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

Throwing Techniques for Ultimate Frisbee

### Abstract

The goal of this study was to determine if certain throwing techniques for the sport of Ultimate Frisbee were advantageous relative to other techniques. The defense can attempt to force a thrower to utilize a specific throw; knowing the advantages of different throws can influence a defender’s decision to force the thrower to use a certain throw.

Motion capture was used to monitor the flight of a disc (Discraft Ultrastar 175g) for three throwing techniques. The two main groups of throws were backhand (BH) and forehand (FH) throws, with the forehand throws divided into a closed forehand grip (CF) and a split forehand grip (SF). Sixteen participants were recruited with experience ranging from 3 years to 8 years based on survey. Throws were analyzed with regards to linear velocity, angular velocity, precession, and accuracy. Players threw a total of 45 throws: five throws for all combinations of the three throwing techniques combined with three objectives: accuracy, maximum spin, and maximum velocity. The order of the nine throwing groups was randomized.

Throws were analyzed for linear velocity, angular velocity, precession, and accuracy. Linear velocity was calculated by measuring the distance traveled in the first 0.02 seconds of flight, and angular velocity was measured by calculating the time required for four unique points on the disc to complete one rotation. Precession was measured by calculating the average angular deviation from the average normal plane of the disc, and accuracy was measured by the distance between the center of the disc and the target at closest approach using a quadratic fit to the known flight path.

There was a very strong linear correlation between linear velocity and angular velocity. There was no difference in linear velocity between backhand and forehand throws, although the closed grip forehand had a higher linear velocity than the split grip forehand. Backhand throws had higher angular velocities than forehand throws for a given speed; there was no difference in angular velocity between closed grip and split grip forehand throws. Backhand throws had less precession than forehand throws, and there was no difference in precession between closed grip and split grip forehand throws. There were no statistically significant differences in accuracy between any of the throws.

These results show that backhand throws tend to have more spin and wobble less making the backhand a superior throw. Throws with less spin have greater instability; as a defender, forcing the thrower to utilize a forehand throw would result in a throw with less stability than a backhand throw. Forehand throws did not perform better than backhand throws for any category tested.

Additionally, new players are often taught that the split-grip forehand is a bad throw, and that the closed-grip forehand should be used instead. The results show that the split-grip forehand performs on par with the closed-grip forehand with the exception of maximum velocity. New players should not be discouraged from using a split-grip forehand while learning the mechanics of the forehand, as the only disadvantage is a slight decrease in maximum velocity.

**Key Words:** Forehand, Backhand, Flick, Frisbee

### Introduction

In the sport of Ultimate Frisbee, players use two primary throws: backhand and forehand. My hypothesis, from personal experience, is that backhand throws will wobble less, have less spin, be more accurate, and travel faster than forehand throws. The aim of this study was to determine if one throw had a comparative advantage with respect to linear velocity, angular velocity, precession, and accuracy. Also, the split-grip forehand is often thought of as an inferior throw relative to the closed-grip forehand. The closed-grip forehand is expected to outperform the split grip forehand.

Players must be able to utilize both throws as the defenders can force players to throw one way or the other by positioning their bodies on a certain side of the thrower. As a defender, knowing advantages and disadvantages of each throw can factor into defensive strategies to increase the chances of a disc being thrown with sub-optimal flight characteristics as a result of differing throwing techniques. This may cause a higher incidence of turnovers due to incomplete passes.

Previous research has shown that a disc thrown with less angular velocity will result in a throw with less stability (1). Therefore, whichever throw has higher angular velocities will be the more stable throw and will be more likely to reduce turnovers. The angular velocity of a disc in flight does not have a significant effect on lift and drag coefficients (2).

### Methods

#### Participants

Participants were recruited by open invitation to the St. Louis Ultimate Association and both the Washington University in St. Louis men’s and women’s club ultimate teams. Participants completed a questionnaire to determine experience and skill level. The skill level was a ten point scale with 1=Beginner, 4=Recreational, 7=Competitive college, and 10=Elite. Of the participants, 5 were placed in the ‘elite’ category and 11 were placed in the ‘non-elite’ category. Experience was divided into seven categories: 0-1 years, 1-2 years, 2-4 years, 4-5 years, 5-8 years, 8-15 years, and 15+ years (see Table 1). No participants were excluded from the study and all participants performed the same number of each test.

Testing occurred at the Washington University School of Medicine Human Performance Lab after the participant signed the IRB-approved informed consent form. No financial compensation was provided for participating in the study.

#### Data Collection

Data were collected using a Motion Analysis system consisting of six high-speed Eagle Digital Cameras at 250 Hz and Cortex software. Three cameras were located above and behind the thrower (relative to the direction of the throw) with one camera directly behind the thrower, and the other two cameras located a couple meters to either side. These cameras were focused on the latter portion of the throw. The other three cameras were located in the same arrangement, but above the target, and focused on the volume around the thrower. This set up provided the largest capture volume so the throw would be in view of at least two cameras at all times. Seven reflective markers were used. One marker was placed on the thumbnail of the throwing hand with one marker in the center of the disc and three markers placed approximately five inches from the center marker in a triangular formation. Additionally, one marker was placed adjacent to one of the perimeter markers to provide an asymmetric model. The final marker was placed on a target, used to evaluate accuracy. Participants stood approximately 2.5 meters from the target and net. Participants threw 45 total throws consisting of nine categories: Backhand Accuracy (BH_A), Backhand Spin (BH_S), Backhand Velocity (BH_V), Closed Forehand Accuracy (CF_A), Closed Forehand Spin (CF_S), Closed Forehand Velocity (CF_V), Split Forehand Accuracy (SF_A), Split Forehand Spin (SF_S), and Split Forehand Velocity (SF_V). The order was randomized for each participant prior to data collection. For each category, participants had two practice throws followed by three throws, which were used for analysis.

#### Variables

The following variables were calculated: linear velocity, angular velocity, precession, and accuracy. For each throw, data processing began when the marker on the disc closest to the thumb marker was 0.3 meters away from the thumb marker indicating the disc had left the thrower’s hand. Linear velocity was calculated by computing the distance traveled over the first 0.02 seconds (5 frames). Angular velocity was calculated by tracking the time required for five different pairs of markers to complete one cycle. A cycle began when one marker’s y-coordinate crossed the other marker’s y-coordinate. The cycle ends after the first marker’s y-coordinate crosses the second marker’s y-coordinate twice. The angular velocity was calculated by averaging the times for each of the five pairs. Precession was calculated by calculating the average angular deviation from the average plane of the disc. The angles were calculated by taking the cross product of two vectors defined by two of the perimeter markers and the center marker. Accuracy was calculated by measuring the closest approach of the projected flight path to the center of the target. The flight path projection was calculated using a quadratic fit to the known flight path.

#### Statistical Analysis

The data were analyzed using SPSS Statistics® and Microsoft Excel®. Two sample paired t-tests were used to compare different throws and two sample t-tests (assuming equal variance) were used to compare elite vs. non-elite players. The significant threshold employed was p < 0.05. Regression analysis was used to determine whether correlations existed between linear velocity and both angular velocity and precession. Since angular velocity varied by linear velocity, the ratio of angular velocity to linear velocity was used to determine which throw achieved the highest angular velocities.

### Results

#### Elite vs. Non-Elite

The only difference found between elite and non-elite players was the maximum speed of throws: elite players had higher maximum velocities than non-elite players. There was no difference in accuracy, precession, or angular velocity to linear velocity ratios. With the exception of maximum velocity, no differences were found between elite and non-elite players; as a result, throw comparisons included both elite and non-elite players (see Table 2).

#### Throw Comparison

No significant differences were found between backhand and closed grip forehand or backhand and split grip forehand velocities. Closed grip forehands were found to have higher maximum velocities than split grip forehands. Backhand throws had an average maximum velocity of 20.1 m/s, closed grip forehand throws had an average maximum velocity of 20.6 m/s, and split grip forehands had an average maximum velocity of 19.2 m/s.

Backhand throws were found to have a higher angular velocity / linear velocity ratio than both closed grip and split grip forehands by more than 4 RPM per meter per second. No differences were found in the angular velocity / linear velocity ratio for closed grip forehands vs. split grip forehands (see Figure 1).

When participants were instructed to throw for maximum spin, throws were found to have higher angular velocity to linear velocity ratios than throws for accuracy and velocity; differences of greater than 5.5 RPM per meter per second were found for all three grips (see Table 3).

No correlation was found between velocity and precession.

No differences were found in accuracy for backhand, closed grip forehand, or split grip forehand throws, with average distances varying by less than 0.03 meters (1.25 inches). Backhand throws were found to have less precession than both closed grip forehands and split grip forehands by more than 35%. No differences were found between closed grip forehands and split grip forehands (see Figure 2)

Strong linear correlations were found between angular velocity and linear velocity when considering throws for maximum velocity and accuracy. values of greater than 0.9 were found for all three categories.

### Discussion

This study has limits that should be taken into consideration. First of all, several of the subjects have learned their throwing techniques from the same group of players, so certain efficiencies or inefficiencies in technique may affect results. Secondly, all participants use a closed-grip forehand; closed-grip forehand throws have been practiced by the participants, whereas split-grip forehand throws have not been practiced. Additionally, participants were throwing in a room with expensive equipment; participants may have altered their throws to ensure they hit the net. Accuracy data may have been inconclusive because the target was located 2-3 meters from the thrower. Also, certain throws may be more accurate for shorter distances and less accurate for longer distances. Limitations of being in a confined space may have prevented any significant results related to accuracy. The cameras also had a difficult time of tracking the higher velocity throws (18+ m/s). As a result, flight paths had to be reconstructed from partial data.

Backhand throws appear to be superior to forehand throws due to the higher angular velocity (see Figure 1) and less precession (see Figure 2) than forehand throws. Morrison found that angular velocity increases the stability of the disc (1) as the angular momentum provides gyroscopic stability, so backhand throws should be more stable than forehand throws. There were no differences in maximum linear velocity or accuracy between backhand and forehand throws. The only difference between the two forehand throws was that closed-grip forehands were thrown faster than split-grip forehands (see Table 3). There were no differences in the angular velocity to linear velocity ratio, precession, or accuracy for split-grip and closed-grip forehands.

Angular velocity can be predicted accurately by knowing linear velocity and intent of throw (maximum linear velocity, angular velocity, or accuracy). No predictors of precession were found in the study.

No previous studies have compared flight characteristics of forehand and backhand throws.

### Conclusion

Based on the results obtained, it would be advantageous to force the opposing team to throw forehand throws. Doing so results in throws with less stability as a result of less angular momentum, and more precession. It is possible that lower angular velocity and higher precession could lead to a decrease in distance traveled and stability. Additionally, higher precession values could expose the disc to more drag, causing the wind to affect the throw more.

Based on the results for forehand throws, the only advantage to throwing with a closed grip is the maximum attainable velocity. By using a closed grip, participants did not show any improvement in angular velocity or precession. Thus, the only instance where a closed-grip forehand is advantageous relative to a split-grip forehand is when a player is trying to throw for distance.

The hypothesis that backhand throws would wobble less was shown to be true and that backhand throws would have less spin was shown to be false. The hypotheses that backhand throws would be more accurate and travel faster were not supported by any results.

Overall, it appears that it would be advantageous to force the offense to throw more forehand throws than backhand throws and new players should not be discouraged from learning to throw a split-grip forehand while learning throwing mechanics.

### Applications In Sport

From a strategic standpoint, teams can change defensive strategies to force the opposition to use an inferior throw. Additionally, new players can be taught advantages and disadvantages of different grips. New players are often taught that the split-grip forehand is inferior to the closed-grip forehand, although the only disadvantage of the split-grip forehand is the maximum speed of the throw. For new players, if the split-grip is more comfortable than the closed grip, they will achieve the same angular velocity and precession as a closed-grip throw.

### Acknowledgements

I would like to sincerely thank Dr. Jack Engsberg for making this research possible. He welcomed my research proposal with open arms, having nothing to gain from the study. Jack has a true passion for helping others and I am extremely fortunate to be one of the many persons he has helped. He has guided me through every step of the research process offering invaluable advice along the way. Jack, thank you for being an amazing mentor and great friend.

### REFERENCES

1. Morrison, V.R. (2005). The Physics of Frisbees. Mount Allison University Physics Department.
2. Hummel, Sarah Ann (2003). Frisbee Flight Simulation and Throw Biomechanics. Office of Graduated Studies of the University of California Davis.

### TABLES

#### Table 1
Participant Survey

Age Years Played Skill Level
22 4.6 8.1
(1.9) (2.5) (1.2)

**Note:** Standard Deviations appear in parentheses below the means.

#### Table 2
Comparison of Elite and Non-Elite Players

BH FH
Elite Non-Elite Elite Non-Elite
Maximum Velocity (m/s) 21.2 (3.0)** 17.7 (2.4) 20.7 (2.7)** 18.3 (1.5)
Accuracy (m) 0.24 (0.13) 0.33 (0.15) 0.25 (0.20) 0.32 (0.19)
Precession (degrees) 2.3 (1.5) 2.6 (1.1) 3.8 (2.0) 3.7 (1.8)
Angular Velocity to Linear Velocity Ratio (RPM per m/s) 44.1 (9.7) 48.0 (6.9) 38.9 (6.5) 38.1 (6.2)

**Note:** BH is backhand, FH is forehand.

** Denotes significantly different from non-elite (p < 0.05)

#### Table 3
Throw Comparison

BH CF SF
A V S A V S A V S
Maximum Velocity (m/s) 20.1 (3.2) 20.6 (2.6)^ 19.2 (2.5)**
Accuracy (m) 0.30 (0.14) 0.28 (0.18) 0.27 (0.21)
Precession (degrees) 2.4 (1.3)**^ 3.7 (1.7) 3.8 (2.2)
Angular Velocity to Linear Velocity Ratio (RPM per m/s) 40.2 (3.4)‡ 40.4 (2.9)‡ 47.8 (11.5)‡ 36.5 (3.1)†‡ 34.8 (2.1)‡ 43.8 (11.3)† 37.3 (2.8)†‡ 36.0 (2.5)‡ 42.9 (7.7)†
42.8 (7.9)**^ 38.4 (7.8) 38.7 (5.7)

**Note:** BH is backhand, CF is closed grip forehand, and SF is split grip forehand. A is accuracy, V is velocity, and S is spin.

** Denotes significantly different from CF (p<0.05), ^ Denotes significantly different from SF (p<0.05)

† Denotes significantly different from V (p<0.05)

‡ Denotes significantly different from S (p<0.05)

### Figures

#### Figure 1
Graph of Angular Velocity vs. Linear Velocity

![Figure 1](/files/volume-15/453/figure-1.jpg)

#### Figure 2
Graph of Precession vs. Linear Velocity

![Figure 2](/files/volume-15/453/figure-2.jpg)

### Corresponding Author

Jack R. Engsberg, PhD
Washington University School of Medicine: Human Performance Laboratory
4444 Forest Park, Campus Box 8505
St. Louis, MO 63108
<engsbergj@wustl.edu>
314 – 286 – 1632

### Main Author

Evan Winograd
Washington University School of Engineering and Applied Science
6985 Snow Way Drive Box 6861
St. Louis, MO 63130
<ewinograd@go.wustl.edu>
713-805-8609

### Author Bios

#### Evan Winograd
Evan Winograd is an undergraduate student studying Mechanical Engineering at the Washington University in St. Louis School of Engineering and Applied Science.

#### Jack Engsberg
Jack Engsberg is a Professor of Occupational Therapy and Neurosurgery at Washington University School of Medicine in St. Louis. His work in the Human Performance Laboratory focuses on rehabilitation for persons with disabilities including cerebral palsy, stroke, scoliosis, spinal deformity, spinal cord injuries, and amputations using high-speed motion capture systems, force plates, electromyography, and an isokinetic dynamometer.

2013-11-22T22:54:13-06:00January 5th, 2012|Sports Coaching|Comments Off on Throwing Techniques for Ultimate Frisbee

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

Qualitative Analysis of International Student-Athlete Perspectives on Recruitment and Transitioning into American College Sport

### Abstract

Recruiting international athletes is a growing trend in American intercollegiate sport, and international student-athletes play an increasingly prominent role in NCAA competition. This research answers the following questions regarding the recruitment of international student-athletes and their transition to college life: (1) what is the most difficult aspect of the international university experience?; (2) what do international athletes identify as the most important factor for a successful transition to American college?; (3) how did international athletes hear about athletic opportunities in the United States; (4) what advice would current international athletes give international athletes considering a move to the United States to participate in intercollegiate sport?; and (5) what would the athletes have done had they not played college sports in the United States? The researchers solicited the assistance of CHAMPS/Life Skills coordinators at 15 Division I schools who distributed surveys to student-athletes, who in turn completed the survey, sealed it in an envelope, and returned in to the coordinator. A total of 355 athletes completed the survey, including 192 international athletes. Homesickness and adjustment to the U.S. culture were identified as the most difficult aspects of the university experience for international athletes, while the most important elements to a successful transition for international athletes were a strong support system from teammates and coaches and also from friends and family in their native country. Only one-fourth of respondents learned about athletic opportunities from coaches in the U.S., while one-fourth of the respondents learned about these opportunities from friends, family, and other athletes. The top piece of advice given by respondents was to realize that playing sports in the U.S. will require important traits like focus, dedication, hard work, and persistence in order to succeed. The results of this study highlight the importance of transitioning international athletes into college life. Once international athletes are on campus, a member of the athletic department staff should oversee the athlete’s transition into college life, focused on combating the top three challenges identified in this research: homesickness, adjustment to U.S. culture, and language. This staff member should serve as a liaison between athletic department personnel and other campus resources to facilitate a smooth transition.

**Key Words:** international student-athletes, recruiting, transition to college

### Introduction

Recruiting athletes from outside of the United States is a growing trend in college athletics as international student-athletes play an increasingly prominent role in NCAA competition (6, 9, 22). For coaches, who must recruit talented athletes in order to be successful, “the pressures to win, and the penalties for losing, are exacting. Many Division I coaches’ jobs are predicated on the strength of their programs, causing them to recruit the best talent they can find, in many cases from the international pool” (19, p. 860). Evidence of a worldwide search for talent is found in the 17,653 international student-athletes that competed in NCAA competition during the 2009-10 school year, a large increase from the just under 6,000 that competed a decade prior (11). Among Division I schools, over one-third of the male and female athletes in both tennis and ice hockey, and over one-eighth of male and female golfers, were born outside of the United States (11). In addition to increasing participation numbers, international athletes have dominated in individual sports like tennis and golf, and led teams to championship performances (13, 22). However, international athletes face many challenges in adjusting to the language, coursework, dorm life, food, cultural expectations, coaching, paperwork, and the style of play in the United States. As international athletes continue to leave their mark on NCAA sports, coaches and administrators benefit from understanding what difficulties come with transitioning to life as a student-athlete in the U.S. and how international athletes learn about the recruitment process.

Previous research has examined the adjustment process for both international students and international athletes to college. While researchers have noted that a lack of groups with which to socialize is a problem for many international students (7, 10, 20), international athletes have the advantage of being immediately placed within a team structure (14). However, athletes may still face similar obstacles to a successful transition including culture shock, cultural differences, academic adjustment, homesickness, discrimination, and contentment (5). Ridinger and Pastore (17) were the first to create a model of adjustment for international student-athletes, which included four antecedent factors (personal, interpersonal, perceptual, and cultural distance), and five types of adjustment (academic, social, athletic, personal-emotional, and institutional attachment), resulting in two outcomes (satisfaction and performance) to define successful adjustment to college.

Researchers have also examined the recruitment of international athletes. Not only can coaches create winning programs through the recruitment of international athletes, but coaches can also maintain successful teams with international athletes through the establishment of talent pipelines (3-4, 21). Bale (3) identified talent pipelines in which concentrations of athletes from certain countries were found in particular NCAA institutions, with coaches hoping that friend-to-friend recruiting will result in attracting elite athletes from a particular foreign country. Bale (3) noted that institutions unable to compete for homegrown talent, due to lack of prestige or unattractive campus location, established talent pipelines with a foreign country. For example, a talent pipeline of elite track and field stars from Kenya was found at schools like University of Texas El Paso and Washington State University, and a pipeline of track talent from Nigeria was identified at the University of Missouri and Mississippi State University (3). Talent pipelines are an important recruiting strategy, particularly when coaches are unable to compete for local athletes or local talent is not available for certain sports (21).

This research seeks to provide answers the following questions regarding the recruitment of international student-athletes and their transition to college life: (1) what is the most difficult aspect of the international university experience?; (2) what do international athletes identify as the most important factor for a successful transition to college?; (3) how did international athletes hear about athletic opportunities in the United States; (4) what advice would current international athletes give international athletes considering a move to the United States to participate in intercollegiate sport?; and (5) what would the athletes have done had they not played college sports in the United States?

### Methods

The sample for this study included N = 355 athletes from 15 NCAA Division I institutions, including n = 192 international athletes. Schools selected for this study were based on a need to collect data from purposive clusters of Division I institutions, given certain factors may influence international student-athletes’ experiences at their United States institution such as school size, the size of the community within which the school is located, and the geographic location of a school (3). Seven schools were members of the Football Bowl Series (FBS) conferences, while eight were not. Eleven conferences were represented in the study. Eight schools were located in large metro areas with populations over 400,000, while seven were located in communities with populations under 170,000. Six schools were located in the eastern third of the U.S., six were located in the Midwest, and three were located in the western third of the country.

The researchers solicited the assistance of CHAMPS/Life Skills coordinators from the 15 schools via phone to see if they would agree to participate in the study. The researchers then collected the names of all international student-athletes listed on website rosters. The coordinators were instructed to distribute the surveys to the student-athletes, who in turn completed the survey, sealed it in an envelope, and returned in to the coordinator. Participation in the survey was voluntary and a letter indicating the participant’s rights were included, per the approval obtained by the university Human Subjects Review Committee.

A total of 192 athletes representing 57 countries responded to the survey for a response rate of 39.6%. The top three countries represented were: Canada, 24%; England, 8.3%; and Puerto Rico, 7.8%. Males accounted for 45% of the sample and females accounted for 55%. The responses from the open-ended questions in the International Student-Athlete Survey were content analyzed. Two raters independently examined the data and codes were developed to categorize written responses (18). To test intercoder reliability, the coders independently examined 20% of the sample. The codebook and coding protocol were clearly understood, as the correction for chance agreement (Scott’s Pi) exceeded .8 for all but one question, which yielded an acceptable .77 (23).

In addition to frequency counts for each question, chi square was utilized to examine differences between demographic variables, including: gender, native area of origin (Canada, Europe, South America), length of time in the United States (0-2 years, 2.5 to 3.5 years, 4+ years), type of sport (team or individual), class standing (freshman/sophomore and junior/senior), whether or not the athlete used a campus visit, number of schools considered (0-2, 3+), and whether or not the athletes had a full scholarship.

### Results

Ten variables were identified for the first question, “what is the most difficult aspect of the international university experience?” Homesickness was the most difficult aspect, accounting for 24.1% of all answers, followed by adjusting to the U.S. culture, 20.5%; and adjusting to the language, 14.7%. Table 1 displays all ten coded answers for question 1. In order to examine the difference between various demographic variables through chi square analysis, the ten answers in Table 1 were re-coded into four variables (language and cultural adjustments, homesickness, athletic and academic transitions, financial and logistical difficulties, and other). First, chi square analysis revealed that European athletes were more likely to note language and cultural adjustments as a difficult aspect of the international university experience than non-European athletes (χ2 (4, N = 278) = 12.1, p = .017). Second, Canadian athletes were more likely to identify financial and logistical difficulties than non-Canadian athletes (χ2 (4, N = 278) = 29.8, p = .001). Third, athletes participating in individual sports were more likely to identify language and cultural adjustments as a difficult aspect than athletes on team sports, while athletes participating on team sports were more likely to identify homesickness than athletes on individual sports (χ2 (4, N = 278) = 11.4, p = .023). Finally, freshman/sophomore athletes were more likely to identify language and cultural adjustments than junior/senior athletes (χ2 (4, N = 278) = 11.7, p = .020).

Seven variables were identified for the second question, “what were the most important factors in helping you transition to university life in the United States?” Over one-third of respondents indicated that a strong support system from teammates and coaches on their college team was important, and 20.2% indicated that a strong support system from friends and family in their native county was important. Table 2 displays all seven coded answers from question 2. The answers in Table 2 were re-coded into two variables (support system identified as important, support system not identified as important). First, chi square analysis revealed that athletes from the Carribean/South America were less likely to cite the need for a support system from coaches, family, and friends than athletes not from that area (χ2 (4, N = 267) = 7.3, p = .006). Second, junior/senior athletes were more likely to identify the importance of a support system from coaches, family, or friends than freshman/sophomore athletes (χ2 (4, N = 265) = 6.9, p = .006).

Eight variables were identified for the third question, “How did you first learn about opportunities to earn university sports scholarships in the United States?” One-fourth of the respondents learned about these opportunities from friends, family, or other athletes, while another one-fourth indicated they learned from individuals who had previously participated in U.S. sports. Only 23.9% learned from personnel related to U.S. college sports (i.e. coaches and administrators). Table 3 displays all 8 coded answers from question 3. Chi square analysis revealed that athletes playing team sports obtained information regarding U.S. college sports differently than athletes participating in individual sports. Team sport athletes were more likely to obtain recruiting information from those involved in U.S. college sports (i.e. coaches and recruiters) than individual sport athletes (χ2 (1, N = 180) = 4.4, p = .030). Additionally, athletes participating in individual sports were more likely to learn about scholarship opportunities through personal relationships with family, friends, and athletes, while team sport athletes are more likely to learn about scholarship opportunities through those involved with the institutional sport structure (i.e. coaches, administrators, recruiting services) (χ2 (1, N = 180) = 4.9, p = .02)

In a related question, international athletes were asked to compare the athletic facilities and athletic opportunities in the United States and their home country. The respondents overwhelmingly indicated that both the facilities and opportunities were better in the United States. Only ten percent of the international athletes believed that either the facilities or opportunities in their home country were better than what was available in the United States.

Fourteen variables were identified for the fourth question, “what advice would current international athletes give international athletes considering a move to the United States to participate in intercollegiate sport?” However, only four variables occurred in greater than 7% of the responses. The top piece of advice given by one-fifth of the respondents was to realize that playing sports in the U.S. will require important traits like focus, dedication, hard work, and persistence in order to overcome challenges. Second, 18.9% encouraged prospective international athletes to do adequate research on schools before deciding which school to attend, such as getting to know the coaches, athletes, and athletic facilities. Third, 14.2% recommended making the decision to play in the United States because it was such as an excellent opportunity. Fourth, 11.8% indicated it is important to consider academics and majors that can be used to obtain employment in their native country, meaning it is important to find the best overall fit between academics and athletics when deciding on a school.

Finally, international athletes were asked, “what would you be doing now if you had not had this opportunity to play for an NCAA university?” Responses were categorized by what the athlete would be doing (i.e. working, attending college, playing sports) and where they would be living (i.e. native country, United States), as presented in Table 4. Only seven athletes indicated they would be attending college in the United States, while 105 respondents indicated they would be attending college in their native country and only 33 would have continued to play sports in their native country.

### Discussion

American NCAA Division I universities provide opportunities for elite athletes from outside the U.S. to pursue their university degree while continuing to train and compete at a high athletic level, an opportunity not possible in many other countries. However, international athletes face challenges in adjusting to life as a student-athlete. It should come as little surprise that international athletes felt the most difficult aspects of playing university sport in the U.S. were dealing with homesickness, cultural differences, and language barriers. Many cross-cultural sojourners find themselves dealing with similar issues once the initial excitement of being submerged in a new culture wears off (1, 12). In fact, the greater the cultural distance between the sojourner’s native country and the host nation, the greater the adjustments international athletes would be expected to make (17). As was demonstrated in the results, Canadians, whose native country is culturally quite similar to the U.S., were much less likely to indicate a concern with homesickness, cultural differences, and language barriers (for many Canadians, the language barrier is non-existent). Canadian athletes were much more concerned with financial and travel logistics. The results also indicated that freshman and sophomores struggle with these issues more than experienced athletes in their junior and senior years.

The respondents to the survey revealed two key strategies to overcoming these difficulties and successfully transitioning into life as a student athlete during the first year on campus. First, international athletes indicated the high importance of understanding what international-student athletes are “getting themselves into” before committing to an NCAA school. Advice dispensed by the sample in this study focused on understanding the dedication and commitment required of an NCAA Division I athlete, knowing the differences between schools, coaches, and athletic programs at various universities, and learning which schools and academic programs could offer international athletes the best opportunities back in their home country after their college career is complete.

This strategy aligns with prior research. Craven (8) suggested the more athletes and coaching staffs are prepared and educated about the cultural differences they may experience while submerged in another culture, the easier their transition and adjustment to the new environment will be. In Bale’s work, several of his subjects suggested the U.S. college experience was not what they thought it would be, as over 30% encountered problems with U.S. coaches, nearly 25% had difficulties adjusting to the climate in their new location, and over 20% lacked motivation with academic work (2). When offered the chance to be a varsity athlete at an NCAA Division I school, many international athletes are initially so excited about the opportunity and chance to travel to the United States that the location and environment of the specific school they attend is not a key factor (15-16). As the results of this study indicate, however, current international athletes believe it is important for international student-athlete prospects to consider many issues beyond just an opportunity to compete in the U.S. college system before making the commitment to attend a U.S. university.

The second key factor in transitioning into life as a student-athlete is the development of a support system first built on teammates and coaches, but also built on family and friends back home. It is important for coaches and teammates to understand that international student-athletes identified developing a support system with them as the most important element of a successful transition. It is clear the relationships developed with the people international athletes spend the most time with are a key determinant to a successful transition. Coaches should also ensure international athletes have the technical support to maintain relationships with those at home through various video, chat, and online communication resources.

Another key finding in this study was that most of the respondents would not have moved to the U.S. or continued to participate in sports without the opportunities presented through American intercollegiate sport. One of the attractions of U.S. college sport is access to high quality facilities and abundant opportunities. Results indicated that the respondents felt the athletic facilities and athletic opportunities available to them as an NCAA Division I athlete were superior to their options in their native country. This finding could potentially be skewed as young athletes who did have access to better facilities and opportunities in their homeland may not have considered playing in the U.S. college system. However, this finding has key implications for sport managers outside of the U.S. Administrators of sport clubs in non-U.S. countries may lose elite athletes at the peak of their career as those athletes choose to accept an NCAA scholarship. If such club administrators hope to retain these athletes, they may need to examine the attraction of competing in the U.S. collegiate sport system (namely competitive opportunities and facilities) and attempt to replicate those factors in their native country. More research examining this specific issue is needed.

Finally, one surprising finding from this study is only a quarter of respondents indicated university athletic department staff, such as coaches and administrators, were the key source of information regarding the opportunity to compete in the United States college system. As illustrated in the introduction to this paper, recruiting is arguably the most important element in developing an elite college athletic program and many university athletic departments dedicate a relatively large percentage of their resources towards this endeavor. Yet the recruiting process does not seem to be overly efficient in reaching international prospects. Many of the respondents in this study indicated family, friends, and acquaintances that had competed in the U.S. college system were more important sources of information about playing opportunities at NCAA schools than were the coaches whose job it is to recruit these athletes. This study illustrates the need for coaches to more effectively and efficiently recruit the international landscape.

### Conclusions

American college sports provide an opportunity for athletes from countries outside the U.S. to continue their playing careers and educational training in the United States where high-level athletic facilities and strong competitive opportunities abound. International student-athletes must overcome many challenges and obstacles upon arrival on campus, including homesickness, adapting to the culture, and learning the language. Coaches and teammates play an important role in helping international athletes develop a support system that will assist in the successful transition to a student-athlete. Athletic administrators also play a key role, as discussed in the next section.

### Applications In Sport

Once international athletes are on campus, a member of the athletic department staff should oversee the athlete’s transition into college life, focused on combating the top three challenges identified in this research: homesickness, adjustment to U.S. culture, and language. This staff member should serve as a liaison between athletic department personnel (i.e. CHAMPS Life Skills coordinators, compliance, eligibility, coaches) and other campus resources (i.e. academic advising, international office) to facilitate a smooth transition. The liaison can coordinate paperwork deadlines, information updates, cultural sensitivity training in the athletic department, and any programming that might benefit the international athletes. Such programming could include a peer mentoring program, utilizing transition to college coursework, placing international athletes with experts in teaching the English language, offering open forums for athletes to socialize with athletes from other teams, developing information packets with multicultural resources in the community and university, and establishing relationships with host families in the community (under the supervision of the compliance office). Acquainting athletes with American college life should begin as soon as possible, either on an official visit or having international athletes arrive on campus as early as possible to adjust to the language, culture, food, teammates, and academic expectations. Finally, developing a strong relationship with the international office is important in order to ensure all government paperwork is completely in an accurate and timely fashion.

Finally, in contrast to domestic athletes who take official and unofficial visits and have many other opportunities to develop relationships with coaches who are recruiting them, international athletes rely on their personal support system (i.e. club coaches, former athletes, family, friends) to gather information on U.S. colleges. NCAA coaches must continue to improve their international recruiting connections with former athletes and club coaches because they are still the top source of information about competing in the U.S. college system. If NCAA coaches want to successfully recruit international athletes, they should focus on improving recruiting connections with key members of an athlete’s personal support system. Previous research by Bale (2-4) has established some institutions are able to develop talent pipelines where information about an institution is disseminated by athletes who competed for a particular school in the past.

### References

1. Adler, P. (1975). The transitional experience: An alternative view of culture shock. The Journal of Humanistic Psychology, 15, 13-23.
2. Bale, J. (1987). Alien student-athletes in American higher education: Locational decision making and sojourn abroad. Physical Education Review, 10(2), 81-93.
3. Bale, J. (1991). The brawn drain: Foreign student-athletes in American universities. Urbana, IL: University of Illinois Press.
4. Bale, J. (2003). Sports geography (2nd ed.). London: Routledge.
5. Berkowitz, K. (2006). From around the world. Athletic Management, 18(6). Available online at <http://www.athleticmanagement.com/2007/01/15/from_around_the_world/index.php>
6. Brown, G.T. (2004, Dec. 6). Foreign matter: Influx of internationals in college swimming tugs on bond between campus and country. The NCAA News, p. 5.
7. Chapdelaine, R., & Alextich, L. (2004). Social skills difficulty: Model of culture shock for international graduate students. Journal of College Student Development, 45, 167-184.
8. Craven, J. (1994). Cross-cultural impacts of effectiveness in sport. In R.C. Wilcox (Ed.) Sport in the global village, (pp. 433-448). Morgantown, WV: Fitness Information Technology, Inc.
9. Drape, J. (2006, Apr. 11). Foreign pros in college tennis: On top and under scrutiny. The New York Times, p. D1.
10. Furnham, A., & Bochner, S. (1986). Culture shock: Psychological reactions to unfamiliar environments. London: Methuen.
11. NCAA. (2010). 1999-00 – 2009-10 NCAA student-athlete race and ethnicity report. Available online at <http://www.ncaapublications.com/productdownloads/SAEREP11.pdf>
12. Oberg, K. (1960). Cultural shock: Adjustment to new cultural environments. Practical Anthropology, 7, 177-182.
13. Pierce, D., Kaburakis, A., & Fielding, L. (2010). The new amateurs: The National Collegiate Athletic Association’s application of amateurism in a global sports arena. International Journal of Sport Management, 11(2), 304-327.
14. Popp. (2006, September). International student-athlete adjustment to U.S. universities: Testing the Ridinger and Pastore model. Paper presented at the annual meeting of the European Association for Sport Management, Nicosia, Cyprus.
15. Popp, N., Love, A., Kim, S, & Hums, M.A. (2010). International student-athlete adjustment: Examining the antecedent factors of the Ridinger and Pastore theoretical framework model. Journal of Intercollegiate Sport, 3, 163-181.
16. Popp, N., Pierce, D., & Hums, M.A. (in press). A comparison of the college selection process for international and domestic student athletes at NCAA division I universities. Sport Management Review.
17. Ridinger, L. & Pastore, D. (2000). A proposed framework to identify factors associated with international student-athlete adjustment to college. International Journal of Sport Management, 1(1), 4-24.
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19. Weston, M. A. (2006). Internationalization in college sports: Issues in recruiting, amateurism, and scope, 42 Williamette Law Review 829.
20. Westwood, M., & Barker, M. (1990). Academic achievement and social adaptation among international students: A comparison groups study of the peer-pairing program. International Journal of Intercultural relations, 14, 251-263.
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### Tables

#### Table 1
Most Difficult Aspects of International University Experience

Response Frequency Percent
Homesickness 67 24.1
Adjustment to U.S. culture 57 20.5
Language adjustment 41 14.7
Adjustment to being an athlete 23 8.3
Other 21 7.6
Time management 19 6.8
Academic transition 18 6.5
Financial insecurity or finding a job 15 5.4
Paperwork 12 4.3
Finding transportation 5 1.8
Total 267

Note: Respondents could have multiple answers in their written response

Intercoder Agreement: Scott’s Pi = .89

#### Table 2
Important Factors for Successful Transition to University Life

Response Frequency Percent
Strong support system from teammates and coaches 91 34.1
Strong support system from friends and family back home 54 20.2
Possess of key personality traits (experience, desire, patience, etc.) 49 18.4
Strong support system from academic advisors, tutors, professors, and administrators 25 9.4
Adapting to U.S. culture and the English language 20 7.5
Other 15 5.6
Time management and organization 13 4.9
Total 267

Note: Respondents could have multiple answers in their written response

Intercoder Agreement: Scott’s Pi = .82

#### Table 3
Source of Information Regarding Athletic Opportunity in the United States

Response Frequency Percent
Family, friends, and athletes 45 25
Individuals who had participated in U.S. athletics previously 44 24.4
Coaches and recruiters involved in U.S. college sports 43 23.9
In native country from high school coach or administrator 29 16.1
Personal research 10 5.6
Other 5 2.8
Sport recruitment service 4 2.2
Total 180

Intercoder Agreement: Scott’s Pi = .87

#### Table 4
Life without American College Sports

Working Attending College Playing Sports Total
Native Country 14 105 33 152
Not Specified 9 15 13 37
U.S. 0 7 0 7
Total 23 127 46 196

Intercoder Agreement: Scott’s Pi = .85

### Corresponding Author

Dr. David Pierce
Ball State University
School of Physical Education, Sport, and Exercise Science
Muncie, IN 47306
765-285-2275
<dapierce@bsu.edu>

2013-11-22T22:56:03-06:00January 4th, 2012|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off on Qualitative Analysis of International Student-Athlete Perspectives on Recruitment and Transitioning into American College Sport
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