Sports Coaching – Page 25 – The Sport Journal

Pay and Performance: An Examination of Texas High School Football Coaches

Submitted by: Scott J. Callan and Janet M. Thomas – Bentley College

Abstract

Salaries paid to high school coaches and team managers have recently generated media and public debate over their justifiability. This research represents an earnings function estimation designed to identify salary determinants for high school football coaches. The theoretical model supporting the analysis builds on models presented in the sports economics literature. To conduct the empirical estimation, we used salary, human capital, performance, and institutional data for coaches of Class 4A and Class 5A 11-man high school football programs in Texas (N = 95). Our results indicate that the determination of overall coaching compensation is significantly affected by human capital investment, measured through experience; by job performance, captured in winning percentage; and by school characteristics, such as location and stadium size.

Pay and Performance: An Examination of Texas High School Football Coaches

Over the past decade, economic investigations of professional sports teams—particularly pay-for-performance studies—have become increasingly prevalent. This emerging research trend has evolved in part because of the broad applicability of economic principles to sporting contexts and also because of the increasing availability of performance and salary data for professional sports participants. Although it has not always been the case, reliable data for selected amateur sports, such as NCAA golf, are also starting to become available, allowing researchers to apply economic reasoning to these varied and important sports environments. (Examples are Callan and Thomas, 2004, 2006, which are investigations of the determinants of success in amateur golf that employed two different samples of NCAA golfers.)

From a theoretical perspective, economic research on sports salaries and performance builds on human capital theory, as first suggested by Becker (1964). Critical to this theory is the belief that education and experience play a significant role in the determination of a worker’s performance and earnings. Simply stated, investments in human capital, such as education, training, and work-related experience, are expected to positively influence compensation.

As for the empirical testing of these theoretical models, most salary investigations within the professional sports literature have focused on individual players as opposed to coaches or managers. It is also the case that most used an earnings function model similar to the one developed by Scully (1974), who studied salary determinants for Major League Baseball players. Consistent with Becker’s (1964) fundamental hypothesis, Scully’s model assumes that a professional baseball player’s development of human capital and skill are critical determinants of his earnings. Since Scully’s original work, numerous studies have adapted his model to other sports settings. For example, Jones and Walsh (1988) examined salary determination for players in the National Hockey League, and Hamilton (1997) did the same for players in the National Basketball Association.

Despite the accumulating research on players’ salaries in various sports, we know of only two papers that adapted Scully’s (1974) original model to an examination of the earnings of team managers or coaches. One is a study by Kahn (1993), and the other is an investigation conducted by Humphreys (2000). A brief overview of each follows.

Kahn (1993) used 1987 data for professional baseball teams to estimate an earnings function for team managers, which in turn was used to analyze managerial quality. Following human capital theory, Kahn’s model specifies earnings as the natural log of manager salary and includes the following as explanatory variables: years of managerial experience; lifetime winning percentage; and a binary variable to control for league (i.e., American or National). Kahn asserts that there are at least two reasons why experience is expected to have a positive effect on earnings. Specifically, more years of experience should reflect (a) greater skills, developed through on-the-job training, and (b) longevity, based on relatively high-quality management ability exhibited over time. Winning percentage captures team performance or success, which also should positively affect earnings, and the binary league variable controls for any league-specific differences in the demand for managerial quality. As expected, Kahn’s results showed that a manager’s experience level and career winning percentage have significant and positive effects on salary, although the league variable was not found to be statistically significant.

Humphreys (2000) used Division I NCAA basketball program data for the 1990–1991 academic year to test for possible gender-based differences in compensation among head basketball coaches. Similar to Kahn’s model, Humphreys’s earnings function defines the dependent variable as the log of annual base salary. Two groups of hypothesized salary determinants are specified: a set of coach characteristics and several control variables to represent the institution where each coach is employed. For the coach characteristics, Humphreys included a dummy variable for gender; experience, in years, to represent investment in human capital; and career winning percentage to measure job performance. In accordance with conventional human capital theory, both experience and winning percentage were assumed to have a positive effect on salary. The institution-specific control variables were intended to capture potential demand-side influences on a coach’s earnings. Included among these were total student enrollment, ticket revenues, and school location. The underlying hypothesis was that greater demand for basketball entertainment, which can be proxied by higher enrollment and larger revenues, should positively influence a coach’s salary.

Humphreys’s empirical estimation across several variations of his model found neither gender nor experience to be significant. However, the results did suggest that performance (measured through career winning percentage) positively affects earnings. Humphreys believed that a high correlation between performance and experience in his sample likely explained the lack of significance found for the experience parameter. Among the institutional control variables, Humphreys found that total enrollment, participation in Division IA games, and ticket revenues exhibited consistently positive effects on collegiate basketball coaches’ salaries.

Clearly, the studies by Kahn (1993) and Humphreys (2000) have helped to identify some of the factors responsible for manager or coach salaries at the professional and collegiate level, respectively. However, to our knowledge, no analogous earnings function estimations exist for noncollegiate amateur coaches, leaving many questions unanswered.

At least until recently, the primary reason for this lack of research on noncollegiate school sports was, apparently, limited or nonexistent data. However, reliable data on high school football in some regions of the United States have now become available. That such a turn of events is timely is evidenced in part by recent media attention to high school coaches’ salaries, particularly in comparison to teachers’ and other school administrators’ salaries. Some journalists report on the relatively high salaries earned by high school football coaches, particularly in the southern and western United States, where high school football is markedly more important to local communities than in other regions (Jacob, 2006; Associated Press, 2006). Others, such as Abramson (2006), counter with a different perspective about coaches’ earnings, referring to long hours worked, particularly in so-called football states like Texas, Florida, and Georgia.

A related issue raised by the media is the extraordinary level of monetary investments made in some high school football programs, an observation that some find particularly striking in the face of funding cuts for educational resources and programs. In a recent issue of a national newspaper, Wieberg (2004) reported on multimillion-dollar projects in Texas, Georgia, and Indiana to build state-of-the art high school football stadiums. This trend, he argued, arises from a competitive race involving high-end facilities and highly paid coaches that has trickled down from the college level. In some states, such competition arises from open enrollment policies, under which schools literally compete for students to preserve their state funding (which is linked to enrollment). Schools also compete for a strong fan base to generate revenues to help support the costs of football programs—including elevated salaries for coaches, some reportedly reaching six figures. Such activity, which is consistent with the demand-side effects on salary suggested by Humphreys (2000), identifies another motivation for exploring the issue empirically.

The present research addressed the critical issues by empirically examining salary determinants for a sample of high school football coaches in Texas. There were a number of reasons for using Texas as the context of the analysis. First, high school football is enormously popular in Texas, and schools there invest heavily in football programs. These observations translate to a favorable opportunity to study demand-side salary determinants for coaches along with the usual human capital factors. Second, and perhaps not unrelated to the first reason, the necessary sample data to conduct an empirical estimation of earnings have become available for the state. Third, because Texas high school football is nationally recognized, we anticipated that our findings concerning Texas coaches would both call attention to underlying issues and stimulate new research on salary determination for those who coach in other parts of the country and in other high school sports.

Method

Sample

Reflecting both data availability and our motivation to capture possible demand-side factors in our model, the sample for this study was 95 head coaches at Class 4A and Class 5A Texas high schools during the 2005–2006 football season. Oversight of high school football in Texas is provided by the University Interscholastic League (UIL). The UIL is a nonprofit organization with a purpose to “organize and properly supervise contests that assist in preparing students for citizenship” (About the UIL, n.d., ¶3); extracurricular activities outside athletics also fall within UIL’s purview. The UIL organizes Texas high school football contests based on schools’ geographic locations and enrollments. It divides football programs into 6-man and 11-man classifications. Most small schools (i.e., those with fewer than 100 enrolled students) participate in 6-man football, but the majority of Texas high school football programs are 11-man programs. The sample for this study was drawn from 11-man programs only.

Giving greater context for our analysis, table 1 presents the breakdown by classification of the 1,033 11-man high school football programs in Texas. The UIL identifies 32 geographic districts within Texas. The average number of football teams within each district ranges from 5.13 in Class 1A, to 7.53 and 7.69, respectively, in the larger 4A and 5A classes. The data indicate that significant enrollment differences exist across these various conferences. Classes 4A and 5A comprise the largest schools, those with enrollments as high as 2,084 and 5,852, respectively.

Table 1

2008–2009 Season Data for Texas High School 11-Man Football Teams, by Class

Class	Number of districts with football programs in the class	Number of schools with football programs	Average number of schools per district	Minimum enrollment	Mid-point enrollment	Maximum enrollment
1A	32	164	5.13	69.00	134.00	199.00
2A	31	205	6.61	201.00	314.75	428.50
3A	32	177	5.53	222.00	599.00	976.00
4A	32	241	7.53	533.00	1,308.50	2,084.00
5A	32	246	7.69	1,515.00	3,684.00	5,852.00

Note. Conference 2A spans 32 districts, but no school in District 24 has an 11-man football program. From “Alignments (updated for 2008–2010),” n.d., retrieved June 14, 2008, from http://www.uil.utexas.edu/athletics/football/

Measures

For each coach in our sample, we collected earnings data for the 2005–2006 academic year from a Dallas Morning News article, creating our empirical model’s dependent variable, SALARY (Jacob, 2006). According to a recent article in the popular press, a Class 4A or Class 5A head coach typically works 70–100 hr per week and is under contract for a 226-day work year (Texas Twist, 2006). Some coaches also teach, and some hold administrative positions such as athletic coordinator or athletic director. Our empirical model defined the variable ADMIN as a binary variable equal to 1 for a coach having administrative responsibilities or to 0 otherwise. We expected that coaches with administrative positions in addition to coaching responsibilities would earn higher salaries than those with coaching responsibilities only. Hence, we anticipated that the estimated parameter associated with ADMIN would be positive.

To capture each coach’s investment in human capital, we defined two distinct measures, GAMES and ROOKIE. Because the number of contests each team plays annually is fairly consistent, the GAMES variable was allowed to serve as a proxy for each coach’s cumulative head coaching experience in years (the data we would have preferred as our measure of human capital investment, had they been available). The GAMES variable actually measured the cumulative number of games for which an individual had acted as a head coach. Increases in this human capital variable were expected to have a positive influence on coaches’ salaries. The binary variable ROOKIE equaled 1 for a coach who was a rookie head coach (i.e., had no more than one year’s experience) and 0 for more experienced coaches. We anticipated that the parameter on this variable would be negative, reflecting the market’s ability to pay a rookie coach a lower salary than a veteran coach.

The sports economics literature suggests that in addition to experience level, how able a coach is, reflected in job performance, is an important determinant of compensation. Both Kahn (1993) and Humphreys (2000) used a coach’s career winning percentage to capture job performance. Following their approach, we defined a variable, WP, to measure the overall career winning percentage for each coach in our sample. If a coach’s winning percentage increased, we hypothesized, his salary will be higher, holding all other factors constant.

We further theorized that a coach’s salary would be influenced by demand-side characteristics (Humphreys, 2000), which would be linked to attributes of the high school employing the coach. One such characteristic was student enrollment, which we measured in the ENROLL variable, obtaining data from PigskinPrep.com, a website devoted to Texas high school football. (PigskinPrep.com’s Class 4A data was found at www.texasfootballratings.com/4ADistEnrollmentRealign.html and its Class 5A data at www.texasfootballratings.com/5ADistEnrollmentRealign.html). Schools with larger enrollments are expected to pay their coaches higher salaries, so we expected to find a positive relationship between ENROLL and SALARY.

Moreover, because Texas football has a following that extends beyond the student body, it was important to include some measure of community demand for the sport. Indeed, H. G. Bissinger (1990) suggests, in his best-selling book Friday Night Lights, that football in Texas is a community event. Therefore, we included the variable STADIUM in our empirical model to measure seating capacity at the facility where each coach’s school played its home games; the Texas High School Stadium Database (www.texasbob.com/stadium) provided the measures for each stadium. STADIUM was intended to capture a community’s market demand for high school football. Adapting Humphreys’s (2000) logic to our model, we expected that high school teams playing in larger stadiums would generate more revenue than those playing in smaller facilities, yielding more funds with which to compensate their head coaches, and hence we expected STADIUM to be positively related to SALARY. While we viewed stadium capacity as a reasonable proxy, we would have preferred including ticket revenues directly in our model, as Humphreys did, had such data been available for the individual Texas high schools. UIL does track football gate receipts for Texas high schools as a group. They totaled $1,102,798 for the 2005–2006 season, more than any other high school sport in Texas generated (West, Davis, and Company, 2008).

Lastly, following Humphreys (2000) we included a location-specific variable, DALLAS, in our model. The measure is a binary variable equal to 1 for a school located in the Dallas school district or to 0 otherwise. The variable controls any salary differences associated with location in the Dallas urban district. Earnings levels in urban districts may differ from those in other districts, due to differences in cost of living and/or population. However, since the relative magnitude of any such effect was not known a priori, the qualitative relationship between SALARY and DALLAS could not be predicted.

Procedures

To estimate the earnings function for each head coach in the sample, we used multiple regression analysis to examine the relationship between earnings and the defined human capital investment measures, job performance, and demand-side characteristics. As the literature suggests is typical, we transformed the dependent variable, SALARY, by natural logs. This transformation meant that the effect of each explanatory variable on earnings could be interpreted as a percentage change.

Results and Discussion

Fundamental statistical analysis was used to describe the variables in our data set. Table 2 presents the basic descriptive statistics for the sample of 95 Class 4A and Class 5A head football coaches. Note that, on average, a coach in this sample earned slightly more than $82,000 per year, and that 9 out of 10 coaches performed some administrative duties. The average coach had participated in approximately 107 games and achieved an overall career winning percentage of 53.41. Because a typical season consists of approximately 10 games, the mean value of 106.8 for GAMES suggests that the average coach in our sample had over 10 years of head coaching experience. Only 7% of the coaches were rookies.

Regarding institution-specific characteristics, the mean value for school enrollment was 2,310 students, and the average high school stadium seated 10,963 fans. The difference between the two measures indicates that demand for Conference 4A and 5A football extends well beyond the student body to the larger community. We also observed that 20% of coaches in the sample were employed at schools in the Dallas school district.

Table 2

Basic Descriptive Statistics for Class 4A and Class 5A Head Coaches (N = 95)

VariableMeanStandard DeviationMinimumMaximum

SALARY	82,179.00	10,457.00	50,117.00	106,044.00
GAMES	106.80	89.67	10.00	401.00
ROOKIE	0.07	0.26	0.00	1.00
WP	53.41	17.30	5.00	84.00
ADMIN	0.91	0.29	0	1.00
STADIUM	10,963.00	3,795.00	3,500	21,193
ENROLL	2,310	849.12	1,076	5,652
DALLAS	0.20	0.40	0.00	1.00

Table 3 presents the multiple regression estimates for our hypothesized earnings function model. (Several model specifications were estimated; overall results for the alternative model specifications did not differ significantly from the results presented in table 3.) On the basis of the adjusted R-squared statistic, our regression model explains over 58% of the variability in the natural log of earnings. The overall fit of our model compares favorably with those presented by other researchers. Each regression model presented by Kahn (1993) and Humphreys (2000) explained less than 50% of the variability in, respectively, professional coaches’ salaries and collegiate coaches’ salaries.

Table 3

Regression Model Parameter Estimates (Dependent Variable = Natural Log of Salary)

Determinant	Parameter estimate
Intercept	11.11†

Human capital variables
GAMES	3.96 E-04†
ROOKIE	-0.09**

Job Performance variable
WP	8.88 E-04†

Institution-specific characteristics
ENROLL	2.94 E-05**
STADIUM	3.55 E-03†
DALLAS	-0.17†

Other factors
ADMIN	0.04

F-statistic	19.81 (p value < 0.001)
R-squared	61.45
Adjusted R-squared	58.34

* p < 0.05, assuming a one-tailed test of hypothesis for ENROLL and two-tailed tests elsewhere. ** p < 0.01, assuming a one-tailed test of hypothesis for GAMES and two-tailed tests elsewhere. † p < 0.10, assuming a one-tailed test of hypothesis for WP and STADIUM.

Turning attention next to the model’s individual parameter estimates, we made a series of important observations, starting with the two measures of human capital investment. First, as anticipated, the algebraic sign on the ROOKIE parameter was negative, meaning that a coach with no more than 1 year of experience received less compensation than veteran coaches. On average, the difference was approximately 9%. Second, the estimated directional effect for a coach’s level of experience, measured through the GAMES variable, was consistent with expectations. Specifically, we found that GAMES had a statistically significant positive effect on a coach’s salary. Holding all other factors constant, each additional year of coaching experience increased salary by, on average, approximately 0.4 percentage points. (We assumed that 10 games represented about 1 year of play; the GAMES parameter estimate hence indicates that each additional game coached translated to a salary increase of about 0.04%, a year’s worth of games thus representing 10 times that salary increase, or 0.4%.) In contrast Kahn’s (1993) investigation of Major League Baseball managers showed that each additional year of experience in professional ball increased a manager’s salary by 2.35%. Humphreys’s (2000) investigation of NCAA basketball coaches did not find the analogous effect on salary to be statistically significant. He argued that a high correlation (0.60) between career winning percentage and years of experience most likely produced the insignificant result for the latter variable. The correlation coefficient between GAMES and WP in our model was markedly lower (0.46).

Holding constant a coach’s investment in human capital, we obtained further results indicating that a coach’s job performance, measured by WP, has a statistically significant positive effect on compensation (a one-tailed test was used). Qualitatively, this result is consistent with those presented by Kahn (1993) and Humphreys (2000). The specific estimated value suggested that an increase of 10 percentage points for WP increased a coach’s salary by approximately 0.9%. Clearly, this finding suggests that winning is important in high school football. However, the common sports adage “Winning is everything” seems an overstatement, at least in the context of how high school football coaches’ salaries are determined.

Quite predictably, our results also indicate that demand-side factors are relevant to the determination of coaches’ overall compensation. For two of the demand-side, institution-specific variables, STADIUM and ENROLL, each of the obtained parameters had the predicted positive sign. Using a one-tailed test, the parameter on STADIUM was statistically significant at the 10% level. This suggests that coaches at schools with larger stadiums, and hence greater demand for high school football, receive higher compensation than those at schools with smaller stadiums. The parameter on ENROLL was positive and statistically significant on the basis of a two-tailed test. As expected, then, larger schools tend to compensate coaches at higher rates than do schools with relatively fewer students. The specific estimated value implies that for every additional 100 students enrolled in a school, its football coach’s salary is about 0.29% higher. The underlying premise is that demand for football games is greater when the student body is larger.

The algebraic sign of the parameter on the urban location variable, DALLAS, was negative and statistically significant at the 1% level. This finding differs from Humphreys (2000), who in his study of NCAA basketball coaches did not find the urban location variable to be significant. It might be the case that the result in our model is specific to the Dallas, Texas, area and cannot be generalized to other urban areas. In any case, we can say that the subsample of Texas high school coaches employed by the Dallas school district earned about 17% less than their counterparts in other districts. This negative effect might reflect a larger population of available coaches in the area, which would mean greater competition for available positions and hence lower salaries. It might also be a function of the relatively low cost of living in Dallas, suggested by consumer price index levels for Dallas versus other areas (U.S. Department of Labor, 2008).

Finally, while the parameter on ADMIN had the expected sign, the finding was not statistically significant. This result may be due to the fact that over 90% of the head coaches in our sample held some type of administrative position in addition to their regular coaching duties. The resulting lack of variability in this measure may be responsible for its insignificance in our earnings function.

Conclusion

It is well documented in the sports economics literature that, holding ability constant, a player’s investment in human capital and his overall performance contribute significantly to the determination of overall compensation. Building on these findings, recent research in sports economics has applied earnings function analysis to an examination of salaries paid to professional and collegiate team managers and coaches. Although this segment of the sports literature is still in its infancy, thus far the empirical findings are generally consistent with those for players. That is, investments in human capital and job performance seem to be significant determinants of managers’ and coaches’ salaries, just as they are of players’ salaries.

In this research study, we extended the analysis of sports managers’ and coaches’ salaries to the noncollegiate amateur level, using a sample of Texas high school football head coaches employed during the 2005–2006 season. Following the approach used in investigations of professional sports, we modeled and estimated an earnings function, using conventional regression analysis. Our model specified a series of potential salary determinants, including human capital measures, a performance variable, and institution-specific demand-side factors.

Our statistical findings indicate that coaches’ salary determinants at the high school level are qualitatively consistent with those identified in the literature for professional and collegiate coaches. Specifically, a high school coach’s development of human capital was shown to be a statistically significant determinant of his salary. Moreover, a coach’s performance or ability to win games, as measured by career winning percentage, also affected his earnings. Lastly, consistent with findings presented by Humphreys (2000), we found that demand-side, institution-specific influences such as the size of the fan base can affect a coach’s compensation.

Taken together, the results of this research, we believe, make an important contribution to the literature examining compensation paid to sports participants, because they broaden its scope to include coaches at the high school level. The findings are timely, as well, given recent media attention to coaching salaries and the associated debate about rising investments in high school sports programs concurrent with funding cuts for education. We are hopeful that, as new data become available, other researchers will seek to validate our findings in other locations and for other high school sports throughout the country. This in turn could help stimulate important dialogue about the level of compensation for coaches relative to other educational professionals and whether that compensation appropriately rewards experience and performance.

References

About the UIL [University Interscholastic League]. (n.d.). Retrieved June 14, 2008, from http://www.uil.utexas.edu/about.html

Abramson, A. (2006, October 31). High school football coaches want pay to stay. Palm Beach Post. Retrieved September 24, 2008, from http://www.palmbeachpost.com/highschools/content/sports/epaper/2006/10/31/a1c_highschoolcoaches_1031.html

Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. New York: National Bureau of Economic Research.

Bissinger, H. G. (1990). Friday night lights: A town, a team, and a dream. Cambridge, MA: DaCapo Press.

Callan, S. J., & Thomas, J. M. (2004). Determinants of success among amateur golfers: An examination of NCAA Division I male golfers. The Sport Journal, 7(3). Retrieved September 24, 2008, from http://www.thesportjournal.org/article/determinants-success-among-amateur-golfers-examination-ncaa-division-i-male-golfers

Callan, S. J., & Thomas, J. M. (2006). Gender, skill, and performance in amateur golf: An examination of NCAA Division I golfers.” The Sport Journal, 9(3). Retrieved September 24, 2008, from http://www.thesportjournal.org/article/gender-skill-and-performance-amateur-golf-examination-ncaa-division-i-golfers

Hamilton, B. H. (1997). Racial discrimination and professional basketball salaries in the 1990s. Applied Economics, 29, 287–296.

Humphreys, B. R. (2000). Equal pay on the hardwood: The earnings gap between male and female NCAA Division I basketball coaches. Journal of Sports Economics, 1(3), 299–307.

Jacob, M. (2006, January 9). High school football coaches cashing in. Dallas Morning News. Retrieved September 24, 2008, from http://www.dallasnews.com/sharedcontent/ dws/spt/highschools/topstories/stories/010806dnspocoachsalaries.2a4475f.html

Jones, J. C. H., & Walsh, W. D. (1988). Salary determination in the National Hockey League: The effects of skills, franchise characteristics, and discrimination. Industrial and Labor Relations Review 41(4), 592–604.

Kahn, L. M. (1993). Managerial quality, team success, and individual player performance in Major League Baseball. Industrial and Labor Relations Review 46(3), 531–547.

Scully, G. W. (1974). Pay and performance in Major League Baseball. American Economic Review, 64, 915–930.

Texas twist: Football coaches earn more than teachers. (2006, August 27). ESPN.com. Retrieved September 24, 2008, from http://sports.espn.go.com/sports/news/story?id=2562629

UIL [University Interscholastic League] alignments (updated for 2008–2010). (n.d.). Retrieved June 14, 2008, from http://www.uil.utexas.edu/athletics/football/

U.S. Department of Labor, Bureau of Labor Statistics. (n.d.). Consumer Price Index, Retrieved July 15, 2008, from http://www.bls.gov/CPI/home.htm

West, Davis, and Company. (2008, January 25). University Interscholastic League: Annual financial report (statutory basis) for the year ended August 31, 2006. Retrieved June 14, 2008, from http://www.uil.utexas.edu/policy/pdf/05_06financial_report.pdf

U.S. Sports Academy2016-10-12T14:56:39-05:00October 7th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management|Comments Off

Active Versus Passive Recovery in the 72 Hours After a 5-km Race

Submitted by: Andy Bosak – West Virginia Wesleyan College; Phil Bishop and Matt Green – University of Alabama; Mike Iosia – Lee University

Abstract

We do not clearly understand what type and duration of recovery works best after a hard run to restore the body to peak racing condition. This study compared 72 hr of active recovery after a 5-km running performance with 72 hr of passive recovery. A sample of 9 male and 3 female runners of above-average ability completed 3 trials within 6 days. Each 5-km trial was followed by 72 hr of passive recovery (PAS) or 72 hr of active recovery (ACT), a counterbalanced protocol. The 2 initial 5-km trials constituted separate PAS and ACT baselines. Mean finishing times did not differ significantly (p = 0.17) between ACT (19:35 + 1.5 min) and baseline (19:41 + 1.7 min); nor was there significant difference (p = 0.21) between PAS (19:30 + 1.5 min) and baseline (19:34 + 1.6 min). Average heart rate for PAS (177.9 + 6.3 b/min) was significantly higher (p = 0.04) than baseline (175.4 + 6.5 b/min), but ACT average heart rate (175.9 + 6.6 b/min) was significantly lower (p = 0.02) than baseline (178.9 + 6.4 b/min). For PAS, perceived rate of exertion at ending (19.8 + 0.6) was significantly greater (p = 0.01) than baseline (19.3 + 0.9), yet for ACT, perceived rate of exertion at ending (19.6 + 0.8) did not differ significantly (p = 0.17) from baseline (19.7 + 0.7). During PAS trials, 2 individuals ran a mean 12.0 + 2.8 s slower, 2 individuals ran a mean 33.0 + 21.0 s faster, and 8 individuals ran within 5.1 + 2.5 s of their first run. During the ACT trials, 1 participant ran 13.0 s slower, 3 participants ran a mean of 34.7 + 13.5 s faster, and 8 nonresponders ran within 5.5 + 2.7 s of baseline. Results indicate that 72 hr of passive and active recovery result in similar mean 5-km performance.

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U.S. Sports Academy2016-10-19T11:20:16-05:00July 7th, 2008|Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

Parameters That Influence Vertical Jump Height

Submitted by: Dawn T. Gulick, James Fagnani, Matthew Long, Kenneth Morris, and Brian Hartzell – Widener University; Marcia Epler – Lebanon Valley College

Abstract

Plyometric activities use rapid switching from eccentric to concentric contractions to increase speed or force of muscle contractions. Training the stretch-shorten cycle by jumping enhances athletic performance. This study sought optimal box heights athletes could drop from to obtain maximal rebound height. Division-III collegiate football players (n = 55) older than18, weighing no more than 100 kg, and with no lower-extremity injury were the participants. Initial data collected measured height, weight, leg length, age, standing vertical jump, and quadriceps strength. Peak torque and work per repetition were calculated for eccentric and concentric quadriceps activity. Participants completed 3 box drops from each of 4 different box heights as vertical rebound was measured. ANOVA showed rebounds did not differ significantly by box height, nor did rebound from any height differ significantly from standing vertical jump. Little to no correlation (Pearson’s r < 0.25) was found between vertical rebound from any height and concentric or eccentric work per repetition or eccentric peak torque. Fair correlation (Pearson’s r = 0.29–0.33) was found between concentric peak torque and vertical rebound from all heights. Leg length correlated moderately (Pearson’s r = 0.56–0.61) with vertical rebound from all heights. Because results indicate greater box drop height is not statistically associated with greater vertical rebounds, using a box height above 0.12 m (the shortest tested here) is likely to increase injury risk without providing any accompanying benefits. The study is limited by the fact that jumping technique was not included as a variable, although clearly technique could be a component in rebounding.

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U.S. Sports Academy2016-10-24T10:17:51-05:00July 7th, 2008|Sports Coaching, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off

Program and Facility Emergencies in Youth Sports, Part II: Dealing with the Event

Submitted by: Richard Gershom, Ashland University and Kimberly Peer, Kent State University

Communication
plays an integral role in the prevention of youth sport injuries, as the
evidence in Part I of this paper suggested. Communication regarding
expectations, policy and procedures, and post-injury protocol can facilitate a
safe youth sport environment. However, preventing youth sport injuries also
involves several other areas, such as protective equipment, strength and
cardiovascular conditioning, environmental and facility management, proper
coaching, and proper nutrition and hydration. According to the American College
of Sports Medicine (1993), 50% of injuries that occur in children and
adolescents are preventable. Even when steps are taken to ensure safe
participation in youth sport programs, however, injuries will happen; what happens
before, during, and after an emergency can make the difference in the eventual outcome
of injuries.

Dealing with
emergencies in youth sport requires sufficient preparation and planning to
ensure prompt resolution of the event. Prevention measures preclude injury or
have the potential to reduce the severity of injuries and should thus be
considered most effective (Roberts, 1998). League administrators and youth
coaches must ask and answer several questions: Who is the most qualified
individual to treat injuries? Are the persons dealing with the emergencies
adequately prepared for a variety of emergency situations? Are coaches properly
trained to coach? Are there mechanisms in place for prompt medical care? Coming
prepared with this kind of information prior to any emergency can promote
optimal medical care and prevent litigation.

Coach and Parent Education

To make decisions
in answer to the questions just reviewed, league administrators must understand
the qualifications of coaches. The National Association of Sports and Physical
Education (NASPE) has developed standards of fundamental competency that
communities, school systems, private leagues, parents, and athletes should require
of coaches. League administrators and parents are responsible for ensuring that
youth coaches are appropriately qualified to supervise the sport in question and
to maintain a safe playing area and environment. Moreover, coaches should be
required to complete (at a minimum) a community course in first aid and CPR;
there are several sport safety courses available as well that are recommended
for all youth coaches.

A critical
component of caring for an injured athlete is familiarity with the medical
history and condition of the athlete. Before activity commences in any sport, each
athlete should undergo a pre-participation physical examination. This examination
should be required of all athletes prior to participation and should be
comprehensive. Necessary checks include a medical physical to assess heart and
lung function; a medical history to identify any pre-existing problems and
family health history; a musculoskeletal examination assessing alignment,
strength, flexibility, and laxity; a “vitals” examination ensuring heart rate,
blood pressure, height, and weight are appropriate for the individual; body
composition assessment; vision screening; and finally, a sport performance
assessment determining whether the individual’s cardiovascular condition
and strength are appropriate for the anticipated exertion.

First Aid Equipment

In addition to
familiarity with each athlete’s health status, it is also key to have
appropriate emergency medical supplies available. Most youth leagues provide
first aid kits or small athletic trainer kits for each team. When preparing a
kit for a team in a given sport, it is crucial to plan for a broad scope of
needs, stocking the kit properly to address all of them. Kits must be prepared before
each practice or contest in order to be of reliable use. Having the correct
supplies could be the difference in delivering essential care to an injured
athlete appropriately.

Although a wide
variety of first aid supplies can be helpful depending on the sport, there are
items of common value across sports. Key items include the following:

information such as phone
numbers, release forms, and emergency cards (as well as paper and pen)
instruments including paramedic
scissors, tape cutters, tweezers, fingernail clippers, fingernail files,
and a microshield or CPR mask
bandages and related supplies including
athletic tape, tape adherent, underwrap, elastic tape, band-aids, gauze
pads, ace wraps, and petroleum jelly
splinting supplies including slings,
safety pins, finger splints and other splints, and crutches
eye care kit including contact
solution, contact case, saline, and a pocket mirror
miscellaneous items including
rubber gloves, antiseptic cleaning solution, insect repellent, water
bottles, ice chests and/or coolers, tongue blades, and felt or foam
padding material

This list is not exhaustive but it provides
the foundation of a well-stocked sport first aid kit. In some sports, kits may
need to be augmented with items such as mouth pieces, nose plugs, analgesic rub,
hand cream, sun glare, and feminine hygiene products. Organization of the kit
is important in emergency situations when first aid must be provided quickly.
Similar items should be stored in the same area of the kit; there should be
nothing unnecessary in the kit obscuring needed items that need to be located
quickly following an injury.

Administrators and
supervising coaches must make certain that each youth coach is qualified to use
and comfortable in using all first aid kit supplies. A general rule is not to
pack in the kit any supply with which the coach or coaches are uncomfortable.
It is important to designate one person to maintain the first aid kit and order,
as needed, items replenishing the kit’s supply.

Although they can
be expensive, first aid kits are highly recommended for all youth sport
programs. League commissioners typically determine who purchases kits and supplies
to stock them. When there is no funding for emergency medical supplies, asking
health care facilities and drug stores to donate supplies is a potential
course; firefighting and other emergency departments may also be willing to
help. League administrators and/or coaches are ultimately responsible for
providing players with the best possible first aid should they be injured; the
expense of good first aid kits is, ultimately, relative.

After an Injury

Providing care is a
top priority in an emergency. Care can be provided best and most quickly when
those involved remain calm while activating appropriate medical assistance. When
a young athlete may be injured, it must always be remembered that nothing less
than his or her well-being is at stake. It is therefore better to err on the side
of cautiousness, when in doubt about the injury or first aid, by seeking
additional medical assistance immediately. It should also be remembered that
children’s and adolescents’ bodies are distinct from the adult’s and cannot
always be treated in the same way. Therefore, it is always recommended that a
young athlete seek medical attention from a physician for any injury that does
not improve in a short period.

Fortunately, most
injuries in youth sports are not complicated and can be resolved with little
medical intervention. Often, the best approach is what has been called, for
ease of memory, RICE, which stands for rest, ice, compression,
and elevation. Rest the injured area by supporting it with a sling, splint,
or crutches. Ice the injury for approximately 20 min at a time. Compress the
area with an elastic bandage to control swelling. Finally, elevate the area
above the level of the heart, also to manage swelling. These steps comprise a standard
and long-advocated treatment for many sport injuries.

When an emergency
has occurred and first aid has been rendered, notification of certain
individuals becomes necessary, when those individuals are not present at the
sport facility. Again, parent phone numbers and the league commissioner’s phone
number, along with emergency numbers, should be kept easily available in the
front of the first aid kit. It is also recommended that useful emergency information
is provided as a courtesy to each visiting team, for example on a reference
card. Having access to emergency numbers and directions to nearby hospitals is
greatly appreciated by teams unfamiliar with an area.

Conclusion

All sports pose
some injury risk. While coaches and administrators should make every effort to
keep that risk as low as possible, they must also ensure that appropriate care
is available in the event an injury does occur. Injury-prevention programs are
advocated by the American Academy of Orthopaedic Surgeons and are readily
available to the general public (Purvis & Burke, 2001). Completing the programs
can help prepare youth coaches to manage emergency situations. Furthermore,
youth sports leagues are well advised to maintain a written emergency plan that
staff know how to implement. The plan should be reviewed yearly by league
officials, coaches, parents, and care providers from the local community’s emergency
medical service. It is important that this plan be reviewed yearly due to the
typically high number of changes in coaching staff each year.

References

American
College of Sports Medicine (1993). The prevention of sports injuries of
children and adolescents. Medicine and Science in Sports Exercise, 25(8),
1–7.

National
Association of Sport and Physical Education. National standards for athletic
coaches. Reston, VA: Author.

Purvis,
J. M., & Burke, R. G. (2001). Recreational injuries in children: Incidence
and prevention. Journal of the American Academy of Orthopedic Surgeons, 9(6),
365–374.

Roberts,
W. O. (1998). Keeping sports safe: Physicians should take the lead. The
Physician and Sports Medicine, 26(5).

U.S. Sports Academy2017-08-07T11:43:32-05:00July 7th, 2008|Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

Marketing the Triathlete

Submitted by: Adam Epstein, South College

This article will explore the triathlon world and address issues that should increase the likelihood of securing product and financial sponsorship for the triathlete. Throw out the textbooks. Forget about product, price, place, promotion for now. The only way you will be successful in marketing an elite triathlete is to have as your client an exceptional triathlete who can win races (or consistently finish in the top three) and give exposure to companies’ products and logos. This is especially true in a bear financial market in which corporate budgets are tight. (There is debate in the triathlon community as to whether to use the world “elite” or “professional” to describe exceptional athletes; triathletes are not generally paid a salary.)

Assuming you do have a truly talented, exceptional triathlete as a client, how do you maximize your client’s sponsorship potential in order to generate the revenue he or she needs to remain in the sport? Unlike other professional athletes, triathletes are not normally part of any team. There are no players’ associations (unions), no collective-bargaining agreements, and no salaries per se in the sport of triathlon. Dollars are generated in two ways: money earned at the races themselves (of which agents usually do not take a percentage) and money generated by sponsorship contracts (that’s where the money is made).

The sports marketer’s goal must be to find sponsors who will reward the triathlete for performance and exposure. Fortunately, triathlon involves swimming, biking, and running (in that order). Such diverse categories allow the sports marketer to seek sponsorship from many companies. Such a wide net of potential sponsors is found in very few sports.

Ironman vs. Olympic Triathlons

The sports marketer must understand that the triathlon world is divided into two major (and very different) categories: the Ironman¹ triathlon and the Olympic-distance triathlon. Only a handful (at best) of the world’s triathletes can excel in both categories. Expect that your client will focus on only one of them. Some sponsors are concerned only with efforts in one category or the other.

The Ironman distance triathlon is truly a grueling race: a swim of 2.4 mi, a bike race of 112 mi, and then a marathon run of 26.2 mi. The Ironman distance races are often finished in the 8-to-10-hr range (you are reading that correctly), by the best competitors. Sponsors are likely to think of the Ironman triathlon when first presented with the opportunity to sponsor a triathlete.

Currently, the Ironman Triathlon World Championship—sometimes called simply Ironman Hawaii—is held in Kona, Hawaii. Another 24 Ironman events constitute the Ironman Triathlon series and are held throughout the world² ; some involve the full race distances, and some are half-Ironman distances. Unless they are among a select few competitors who earn “lottery” slots, triathletes must qualify to compete in the Ironman Triathlon World Championship, based upon their elite or amateur status.

Much of the reason for the Ironman triathlon’s success is that NBC television broadcasts the Ironman Triathlon World Championship every year. Outside this event, virtually no other triathlon is broadcast on a major network, except during the Olympic Games.

The Olympic-distance triathlon differs markedly from the Ironman triathlon. The Olympic version comprises a 1.5-k swim (.9-mi), a 40-k bike ride (24.8-mi), then a 10-k run (6.2-mi). This sport of sprint triathlon made its Olympic debut in 2000 at the Sydney Olympics. Unlike the Ironman distance, this distance is truly a swim, bike, and run sprint. The Olympic distance, also called ITU distance (for International Triathlon Union, the international federation for the sport of triathlon), is often filled with loops on the bike and is designed to be spectator friendly. Such races are often completed in the 2-hr range by the best competitors. Triathletes earn points based upon a formula that weighs the size of the triathlon and the rankings of other participating competitors. World rankings are established under the ITU point system. Networks such as the Outdoor Life Network, ESPN, and ESPN2 broadcast Olympic-distance triathlon events, but often at off-peak hours of the day or night.

Important Triathlon Organizations and Other Resources

USA Triathlon (www.usatriathlon.org) is the national governing body for the sport of triathlon in the United States and is one of many national governing bodies under the purview of the United States Olympic Committee. The International Triathlon Union is another leading triathlon organization and maintains a website (www.triathlon.org). Visiting the two groups’ websites will allow the novice sports marketer to learn which sponsors already participate in the sport. Contacting these sponsors is a good first step. Recognize, however, that the ITU has very clear guidelines about the size and number of logos that may appear on a competitor’s jersey (Ironman triathlons do not have such limitations).

Beyond the websites, the novice sports marketer’s research might start with subscriptions to Triathlete magazine (www.triathlete.com) and Inside Triathlon (www.insidetri.com). Additionally, Katherine Williams’s Triathlon Sourcebook provides the names and e-mail addresses of athletes, coaches, companies, and events. The book is published every other year (1997, 1999, 2001); the next one is due in January 2003. It usually sells for around $30, a worthwhile investment for an agent, and you can contact the author directly at kwilliams@triathloncentral.com. Finally, visiting sponsors’ websites can give you an idea of how serious they are about participating in the sport of triathlon. Fortunately, communicating via e-mail controls up-front costs in terms of promotion of the triathlete.

Company Sponsorship and Contracts

Clearly, a sports marketer’s best first step toward securing sponsorship for an elite triathlete is to find sponsors who are already involved in the sport. Table 1 lists various product areas in which there are manufacturers who have sponsored elite triathletes.

Table 1: Triathlete sponsors and their product categories

Product Category	Examples of Sponsors
	Saucony / Speedo / TYR / Adidas / Nike
bikes	Javelin / Trek / Elite / Cannondale
aero bars	Profile
wheels and components	Spinergy / HED / Zipp
tires	Continental
shoes and pedals	Speedplay / Carnac / Time
helmets	Rudy Project / Giro
wetsuits	Speedo / Orca
glasses	Rudy Project / Oakley
nutrition	Twinlab / Met-Rx / Powerbar / Clifbar
hydration	Gatorade / Endurox / Push / Fuel Belt
bike case	Tri-All Sports
other/out-of-sport products	Timex / financial services company

If you are lucky, you will find a few sponsors who are not involved in the sport of triathlon and wish to sponsor a triathlete. However, almost all sponsors expect some sort of return on their advertising investment. In seeking sponsors outside the triathlon sport, expect to receive a lot of “no thank-yous” and letters or e-mails of rejection (unless your triathlete—at either distance—is a world champion or national champion). The most likely time to find an out-of-sport sponsor is just prior to the Olympics and shortly afterward, if the triathlete wins a medal. Once a sponsor is interested, a contract may be written. In general, sports contracts focus on salary and on performance and exposure bonuses. Some companies provide only product; others frown on salary unless the triathlete is an experienced, elite professional. Use caution if you are considering a multi-year contract, especially if your client’s performance (and exposure) is anticipated to increase over time.

Publicizing an Elite Triathlete Client

Vital to your success in serving the elite triathlete client is exploring the designs of competitors’ websites. Typically, sponsors’ logos are posted and offer links to the sponsors’ websites (in turn, expect a sponsor’s site to offer links to the athlete’s site). Visiting www.usatriathlon.org will help you locate elite triathletes’ websites; the ITU website also provides links to some nice websites. Do not reinvent the wheel when working to promote a triathlete via a website; do realize that building a website costs money, and ensure that both you and your client know who will pay costs associated with the site, including the hosting fees and charges for updates and structural changes. (Updating the website in a timely manner is important.)

Attending triathlon events is always a plus for the marketer. Meeting personally with sponsors is vital to business relations and your own exposure. Often, an expo takes place in conjunction with larger triathlon events; it can be a prime time to meet with sponsors maintaining booths for promoting their products and services. Since the agent assists in the coordination of publicity for the triathlete, contacting sponsors ahead of time at an expo is a plus; of course a cell phone is essential.

Conclusion

Remember, the agent is only as good as the triathlete. A client in the news—providing exposure for sponsors—is the client most able to obtain sponsorship. Nevertheless, representing your client responsibly by completing preliminary research and establishing and maintaining contacts increases the likelihood that your client will enjoy considering as many sponsorship opportunities as possible. To really appreciate the sport and those who compete in it, you might even try swimming, biking, and running on your own.

Making a living as a triathlete’s agent may not be possible. However, it is often just as rewarding to hear the cheers of fans at races and to know that you had a small something to do with the success of a triathlete. The satisfaction lasts even when the race is just a memory, and the race-results are only a link on the Internet.

Author Note

Adam Epstein, J.D., M.B.A., chairs the legal studies department at South College in Knoxville, Tennessee. Epstein also serves as an adjunct assistant professor of sport management at the University of Tennessee. He has taught undergraduate and graduate courses in legal studies, paralegal studies, sport management, and business management since 1994.

¹Registered trademark of World Triathlon Corporation, Inc.
²See www.ironmanlive.com

U.S. Sports Academy2015-10-24T01:32:24-05:00April 16th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science|Comments Off