Work-Family Conflict and Related Theories in NCAA Division II Sports Information Professionals

### Abstract

Work-family conflict (WFC) is defined as “the discord that arises when the time devoted to or time spent fulfilling professional responsibilities interferes with or limits the amount of time available to perform family-related responsibilities” (20, 21). A successful career in sports information requires long, demanding hours which can make finding balance between work and family difficult. Sports information professionals (SIDs) participate in public relations activities designed to promote the teams they represent (19, 26). Responding to increasing interest in college sports, the demand for information about collegiate athletic departments has increased (13). In order to meet this demand for information, SIDs are responsible for producing content for electronic and print media on a regular and timely basis. The work done by sports information professionals has been characterized as 24 hours a day, 7 days a week work (11). Therefore, balancing work and home life has become a topic of increasing interest for those working in this field.

The purpose of this study was to determine if work-family conflict exists in NCAA Division II SIDs and to examine the impact of WFC on the related theories of life satisfaction (LS), job satisfaction (JS), job burnout (JB), and career commitment (CC). E-mails containing a link to the online survey were sent to the highest ranking sports information professional in each NCAA Division II institution. Informed consent was obtained prior to obtaining access to the survey. The survey contained Likert scale items for WFC, LS, JS, JB, and CC, demographic information, and open ended items relating to positive aspects and challenging aspects in performing the duties of a sports information professional. Of the 273 individuals contacted, 98 (36%) completed the survey. Results indicated these professionals do suffer from work-family conflict as 84% reported high levels of conflict, while only 8% reported low levels of conflict. Examination of the other scales revealed that these professionals are fairly satisfied with life and job factors, but some do experience from a fair degree of job burnout. Further analysis revealed that those with more children in the home had greater WFC. Finally, correlation and regression analyses revealed significant statistical relationships between each scale and indicated that WFC could successfully predict variations in LS, JS, JB, and CC.

**Key Words:** sports information, media relations, work family conflict

### Introduction

Work-family conflict (WFC) is defined as “the discord that arises when the time devoted to or time spent fulfilling professional responsibilities interferes with or limits the amount of time available to perform family-related responsibilities” (20, 21). This type of conflict appears when the demands of one’s professional life interfere with the demands of one’s personal life. Stated another way “participation in the work role/family role is made more difficult by virtue of participation in the family role/work role” (16). WFC has been studied extensively in the corporate environment (2, 9). This is a growing line of inquiry in the sport context and has received visible support from the National Collegiate Athletic Association (NCAA). For example, the NCAA has created a work-life task force to address these issues (10) and the topic has been prominent at [NCAA National Conventions](http://www.ncaa.org) beginning in 2008. Results from a recent study found that NCAA Division I sports information professionals do experience high levels of work-family conflict (14).

Sports information professionals (SIDs) participate in public relations activities designed to promote the teams they represent (19, 26). Responding to increasing interest in college sports, the demand for information about intercollegiate athletic departments has increased (13). In order to meet this demand for information, SIDs are responsible for producing content for electronic and print media on a regular and timely basis. They develop a wide range of publications and new media, compile and manage statistics, meet the needs of the media, manage budgets, organize events, and supervise personnel all while maintaining their composure in highly stressful situations (12, 26). SIDs report feeling overwhelmed with the increasing demands of desktop publishing and electronic media (16). A successful career in sports information requires long, demanding hours which can make finding balance between work and family difficult. Therefore, balancing work life and home life has become a topic of increasing interest for those working in this field, including SIDs at the NCAA Division II level.

In an attempt to define, brand, and uniquely position NCAA Division II, the NCAA launched a strategic initiative that incorporates a hexagon of principles (learning, balance, resourcefulness, sportsmanship, passion, and service) to clearly define and uniquely position [Division II](http://www.ncaa.org/wps/wcm/connect/82af4f004e0daa1e9b7ffb1ad6fc8b25/SPPlatformInColor.pdf?MOD=AJPERES&CACHEID=82af4f004e0daa1e9b7ffb1ad6fc8b25 ). In addition, the Division II presidents have established the first phase in a two phase process designed to promote more balance between work and life for coaches and student-athletes. The “Life in the Balance” principle reduces contest dates in 10 sports thus streamlining the seasons and includes a provision for a seven-day break from practice and competition for basketball. These actions are designed to provide time off for [players and team staffs](http://www.ncaa.org/wps/wcm/connect/public/ncaa/academics/division+ii/life+in+the+balance). It is reasonable to infer that this increased focus on a balanced life, including the streamlining of seasons and reduction in contests, would promote more opportunity for work-life balance for athletic department members, including sports information professionals.

The NCAA Division II strategic positioning initiative is designed to establish a way of life on the Division II campus as uniquely different from the way of life on campuses at other institutional classifications. Several studies exist that examine the job characteristics for athletic directors at the various institutional classifications. Previous research indicates that there are very few differences among the characteristics of the organizations and the styles of administration in NCAA (all levels) and NAIA athletic departments (25). Further, Copeland and Kirsch (4) found no significant differences in job stress for NCAA athletic directors regardless of institutional classification (Division I, Division II, or Division III). Additionally, these athletic directors reported that they almost always experienced some level of job related stress (4). Given the similar organizational characteristics and administrative styles, including the similarly stressful nature of the role of the athletic director in intercollegiate athletics, it is reasonable to infer that those with other roles within athletic departments at various institutional classifications might experience similar challenges to their colleagues across divisions. In fact, the stresses faced by SIDs in NCAA Division I might also be faced by those in NCAA Division II institutions. Hatfield & Johnson (14) reported that a majority of the NCAA Division I SID participants experienced work-family conflict.

Studies examining work-family conflict in sport have focused primarily on athletes, coaches, athletic trainers, and administrators at the NCAA Division I level (6, 7, 8, 14, 15, 17, 18, 22, and 24). Male and female coaches have experienced work-family conflict (24). Work-family conflict has been closely examined in NCAA Division I athletic trainers (17, 18). Results from these studies indentified long hours, required travel, overlapping responsibilities, drive to succeed, and commitment to the profession as qualities that contribute to the challenges sport professionals face in managing work-family conflict (6, 7, 8, 15, 17, 18, 22, 24). SIDs are another group of athletic department staff members who work in similarly demanding positions. In a study examining work-family conflict and related theories in sports information professionals, Hatfield & Johnson (14) found that 86% of participating SIDs reported experiencing work-family conflict. These professionals identified “balancing work and family life, especially on the weekends;” “balancing work/family life and prioritizing the things that must get done and putting others aside to spend time with family;” “meeting all the job demands with a small staff and meeting the demands at home as a husband and father of two young children;” and “balancing travel/events with family…more is always added, nothing is ever taken away” as some of their greatest challenges in performing their job duties (14).

Work-family conflict does not exist in isolation. Work-family conflict has been negatively related to life satisfaction and job satisfaction in athletic trainers and sports information professionals (14, 18). Work-family conflict has been positively correlated with job burnout and intent to leave the profession (14, 20). Work schedules that require long hours with little flexibility have been tied to job dissatisfaction and burnout in athletic department employees (14, 17). Further, in so much as time is a limited resource, time spent on one activity, work, is time not spent on another activity, family. Therefore, attempts to balance work and family while managing other, related constructs as experienced by SIDs warrants formal examination. The purpose of this study was to determine if work-family conflict exists in NCAA Division II sports information professionals and to examine the impact of work-family conflict on the related theories of life satisfaction (LS), job satisfaction (JS), job burnout (JB), and career commitment (CC).

### Methods

#### Participants

Sports information professionals in each of the 273 NCAA Division II member institutions were invited to participate, and 98 SIDs completed surveys. Participants in this study were the highest ranking sports information professionals in their respective NCAA Division II athletic departments. Titles for these professionals might include, but are not limited to, any of the following: sports information director, assistant athletic director for media relations, or associate athletic director for sport communications.

#### Procedures

There are 273 NCAA Division II institutions listed on the [NCAA portal](http://www.ncaa.org). The portal was used to provide access to the website for each Division II institution. Once on the website, the highest ranking communications professional in the athletic department was identified and an email inviting that individual to participate in the study was sent. A link to the survey was provided in the email. Informed consent was obtained prior to obtaining access to the survey. Following the initial invitation to participate, two additional reminders were sent. The survey was open for six weeks.

#### Instrumentation

An online survey was assembled to include five scales that had previously been tested for validity and reliability (12) and included a section for demographic information and open ended items to address the positive aspects and challenging aspects in performing the duties of a sports information professional. The following five scales were used:

*Work-Family Conflict.* Work-family conflict was assessed using the 5-item Netemeyer et al. (20) scale that included a 7-point Likert-type scale (1 = *strongly disagree* or *low work-family conflict* to 7 = *strongly agree* or *high work-family conflict*) for responses.

*Life Satisfaction.* Life satisfaction was assessed using the 5-item Diener (5) Satisfaction with Life Scale that included a 7-point Likert-type scale (1 = *strongly agree* or *high life satisfaction* to 7 = *strongly disagree* or *low life satisfaction*) for responses.

*Job Satisfaction.* Job satisfaction was assessed using the 6-item Agho, Price & Mueller (1) scale that included a 5-point Likert-type scale (1 = *strongly agree* or *low job satisfaction* to 5 = *strongly disagree* or *high job satisfaction*) for responses.

*Job Burnout.* Job burnout was assessed using the 21-item Pines & Aronson (23) Burnout Measure that included a 7-point Likert-type scale (1 = *never* or *low job burnout* to 7 = *always* or *high job burnout*) for responses.

*Career Commitment.* Career commitment was assessed using the 7-item Blau (3) scale that included a 5-point Likert-type scale (1 = *strongly agree* or *high career commitment* to 5 = *strongly disagree* or *low career commitment*) for responses.

#### Data Analysis

The quantitative data was calculated using SPSS version 16. Demographic data was collected for gender, age, EEOC status, educational background, number of children under the age of 18 living in the household, and number of years in the field. Each scale was totaled and percentages for the “agree” (agree, somewhat agree, strongly agree), “neutral”, and “disagree” (disagree, somewhat disagree, strongly disagree) responses were calculated for each scale. Cross-tabulations between demographic categories and the WFC scale were run to determine if any of these factors had an impact on WFC. Finally, correlation and regression analysis was run to examine the relationships between the scales and to determine the predictive ability of WFC on each of the other scales. Qualitative data from the open ended items were utilized to support the results from the quantitative analyses.

### Results & Discussion

Of the 273 Division II sports information professionals contacted, 98 responded to the survey, for a response rate of 36%. Within the group of respondents, 85% were male (n = 83) and 11 % were female (n = 11). Four individuals (4%) chose not to include their gender. With regard to family status, 32% were single (n = 31), 61% were married (n = 60), 1% was widowed (n = 1), 1% was divorced (n = 1), 1% was in a domestic partnership (n = 1), and 4% (n = 4) did not indicate a family status. Eighty six percent of the sample was Caucasian (n = 84), five percent were African American (n = 5), one percent was Hispanic (n = 1), two percent were of mixed heritage (n = 2), and six percent did not respond to EEOC status (n = 6). Most of the respondents were sports information directors (70%, n = 69), with a few indicating they were assistant or associate athletic directors (27%, n = 25). Four of the participants did not indicate a title (n = 4).

The results clearly show that Division II sports information professionals (SIDs) do experience levels of work-family conflict. Eighty four percent of the participants responded that they had high levels of work-family conflict while only eight percent indicated they did not feel their work conflicted with their personal lives. Responses from open-ended questions also support this finding including: “having to work seven days a week and having very little family time;” “trying to manage family time with work demands. More games are moving to weekends to avoid missed class time, but it doesn’t help staff members;” and “keeping an equal life-work balance through the entire year, not just in the summer months when there are no sports.”

With regard to the life satisfaction scale, 59% of the respondents indicated that they were happy with their current life situation, 28% indicated that they were not happy with their current life situation and another 13% responded neutral with regard to this set of questions. Even though over half of the participants did report that they are happy with their current life situation, the researchers were expecting this number to be higher as anecdotal evidence indicated that although these types of sport professionals do work long, demanding hours, the great percentage seemed to be happy with their lives. Therefore, the fact that almost 30% reported being somewhat unhappy further indicates there may be some work-life balance issues with this population. One respondent suggested that being “able to work flexible hours outside of events. Telecommute when possible. Go into the office after the kids are in bed” was a positive aspect of the job. Other responses included: “…involving my family in my work so I can accomplish my duties and spend time with family at the same time” and “nothing less than 100% is enough…my drive keeps me going and my family is heavily involved in the school in which I work which is good and bad.” These statements reinforce the crossover between these job and life characteristics.

Results related to the job satisfaction scale indicated that overall these professionals are satisfied with their present situation, as 80% responded that they were satisfied with their current jobs, while only nine percent reported being dissatisfied. This certainly indicates that while there are issues in this profession, the gross majority are pleased with their careers at this point in their professional lives. Respondents indicated that interacting with student-athletes and coaches, being a fan of one team, and the game-day atmosphere were positive aspects of their jobs.

Fifty five percent of the participants did not indicate high levels job burnout while 43% did indicate some level of burnout on a fairly frequent basis, according to results from the job burnout scale. Again, even though the majority of the participants do not report experiencing high levels of burnout, the fact that 43% do suffer from some level of burnout is an important finding and one indication that these individuals may experience more burnout as they progress through their professional careers as most of the participants were less than ten years into the profession. Some respondents provided work place examples related to burnout including the following: “Balancing what I physically, mentally and emotionally CAN do with what I WANT to do;” “too much work, not enough pay;” “no full-time help;” “limited staff (just me) covering 16 sports;” and “the ever changing and growing list of responsibilities.”

Results from the career commitment scale were interesting as 56% indicated that they were happy with their careers, while 41% had some level of uncertainty. This, again, further illustrates that most of these professionals do enjoy what they do although some may choose a different focus if they could “do it over again.” Positive comments related to career commitment included: “I love daily interaction with student-athletes, nothing beats the atmosphere of a college campus and the chance to make a difference in the lives of student-athletes” and “ability to develop working relationships with players and coaches. Ability to call the program ‘my own.’ Opportunity to tailor my work to the needs of my media market.” Others provided comments identifying challenges to their career commitment: “dealing with unrealistic objectives from superiors who have not the first clue what this job entails;” “I’m a one-man show. I currently do not have any full-time assistant[s] so I must complete all tasks;” and “managing expectations of administration in face of new technologies.”

To further disaggregate the data, cross-tabulations were run to determine if the responses on the work family scale were different based on gender, EEOC status, years of experience in the field, and number of children under age 18 in the home. When compared on gender, 100% of the female respondents indicated they did feel at least some degree of work-family conflict (see Table 1 for complete results). Results related to males showed 92.8% had some level of work-family conflict, while 1.2% was neutral and 6% indicated there was little or no work-family conflict. Comparison on EEOC status revealed similar results across the different categories as most felt a fair degree of work-family conflict and very few responses indicated little or no conflict (see Table 2 for complete results).

Data for years of experience as it relates to work-family conflict also showed very few differences across categories. Ninety three percent of those with ten or less years of experience indicated at least some level of conflict, compared to 96% of those with 11-20 years of experience, and 92% of those with over 20 years of experience (see Table 3 for complete results).

The most significant results of the cross-tabulations were associated with the number of children under the age of 18 in the home (see Table 4 for complete results). First, it was interesting to note that approximately 55% of the participants in the study reported having no children under the age of 18 living in the household. There could be several explanations for this result. Since many of these individuals are less than ten years into their careers they may not be at a point in their life where they want to start a family, but it may also indicate that their work schedules are interfering with the ability to start a family. Data from the cross-tabulation definitely showed differences based on the number children under age 18 in the household. Greater numbers of children in the household was associated with greater work-family conflict. Of those with three or more children, none indicated they were neutral or had little or no conflict, while 10.3% of those with two or less children under the age of 18 reported neutral or low rates of conflict.

Correlations were run to examine the degree of relationship between each of the scales. The correlations show significant relationships between each of the scales utilized in the study (see Table 5). Approximately half of the correlations were moderate (0.4 to 0.7) while the other half were low (0.2 to 0.4) but still all correlations were statistically significant at the 0.05 alpha level. These data clearly show there is a relationship between work-family conflict and each of the other scales, as well as, each of the other scales with each other.

After determining there were significant correlations between the scales, regression analyses were run between the work-family scale and each of the other scales to determine if work-family conflict could successfully predict the variations in the scores on the other scales (see Table 6 for complete results). The work-family conflict scale was able to predict each of the other scales effectively, indicating that work-family conflict is significantly related to life satisfaction, job burnout, career commitment, and job satisfaction for this group of Division II sports information professionals. Although work-family conflict was able to predict each of the other scales, the regression between work-family conflict and job burnout was substantially higher than the others, which indicates those experiencing from work-family conflict also seem to be experiencing a fair degree of job burnout.

The results of this study compare remarkably with a previous study by these authors investigating the same research questions with Division I sports information professionals (14). Eighty six percent of Division I SIDs reported having work-family conflict which compares favorably to the 84% reported in this study. All of the other scales had very similar results as well, certainly indicating that the stresses faced and the impact of these stressors on the lives of sports information professionals is very similar from Division I to Division II. The Division II SIDs did report slightly higher job burnout than their Division I counterparts (43% to 41%) which could be related to less staff and help, and additional responsibilities that may include coaching, other administrative responsibilities, etc., at the Division II level. The results from the correlation and regression data also mirrored the results from the Division I study.

### Conclusions

With increased coverage of Division II athletic events comes increased work for those providing information and promoting the athletes and teams to media outlets, fans, and other interested parties. As this demand for information increases, the potential for work-family conflict and related issues could certainly increase as well. The purpose of this study was to determine if work-family conflict exists in Division II SIDs, and if so, what is the relationship between work family conflict and life satisfaction, job satisfaction, career commitment, and job burnout? It is clear that Division II sports information professionals do experience work-family conflict, much like their Division I colleagues, and there is a significant relationship between these concepts. The correlation and regression analyses clearly show that work-family conflict can predict variations on each of the other scales. It is important for those in administrative positions to understand the demands on the SIDs and try to provide ways to reduce the impact of work-family conflict as it certainly could have potential negative results for the professionals.

### Application To Sport

Since SIDs serve as a liaison between collegiate athletic departments and media outlets, fans, and other interested parties, work-family conflict and job burnout could lead to increased stress among these professionals and could impact all entities associated with these athletic departments, including the athletes, other athletic administrators, and the university as a whole. This study has clearly demonstrated that these professionals do suffer from work-family conflict, and that WFC is related to increased job burnout and decreased life satisfaction, job satisfaction, and career commitment. Therefore, it is certainly plausible that this could lead to increased stress and negative impacts, therefore, it is important for athletic administrators to address this issue with their employees and try to find ways to decrease this conflict.

### Tables

#### Table 1
Cross-tabulation of work-family conflict by gender

Gender
Response Male Female
Strongly Disagree 0 0
Disagree 0 0
Somewhat Disagree 6.0 0
Neutral 1.2 0
Somewhat Agree 19.3 36.4
Agree 34.9 36.4
Strongly Agree 38.6 27.2

#### Table 2
Cross-tabulation of work-family conflict by EEOC

EEOC
Response Caucasian African-American Hispanic Mixed Heritage
Strongly Disagree 0 0 0 0
Disagree 0 0 0 0
Somewhat Disagree 3.6 20 0 0
Neutral 1.2 0 0 0
Somewhat Agree 23.8 0 0 0
Agree 35.7 20 100 50
Strongly Agree 35.7 60 0 50

#### Table 3
Cross-tabulation of work-family conflict by years of experience

Years of Experience
Response 0-10 years 11-20 years 21-30 years 31+ years
Strongly Disagree 0 0 0 0
Disagree 0 0 0 0
Somewhat Disagree 5.4 4 8.3 0
Neutral 1.8 0 0 0
Somewhat Agree 21.4 24 16.7 0
Agree 32.1 40 41.7 0
Strongly Agree 39.3 32 33.3 100

#### Table 4
Cross-tabulation of work-family conflict by number of children under age 18 in the home

Number of children under 18 in home
Response 0 1 2 3 4+
Strongly Disagree 0 0 0 0 0
Disagree 0 0 0 0 0
Somewhat Disagree 1.9 6.7 13.6 0 0
Neutral 3.8 0 0 0 0
Somewhat Agree 30.2 6.7 18.2 20 0
Agree 24.5 40 50 40 50
Strongly Agree 39.6 46.7 18.2 40 50

#### Table 5
Correlations (actual correlation coefficients) between subscales

Scales Work-family Conflict (WFC) Life Satisfaction (LS) Job Satisfaction (JS) Job Burnout (JB) Career Commitment (CC)
WFC 0.3962* 0.292* 0.485* 0.395*
LS 0.362* 0.418* 0.680* 0.471*
JS 0.292* 0.418* 0.405* 0.664*
JB 0.485* 0.680* 0.405* 0.315*
CC 0.395* 0.471* 0.664* 0.315*

* p < .05

#### Table 6
Regressions between WFC and each scale

Regression R squared F ratio P value
Work-family Conflict vs. Life Satisfaction 0.131 14.327 0.000
Work-family Conflict vs. Job Satisfaction 0.085 8.867 0.004
Work-family Conflict vs. Job Burnout 0.235 28.233 0.000
Work-family Conflict vs. Career Commitment 0.156 17.214 0.000

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### Corresponding Author

Laura M. Hatfield, Ph.D.
Assistant Professor, Sport Management
University of West Georgia
Carrollton, GA 30118-1100
<lhatfiel@westga.edu>
678.839.6191

### Author Biographies

#### Laura M. Hatfield

Laura M. Hatfield (Ph.D., University of Southern Mississippi) is an assistant professor of sport management in the Department of Leadership and Applied Instruction at the University of West Georgia in Carrollton, GA. She teaches undergraduate courses organizational theory, organizational behavior, and communications. Her research interests include work-family conflict, organizational communication, and the scholarship of teaching.

#### Jeffrey T. Johnson

Jeffrey T. Johnson (Ph.D., Georgia State University) is an associate professor of sports science in the Department of Leadership and Applied Instruction at the University of West Georgia in Carrollton, GA. He teaches undergraduate and graduate courses in anatomy and physiology, biomechanics, and exercise physiology. His research interests include pathological walking and running, sport mechanics, and work-family conflict.

2013-11-22T22:50:34-06:00April 9th, 2012|Contemporary Sports Issues, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on Work-Family Conflict and Related Theories in NCAA Division II Sports Information Professionals

As Goes The Spoils, So Go The Victories: Exploring Major League Baseball’s Playoff Bonus System

### Abstract

This paper explores the playoff bonus system used by Major League Baseball. The unique incentive structure used is positively related to organizational success, and is studied using a Grounded Theory methodology. Exploration and analyses found that World Series winning teams prospectively distributed ten percent (10%) more shares than losing teams and repeat winners distributed still more. By developing an incentive structure as an intermediate step in the performance environment and linking the bonus’s value to future performance, decision makers may be able to positively impact organizational outcomes – particularly over repeated periods.

**Key words:** Major League Baseball, bonus system, payment, incentives

### Introduction

In October of 2007, the Colorado Rockies baseball team made the news for two reasons. First, they won 20 games in the last month of the season to wipe out a six game deficit and make the playoffs. The team became media favorites as announcers told and retold the story of how a franchise with one of the lowest payrolls in the game eventually became World Series champions. Moreover, reporters lauded the fact that success came not through the actions of one or two superstars, but by working together. This second reason for the media’s attention was the Rockies players’ decision to award a full playoff share to the family of Mike Coolbaugh.

Mike Coolbaugh was a minor league coach in the Rockies organization whose tragic and untimely death occurred on July 22, 2007 when he was struck by a foul ball during a Tulsa Drillers baseball game. At the time, he was working as a first base coach for the Drillers: a Double-A affiliate of the Colorado Rockies. The value of the playoff share eventually grew to $233,505.18 and earned the team a ‘Sportsman of the Year Award’ nomination from Sports Illustrated (38).

The purpose of this paper is to explore and explain the historical dynamics of the Major League Baseball’s (MLB’s) playoff bonus distribution system. That high performing teams’ members would be willing to sacrifice some of their own potential earnings to support their organizations’ staff members is, on the face of it, contrary to the ‘self-interests explanations’ (SIE; 14) commonly used in management, economic, and sociology theories’ literatures. Instead, it is more akin to a transformational leadership style commensurate with higher levels of organizational identification (30).

A Grounded Theory approach is used and a three-phase of the research methodology outlined by Schollhammer (43) – a relationship between share distribution and outcomes were hypothesized, an assessment of available data in the historic record was conducted, and the outcomes were evaluated based on current theories – are described. Both working managers and academic researchers can use the approach used herein to study the organizational phenomenon that commonly arise in Sport.

#### New Contribution

The paper makes two contributions managers can use to study and improve their organization’s performance. First, we present the MLB system as a case for exploring alternative frameworks for financial incentive structures. In particular, the roles of players’ decisions, rather than managers’ contributions (44), in influencing organizational success. Second, we analyze historic data, testing hypotheses developed using the grounded theory approach, to assess how patterns in share distribution practices are related to organizational performance outcomes.

For management researchers, MLB has offered a fertile empirical test bed. With the availability of data sets sometimes spanning over 100 years, baseball provides a natural experiment through which organizational behavior can be studied. In this case, we analyze the bonus system framework that can be explored in relationship to other more widely used approaches using controlled experimental designs that rely on the availability of data (44). Once completed, such research can provide decision-makers the ability to engage in a strategic process of setting appropriate performance indicators similar to those used in the “Moneyball” approach to player selection (49).

The background presented here follows an inductive/deductive processes used to study phenomenon where event timing is a critical element in the outcomes of interest. First, a brief description of the analytic strategy is given. Second, the historical context of MLB’s playoff bonus system is described. Next, data drawn from the MLB’s records is analyzed. Third, a theory is generated to explain the bonus system employed in baseball. Finally, we discuss the implications of timing in the development of incentive systems.

#### Background

Suddaby (48) both defined and outlined the use of Grounded Theory. Grounded Theory is as an approach positions phenomena as embedded in context that provides meaning that is often missing in traditional research. In other words, in the traditional model a researcher builds a hypothesis, constructs or identifies sources of data to test that hypothesis, and then tests that data to either support or reject the proposition developed. Suddaby argued that to do so ignores the perspective of the “actors” whose behaviors the data presupposes to study. Without exploring the intent of the actors, findings may be misrepresentations of the data. Further, to ignore the perspective of the actors is to ignore potential new theoretical lines of inquiry that emerge from their explanations.

The use of Grounded Theory in any context should then be defended as appropriate. Grounded Theory is most suited to efforts to understand interdependent processes, one where actors engage in an inter-subjective experience (48). The methodology is more effective when it considers extended time periods and when the relationships observed in the initial instance of the phenomenon are found to exist in other times or settings (16). Finally, the use of Grounded Theory is focused on how subjective experiences can be abstracted into theoretical statements about causal relations between actors.

Finally, it should be noted that individuals engaged in Grounded Theory research have an especial duty to explain the qualitative motivations in the research. As Grounded Theory should begin with a discussion of the data and then move to an exploration of the qualitative factors that directed inquiry, such an approach is often subjugated to the needs for traditional presentation forms for the sake of clarity – involving the presentation of a theoretical overview. In this paper, a three-step approach to exploring the phenomenon’s key elements and their relationships is employed, based on the strategies for sensemaking proposed by Langley (29). Collectively, the three phases of research used to understand the observed phenomenon – Describe, Analyze and Theorize – are referred to as the ‘DAT’ methodology. This method is particularly useful when data is temporally embedded in events rather than simple variables.

In the first phase, Define, a common narrative is developed from the process data based on unique instances of the phenomenon. The Define phase seeks to outline the context in sufficient detail from the actors’ perspective. Such an approach is requisite to assist in the narrowing of the theoretical frame for the study. Put another way, while many explanations are possible, they can be further limited by the context of the actors in situ.

The second phase, Analyze, focuses on the construction and execution of an analytic approach through which data on the dynamics identified in the Define phase are both gathered and processed in a manner that allows for simultaneous classification, to reduce complexity, and hypothesis driven statistical examination. These first two approaches are used to envelop and specify the phenomenon’s characteristics from both ends of the inductive research spectrum.

The final phase provides for the opportunity for Theory building based on an understanding of context and an assessment of data on the phenomenon being studied. Here, an alternative approach to sensemaking is used where different theoretical perspectives are applied in a deductive manner to determine how the phenomenon is best explained. The remainder of the paper follows the DAT format for investigating the incentive system used by MLB.

#### Phase 1: Describing the Phenomenon with Narrative

Groves argues that team dynamics are at play when “the decision makers base their decision choices on different information, yet are motivated by a common goal (19).” Further, Groves also notes that employee behavior may be more accurately analyzed in terms of the compensation they receive from the organizations for their participation in it. There are three features of the MLB Playoff bonus system that depart from most incentive and reward programs used by professional firms – the process of valuation, control and criteria.

Valuation addresses the resources set aside for the purposes of creating an incentive structure. In the broader business context, valuation is often a function of organizational history. The amount of resources set aside for the purpose of incentive distribution is not uniform as organizations specify the parameters on a case-by-case basis. Valuation can be understood not only in terms of how much, but also in terms of when the establishment of the specific value takes place. Further, valuation speaks not only to the pool of available resources for such incentives, but whether that pools is absolute or relative to firm performance.

For instance, an organization may have developed a practice of setting aside 10 percent of pre-tax profits for the purpose of distribution by management. For instance, an organization may decide that it sets aside 25 percent of any profit for distribution whereas another might only set aside that 25 percent if the organization exceeds last year’s profit levels. Additionally, organizations may simply allocate a fixed sum once a specific objective or subjective threshold is accomplished, for example, when the organization’s profits exceed 10 million dollars, one million is put into the profit sharing pool.

The second feature of bonus financial incentive systems is control. In this context, control speaks to the authority that allocates the bonus dollars. In most cases it is facilitated through the management structure. Control may be facilitated both directly and indirectly. Direct control is exercised when management evaluates individual performance to determine the degree to which an individual should be rewarded using the incentive pool. Indirect control is exercised when the organization codifies in contracts that specify the conditions under which an individual has a right to access the pool. For the most part, this process is considered a management responsibility.

Finally, the issue of criteria addresses the constraints and rules associated with participation in the pool. Organizations may, for instance, develop a pool for the sales staff. Such a pool is often aligned with compensation schemes to recognize high performers and represent constraints on the criteria for participation. Other organizations may create committees whose task it is to determine which employees meet the criteria for inclusion in the pool.

##### The Players’ Bonus in MLB

The bonus incentive model in MLB has an unusual scheme in that the system of valuation and control are far from the norm. Additionally, the criteria for distribution diverge from many bonus systems. To understand the formalization of this incentive structure, it is necessary to describe how the value of a share is calculated. We shall do so here in the order in which events happen so as to make specific points about the nature of what is both known and unknown at that point in the process.

##### Identifying the phenomenon’s key constructs

In 1903 the National Agreement united the American and National Leagues. This agreement created the World Series and stipulated that a portion of the playoff series gate receipts go directly to the participating teams’ players. At the time, prior to major radio or television contracts, the gate represented a significant portion of most teams’ total revenue. Therefore, the bonus system was a major revenue sharing scheme well ahead of its time (40). Further, the bonus system’s design and longevity are both rarities in the modern industrial era.

MLB has a playoff reward program and players on winning teams can earn substantial bonuses. Under the MLB playoff structure implemented in 1995 (the current system allows for three divisional champions and one ‘wild-card’ in each league), the players pool (P*) is comprised of 60 percent of the gate receipts from the first three rounds of the Division Series and 60 percent of the gate receipts from the first four rounds from the League Championship Series and World Series across the entire playoff contest. By only using the gate from the initial games, the formula presented in equation 1 does not incentivize the extension of the series. These receipts are divided as team shares among 12 clubs using a performance multiplier (MT) that escalates as the team progresses further in the playoffs, described in Table 1. The team share (PT) is then the fraction of the players’ pool that is available to divide among players.

**equation goes here**

This dynamic represents the first divergence from traditional bonus models. In MLB, the valuation process is prescribed but disjointed. Specifically, equation 1 specifies the value of the MLB players’ pool (P*), making it clear which teams are eligible for participation at the front, and allowing teams to estimate, based on historic trends the value associated with different performance outcomes. Individual team’s payouts escalate as they progress further in the playoffs with the World Series Champion getting the largest sum as prescribed by both Table 1 and equation 2. In 2006, the Players’ pool was $55.06 million and the St. Louis Cardinals took the lion’s share of $20.2 million (36 percent) by winning the World Series. That equates to $300,000 per player on the World Series winning team.5 (See Table 1)

At this point, a second divergence occurs from traditional models along the issue of control. In most cases the control of bonus dollars resides in management, however in the case of MLB, only players may allocate a portion of the players pool and they may do so in any manner they wish. This dynamic is significant in two ways. First, management has no ability to control the distribution of shares. Second, the distribution of shares occurs before the beginning of play in the post season. In other words, the employees create a system to allocate future performance-based earnings using a fractional system based on the concept of a “share” (Vs) as described in equation 3.

_Sporting News_ writer Todd Jones, a former MLB pitcher, notes that only players who have been on the roster the entire season are given a say in the allocation of shares. Players taken off the rosters for an extended period of time are generally not eligible to vote, and all players eligible to vote are all given a single share. The meeting then does not focus on the shares allocated to those in the room, rather it focuses on the shares to be allocated to those not in the room, and several players note that these meetings can become quite heated. Players often create “fractional shares” or simple “cash awards” (A) to recognize the work of auxiliary personnel. Because the allocation allotted to the team is fixed, every additional share authorized reduces the relative value of each player’s share. Additional discussion about the meaning of shares will be addressed in the next section. (See Table 2)

In summary, the MLB system of playoff shares provides a unique perspective into a system with a long history that is framed in such a way that empowers plays by giving them both control and allowing the players to determine the criteria. These decisions have an effect on valuation that is related to both performance and the share distribution decisions of the organization’s players. Each additional share distributed by the players reduces the value of the share each rostered player receives.

##### The meaning of a share

The authors were unable to secure interviews through the Major League Baseball Players Association. As a result, the need to explore what these shares meant to the players was critical to our DAT approach. Using Lexus-Nexus, the authors retrieved all news stories available on the service since 1980 using the search terms “playoff shares” and “baseball.” These 691 articles were then reviewed for the purpose of identifying what a share meant to the players. From those articles, duplicates were removed and articles outside of scope, as in the case of stories focusing solely on the value of the share as determined after the World Series or commentary provided by the writer. The remaining data was further segmented into direct quotes and evidence-based commentary whose origin was the players and their share deliberations.

Clearly players would forgo the value of a playoff share for the World Series title, but the underlying message was that the bonus is considered to be very significant and given in recognition of time served on the roster rather than performance of a specific player. For instance Astros General Manager Tim Purpura was quoted as saying, “The players that are on the roster as of June 1 typically get a full share… any players who were (on the roster) part of the season, they get voted on (by the players) (32).” A similar sentiment was made by the players of the Boston Red Sox who gave partial shares or cash rewards to anyone who wore the uniform, as well as a number of front office staff (24).

However, because the players make the decision according to their own preferences, time is no guarantee of a share. The players of the Toronto Blue Jays awarded former Chiefs outfielder Rob Ducey a small fraction of a share (16%), which was far less than others like Tom Lawless (59%), Rob MacDonald (25%), Mike Flanagan (20%) and Tom Quinlan (17%) even though Ducey was on the team longer than the other players (33).

Players have seen the exercise of their rights over the control of the playoff shares as a form of power. In 2002 the hitters in the San Diego Padres were so impressed by the work of one of the minor league coaches that they were able to “finagle” him onto the major league roster. He quit his job as a minor league coach but replaced it not only with a six-figure salary but a full playoff share after the Padres reached the World Series (7). Additionally, in 1996 the playoff shares were at times withheld from those crossed the picket lines. Mike Busch failed to receive a playoff share from the rostered players, despite a direct plea by then manager Tommy LaSorda. “What matters is the name on the front of your shirts, not the name on the back,” (17).

The dynamics inside these team meetings were noted as potentially contentious. Baltimore manager Johnny Oates, a former player, commented that he never liked team meetings but he did enjoy splitting up the playoff shares at the end of the year (22). A day after clinching the American League Western Division title, California Angels’s Rod Carew stormed out of a Sunday meeting in which the Angels decided how to divide playoff shares and World Series money among themselves. Carew would say only, “Money does strange things to people.” Carew, a native of Panama, apparently interpreted the decision to offer two Latino pitchers a half-share as discriminatory and angrily left the meeting. Reggie Jackson and Doug DeCinces were reported to have tried to restore the peace, but were unsuccessful (45).

Interestingly, the 1991 story of Dave Pavlas, Matt Howard and Dale Polley stood out in that it was the single instance where management, in this case George Steinbrenner of the New York Yankees, gave individuals each a check for $25,000 and a 1996 World Series ring, when the players voted to give them nothing. Steinbrenner also traded the primary force behind the decision to withhold the share, Jim Leyritz, the following day (4, 42).

Another ongoing commentary by the players is the financial value of a share for younger players. A few players identified the impact of a share on people who worked to create a winning team. In 1991 Norm Charlton of the Cincinnati Reds said, “You get two things for winning the World Series – a ring and our playoff shares.” He pointed out that for many of the younger players, the value of a share was the only recognition they received for winning a division or league championship. Further, he argued that the amount given was substantial relative to the salaries earned by some rookies (6).

Additionally, in organizations that traded players midseason, the players used shares to recognize these individuals’ contributions, even after having left to work for another competing organization. In 2004, the Boston Red Sox voted Nomar Garciaparra to receive a full share even though he was traded to the Chicago Cubs (18). When infielder Jeff Cirillo was released from the Minnesota Twins to the Arizona Diamondbacks, his final message was, “Good luck, and don’t forget about the old guy in the playoff share meeting” (34). This was again the case when the Florida Marlins players voted to give Pat Rapp of the San Francisco Giants a full playoff share for his contribution to the team before his trade (1).

In 2009, Major League Baseball (MLB) reported that the players for the Milwaukee Brewers offered former manager, Ned Yost, one of the Brewers 48 full postseason shares. The firing of Ned Yost marked the first time in major-league history — except the strike-split 1981 season — that a manager was fired in August or later with his team in playoff position. In their press release, MLB quoted an unnamed player who said “There are unwritten rules about how to do things, and it was the right thing to do. If you’re there more than 50 percent of the season, you’re pretty much getting a full share.” That same year, Nick Adenhart was killed in a car accident hours after starting as a pitcher for the Angels franchise. The Angels players voted to provide the late pitcher’s family a full share.

In summary, we found that players expressed a clear understanding of the diminishing return of the effect of the issuance of additional shares, that issues of equity, fairness and a sense of team spirit was a consistent theme, and that shares were also an expression of power by the players that could be exercised to make individuals feel either included or excluded. The series of events and decisions made by the Colorado Rockies described at the paper’s outset served as the impetus for the study at hand. However, it is clear that players use shares to recognize efforts of non-players as well as remediate perceived slights by management. While other teams have engaged in similar behaviors, none received the widespread coverage that the Rockies’ gesture did, in no small part because of their successful run to the World Series. The authors speculated that differences in share distribution patterns between teams might predict playoff series’ outcomes.

##### Shares and their relationship to outcomes

Emergent in this discussion is the idea that playoff shares can serve as an incentive for teams. Further, the structure involves an intricate timing that alters the manner in which the share itself is perceived. Table 1 speaks to the steps associated with the valuation, control and criteria used in share calculations. An initial component of evaluation is assigned in the securing of a place in the playoffs. At that point, team members know they are eligible for a share, however the specific value of that share remains unknown as it is based on future team performance. Rather than making the decision after the fact, the meeting that sets the criteria occurs place before any of the playoff series are played. This means that eligibility for a share is established before the value of that share in absolute terms is known. What is known is the value in relative terms.

The baseball system creates certainty around a payout whose value is unknown. While this may be a small point, it is a significant one. The implication of increased information provides short-term certainty other recognition systems often fail to provide. In 2008, 21 percent of all MLB players were paid less than $400,000, and the median payroll for a player was $1 million dollars. A playoff share will constitute a significant bonus for most of MLB players. For non-uniformed players, a share can triple or quadruple an annual salary.

The playoff shares system is distributed based on a criterion of past performance, where the control resides at the professional level to recognize peer performance, and creates a value that is based on future performance. Therefore, the major hypotheses are designed to explore share distribution and winning the World Series.

Hypothesis 1: Winners of the World Series will distribute significantly more playoff shares than teams that lose the World Series.

The expansion of the playoffs in 1995 provides a second period with potentially greater variability in the share distribution–playoff outcome relationship. With more teams in the playoffs over more rounds, the inference that distributing more shares is related to performance can be tested more rigorously. Therefore, the additional hypothesis is:

Hypothesis 2: Since 1995, Winners of the World Series will distribute significantly more shares than teams that lose the World Series.

It is posited that winning teams will distribute more shares. It stands to reason that players across the league will either implicitly or explicitly internalize this information. As a result, the number of shares distributed on average should increase over time. Therefore, another hypothesis arises:

Hypothesis 3: Baseball teams making the playoffs will distribute more shares over time.

It is possible to further explore the learning phenomenon by focusing on the teams that repeatedly make the playoffs. Having the firsthand experience assessing the playoff bonus – performance outcome relationship, teams that have won previously will seek to ensure their competitive advantage by increasing their distribution disproportionately. Therefore, the following hypothesis is made:

Hypothesis 4: World Series winning teams will distribute disproportionately more shares over time.

Based on this series of hypotheses, the Analytic Phase of the DAT process was undertaken.

### Methods

Baseball has been the subject of many empirical studies. One reason for the large number of studies is the availability of reliable and valid data on player demographics, performance outcomes, pay rates and incentives. Rottenberg (41) explored the market factors of the baseball labor market . A second reason for the interest in baseball’s statistics is the stability of the sport over time allowing players from various eras to be compared with some fidelity – the ‘dead ball’ and ‘steroid’ eras not withstanding.

With respect to compensation, Kahn (26) explored the relationship between managerial quality and salary. However, most of the studies to date have generally focused on baseball as a market phenomenon (5, 12, 13, 28, 35, 39). There have, to date, been few, if any, studies that have looked at the playoff shares incentive structure as described herein.

Using data publicly available on the number and value of World Series shares issued going back to 1903, the researchers tested the hypothesis that teams that were more egalitarian in the distribution of shares to non-uniformed players would be more successful. Because, within a year the size of teams should be relatively comparable, a simple different t-test was employed on the number of shares winning teams distributed prior to the start of the playoffs and the number of shares that the losing teams distributed. As only World Series shares were available from MLB, this hypothesis could only focus on winners and losers of the final series, and not on any of the championship and pennant races that allowed a team to compete in the final series. Based on the meaning behind shares, we argue that the differences within a single season can be attributed to shares allocated to players that played an incomplete season and auxiliary staff.

A second data gathering exercise focused on the playoffs that occurred after the introduction of the wild card in 1995. Wild card teams are ineligible for shares unless they win their division. Because this change to the system created new dynamics, we focused on the almost 30 years of data that emerged since that time. Again, data on shares were gathered from publically available data sources, including but not limited to MLB, _U.S. News and World Report_, and the Associated Press.

Analysis of the data was conducted using SPSS 17. Hypotheses 1-4 were tested using a t-test. This research involved the exclusive use of secondary data and as such was exempt research study per university institutional research board regulations.

### Results And Discussion

Phase 2: Analyses of the Available Data

Analyses of the relationship between World Series performance and playoff share distribution supports the assertion that winning teams are more egalitarian in their distribution of shares, authorizing more than their losing competition. Further, the dynamics of share distribution have been altered as the rules of the game have changed, most significantly with the initiation of free agency and the alteration to the playoff series’ format following expansions (23). Table 2 outlines the pattern of share distribution by franchise. (See Table 3)

Winning teams have historically been more generous (Hypotheses 1 is supported)

Over the 102 years of the World Series through 2006, we first calculated the difference in shares offered between winning and losing teams. Over that time period, winners distributed 88.93 more shares than losers, resulting in an average differential distribution of 0.872 shares for winning teams over losing teams (s = 3.65). The data supports the proposition that, within each year, winning teams offered more shares than losing teams (t = 2.407, p < 0.001). (See Figure 1)

Another way to look at the problem is to study the winner’s premium and compare it with the winner’s counter-premium. The winner’s premium is the number of shares issued by the winning team above what the losing team offered. For instance, when the winning and losing teams both issue 10 shares, an identical number of shares, neither is offered a premium. However, when the winning team issues 15 shares and the losing team issues 10 shares, the winner’s premium is said to be 5. The converse, the winner’s counter-premium exists when the winning team offers less shares that the losing team. The use of the term premium identifies the magnitude of the difference between winning teams that more distributive and those that are less relative to their competition. Figure 1 presents the winner’s premium/counter-premium in the form of a control chart where the y-axis is keyed to the standard deviation. Most statisticians would argue that the process only moves out of control in recent years, interestingly enough in a manner that coincides with the inauguration of the wild card rules of 1995. There is an additional anomaly that coincides with the expansion of MLB in 1965. (See Figure 2)

In the historic case, the winners’ premium has been 3.26 shares, based on 195.8 shares over 60 instances where the winner of the World Series is the team that has offered more shares. The winner’s counter premium has been 2.89, based on 102.2 shares over 38 instances where the winner of the World Series is the team that has offered more shares. Further, winners were 1.6 times likelier to have offered more shares than losers. When more generous teams won, they generally authorized 3.26 shares more than their losing competition. When the less generous team won, the difference between the winners and losers was actually smaller (2.7 shares).

Additionally, it should be noted that repeated appearance in the playoffs is related to salary. An analysis of shares authorized correlates to the payroll of the team (r2 = 0.26). However, it should be noted that making it to the playoffs multiple times does not necessarily correlate with higher salaries. A similar analysis was conducted by adding the frequency the team appeared in the playoffs for that same time period. In that case, the quality of the correlation dropped (r2adj=0.22).

Therefore, a minimum share distribution spread may exist before differential performance is realized. Therefore, we find that Hypothesis 1 is supported in both analyses.

Additionally, we focused on the distribution parameters since the baseball playoff series was restructured in 1995 through the most recent data available in 2008. The results in this case were not significant. Over that time period, winners distributed 0.74 more shares than losers, resulting in a negligible differential distribution of 0.05 shares for winning teams over losing teams (s = 5.53). In those same years, the winning team offered more shares as often as the losing team – six of twelve times the World Series were won by the organization offering the winner’s premium. However, further analysis of the data shows that the winner’s premium of these years was 6.05 and the winner’s counter-premium was 4.33. This provides some evidence that effect is relatively weak and that limiting the time to twelve years may have undermined the power of the analysis. Therefore, while Hypothesis 2 is not supported, there is counter evidence that may need further study.

Overall, in the historical model, winners were 1.6 times likelier to have offered more shares than losers. Further, when more generous teams won, they generally authorized 3.26 shares more than their losing competition. When the less generous team won, the difference between the winners and losers was actually smaller (2.7 shares). Therefore, a minimum share distribution spread may exist before differential performance is realized. This analysis limited to the World Series between the years of 1995 to 2008 found winners were as likely to have offered more shares than losers. Further, when more generous teams won, they generally authorized 6.05 shares more than their losing competition. When the less generous team won, the difference between the winners and losers was actually smaller (4.33 shares).

Teams have become more generous over time (Hypothesis 3 is supported). There has been a general trend to increase the number of playoff shares. On an annual basis, teams in the World Series have increased the average number of shares distributed annually by 0.31 (r2 = 0.78). Since 1995, the average number of shares authorized by teams in the World Series has increased 1.03 shares annually, with an Adjusted R-squared of 0.330. When that analysis is expanded to include the divisional champions for both the American and National League, the distributional structure does not change (1.10 v. 1.03 shares annually), but the explanatory factor is increased significantly (Adjusted R-squared = 0.61), suggesting that the increase is foundational and the diminished explanatory power can be attributed to the relatively small data set in the subset analysis. Therefore, Hypothesis 3 is supported in both the full sample and the period from 1995 to 2008.

Repeat winners are still more generous (Hypothesis 4 is supported). Among World Series winning teams, there has been a consistent increase in the number of shares distributed on average. For every additional World Series a team has won in the past 1995 to 2008 period, teams offered an additional half-share (r2 = 0.32). Winning teams have developed a winning formula with respect to share distribution. Further, that players learn to forgo the initial temptation for engaging in SIE behaviors at the outset is important, but not predicted in most of the theories employed by management, economic and sociology researchers. Therefore, it would be beneficial to have a set of guiding principles prior to implementing such an incentive system; hence, the need for a theoretic exploration of MLB’s bonus system.

#### Phase 3: Theoretic Assessment of MLB’s Bonus System

The narrative and empirical evidence offer a new opportunity to explore the theoretic paradigms used to explain and create bonus systems that promote organizational missions. Issues of power, equity and pro-social behavior, and performance forecasting characterize playoff share distribution decisions and their positive impact on organizational outcomes. They also constitute decision-making in an environment where the motivation can range from purely self-serving (e.g., Self-Interested Economics (SIE) explanations) to completely egalitarian. With respect to SIE motivations, Principal-Agency is the most widely used model for exploring compensation issues in organizations – Principal-Agency Theory (8). At the other end of the spectrum is Equity Theory that predicts the remuneration system will distribute shares across all participants at equal levels. The range of theories is explored from most self-interested to most egalitarian.

##### Alternative theory one – principal-agent theory

Bonus systems are often used as an extension of governance policies to promote congruence between principals’ and agents’ goals. Therefore, the topic of incentive-reward systems often arises in the economics’ literature as a search of Google Scholar using the term “bonus system theory” reveals. In particular, economics views such systems through the lens of contractual arrangements where an agent’s rationale behavior is to maximize their utility by fulfilling the contract and doing nothing more (50).

The limits and boundaries of agency theory lie in its model of human motivation. In the case of baseball, economic models based on the ‘rational’ or ‘economic man’ would suggest that the optimum distribution for the individuals (agents) acting on their own behalf would be to distribute a maximum of 40 full shares (47). Given that few if any teams go a full season without roster changes, the number of full share equivalents distributed should be less than 40 with only fractions given to individuals who have been on the roster for partial period.

The near-term financial incentives are for the empowered players to act in their own interest without consideration for non-uniformed stakeholders or players no longer on the roster. The reality is far from this. In 2006, players on each of the teams in the playoffs authorized the creation of 52.86 shares, on average. Therefore, the prima fascia evidence indicates that one of Agency Theory’s main tenets is violated by the bonus system used by MLB. Beyond the shortcomings of economic theories identified herein, other researchers have suggested that other organizational theories may have greater explanatory power (2, 50). In particular, they point to Stewardship Theory for addressing the egalitarian features of the bonus system used by MLB instead of Agency Theory (9, 10, 15, 50).

##### Alternative theory two – stewardship theory

Stewardship Theory describes a phenomenon where an agent’s goals are subordinated to a broader organizational or societal aims. Stewardship Theory assumes an individual’s decision model is ordered such that pro-organizational, collectivistic behaviors have higher utility functions than individualistic, self-serving behaviors. A steward protects and maximizes the organization’s collective wealth through superior firm performance, because, by so doing, the steward’s utility functions are maximized. Alternatively, the steward may calculate that working toward organizational, collective ends is the best trade-off between personal needs and organizational objectives. Hence, the utility gained from pro-organizational behavior is higher than the utility that can be gained through individualistic, self-serving behavior.

The main problem with Stewardship Theory, as it applies to MLB’s bonus system, is that incentive pay is antithetical to the primary model of behavior anticipated. From a Stewardship Theory perspective, how bonuses are used or distributed within organizations would have little influence on employees’ individual motivations to work hard toward organizational goals. Given players are earning a living wage, which is assured with the minimum pay agreed to through the Collective Bargaining Agreement, no other motivation than those intrinsic to the organization’s aims should be needed.

There is also objective evidence that Stewardship Theory is not applicable to MLB. In addition to the 1919 Black Sox scandal, a reference to game fixing by the Chicago White Sox, the widespread use of steroids more recently provide both specific and general examples that players are not fully vested stewards protecting the integrity of the game’s values and traditions. With respect to the latter, steroid use, not only have players cheated and skewed the on-field records, they have also lied repeatedly to cover their misdeeds, and have engaged in this behavior in what seems to be a collective mind rather than individual exception (37). The players were able to engage in this behavior for an extended period of time under the protection of their union’s collective bargaining agreement (46). While steroid use was widespread, and alleged to have been endemic to some teams, the difference between individual versus collective decisions may be explained using another pair of theories.

##### Alternative theory three – expectancy and equity theories

Psychological paradigms such as Expectancy and Equity Theories have been applied to organizational compensation questions and baseball in particular. These theories explore the positive versus negative aspects of individuals’ behaviors under pay-for-performance situations. There has been significant work in the area of equity and expectancy theories in baseball. Lord and Hohenfeld (31), Duchon and Jago (11), Hauenstein and Lord (21), and Harder (20) studied the performance of baseball players who were either free agents or had been through salary arbitration. This line of research has looked at the pay-for-performance question based on salaries, not collectively distributed bonuses. Further, a major reason for implementing a bonus system is to avoid the managerial transaction costs of micro-managing employees’ individual expectations and trying to equitably fulfill them. Indeed this is a major benefit of creating an incentive system controlled directly by the employees.

The teams’ distributions of playoff shares and performance outcomes allow for some inferences to be made with respect to Expectancy and Equity. In particular, the distribution of more shares could be equated to greater perceived equity, which in turn leads to improved performance. However, the distribution of more shares could be defined as egalitarian without any regard to merit with equal veracity. Therefore, expectancy and equity theory are problematic under the given circumstance for a manager wishing to take advantage of a program with a proven record of success in promoting organizational goals.

There are other theories that can be applied to MLB’s bonus system and other potential propositions arise from the results herein. Institutional Theory could be used to explain the increasing amount of shares being created as teams engage in a mimetic process based on the winners’ behaviors. Alternatively, human resources researchers might propose that there will be greater player retention among more egalitarian squads. Both of these lines of inquiry will require additional information that is best gained by going directly into the process.

### Conclusion

MLB’s playoff bonus system exhibits many desirable characteristics, but it does not conform to the most commonly used found in the design of such systems. It is important to reiterate the unusual features of the system. Shares have nominal value at the point they are issued, and gain in value as a team progresses through the playoff series by performing competitively. Allocations generally include some aspect of retrospective acknowledgement – shares are allocated to the people that the core players believe have worked hard to get their team to the playoffs, paid against a value defined by future performance. These people can be players, support staff, or managers. As a result, beneficiaries of shares have full information about the distribution of shares without knowledge of the value of those shares. However, because these shares are allocated prospectively, share recipients can alter behaviors in ways that maximize the value of those shares. In this respect, the system constitutes an incentive structure on a pay-for-performance basis.

The link between performance and reward distribution is one mechanism that management scholars have long advocated as an important motivational or demotivational tool (25). In particular, the use of group incentive plans are recommended when the group is small, team members are engaged in the same kind of work, the payment is clearly linked to the performance, the output depends on the workers, the operational cycle is fairly short, the bonus is substantial relative to standard pay, and there is an atmosphere of mutual trust between management and workers (3, 36). The MLB playoff bonus system is such an incentive plan and has been in existence since 1903. Further, it has an unusual feature in that the players determine playoff bonus share distribution, not only amongst themselves, but also to managers, trainers and other staff members in the organization.

The data from this analysis supports a bonus system that creates incentives for individuals that linked performance to outcomes in ways determined by the principal actors, but not actively managed by owners may support performance in substantive ways. Further, to the extent that such efforts are seen as egalitarian and recognizes the broader contributions of individuals on the margin, such distributions seem to support overall organizational success. Additionally, and maybe more significantly, this system defines the reward structures prospectively. While most players would consider winning the World Series the ideal outcome, this system creates process incentives in addition to outcome incentives. This represents a significantly different method for incentivizing outcomes. Most bonus systems are designed and allocated after the fact with little to no information given to the recipients of the reward.

MLB players have elected to distribute progressively more playoff bonus shares as time progresses. Therefore, there is some form of institutional knowledge accruing. Further, that teams with more experience distribute still more shares than their competitors is an indication that the feedback mechanism functions in a positive manner (27). Collectively, the hypotheses demonstrate the efficacy of MLB’s bonus system.

### Applications In Sport

The distribution of annual bonuses by professionals to themselves and other organizational members is a common feature of law practices, investment banking firms, consulting practices and some other closely held businesses. Bass suggests that group incentive plans are recommended under specific instances. In practice, these distributions are typically allocated retrospectively when the bonus pool’s amount is known using performance standards determined ex post facto. Generally, the professionals (e.g., partners) determining the distribution are the firm’s management.

Studying the distribution of World Series playoff shares provides a unique glimpse into an incentive scheme. The MLB Playoff Shares system uses a valuation system where non-managerial professionals determined share allocation a priori and the bonus amount is not assured, and the performance measure – winning the World Series – is absolute. The baseball model is a system where a bonus is structurally defined, controlled by the agents of the organization with the greatest direct ability to impact outcomes, and is retrospectively allocated but prospectively incentivizing. The incentive structure created is therefore both prospective and retrospective, and moreover, the timeline presents a short horizon to gain, addressing the common pool resource problem of long-term versus short-term feedback. Therefore, if the incentive scheme is an efficacious one, it may warrant broader adoption amongst other professionally led organizations seeking to improve their performance.

### Tables

#### Table 1
Performance Multiplier

Title Number Performance Multiplier
World Series Champion 1 0.36
League Champion (World Series Loser) 1 0.24
League Champion Series Runners Up 2 0.12
Division Series Runners Up 4 0.03
Second Place Finishers (Non-Wild Card Clubs) 4 0.01

#### Table 2
MLB’s Bonus System’s Design

Construct Definition
Between Team Pool Determination Formula-driven and invariant from year-to-year. System rewards teams, not individuals. Team’s payout varies based on how they finish in the playoffs.
Allocation Timing Share distribution is determined prior to share valuation.
Within Team (Work Group) Control Front-line employees – players – determine their own and staff members’ shares in an informal process designed to incentivize team aims.
Allocation Criteria Measurement models unknown. Only players that were on the roster the entire season have control of the bonus allocation. Rosters are expanded for the post season; therefore, some aspect of tenure likely plays a role. In addition, the large wage disparity between ‘star’ players and other organizational members may result in a premium on altruistic distribution schemes.
Valuation A share’s realized value is not known until the team’s season ends. Teams that perform better vis-à-vis other units receive progressively greater rewards.

#### Table 3
Share distribution by winning franchise, sorted by Average Share Premium (1903 to 2008)

World Series Championship Franchise Number of Wins Average Share Premium
Chicago White Sox 3 -5.83
Florida Marlins 2 -5.55
Minnesota Twins 2 -2.00
Atlanta/Milwaukee/Boston Braves 3 -1.87
Cleveland Indians 2 -1.20
Toronto Blue Jays 2 -1.10
St. Louis Cardinals 10 -0.88
Washington Nationals 1 -0.80
SF/NY Giants 5 -0.44
LA/Brooklyn Dodgers 6 0.37
Detroit Tigers 4 0.70
Oakland/Philadelphia Athletics 9 0.76
Anaheim Angels 1 1.20
Chicago Cubs 2 1.20
Cincinnati Reds 5 1.40
Pittsburgh Pirates 5 1.50
Baltimore Orioles 3 1.53
Boston Red Sox 5 1.95
Kansas City Royals 1 2.70
New York Yankees 26 2.73
Arizona Diamondbacks 1 3.00
Boston Americans 1 4.00
Philadelphia Phillies 1 4.45
New York Mets 2 6.20

### Figures

#### Figure 1
Premium/Counter-premium Control Chart (World Series: 1903 – 2008, y-units are 1)
![Figure 1](//thesportjournal.org/files/volume-15/459/figure-1.png “Premium/Counter-premium Control Chart (World Series: 1903 – 2008, y-units are 1)”)

#### Figure 2
Share distribution of Playoff Teams by Frequency of Participation (1995 through 2008)
![Figure 2](//thesportjournal.org/files/volume-15/459/figure-2.png “Share distribution of Playoff Teams by Frequency of Participation (1995 through 2008)”)

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### Corresponding Author

Eric W. Ford, MPH, Ph.D.
Forsyth Medical Center Distinguished Professor of Management
The University of North Carolina Greensboro
PO Box 26165
Greensboro, NC 27402
<ewford@uncg.edu>
Phone: 806-787-3267
Fax: 336-334-5580

2015-01-31T01:09:26-06:00April 9th, 2012|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on As Goes The Spoils, So Go The Victories: Exploring Major League Baseball’s Playoff Bonus System

Upon Further Review: An Empirical Investigation of Voter Bias in the Coaches’ Poll in College Football

### Abstract

#### Purpose

The popularity of NCAA football continues to rise at an exponential rate. As revenues increase, the difference between a BCS bowl berth and a non-BCS bowl berth can be millions of dollars. Thus, the process of how schools are selected to play in a BCS bowl game is very important. In this paper, we analyze one of the components of the BCS ranking system: the Coaches’ Poll.

#### Methods

Data from the final regular season Coaches’ Poll from 2005 through 2010 were analyzed in order to explore whether coaches were biased in their voting in three different areas: voting for their own team, voting for teams in their conference and voting for teams from Non-Automatically Qualifying (N-AQ) conferences.

#### Results

Through analyzing a Coach’s Difference Score (CDS), we found that coaches had a positive bias towards their own team. That is, they vote their own team higher than their peers. We also discovered that coaches tend to vote schools from their own conference higher than do coaches from outside that conference. Finally, we concluded that coaches from the six Automatically Qualifying (AQ) conferences were biased against schools from the smaller N-AQ conferences.

#### Conclusions

After discussing potential reasons why all these biases occur, several questions for future researchers to explore are put forth. Then, we make several suggestions to improve the voting process in order to make it as objective as possible.

**Key Words:** College Football Coaches’ Poll, Voting Bias, BCS Implications

### Introduction

Every year in college football, a debate occurs about which team should be ranked higher than another, and 2010 was no different. With three teams finishing their regular seasons undefeated, it was up to the Bowl Championship Series (BCS) rankings to determine which two teams would play for the national championship. While Auburn defeated Oregon in Glendale, Arizona, on January 10, 2011, and was crowned champion, fans over a thousand miles away in Fort Worth, Texas were left to wonder, “Could TCU have beaten Auburn?” Thus, the scrutiny of the BCS continues.

The BCS system was started in 1998 as a way to bring the top-two ranked teams face to face in a bowl game to determine a national champion (3). Prior to the BCS, the bowls tried to match number one versus number two, but with guaranteed conference tie-ins, such as that of the Pac 10 and the Big 10 to the Rose Bowl, it was not always possible. When the Rose Bowl relented, the BCS was born. According to the official BCS website, “The BCS is managed by the commissioners of the 11 NCAA Football Bowl Subdivision (“FBS”) (formerly Division I-A) conferences, the director of athletics at the University of Notre Dame, and representatives of the bowl organizations. The conferences are the Atlantic Coast, Big East, Big Ten, Big 12, Conference USA, Mid-American, Mountain West, Sun Belt, Pacific-10, Southeastern and Western Athletic” (3).

As of 2005, the BCS standings are determined by averaging three different rankings: the Harris Poll, computer rankings and the Coaches’ Poll. The Harris Poll is run by a marketing research firm, Harris Interactive, and is “comprised of 114 former college football players, coaches, administrators and current and former members of the media…randomly selected from among more than 300 nominations” (10) from the FBS. The final computer ranking used is an average of the rankings from six different firms/individuals that mathematically calculate a team’s ranking based on wins, strength of schedule, etc. (3). The Coaches’ Poll is run by *USA Today* and the American Football Coaches Association (AFCA) and is approximately 60 coaches–50% of the coaches in each conference are randomly selected to vote (18).

This research explores one component of the BCS: the Coaches’ Poll. In particular, we investigate to what extent coaches have been biased in their voting. Bias, as defined herein, is considered to be present when a coach ranks a team significantly different than the other voting coaches in the poll. Why is this important? With teams often being separated by a few tenths of a point in the BCS standings, ensuring the integrity of the rankings is critical. The BCS standings can determine a team’s bowl game and/or a coach’s bonus. For example, Iowa coach Kirk Ferentz received a $225,000 bonus for finishing in the Top 10 BCS rankings in 2009, and another $175,000 bonus for playing in the BCS Orange Bowl that season (8). In 2010, the BCS bowl payout was 17 million dollars (6) with the non-BCS bowl payout being much less (e.g., The 2010 Capital One Bowl had the highest non-BCS Bowl payout of 4.25 million dollars (6)). So, biased decisions may not only affect the coaches, who make these decisions, but other coaches and universities, as well.

Prior to 2005, the coaches’ votes were not made public. Then, in response to added pressure for transparency, a vote by all FBS coaches made the final regular season Coaches’ Poll public by agreeing to have the ballots published in *USA Today* (7). However, the decision was not unanimous. According to Texas coach Mack Brown, who was initially not in favor of making the votes public, “It can put coaches in a difficult situation” (7). How did the first year of public voting go? According to Sports Illustrated writer, Stewart Mandel, it was “the equivalent of a high school student-council election” with “Oregon coach Mike Bellotti, his team about to be squeezed out of the BCS by Notre Dame, placing the Ducks fourth and the Irish ninth,” and “Arkansas coach Houston Nutt ranking SEC rival Auburn third and Big East champion West Virginia … nowhere.” (14). Even Coach Steve Spurrier of the University of South Carolina has questioned the validity of the Coaches’ Poll remarking, “I guess we vote ’cause college football is still without a playoff system. I really believe most coaches do not know a whole lot about the other teams” (9).

With increasing scrutiny of the coaches’ voting patterns, the AFCA hired The Gallup Organization in early 2009 to analyze the coaches’ voting and make recommendations. “The perception is that there’s a huge bias, and we’ve never really found that,” claimed former Baylor coach and current AFCA Director Grant Teaff (2). One of Gallup’s key recommendations was to make the coaches’ final regular season votes private. However, after seeing the response to a *USA Today* poll of over 4,000 readers that found 79% of fans felt the coaches’ final regular season votes should remain public as “it is important they are accountable,” (20) the AFCA put the decision to a vote of all FBS head coaches, and the results indicated that the final regular season votes should remain transparent. Consequently, the AFCA changed their mind and kept the final regular season votes public in 2010 (1).

Even with the continued visibility of the voting, one thing remained consistent in 2010: scrutiny. For example, in the final vote of 2010, one coach returned his ballot with TCU ranked number one, which is against the AFCA rules (the AFCA instructs every coach to list the winner of the BCS National Championship Game as the top ranked team) and two other coaches in the poll failed to turn in their ballots at all (18). While the final votes of each coach are not made public, these types of mishaps still fuel the debate: Should coaches have a part in the BCS rankings?

Previous researchers have discovered that individuals can be biased towards others in society (11, 12), and that people can also be biased when voting (16, 17). Specifically, researchers have examined voter bias in college football polls. For instance, Coleman et al. (5) concluded that voters in the 2007 Associated Press college football poll were biased in a number of different ways, including voter bias toward teams in their home state. In another study, Campbell et al. (4) discovered that “the more often a team is televised, relative to the total number of own- and opponent-televised games, the greater the change in the number of AP votes that team receives,” (p. 426) when they analyzed the AP votes from the 2003 and 2004 college football seasons. A study by Paul et al. (15) also examined AP voting bias, but included coaches’ voting as well. Their research looked at both of these polls from the 2003 season, and they determined that the spread or betting line on a game is “shown to have a positive and highly significant effect on votes in both polls. A team that covers the point spread will receive an increase in votes in both polls. A team that wins, but does not cover the point spread, will lose votes” (15, p. 412). In 2010, Witte and Mirabile (21) extended the literature by examining several seasons of Coaches’ Poll data, and they concluded that voters tended to “over-assess teams who play in certain Bowl Championship Series (BCS) conferences relative to non-BCS conferences” (p. 443).

While research on the voting bias in the college football polls exists, few researchers have investigated the bias in the Coaches’ Poll to any great depth. Hence the purpose of this research is to determine if college football coaches are biased when they vote and, if they are, what kind of biases they hold. Specifically, we look at three areas of potential bias put forth in the following null and alternate hypotheses:

> H1o: Own-School Bias – Coaches do NOT rank their own teams significantly different than other coaches voting.
>
> H1a: Own-School Bias – Coaches do rank their own teams significantly different than the other coaches voting.
>
> H2o: Own-Conference Bias – Coaches do NOT rank teams within their conference significantly different than coaches from outside the conference vote those same teams.
>
> H2a: Own-Conference Bias – Coaches do rank teams within their conference significantly different than coaches from outside the conference vote those same teams.
>
> H3o: N-AQ Bias – Coaches from schools in the AQ conferences do NOT rank N-AQ teams significantly different than the N-AQ coaches voting.
>
> H3a: N-AQ Bias – Coaches from schools in the AQ conferences rank N-AQ teams significantly different than the N-AQ coaches do.

Combining the three hypotheses, a model for Coaches’ Voting Bias is shown in Figure 1.

The first hypothesis investigates whether coaches can be objective when ranking their own teams. Do coaches rank their own teams about the same as other coaches rank that team, or, do coaches tend to over-estimate their own team’s ranking? The second hypothesis explores whether coaches rank teams in their own conference impartially. Many times a team’s quality of wins and losses can impact the perception of how good they are, and if a coach makes teams in their conference look superior to those from other conferences, perceptions of the strength of their own team may increase. Our final hypothesis examines what is commonly called “big school bias.” Namely, do coaches from the six traditional power conferences that have automatic qualification tie-ins with the BCS (AQ teams) tend to underestimate the strength of teams from the smaller conferences, the winners of which do not automatically qualify for a BCS bowl (N-AQ teams)?

### Methodology

The sample for this research was the final regular season coaches’ ballots for the 2005 through the 2010 college football seasons published in the *USA Today Coaches’ Poll*. In each of these years, a coach who is selected to vote ranks his top 25 teams by awarding 25 points to his top ranked team, 24 points to his second ranked team and decreasing in a similar manner until the 25th ranked team is awarded a single point. Appendix A lists the various coaches and the years during which each has been a member of the *USA Today Coaches’ Poll*. Table 1 aggregates the data by conference.

Because the number of coaches who vote each year varies slightly, a simple linear transformation of the total point system was employed herein by calculating a voter’s “difference score,” which is the average number of points a team received subtracted from the points that the voter awarded them. For instance, in 2008, the #20 Northwestern Wildcats received a total of 334 points and 61 coaches voted in that poll. So, Northwestern’s average points per coach is calculated as 334/61 = 5.475. In that poll, Coach Bret Bielema of Wisconsin gave Northwestern 8 points. Thus, his difference score would be 8–5.475 = 2.525. On the other hand, Coach Art Briles of Houston gave Northwestern only 4 points that year resulting in a difference score of 4 – 5.475 = -1.475.

In general, a positive difference score suggests that a coach ranked a team higher than that team’s average score, while a negative difference score indicates that a coach ranked a team lower than its average score. A small difference score represents a case where a coach has ranked a team very close to the average ranking of his peers. In contrast, a large difference score would suggest a coach disagreed with his peers about where a team should be ranked. The total of the 25 difference scores for each individual coach will sum out to zero each year as every time a coach votes a team higher than his peers, he must vote another team (or a combination of teams) lower than his peers. Likewise, when all the coaches’ difference scores for a single team are summed, there will be a difference score of zero (i.e., for every coach that votes a team higher than their final average, there must be a coach, or combination of coaches, that votes that team lower). Thus, the key unit of analysis in this study is a term we have labeled the Coach’s Difference Score or CDS.

One thing to note about the Coaches’ Poll is just how few votes can separate teams. Roughly seventeen percent of the point differences between two contiguous positions in the poll were determined by fifteen or fewer points. In fact, in fifteen occurrences, which is an average of 2.5 per year, less than six total points separated two teams, including in 2008 where a single point separated #1 Oklahoma (1,482 points) and #2 Florida (1,481 points).

### Results

In order to test Hypothesis 1, which explores whether there are any discernible patterns in how coaches rank their own schools, we employed a simple t-test. If there are no significant biases (as a collection), coaches that vote on their own teams will have a mean CDS score of zero (i.e., for every coach that ranks his team higher than his peers, a corresponding coach would rank his team lower than his peers). However, if coaches tend to error consistently on one side or the other from their peers, then the difference score for those coaches will not be equal to zero. We tested each of the six years individually and collectively. The results are summarized below in Table 2.

As illustrated in Table 2, in all six years, the CDS, which ranged from a low of 1.61 (in 2008) to a high of 3.12 (in 2007), were significantly different than zero at a p < .01 level. This result leads us to reject the null hypothesis that there is no bias in the way coaches vote their own school. The result indicates that coaches do tend to rank their own teams significantly higher than do other coaches. Over the entire sample, coaches, on average, ranked their own team 2.32 positions higher than did their peers. We explored this result further by performing an ANOVA test across the six periods to see if any one year’s bias was significantly higher or lower than the other years. The ANOVA result was not significant (F = 1.083, p = .373) leading us to conclude that there is no statistical evidence to suggest that the bias changes from one year to the next. While this result might seem trivial on the surface, it tells us that no matter how much the composition of the voting group changes (e.g., only eight of the sixty-two coaches that voted in 2005 were still voting in 2010), the coaches vote in a fairly homogeneous way when it comes to ranking their own teams.

In order to test Hypothesis 2 for within conference bias, we assessed the CDS for each voting coach, with regard to their respective conference members. To control for own-school bias, we did not include a coach’s own team in the analysis. We tested for this own-conference bias for individual seasons, as well as, collectively, for the entire span of years examined. We employed the same set-up and methodology (i.e., a t-test) that we used to test H1. Table 3 reveals the results of these t-tests.

All of the t-test results were statistically significant (p < .001) leading us to reject the null hypothesis. This result suggests that coaches do rank their own conference members higher than do coaches outside the conference. While the CDS overall mean of 1.19 might seem small, keep in mind that some conferences have as many as seven voting members, and others as few as three, which could lead to an average favorable bias of nearly 5 points (1.19 * (7 – 3)) for the teams in a conference with seven voting members.

To explore this bias further, we conducted two additional ANOVA tests. The first to discover whether the mean CDS had changed across the six years and the second to determine if any one conference’s coaches have a higher CDS than those of other conferences. With an F-statistic of 4.286, the first ANOVA test was significant at the p < .001 level. A post-hoc Tukey test (p < .05) indicated that own-conference bias was significantly higher in 2007 (with a mean of 1.95), than in each of the other years. This result might be explained by noting that, in 2007, there were only two teams from the traditional AQ conferences, Ohio State and Kansas, which had one loss or less, while a total of ten teams had two losses, potentially making it very difficult to sort out what schools rounded out the Top 10. As a result, coaches ranked the schools with which they were familiar (their own-conference schools) higher than other schools. That year was the only one within our sample range that had such a grouping of teams with similar records. Sixteen different teams received top 10 votes that year, which was the largest amount for any year.

We then turned our attention to analyzing own-conference bias broken down by conference membership. Table 4 gives the descriptive statistics for each conference. The ANOVA analysis suggests that we cannot accept the null hypothesis of equal bias across conferences (F = 4.286, p < .001). Post-hoc Tukey comparisons reveal that the own-conference bias of voters from the WAC was significantly greater than the bias from coaches in the ACC, Big 10, Big 12, C-USA, MAC, Pac 10 and SEC (p < .01). The primary beneficiaries of this effect were Hawaii in 2007, with the four WAC coaches voting Hawaii an average of 5.27 positions higher than non-WAC coaches, and Nevada in 2010, with the four WAC coaches voting Nevada an average of 5.4 positions higher than the non-WAC coaches.

Our final hypothesis, H3, investigates whether bias exists in the way coaches from AQ conferences, whose champions automatically qualify for a BCS spot, vote versus the way coaches from conferences that do not have automatic tie-ins, referred to as N-AQ conferences, vote. The six AQ conferences include: ACC, Big 10, Big 12, Big East, SEC and Pac 10. The remaining five conferences are categorized as N-AQ: C-USA, MAC, MWC, Sun-Belt and WAC. Ultimately, we are testing what many journalists call “big school bias”–whether or not coaches from the six AQ conferences are biased against the smaller N-AQ schools. To test for this bias, we assessed how coaches from the AQ schools ranked the N-AQ schools, compared to how coaches from the N-AQ schools ranked the other N-AQ teams. If there is no bias present, the means of the CDS of the two groups of coaches would be equal to each other. In order to control for the previous bias that we have demonstrated, we removed how an N-AQ coach votes on his own team and teams within his conference. For example, for Gary Patterson, the head coach of Texas Christian University (TCU), we analyzed his voting record for all the schools from C-USA, MAC, Sun-Belt and WAC, but did not include his voting record on schools from the MWC, the conference that Patterson’s TCU team played in during this time period, or his voting record for TCU. We performed this test on each of the six years, individually, as well as collectively. The results are presented below in Table 5.

In five of the six years, there was a statistically significant bias (p < .05). The largest amount of bias occurred in 2007, when AQ coaches ranked N-AQ teams an average of 1.92 spots below the positions assigned them by N-AQ coaches. The only year without bias was in 2009. An investigation of this year showed a couple of possible explanations. First, in two of the three previous years, N-AQ teams had significant BCS bowl wins. For example, after the 2006 regular season, Boise State defeated, then #8, Oklahoma in the Fiesta Bowl, and after the 2008 regular season, Utah beat, then #4, Alabama in the Sugar Bowl. Second, during the first few weeks of the 2009 season, when teams generally play out-of-conference games, several teams from N-AQ conferences had wins over good teams from AQ conferences: TCU beat a Clemson team that would win nine games and go on to win their division in the ACC, Boise State beat the #16 ranked Oregon Ducks, a team that won the Pac 10 and went on to play in the Rose Bowl, and BYU beat then #3 ranked Oklahoma. These high profile wins may have played a significant role in reducing the bias against N-AQ teams.

When the six-year period is looked at collectively, the AQ coaches ranked the N-AQ teams 0.80 places lower than the N-AQ coaches ranked those same teams (p < .001). While this result might, at first, seem like a small margin, recall that as Table 1 shows, in an average year, there are 10.5 more AQ coaches than N-AQ coaches voting–and the resulting bias can thus have a significant effect on the overall point totals and rankings.

### Discussion

This study demonstrated that coaches who are selected to vote in the *USA Today Coaches’ Poll* are subject to at least three different kinds of bias. First, coaches are biased toward their own teams. On average, coaches rank their own school 2.32 positions higher than do their peers. Indeed, the effect is so prevalent that 92.1% (or 82 out of 89) of coaches whose school finished in the top 25 ranked their own school higher than the average of the other coaches. In two years, 2007 and 2010, every single coach ranked their team higher than its final position. Twenty-eight of the 89 coaches (31.5%) ranked their school at least three positions higher than the average of their peers, and 11 of 89 (12.4%) voted their team at least five positions higher. One coach even voted his team 9.71 positions higher than the average of the other coaches’ rankings. In contrast, the maximum amount a coach ranked his team lower than did his peers was 1.18 positions. This bias seems to be a natural phenomenon. Social psychologists have extensively studied the concept of illusory superiority (11, 12), which describes how an individual views him or herself as above average, in comparison to their peers.

The second form of coaches’ bias found was bias toward their own conference. Over the six-year period from 2005 to 2010, coaches voted their conference members 1.19 positions higher than their average ranking. Representative examples of this type of bias are worth discussing. For example, in 2009, Mississippi received 87.5% of their total points from SEC conference coaches, who made up less than 12% of the voters. Similarly, in 2008, the Iowa Hawkeyes received 62% of their votes from Big Ten coaches, who made up less than 10% of the voting population. Further evidence of this effect is apparent when you compare two teams that finished very close in the rankings. For example, in 2009, Oregon and Ohio State were #7 and #8, respectively, in the poll, and they were separated by only 19 points. All five PAC 10 coaches voted Oregon ahead of Ohio State, while four of the five Big 10 coaches voted Ohio State ahead of Oregon (Interestingly, Jim Tressel, the coach of the Buckeyes was the only Big 10 coach to put Oregon ahead of Ohio State). A similar phenomenon happened in 2010 when Oklahoma and Arkansas were tied for the #8 ranking. Six of the seven Big 12 coaches voted Oklahoma higher, while five of the six SEC coaches voted Arkansas higher.

When comparing the bias across conferences, the WAC was found to be the most biased voting their own teams on average 2.97 places higher. Perhaps, this is due to the WAC coaches trying to overcompensate for the perceived bias that other voting coaches have against this N-AQ conference. In 2007, Hawaii went undefeated, yet finished #10 in the overall rankings behind seven teams from AQ conferences that had two losses, in large part due to the voting of AQ coaches. A similar result occurred in 2006 when Boise State went undefeated and finished #9 in the rankings behind three AQ teams with two losses.

The third form of bias discovered in this research was that of coaches toward N-AQ conference teams. Looking more closely at the numbers shows that while this bias does exist, it seems to be diminishing over time. The effect was at its highest in 2007 when AQ coaches ranked N-AQ teams 1.92 positions lower than did the N-AQ coaches. As previously mentioned, there was no significant difference in the CDS with regard to N-AQ teams in 2009, and this might be due to some significant wins N-AQ schools have had over AQ schools in recent years. Moreover, it will be interesting to see if the N-AQ bias is further reduced in the 2011 season after TCU’s defeat of Big 10 Champion Wisconsin in the 2011 Rose Bowl, which led the Horned Frogs to a #2 ranking in the final standings–the highest for any N-AQ team during the period we surveyed. The Rose Bowl win brought the N-AQ teams to a very impressive five wins to two losses in their BCS Bowl appearances. Their 71.4% winning percentage is higher than that of any of the AQ conferences.

There are a number of limitations to this study. For one, the data was limited to the final regular season *USA Today Coaches’ Poll*, as that is the only data made public. If more data is provided in the coming years, future researchers will be able to investigate whether coaches’ bias varies throughout the season. Greater availability of data will also allow researchers to use more sophisticated time series data analysis techniques, such as logistic regression. Secondly, the sample sizes for some of our subgroups was rather small. For example, because only one MAC team made the top 25 rankings, only five MAC coaches’ votes were used to assess the MAC’s own-conference bias. As more data is collected over time, and possibly more MAC teams make the top 25 poll, future researchers can replicate this study on a larger sample.

There are many fruitful areas remaining for future researchers to continue exploring bias in the Coaches’ Poll. Researchers can analyze where bias is the strongest – are coaches most biased when ranking teams in the top third of the standings, the middle third, or the bottom third, etc.? Previous research has shown that TV exposure impacts how media members vote (4); future researchers can determine whether it has any effect on the way coaches vote. The 2011 and 2012 seasons will see a shift in conference membership. Future researchers can attempt to discover what effect this has on bias. One particular study could examine Utah and TCU–two N-AQ teams who are moving to AQ conferences, the PAC 10 and the Big East, respectively. Will AQ coaches now see these teams as AQ teams or will they continue to see them, and thus penalize them, as being N-AQ teams? Finally, a last ripe area for exploration involves gathering the coaches’ opinions on the subject. Do coaches think that they themselves are biased? Do they think their colleagues are biased? And if coaches do think other coaches are biased, do they try to compensate for it?

### Conclusion

One thing is certain. The current BCS system has flaws, which leads to frequent fan and media criticism. While every system, including a playoff, has advantages and disadvantages, the BCS should continually evaluate itself in an effort to make improvements. If it does not, the scrutiny will only increase over time. For example, Wetzel, Peter and Passan’s 2010 book, Death to the BCS, has garnered much attention in the media. The authors refer to the BCS as an “ocean of corruption: sophisticated scams, mind-numbing waste, and naked political deals” (19). In fact, after reading this book, Dallas Mavericks’ Owner, Mark Cuban, formed his own company in late 2010, in an effort to create a play-off system that would challenge the BCS in the future (13).

In our opinion, BCS officials should consider making several changes. For one, they should use an email-based ballot to make it easier for coaches to vote, instead of the antiquated phone-in ballot system currently used. Moreover, they should not require all ballots to be turned in so soon after the weekend games. Coaches simply do not have enough time to thoroughly analyze all of the teams within 24 hours of finishing their games. The BCS could consider moving the voting deadline to later in the week. Secondly, coaches should not be allowed to vote for their own team–if this rule were implemented own-school bias would be eliminated. These last two recommendations are not new; both were made by Gallup when they were hired by the AFCA to examine the Coaches’ Poll in 2009 (20). While the AFCA decided not to implement them, we feel that given the dollars involved in the BCS rankings, these would be easy improvements to the system. Lastly, why not let every FBS coach vote? Normally, a sample or sub-set of the population is used due to the expense of a census. But, in this case, the population is not very large, with only 120 FBS coaches, nor is the process very complex or time-consuming. By allowing all coaches to vote, it may help reduce the amount of own-school benefit that about half of the teams are currently receiving. Moreover, as most conferences are roughly the same size, this measure would also help reduce the disparity in the number of voters from each conference, thus minimizing the effect of own-conference bias.

Overall, our research has highlighted some important issues with the Coaches’ Poll. Bias in voting has occurred in the political arena in many different forms (16) and researchers have discovered that the amount of information voters possess can impact voting preferences (17). Perhaps the AFCA could do the same with voting coaches using our research results. If the coaches were to see how much bias occurs and the different forms of bias that are present in the voting, they may be encouraged to vote more objectively.

Under the current system, we found three different forms of bias present in the *USA Today Coaches’ Poll*: bias toward own-team, bias toward own-conference and bias toward teams in N-AQ conferences. These are significant findings as the Coaches’ Poll is an important part of the BCS standings that accounts for one-third of the BCS formula; a formula that, in turn, can mean the difference between a team going to a bowl with a payout of $17 million versus a fraction of that amount.

### Application In Sport

This research has several applications for those in sport. For one, BCS and college football administrators now have a better understanding of the biases that coaches employ (intentionally or unintentionally) when voting. Hopefully, some changes, as suggested in our Conclusion section, can be made to improve the process. In addition, sport management researchers and students can continue to analyze the numbers in the future to investigate other forms and levels of bias, now that this study has provided a framework, namely the CDS, as a basis for voting comparisons.

### References

1. Anonymous (2009, November 6). AFCA to continue release of final regular season Coaches’ Poll ballots. Retrieved from <http://www.afca.com/ViewArticle.dbml?DB_OEM_ID=9300&ATCLID=204828450>
2. Associated Press. (2009, May 6). Coaches mull changes in football poll. Retrieved from <http://sports.espn.go.com/ncf/news/story?id=4147492>
3. BCSfootball.org. (n.d.). Retrieved from <http://www.bcsfootball.org>
4. Campbell, N. D., Rogers, T. M., & Finney, R. Z. (2007). Evidence of television exposure effects in AP top 25 college football rankings. Journal of Sports Economics, 8 (4), 425-434.
5. Coleman, B. J., Gallo, A., Mason, P. M., & Steagall, J. W. (2010). Voter bias in the associated press college football poll. Journal of Sports Economics, 11 (4), 397-417.
6. CollegeFootballPoll.com. Retrieved from <http://www.collegefootballpoll.com/2010_archive_bowls.html>
7. Collins, M. (2009, May 31). College football coaches choose darkness: Coaches Poll changes in 2010. Bleacher Report. Retrieved from <http://bleacherreport.com/articles/189684-college-football-coaches-choose-darkness-coaches-poll-changes-in-2010>
8. Dochterman, S. (2010, January 8). Orange Bowl visit, No. 7 final ranking worth millions in bonuses, raises to football program. The Gazette. Retrieved from <http://thegazette.com/2010/01/08/orange-bowl-win-worth-millions-in-bonuses-raises-to-football-program/>
9. Dodd, D. (2009, July 24). Spurrier’s fraudulent SEC vote makes fraud of coaches poll, too. CBS Sports. Retrieved from <http://www.cbssports.com/collegefootball/story/11982516>
10. Harris Interactive. (n.d.). Retrieved from <http://www.harrisinteractive.com>: <http://www.harrisinteractive.com/vault/HI-BCS-HICFP-FAQs-2010-10-15.pdf>
11. Hoorens, V. (1995). Self-favoring biases, self-presentation, and the self-other asymmetry in social comparison. Journal of Personality, 63 (4), 793-817.
12. Hornsey, M. J. (2003). Linking superiority bias in the interpersonal and intergroup domains. The Journal of Social Psychology, 143 (4), 479-491.
13. MacMahon, T. (2010, December 16). Mark Cuban exploring BCS alternative. ESPN Dallas. Retrieved from <http://sports.espn.go.com/dallas/nba/news/story?id=5924399>
14. Mandel, S. (2005, December 7). The real BCS controversy. Want evidence of bias? Just look at coaches’ votes. Sports Illustrated CNN. Retrieved from <http://sportsillustrated.cnn.com/2005/writers/stewart_mandel/12/07/mailbag/index.html>
15. Paul, R. J., Weinbach, A. P., & Coate, P. (2007). Expectations and voting in the NCAA football polls: The wisdom of point spread markets. Journal of Sports Economics, 8 (4), 412-424.
16. Sigelman, C. K., Sigelman, L., Thomas, D. B., & Ribich, F. D. (1986). Gender, physical attractiveness, and electability: An experimental investigation of voter biases. Journal of Applied Social Psychology, 16 (3), 229-248.
17. Taylor, C. R., & Yildirim, H. (2009, June). Public information and electoral bias. Retrieved from <http://econ.duke.edu/~yildirh/elections.pdf>
18. *USA Today*. (2011, January 11). Top 25 Coaches’ Poll. Retrieved from <http://www.usatoday.com/sports/college/football/usatpoll.htm>
19. Wetzel, D., Peter, J. & Passan, J. (2010). Death to the BCS: The Definitive Case Against the Bowl Championship Series. Retrieved from <http://www.deathtothebcs.com>.
20. Whiteside, K. (2009, May 28). Football coaches to keep poll ballots secret starting in 2010. *USA Today*. Retrieved from: <http://www.usatoday.com/sports/college/football/2009-05-27-coaches-poll-votes_N.htm?loc=interstitialskip>
21. Witte, M. D., & Mirabile, M. P. (2010). Not so fast, my friend: Biases in college football polls. Journal of Sports Economics, 11 (4), 443-455.

### Figures

#### Figure 1
A Model of Coaches’ Bias in Voting

![Figure 1](/files/volume-15/458/figure-1.jpg “A Model of Coaches’ Bias in Voting”)

### Tables

#### Table 1
Voter Composition by Conference

![Table 1](/files/volume-15/458/table-1.png “Voter Composition by Conference”)

#### Table 2
T-Test Results of Own-Team Bias

Year Mean Difference Std. Error df t-stat Significance
2005 1.69 .40 15 4.23 .001
2006 2.51 .47 18 5.29 .000
2007 3.12 .57 13 5.49 .000
2008 1.61 .47 12 3.43 .005
2009 2.63 .83 11 3.19 .009
2010 2.38 .52 16 4.59 .000
All Years 2.32 .22 90 10.57 .000

#### Table 3
T-Test Results of Own-Conference Bias

Year Mean Difference Std. Error df t-stat Significance
2005 1.03 .18 129 5.75 .000
2006 0.93 .17 121 5.59 .000
2007 1.95 .21 130 9.43 .000
2008 1.09 .19 125 5.69 .000
2009 1.09 .21 120 5.10 .000
2010 0.98 .16 116 6.25 .000
All Years 1.19 .08 746 15.32 .000

#### Table 4
Descriptive Statistics for Own-Conference Bias

Conference N Mean Difference Std. Error
ACC 111 1.02 .17
Big 10 119 1.20 .17
Big 12 128 0.68 .17
Big East 52 1.61 .26
C-USA 10 0.49 .45
MAC 5 -1.19 .93
MWC 42 1.48 .30
Pac 10 79 1.29 .29
SEC 175 1.25 .17
WAC 26 2.97 .48

#### Table 5
T-Test Results of AQ vs. N-AQ Bias

Year AQ Mean N-AQ Mean difference t-stat significance
2005 -0.41 0.45 -0.85 -1.95 .056
2006 -0.57 0.54 -1.11 -3.74 .000
2007 -1.05 0.87 -1.92 -3.88 .000
2008 -0.29 0.39 -0.68 -2.48 .014
2009 -0.34 0.01 -0.35 -1.36 .176
2010 -0.35 0.20 -0.55 -2.75 .006
All Years -0.46 0.34 -0.80 -6.38 .000

### Appendices

#### Appendix A
Coach Composition of Coaches’ Poll

![Appendix A – Part 1](/files/volume-15/458/appendix-a-part1.png “Coach Composition of Coaches’ Poll”)
![Appendix A – Part 2](/files/volume-15/458/appendix-a-part2.png “Coach Composition of Coaches’ Poll”)
![Appendix A – Part 3](/files/volume-15/458/appendix-a-part3.png “Coach Composition of Coaches’ Poll”)

#### Appendix B
Team Rankings in the Coaches’ Polls Analyzed

![Appendix B](/files/volume-15/458/appendix-b.png “Team Rankings in the Coaches’ Polls Analyzed”)

### Authors

#### Michael Stodnick, Ph.D.
Assistant Professor, College of Business
University of Dallas

#### Scott Wysong, Ph.D.
Associate Professor, College of Business
University of Dallas

### Corresponding Author

Scott Wysong, Ph.D.
Associate Professor, College of Business
University of Dallas
1845 E. Northgate Dr.
Irving, TX 75062
<swysong@gsm.udallas.edu>
972-721-5007

2013-11-22T22:51:58-06:00February 29th, 2012|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on Upon Further Review: An Empirical Investigation of Voter Bias in the Coaches’ Poll in College Football

Acute Effects of Combined Elastic and Free-weight Tension on Power in the Bench Press Lift

### Abstract

The present study investigated the acute effects on power following the bench press exercise with a combination of elastic band and free-weights vs. free weight only. Eight college-aged males and females participated in this study. All 8 subjects were college track and field athletes that participated in throwing events. The participants performed two bench press training sessions that consisted of three sets of five repetitions. One session used a combination of elastic band (15% of total resistance) and free-weight exercise (85% of total resistance), while the other session consisted only of a free-weight exercise (100%). Power was measured twice at 50% of their one repetition maximum (1 RM) at the conclusion of each lifting session. Analysis via repeated measures Ancova (Treatment by Time covaried for gender) revealed a significant effect for Time (F= 5.951, p=0.05) and a significant two way interaction for Treatment*Time (F=54.093, p<0.001). The present investigation demonstrated an initial power measurement that was greater for the combined group rather than the free-weight only group. This information is potentially beneficial for many different groups of trainee’s.

**Key Words:** Elastic tension, Strength Training, Acute Training Effect

### Introduction

Recently, there have been a number of investigations that have assessed the impact of combined elastic band and free-weight exercise. These bands have been shown to provide predictable variable resistance when applied to free weight exercises such as the back squat and bench press (5,7). Exercise professionals are continually trying to discover novel ways to increase strength and power gains. Wallace et al. (12) demonstrated that power was acutely increased in the back squat exercise with the addition of elastic tension. It was suggested from this research that an 80% free-weight/20% elastic tension ratio might be optimal. Stevenson et al. (10) also found that the combination of elastic band and free-weight exercise during the back squat can significantly increase rate of force development. Experienced power lifters and strength and conditioning professionals have claimed elastic band resistance combined with traditional training produces strength gains for several years (4,8,9). Anderson et al. (1) demonstrated an increase in the bench press and squat exercise strength after training with the addition of elastic tension for an athletic population. In this study, the back squat 1-RM improvement was nearly three times higher for the combined group. In addition, the bench press increase was doubled for the combined group. Furthermore, the combined group’s lower body average power increase was nearly three times better than the free-weight only group. Anderson et al. (2008) used the 80/20 ratio that was suggested by earlier studies. Anderson’s study demonstrated that combined elastic band and free-weight exercise was a viable option to use to train experienced lifters. That study also demonstrated that the group using the combination exercise experienced slightly less resistance at the bottom of the movement when the joints may be under maximal stress in free-weight training. Thus, band training may also provide reduced risk in back squat and bench press exercises.

Triber et al. (11) concluded that the combination of elastic and free-weight exercise provided beneficial effects on strength and functional performance in college-level tennis players. The experimental group experienced significant gains in both internal and external rotation torque. That same study concluded that an elastic band training program strengthened the rotator cuff muscles of collegiate baseball pitchers (11). Band training has the unique ability to target specific muscles, which can be beneficial for numerous sports teams. Using a combination of elastic band and free-weight exercise can also mimic the strength curve of most muscles better. A muscle’s strength curve denotes the alteration in strength of that muscle during the entire range of motion in a certain movement (13). Along these lines, it has been reported that combined elastic and free-weight exercises provided greater force during the first 25 percent of the eccentric phase and last ten percent of the concentric phase of a lift as compared to free-weights alone (3).

Elastic tension has also been reported to impact the neuromuscular performance. Page and Ellenbecker (6) claim that elastic band exercise imparts a higher neuromuscular control resulting in improved balance, gait and mobility. As stated, the gains resulting from the combination of elastic band and free-weight exercise are abundant and the use of this treatment is growing among professionals; though the acute effects on power have yet to be documented. Therefore, the purpose of the present investigation was to determine how if at all, combined elastic tension applied to a normal bench press training session affects power.

### Methods

The present investigation was approved by the local institutional review board and employed a within subjects design, with random assignment. The participants gave informed consent prior to participating and included: four male (age: 20.5±2.1yrs, height:1.82±0.07m, weight: 112.68±15.03kg) and four female (age: 19.9±1.7yrs, height: 1.76±0.05m, weight: 100.78±28.47kg) college track and field athletes involved in the throwing events (shot put, discus, hammer). The participants performed in a counterbalanced within-subjects design, two bench press training sessions that consisted of 3 sets of 5 repetitions at 85% of their 1-RM. The athletes had recently undergone a 1-RM assessment as part of practice; which was supervised by the research team and the weight selected for the treatment was based on this assessment.

One session consisted solely of resistance provided by a standard Olympic barbell with plates, which equated to 85% of the athletes previously determined one repetition maximum, the second session consisted of combined resistance where 85% of 1 RM was derived from 85% tension provided by an Olympic barbell with plates and 15% provided by Elastic Bands (Jump Stretch Inc., Youngstown, OH.). The 85% free weight and 15% elastic tension treatment was based upon previous research performed in our laboratory that suggested that this was an appropriate split for effective training between the isotonic tension provided via free weight and variable resistance by the elastic bands (2).

Immediately after the training sessions, the participants were asked to bench press 50% of 1RM at maximum velocity, in order to generate the greatest amount of watts possible. The participants performed two lifts at 50% of 1RM after each treatment, separated by a rest period of 90 seconds. The two sessions were separated by a 72 hour wash out period as to avoid undo fatigue affecting the results. The order of treatment was randomized so that half the participants lifting under the combined elastic band and free weight condition went first, with the other half lifting in the free weight only condition went first. During the second visit the participants lifted under the other treatment.

Instruments

Power was measured twice, with a minimum of 90 sec rest between measurements at 50% of 1-RM, following the conclusion of both lifting sessions, using a Max Factor tether type potentiometer (Max Rack Inc, Columbus, OH.). This instrument demonstrated reliability in pilot testing with Intraclass correlations of greater than 0.99 on repeated measures testing.

Statistical Analysis

Results of the present investigation were analyzed via a treatment (Combined free-weight and elastic tension vs. free weight only) by time (attempts 1,2) repeated measures Ancova (covaried for gender). The inclusion of the covariate was necessary based upon the natural differences in strength that existed between the male and female athletes in the present investigation. All statistical tests were performed with the use of a modern statistical software package (SPSS ver 17.0 for Macintosh). The criteria for statistical significance was set a priori at alpha <0.05.

### Results

Intraclass correlation analysis suggested good reliability on all measures for the present investigation (>0.99). Analysis performed via repeated measures Ancova (Treatment by Time covaried for gender) revealed a significant main effect for Time (F= 5.951, p=0.05) and a significant two way interaction for Treatment*Time (F=54.093, p<0.001).

The subjects initial measurements of power immediately following the training session was higher in the combined elastic treatment (437.5+34.89 watts) as compared to the free-weight only condition (391.88+41.01 watts). (see Table 2)

### Discussion

The current study extended previous studies by using both male and female participants that were college track and field athletes. All 8 subjects were involved in throwing events and therefore trained regularly with resistance exercises such as a bench press with the involvement of both elastic and free-weight training. The present investigation revealed a differential response in power following training sessions that utilized combined elastic and free weight tension as compared to free weight only.

Affects have been seen with a combination of elastic band and free-weight tension in the past. Bellar et al. (2011) reported around a 5lbs increase in 1RM bench strength after only 3wks of training with a combination of elastic bands and free weights. Anderson et al. (2008) reported changes in power production with athletes who utilized a combination of elastic and free-weight tension. The current study builds upon these findings and notions by experts in the field (Mannie 2005, Simmons, 2007) who suggest adding elastic tension can have acute effects. Based upon these data, during the course of an upper body lifting session it appears that athletes are able to maintain more power when training with a combination of elastic tension and free-weights.

The recorded power was notably different between the sessions that used a combination of an Olympic barbell and an elastic band and those that only used an Olympic barbell. The difference between the two separate 50% 1-RM power assessments for the combination group was only 1 watt, while the difference between the free-weight only group was close to 46 watts. This finding is notable as the attempts post combined training were essentially identical, whereas the first attempt under the free weight only treatment was lower than the second by 46 watts. This suggests that the free weight only treatment may have acutely resulted in a reduction in power production capability that was washed out by the second attempt. The first power output between the two treatments differed by almost 35 watts. After the 90 second rest, the second power output of each group was extremely close, differing by 10 watts. The initial measurement of power following the training was higher for the group that performed the bench press with the combination of the elastic band and the free-weight, but the two different groups seemed to retain the same amount of power at the end. The overall results of the study suggest that in the immediate period following bench press training, athletes who use combined elastic and free weight tension will be better suited to activities that rely on greater power production, such as throwing a shot put. This finding is important as coaches often pair activities in complex training schemes.

### Conclusions

The present investigation has shed light onto the acute affects of combining elastic tension with free-weight exercise on power production in athletes. Further research should continue to explore the effects of power, strength, rate of force development, velocity, eccentric activity and neuromuscular stimuli when performing combination activities with both elastic band and free-weight exercises. It is plausible that given the data from the present investigation, chronic adaptations to training with elastic resistance in combination with free-weights may have been caused by lesser reductions in power during acute training sessions. If this acute effect does manifest in this fashion, then it would have ramifications as to the training volumes athletes utilize with this modality to gain maximum adaptations. The current research on the topic of combining elastic and free weight training is very limited and mostly focused on the back squat and bench press. Hence, investigations and applications on diverse exercises should be considered in forthcoming research.

### Applications In Sport

Based upon the present investigation, it would immediately appear at the conclusion of a training session that athletes retain more power production post combined elastic and free-weight training as compared to free-weight training alone. This information is potentially beneficial to professionals who work with athletes, as complex training is often incorporated into the program design. This form of training often involves the performance of a skill related activity post-resistance training bout.

### Tables

#### Table 1
Participant characteristics given in Means ± SD.

Gender Age (yrs) Height (m) Weight (kg)
Male (n=4) 20.5 ± 2.1 1.82 ± 0.07 112.68 ± 15.03
Female (n=4) 19.9 ± 1.7 1.76 ± 0.05 100.78 ± 28.47

#### Table 2
Watts Produced by Treatment and Attempt given in Means ± SD.

Treatment Attempt 1 (Watts) Attempt 2 (Watts)
Combined Elastic and Free-weight 426.5 ± 257.0 427.5 ± 229.2
Free-weight Only 391.9 ± 206.3 437.5 ± 242.6

### References

1. Anderson, C.E., Sforza, G.A., Sigg, J.A. (2008) The effects of combining elastic and free weight resistance on strength and power in athletes. Journal of Strength and Conditioning Research, 22(2), 567-574.
2. Bellar, D., Muller, M., Ryan, E.J., Bliss, M.V., Kim, C-H, Ida, K Barkley, J.E., Glickman, E.L. (2011) The Effects of Combined Elastic and Free Weight Tension vs Free Weight Tension on 1 RM Strength in the Bench Press. Journal of Strength and Conditioning Research, 25(2), 459-463.
3. Israetel, M.A., McBride, J.M., Nuzzo, J.L., Skinner, J.W., Dayne, A.M. (2010) Kinetic and kinematic differences between squats performed with and without elastic bands. Journal of Strength and Conditioning Research, 24(1): 190-194.
4. Mannie K. Strike up the band training, the benefits of variable resistance. (2005) Coach Athletic Director, 75, 8-13.
5. Neelly, K., Carter, S.A., Terry, J.G. (2010) A study of the resistive forces provided by elastic supplemental band resistance during the back squat exercise: a case report. Journal of Strength and Conditioning Research, in press. Epub ahead of print retrieved June 20, 2011, from <http://journals.lww.com/nscajscr/Abstract/2010/01001/A_Study_Of_The_Resistive_Forces_Provided_By.119.aspx>
6. Page, P., & Ellenbecker, T. S. (2005). Strength Band Training. In Strength Training with Elastic Resistance [Excerpt]. Retrieved from Farnsworth Group website: <http://www.champaign411.com/sports_fitness/excerpts/strength_training_with_elastic_resistance>
7. Shoepe, T.C., Ramirez, D.A., Almstedt, H.C. (2010) Elastic band prediction equations for combined free-weight and elastic band bench presses and squats. Journal of Strength and Conditioning Research, 24(1), 195-200.
8. Simmons, L. (2007, March 5). Advanced programs for beginners. In Elite Fitness Systems [Article]. Retrieved March 22, 2011, from Elite Fitness Systems website: <http://totalphysiqueonline.com/2007/03/05/advanced-program-for-beginners/>
9. Simmons, L. (2009, July 15). Training athletes vs. full meet powerlifters [Web log post]. Retrieved from <http://www.wannabebig.com/training/powerlifting-and-functional-strength-for-athletics/q-a-with-westside-barbells-louie-simmons/>
10. Stevenson, M. W., Warpeha, J. M., Dietz, C. C., Giveans, R. M., & Erdman, A. G. (2010). Acute effects of elastic bands during the free-weight barbell squat exercise on velocity, power, and force production. Journal of Strength and Conditioning Research, 24(11), 2944-54.
11. Treiber, F. A., Lott, J., Duncan, J., Slavens, G., & Davis, H. (1998, July). Effects of theraband and lightweight dumbbell training on shoulder rotation torque and serve performance in college tennis players. Am J Sports Med, 26(4), 510-15.
12. Wallace, B.J., Winchester, J.B., McGuigan, M.R. (2006) Effects of elastic bands on force and power characteristics during the back squat exercise. J. Strength Cond. Res., 20(2), 268-27.
13. Woodrup, J. (2008). Band Training for Explosive Vertical Gains. In Vertical jumping [Article]. Retrieved March 22, 2011, from Vertical Jumping website: <http://www.verticaljumping.com/band_training.html>

### Corresponding Author

David Bellar
225 Cajundome Blvd
Department of Kinesiology
University of Louisiana Lafayette
<dmb1527@louisiana.edu>

### Author Bios

#### Sara Prejean

Sarah Prejean is an undergraduate student studying exercise science in the department of kinesiology at the University of Louisiana at Lafayette

#### Lawrence Judge

Lawrence Judge is an associate professor and coordinator of the graduate coaching program at Ball State University. Dr. Judge has a long-established background in coaching track and field athletes and an extensive research background in coaching behavior, moral issues, and competitiveness versus participation in athletics, specifically in youth sports.

#### Tiffany Patrick

Tiffany Patrick is an undergraduate student studying exercise science in the department of kinesiology at the University of Louisiana at Lafayette

#### David Bellar

David Bellar is an assistant professor and director of the human performance lab in the department of kinesiology at the University of Louisiana at Lafayette. Dr. Bellar has a background in coaching track and field athletes, and researching performance attributes within this population.

2013-11-22T22:55:06-06:00January 4th, 2012|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management|Comments Off on Acute Effects of Combined Elastic and Free-weight Tension on Power in the Bench Press Lift

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

### Abstract

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

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

### Introduction

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

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

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

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

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

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

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

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

Ambady and Rosenthal (9) researched intuitive judgments on teacher

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

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

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

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

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

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

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

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

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

### Methods

#### Participants

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

#### Instrumentation

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

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

#### Procedures

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

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

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

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

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

#### Data Analysis

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

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

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

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

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

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

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

### Results

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

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

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

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

### Discussion

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

### Application in Sport

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

### Tables

#### Table 1
Descriptive Statistics

M SD Skewness (SE = 0.17) Kurtosis (SE = 0.34) M SD Skewness (SE = 0.17) Kurtosis (SE = 0.34)
Target Accuracy
Consensus Self-Other Agreement
Effectiveness Attribute -.27 0.25 0.65 0.69
Acceptance -.33 0.28 0.65 0.65 .03 0.30 -0.57 0.33
Active -.16 0.25 0.10 -0.44 .16 0.28 -0.08 0.30
Attentive .23 0.27 -0.69 0.91 .11 0.28 -0.20 0.03
Competent -.15 0.23 0.69 1.40 .19 0.28 -0.15 -0.19
Confidence .15 0.25 -0.07 0.18 -.05 0.28 0.23 -0.12
Dominance -.11 0.24 0.30 1.10 .27 0.25 -0.90 -0.05
Empathic -.17 0.28 0.45 0.56 .42 0.32 -1.20 0.60
Enthusiastic .45 0.24 -0.99 1.50 -.11 0.30 0.64 1.80
Honesty -.07 0.27 0.25 -0.10 -.08 0.26 0.33 0.42
Likeability .20 0.23 -0.22 0.21 .01 0.29 0.48 0.02
Optimistic .00 0.23 0.13 0.15 .18 0.28 -0.59 0.43
Professional -.09 0.25 0.02 -0.35 .22 0.27 -0.42 0.10
Supportive -.17 0.25 0.35 0.11 .01 0.27 0.00 0.10
Supportive -.17 0.25 0.35 0.11 .01 0.27 0.00 0.10
Warm -.13 0.28 0.16 -0.10 -.09 0.29 0.23 -0.08

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

Gender
Males Females
Attributes M SD M SD
Effectiveness -.28 0.28 -.26 0.23
Acceptance -.33 0.30 -.33 0.26
Active -.18 0.23 -.14 0.25
Attentive .20 0.30 .25 0.24
Competent -.14 0.24 -.16 0.23
Dominance -.12 0.25 -.11 0.23
Empathic -.18 0.33 -.17 0.24
Enthusiastic .40 0.24 .48 0.23
Honesty -.08 0.30 -.05 0.24
Likeability* .14 0.23 .25 0.22
Optimistic -.04 0.25 .03 0.22
Professional -.13 0.26 -.07 0.24
Supportive -.18 0.28 -.15 0.22
Warm -.13 0.30 -.13 0.27

* p < .01

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

Race
African-Americans Caucasians
Attributes M SD M SD
Effectiveness -.26 0.25 -.30 0.25
Acceptance -.31 0.29 -.39 0.24
Active -.31 0.29 -.39 0.24
Attentive .24 0.28 .20 0.22
Competent -.15 0.22 -.13 0.26
Dominance -.09 0.24 -.17 0.18
Empathic -.17 0.28 -.19 0.29
Enthusiastic .45 0.25 .42 0.23
Honesty -.06 0.28 -.09 0.23
Likeability .19 0.23 .26 0.24
Optimistic -.02 0.23 .06 0.23
Professional -.10 0.26 -.07 0.22
Supportive -.18 0.25 -.15 0.25
Warm .14 0.28 -.11 0.27

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

Attributes df F p
Acceptance 4 0.85 0.50
Active 4 0.29 0.89
Attentive 4 0.96 0.43
Competent* 4 3.57 0.01
Confidence* 4 3.67 0.01
Dominance 4 0.31 0.87
Empathic 4 0.32 0.86
Enthusiastic 4 3.22 0.01
Honesty 4 0.70 0.59
Likeability 4 1.14 0.23
Optimistic 4 0.94 0.45
Professional 4 0.71 0.59
Supportive 4 1.51 0.20
Warm 4 1.45 0.22

* p < .01

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### Corresponding Author

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

2013-11-22T22:55:49-06:00January 4th, 2012|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on Coach Effectiveness and Personality Assessments: An Exploratory Analysis of Thin Slice Interpersonal Perceptions
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