER, N.: Olympismus. In: RÖTHIG, P./PROHL, R. (eds.): Sportwissenschaftliches Lexikon. 7th ed., Schorndorf, Hofmann, 2004, 414-416.
  27. MÜLLER,N. (red.). Fair play for all. Declaration on Fair play. Paris: International Fair Play Committee, 1991.
    OERTER, R.: Hochleistungssport unter entwicklungspsychologischer Perspektive. In: Leistungssport 12 (1982) 1, 6-12.
    OTT, K.: Ethische Ansätze. In: GRUPE, O./MIETH, D. (eds.): Lexikon der Ethik im Sport. Schorndorf 1998, 131-142.
  28. PATSANTARAS, N.: Trainer(in)/Training/Trainerethos. In: GRUPE, O./MIETH, D. (eds.): Lexikon der Ethik im Sport. Schorndorf 1998, 566-569.
  29. RICHARTZ, A.: Jugendliche Leistungssportler im Internat – typische Beziehungsmuster und ihre pädagogische Bedeutung. In: Leistungssport 28 (1998) 1, 12-17.
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  31. WEBER, M.: Politik als Beruf. In: WINKELMANN, J. (ed.): Gesammelte politische Schriften. Tübingen 1971, 505-560.
  32. WEISCHENBERG, K.: Kindheit im modernen Kinderhochleistungssport. Untersuchungen zur alltäglichen Lebensumwelt von C- und D- Kader Athletinnen im Kunstturnen auf der Grundlage eines konkreten Kindheitsverständnisses. Frankfurt am Main – Berlin – Bern – New York – Paris – Vienna 1996.
2015-03-20T11:05:34-05:00January 6th, 2005|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on The Implementation of Ethical and Social Standards in Youth High-Performance Sport on the Basis of Olympic Ideals
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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
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3. James, B. (1983). The Bill James Baseball Abstract 1983. New York: Ballantine
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4. James, B. (1984). The Bill James Baseball Abstract 1984. New York: Ballantine
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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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