An application of means-end theory to analyze the college selection process of female athletes at an NCAA division II university

Abstract

While considerable academic attention has been given to the college selection process of student athletes, it has typically relied strictly on survey responses to determine the relative importance of numerous factors. This research applied means-end theory to the problem of understanding college selection among female student athletes at an NCAA Division II university. Through interviews with participants (N=25), the researchers were able to utilize the laddering technique (Reynolds & Gutman, 1988) to identify not only attributes of the university that were salient to the participants as they made their college selection, but also to probe deeper to determine the underlying values that made the factors important. The values cited by participants were security, achievement, belonging, and fun and enjoyment. This study highlights the function of means-end analysis to investigate college selection among student athletes going beyond the superficial identification of important factors. Via means-end interviews, researchers can determine why varied factors are important to individuals.

Review of Literature

College selection is often a difficult process for students in general and is even more complicated for student athletes, particularly those who are recruited by numerous schools (Klenosky, Templin, & Troutman, 2001). To date, considerable academic attention has been paid to assessing the relative importance of factors student athletes consider during their college selection process. The traditionally used method has been to present student athletes with a survey through which various factors were rated. The factors receiving the highest mean scores were then considered to be the most important to the prospects at the time that they made their final college selection. Factors that were commonly cited as important in the college-selection literature in regard to student athletes were concisely detailed in Kankey and Quarterman (2007), and included: (a) opportunity to play (Forseth, 1987; Johnson, 1972; Konnert & Geise, 1987; Slabik, 1995); (b) academic factors (Bukowski, 1995; Cook, 1994; Forseth, 1987, Mathes & Gurney, 1985; Reynaud, 1998; Slabik, 1995); (c) amount of scholarship (Doyle & Gaeth, 1990; Ulferts, 1992); and (d) head coach (Cook, 1994; Mathes & Gurney, 1985; Slabik, 1995).

Recent studies in this area utilized the traditional method for college selection studies. In both studies, Finley (2005), and Kankey and Quarterman (2007), original surveys were constructed and tested for validity and reliability. Surveys were then distributed in packets to coaches with an accompanying cover letter, instructions for administering the survey, and an addressed and stamped return packet. Both studies utilized five-point scales to elicit scores intended to reflect relative importance of numerous factors. Kankey and Quarterman (2007) elected to use a scale ranging from 5 (extremely important) to 1 (unimportant), while the scale used by Finley (2005) was a traditional Likert scale, ranging from 5 (very important) to 1 (very unimportant), with a neutral category.

Karney and Quarterman (2007) surveyed members of NCAA Division I softball teams in Ohio. Participants (N=196) represented 10 of the 11 programs in the state. The descriptive analysis demonstrated that this population considered availability of major or academic program, head coach, career opportunities after graduation, and social atmosphere of the team to be the most important college choice factors, with the mean score for each being above 4 (very important).

Finley (2005) sought to determine the most salient aspects of college selection among NCAA Division III cross country runners (N=427) from around the country. Results indicated that academic reputation, major or degree program, atmosphere of the campus, and the success of the cross country program were the most important. Finley (2005) also determined that the importance of team-related factors was related to the gender and ability of the athletes. Finley split the sample by gender and then subdivided each gender-group into higher and lower ability groups based on the best cross country time each participant had recorded in high school. Several factors proved to be more important to higher ability males than the other groups: The team’s performance in the prior season, the team’s performance over the last several seasons, the performance of individuals on the team last year, and the number of award-winning athletes from the program were all more important to higher-ability males than to lower-ability males or female cross country runners in both the higher and lower ability groups.

While the aforementioned research was important and contributed to the understanding of the college selection of student athletes, it did not address the question of why these factors are important. Klenosky, Templin, and Troutman (2001) introduced a new strategy for assessing college selection criteria with an eye for understanding the underlying values of the student athletes at the time they selected a college. Specifically, the researchers sought to address the “why” question through interviews with 27 NCAA Division I football players at a single university. Their application of means-end theory (Gutman, 1982) demonstrated that college-selection research can move beyond the survey format to answer the more robust question of why particular factors are important to specific participants. The football players described such factors as facilities, the coach, schedule, and academics as important. Players linked these factors to such consequences as getting a good job, personal improvement as a player, playing on television, and feeling comfortable. In turn, these consequences supported the football players’ values of feeling secure, a sense of achievement, a sense of belonging, and having a fun and enjoyable experience. While Klenosky, Templin, and Troutman (2001) successfully introduced Gutman’s means-end theory to the study of college selection by student athletes, they acknowledged that further studies should explore other levels of competition, and female student athletes. This research sought to make that contribution to the college selection literature.

Means-End Theory

Developed by Gutman (1982), means-end theory allows researchers to explore consumer choice beyond the superficial level to understand the emotional underpinnings that drive consumers’ decisions. Through interviews, researchers guide participants through levels of abstraction, moving from the superficial factors that guide their choice, to the consequences that they perceive will arise (consumers seek to maximize positive outcomes) from their choice, and finally to the personal values they are attempting to reinforce. From each attribute of a program or school that an interviewee describes as important, a means-end chain is created to explore the interconnections between the attribute, the anticipated consequences that arise from the attribute, and finally to the personal value being reinforced. The defining aspect of an interview utilizing this theory is to present the participant with the simple question, “Why is that important to you?” After they name a factor or attribute that was important in their college selection, the researcher simply seeks to determine why that factor was important. This generally leads to a connection to a consequence. Asking why the consequence was important leads into further abstraction, to a statement of a value.

According to the theory, individuals base decisions on factors that are likely to lead to desired consequences (Gutman, 1982). The privileging of one consequence over another reflects the value set of the person empowered with the choice, and they will make selections that reinforce what they have deemed valuable (Klenosky, Templin, & Troutman, 2001). While two athletes might cite the location of a school as an important factor on a traditionally used survey format, it would be unclear whether they value location because of proximity to family, the effect of weather on their sport performance, preference for a rural or suburban lifestyle, or for myriad other reasons. Through the application of means-end theory, researchers can make this determination. As applied to college selection, for example, an athlete might rate facilities as an important factor (attribute) in her college selection. Further questioning (via the “why is that important” question) can elicit the response that facilities were import because she believed it would help her play better (consequence). Finally, she might describe that playing better would reinforce her desire for personal achievement (value). See Table 1 for an example of interview responses and the corresponding coding.

Table 1

Example of two interview ladders and the corresponding coding for each

Table 1

Research Goal

 

The present study sought to apply means-end theory to determine the attributes, consequences, and values that underpinned college selection for female student athletes at an NCAA Division II institution.

Method

Procedure

 

Semi-structured interviews were conducted with two researchers and individual student athletes. The participants were asked to recall the colleges that they seriously considered as they made their final college selection. Participants were then asked to list factors (attributes) that they relied on as they selected their college over their other finalists. The researchers then utilized the laddering technique as described by Reynolds and Gutman (1988) and later applied to student athletes and college selection by Klensoky, Templin, and Troutman (2001) to create means-end chains, in which each attribute was explored via the question, “Why is that important to you?” This would elicit a response suggesting how this attribute would benefit the participant (consequence). Then the “Why is that important to you?” question would be used to move the participant into deeper reflection, moving from the consequence to a personal value. Participants would create from two to four chains and interviews generally lasted ten to fifteen minutes.

To elicit the most thoughtful and honest answers possible, the researchers utilized the interview methods suggested by Reynolds & Gutman (1988). These included conducting interviews in a non threatening environment (a library area was used, which represented a more neutral site for participants than would a professor’s office or a classroom), making an effort to position the participant as the only expert regarding their college selection, with emphasis being placed on reassuring them that there was no right or wrong answer, and showing interest in responses while refraining from giving cues suggesting judgment. Following each interview, the researchers used interview notes to create means-end chains, which connected each attribute cited by the participants with the corresponding consequences and values stemming from it. Discrepancies were resolved jointly, relying as strictly as possible on key words and phrases used by the participants and recorded in the interview notes.

Participants

The participants in this study were 25 female student athletes at an NCAA Division II university in Florida during the 2005-2006 academic year. Participants represented a variety of sports, including basketball, soccer, softball, golf, tennis, rowing, and cross country.

Results

 

In total, 77 means-end chains were created, an average of 3.08 per participant. Coding of the means-end chains revealed eight attributes cited as important to the selection of the student athletes’ current college. These attributes led to eight potential consequences, which, in turn, led to four values.

Table 2

Summary of all attributes, consequences, and values identified throughout the interview process

Table 2

Using the coded data, an implication matrix was constructed (Table 3) as a summary of the connections between attributes, consequences, and values. In addition to showing the number of participants that mentioned a concept (under N), the matrix also lists the number of total times the concept was mentioned. Each cell reflects the number of times the concept was mentioned. For example, location linked to the consequence of feel comfort (C1), three times and connected to the consequence of adventure (C3) twelve times. Location also connected to the value fun and enjoyment (V1) fifteen times. The implication matrix was then used to construct a Hierarchical Value Map (HVM).

table 3

Implication Matrix for female student athletes’ college selection

N Chains C1 C2 C3 C4 C5 C6 C7 C8 V1 V2 V3 V4
Attributes
A1 Location 22 30 3 1 12 1 5 8 15 5 8 2
A2 Scholarship 16 16 13 3 7 9
A3 Academics 7 7 7 3 1 3
A4 Coach 7 7 5 2 3 2 3 2
A5 Facilities 6 6 1 5 1 5
A6 Friend
on the team
4 4 4 3 1
A7 School
Size
4 4 3 1 2 2
A8 Open Spot 3 3 3 2 1
Consequences
C1 Feel Comfort 15 16 8 1 2 5
C2 Financial Comfort 14 14 4 10
C3 Adventure 12 12 12
C4 Get a Good Job 9 9 3 1 5
C5 Can Improve 8 10 10
C6 Friend & Family 7 8 2 5 1
C7 Feel
Valued
5 5 4 1
C8 Playing
Time
3 3 2 1
Values
V1 Fun
& Enjoyment
20 27
V2 Achievement 14 21
V3 Security 13 22
V4 Belonging 5 7

As information from the implication matrix was transferred into the HVM, the researchers selected a cutoff level of two. A cutoff level establishes how frequently a connection had to be made to be depicted in the HVM. Thus, only connections made two or more times are illustrated with a line. Eliminating connections made only one time reducing clutter in the HVM. To assist the reader in interpreting the HVM, an illustrative example is presented (Figure 1). The complete HVM follows (Figure 2). Consistent with the literature (Klenosky, Templin, & Troutman, 2001), values are presented at the top of the map to represent their abstract nature in college selection (they appear within triangles and are spelled with all capital letters). Consequences are represented across the middle (within circles and beginning with a capital letter), and attributes appear at the bottom (within rectangles and all lower case letters) to reflect that they were merely the beginning point in each chain and are the most superficial level of information gathered. Further, the lines between attributes, consequences, and values represent the frequency of the connection between these concepts (more frequent associations depicted with thicker lines). The size of each shape also reflects the number of participants mentioning it, with more frequently mentioned concepts dominating more space. Finally, the first number in each shape reflects the number of participants that mentioned the concept, while the number in parenthesis is the number of times the concept was mentioned in total, reflecting that some concepts would be mentioned multiple times by a single participant.

Figure 1

Figure 1. An illustrative example of an HVM section

Figure 2

Figure 2. Hierarchical Value Map for female student athletes’ college selection

Discussion

 

Analysis of the HVM revealed several noteworthy findings. First, location was a primary attribute for the selection of this university over other universities the athletes considered as they made a final decision. In fact, 39% of all the chains created in this study began with the attribute of location. While it might not be surprising that a university in the state of Florida is selected for its location, this fact underscores the importance of a means-end analysis. While a college selection survey would also reveal that location was important, it would not discover the true reason for the importance of this attribute. The means-end analysis demonstrated that the attribute of location was important for several different reasons. Of the 30 chains beginning with location, 12 went to the consequence of adventure and then continued on to the value of fun and enjoyment. Other participants indicated that location was important because it kept them close to friends and family, which had a strong connection to the value of security. Others expressed that they simply are comfortable here, which largely connected with fun and enjoyment. Finally, some participants (in outdoor sports) noted that the weather in Florida would allow them to improve their sport performance (largely due to an extended season), which supported the value of achievement.

The different values that underpinned the importance of location supported the belief that college selection is a complicated process and that a single attribute of a campus can be important to prospective student athletes for a wide variety of reasons. This fact should be particularly interesting to coaches who spend considerable time and effort in the recruitment process and could misinterpret a prospects’ motivation for selecting a particular university. For example, coaches might feel confident that a student athlete selected a college because of location and may even presume to know that it is related to a consequence, such as improving sport performance, whereas in the mind of the student athlete, lying on the beach might be the true motivator because she is more driven by her value of fun and enjoyment than by the value of achievement.

Second, the attribute of receiving an athletic scholarship was also frequently mentioned. It was important to 16 of the 25 participants (64%). Predominantly it led to the consequence of financial comfort, which, in turn led to the value of security. For a few participants, however, the consequence of financial comfort led to the value of achievement, which reflected their belief that financial comfort was essentially earned through their years of dedication to sport participation. Comments made during the interviews suggested that the participants viewed the scholarship as a literal indication that they had achieved within their sport and that their achievement became measurable and worthwhile through the scholarship offer. Participants reported being offered scholarship packages of widely varying values and thus scholarship became an important attribute in differentiating between schools. The Klenosky, Templin and Troutman (2001) study did not reveal scholarship as an important attribute among the Division I football players because each participant in the sample reported being recruited by over 20 schools and thus scholarship was likely a non-issue in differentiating between schools.

Third, the attributes of the coach and academics were mentioned by surprisingly few participants. These attributes were seldom used by participants to differentiate their school from others at the time they made their final selection. Still, it is interesting to see that these attributes trailed location and scholarship by a wide margin. For the seven participants who mentioned academics, all of them linked it to the consequence of getting a good job, as opposed to more altruistic notions such as gaining knowledge or growing as a person.

Fourth, the consequence of feeling comfort was frequently mentioned and stemmed from a variety of attributes. School size, location, a friend on the team, and the coach were all attributes that seemed to assure the participants that this school would be a good fit for them and provide a place in which they would feel comfort. This information is valuable for coaches who actively recruit prospects. It is possible that a key to securing recruits is in convincing them that the attributes of the college, team, and campus will help the prospect feel comfort.

Fifth, the value of fun and enjoyment underpinned the college selection for many participants (it was mentioned by 20 of the 25 participants (80%), and several participants had multiple chains end with this value). However, the source of fun and enjoyment was extremely varied. At the time the college selection was made, participants believed that playing time, adventure (from location), proximity to friends and family, a comfortable atmosphere, and opportunity to get a good job all led to the possibility of fulfilling the value of fun and enjoyment.

This study contributes to the college selection literature and furthered the work of Klenosky, Templin, and Troutman (2001) to utilize means-end theory to determine the values that student athletes rely on in this process. However, there were limits to the study. Most notably, it only represented student athletes from one university and results do not generalize to female student athletes overall. Different results could occur among student athletes at other schools based on such traits as school size, region of the country, and NCAA division.

Conclusion

 

College selection is a complicated and difficult process for student athletes, which is often made even more confusing by the recruitment process. While traditionally researchers have sought to understand college selection by drawing from sizable data sets gathered via surveys, that method fails to explore fully the complexity of any given attribute (such as location). By applying means-end theory researchers can probe further and determine the values on which prospects are basing their selection. Further, a general understanding of means-end theory could be important for coaches to improve the process of attracting prospects in an increasingly competitive college sports climate. It also can assist coaches in understanding what is important to the student athletes once they matriculate to campus.

For the participants in this study, security, achievement, belonging, and fun and enjoyment were the guiding values for college selection. Future research should extend the use of means-end analysis to student athletes in other contexts, for example by sport, NCAA division, and region of the country.

References

 

Bukowski, B. J. (1995). Influences on student college choice for minority and non minority athletes at a Division III institution (Doctoral dissertation, University of Wisconsin, Madison, WI). Dissertation Abstracts International, 56(7), 126.

Cook, T. (1994). Factors female freshmen student-athletes use in deciding between a NJCAA college and a NAIA college. Unpublished master’s thesis, University of Kansas, Lawrence, KS.

Doyle, C. A. & Gaeth, G. J. (1990). Assessing the institutional choice process of student athletes. Research Quarterly for Exercise and Sport, 61(1), 85-92.

Finley, P. S. (2005). An analysis of team Web site content and college choice factors of NCAA Division III cross country runners (Doctoral dissertation, University of Northern Colorado, Greeley, CO). Dissertation Abstracts International, 66(04), 1291.

Forseth, E. (1987). Factors influencing student-athletes’ college choice at evangelical, church-supported NAIA institutions in Ohio (Doctoral dissertation, The Ohio State Univesity, Columbus, OH). Dissertation Abstracts International, 48(01), 172.

Gutman, J. (1982). A means-end chain model based on consumer categorization processes. Journal of Marketing, 46(2), 60-72.

Johnson, E. A. (1972). Football players’ selection of a university. Unpublished master’s thesis, University of Utah, Salt Lake City, UT.

Kankey, K., & Quarterman J. (2007). Factors influencing the university choice of NCAA Division I softball players. The SMART Journal, 3(2), 35-49.

Klenosky, D. B., Templin, T. J. & Troutman, J. A. (2001). Recruiting student athletes: A means-end investigation of school-choice decision making. Journal of Sport Management, 15, 95-106.

Konnert, W., & Geise, R. (1987). College choice factors of male athletics at private NCAA Division III institutions. College and University, 63(1), 33-44.

Mathes, S., & Gurney, G. (1985). Factors in student-athletes’ choice of colleges. Journal of College Student Personnel, 26(4), 327-333.

Reynaud, C. (1998). Factors influencing prospective female volleyball student-athletes’ selection of an NCAA Division I university: Towards a more informed recruitment process (Doctoral dissertation, Florida State University, Tallahassee, FL). Dissertation Abstracts International, 59(02), 445.

Reynolds, T. J., & Gutman, J. (1988). Laddering theory, method, analysis and interpretation. Journal of Advertising Research, 28(1), 11-31.

Slabik, S. L. (1995). Influences on college choice of student-athletes at National Collegiate Athletic Association Division III institutions. Unpublished doctoral dissertation, Temple University, Philadelphia, PA.

Ulferts, L. (1992). Factors influencing recruitment of collegiate basketball players in institutions of higher education in the upper Midwest (Doctoral dissertation, University of North Dakota, Grand Forks, ND). Dissertation Abstracts International, 54(03), 770.

Authors Note:
Correspondence for this article should go to Peter Finley, H. Wayne Huizenga School of Business and Entrepreneurship, 3301 College Avenue, Fort Lauderdale-Davie, Florida 33314, (954) 262-8115, pfinley@huizenga.nova.edu.

2016-10-20T10:36:52-05:00April 2nd, 2008|Sports Coaching, Sports Exercise Science, Women and Sports|Comments Off on An application of means-end theory to analyze the college selection process of female athletes at an NCAA division II university

Service Learning in Sport Management: A Community Health Project

Abstract

Service learning is increasingly popular in schools, colleges, and universities. Service learning is a form of experiential learning and is an ideal pedagogical strategy to teach students about sport management. Students engaged in service learning typically become involved in specific community-based projects that are a part of their class requirements. These projects usually meet a real community need and link classroom content with community projects and reflection. Students can benefit tremendously from an educational experience that combines service learning and sport management. They can reap benefits in the areas of academic learning, civic responsibility, personal and social development, and opportunities for career exploration. A well-planned and well-executed service learning project can expand the student’s sport management experience well beyond events, contests, and classroom lectures. It can bridge the gap between the school and the community by providing a way for students and community organizations to come together for a worthy cause, making learning more meaningful. The purpose of this article is to examine how sport management classes can be designed and implemented as service learning projects that address critical community health challenges. Specifically, this article addresses service learning design that could be applied to any community health problem. The example used here is fund raising for malaria mitigation projects distributing bed nets as a low-cost means of prevention. The article describes the actual service project and discusses ways to encourage students to deepen their civic engagement to meet critical community and global needs.

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2016-10-19T11:05:37-05:00April 2nd, 2008|Contemporary Sports Issues, Sports Coaching, Sports Management|Comments Off on Service Learning in Sport Management: A Community Health Project

Retaining Current Vs. Attracting New Golfers: Practices among the Class A Carolinas Professional Golf Association Membership

Abstract

Golf rounds declined in the U.S. from 2001 to 2004. The southeast region of the country has started to show increases in golf rounds. A possible explanation for this turn-around can be found in the theory of reasoned action. A survey among Professional Golf Association Class A Members in the Carolinas section of the PGA shows the utility of retaining current avid golfers is greater than the utility of attracting new golfers. Implications for managing golf clubs nationally are discussed.

Introduction

The golf industry in the U.S. has recently been stagnant or declining in the number of rounds of golf played annually. The National Golf Foundation (NGF) has reported a national decline from 2001 to 2004 of -4.5% (NGF, 2004). A similar trend (-4.3%) has been observed in the southeast region during that period. Some observers (Graves, 2006; Harrack, 2006) have suggested that concentrating on getting avid golfers to play more rounds is a better approach than trying to attract new golfers to a club.

More recently, the NGF (ngf.org, 2007) is reporting a national upturn in golf rounds of +0.8% with the southeast region showing a robust growth of +4.5%. The question of why the southeast region is doing so well is one of management priorities. The theory of reasoned action (Hawkins, Mothersbaugh, and Best, 2007) provides a framework for understanding how managers of business enterprises make decisions. Professional Golf Association (PGA) members who run golf club enterprises are no different than the chief executive officer of a Fortune 500 company in the decisions they need to make. A business manager needs to identify how to grow the business and make a profit. Operational goals have varying priority and utility in this effort, and golf club managers have intensified their commitment to growing rounds of golf (Staw, 1981).

Theory of Reasoned Action

The theory of reasoned action specifies the decision-making task confronting PGA members who manage golf clubs. Historically, psychologists (Baron, 2000), game theorists (Von Neuman and Morgenstern, 1972), sociologists (Homans, 1961), economists (Elster, 1986), and marketers (Johnson and White, 2004) have all embraced theory of reasoned action concepts.

The concepts embodied in the theory of reasoned action include: 1) bounded rationality (only a few evaluative criteria can be considered simultaneously implying limited capacity), 2) making trade-offs (applying the evaluative criteria to viable alternatives in a compensatory way), 3) the superior option is revealed as the one with the highest utility value to the business manager. Thus, the PGA member managing a golf club must decide what is important and which of those important goals will lead to the best business outcome.

The theory of reasoned action has a measurement methodology known as utility calculations (Baron, 2000). The basic concept, evaluative criteria, involves various dimensions, features, or benefits sought in attempts to solve a specific problem, such as reaching operational goals at a golf club. Managerial decisions involve an assessment of one or more evaluative criteria related to the potential benefits or costs that may result from a decision of which goals to pursue.

Thus, evaluative criteria are typically business activities associated with either benefits desired by managers or the costs they must incur. Depending upon the business situation, management evaluative criteria can differ in terms of type, number, and importance. Thus, a study of business management decision making involves an evaluation of both the importance of the business activity and the business performance resulting from specific criteria. Determining an evaluation of business activity options can be accomplished in two ways: 1) direct methods where PGA members are simply asked about the importance and satisfaction with performance concerning business activities they may use in a particular decision situation, and 2) indirect techniques, where it is assumed PGA members will not or cannot state their views on these issues. The approach taken here is direct assessment described below in the methodology.

The purpose of this project was to conduct a survey of PGA Class A professionals who manage golf course enterprises in the CPGA region to determine what their priorities are regarding operational goals they see as related to stimulating growth in rounds of golf played at their clubs.

Hypothesis

It is hypothesized that PGA Class A Members managing golf clubs in the CPGA will consider the utility of retaining current golfers to be larger than the utility of attracting new golfers.

Methodology

The Class A PGA members survey provided data concerning golf course management practices utilizing an e-mail recruitment and VOVICI (formerly WebSurveyor). The issues involved include:

1.    Making the questions easy to understand and answer;

2.    Measuring the relevant concepts such as importance and performance;

3.    Asking appropriate demographic questions;

4.    Having a relevant e-mail list;

5.    Having a short and effective invitation;

6.    Sending the e-mail invitation at an effective time; and

7.    Using follow-ups as necessary.

Faculty handled items one through four above and utilized WebSurveyor to create the survey instrument. The e-mail recruiting list came from the PGA; thus it was relatively fresh and accurate. Items five, six, and seven were handled by the students after instruction from faculty.

There were 72 students in two Retail Management classes who participated in fielding the PGA web survey. Each student had a list of approximately 20 PGA members to contact through e-mail. The first round of e-mail invitations produced few completed survey responses without an endorsement letter. The second round of e-mail invitations included an endorsement letter from the Secretary of the Carolinas Section of the PGA. In addition, a specific subject line was provided that said, “A Message from Karl Kimball, Secretary of the Carolinas Section of the PGA.” Students were also required to copy the Retail Management professor on all outgoing e-mails to keep track of their efforts so they could receive course credit and so the PGA respondents could receive a summary of the results after the responses were analyzed.

This approach to survey control ensured that e-mail invitations were sent out in a timely fashion, had an appropriate and inviting subject line, included an endorsement by an appropriate source, and offered an incentive for participation in the form of a summary of the results (Goodman, 2006). As a result, 107 completed surveys were available for analysis.

Measuring business managerial judgments of the importance of and satisfaction with performance on specific operational goals can include rank ordering scales, Semantic Differential scales, or Likert Scales. Likert Scales were used here.

Using a Likert Scale to measure importance and satisfaction with performance against operational goals in the theory of reasoned action applied to golf club management comes in the form of a calculated utility score. Here, utility is defined as the product of each operational goal’s rated importance score and its rated satisfaction with performance score measured on a Likert Scale. For this study, it is the importance and satisfaction score associated with retaining current golfers as well as attracting new golfers using the Importance and Satisfaction Likert Scales below.

Importance

5 = Extremely Important
4 = Somewhat Important
3 = Neutral
2 = Not Very Important
1 = Not Important At All

Satisfaction with Performance

5 = Very Satisfied
4 = Somewhat Satisfied
3 = Neutral
2 = Somewhat Dissatisfied
1 = Very Dissatisfied

In addition to measuring importance and satisfaction with performance concerning retaining and attracting golfers to the club, a series of demographic items were included in the survey such as type of course, golf population served, tenure of the course manager as a Class A Member, and the time at the current club spent by the Class A Member of the PGA.

Results

Sample Characteristics

The results of the demographic items appear below and indicate the survey produced a wide variety of clubs where the PGA members are located.

Type of Course: The majority of PGA members were at either private (34.7%) or semi-private courses (28.7%), with some at public courses (17.8%), resorts (11.9%), or other type of courses (6.9%).

Golfing Population Served: Almost half of the PGA members described their golfing population as mostly permanent residents (49.5%) with few serving either mostly out-of-town visitors or mostly part-time residents (6.9% each), while close to a third (30.7%) have a golfing population balanced among these three groups.

Tenure as a PGA Class A Member: Few PGA members in the survey have been in Class A for 5 years or less (8.9%) or between 6 and 10 years (14.9%). Almost half have been in Class A between 11 and 20 years (48.5%) and less than a third have been in Class A for more than 20 years (27.7%).

Time Served at Current Club: Over a third of these Class A members have been at their current club for either 2 to 5 years (37.6%) or more than 10 years (38.6%). A minority have been in place either 6 to 10 years (18.8%) or 1 year or less (5.0%).

Importance of Retaining Current Golfers and Attracting New Golfers

The results for the importance of reaching the operational goals of attracting new golfers and retaining current golfers appear in Table 1. PGA members rated both operational goals as extremely important.

Table 1: Means and Standard Deviations for Operational Goal Importance

Operational Goals Mean Response Standard Deviation
Attracting New Golfers 4.64 0.622
Retaining Current Golfers 4.80 0.531

Satisfaction with Performance in Retaining Current Golfers and Attracting New Golfers

Results for satisfaction with performance with operational goals appear in Table 2. PGA members were somewhat satisfied with performance against the two operational goals.

Table 2: Means and Standard Deviations for Satisfaction with Performance in Reaching Operational Goals

Operational Goals Mean Response Standard Deviation
Attracting New Golfers 4.10 0.572
Retaining Current Golfers 4.19 0.741

Utility of Retaining Current Golfers and Attracting New Golfers

Results for the calculated utility scores for the two operational goals appear in Table 3. A paired-t test was done on the mean responses for the two operational goals and indicates retaining current golfers has significantly higher utility to the PGA members compared to attracting new golfers (t [90] = -2.44, p <.02 two-tailed).

Table 3: Means and Standard Deviations for Utility in Reaching Operational Goals

Operational Goals Mean Response Standard Deviation
Attracting New Golfers 19.02 3.85
Retaining Current Golfers 20.09 4.32

A final issue concerns whether or not reaching these operational goals is producing an increase in rounds played and how that utility is realized and that increase is accomplished.

Change in Rounds Played

A series of survey items dealing with number of rounds played per year at the club was also included. These items included total number of rounds played, number of rounds at a discounted price, number of rounds as part of a golf and lodging package, and number of complementary rounds. Table 4 shows the change in number rounds reported by the PGA members.

Table 4: Percentage Reporting Changes in Rounds Played

Percentage Reporting Changes in Rounds Played Increasing Stable Declining
Number of Rounds Played Per Year 45.9% 42.9% 11.2%
Number of Rounds at a Discounted Price 24.4% 50.0% 25.6%
Number of Rounds as Part of Golf and Lodging Package 22.8% 63.3% 13.9%
Number of Complimentary Rounds Played 6.5% 76.3% 17.2%

The net percentage of PGA members reporting change in rounds played can be found by subtracting the percentage reporting a decline from the percentage reporting an increase in the number of rounds while ignoring those who are stable. Thirty-five percent of the PGA members reported net rounds are increasing. This increase was attributed to golf and lodging packages bringing more golfers to the course (+9%). In addition, declines in discounted (-1%) and complementary rounds (-11%) were reported. The figure below displays these results for the net percentage of PGA Class A Members reporting changes in net rounds played.

Net Percentage Reporting Change in Rounds Played
Conclusions and Implications:

Figure 1. Net Percentage Reporting Change in Rounds Played

Conclusions and Implications

Support for the hypothesis that CPGA Class A Members would show more utility for getting additional rounds from current golfers compared to attracting new golfers indicates they have solved the problem of declining rounds of golf in accordance with the theory of reasoned action. These club professionals realized that getting additional rounds of golf from golfers who patronize their clubs is more effective than trying to attract new golfers with discounted rounds and complementary rounds. Any costs associated with golf and lodging packages were more than compensated for by a substantial increase in rounds per year.

For the PGA membership to increase rounds nationally, the focus should be on retaining current avid golfers to increase rounds and get them to the club by offering golf and lodging packages and reducing discounted and complementary rounds to attract new golfers. Growth can be restored in this manner for golf rounds in the U.S.

References

Baron, J. (200). Thinking and Deciding, 3rd edition, Cambridge, UK: Cambridge University Press.

Elster, J. Ed. (1986). Rational Choice. Oxford, UK: Basil Blackwell.

Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.

Goodman, G.F. (2006). Five common email marketing mistakes. http:// www.Entrepreneur.com>

Graves, R. (2006), Golf Ranges Drives Profits: Today’s range is a practice center, learning center, clubfitting center, Etc. PGA Magazine, (August 1), 37-57.

Harack, T. (2006), Pushing forward: A proactive recruitment program can help stimulate stagnant membership roles, Golf Business, 12 (August), 26-27.

Hawkins, D., Mothersbaugh, D., and Best, R. (2007). Consumer Behavior: Building Marketing Strategy, 10th ed. Boston, MA: McGraw-Hill: Irwin.

Homans, G. (1961). Social Behaviour: Its Elementary Forms. London: Routledge and Kegan Paul.

Johnson, D., and White, J. (2004). A new integrated model of noncompensatory and compensatory decision strategies, Organizational Behavior and Human Decision Processes, 95, 1-19.

National Golf Foundation (2004), Rounds Played in the United States, 2004 Edition.

National Golf Foundation Press Release (2007), Rounds Played in the United States, 2007, <http://www.ngf.org/cgi/whonews.asp?storyid=191>

Staw, B.M. (1981). The escalation of commitment to a course of action. Academy of Management Review, 6, 577-587.

Von Neuman, J, and Morgenstern, O. (1972). Theory of Games and Economic Behavior, Princeton, NJ: Princeton University Press.

2016-10-19T11:00:39-05:00March 14th, 2008|Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off on Retaining Current Vs. Attracting New Golfers: Practices among the Class A Carolinas Professional Golf Association Membership

Show Me the Money! A Cross-Sport Comparative Study of Compensation for Independent Contractor Professional Athletes

Abstract:

Numerous pay equity studies have been conducted. Many have examined the compensation of professional athletes. However, few studies have compared athlete compensation across sports, which is the objective of this research. Focusing on independent contractor athletes, several analyses were performed to determine how one type of athlete’s (e.g., horse jockeys) earnings from competition (excluding sponsorships, appearance fees, etc.) compare to other types of contracted athletes, such as race car drivers, golfers, bull riders, tennis players, etc. Overall, this exploratory study sheds insight into how the different groups of athletes are paid, and, more importantly, provides a framework for future research that examines the compensation inputs (versus outputs) of each of these groups.

Introduction:

Salaries for professional athletes continue to escalate each year. From Alex Rodriguez’s record $252 million contract to David Beckham’s $50 million per year enticement to join the LA Galaxy soccer team, most sports fans believe that professional athletes, in general, are overpaid and not worth their salaries. Yet for the professional athlete, maximizing compensation is critical, given the short careers and health risks associated with professional sports. Thus, athletes and their agents often look to see what others within their sport are paid in an effort to negotiate for more money. Few, if any, have compared athlete compensation across sports. While a cross-sport comparison might not be necessary in team professional sports (e.g., MLB, NHL, NFL, NBA) given the strength of their collective bargaining agreements, independent contractor professional athletes (e.g., jockeys, bull riders, golfers, race car drivers) need this type of analysis. The purpose of this research is to compare independent contractor professional athlete salaries across sport using four perspectives: total payout to athletes per sport, percentage of winnings per sport, top individual earner by sport and mean earnings for the top 50 athletes in each sport. These perspectives will allow independent contractor professional athletes to better analyze the “fairness” or equity of compensation.

Background:

Ever since jockey Gary Birzer was paralyzed while racing in 2004, the jockeys’ guild and track owners have had an acrimonius relationship, to say the least. While much of the ongoing battle has centered on who should pay for the jockeys’ long-term disability coverage, the dispute has recently turned to jockey compensation. The jockeys believe they are underpaid, given the amount of overall purse money involved in the sport and the inherent health risks of racing horses. In contrast, the race tracks believe that the horse owners and trainers deserve the bulk of winnings, given their financial risk and knowledge. With the sides at a stalemate, Corey Johnsen, President of Lone Star Park at Grand Prairie and current President of the Thoroughbred Racing Associations (the trade organization for the tracks) commissioned this study. By collecting extensive data across a number of sports, Johnsen sought to provide a starting point for discussions between the tracks and the jockeys’ guild in an effort to resolve the dispute.

Review of Literature:

A number of management and economic researchers have investigated compensation equity or justice across many industries. While it is beyond the scope of this paper to cover all of this literature, this section highlights a few key citations.

Carrell and Dittrich (1978) were two of the first to provide a comprehensive look at pay equity. The authors looked at the components of pay equity, such as an individual’s perceived inputs and outputs. In addition, they examined how an individual adjusts performance when he or she perceives pay to be inequitable. Adding to the body of existing literature, Younts and Mueller (2001) measured the importance individuals place on compensation justice, in particular distributive justice (i.e., the outcomes or rewards received). Similarly, St-Onge (2000) extended the literature by evaluating the influence of several individual variables on pay-for-performance perceptions, specifically looking at actual pay-for-performance, trust in decision-makers, perceived procedural justice, outcome satisfaction and size. Using social equity theory, Sweeney and McFarlin (2005) found that an individual’s pay satisfaction was based on that individual’s wage comparisons with similar and dissimilar others.

Looking specifically at athlete compensation, the majority of previous studies have dealt with team sports, and, in particular, Major League Baseball, given its unique market structure and history (e.g., anti-trust exemption, owner collusion in the 1980s, etc.). For instance, Slottje, Hirschberg, Hayes, and Scully (1994) used Frontier estimation to measure wage differentials in Major League Baseball and discovered that free agency status significantly influenced wage inefficiency. Similarly, Scully (2004) examined the effects of free agency on compensation as a share of league revenue and the dispersion of compensation among players across the four major professional sports: MLB, NBA, NFL, and NHL. As expected, he concluded that both were higher when leagues permitted a free labor market. Looking more closely at one professional sport, NHL hockey, Idson and Kahane (2000) empirically investigated the effects of co-worker productivity by examining individual variables, such as height, weight, points scored, plus/minus ratios, star status, and team/coach variables, such as team revenues, coach’s tenure, and coach’s winning percentage in the league. They concluded that “estimates of the effects of individual attributes on compensation are upwardly biased when team effects are not taken into account in standard salary regressions” (p. 356). Lastly, Nero (2001) used multiple regression analysis to measure the salary effectiveness of independent contractor athletes in one sport: PGA tour golfers. In his study, he analyzed the impact of a pro golfer’s driving distance, fairways hit, putting average per green, and sand saves (i.e., the percentage of times a player uses at most two shots to score from a greenside sand trap) on earnings. He concluded that putting average had the greatest influence on a golfer’s earnings.

Analysis:

  1. To compare jockey compensation to other independent contractor professional athletes, eleven sports were chosen (see Table 1). Salary data was compiled using primary research, including telephone and face-to-face interviews with representatives from various sports and secondary research utilizing websites, sports journals, magazines, and newspapers.

Table 1: Independent Contractor Sports for Comparison

  1. Professional Rodeo Cowboys Association (PRCA)
  2. Professional Bull Riding (PBR)
  3. Women’s Professional Rodeo Association (WPRA)
  4. Professional Golfers’ Association of America (PGA)
  5. Ladies Professional Golf Association (LPGA)
  6. Association of Tennis Professionals (ATP)
  7. Women’s Tennis Association (WTA)
  8. National Association of Stock Car Racing (NASCAR)
  9. National Hot Rod Association (NHRA)
  10. Association of Volleyball Professionals (AVP)
  11. Union Cycliste International (UCI)

Results:

Total Payout per Sport

Table 2 reveals the total payouts (excluding additional income such as endorsements or appearance fees) for horse racing (NTRA: National Thoroughbred Racing Association) and the independent contractor athletes in eleven other sports in 2005 and 2004.

Table 2: Total Payout by Sport

Assn 2005 2004
NTRA $1,085,000,000 $1,092,100,000
PRCA $37,553,821 $18,881,001
PBR $5,615,563 $3,491,450
WPRA $5,000,000 $4,000,000
PGA $266,112,055 $256,740,000
LPGA $44,400,000 $42,075,000
ATP $90,287,231 $88,549,527
WTA $59,190,883 $56,614,168
UCI $85,050,000 $85,050,000
NASCAR $275,276,253 $264,210,961
NHRA $13,500,000 $12,600,000
AVP $3,500,000 $3,000,000
Totals $1,970,485,806 $1,927,312,107

As illustrated, the amount of money paid out (i.e., purse money) in horse racing far exceeds the other sports. In fact, the second and third ranked sports, NASCAR and PGA, have purses approximately 25% of those of the NTRA. However, when the payouts for jockeys-only (i.e., the percentage of the purse that is paid to the jockeys by the owners) is compared to the athletes in the eleven other sports, the amount is much less than the compensation paid to PGA golfers and NASCAR drivers (see Figure 1).

Figure 1
Figure 1
Percentage of Winnings Per Sport

Figure 2 provides another perspective of the data in Figure 1. When we analyzed the dollars paid to jockeys as a percentage of the total purses (payouts), we found that jockeys, on average, received 7.5% of the available purse. This is much lower than a number of the other sports. Figure 3 provides the breakdown of purse percentage for first, second, and third places. As shown, NTRA jockeys receive 6% of the available purse for winning a race and 1% and .55% for second and third places respectively. In contrast, PRCA and PBR athletes received at least 20% of the purse for a second place finish.

Figure 2
Figure 2

Figure 3
Figure 3
Note: NTRA represents just the jockey’s percentages

Top Individual Earner by Sport

While using one individual in each sport is not an accurate representation of all athletes within the sport, comparing the top earner provides a glimpse of the earning potential per sport. As Figure 4 reveals, the top jockey, John Velasquez did not earn as much as Tiger Woods (PGA), Roger Federer (ATP), Maria Sharapova (WTA), Annika Sorenstam (LPGA), or Tony Stewart (NASCAR). However, he still makes nearly $2 million per year in earnings.

Figure 4

Top 50 Earners by Sport

Given the drawbacks of looking at the top earner in each sport, the top 50 earners in each sport were compared (see Figure 5). As shown, the average salary of the top 50 jockeys ranks fifth and is approximately $500,000 per year.

Figure 5
Figure 5
Note: NTRA represents just the jockeys (excludes owners and trainers)

Conclusions, Limitations, and Future Research:

Although a very simplistic bivariate analysis was used, this research provides important information for both sides of the jockey compensation debate. From the perspective of the tracks, over $1 billion is paid out in purses each year, with millions going to the jockeys, making horse racing a very lucrative sport. In contrast, from the jockeys’ perspective, they only receive 6% of the purse earnings, if they win, and 7.5% overall. While both sides have their stance, this research should allow the two sides to come together and begin discussions on how to solve this labor dispute. For instance, there might be a way to re-distribute the earnings to allow the second and third place finishers to earn more.

As with any research, there are limitations to this study. For one, this analysis excludes additional monies that athletes receive outside of competing (e.g., appearance fees, endorsements, etc.). Secondly, this research was limited by the bivariate analysis.

In the future, researchers should comparatively examine the sponsorship/endorsement dollars that independent contractor professional athletes receive across the twelve sports. This would provide a more thorough comparison of the total compensation these athletes receive. In addition, more sophisticated statistical analyses are needed to compare athletes across sports via multiple regression analysis or related techniques. As Porter and Scully (1996) pointed out, an individual athlete’s performance is difficult to measure because it is “a serially repeated and rewarded event. Among the individuals competing in professional sports, the distribution of earnings is determined by the distribution of awards for performance, the distribution of talent (expected performance), and by work effort (the number of competitions undertaken)” (p. 149).

Hopefully, future researchers will be able to examine in more detail the pay equity of independent contractor professional athletes.

References:

Carrell, M. and J. Dittrich (1978). Equity theory: The recent literature, methodological considerations, and new directions, The Academy of Management Review, Vol. 3, 202.

Idson, T. and L. Kahane (2004). Teammate effects on pay, Applied Economics Letters, Vol. 11 (12), 731.

Nero, P. (2001). Relative salary efficiency of PGA tour golfers, American Economist, Vol.45 (2), 51.

Porter, P. and G. Scully (1996). The distribution of earnings and the rules of the game, Southern Economic Journal, Vol. 63 (1), 149.

Scully, G. (2004). Player salary share and the distribution of player earnings, Managerial and Decision Economics, Vol. 25 (2), 77.

Slottje, D., J. Hirschberg, K. Hayes and G. Scully (1994). A new method for detecting individual and group labor market discrimination, Journal of Econometrics, Vol. 61 (1), 43.

St-Onge, S. (2000). Variables influencing the perceived relationship between performance and pay in a merit pay environment, Journal of Business and Psychology, Vol. 14 (3), 459.

Sweeney, P. and D. McFarlin (2005). Wage comparisons with similar and dissimilar others, Journal of Occupational and Organizational Psychology, Vol. 78, 113.

Younts, C.W. and C. Mueller (2001). Justice processes: Specifying the mediating role of perceptions of distributive justice, American Sociological Review, Vol. 66 (1), 125.

2016-10-20T10:28:15-05:00March 14th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on Show Me the Money! A Cross-Sport Comparative Study of Compensation for Independent Contractor Professional Athletes

Cross-Country Skiing USSA Points as a Predictor of Future Performance among Junior Skiers

Abstract:

Junior cross-country skiers’ performances prior to participation in the 2006 Junior Olympics were compared to their results in the 2006 Junior Olympics using USSA points as a measure of performance.  Junior class and division (team) were also included as independent variables.  Prior performance as determined by USSA points is a poor indicator of performance in the Junior Olympics.

Introduction:

Cross-country skiing times from different races, even those of the same length, are not comparable because the terrain is different for each race.  Furthermore, snow conditions may vary, even from hour to hour, on the same course.  Merely comparing times of skiers over similar distances is not an accurate comparative assessment of skiers’ abilities.  The United States Ski and Snowboard Association (USSA) points list was developed to allow comparison between skiers who may have entered several different races.  USSA points are awarded to registered cross-country skiers for participation in sanctioned ski races.  A lower value in USSA points indicates that a skier is a better, more competitive skier.  USSA points and similar International Ski Federation (FIS) points are used to help select the U.S. national teams, to seed racers in both mass and interval start races, and to monitor the progress of athletes in physiological studies (Bodensteiner & Metzger 2006; Staib, Im, Caldwell, & Rundell 2000).

The USSA formula that allocates points to skiers is based on race performance. It includes a number of variables that capture the relative ability of skiers in the race.  Who enters the race and how they place are used in determining the penalty.  Each race’s penalty is based upon the current USSA points of top finishers in the race.  The type of start or race and a minimum penalty also are used in the calculation of USSA and FIS points assigned to a skier’s race (Bodensteiner & Metzger 2006, International Ski Federation, 2006).  Despite the common and, at times, mandatory use of the system, the USSA point system has been criticized by racers and coaches over the years for failure to accurately capture a skier’s ability (Anonymous, 2006; Smith, 2002; Trecker 2005).

Methods:

Given the importance and criticism of USSA points, this study develops a systematic comparison of prior USSA points results of skiers to their USSA points earned in a common competition.  One would hypothesize that a skier’s points prior to a competition would predict a skier’s points earned within the competition.   Points earned by Junior skiers (ages 14 to 19) in the 2005-2006 season are compared to USSA points in the 2006 Junior Olympics.  The use of linear regression allows one to determine if a linear relationship exists between prior performance and performance in the Junior Olympics and whether other, easily obtained variables can improve the ability to predict performance at the Junior Olympics.  (Hill, Griffiths, & Judge, 1997; Johnston, 1984)

Before the Junior Olympics, skiers earned USSA points in different races throughout the northern part of the United States.  Skiers within any of the ten USSA districts competed against each other, but there was limited competition among skiers from different districts.  The top 400 skiers then competed in the Junior Olympics in March, 2006 in Houghton, Michigan.  The end of season Junior Olympics allows skiers to be directly compared on the same course and with the same snow conditions, so USSA points assigned in these races can be used in this study free of the bias of course and snow conditions.

A general linear model (equation 1) with USSA points earned in the Junior Olympics as the dependent variable and USSA points prior to the Junior Olympics, junior class (J2, J1, or OJ) division (team) were used as independent variables.  The parameters c and ak (where k = 1, 2, and 3) were estimated.  Estimated parameters in bold are matrices of parameters associated with a matrix of dummy variables.  Equation 1 is the most comprehensive linear model used.

yi = c + a1*Pi + a2* JCLASSi + a3*DIVi + ei          equation 1

Where

yi = USSA points in the 2006 Junior Olympics for the ith skier,

c = an estimated constant,

Pi = USSA points prior to the Junior Olympics for the ith skier,

a1 = the estimated parameter associated with Pi,

JCLASSi = a matrix of junior classes with dummy variables for OJ, J1, and J2 where the value is 1 in the ith skier’s junior class and zero for other classes,

a2 = a matrix of estimated parameters associated with JCLASSi,

DIVi  = a matrix of regional divisions with dummy variables for Alaska, Great Lakes, Midwest, Intermountain, Rocky Mountain, Mid-Atlantic, New England, Far West, High Plains, and Pacific Northwest where the value is 1 in the ith skier’s division and zero for other divisions,

a3 = a matrix of estimated parameters associated with DIVi, and

ei = the residual value for the ith skier.

The model was run using USSA points from all three individual races at the Junior Olympics (yi): freestyle, classic, and sprint.  USSA points prior to the Junior Olympics included (Pi) for distance, sprints, and overall points were used in separate regressions.  Thus, there are several versions of equation 1 that use different techniques (classic and freestyle) and USSA disciplines (sprint, distance, and overall).

While equation 1 represents the most extensive model tested, other models using a subset of the independent variables were also tested to determine the stability of the model.  When sets of independent dummy variables would have resulted in a full rank matrix, one of the variables was not included in the regression.   Technical definitions associated with cross-country skiing terms can be found in the USSA’s Nordic Competition Guide (Bodensteiner & Metzger, 2006). Analyses were run using the GLM procedure in SAS 9.1 for Windows.

Data:

Pre-Junior Olympics distance, sprint, and overall USSA points; names; USSA numbers (to confirm this data with results from the Junior Olympics); junior class (J2, J1, or OJ); and year of birth information were obtained from the national list of USSA points, which had been updated just prior to the Junior Olympics.  Data were downloaded on March 27, 2006.  Junior Olympic classic, freestyle, and sprint USSA points; skier’s division (team); name; and USSA number were obtained from itiming.com via the web in the week following the 2006 Junior Olympics.  In all cases, as complete a data set as possible was used in the regression.  However, some skiers entered the Junior Olympics without prior USSA points or with only a partial set of information.  The most common missing data were USSA sprint points prior to the Junior Olympics.  Whenever a valid number was available for a skier, that skier was entered in the data set for a particular regression analysis.  In a few cases, skiers did not start or finish a race or were disqualified during the race.  The largest data set included information for 271 skiers.

Results:

USSA Points prior to the Junior Olympics – the simplest models.

The first part of the statistical analysis was to determine if USSA points alone could predict USSA points in the Junior Olympics.  The model used to test this question was:

yi = c + a1*Pi + ei          equation 2

Since skiers have sprint, distance, and overall points prior to the Junior Olympics and compete in sprint, freestyle distance, and classic distance events, there are six logical combinations of dependent and independent variables.  Table 1 shows the results of each regression.

Table 1:  Results from the regression of USSA points earned at the Junior Olympics (yi) on USSA points earned prior to the Junior Olympics (Pi).  Equation 2

yi JO Points (Source) Pi Prior (Source)  

estimated c

 

estimated a

 

r2

Freestyle Overall 87.1 0.57 0.59
Freestyle Distance 82.8 0.59 0.59
Classic Overall 116.9 0.79 0.36
Classic Distance 106.7 0.85 0.37
Sprint Overall 74.4 0.80 0.54
Sprint Sprint 84.8 0.60 0.49

Note:  All estimated parameters were significant at the 0.0001 level.

At best, the USSA points earned prior to the Junior Olympics predict only 59% of the variability in the final USSA points earned at the Junior Olympics.  Equation 2 is least effective when used to predict the classic results, explaining only 36% of the variability when the independent variable is Overall USSA points prior to the Junior Olympics.  Figure 1 shows the relationship between the Overall USSA points prior to the Junior Olympics and USSA points earned in the Junior Olympics classic race.  The top five skiers based upon prior USSA points also ended up with results close to what one would expect.  However, after this elite group of skiers, the prior USSA points exhibit poor predictive ability for the remaining skiers.  Some skiers with relatively high USSA points skied well and moved up dramatically at the Junior Olympics.  The reverse was also true; some skiers skied less competitively than one would have predicted from their prior USSA points.  While this is to be expected to some extent (athletes have good and bad days), the large number of skiers who deviated from the expected indicates something other than a few atypical performances by a small number of skiers has occurred.  While the correlation between prior USSA points and the freestyle and sprint race results were better than the classic, the same general pattern is evident the results of these two races are plotted.  The top skiers were identified by prior USSA points while predictive power diminishes for average and relatively weaker skiers at the Junior Olympics.  In fact, even finish order is poorly predicted by prior USSA points.

Figure 1
Figure 1.  Relationship between Overall USSA points prior to the Junior Olympics and USSA points earned in the classic race at the 2006 Junior Olympics.

Figure 1 also shows that this data set is heteroscedastic.  The heteroscedasticity of the data is discussed in the Appendix.

USSA Points prior to the Junior Olympics – adding independent variables

Given that USSA points earned prior to the Junior Olympics are relatively poor predictors for results at the Junior Olympics, whether or not it is it possible to use other readily available information to improve the estimate of where a skier would finish is of importance. Equation 1, a more robust model, was estimated for the same six data sets used for equation 2.  Equation 1 includes the JO class of the ski and the division (team) of the skier. The r2 associated with each equation is shown in Table 2.

Table 2.  Comparison of Equation 2, only prior JO points, with Equation 1, prior JO points, Junior class, and division (team).

yi JO Points (Source) Pi Prior
(Source)
equation 2
r2
equation 1
r2
Freestyle Overall 0.59 0.69
Freestyle Distance 0.59 0.68
Classic Overall 0.36 0.51
Classic Distance 0.37 0.52
Sprint Overall 0.54 0.65
Sprint Sprint 0.49 0.64

Using Junior class and division and team of the skier improved the r2 for all six combinations of Junior Olympics USSA points and points earned prior to the Junior Olympics.  Unfortunately, the best r2 is 0.69, indicating that there is still a substantial amount of unexplained variability in the data set.  Equation 1 is an improvement, but still does not leave one with the ability to use the model with confidence if the purpose is to use past performance to predict expected performance.

Because there is little difference between the use of overall points and other prior USSA points as independent variables in equation 1, only results for equation 1 with overall points are reported.  Table 3 shows the variables, estimated parameters, and P values for each independent variable for the classic, freestyle, and sprint races at the 2006 Junior Olympics.

Table 3.  Estimated parameters and probability level for the parameters, in parentheses, for equation 1.  Estimations are for all three individual events at the Junior Olympics using skiers’ overall USSA points, division (team), and junior class as independent variables.

Independent           Estimated Parameter and P Value (Pr > |t|)
Variable                Classic               Freestyle                 Sprint        
Constant               135.90                 83.47                   44.38
(<0.001)            (<0.001)                 (0.003)
OVERALL                  0.89                  0.55                     0.77
(<0.001)            (<0.001)              (<0.001)
NE                       -46.43               -17.78                  -22.73
(0.005)              (0.015)                 (0.063)
MA                        -7.61                  4.50                     5.13
(0.731)              (0.647)                 (0.743)
GL                       -28.74               -21.40                   53.06
(0.102)              (0.044)                 (0.012)
MW                         1.15                 -6.50                     0.87
(0.961)              (0.405)                 (0.946)
HP                         50.07                 56.54                   69.19
(0.047)            (<0.001)              (<0.001)
IM                         -5.15                 20.21                   58.61
(0.754)              (0.006)              (<0.001)
RM                        -4.40                 -3.12                   33.66
(0.794)              (0.677)                 (0.004)
FW                       -32.77               -17.09                   51.88
(0.090)              (0.047)              (<0.001)
PN                         -2.75                  0.63                   23.66
(0.887)              (0.942)                 (0.079)
J1                        -16.16                  9.91                   26.69
(0.163)              (0.053)                 (0.002)
J2                        -93.08                  8.23                   13.23
                          (<0.001)              (0.211)                 (0.231)                
Notes:  Alaska and OJ are omitted to avoid estimation of a full-rank matrix.
NE = New England, MA = Mid-Atlantic, GL = Great Lakes, MW = Midwest,
HP = High Plains, IM = Intermountain, RM = Rocky Mountain, FW = Far West,
PN = Pacific Northwest.

Each of the equations is estimated with Alaska omitted as a team and the OJ class omitted.  This prevents full rank estimation of the equation.  The Classic estimation shows that New England and Far West skiers ski relatively faster than Alaskan skiers given their predicted times.  High Plains skiers are slower than predicted relative to the Alaskan skiers.  The estimated parameters for other divisions are not significantly different from zero.  In the freestyle race, the estimated parameter for the dummy variable representing skiers from the New England, Great Lakes, and Far West indicated that, given their prior USSA points, members of these teams were relatively faster than the Alaskan skiers as indicated by USSA points earned in the Junior Olympics race.  The phrase “relatively faster” is important.  In general, Alaskan skiers finished ahead of Great Lakes skiers, although the estimated parameter associated with the Great Lakes is negative.  The dummy variables for teams improve the estimation by adjusting for a skier’s team given the other variables used in the estimation, especially the overall USSA points prior to the Junior Olympics.  Using Alaska and the Great Lakes as an example, the average Alaskan skier entered the Junior Olympics with a better USSA points ranking and than the average Great Lakes skier.  The Alaskan skiers also outperformed the Great Lakes skiers on average at the Junior Olympics.  However, in the freestyle competition at the Junior Olympics, the Great Lakes skiers’ improvements from predicted to actual performance was substantially better than that of the Alaskan skiers.  Dummy variables capture this distinction.

In the freestyle race, the estimated parameters for the High Plains and Intermountain teams were positive.  In the sprint race, the teams from New England again had a significant, negative estimated parameter while the Great Lakes, High Plains, Intermountain, Rocky Mountain, Far West, and Pacific Northwest all had significant, positive estimated parameters.  Both the Far West and Great Lakes had significant, negative estimated parameters in the freestyle race but significant, positive estimated parameters in the sprint race.  (New England skiers can take heart that they outperformed their expected results and won the Alaskan Cup despite whatever disadvantage may accrue to weaker seeding.)

The estimated parameter for junior class was also significant for one of the classes in each of the equations, indicating that including class in the estimate improves the equation.  Junior class can help predict USSA points earned.

Stability of the Models

It would be tempting to state that the use of additional variables improves the equation and would help somebody trying to use prior USSA points in estimating performance or performance gains.  However, several factors argue against this.

1.  This data set represents only the top junior skiers, ages 14 to 19, over one season.

2.  The three versions of equation (1) estimated with classic, freestyle, and sprint results from the Junior Olympics are not similar.  Both the constant and parameter associated with the overall points vary considerably with the different estimations, indicating that the model is not stable.

3.  The parameters associated with dummy variables representing divisions (teams) and junior classes are not consistent and, in some cases, change dramatically from estimation to estimation.  For example, Great Lakes skiers have a positive and significant parameter associated with the dummy variable in the freestyle equation, but they have a negative and significant parameter associated with the dummy variable in the sprint equation.

4.  The r2 values associated with all equations estimated are not strong enough to justify the use of the model to predict the future results of skiers.

Given these concerns, it is likely that estimating these equations using data from other years or older skiers would generate substantially different equations.  It is unlikely that the model would be stable (that is, the estimated parameters would be similar), if different versions of the model were estimated or different data sets were used.

Conclusions:

This paper provides a clear test of the ability of USSA points to compare the relative ability of skiers.  The initial points of skiers earned in their best races prior to the Junior Olympics were used to estimate a linear regression model with points earned in three separate races at the Junior Olympics less than a month after the prior points list was released by the United States Ski and Snowboard Association.  The prior points were a poor predictor and the general model showed poor stability from estimation to estimation.  While these results were derived from a data set composed of junior skiers, they support the broader anecdotal concerns about USSA points.  This study provides a reliable quantitative basis for those concerns with a substantial and consistent data set.  Most observers of cross-country ski racing would not be surprised by these results.  However, the instability in the data set is striking and is less easily observed through casual observation of ski results.  Not only are the predictions relatively poor, those poor predictions vary with the subset of the data and the specific model used to make the prediction.  USSA points should be used with caution and with other information for critical decisions in cross-country ski racing.  Their value in monitoring skier performance in physiological trials is questionable.

References:

Anonymous.  (2006).  U.S. Olympic Cross Country Team Announced.  Retrieved October 6, 2006 from http://www.fasterskier.com/news2962.html  .

Bodensteiner, L., & Metzger, S.  (2006).  2006 USSA Nordic Competition Guide.  Park City, UT.

Hill, C., Griffiths, W., & Judge, G.  (1997).  Undergraduate Econometrics.   J. Wiley & Sons, New York.

International Ski Federation.  (2006).  Cross Country Rules and Guidelines of the FIS Points 2006/07.  Retrieved October 11, 2006 from http://www.fis-ski.com/data/document/pktrgl0607-neu.pdf

Johnston, J.  (1984).  Econometric Methods (3rd ed.)  McGraw-Hill, New York.

Smith, C.  (2002).  U.S. Olympic Team Selection.  Retreived July 17, 2006 from http://www.xcskiracer.com/rants.shtml

Staib, J.L., Im, J.,Caldwell, Z., & Rundell, K.W.  (2000).  Cross-country ski racing performance predicted by aerobic and anaerobic double poling power.  Journal of Strength and Conditioning 14(3), 282-288.

Trecker, M.  (2005).  Following the Olympic Trials, Who’s Hot, Who’s Not, and the Strange Anomalies of USSA Scoring.  Retrieved July 17, 2006 from http://www.fasterskier.com/opinion2749.html

Appendix – Heteroscedasticity in the Data Set:

This portion of the study on heteroscedasticity is placed in the appendix because most people interested in skiing will not be interested in statistical methods and assumptions.  They want to know if current USSA points predict future skiing results.  However, from an analytical viewpoint, improper use of statistics can lead to incorrect results and correct procedures lead to improved analysis.  One assumption of linear regression is that the variance of the random error term is 2 for all x.  If this is not the case, then the estimate remains linear and unbiased but it is no longer the best linear unbiased estimator and standard errors are often incorrect (Johnston, 1984).  Confidence intervals and results of statistical tests can be misleading.  This appendix covers four topics:  heteroscedasticity in equation 2, correcting for heteroscedasticity using data transformations, heteroscedasticity in the complete data set, and a brief conclusion.

Heteroscedasticity in equation 2

Equation 2 is the intuitive equation to test whether prior performance as measured by USSA points can predict future performance.

yi = c + a1*Pi + ei          equation 2.

Figure 1 shows a much wider variance in the dependent variables as USSA points increase.  White’s test for heteroscedasticity indicates a probability of greater than 99.99% that heteroscedasticity does exist (test statistic= 15.37 with two degrees of freedom).

Correcting for heteroscedasticity using data transformations

Data may be adjusted using transformations to eliminate heteroscedasticity (Hill et al, 1997, Johnston 1984).  In the data set used in this study, the variance in the residuals is larger for the larger values of the independent variable.  Two logical transformations are to take the logarithm of the independent variable and the square root of the independent variable.  Separate regressions were estimated using equation (2) where

(a)  Pi = the square root of the competitors USSA points earned prior to the Junior Olympics and

(b)  Pi = the natural logarithm of the competitors USSA points earned prior to the Junior Olympics.

In both cases, the r2 value improved less than 0.02, and the White’s test indicated that heteroscedasticity remained a problem.

Heteroscedasticity in the complete data set

The complete data set, including division and junior class of the competitor, not only improves the estimation, it is less likely heteroscedasticity exists.  White’s test for heteroscedasticity indicates a probability of approximately 80% that heteroscedasticity does exist (test statistic= 49.46 with 42 degrees of freedom).  Most researchers would not reject the null hypothesis at this level.  This indicates that the additional independent variables have the greatest impact on improving prediction for skiers with the higher (less competitive) prior USSA points.

Conclusion:

The original goal of this study was not only to determine what statistical model would work best for the data, but to determine if USSA points were a good predictor of future performance of athletes.  From a practical standpoint, a complex model used in the prediction would indicate that USSA points alone are a poor predictor and a complex model would be difficult to justify and administer.  The heteroscedasticity and the development of more complicated, but still unstable, models reinforce the results of the main paper.  Prior USSA points are poor predictors of Junior races.

2016-10-20T10:03:26-05:00March 14th, 2008|Sports Coaching, Sports Management|Comments Off on Cross-Country Skiing USSA Points as a Predictor of Future Performance among Junior Skiers
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