NCAA Website Coverage: Do Athletic Departments Provide Equitable Gender Coverage on Their Athletic Home Web Pages?


The purpose of the current research was to perform a content analysis on the gender coverage provided on intercollegiate athletic home Web pages. One of the primary reasons why the research is necessary is because it focuses on a not-for-profit media outlet with Title IX and ethical constraints due to the fact that the athletic departments are a part of their coinciding universities. Overall, when in comparison to the NCAA athlete and team independent standards, the results demonstrated that women were underrepresented in comparison to men within each of the units of measurement (e.g., advertisements, articles, multimedia, and photographs) presented within the study. The implications of the results are discussed further within the text. The data within the current study was collected from a dissertation that was performed by the author while attending Indiana University.

Keywords: intercollegiate athletic websites, gender coverage, college athletics

The Internet is a contemporary communication medium that provides sport organizations with the opportunity to communicate with both current and potential fan bases (Lombardo, 2007). In today’s realm of sports media, the Internet has become a major media source for fan consumption. Currently, there are hundreds of millions of Internet users worldwide, and the number of individuals accessing the World Wide Web increases at a rapid rate each year (Internet World Stats, 2007). Particularly, the Web has become a primary outlet for news consumption. While only four percent of the population went online to access news in 1995, today nearly 26% of the population accesses news content on the Web on a weekly basis (The Pew Research Center [TPRC], 2007). Furthermore, of the individuals accessing the Internet regularly, 46.5% claimed that sports were a primary entertainment source while browsing the Web (TPRC, 2007).

The mass consumption of sports news on the Internet alone makes it essential for scholars to focus on the sports coverage being provided on the Web. In addition to the growing interest, the Internet is also a unique medium, because it provides athletic teams and programs with an outlet to promote their product to fan segments. As a result, intercollegiate athletic programs have the ability to control the coverage being provided to each of their individual teams on their athletic home Web page. Thus, the athletic departments also have the unique opportunity to control the gender coverage being provided on their individual websites.

Since the athletic programs are part of their coinciding universities, the expectation would be that the athletic departments are providing equitable gender coverage on their websites due to Title IX constraints. Under Title IX, athletic institutions are required to provide women with equal opportunities within the general benefits and services program areas (Policy Interpretation, 2007). More specifically, in the “laundry list” of items stated under the third category of Title IX, athletic programs are expected to provide equitable promotions for women (National Association for Girls and Women in Sport [NAGWS], 2007). While the Internet coverage makes up only a portion of the promotional activities within the athletic department, it is still a viable concern when focusing on gender equity within college athletic programs. Furthermore, due to the fact that the universities are part of the National Collegiate Athletic Association (NCAA), you would expect that the gender coverage would be equitable from an ethical standpoint as well. The current research attempted to understand the coverage provided on intercollegiate athletic websites by examining the gender coverage provided during an academic school year.

Review of Related Literature

In today’s society, the media has a major influence on the beliefs of individuals residing within our culture (Duncan, Messner, Williams, & Jensen, 1994; Kane, 1988). In fact, Coakley (1998) explained that by ignoring certain aspects of female participation in sport, the sports media is essentially shaping the public’s opinion on the value of female sports. Cunningham, Sagas, Satore, Amsden, and Schellhase (2004) added that “if girls and women are not represented in an equitable fashion by the media, then girls are not afforded the necessary exemplars to emulate” (p. 861). Thus, as a result, there is a chance that the future participation in sports can suffer, and as a result Pedersen (2002) explained that “females can lose out on the benefits provided in sports that can help them develop both professional and personal skills” (p. 420).

When focusing on past gender studies within sports settings, research has shown that women receive inequitable coverage allocations within each of the media outlets examined (Bishop, 2003; Cunningham, 2003; Duncan & Sayaovong, 1990). Recently, scholars have indicated that a difference exists in the gender coverage provided within for-profit (Cuneen & Sidwell, 1998; Fink & Kensicki, 2002) and not-for-profit (Huffman, Tuggle, & Rosengard, 2004) media outlets. Sagas, Cunningham, Wigley, and Ashley (2000) explained that a primary difference in the two types of media outlets is that for-profit sources tend to cater to the wants and needs of their customers in order to remain profitable. Cunningham et al. (2004) added the following:

Given the dependence upon consumers and consumer preferences among for-profit media sources, an alternative approach is to study the representation of men and women in not-for-profit media outlets, such as university newspapers, athletic department Internet Web sites, and/or the NCAA News, a publication of the National Collegiate Athletic Association (p. 862).

The NCAA News is a not-for-profit media outlet that has received attention from scholars in past research. Overall, research within the publication has demonstrated more favorable results for women when in comparison to for-profit media outlets (Shifflet & Revelle, 1994). Cunningham et al. (2004) confirmed the improvement in gender coverage in not-for-profit media outlets when reporting that women received 42.4% of the article coverage and 39.7% of the photographic coverage within the publication. The coverage rates presented in the study represent two of the most favorable coverage allocations for women in any media outlet.
An additional emphasis in research on not-for-profit media outlets has been the examination of gender coverage in media outlets with campus affiliation. Outside of the previous studies on the NCAA News (Cunningham et al., 2004; Shifflet & Revelle, 1994), the research on media outlets with a campus affiliation has demonstrated some of the most favorable coverage rates for women within intercollegiate athletic settings (Wann, Schrader, Allison, & McGeorge, 1998). One of the primary reasons for the more favorable coverage rates for women is the influence of Title IX on publications with campus affiliation. Additionally, Huffman et al. (2004) explained the following:

Because student journalists working for campus media belong to a generation that grew up with Title IX and because they live in college communities that include male and female student athletes, these student journalists might be more likely than professional media practitioners to cover athletes in a way that results in gender equity (p. 480).

While the coverage allocations have improved for women within not-for-profit media outlets, research has demonstrated that women are not fully represented within the campus media sources. In an analysis of campus newspapers, Wann et al. (1998) found that women were underrepresented when in direct comparison to both the female participation and enrollment rates at each of the coinciding universities examined in the study. In a similar study, Huffman et al. (2004) reiterated the previous results when demonstrating women received 27.3% of the overall newspaper coverage. Thus, despite small improvements, the results confirm that women are not fully represented within campus newspapers.

Recent research has also extended the analysis of media outlets with campus affiliation by focusing on the gender coverage provided on intercollegiate athletic websites (Sagas, Cunningham, Wigley, & Ashley, 2000). Sagas, Cunningham, Wigley, and Ashley (2000) provided an initial analysis when concluding that women’s softball teams were not fairly represented when in comparison to men’s baseball teams. Additionally, in a follow-up study, Cunningham and Sagas (2002) again demonstrated that the women’s softball team received less coverage than the men’s baseball team. On a positive note, the study demonstrated no difference in the coverage provided to the men’s and women’s basketball teams.

The purpose of the current study was to analyze the overall gender coverage provided to each of the teams contained within athletic departments on intercollegiate athletic websites. An analysis of the overall gender coverage provided on intercollegiate sites to each of the teams in the athletic department is essential for a couple of key reasons. First, as shown in the review of literature, it is clear that there is a limited amount of research available on the gender coverage provided on intercollegiate athletic websites. Further analysis would be beneficial in building new information on the media outlet. Second, in the limited research available, scholars have focused solely on the comparison between two to four similar female and male sport teams. Thus, the analysis of the coverage provided to each of the various teams housed within a college athletic department would provide new insight into the overall gender coverage rates offered on intercollegiate athletic websites. As a result, the current research provides additional depth that is useful to the literature on sports media coverage. Through an analysis of past related studies, the following hypotheses were created to guide the current research:

(1) Women will receive significantly less total overall [1A, 1B, 1C, 1D] coverage on intercollegiate athletic home Web pages than men, when in comparison to coinciding NCAA athlete and team gender participation rates.
1A) Advertisement
1B) Article
1C) Multimedia
1D) Photographic

(2) Women will receive significantly less non-scroll [2A, 2B, 2C, 2D] coverage on intercollegiate athletic home Web pages than men, when in comparison to coinciding NCAA athlete and team gender participation rates.
2A) Advertisement
2B) Article
2C) Multimedia
2D) Photographic


The current research was a content analysis of the gender coverage provided on intercollegiate athletic home Web pages over an academic year. Particularly, the current research involved the analysis of the following four units of measurement on each individual athletic home Web page: advertisements, articles, multimedia content, and photographs. The decision was made to include the four categories, because it offers an opportunity to segment the coverage being provided on the websites. Thus, there was an opportunity not only to understand the overall gender coverage, but also to understand the gender coverage within higher quality coverage areas. Due to the nature of websites, there was an opportunity to further segment the coverage due to the fact that the sites offer advertisements and multimedia content. The advertisement content was characterized by the block advertisements provided to individual teams on athletic websites. The multimedia content was characterized as the audio and video content dedicated to individual teams on the home Web pages.

The data were collected from 30 athletic home Web pages during an academic school year. The data collection process involved a random selection of 30 programs from the NCAA Division I-A database. The sampling frame selected for the analysis was the 2005-2006 academic school year. Particularly, the following stratified samples were chosen to obtain a sample representative of each sports season presented during the school year: fall (October – December), winter (January – March), and spring (April – June). As recommended by Riffe, Lacy, and Fico (2005), a one-week random sample was taken from each of the sports seasons. Thus, the study included an analysis of 630 home Web pages during the academic year.

Data Collection
The data collection process involved a series of protocol that were developed to ensure reliability in the study. In order to accurately assess the coverage within each unit of measurement, the following measures were created to guide the coders during the data collection process: gender, location, and square inch coverage. As recommended by Malec (1994), the gender measure only included female and male, and did not include the “combined” and “neither” categories. In addition, the current research utilized a location measure that identified the area of the Web page where the coverage occurred. Similar to the front page newspaper coverage examined by Pedersen (2002), the study examined the non-scroll coverage directly available upon immediate access to the media outlet. In this case, the coverage was coined as “non-scroll” coverage, and this was characterized by the unit of measurement coverage appearing on the website prior to scrolling down the webpage. When multiple rotating stories were presented, each of the storylines were collected and considered as non-scroll coverage.

Data Analysis
Upon the completion of the data collection, the data were combined and calculated for data analysis. In order to examine the gender coverage differences, the Chi Square test was utilized in order to analyze the coverage within each of the units of measurement. Riffe, Lacy, and Fico (2005) explained that the Chi Square test is the most common statistical method used in content analysis research. Additionally, as stated by Pedersen (2002), it is necessary to develop an independent standard in order to compare the results to the expected outcome. The current research utilized the same independent standards adopted by Cunningham et al. (2004) in their analysis of the NCAA News: (1) NCAA individual athlete gender participation rates, and (2) NCAA team gender participation rates. The NCAA Sports Sponsorship and Participation Rates Report (NCAA Sports, 2006) was used to calculate both the percentage of athletes (women = 42.1%; men = 57.9%) and teams (women = 53.2%; men = 46.8%) participating in the NCAA. The rates were calculated according to the teams that were included in the study.


Overall, the analysis of 630 intercollegiate athletic home Web pages produced 43,866 square inches for analysis. As shown in Table 1, the results demonstrated that the units of measurement each received the following square inch coverage allocations: advertisements (7,712 square inches), articles (19,311 square inches), multimedia (1,522 square inches), and photographic (15,321 square inches). Similarly, when focusing on location of the units of measurement, the results revealed that 57% of all of the coverage was considered non-scroll coverage. The results of the overall and non-scroll coverage for each of the units of measurement are presented in the following sections.

Table 1
Gender Coverage Allocations within the Four Units of Measurement

Gender Advertisement Article Multimedia Photograph
Men 5420(70.3%) 11587(60.0%) 1189(78.1%) 9240(60.3%)
Women 2292(29.7%) 7724(40.0%) 333(21.9%) 6081(39.7%)
Total 7712(100%) 19311(100%) 1522(100%) 15321(100%)

Note. Data in Square Inches and Percentages.

Article Coverage
The analysis of the article unit of measurement helped demonstrate the article coverage provided to women and men on intercollegiate athletic websites. In comparison to the other four units of measurement presented in the study, the results demonstrated that women received a slightly more favorable coverage allocation within the article unit of measurement. Overall, women received 40.0% of the total article coverage included in the study. Despite receiving a slightly higher coverage allocation, the Chi Square comparison (Table 3) revealed a significant difference than men when in comparison to the 42.1% female athlete participation rate (x² = 34.95, df 1, p < .05) and 53.2% female team participation rate (x² = 1351.86, df 1, p < .05).

Further analysis of the article unit of measurement demonstrated that women received a less favorable coverage allocation when focusing on the location of the coverage. In comparison to the number of female athletes active at the intercollegiate level, the results showed that the 36.4% non-scroll article coverage rate provided to women was significantly below the 63.6% coverage allocation offered to men (x² = 1351.86, df 1, p < .05). Similarly, when in comparison to team participation rates, the results illustrated that women were once again underrepresented when in comparison to men (x² = 868.57, df 1, p < .05).

Advertisement Coverage
In the analysis of the advertisement unit of measurement, the results demonstrated that women received 29.7% of all of the advertisement coverage included on the intercollegiate websites. In comparison, males received 70.3% of the overall advertisement coverage included during the study. As shown in Table 4, when in comparison to the overall female athlete (x² = 484.87, df 1, p < .05) and team participation rates (x² = 1707.68, df 1, p < .05), the advertisement allocation provided to women was significantly less than the advertisement coverage provided to men on the athletic sites.

Similar to the previous article unit of measurement, women received an even less favorable coverage allocation when focusing on the non-scroll advertisement coverage. In fact, the difference between the overall advertisement coverage and the non-scroll advertisement coverage represented an 8.8% decrease in coverage. When in comparison to athlete participation rates, the results confirmed that women received significantly less advertisement coverage in prime locations when in comparison to men (x² = 638.99, df 1, p < .05). Further analysis demonstrated that women were further underrepresented when in comparison to NCAA team participation rates (x² = 1452.13, df 1, p < .05).

Multimedia Coverage
Overall, when in comparison to the other units of measurement, the multimedia coverage area contained the least favorable coverage allocations for women. Particularly, as illustrated in Table 5, the investigation showed that the 21.9% multimedia coverage allocation provided to women was significantly less than the 78.1% coverage allocation provided to men (x² = 254.50, df 1, p <.05). Furthermore, when in comparison to team participation rates, the results demonstrated that women received slightly less favorable coverage allocations x² = 597.16, df 1, p < .05). Thus, women received even less coverage within units of measurement with a higher potential to influence fan consumption habits.

Similar to the article and advertisement coverage, the analysis of non-scroll multimedia coverage revealed a coverage allocation slightly below the 21.9% overall multimedia coverage rate provided to women. Overall, the Chi Square analysis helped determine that the 20.4% non-scroll multimedia coverage rate provided to women was significantly less the 79.6% coverage rate provided to men (x² = 164.56, df 1, p < .05). Similarly, the analysis also confirmed that females were severely underrepresented as well when in comparison to the NCAA team participation rates (x² = 367.64, df 1, p < .05).

Photographic Coverage
Overall, when in comparison to the other units of measurement, the photographic coverage area represented the second most favorable unit of measurement coverage for women. Despite demonstrating a more favorable coverage allocation, the 39.7% photographic coverage allocation provided to women was significantly lower than the 60.3% coverage allocation provided to men when in comparison to the individual athlete independent standard (x² = 36.5, df 1, p < .05). Similarly, the results also confirmed that women were underrepresented in comparison to men when focusing on the NCAA team coverage rates (x² = 1123.05, df 1, p < .05).

Despite still remaining underrepresented when in comparison to men (x² = 100.33, df 1,
p < .05), the 37.7% non-scroll photographic coverage allocation provided to women was the most favorable non-scroll unit of measurement rate provided to women during the investigation. While the coverage allocation is somewhat favorable, the results showed that females still received significantly less coverage than men when in comparison to the 53.2% female NCAA team participation rate (x² = 1248.36, df 1, p < .05). Thus, as a result, women received significantly less coverage than men in each of the units of measurement examined during the study.


Similar to the study performed by Cunningham et al. (2004), the essential question when analyzing the gender results is to ask the question whether the glass is half full or whether the glass is half empty. In other words, the significance of the results provided to females within the study was dependent upon how you chose to interpret the data. On one hand, there was a unique opportunity to demonstrate a favorable response when the data were compared to past content analyses focusing on gender coverage in sports media outlets (Bishop, 2003; Fink & Kensicki, 2002). On the other hand, the results were not as promising when the data were compared to NCAA athlete and team gender participation rates (NCAA Sports, 2006). Depending on the area of focus, the glass could have either been half full or half empty.

A Revisited Perspective – Half Empty
An ideal starting point for analyzing the coverage allocations provided to women in the current study involved the direct comparison of results to present NCAA gender participation rates. When focusing on the comparison with NCAA athlete (42.1%) and team (53.2%) gender participation rates, the results revealed that the women were underrepresented in comparison to males in each of the units of measurement analyzed. In addition to the investigation of overall coverage allocation and units of measurement coverage allocations, the current research added depth by focusing on the coverage provided to women in prime website locations. Similar to a study performed by Pedersen (2002), the results of the study confirmed that women received slightly less favorable coverage allocations when focusing on the non-scroll coverage. Thus, the results confirmed that women received less attention than men in locations with more potential to reach fan segments.

In addition to the analysis of non-scroll coverage, the current research also provided additional insight by further segmenting the types of coverage offered on intercollegiate athletic websites. Overall, the segmentation provided the opportunity to examine the gender coverage being provided in the units of measurement with a higher potential to influence fan consumption habits. Thus, the lower coverage allocations within the advertisement (29.7%) and multimedia (21.9%) units of measurement for females is somewhat disappointing considering the coverage areas tend to draw more attention than your traditional article and photographic units of measurement.

The lack of coverage allocated to females on websites is a critical issue for a variety of different reasons. As illustrated by Cunningham et al. (2004), when females are not provided equitable coverage, then younger generations of athletes are not provided with role models to emulate. Thus, there is an opportunity that future participation interest in female sports will suffer because athletic departments are sending the message that female athletic teams are not important. Furthermore, with a potential lack of opportunities, females can lose out on important professional skills that are learned through participation in sports. In order to ensure that females are provided with an equal opportunity to succeed within intercollegiate athletics, athletic departments must provided equitable coverage allocations to female athletes.

A Varying Perspective – Half Full
An additional perspective on the gender coverage that was provided during the study is that the results were promising when in comparison to past content analyses on sports media outlets (Huffman et al., 2004). As previously mentioned, the results can potentially be seen as a step forward for women when judging them based upon past research focusing on for-profit media outlets. For example, when in comparison to the 10% of overall article and photographic coverage provided to women in Sports Illustrated (Fink & Kensicki, 2002), the article (40%) and photographic (39.7%) coverage provided to women in the current study helps demonstrate an overall improvement in the type of coverage being offered to female athletes.

An additional area of consideration when evaluating the results from the current study involves the direct comparison to content analyses examining not-for-profit media outlets (Sagas et al., 2004; Shifflet & Revelle, 1994). When in comparison to the not-for-profit media outlets, the results of the study are still somewhat promising. Overall, while the 40% article coverage rate is slightly lower than the allocation reported by Cunningham et al. (2004), the results confirmed an identical photographic coverage rate (39.7%) when in comparison to the previous study. Despite the fact that the article coverage is slightly lower than that which was reported by Cunningham et al. (2004), the results are still very promising considering the fact that the study focused on the coverage being provided on intercollegiate athletic websites. In contrast, the previous study by Cunningham et al. (2004) had focused on the gender coverage within the NCAA News. Thus, the results overall helped confirm that the glass seems to be half full due to the fact that women were being taken seriously within the not-for-profit intercollegiate athletic websites.


In future years, it is critical that minority groups of athletes receive an equal opportunity to succeed within intercollegiate athletic environments. In order to ensure equitable participation opportunities, athletic departments must monitor coverage on their home Web page to ensure that females are receiving fair coverage allocations. Particularly, there needs to be an emphasis on higher quality coverage areas to ensure that female sport teams are being provided with significant advertisement and multimedia content. Additionally, it is critical that females are provided with sufficient amounts of non-scroll coverage so that they are recognized as important entities to athletic programs in future years.

In addition to the previously addressed concerns, the gender coverage on intercollegiate athletic websites is also important for another crucial reason: the intercollegiate websites set gender coverage precedence for independent media outlets without NCAA affiliation. After all, when athletic departments provide inequitable gender coverage on their home websites, they are sending a message to independent media outlets that female sports participation is not important. As a result, independent media outlets such as Sports Illustrated and USA Today have even less incentive to cover female athletics in their publications. Thus, it is critical that athletic departments understand the importance of setting a positive precedence for independent media outlets.

In the future, it will be important that scholars continue to focus on the gender coverage being provided on intercollegiate athletic websites. A limitation of the current research is that it focused on the gender coverage on the websites during an academic year. In order to provide additional insight, future research should examine the gender coverage over a longer time frame to determine whether the coverage provided to females is improving over time. Additionally, scholars could also provide additional depth to the study by investigating the gender coverage provided during the summer months.

In addition to the investigation of intercollegiate athletic websites, future studies should also focus on identifying the gender coverage being provided on a variety of different sites featured on the Internet. For example, scholars could focus on the units of measurement coverage provided on conference websites to determine the message being sent by NCAA conferences. Furthermore, in addition to the gender coverage provided on sites with NCAA affiliation, future research should also examine the individual team coverage being provided on websites. The identification of individual team coverage not only provides data to alleviate gender inequalities, it offers an opportunity to understand the men’s nonrevenue teams receiving inequitable coverage allocations.


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Is Revenue Sharing Working for Major League Baseball? A Historical Perspective


This article attempts to evaluate whether the system of revenue sharing in Major League Baseball since 2000 (after the Blue Ribbon Panel report) has had a statistically significant effect on team revenues, payroll, attendance, and performance. Analysis of data for two distinct time periods, 1995-2000 and 2001-2007, suggests that since the adoption of the current revenue sharing system (1) the ratio of the highest to lowest team revenue has decreased; (2) the marginal effect of revenue on performance as measured by percentage of wins during the regular season has improved in a way that has benefitted lower-revenue teams; (3) the payroll expenditures of the lowest revenue quartile teams have increased significantly; and (4) attendance levels for the lowest revenue quartile teams have increased slightly. Historical trend analysis suggests, however, that the system is working slowly.

Revenue Sharing in Major League Baseball

In the last decade, Major League Baseball (MLB) has witnessed the dominance of the same few teams during the regular season. The Yankees have reached the World Series six times and have won four championships in the past ten years, while the Atlanta Braves reached the World Series five times in the 1990s. The Red Sox have won two championships in the last five years, and a glance at the end-of-year standings shows slim differences in division winners. Some studies (Schmidt & Berri, 2001) have argued against the popular contention that MLB competitive balance is on the decline. However, the imbalance, real or perceived, has caused dissatisfaction in many fans and owners, and is often mentioned as the biggest problem facing the game (Lewis, 2007 and Lewis, Sexton, & Lock, 2007).

In 2000, The Blue Ribbon Panel, an independent panel appointed by MLB Commissioner Bud Selig to look into the issue, cited “the large and growing disparity between what are called local revenues” (Levin, Mitchell, Volcker, & Will, 2000, p. 6) as one of the reasons for the chronic lack of competitive balance. Local revenues consist of gate receipts, local television, radio and cable rights fees, ballpark concessions, advertising and publications, parking, suite rentals, postseason, and spring training. In a 2007 study, Gennaro found that local revenue contributes 70-80% to a team’s total revenue. Therefore, economic factors such as attendance, per capita income, and other Standard Metropolitan Statistical Area census figures are largely responsible for the level of total revenue baseball teams receive.

The latter is a problem because all teams participate in the same national labor market. MLB does not have a salary cap; therefore, a team can spend any amount they wish on their payroll. The teams with the most revenue have the most available funds and are therefore able to make offers that cannot be matched by lower revenue teams. The amount of a club’s revenue is considered a key factor in determining the amount of that club’s payroll, and it has been argued that the size of a club’s payroll is the most important factor in determining how competitive the club will be (Levin et al., 2000, p. 36, and Hall, Szymanski, & Zimbalist, 2002).

The Blue Ribbon Panel found that “…these problems have become worse since 1994 and unless addressed seem likely to remain severe” (Levin et al., 2000, p. 1). The panel went on to suggest that the commissioner should instate revenue sharing, a competitive balance tax, central fund distributions, as well as a competitive balance draft, and should allow franchise relocation. In 1997, MLB officially granted the commissioner new powers to distribute the central fund revenues in unequal amounts, as opposed to the previously used method of distributing these central fund revenues equally. More recently, in October of 2006, MLB and the players association reached a five-year agreement on the revenue sharing policy. The agreement requires all 30 teams to pay 34% of their local revenues into a common pool, and that pool is split evenly among the 30 teams (Jacobson, 2008, p. 1). In 2007 alone, $312 million of wealth was transferred from high to low revenue teams (Fatsis, 2006, p. 2).

The system of revenue sharing, however, has instilled much controversy within the league. “The big clubs say some teams simply shouldn’t get the money” (Fatsis, 2006, p. 1). Their argument is that certain teams have not shown that they are actually using the proceeds from revenue sharing to improve performance. Rather, they claim some owners hoard the profits while their teams’ struggles amplify. The Kansas City Royals are the epitome of this argument. Since 2000, Royals’ ticket sales have declined 18%, while the team valuation has increased from $96 million to $282 million (Vardi, 2007). The Royals’ revenue sharing proceeds have doubled since 2002, while their payroll has increased only 6% annually. Similarly, the Tampa Bay Rays (formerly, the Tampa Bay Devil Rays) decreased their payroll from $35 million in 2006 to $24 million in 2007, even though they received $30 million in revenue sharing. The team’s performance in the 2008 season appears to refute the argument, as the Rays won the American League Pennant and reached the World Series. The real question, however, is whether the Rays’ successful season is the exception, a Cinderella story attributable to the team’s chemistry and camaraderie, or whether it is a result of the incentives and opportunities provided by the current revenue sharing system.

The problem with the incentives the current system of revenue sharing provides is that transfers are based on local revenues. If teams that receive money from revenue sharing actually used it to increase their clubs’ competitiveness, more fans would show up to games. The increase in attendance would lead to an increase in local revenue. Thus, teams with lower local revenues may consciously choose not to invest into their payroll, as doing so would decrease the amount of revenue sharing proceeds they would receive.

This study tries to assess whether the latter has indeed been the case. It evaluates whether the current method of revenue sharing (since 2000) has been providing an incentive for teams to invest in their payroll, improve performance, and attract fans. The positive correlation between payroll expenditure and on-field performance has been well-documented in the literature, and is related to long-term competitive balance, which in turn has been shown to increase attendance and improve the popularity of a team (Schmidt & Berri, 2001). This article, however, does not address explicitly the issue of whether the competitive balance in MLB has improved overall, as Schmidt & Berri (2001) do in their economic inequality analysis through estimating the Gini coefficient, or as Quirk & Fort (1997) do through examining the dispersion and season-to-season correlation of team winning percentages. Instead, it examines recent historical team performance, revenue, payroll, and attendance statistics, with the goal of determining whether a statistically significant difference in trends and team behavior has taken place since 2000.

The study begins by performing a revenue analysis, comparing the years before and after the revision of revenue sharing in search of a noticeable change among the poorer teams. It proceeds with a payroll analysis: it examines whether larger revenues have led to larger payrolls, and whether the level of payroll has had a significant impact on the number of total team wins. An attendance analysis is also performed to assess whether attendance for poorer teams has been affected by the implementation of revenue sharing. The article concludes with a summary of findings.

The data used in this analysis include team total revenue, local revenue, payroll expenditure, attendance, and performance. Financial data have been discounted back to the year 1995 by the consumer price index in each year, to account for inflation. All data date back to the year 1995 for two reasons. First, this allows for a fair comparison since this is the beginning data point that the Blue Ribbon Panel used when they found that market size was creating a problem. Second, there was a player strike during the 1994 season, so the data for that particular year are incomplete. The data came from three main sources: the Blue Ribbon Report (Levin et al., 2000), Forbes’ website, and Sports Reference’s website. Financial data prior to the 2000 season were collected from the Blue Ribbon Report, while data following and including the 2000 season were collected from Forbes. A two-year overlap (1998 and 1999) was compared to ensure consistency among the different sources.

Revenue Analysis

To evaluate whether the system of revenue sharing is working, the data used by the Blue Ribbon Panel was updated, and different statistical techniques for comparative analysis were applied.

First, the average difference between team revenues in the two time periods was examined. Between 1995 and 1999, the Washington Nationals (formerly the Montreal Expos) had the lowest local revenue, averaging only $16.332 million per season. In contrast, the New York Yankees, MLB’s wealthiest team, averaged just over $118 million during the same time period. The equation of the line of best fit for all 30 teams was , where y is the local revenue in dollars, and x is the team rank in terms of revenue. In other words, each subsequent team had $2.830 million more in local revenue than the previous team in the ranking. It is worth noting that the New York Yankees were a major outlier, and had a significant revenue advantage over every other team.

The situation, however, appears to have changed after 2000. The relationship between revenue and team rank was described by the equation y=23,986,266 + 2,267,401x. The noticeably reduced slope of 2.267 million suggests that the teams are on a more equal playing field now than in the years before 2000. Between the 2000 and 2007 seasons, the poorest team in terms of average local revenue was the Washington Nationals, but their average local revenue was $26.961 million since revenue sharing, compared to $16.332 million before revenue sharing.

Next, revenue growth rates were analyzed for the years before and after 2000. In the years leading up to 2000, the club with the lowest local revenue (the Washington Nationals) witnessed an average growth rate of -14.6% per year, while the team with the highest local revenue witnessed an average growth rate of 12.7% per year. The gap was huge, and growing. Since the introduction of revenue sharing, the club with the lowest local revenue has witnessed a growth rate of 21.4%, compared to the team with the highest local revenue, which witnessed only 1.7% growth. While both revenue streams are growing, it is important to note that the minimum revenue club is growing at a faster rate than the maximum revenue club.

The ratio of revenues of the highest revenue team and the lowest revenue team is shown in Figure 1. The ratio increased every year prior to 2000, and peaked in 1999. In 2000, the ratio significantly decreased, and has been decreasing ever since. To remove potential outliers, the disparity between clubs’ local revenues was examined also through a quartile analysis, where Quartile 1 represents clubs with the highest local revenues, and Quartile 4 represents clubs with the lowest local revenues. The average quartile revenue ratio analysis is also shown in Figure 1. The quartile ratio appears to be declining slowly, similarly to the maximum and minimum ratio.

It is apparent that the new system of revenue sharing stopped the increasing spread between the two ratios, and actually mitigated it substantially. Since the year 2001, the ratio has flattened, suggesting that although revenue sharing did appear to stop the increasing revenue spread, it is not actually decreasing it.

However, this may simply be a result of how new revenue sharing is. Prior to revenue sharing, Quartile 1 clubs witnessed an 11.31% year-on-year increase, while Quartile 4 clubs only had a 5.08% year-on-year increase. Since the introduction of revenue sharing, however, average annual growth for Quartile 1 teams has been only 0.72%, compared to 8.02% for Quartile 4 teams. The change in growth seen through each quartile suggests that given enough time, this spread may slowly converge.

Figure 1. Revenue differences, 1995-2007.

A two sample t-test of Quartile 1 and Quartile 4 average revenues statistically verified (p-value < 0.000) that there is a significant change in Quartile 4 revenue relative to Quartile 1 revenue since the revision of revenue sharing in 2000.

The final issue that was addressed was whether the amount of team revenue had a larger or smaller impact on the percentage of wins in the regular season after the current revenue sharing system was adopted. Two separate regressions were run on the data before and after the year 2000, with percentage wins as the response variable, and revenue as the explanatory variable.

The results of the regression analysis, shown in Table 1, indicate that the relationship between revenue and percentage wins has indeed changed since the implementation of revenue sharing in 2000. The change is statistically significant, as demonstrated by the dramatically different slope coefficient and the fact that the 95% confidence intervals for the slopes do not overlap. The marginal effect of revenue on percentage of wins has decreased about twofold after 2000, in a way that benefits lower income teams.

Payroll Analysis

As Figure 2 illustrates, the maximum payroll steadily increased until the year 2005, and the minimum payroll has fluctuated. On average, the minimum payroll has grown 9% per year since 2000 and the maximum payroll has grown 5.2% since 2000. However, as shown in Figure 3, the ratio of the maximum to the minimum payroll does not appear to have improved substantially. In fact, in 2006 the ratio of highest to lowest payroll teams reached an all-time high, even surpassing the ratios before the revision of revenue sharing in 2000.

When revenue quartiles rather than individual teams are considered, however, the picture is not as bleak. In 1995-1999, the average payroll growth per year of Quartile 1 revenue teams was 10.42%, while the average payroll growth of Quartile 4 teams was only 1.75%. By contrast, in 2000-2007, payroll expenditure for Quartile 1 revenue teams increased 1.14% per year on average, while payroll expenditure for Quartile 4 teams increased 5.86% per year. The actual dollar spread in payroll expenditure between Quartile 1 and Quartile 4 teams, however, is still increasing (Figure 4).

Still, a two-sample t-test on Quartile 4 teams’ mean payroll expenses before and after 2000 revealed that the increase in payroll after the adoption of the current revenue sharing system was statistically significant (p-value < 0.000). It is also interesting to note that after 2000, wins during the regular season were less determined by the size of a club’s payroll than before 2000. This is reflected in the decreased slope coefficient for the regressions with percentage wins as the response variable and payroll expense as the explanatory variable for 1995-1999 and 2000-2007. The decrease in the slope is statistically significant (Table 2).

Attendance Analysis

Trends in team attendance statistics were examined for the 1995-2000 and 2001-2007 time periods. In order to serve as a fair comparison across the various teams and stadium capacities, percentage attendance was used, and was adjusted for the multiple capacity renovations on many stadiums between 1995 and 2007.

Figure 5 shows the average percentage home attendance for teams in each quartile. The chart reveals dramatic differences in attendance. In 2007, revenue Quartile 1 teams had 92.72% attendance, while the poorer teams in Quartile 4 only had 48.50% attendance. Correlation does not prove causation, however, so it is difficult to say whether high attendance figures result in high revenue, or if high revenue is attributable to a more competitive team, thus increasing attendance.

The average Quartile 1 attendance has been relatively constant at 88%. The average Quartile 4 attendance, on the other hand, has been increasing, from 28% in 1995 to 48.5% in 2007. Perhaps not coincidentally, the spread between Quartile 1 and Quartile 4 attendance began to decrease after the 2000 season, which is the season in which revenue sharing was revised. Since then, the spread has been decreasing at a moderately slow rate.

In order to test the significance of the change in percentage attendance in the year’s prior and following revenue sharing, a t-test of Quartile 4 attendance data was conducted. The average percentage attendance of Quartile 4 teams was 31.79% before 2000, and 40.10% in the years following 2000. The results of the t-test indicate that this increase in mean attendance is statistically significant (p-value < 0.000).

However, an examination of growth rates shows just how small this change is. In the years prior to revenue sharing, percentage attendance for teams in Quartile 1 grew 1.28% per year, while percentage attendance for teams in Quartile 4 grew 4.75% per year. In the years following revenue sharing, percentage attendance in Quartile 1 increased at a smaller growth rate of 1.05%, while Quartile 4’s growth rate increased to 4.87%. That raises the question of whether the current revenue sharing system is comprehensive enough. Taking into account the slight change in growth rates, the statistically significant increase in Quartile 4 attendance, and the decreasing quartile spread, it appears that revenue sharing may be working on an attendance level, but it is only doing so ever so slowly.

Concluding Remarks

The goal of this study was to examine whether there was statistical evidence that the system of revenue sharing started by the MLB Commissioner in 2000 provided the proper incentives for clubs to invest in improving their teams and performing better. The expectations at the beginning of the study were that the revenue sharing system had decreased incentives for revenue-maximizing teams to pursue better performance, as teams could maximize their profitability by spending less on team improvement and waiting to receive their share from the system. This had been the prevailing opinion in the press, and a source of much debate and criticism.

Interestingly, however, the statistical analysis on historical trends did not find enough evidence to support this view. While many fans argue that the standings have not changed much, this research found statistical evidence that revenue sharing has had a small effect on team behavior, and in many aspects, team performance and investment in payroll have improved for teams with limited financial resources.

The analysis shows statistically significant change in the revenue, payroll expenditures, and attendance, especially for poorer teams; however, it also indicates that the system is working slowly. To speed up the process of achieving a level of comparable competitiveness, perhaps the system needs to implement incentives to motivate teams to spend revenue sharing money on payroll, or simply only award money to those that do. These suggestions, based on the empirical findings in this article, support an observation of Miller (2007), who argues that a revenue-sharing system that rewards quality low revenue teams can alter the outcome of the game while requiring a lower proportion to be taken from high revenue teams.


Fatsis, S. (2006, April 28). Playing hardball – baseball owners square off over how much richest clubsshould share with the rest. The Wall Street Journal.

Forbes, LLC. Baseball Team Valuations [Data file]. Retrieved March 3, 2008 from Forbes Website:

Gennaro, V. (2007). Diamond dollars: The economics of winning in baseball. Hingham, Massachusetts: Maple Street Publishing.

Hall, S., Szymanski, S., & Zimbalist, A. S. (2002). Testing causality between team performance and payroll: The cases of Major League Baseball and English soccer. Journal of Sports Economics, 3(2), 149-168.

Jacobson, D. (2008, March 3). MLB’s revenue-sharing formula. BNET Business Network. Retrieved January 15, 2009 from BNet Business Network Website: html

Levin, R. C., Mitchell, G. J., Volcker, P. A., & Will, G. F. (2000). The Report of the independent members of the commissioner’s Blue Ribbon Panel on baseball economics. Retrieved January 15, 2009 from Major League Baseball Website:

Lewis, H. F., Sexton, T. R., & Lock, K. A. (2007). Player salaries, organizational efficiency, andcompetitiveness in Major League Baseball. Journal of Sports Economics, 8(3), 266-294.

Lewis, M. (2007, November 3). Baseball’s losing formula. The New York Times.

Miller, P. A. (2007). Revenue sharing in sports leagues: The effects on talent distribution and competitivebalance. Journal of Sports Economics, 8(1), 62-82.

Quirk, I. & Fort, R. (1997). Pay dirt: The business of professional team sports. Princeton, NJ: Princeton University Press.

Schmidt, M. B., & Berri, D. J. (2001). Competitive balance and attendance: The case of Major League Baseball. Journal of Sports Economics, 2(2), 145-167.

Sports Reference, LLC. Expanded standings [Data files]. Retrieved October 30, 2008 from Baseball Reference Website:

Vardi, N. (2007). A royal mess. Forbes, 179, 40-41. Retrieved January 15, 2009 from Forbes Website:

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Strategic Planning in University Athletic Departments in the United Kingdom


The study’s purposes were to (a) determine the extent to which university athletic departments in the United Kingdom use strategic planning, (b) identify key factors discouraging strategic planning, and (c) examine relationships between use of strategic planning and the variables university size and athletic director’s background. Of athletic departments studied, 59.5% were strategic planners that wrote long-range plans, assessed external and internal environments, and based strategies on department mission and objectives. The remaining 40.5% were nonstrategic planners using just some components of the strategic planning process, as either users of short-range written plans and budgets, for the current fiscal period; users of unwritten short-range plans maintained in an administrator’s memory (intuitive planners); or users of no measurable planning procedures.

Keywords: planning, strategic planning, strategy, university athletic departments

Private and public organizations today use a structured planning process to select appropriate long-term objectives and develop means to achieve these objectives (Christensen, Berg, Salter, & Stevenson, 1985; Elkin, 2007; Mintzberg, Lampel, Quinn, & Ghoshal, 2003; Wheelen & Hunger, 2008). The business sector of society has long recognized that continued profitability requires maintaining a strategic fit between organizational goals and capabilities and changing societal and economic conditions. As its environment changed, the business sector developed planning systems which made possible coordinated and effective responses to increasing unpredictability, novelty, and complexity (Ansoff, 1984). Strategic thought and practice generated in the private sector can also help public and nonprofit organizations anticipate and respond effectively to their dramatically changing environments (Bank, 1992; Bryson, 1988; David, 1989; Duncan, 1990; Espy, 1988; Laycock, 1990; Medley, 1988; Nelson, 1990; Robinson, 1992; Wilson, 1990).

Today’s colleges and universities have experienced rapid change. Educational administrators are confronted with changes associated with aging facilities, changing technology, changing demographics, increasing competition, rising costs, funding cuts, and so on. The educational sector has begun to recognize that strategic planning is necessary in order to maintain responsiveness to the rapidly changing environment (Agwu, 1992; Busler, 1992; Hall, 1994; Williams, 1992). Since athletic programs are so much a part of colleges and universities, athletic departments face the same problems as do the institutions to which they belong. If athletic departments are to respond well to change, they must anticipate it and adapt programs and resources to meet their mission and objectives in new situations (Bucher, 1987; Kriemadis, Emery, & Puronaho, 2001). Strategic planning may help athletic departments do this and may further point them to the strategies necessary to achieve their missions and objectives (Dyson, Manning, Sutton, & Migliore, 1989; Ensor, 1988; Gerson, 1989; Kriemadis, 1997; Smith, 1985; Sutton & Migliore, 1988).
Duncan (1990) stated that strategic planning is a method of decision making developed in the private sector that has been adopted by public sector organizations. Proponents of strategic planning argue that traditional long-range planning fails in the contemporary world, and strategic planning is now the powerful tool for organizations to cope with an uncertain future.

The service sector today includes a growing nonprofit segment, including social services, schools and universities, research organizations, sports organizations, religious orders, parks, museums, and charities. Strategic planning is earning its place in the management systems of service businesses (Kriemadis, 1997; Kriemadis et al., 2001; Sutton & Migliore, 1988; Wilson, 1990). Pearce and Robinson (1985) have argued that strategic planning consists of the following steps:

1. Determining the culture, policies, values, vision, mission, and long-term objectives of the organization.
2. Performing external environmental assessment to identify key opportunities and threats.
3. Performing internal environmental assessment to identify key strengths and weaknesses.
4. Developing long-range strategies to achieve the organization’s mission and objectives.
5. Establishing short-range objectives and strategies to achieve the organization’s long-range objectives and strategies, a process called strategy implementation.
6. Periodically measuring and evaluating performance, a review known as strategy evaluation.

Steps 1–4 together are referred to as strategy formulation.
A number of authors (Ansoff & McDonell, 1990; Barry, 1986; Bryson, Freeman, & Roering, 1986; Bryson, Van de Ven, & Roering, 1987; Elkin, 2007; Kotler, 1988; Mintzberg et al., 2003; Rowe, Mason, Dickel, & Snyder, 1989; Steiner, 1979; Wheelen & Hunger, 2008) argue that, in turbulent environments, strategic planning can help organizations to

  • think strategically and develop effective strategies
  • clarify future direction
  • establish priorities
  • develop a coherent and defensible basis for decision making
  • improve organizational performance
  • deal effectively with rapidly changing circumstances
  • anticipate future problems and opportunities
  • build teamwork and expertise
  • provide employees with clear objectives and directions for the future of the organization and increase employee motivation and satisfaction

Wheelen and Hunger (2008) and Newman and Wallender (1987) stated that basic management concepts should be applied to both profit and nonprofit organizations. The present study is useful in extending the basic management concept of strategic planning to university athletics. It may help athletic administrators to further their understanding of the strategic planning process in their respective athletic departments.

Management of University Athletic Departments in the U.K.

Both the nature and context of sports programs in the United Kingdom—and specifically of sports in higher education there—have changed in unprecedented ways in the last decade. For instance, public income per student has declined by 40% in real terms, and universities have responded by rapidly expanding student numbers and developing alternative income-generation activities involving nongovernmental sources (Lubacz, 1999).

Sports in the university sector in the U.K. has historically been managed by each university’s athletic union, a largely student-run body attached to the student union. The role of the athletic union, the fact that students belonging to it are untrained, and the voluntary nature of athletic union offices (filled annually by election) have rendered management of university sports largely ineffective, strategic planning virtually nonexistent. But sports’ profile has increased considerably, as has the value attached to sports. Many universities in the U.K. have already recognized that by managing their sports programs more effectively, fully endorsing a corporate-type strategy within their athletic departments, they should be able to develop new opportunities at local, regional, national, and even international levels. To establish a rationally planned and coordinated approach to sports, many universities have introduced relatively formal sports management structures. These have often involved full-time paid positions emerging from either academic departments, central services, or, more directly, from a university’s student union.

Because the scale and scope of such developments in university athletic departments over the last five years have varied widely, university sports in the U.K. now involves many diverse approaches to management. At one extreme, some universities still feature programs run entirely by students for students. At the opposite end of the continuum, some universities have recently created institutes of sports that are separate cost centers employing up to 20 staff members or more. Such institutes of sports aim to fully realize roles that may include (a) encouraging and supporting sports participation by students and staff, (b) establishing the university’s place as a center of excellence in sports, (c) managing the university’s sports facilities, programs, and events, and (d) organizing short courses, seminars, conferences, research, consultancy, and publications that reflect both university expertise and strong international, European, and regional links enjoyed by the university (Ilam, 1999).

Thus the functions of university sports and the nature of university sports programs are now considerable in some cases, much broader than campus athletic clubs and student competitions. Stakeholders can include internal and external clientele: participants, spectators, coaches, administrators, sponsors. Sports products and services can relate to anything from merchandising to organizing short courses; from national athlete awards to requirements of degree study in sports-related areas. University sports facilities can be used for a variety of leisure purposes over all 52 weeks of a year, and the meaning of recreational sports can extend to providing personalized health fitness programs. Consequently, within higher education, sports has a growing, diversifying audience, only one part of which is involved with competitive performance. Many universities have positioned themselves accordingly, establishing the balance and management practices to meet new needs.

Where universities and their students wish to compete against one another, either nationally or internationally, they must become institutional members of the British Universities Sports Association (BUSA). This voluntary association has its origins in the first intervarsity athletic meeting between nine institutions from England and Wales, held in 1919. Since that time, membership eligibility has been limited to U.K. institutions of higher education, but in 1999 BUSA had 148 members and some 200,000 students participating in nationally organized championships in 43 different sports (BUSA, 1999).

The present study addressed two research questions: (a) To what extent do university athletic departments in the United Kingdom use the basic management tool of strategic planning? and (b) What are the key factors discouraging athletic departments’ use of strategic planning? In addition, the study tested the following two hypotheses:

Hypothesis 1. The extent to which strategic planning is used by the athletic department of a U.K. university is independent of the university’s size.
Hypothesis 2. The extent to which strategic planning is used by the athletic department of a U.K. university is independent of the background of the university’s athletic director.


The population for the present study consisted of 101 of the 148 institutional members of the British Universities Sports Association (BUSA). The 101 BUSA members studied represented all U.K. universities that had participated in more than 10 sports competitions during 1999 and that furthermore employed a full-time coordinator of sports. These criteria were established in order to ensure participation by sports planning units large enough to pursue the kind of strategic planning under investigation. Surveys were sent to the athletic departments of the 101 BUSA members. Out of these, 37 responded (37% response rate). Nonrespondents’ characteristics did not appear to follow a pattern of geographical location or institutional size. This fact, combined with the response rate, suggests that results of the study can be generalized to the target population.

Data describing the 37 participating athletic departments’ strategic planning practices were collected using a questionnaire developed by the author and validated by a panel of experts in strategic planning, higher education, management, and sports management. The reliability of the survey instrument was determined via Cronbach’s alpha (a); all alpha coefficients were within acceptable ranges for comparable instruments (Nunnally, 1967). Coefficients for each subdimension were as follows: general planning factors, a = .67; external factors, a = .89; internal factors, a = .87; constraint factors, a = .82; type of plan factors, a = .74; short- and long-range plans factors, a = .68. A pilot study was also conducted, and recommended improvements were incorporated in the final research instrument.


Data from the survey instrument showed that 75.7% of the responding athletic departments have developed a vision statement, and more than 90% have developed a mission statement, conducted a SWOT (strengths, weaknesses, opportunities, threats) analysis of the internal and external environment, and developed long-range and short-range plans (Table 1). In addition, 73% of the surveyed athletic departments reported that they evaluate the performance of their planning process, while 78.4% reported that they evaluate the performance of the athletic department.

Table 1
Activities Included in Surveyed Athletic Departments’ Current Planning Processes

Item Frequency Percentage
Vision statement
Yes 28 75.7
No 9 24.3
Mission statement
Yes 35 94.6
No 2 5.4
Evaluation of strengths and weaknesses
Yes 34 91.9
No 3 8.1
Evaluations of opportunities and threats
Yes 34 91.9
No 3 8.1
Formulation of goals and objectives
Yes 35 94.6
No 2 5.4
Formulation of long-range plans
Yes 35 94.6
No 2 5.4
Formulation of short-range plans
Yes 35 94.6
No 2 5.4
Formulation of planning process
Yes 27 73
No 10 27
Performance Evaluation
Yes 29 78.4
No 8 21.6

However, the percentage fitting all three criteria specified to indicate authentic strategic planning was smaller, only 59.5% (Table 2). The three criteria are (a) the formalization of long-range written plans; (b) the assessment of the external and internal environments; and (c) the establishment of strategies based on a departmental mission and objectives. The remaining 40.5% of the surveyed athletic departments were identified as nonstrategic planners not meeting the three criteria, although they may have indicated that they did pursue some components of the strategic planning process. Athletic departments in the nonstrategic planner group were excluded from the present analysis, because their planning endeavors represented the use of only short-range written plans and budgets, for the current fiscal period; or the use of only unwritten short-range plans maintained in an administrator’s memory (intuitive planners); or no use of measurable planning procedures at all.

Table 2
Surveyed Athletic Departments’ Level of Planning

Type of Plan Used Frequency Percentage
Structured long-range plan 22 59.5
Operational plan 11 29.7
Intuitive plan 3 8.1
Unstructured plan 1 2.7

The study found that at least 50% of the responding athletic departments reported that they weighed three external factors—competition, community opinion, and government legislation—to a “very great or great” extent when formulating their plans (Table 3). In addition, at least 78.3% of the responding athletic departments reported that they weighed three internal factors—financial performance, adequacy of facilities, and department staff performance—to a “very great or great” extent when formulating plans (Table 4). The study also found that at least 75.7% of the responding departments considered financial plans and human resource plans to a “very great or great” extent during their planning activities (Table 5).

Table 3
Frequency and (Percentage) of External Factors Considered to Three Different Extents by Athletic Departments During Plan Formulation, in Descending Order of Consideration

External Factor Very Little or Little Some Very Great or Great
Competition 4(10.8) 10(27.0) 23(62.1)
Community opinion 7(19.0) 12(32.4) 18(48.6)
Government legislation 10(27.0) 9(24.3) 18(48.6)
Economic/tax 10(27.0) 12(32.4) 15(40.5)
BUSA trends 10(27.0) 13(35.1) 14(37.8)
Demographic trends 4(10.8) 20(54.1) 13(35.1)
Political trends 17(47.9) 14(37.8) 6(16.2)
Spectators 22(59.4) 14(37.8) 1(2.8)

aCorresponding Likert-type scale self-measures: 1 (very little), 2 (little), 3 (some), 4 (great), 5 (very great).

Table 4
Frequency and (Percentage) of Internal Factors Considered to Three Different Extents by Athletic Departments During Planning Process, in Descending Order of Consideration

Internal Factor Very Little or Little Some Very Great or Great
Financial performance 2(5.4) 35(94.6)
Adequacy of facilities 1(2.7) 3(8.1) 33(89.2)
Staff performance 3(8.1) 5(13.5) 29(78.3)
Athletic performance 4(10.8) 12(32.4) 21(56.7)
Coaches’ opinion 6(16.2) 16(43.2) 15(40.5)

aCorresponding Likert-type scale self-measures: 1 (very little), 2 (little), 3 (some), 4 (great), 5 (very great).

Table 5
Frequency and (Percentage) for Management Factors Incorporated to Three Different Extents by Athletic Departments During Planning Activities, in Descending Order of Consideration

Management Factor Very Little or Little Some Very Great or Great
Financial plan 2(5.4) 3(8.1) 32(86.5)
Human resource plan 3(8.1) 6(16.2) 28(75.7)
Facilities master plan 2(5.4) 10(27.0) 25(67.5)
Marketing plan 9(24.3) 11(29.7) 17(45.9)
Contingency plan 17(45.9) 13(35.1) 7(18.9)

aCorresponding Likert-type scale self-measures: 1 (very little), 2 (little), 3 (some), 4 (great), 5 (very great).

What are the key factors that discourage UK university athletic departments from engaging in strategic planning activities? Insufficient financial resources and time were identified by this study as factors that, to a “very great or great” extent, discourage 35% or more of the athletic departments from engaging in strategic planning activities.

Table 6
Frequency and (Percentage) for Factors Discouraging Athletic Departments from Strategic Planning, to Three Different Extents (in Descending Order of Influence)

Discouraging Factor Very Little or Little Some Very Great or Great
Insufficient financial resources 8(21.6) 12(32.4) 17(45.9)
Insufficient time 15(40.5) 9(24.3) 13(35.1)
Insufficient training in planning 20(54.0) 12(32.4) 5(13.5)
Inadequate communication 23(62.1) 9(24.3) 5(13.5)
Staff’s resistance 27(72.9) 5(13.5) 5(13.5)
Lack of a planning policy 27(72.9) 5(13.5) 5(13.5)
Planning is not valued 30(81.1) 5(13.5) 2(5.4)

aCorresponding Likert-type scale self-measures: 1 (very little), 2 (little), 3 (some), 4 (great), 5 (very great).

Both hypotheses tested by the study were supported. Chi-square analysis X2(2, N=37)=2,811, p=0,245 showed that the extent to which an athletic department uses strategic planning is indeed independent of the size of the university. No significant relationship was found between the extent of strategic planning and university size (p = 0.57). Similarly, Chi-square analysis X2(3, N=37)=7,192, p=0,66 showed that the extent to which strategic planning is used by athletic departments is independent of their athletic directors’ backgrounds. No significant relationship was found between the extent of strategic planning and the background of athletic directors (p = 0.35).

Discussion, Implications, Recommendations

In this study of member institutions in the British Universities Sports Association, more than 75% of responding athletic departments indicated that they were involved in such strategic planning activities as developing a vision statement, developing a mission statement, formulating goals and objectives, establishing short- and long-term strategies, and developing plan and performance evaluation procedures. However, only 59.5% of the sample could be classified as practicing authentic strategic planning, defined here as participation in three specific things: the formalizing of long-range written plans, the assessing of the external and internal environments, and the establishing of strategies based on departmental mission and objectives. With more than 40% of the athletic departments practicing either nonstrategic planning or no planning, the need clearly exists to outline formal strategic-planning committees, processes, and systems for these departments’ better management.

According to Harvey (1982), a strategic plan is developed in order to gain or maintain a position of advantage relative to one’s competitors. Following the development of the strategic plan, its implementation becomes critical. The present study did not rigorously assess such implementation, and it remains to be determined whether athletic departments that can be identified as strategic planners are also actual implementers of their strategic plans. Such knowledge would be useful for decisions about committing athletic department resources to reach desired objectives.

The present study did provide evidence that whether and how much a university athletic department engages in strategic planning is unrelated to the size of the university. David (1989) noted that small firms pursue a less formal kind of strategic planning than large firms do. Despite this study’s first hypothesis, then, it was a surprise to this author that large universities’ and small ones’ athletic departments generally pursue strategic planning and a strategic approach to decision making in rather similar fashion.

Evidence was also provided by the study suggesting that the extent of strategic planning carried out by the athletic departments is unrelated to athletic directors’ backgrounds. Some of the athletic directors who participated in the survey had private-sector work experience. Nevertheless, either knowledge of and experience with strategic planning was not transferred to the university environment, or such knowledge and experience had not been a meaningful part of the private-sector background. Failure to transfer knowledge and experience may, however, be attributable in some cases to athletic department decision makers’ lack of access to financial and human resources. Alternatively, it could be that some university administrations do not encourage formulation and implementation of strategic plans.
The findings presented above have implications for the development and use of the strategic planning process in athletic departments. First, since the most significant constraints on strategic planning, according to the survey, were insufficient financial resources and insufficient time, athletic departments need to recognize, and then to remove, these constraints if they are to enjoy the benefits of an implemented strategic plan. Second, if athletic departments are to respond to the scientific literature by accepting strategic planning as an important administrative responsibility, then departments must address a third significant constraint, insufficient training and experience in strategic planning procedures. They can do so by providing staff with strategic-planning educational opportunities. Programs meant to develop skills like human relations, analytical thinking, time management, and participatory decision making can greatly assist athletic departments in preparing to carry out the strategic planning process. In taking these two steps, athletic departments will encourage the perception of strategic planning as one of the primary responsibilities of management—not an auxiliary task.

The literature about strategic planning in intercollegiate athletics remains limited for now, even though interest in the topic appears to be growing. Further studies are needed, and the present study’s findings indicate that some of these future investigations might take up the following:

Three to five years from now, a follow-up study with the same sample of BUSA member institutions should seek out any changes in the way the university athletic departments are using the strategic planning process.

Also, further investigation with the same population might assess the extent of strategic planning from a qualitative perspective, one concerned with data from interviews, observation, and the study of official documents. Through observation and interview, for example, such issues as the membership of a strategic planning committee, the type of data applied to strategic planning, the methods by which those data were obtained, the leadership behavior involved in strategic planning, and resistance encountered to strategic planning could all be addressed in detail. Through study of official documents, researchers might gauge the extent to which documents reflect strategic issues like the assessment of external and internal environments.

Another useful investigation might be the evaluation of the relationship between how extensive the strategic planning activities of an athletic department are and the financial performance or productivity of the department. Such a study would require establishing appropriate measures of financial performance or productivity. An example would be the percentage of self-generated, not university-provided, revenue (e.g., sponsorships, concessions, ticket sales); or alternatively, the national performance of the total athletic program provided by the department.

Finally, future research should be undertaken to establish a valid, reliable strategic planning survey instrument for use in any United Kingdom university athletic department to evaluate the quantity and quality of its ongoing strategic planning activities, as well as the quality of the implementation of strategic plans it has previously developed.


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Impact of Cold Water Immersion on 5km Racing Performance


Much effort over the past 50 years has been devoted to research on training, but little is known about recovery after intense running efforts. Insufficient recovery impedes training and performance. Anecdotal evidence suggests that cold water immersion immediately following intense distance running efforts aids in next day performance perhaps by decreasing injury or increasing recovery. The purpose of this study was to compare 5 km racing performance after 24 hrs with and without cold water immersion. Twelve well-trained runners (9 males, 3 females) completed successive (within 24 hours) 5 km performance trials on two separate occasions. Immediately following the first baseline 5 km trial, runners were treated with ice water immersion for 12 minutes followed by 24 hrs of passive recovery (ICE). Another session involved two 5 km time trials: a baseline trial and another trial after 24 hrs of passive recovery (CON). Treatments occurred in a counterbalanced order and were separated by 6-7 days of normal training. ICE (20:08 ± 2.0 min) was not significantly different (p = 0.09) from baseline (19:59 ± 2.0 min). CON (19:59 ± 1.9 min) was significantly (p = 0.03) slower than baseline (19:49 ± 1.9 min). ICE heart rate (175.3 ± 7.6 b/min) was significantly (p = 0.02) less than baseline (178.3 ± 9.8 b/min), yet CON heart rate (177.3 ± 6.3 b/min) was the same as baseline (177.3 ± 7.3 b/min). ICE rate of perceived exertion (19.2 + 1.0) was significantly less (p = 0.03) than baseline (19.8 ± 0.5) while CON rate of perceived exertion (19.5 ± 0.8) was not significantly different (p = 0.39) from baseline (19.6 ± 0.8). Seven individuals responded negatively to ICE running a mean 24.0 ± 13.9 seconds slower than baseline. Nine individuals responded negatively to CON by running a mean 17.4 ± 12.1 seconds slower than baseline. Three individuals responded positively to ICE running a mean 20.33 ± 6.7 seconds faster during second day performance. Three individuals responded positively to CON by running a mean 13.3 ± 6.8 seconds faster than baseline. In general, cold water immersion minutely reduced the decline of next day performance, yet individual variability existed. Efficacy of longer durations of cold water immersion impact after 48 hrs and on distances greater than 5 km appear to be individual and need to be further explored.

Key words: cryotherapy, ice water immersion, passive recovery, running


Recovery from hard running efforts plays a vital role in determining when a runner can run at an intense level again (Fitzgerald, 2007). Hard training, followed by adequate recovery, allows the body to adapt to the unusual stress and become better accustomed and more prepared for the same stress, should it occur again (Fitzgerald, 2007; Sinclair, Olgesby, & Piepenberg, 2003). Balancing hard efforts with periods of rest is essential in improving performance during endurance efforts.

The recovery process from endurance efforts tends to revolve around repairing damaged muscle fibers and replenishing glycogen stores (Gomez et al., 2002; Nicholas et al., 1997). Methods proposed to enhance recovery, such as cold water immersion, potentially decrease swelling and the severity of delayed onset of muscle soreness (DOMS), which possibly benefits endurance (i.e. running) and anaerobic performance (Higdon, 1998; Vaile, Gill, & Blazevich, 2007).

Cold water immersion is a common practice among collegiate and professional athletes following intense physical efforts. Anecdotal evidence from several National Athletic Trainers’ Association (NATA) collegiate head athletic trainers suggests that cooling the legs after a hard training effort may benefit the next day’s performance. Popular running and athletic magazines (e.g., Runner’s World, Running Times, etc.) have continually suggested that applying cold water to the legs of a runner facilitates a better perceived feeling for the next run on the following day. Yet, despite its widespread use there is no scientific data supporting the notion that cooling the legs after a hard distance running effort will improve performance 24 hrs later.

The use of cold as a treatment is as ancient as the practice of medicine, dating back to Hippocrates (Stamford, 1996). The therapeutic use of cold is the most commonly used modality in the acute management of musculoskeletal injuries. Running is a catabolic process, with eccentric muscle contractions leading to muscle damage. Applying cold to an injured site decreases pain sensation, improves the metabolic rate of tissue, and allows uninjured tissue to survive a post-injury period of ischemia, or perhaps allows the tissue to be protected from the damaging enzymatic reactions that may accompany injury (Arnheim and Prentice, 1999; Merrick, Jutte, & Smith, 2003). The use of cryotherapy, between sets of “pulley exercises” (similar to a seated pulley row), decreased the feelings of fatigue of the arm and shoulder muscles of 10 male weight lifters (Verducci, 2000), while other cryotherapy research involving recovery from intense anaerobic efforts has yielded equivocal results (Barnett, 2006; Cheung, Hume, & Maxwell, 2003; Crowe, O’Connor, & Rudd, 2007; Howatson, Gaze, & Van Someren, 2005; Howatson and Van Someren, 2003; Isabell et al., 1992; Paddon-Jones and Quigley, 1997; Sellwood et al., 2007; Vaile, Gill, & Blazevich, 2007; Vaile et al., 2008; Yackzan, Adams, and Francis, 1984). However, methods of cryotherapy effective for enhancing recovery from distance running efforts have not been examined.

Long duration or high intensity running contributes to muscle cell damage (Fitzgerald, 2007; Noakes, 2003). Edema, a by-product of muscle damage can cause reduced range of joint motion. Because cryotherapy has been shown to decrease inflammation (Dolan et al., 1997; O’Conner and Wilder, 2001), it is logical to assume that this treatment may reduce the severity of DOMS. Less pain may permit an athlete to push themselves harder potentially improving performance. Despite the fact that previous research has shown that 24 hrs alone is not sufficient recovery from 5 km running performance (Bosak, Bishop, & Green, 2008), it might be possible that combining cold water immersion with 24 hrs of recovery could potentially hasten the recovery process. Therefore, the purpose of this study was to compare 5 km racing performance after 24 hrs of passive recovery with and without cold water immersion.



Participants for the study were 12 well trained male (n = 9) and female (n = 3) runners currently engaged in rigorous training. Runners from the local road running and track club, local triathlon competitors, as well as former competitive high school and college runners, were recruited by word of mouth. Participant inclusion criteria included the following: 1) Subjects must have been currently involved in a distance running training program; 2) Their 5 km times previously run had to be at least 16-22 min for male runners or 18-24 min for female runners; 3) They had to be currently averaging at least 20-30 miles (running) per week; 4) They had to have previously completed at least five 5 km road or track races; 5) They had to have a VO2max of at least 45 ml/kg/min (females) or 55 ml/kg/min (males); and 6) They had to provide sufficient data (from running history questionnaires, physical activity readiness questionnaires, and health readiness questionnaires) that reflected good health.

Participants completed a short questionnaire regarding their running background, racing history, and current training mileage. All participants were volunteers and signed a written informed consent outlining requirements as well as potential risks and benefits resulting from participating.


Participants were assessed for age, height, body weight, and body fat percentage using a 3-site skinfold technique (Brozek and Hanschel, 1961; Pollock, Schmidt, & Jackson, 1980). Participants were fitted with a Polar heart rate monitor, and then completed a graded exercise test (GXT) to exhaustion lasting approximately 12-18 min. VO2max, heart rate (HR), and ratings of perceived exertion (RPE) were collected every minute.

All GXTs were completed on a Quinton 640 motorized treadmill. The test began with a 2 min warm-up at 2.5 mph. Speed was increased to 5 mph for 2 min, followed by 2 min at 6 mph, 2 min at 7 mph, and 2 min at 7.5 mph. At this point, incline was increased two percent every 2 min thereafter until the participant reached volitional exhaustion (i.e. they felt like they could no longer continue running at the required speed and grade). Once the participant reached volitional exhaustion, they were instructed to cool down until they felt recovered.

Approximately five days later, participants performed their first 5 km race (performance trial) between the hours of 6:30 am to 7:30 am. The time of day for each performance trial was consistent throughout the entire study. All performance trials were completed on a flat hard-surfaced 0.73 mile loop. Prior to each trial, participants completed visual analog scales, before and after a 1.5 mile warm-up run, regarding their feelings of fatigue and soreness within local muscle groups (quadriceps, hamstrings, gastrocnemius), and for lower and total body muscle groups. Visual analog scales were 15 cm lines, where participants placed an “X” on the line indicating their feelings (with 0 = no fatigue or soreness and 15 = extreme fatigue or soreness). The focus of the visual analog scales was to determine if participants felt the same before the start of every time trial. Participants were also required to rate their perceived exertion (RPE) after the warm-up and prior to the start of each 5 km, during each trial, and at the end of each performance trial to determine if feelings of effort remained consistent between each trial, as well as during each lap and at the end of each trial.

Runners underwent a 1.5 mile warm-up prior to every 5 km performance trial (Kaufmann and Ware, 1977). Participants completed four 5 km performance trials within nine days. Two 5 km performance trials (baseline and CON) were separated by 24 hrs of passive recovery. Passive recovery was deemed as no exercise or extensive physical activity during the allotted recovery hours. Two 5 km performance trials (baseline and ICE) were also separated by 24 hrs of passive recovery, but with 12 minutes of 15.5ºC water immersion immediately following the baseline trial. The two sessions of 5 km performance trials were counterbalanced and were separated by 6-7 days of normal training. Each trial session therefore, had a separate baseline preceded by 24 hrs of passive recovery.

Ideal cryotherapeutic water temperature has not been determined, yet various head collegiate athletic trainers prefer that the water temperature does not dip below 13ºC (55.5ºF) since many people find water temperatures below 13ºC uncomfortable (O’Connor and Wilder, 2001). Also, the duration of ice baths generally lasts 10-15 minutes and is usually applied immediately after a hard training session (Crowe, O’Connor, & Rudd, 2007; Schniepp et al., 2002; Vaile et al., 2008). Hence, in this study, 15.5ºC (60ºF) was the temperature for the cold water and the athletes were immersed for 12 min.

During each time trial, average heart rate and ending RPE were recorded in order to determine if effort for each 5 km was consistent. All participants competed with runners of similar ability to simulate race day and hard training conditions, while verbal encouragement was provided often and equally to each participant. At the end of every performance trial, each runner was instructed to complete a low intensity 1.5 mile cool-down. Each total testing trial required approximately 60 min.

Statistical Analysis:

Basic descriptive statistics were computed. Repeated measures of analysis of variance (ANOVA) were employed for making comparisons between CON and baseline and PAS and baseline performance trials for the following variables: finishing times, HR, RPE, and fatigue or soreness responses. All statistical comparisons were made at an a priori p < .05 level of significance. Data were expressed as group mean + standard deviation and individual results.

In order to evaluate individual responses, data from each participant’s first run was compared to the second run using a paired T-test. The least significance group mean difference (p < 0.05) was determined and group mean finishing time was adjusted to determine the amount of change in seconds needed for significance to occur. The time change between the first trial run and the adjusted trial run baseline was divided by the first trial run and expressed as mean number of seconds or percent for both the ICE (9.3 seconds or 0.8%) and CON (9.5 seconds or 0.8%) trials. The percent values were applied to each individual baseline time in order to determine how many seconds (positive or negative) the second performance trial time had to be over or under the first performance trial, in both CON and ICE conditions, to quantify as a response. Participants were then labeled as non-responders, positive-responders (faster after treatment), and negative-responders (slower after treatment).


Descriptive characteristics are found in Table 1. The participants were between the ages of 18 and 35 (the majority of subjects were between ages 20-28) years. All participants were trained runners or triathletes (where running was their specialty event).

Mean finishing times, HR, and RPE for CON and ICE trials are found in Table 2. CON was significantly (p = 0.03) slower (10 seconds) than baseline, where as ICE was not significantly different (p = 0.09) from baseline. No significant differences were found between CON HR vs. baseline, but ICE HR was significantly (p = 0.01) less than baseline. No significant differences (p = 0.39) were found between CON RPE and baseline, yet ICE RPE was significantly (p = 0.03) less than baseline.

Figure 1 shows individual changes in finishing times for all CON and ICE performance trials. To be considered a non-responder, the individual time change had to fall within 0.8% of baseline performance for ICE and CON. Positive and negative responders (Table 3) were identified when individual time change was greater than 0.8% for CON and ICE trials, with a positive responder being one whose second performance trial time improved (expressed as a negative value) and a negative responder being one whose second performance trial time slowed (expressed as a positive value).

Seven individuals responded negatively to ICE by running a mean 24.0 ± 13.9 seconds slower during the second trial (Table 3). Three individuals responded positively to ICE by running a mean 20.3 ± 6.7 seconds faster than baseline. Two individuals were considered non-responders to ICE with a mean time change of 2.5 ± 0.7secs.

Seven individuals responded negatively to CON by running a mean 20.6 ± 9.0 seconds slower than baseline (Table 3). Three individuals responded positively to CON by running a mean 13.3 ± 6.8 seconds faster than baseline. Two individuals were non-responders to the CON trials with a mean time change of 6.5 ± 0.7 seconds. It is important to note that the seven individuals who were negative responders to ICE were not the same seven participants who responded negatively to CON. Also, the three participants who responded positively to ICE were not the same three individuals who responded positively to CON. Finally, the non-responders to ICE were not the same non-responders to CON.

Soreness and fatigue scores (Table 4) on the pre-and post-warm-up fatigue or soreness visual analog scales were not significantly different between CON and baseline versus ICE and baseline.


The effects of cold-water immersion on recovery and next day performance in 5 km racing have not been previously evaluated. Therefore, the primary purpose of this study was to compare 5 km running performance after 24 hrs of passive recovery with and without cold water immersion. This study appeared to indicate that cold water immersion does not dramatically help performance (regarding the group of runners as a whole) during second day 5 km trials.

Twenty-four hours of passive recovery may allow for normalization of muscle and liver glycogen, yet muscle function and performance measures may not be fully recovered (Foss and Keteyian, 1998). Hence, 24 hrs of recovery, by itself, may not be sufficient to allow for a return to optimal performance (Bosak, Bishop, & Green, 2008). When racing (e.g., a 5 km distance) on consecutive days, race times may be slower on the second day due to magnified perception of pain and impaired muscle function associated with DOMS (Brown and Henderson, 2002; Fitzgerald, 2007; Galloway, 1984). Since cold water immersion may speed up the recovery process (Arnheim and Prentice, 1999; Vaile et al., 2008) it is logical to assume that cold water immersion immediately after a 5 km race or workout could attenuate soreness potentially minimizing performance decrements on successive days.

There were no significant (p = 0.09) differences in 5 km performance between ICE and baseline, indicating that mean performance during ICE was not significantly slower (9 seconds) than baseline (refer to Table 2). However, CON performance was significantly (p = 0.03) slower (10 seconds) than baseline. Hence, due to significant differences occurring between ICE and baseline, it appears that cold water immersion slightly attenuated the rate of decline on successive 5 km time trial performance. However, the time difference between CON and baseline versus ICE and baseline was a mere second. Therefore, from a practical standpoint, cold water immersion was no more beneficial than CON on successive 5 km performance.

Despite the minimal differences between CON (10 seconds) and ICE (9 seconds) trials regarding mean time change, it is important to focus on the effects of cold water immersion on individual runners (Figure 1). Because some runners ran slower during successive performance trials while other runners ran faster, the mean finishing times do not necessarily give a true impression of the benefits or liabilities of the specific treatments involved in this study. As it is with most ergogenic aids, individual variability suggests what works (e.g., ice) for one person may not work the same for another person. It is possible that the treatment may often not have an effect at all, as similar to what occurred with several prior anaerobic performance studies (Barnett, 2006; Cheung, Hume, & Maxwell, 2003; Crowe, O’Connor, & Rudd, 2007; Howatson, Gaze, & Van Someren, 2005; Howatson and Van Someren, 2003; Isabell et al., 1992; Paddon-Jones and Quigley, 1997; Sellwood et al., 2007; Vaile et al., 2008), which was also the case in this study as two individuals were considered non-responders to ICE with a mean time change of 2.5 ± 0.7 seconds between ICE and baseline, while two other participants were non-responders to CON with a mean time change of 6.5 ± 0.7 seconds between CON and baseline.

Three individuals responded positively (Table 3) to ICE, running a mean 20.33 ± 6.7 seconds faster, indicating that cold water immersion may have actually allowed these individuals to run faster on the second day. However, 3 different individuals responded positively to CON, running a mean 13.3 ± 6.8 seconds faster than baseline. The mechanism by which cold water immersion aids in recovery, from endurance performance, remains somewhat unclear and equivocal (Schniepp et al., 2002; Vaile et al., 2008). Yet, several runners who did run faster during ICE trial, verbally indicated that prior to the second trial, their legs felt better (regarding fatigue and soreness) than they had prior to CON. Thus, the notion of feeling better may have allowed the runners to perform faster.

Seven individuals responded negatively (Table 3) to ICE, running a mean 24.0 ± 13.9 seconds slower. However, they were not the same seven individuals who responded negatively to CON, who ran an average of 20.6 ± 9.0 seconds slower than baseline. As was the case with Schniepp et al. (2002) endurance cycling recovery study and various anaerobic performance studies (Crowe, O’Connor, & Rudd, 2007; Sellwood et al., 2002; Vaile et al., 2008; Yackzan, Adams, & Francis, 1984), it appears ICE may have had a more negative effect, for these individuals, on second day performance compared to CON.

Three individuals responded positively to CON running a mean 13.3 ± 6.8 seconds faster during the second day performance trial. It is unclear why some participants ran faster during CON. There were no consistent patterns of HR and increased or decreased performance with all participants during all CON and ICE trials. As a group, no significant differences were found between CON vs. baseline, regarding HR (p = 1.00) and RPE (p = 0.39), despite significant differences (p = 0.04) occurring in mean finishing time. However, mean finishing times for ICE were similar, yet significant differences were found between ICE vs. baseline for both HR (p = 0.01) and RPE (p = 0.03). Hence, there does not appear to be a consistent pattern between performance times and HR and/or RPE.

It can be assumed that a lower HR may be associated with slower times, since HR and intensity levels tend to be linearly related. However, only participants 1, 5, and 6 consistently ran slower during both CON and ICE second day performances with lower HR during both trials. During the ICE trials, only participants 1, 5, 6, and 9 ran slower and had a lower HR. During the CON trials, only 1, 3, 5, 6, ran slower and had a lower HR. Also, soreness and fatigue scores (Table 4) on the pre and post warm-up fatigue or soreness visual analog scales were not significantly different between CON and baseline versus ICE and baseline. These results indicate that all runners tended to feel the same prior to each second day 5 km trial. Therefore, since inconsistencies exist between HR and performance trials and no significant differences were found regarding RPE and fatigue or soreness visual analog scales, it is assumed that each participant completed each trial with similar effort.


The current findings of this study suggest that cold water immersion does not sufficiently enhance recovery (specifically regarding the group of runners as a whole). However, three runners benefited from cold water immersion. Hence, what works for one person may not work for another person. Thus, it may be beneficial for runners to undergo this protocol in order to see which type of recovery method improves their recovery process. Secondly, the results of the study may give credence to some runners’ perception of feeling better due to cold water immersion after a hard running effort. However, one should remember that individual variability existed in response to treatment (ice immersion) within the current study. Future research is needed to see if a greater length of time or slightly lower water temperature in cold water immersion will decrease the rate of decline more or if the effects of cold water immersion are even more predominant on second day performance of distances greater than 5 km.


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Implementing a Breathing Technique to Manage Performance Anxiety in Softball


An intervention strategy was developed, implemented, and evaluated that aimed at minimizing performance anxiety. The goal was to guide NCAA Division I softball athletes in using a breathing technique that, by contributing to the management of performance anxiety, would help each athlete reach full potential on the softball field. The strategy focused on the effects of the breathing technique on the participants’ heart rates, in relation to daily anxiety events; a heart rate monitor and anxiety logs were used to obtain data. All 4 of the athletes studied indicated improvement at various stages in the program.

Top high school athletes who are recruited to college teams may have seen little—or even no—failure in their athletic careers to that point. But at the college level, there are aspects that separate the good athlete from the great. The separation can be the difference between winning and losing. For some athletes, the first real failure is faced during the freshman year in college. First-year college softball athletes, for instance, may begin to realize that the game is quicker, the opponents are stronger, and their teammates share skills and strengths similar to their own. Some athletes rise to the challenge, while others become anxious and may fail.

It is not uncommon for a coach to witness an athlete succumb to such anxiety. Sometimes, an athlete simply walks to the plate mentally defeated, before a single pitch has been thrown. But coaches strive to prepare all athletes with the skills they need to perform in these situations. So why do some athletes perform well, while others cannot?

Those who perform well manage their emotions in critical situations, in a way that enables them to develop an ideal mental state that fosters maximum performance. During competitions in which they fail to achieve this state, many athletes become victims of their own anxieties. The performance psychologist David Roland defined performance anxiety as stage fright, suggesting artists feel apprehensive about approaching the stage and performing (1997). Athletes may also suffer from stage fright and may experience a debilitating effect on their performance.

Anxiety and Performance
In this study, anxiety is identified specifically as competitive anxiety, anxiety experienced while competing. Anxiety is under the umbrella of arousal, but it is typically associated with negative cognitive thoughts, such as worry or a perception of threat (Gill, 2000; Landers & Arent, 2006). An example might be a field goal kicker who is remembering an earlier missed field goal attempt or a batter who does not want to hit in a close-scoring game. Anxiety is a complex behavior with emotional, mental, and physical dimensions. Typically, anxious individuals experience a variety of bodily and mental symptoms such as loss of concentration, thoughts about failure, agitation, increased breathing (Roland, 1997).

Purpose of Study

Performance anxiety is rarely addressed, though coaches often identify certain athletes as “chokers” under pressure. The current study’s purpose, therefore, was to develop a strategy that would guide softball athletes in becoming relatively more aware of their somatic anxiety levels and offer a means of minimizing their performance anxiety.


The participants in this study were 4 members of a 2008 NCAA Division I collegiate softball team ranging from 18 to 21 years of age, 2 pitchers and 2 position players. One pitcher was a first-year student; the other 3 athletes were upperclassmen. No participant had prior exposure to or experience with breathing techniques or breathing exercises.

Procedure and Data Collection
The study included three stages. The first stage was completed with an overview of the relationship between performance and anxiety. The second stage involved working with a sports psychologist to determine a breathing technique strategy and to implement the strategy. The third stage involved measuring the effects of the program on the 4 athletes.

Anxiety Measurement Tools
This study incorporated the Sport Competition Anxiety Text (SCAT) first developed by Martens in 1977 as a self-report measurement of competitive anxiety. The SCAT consists of 15 questions that measure how a person feels during competition (Martens, 1990). This test has met the accepted standards for psychological tests and has been deemed valid and reliable (Gill, 2000). Smith et al. (1990) expanded on Martens’ SCAT and multidimensional models, developing the Sport Anxiety Scale (SAS), a sports-specific anxiety scale for sports-specific measurements of anxiety using cognitive traits (worry, concentration) and somatic traits (heart rate, breathing) (Gill, 2000).
Also used in the current study was the Competition State Anxiety Inventory 2 (CSAI 2), which is a multidimensional inventory intending to measure sports-specific state anxiety. The CSAI 2 also separately evaluates cognitive worry and somatic anxiety (Gill, 2000). It consists of 27 questions.

Breathing Techniques
Breathing techniques have been used for years to manage anxiety. Practicing slow, deep breathing is one way to control the autonomic response to anxiety (Gill, 2000). Ungerleider (2005) identified how breathing impacts performance. He indicated that oxygenated blood can energize the brain, nerves, and muscles. The present study incorporated Ungerleider’s breathing technique, a regimen that involves breathing using the diaphragm. The technique helps fill the lungs from bottom to top, impacting the amount of air taken into the body and thus how much oxygenated blood is available throughout the body.

Program and Intervention
During Week 1 and Week 2 of the study, baseline data for heart rates were recorded. The data included heart rate measures taken during practice, during drills, and during games, without the participants’ receiving any intervention or instructional strategies. Each participant’s heart rate was recorded daily on the heart rate monitor and the data transferred to a computer program that indicated the daily levels.

During these initial weeks, the athletes were introduced to the heart rate monitors they would wear at every practice and game for 5 weeks, to measure the effect of the intervention strategies. The participants utilized Team Polar Heart Rate Monitors. At the conclusion of each practice or game, each monitor was linked to a computer system and the collected information was downloaded to a computer. The athletes were instructed to “spike” their heart rates at a specific time prior to each practice or game. To spike the heart rate, a participant sprinted 200 feet. At the conclusion of the sprint, a stopwatch was started and stopped at 2 minutes and 30 seconds. The stopwatch was used to identify specific times in practices and games as a time stamp when the heart spiked. These time stamps were recorded, noting specifically what the athlete was doing during this interval. These time stamps were also used and matched with specific anxiety events in order to indicate specific heart rate levels.

Week 3 was spent instructing the athletes concerning somatic anxiety and introducing them to a breathing technique and anxiety log devised by sports and performance psychologist Dr. Chris. Carr (personal communication, December 21 2007 ). For the next 3 weeks, the 4 athletes were asked to practice the breathing technique 1 time each day, during their spare time. They were also to complete the anxiety log immediately following practice and games. They were taught to identify the anxiety responses of muscle tension, changes in heart rate, changes in breathing, and sweating. These lessons were implemented so that the participants would understand somatic anxiety responses and be able to maintain the anxiety log in greater detail.

The 4 athletes were taught to use the breathing technique for relaxation; eventually, they were able to use the technique to relax even in noisy environments. The technique required the athlete to lie down, get comfortable, and begin rhythmic breathing (i.e., inhaling into the belly and exhaling). As breathing began to slow, participants were instructed to focus on the release of tension, relaxing every muscle of the body from top to bottom. The sole purpose behind the breathing technique was to control tension in the muscles.

The anxiety log was used by the participants to record their anxiety levels during practice and competition. In the log, a participant assigned a number 1 through 10 (1represented weak, 10 represented high) to her anxiety level and listed any applicable somatic anxiety responses associated with that number. The anxiety log was also used to indicate when specific events at practice or in a game had been accompanied by anxiety. These specific events were coded with chronological times, so that they could ultimately be coded with specific heart rates. For example, if an anxiety log noted that a participant had felt anxious during her third time at bat, when runners were in scoring position, the researchers then located the chronological time of her third at bat and matched it with a specific heart rate associated with the participant’s third at bat.

In Week 4 and Week 5, the participants were asked to note any onset of somatic anxiety responses and to immediately begin a shortened breathing exercise to regain control of those somatic responses. They were asked to record in their anxiety logs how successful they had been at reestablishing control. This use of the shortened breathing exercise during Week 4 and Week 5 was in addition to their daily assigned breathing exercises.

Data Analysis
Using the heart monitor data, the researchers determined each participant’s average heart rate during Weeks 1–2 (baseline), during Week 3 (implementation), and during Weeks 4–5 (program). Each day during the program phase (Weeks 4–5), participants had noted at least one specific anxiety event and described the heart rate accompanying that event as either high, average, or low. The daily heart rates overall during the baseline, implementation, and program phases were then described as being accompanied by, on average, either a high, average, or low heart rate. Using these averages, evaluation was made of whether and to what extent the participants’ heart rates during anxiety events had changed over the course of the 5-week program.

The anxiety logs were also collected and reviewed for evidence of how the intervention techniques affected participants during the implementation and program phases. Daily anxiety log levels were also matched with heart rates (high, average, low) during anxiety events, using the time stamps on these events as they occurred during practices and games. Anxiety logs and heart rate data were then compared for each anxiety event, generating evidence of how the breathing techniques had affected the participants.

Results, Discussion, and Conclusion

For all 4 athletes in the study, a decrease in average heart rate was recorded during the implementation phase of the study; 3 athletes experienced a decrease in average low heart rate during this period. In addition, 75% of participants saw their average low heart rate decrease, while 50% saw their average high heart rate decrease.

Player KM, one of the position players in the sample, started the study with an average spiked heart rate of 142 bpm (beats per minute) (see Figure 1). After entering the implementation phase, Player KM’s heart rate decreased slightly, to 140 bpm, and in the program phase it ranged up to 151 bpm (see Figure 1). This is possibly due to the anxiety events Player KM dealt with in the concluding 2-week program phrase. In two instances, she recorded extremely high heart rates, and she noted in her anxiety log, “When I first made the error I was mad I could feel my heart racing, I did my breathing, felt tense and still remained angry while breathing, couldn’t get my body to relax and recover.”

Player AS, the other position player, recorded an average heart rate of 149 bpm during the baseline phase, of 144 during the implementation phase, and of 139 during the program phase (see Figure 1). She had a greater improvement in the implementation and program phases of the study . On numerous occasions, Player AS noted in her anxiety log that she had felt her body relax as she continuously participated in the breathing exercise. Player AS’s log included precise detail about her recognition of somatic anxiety responses, as for example, “hitting off Coach, emotions took over, started to think about other things, started to get tight all over and it became harder to breath[e] with every swing.” Across the implementation and program phases, Player AS became more aware of her somatic responses, resulting in her ability to immediately implement the breathing exercises.

In the study Player SR, a pitcher, recorded a baseline of 150 bpm, an implementation phase average of 141 bpm , and a program phase average of 150 bpm (see Figure 1). Player SR’s average heart rates decreased during implementation but then returned to 150 bpm during the program phase. While pitching during the final 2 weeks of the season, which coincided with the program phase, Player SR gave up many hits. Her anxiety log indicated that she failed to control her anxiety levels: “Heart pounding tension in upper body, tried to breath[e] and calm myself, but it didn’t help.”

Player DA, another pitcher, exhibited little to no decrease in average heart rate during the implementation phase, moving only from 141 to 140 bpm (see Figure 1). However, during the program phase, her average heart rate fell to 132 bpm. As was validated by the heart rate data and her anxiety log, Player DA managed to control her emotions during stressful situations. She noted, “Was calm prior to inning feeling relaxed, as I got behind in the count I refocused on my task, began breathing and felt body regain control.”

Figure 1. Average participant heart rates (in beats per minute) across baseline, implementation, and program phases of breathing technique study.

By Week 3, after implementation, Player AS and Player 4 had decreased their average heart rates, and they continued to decrease them throughout the program phase (see Figure 1). Very little change occurred in any participant’s recorded “average of low ” heart rate (see Figure 2). Player KM recorded a baseline of 131 bpm, an implementation-phase rate of 127 bpm, and a program-phase rate of 141 bpm (see Figure 3). In the 2 weeks of the program phase, Player KM exhibited a higher heart rate, while implementing the breathing technique. As she indicated in her anxiety log, she “couldn’t bring my breathing down and could feel heart pounding after error even after attempting to breath[e].” This inability to gain control of her breathing had a direct role in Player KM’s heart rate remaining high. Player AS had a baseline of 111 bpm, an implementation-phase rate of 126, and a program-phase rate of 118 (see Figure 2). No anxiety events were recorded in Player AS’s anxiety log during the implementation period that would seem to account for her elevated average heart rate. For Player SR, the average of low heart rates of 137 bpm, 121 bpm, and 134 bpm, respectively, were recorded during the baseline, implementation, and program phases (see Figure 2). Player SR noted in her anxiety log that, “As I recognized my heart rate increasing I began to notice how quickly I could make it slower while just breathing.” Player SR indicated that on occasion while using the breathing techniques, she felt her body became calm, and when it did she could lower her heart rate almost at will.

The least amount of change in “average of low” heart rate levels was exhibited by Player DA. For her, rates of 126, 124, and 120, respectively, were recorded during the baseline, implementation, and program phases (see Figure 2). Player DA indicated in her anxiety log that she had a good awareness of her body and that as her heart rate stayed consistently low, so did her anxiety level, which was the intent.

Player AS showed an increase from her baseline average low to her program average low; whereas Player SR and Player DA showed a decrease , which was the intent (see Figure 2). This may be explained by the fact that all participants were to reach a standard low heart rate associated with a reduction in somatic anxiety responses. For example, if Player DA feels few somatic indications of anxiety while experiencing a heart rate of 125 bpm, then whatever the situation on the field, another player who reaches 125 bpm may not perceive a need for a heart rate below 125 bpm.

Figure 2. Average of lows (in beats per minute) across baseline, implementation, and program phases of breathing technique study.

The recorded average high heart rate for each player accompanying specific anxiety events noted in the anxiety log indicated that for 2 players, average high heart rate significantly decreased, whereas for 2 it increased. Player KM’s average high heart rate increased; she recorded 152, 153, and 161 bpm for the baseline, implementation, and program phases, respectively (see Figure 3). These increases in average high heart rate can be linked to Player KM’s anxiety log, which indicates that she twice became extremely frustrated prior to attempting to implement the breathing technique.

Player AS displayed impressive improvements in her average high heart rate level. She recorded lower levels throughout the study: 165 bpm, 163 bpm, and 159 bpm, respectively, for the baseline, implementation, and program phases (see Figure 3). Player AS indicated in her anxiety log her ability to recognize somatic anxiety responses immediately and follow through by beginning the breathing technique. Using the breathing technique, she saw her high heart rate gradually decrease , which was the intent.

Player SR recorded heart rates of 163 bpm, 151 bpm, and 167 bpm, respectively, for the baseline, implementation, and program phases (see Figure 3). Her anxiety logs indicated that she was well able to recognize anxiety events and somatic responses, but she failed to implement the breathing technique right away when the responses were noted.

For Player DA, average high heart rate decreased; she recorded a rate of 151 bpm during the baseline phase, 156 bpm during the implementation phase, and 144 bpm during the program phase (see Figure 3 ). Player DA’s significant heart rate decrease over the final two weeks of the season and the study may be directly related to her mastery of the breathing technique.

Figure 3. Average of high (in beats per minute) across baseline, implementation, and program phases of breathing technique study.

The purpose of this study was to evaluate the use of a breathing technique to decrease performance anxiety in collegiate softball athletes. Measures of the athletes’ heart rates showed that each of the 4 was able to use the breathing technique to decrease heart rate at some point during the implementation and program phases of the study. Overall, however, Player AS and Player DA reached the intent by exhibiting a decrease in heart rate, but Player KM and Player SR did not.

The anxiety logs describe the participants’ experiences beyond simple heart rate fluctuations. At the conclusion of the baseline phase, all of them noted improvement in their ability to recognize anxiety events; all but one used precise detail to describe personal somatic responses to anxiety.

Once the players understood somatic anxiety responses, they were asked to utilize the breathing technique during anxiety events. Athletes acknowledged and rated the anxiety they experienced. For Week 4 and Week 5, the program phase, the players’ anxiety logs described lower anxiety levels and indicated fewer feelings of apprehension or nervousness. Player SR rated one specific event as an 8: “Runners on 1st and 2nd, tension in shoulders, negative thoughts. Told myself to stay positive and breath[e] . . . got out of the inning!” This is one example where a player rated the event anxiety level, recognized somatic responses, and implemented the breathing technique, deriving a positive result. This same player’s heart rate, however, exhibited an increase in average and average high levels across the study (see Figures 1 and 3), and the evidence from the anxiety log is not supported by the player’s heart rate data.

All 4 players’ anxiety logs indicated at some point that the newfound awareness of somatic responses to anxiety made the experience of anxiety less intimidating . The players indicated that they felt in control as they managed their breathing patterns. The 4 athletes reported that while engaging in the breathing exercises, they felt able to regain control over their anxiety.

Many factors, some unknown, may affect an athlete’s anxiety level, and the factors may or may not be controllable during studies. Nevertheless, this study of a proposed anxiety-reducing breathing technique created successful results for the 4 softball athletes studied, decreasing their heart rates during various anxiety events at softball practices and games.


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