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Location Model in the National Football League: Predicting Optimal Expansion and Relocation Sites

U.S. Sports Academy2016-10-12T14:55:37-05:00March 14th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management|

Abstract:

The National Football League has experienced both expansion and relocation of its franchises in the past decade. It is a dynamic market; the relocation of a NFL franchise is an annual possibility. This study looked at the demographic and economic factors that determine the current locations of NFL teams. The top 50 metropolitan areas were empirically examined to explain why some cities have an NFL team and others do not. These factors included population, per capita income, the number of other sports franchises, and the number of Fortune 500 companies, geographic factors, and television ratings for “Monday Night Football.” This model can identify cities for possible expansion and those that would serve as relocation sites in the future. Special attention was paid to the Los Angeles and New Orleans markets.

Introduction:

The National Football League has experienced a dynamic period of expansion and relocation in the past decade; the league seeks to position itself with the optimal configuration for long-term growth of the professional football market. Although expansion is not a current short-term goal for the NFL, the relocation of weak teams remains an annual possibility.

Moving an existing sports franchise is not new. After the 1995 season, Los Angeles lost both of its football teams. The Rams moved from the old Rose Bowl in Pasadena to the brand-new TWA Dome in downtown St. Louis. The Raiders moved from the Los Angeles Coliseum to the newly renovated Oakland Coliseum. In 1996, the Cleveland Browns moved into a new stadium in Baltimore and became the Ravens. Most recently, the Houston Oilers moved to Tennessee and became the Titans in 1999.

The city of Los Angeles, which lost its chance to gain an expansion team in 2002 to Houston because it was unable to approve financing for a new stadium, remains without a team. Although this leaves the second largest television market without its own team, it also offers a credible relocation threat for existing team owners in their new stadium negotiations with local authorities.¹

Expansion and relocation of franchises in professional sports leagues have been studied for baseball and basketball; however, as far as the researchers know, a location model had never been created for the National Football League. Bruggink and Zamparelli (1999) produced a location model for Major League Baseball. The top 50 metropolitan areas were chosen for the sample. The explanatory variables for the regression model are population, population growth, and per capita income, in addition to the number of other professional sports teams in the area, the number of headquarters for Fortune 500 companies in the area, and the distance to the closest city with a baseball team. As one would expect, all variables had positive coefficients in the regression model. A more recent sports model by Rascher and Rascher (2005) estimated the probability that a particular city will have a National Basketball Association team by using the same core set of variables and adding factors such as the average NBA Nielsen television ratings for each city.

One interesting application of our model was applied to the New Orleans Saints football team. Even before the Hurricane Katrina damage to the Superdome, the owners of the Saints hinted that a move to a new location was in the offing.² Of course, this is the typical ploy to gain public subsidies for a new or improved stadium, but the closure of the Superdome for the 2005 season made this relocation potential very real. The re-scheduled 2005 games found the Saints playing in the welcoming city of San Antonio with capacity attendance at the Alamodome. Although the Saints played the 2006 season in a repaired Superdome, they are in position to pursue one of two options after 2006: 1) stay in New Orleans with a long term city commitment to help build a new or improved stadium, or 2) move to a new location in Los Angeles (Maske and Shapiro, 2005) or San Antonio (Orsborn 2006). Our location model used estimates of New Orleans depopulation to give a perspective on the potential consequences for the city’s viability as a home to a NFL franchise.

Location Model

There are a number of factors that influence the selection of cities for NFL relocation or expansion. Based on demographic and economic factors in the largest 50 metropolitan areas, the researchers constructed a logit model to determine the relationship of these factors (X₁, X₂, X₃, X₄, X₅, X₆, X₇) to the expected conditional probability (P_i) that each city (represented as i) would have one or more NFL teams:

P_i = E (Y_i = 1| X_1i,X_2i, X_3i, X_4i, X_5i, X_6i, X_7i)

where

Y = 1 if metropolitan area has one or more NFL teams; 0 otherwise

X₁ = POP2000 = population in metropolitan area i (millions)

X₂ = POPGROWTH = % population growth of a city the decade before the current team
located in city i (if the city does not have a team, the current
decade of growth is used)

X₃ = INC = income per capita in metropolitan area i ($1000)

X₄ = DISTANCE = distance to closest football franchise city (miles) from city i

X₅ = F500 = the number of Fortune 500 company headquarters in metropolitan area i

X₆ = OTHER/POP2000 = the number of other professional sports teams per million
population in city i (men’s basketball, hockey, and baseball)

X₇ = NIELSEN = “Monday Night Football” Nielsen ratings in metropolitan area i using
the 2002-3 season

The NFL wants to expand or relocate to a metropolitan area with a large and growing population in order to maximize stadium revenues from attendance, concessions, and parking fees. The researchers expected positive coefficients for the population and population growth. In addition to market size, standard microeconomic demand theory suggests that per capita income will be a positive influence. According to a 2003 survey, football fans had the highest median salary in all sports at $55,115 (USA Today, 2003).

The distance to the closest NFL franchise city is a major aspect for the location of a franchise. The NFL does not want all of its teams in one part of the country because there will be interest only in that part of the country, not the whole United States. This is especially important for a sport driven by national television revenues. Furthermore, locating a new franchise close to an existing franchise (generally up to 75 miles) would financially hurt the owner of the latter. The league does not approve of these territorial infringements.

The number of other professional sports teams (baseball, basketball, and hockey) in the metropolitan area can have an impact on the demand for football games. The other sports could be considered substitute goods, or, on the other hand, a measure of fan interest for sports in general (a complement good). Rascher and Rascher (2005) found a negative and significant relationship between the number of other teams and the probability that a city had a professional basketball team. However, Bruggink and Zamparelli (1999) found just the opposite for baseball, supporting the fan intensity argument. The difference could be in overlapping the season with other sports. Basketball overlaps the most, competing with hockey, football, and the beginning of baseball. Baseball and football face less severe overlaps, and at times have the season to themselves.

Another location factor is the number of headquarters for Fortune 500 corporations in the metro area (F500). NFL owners are allowed to keep all the stadium revenue from luxury box receipts (i.e., no revenue sharing), and corporations are the largest patrons of these seating sections (which are also called corporate sky boxes). This is one of the reasons that owners desire new stadiums, because it affords them an opportunity to maximize the number of luxury box seats.

The last location factor is the number of households in the metro area watching football on television. In this study, the average Nielsen ratings³ for the 2002-3 “Monday Night Football” games are used for the 50 metropolitan areas. “Monday Night Football” games were selected because the same game is watched by the entire nation, whether a city has a NFL team or not. Nielsen ratings are particularly important for the National Football League because there are no local television contracts, only a national contract divided equally among the 32 teams, and it is the single largest source of income.

Empirical Results and Simulation:

The preliminary estimation of the model included the top 50 metropolitan areas (those with populations of approximately one million or more). About half had one or more teams. However, with Los Angeles in the data set the estimated coefficient of the population variable is negative instead of positive. This preliminary model showed how sensitive the population factor is to the inclusion of Los Angeles (by far, the largest metro area in the sample that has no team). As discussed earlier, league owners receive value from having a viable city without a team because it poses a credible threat for a team to relocate, allowing them to negotiate with local government for sports subsidies. In this sense, the no-team status of Los Angeles is not a market outcome but a strategic ploy. When Los Angeles was excluded, the researchers actually had a sample of cities that was more representative of market conditions. The estimated model without Los Angeles had a positive coefficient for population and a better overall fit. This sensitivity is the reason the researchers relied on this as the final model for the predictions.

In this study, the researchers: 1) examined the statistical results, 2) tested the within-sample predictions for each city, 3) determined a list of viable cities for the NFL expansion or relocation, and 4) ran a simulation on the effect of New Orleans’ recent depopulation on its viability of retaining its football team.

Table 1 shows all the statistical results for the fitted model using the logistic function shown below. The logistic function is the natural log of the odds ratio in favor of a metropolitan area having a team (P = 1 if the metro area has a team, P = 0 if it does not). There are three advantages to using the logistic curve rather than an ordinary least squares regression: (1) the predicted probabilities for a city having a team are constrained to lie between 0 and 1 for the logistic curve, whereas for a linear regression model the predicted probabilities could exceed 1 or fall short of zero, both of which are impossible values, (2) the slope coefficients in the logit model are more realistic than ordinary least square because they vary in magnitude, depending on the size of the corresponding explanatory variable, and (3) the variance is more constant in the logit model than with ordinary least squares, which makes the t-tests more valid.

Table 1: Location Model

All the coefficients but one have the correct sign. Four of the seven are statistically significant at a 10% level or better in a one-tailed test. The statistically significant coefficients are for the following variables: the distance from the nearest NFL city, the number of other sports teams in the city per million population, the “Monday Night Football” television ratings, and the number of Fortune 500 headquarters in the city. The income variable missed being significant at only a 10% level. The only high correlation among the independent variables is between F500 and POP2000. This may explain why the coefficient of POP2000 does not appear statistically significant.

Table 2 shows the standardized coefficients in the logit model. Standardized coefficients scale the coefficients in the model using the standard deviations of the each independent variable and the dependent variable. By this method, the Fortune 500 and population variables have the most effect on whether a city has a team or not. Each have more the twice the size and therefore twice the impact than the other standardized coefficients. Distance and the presence of other professional teams rank next in importance followed by Nielsen ratings and income.

Table 2: Standardized Coefficients

Table 3 shows the results of the in-sample forecasts. The logit model correctly predicted the current NFL franchise status in 45 out of the 50 metropolitan areas. The missed predictions included Los Angeles (this outcome is made using an out-of-sample prediction), San Antonio, Salt Lake City, Buffalo, and Jacksonville. For the first three misses, the model predicted the cities would have teams but they do not (for our purposes, any probability greater than 0.50 means the city should have a team). Buffalo and Jacksonville have teams but the model predicted that they do not.

Table 3: Predictions* All predictions are within-sample, except for the out-of-sample forecast for Los Angeles.

Table 4 shows the top five candidate cities to have teams either through expansion or relocation. The logit model predicted that Los Angeles would have a team with a probability of 1.0, which is not surprising given that it once had two teams. However, if NFL owners continue to use the city as a credible threat, and if a new stadium is not in the package for the Los Angeles team, then the other cities in this list deserve consideration. Next is San Antonio, where the Saints have already tested the waters with great success. The logit model estimated that San Antonio had a probability of 0.56 in obtaining an NFL franchise either through relocation or expansion. Salt Lake City is a more marginal candidate at 0.51, and the model suggests that both Sacramento and Columbus are not viable candidates (their predicted probabilities are less than 0.5).

Table 4: Predicted Probabilities for Candidate Cities from the Sample

League expansion and the open Los Angeles market have been discussed in the NFL by both the outgoing Commissioner Paul Tagliabue (NFL.com, 2004) and the new Commissioner Roger Goodell (Farmer, 2007), but there is no short term timetable for expanding to a 33^rd team or moving a troubled franchise there. Nonetheless, the league has been working with investor groups representing sites at the Los Angeles Coliseum and the Rose Bowl in Pasadena (NFL.com 2004). Besides New Orleans, Buffalo and Jacksonville are mentioned as cities that might lose their franchises (The Sports Economist, 2006).

Do the Saints Go Marching Back In?

Under pre-Katrina conditions, New Orleans had a probability of 0.85 for its in-sample prediction for having a team (see Table 3). But this changes when the potential depopulation of New Orleans is considered. The extensive damage to the city of New Orleans was not only to the industrial and commercial structures. Whole residential sections of the city were destroyed and depopulated.

The model prediction included depopulation in two parts. First, the New Orleans metropolitan area population was reduced by 10, 20, 25, and 30%.⁴ Second, the Nielsen television ratings were correspondingly reduced. No adjustment was made to the Fortune 500 headquarters because New Orleans has only one such company, Entergy Corporation, and it will remain in the area.

Table 5 shows the simulation results for different assumptions about permanent depopulation for New Orleans. The most recent population estimate from the Census Bureau dates from July 1, 2006. At this time, a 400,000 loss was announced (Whoriskey, 2006). This 30% decline would put the predicted probability for New Orleans at approximately 0.43. Only Buffalo and Jacksonville have teams with lower probabilities than this. But this worse case scenario is outdated. A portion of the 400,000 have returned to the metro area since June 2006⁵, but how many will ultimately return? At this time, there is no planned Census Bureau update for the New Orleans metropolitan population.⁶ Should half of the displaced 400,000 return, the model would put place a probability of 0.59 that New Orleans will have (in this case keep) a team. The most optimistic non-official estimate (as of December 20, 2006) put the metro area at 1.2 million (Savidge, 2006). This is less than 10% depopulation, and the model provided a more comfortable 0.74 probability of having (retaining) a football team.

Table 5: Predicted Probability of a Team in New Orleans with Depopulation

Conclusion:

Expansion and relocation of franchises in the National Football League remains an active topic when one considers the fates of both the Los Angeles and New Orleans markets. Although expansion is not a current short term goal for the NFL, relocation of teams in weak markets remains an annual possibility. The researchers have estimated a model that identifies those weak teams based on economic and demographic factors, and, more importantly, identifies candidate cities for new or relocated teams. Buffalo, Jacksonville, and a depopulated New Orleans are vulnerable to losing their teams, while Los Angeles and San Antonio are viable candidates to offer new homes to teams. What happens next depends on the interests of the current owners and the investor groups in the candidate cities, as well as the state and local government support for new stadiums in the old or new locations.

Endnotes:

¹ “The most recent NFL expansion, when the league was deciding between Houston and Los Angeles, is instructive on this point. In general terms, the decision between the two locations hinged on two considerations regarding the Houston and Los Angeles markets. First, the league considered the financial contribution that either location would make to the league. Second, it considered the value of an open location and the negotiating advantages it provided to current league membership. Keeping the best believable threat location helps owners in negotiations with their current host cities (Fort, 2006, p. 393).

² Gary Roberts, a professor at Tulane University Law School and an expert in sports business issues, states “Everyone knows New Orleans was a marginal major league market. More and more, the NFL has come to rely on corporate dollars and New Orleans doesn’t have a very large corporate base” (Isidore, 2005).

Then Football Commissioner Tagliabue comments on whether New Orleans can support an NFL team long term: “[team owner] Benson has strong personal and professional ties to San Antonio, the suspicion remains that he would prefer to permanently locate the franchise there. Benson fears that the rebuilding of New Orleans, a process expected to take years, will threaten the team’s financial viability” (Pasquarelli, 2005).

³ A Nielsen TV rating is the percentage of households watching that particular television program out of all households with televisions. A TV share is the percentage of televisions in use that are watching that particular program.

⁴ The population growth variable was not altered because its coefficient 1) had the wrong sign, 2) a very small magnitude, and 3) was highly insignificant statistically.

⁵ “As a city in flux New Orleans remains statistically murky, but demographers generally that the population replenishment after the storm, as measured by things like the amount of mail sent and employment in main economic sectors, has leveled off” (Dewan. 2007).

⁶ The Census Bureau is “just not equipped to provide real time population estimates in a situation that is changing as rapidly as New Orleans” (Plyer, 2007).

References:

Brooks, Rick A celebration at the Superdome, Wall Street Journal Online September
23, 2006

Bruggink, Thomas H. and Justin Zamparelli Emerging markets in baseball: An
econometric model for predicting the expansion teams’ new cities, Economics:
Current Research Praeger Press (1999): 49-59.

Dewan, Shaila Fed-Up New Orleans residents are giving up, New York Times
February 16, 2007. www.nytimes.com/2007/02/16/us/nationalspecial/16orleans

Fort, Rodney, Sports Economics, Pearson Prentice-Hall (2006)

Hage, Jim A city is up and running, washingtonpost.com January 1, 2006

Isidore, Chris New Orleans’ muddy sports future, Money.cnn.com September 22,
2005

Maske, Mark, and Leonard Shapiro, Saints could end up in L.A.
Washingtonpost.com October 27, 2005

NFL.com NFL eyes Los Angeles for 2008, NFL.com wire reports May 25, 2004.

Orsborn, Tom. Football: The San Antonio…Chargers? Mysantonio.com January 13, 2006

Pasquarelli, Len. New Orleans Saints, ESPN.com December 30, 2005.

Plyer, Allison. Correspondence with Allison Plyer of Greater New Orleans Nonprofit
Knowledge Works (gnocdc.org) January 19, 2007.

Rascher, Daniel, and Heather Rascher Optimal markets for NBA expansion and
relocation, Sports Economics Perspectives Issue 2 November 2005

Savidge, Martin New Orleans returning slowly, MSNBC.com December 20, 2006.

Wetzel, Dan. Calling a T.O. for N.O., Sports.yahoo.com September 4, 2005

A State Analysis of High School Coaching Certification Requirements for Head Baseball Coaches

U.S. Sports Academy2016-10-12T14:53:11-05:00March 14th, 2008|Contemporary Sports Issues, Sports Coaching, Sports Facilities, Sports Management|

Abstract:

The purpose of this study was two-fold: 1) to investigate the coaching certification status for high school athletic leagues’ head baseball coaches and 2) to recommend a model high school certification program for head baseball coaches in the State of Hawaii. To meet selection criteria, the participating high schools must compete in both varsity and junior varsity baseball. The population surveyed for this study included all 59 athletic directors from the five athletic leagues within the Hawaii High School Athletic Association (HHSAA). The 14-item survey instrument contained four sections: (1) certifications, (2) experience, (3) professional growth, and (4) education. The results indicated that a small percentage of HHSAA athletic directors required a national coaching certification. Secondary findings indicated that a small percentage of HHSAA athletic directors required previous playing and coaching experiences, attendance at coaching-training seminars, and a high school diploma. Importantly, 95% of HHSAA members required background checks from their head baseball coaches.

Introduction:

There are about 6.5 million U.S. athletes that participate in interscholastic sports each year (National Federation of High School Association {NFHSA, n.d.}, 2004). Approximately 800,000 men and women coached these athletes in the school system (NFHSA, 2004). Thirty years ago, the majority of coaches were certified teachers. Today, most high school coaches are not certified (National Association of State Boards of Education {NASBE}, 2003). Currently, less than 8% of school coaches receive a specific education to coach (Martens, Flannery, and Roetert, 2003). Only 13 states specify that coaches must have a teaching certificate, and all of these states allow exceptions to this rule (NASBE, 2003).

Advocating for U.S. quality coaching and coaching education began in the 1960’s from the National Association for Sport and Physical Education (National Association for Sport and Physical Education {NASPE}, 2006). Over the next 40 years, NASPE would partner with various national organizations in spearheading the national movement for high school coaching certification. By the mid-1980’s, this national coaching movement was advanced by the American Sport Education Program (American Sport Education Program {ASEP}, 2007). ASEP, founded by Rainer Martens in the early 1970’s, had by 1986, 1,400 certified instructors who trained more than 50,000 coaches across America (ASEP, 2006). In 1991, this coaching educational movement was expanded when AESP joined forces with the National Federation of State High School Association (NFHS) (ASEP, 2006).

In addition to ASEP advancing the coaching educational movement in the 1990’s, in 1991, another U.S. national coaching certification program, called the Program for Athletic Coaches Education (PACE), was adopted (Seefeldt and Brown, 1991). Currently, PACE consists of six coaching areas: (1) Philosophy, (2) Growth and Development, (3) Sports Medicine, (4) Psychology, (5) Litigation/Liability, and (6) Sports Management (Seefeldt and Brown, 1991).

As a result of the 1990’s coaching education movement, by 1998, 66% of state agencies provided funding for or offered staff educational development to high school coaches (Burgeson, Wechsler, Brener, Young, & Spain, 2001). By 2000, 40% of the states required coaches to be certified in first aid and CPR, and 34% required coaches to complete a coaches’ training course (Burgeson et al., 2001).

As a result of the lack of the states’ initiative of requiring or recommending CPR and first-aid certification for all coaches, in 2003, the NFHS recommended that all coaches (experienced and non-experienced): (1) possess a current and valid CPR and first aid certification and (2) complete a planned systematic coaching education curriculum by 2006 (NASBE, 2003). In addition, the NFHS recommended that even certified teachers serving as head coaches maintain their professional development by completing a minimum of one coaching education course per year during their coaching tenure (NASBE, 2003).

In 2005 the NFHS, in partnership with ASEP, adopted NASPE’s National Standards for Sport Coaches (NASPE, 2006). The purpose of the guide was to “provide direction for coaching educators, sport administrators, coaches, athletes and their families, and the public regarding the skills and knowledge that coaches should possess” (NASPE, 2006). In addition, NASPE oversees the National Council for Accreditation of Coaching Education (NCACE) (NASPE, 2006). NCACE reviews coaching education and certification programs that seek accreditation based on compliance with the National Standards for Athletic Coaches (NASPE, 2006).

Due to the relentless national efforts of NASPE, NCACE, NFHS, ASEP, and PACE, in 2001, high schools began emphasizing coaching education primarily for their respective head coaches. Among the co-ed middle/junior and senior high schools that offered co-ed interscholastic sports (99.2%), 51.7% required their head coaches to complete a coaches’ training course (Burgeson et al., 2001). In addition, 51.3% and 45.6% of these secondary schools required head coaches to be certified in first aid and CPR, respectively (Burgeson et al., 2001).

Currently, there are 40 states that have adopted, recommended, or required one of two national certification programs (ASEP or PACE) for their respective head coaches (Jackowiak, 2003). Currently, ASEP continues to work with 40 state high school associations to provide coaching educational information for more than 25,000 coaches per year (ASEP, 2006).

If Hawaii’s secondary high school coaching environments are similar to the U.S. coaching scene, Hawaii’s high school athletes may be exposed to unqualified coaches. Since baseball is played in all Hawaii’s high schools that compete in interscholastic sports, the investigators examined Hawaii’s head baseball coaches’ educational status to determine Hawaii’s high schools’ coaching certification status.

Purpose:

The purpose of this study was two-fold: 1) to investigate the coaching certification status for high school athletic leagues and 2) to recommend a model high school certification program for head baseball coaches in the State of Hawaii. To meet selection criteria, the participating high schools must compete in varsity and junior varsity baseball. The study specifically addressed the following research questions:

(1) What types of coaching qualifications or certifications exist within the 50 states’ high school athletic associations?

(2) What types of coaching qualifications or certifications exist in Hawaii’s public and private highs schools?

Method:

Every athletic director in all 59 public and private high schools in the state of Hawaii completed the survey. The 14-item survey contained four sections: (1) certifications, (2) experience, (3) professional growth, and (4) education. Each question had a yes or no response. Frequency distributions and percentages of the athletic directors’ responses were determined in order to compare the similarities and differences among the five high school leagues, and between public and private high school leagues. Data were analyzed using descriptive statistics.

Results:

A total of 59 athletic directors completed usable questionnaires, which represented a 100% return rate. Table 1 highlights the responses among HHSAA’s five high school leagues. Table 2 compares collectively the similarities and differences between public and private school leagues. In addition, Table 3 indicates the certification status among the 50 states.

Table 1: HHSAA League Comparisons

As indicated in Tables 1 and 2, comparison of HHSAA’s leagues’ athletic directors’ (n=59) responses with regards to their respective head baseball coaches’ four-area certification status is as follows: (1) In Certifications, HHSAA high school leagues’ athletic directors (88.14%) did not require any formal coaching certification for their respective head baseball coaches, and interestingly, 67.8% didn’t require CPR and First Aid certification; (2) In Experience, HHSAA (84.75% and 84.5%, respectively) did not require their respective head coaches to have any past high school playing experience nor previous coaching experience, but HHSAA (94.92%) did require their league officials to conduct substance abuse and criminal background checks on their respective head baseball coaches prior to their coaching; (3) In Professional Growth, only 13.56% of HHSAA required their respective coaches to participate in any coaching education-training program prior to becoming a head baseball coach. Only 33.9% and 35.59% respectively of HHSAA required annual coaching education-training seminars and passing a rules/regulations examination; in contrast, HHSAA (94.92%) required parental-coaching meetings where head coaches addressed team goals, parent-coaching behavior, team rules, player responsibilities, and player discipline issues; and (4) In Experience, HHSAA (57.63 %) required at least a high school diploma from their respective head baseball coaches.

Table 2: Private vs. Public

Data in Table 2 revealed the following findings comparing the HHSAA pubic high schools’ and private high schools’ athletic directors’ collective responses in the four-area coaching standards: (1) Certification– Only public (13.64%) and private (6.67%) high schools’ athletic directors required their respective head baseball coaches to have national coaching certification and CPR/First Aid (38.64%, 13.33% respectively); (2) Experience– Interestingly, only a small remnant public or private high schools’ athletic directors’ required their respective head baseball coaches to have any previous high school playing experience (18.18% and 6.67% respectively) and previous coaching experience (11.36% and 26.67%, respectively); in contrast, HHSAA required substance abuse and criminal background checks (95.45% and 93.33%, respectively); (3) Professional Growth– Only public (15.91%) and private (6.67%) athletic directors required their respective head baseball coaches to attend coaching education-training seminars prior to employment and to attend annual coaching education-training seminars (34.09% and 33.33%, respectively); and (4) Education– The majority of public (61.36%) athletic directors required their respective high school head baseball coaches to have at least a high school diploma, in contrast to private athletic directors (46.67%).

Table 3: Head Coaching Requirements by StateX = Required, R=Recommended

Discussion:

In the Certifications section of the questionnaire, the findings indicated that a very small percent of HHSAA’s leagues’ athletic directors required a national certification policy and CPR/First-Aid certification. In contrast, HHSAA (84.75%) offered coaching education-training seminars for its head baseball coaches. If HHSAA doesn’t’ require its respective coaches to complete a recognizable national certification program, including CPR and First Aid, then coaches have to further their professional growth by attending their leagues’ recommended coaching education-training sessions.

In the Experience section of the questionnaire, a small percent of HHSAA’s athletic directors required previous high school playing and coaching experience in baseball. Nevertheless, nearly all (94.92%) HHSAA’s athletic directors required substance abuse and criminal background checks on their head baseball coaches. The difference in these requirements may be due to the importance of coaches’ character, rather than playing and coaching experience.

In the Professional section of the questionnaire, a minimal percent of HHSAA’s athletic directors required their head baseball coaches to attend coaching education-training seminars prior to employment, and to attend annual coaching education-training seminars after employment. A related finding revealed that a high percent of HHSAA’s athletic directors (84.75%) offered coaching education-training sessions for their head baseball coaches. Obviously, HHSAA recognized the importance of coaching education-training sessions, but HHSAA possibly encountered attendance problems in the past in coaches or baseball coaches who have not positively reviewed the coaching education-training curriculum.

The Education section of the questionnaire indicated that over half (57.63%) of the HHSAA’s athletic directors required at least a high school diploma for their head baseball coaches. This low percentage may be due to a lack in initiating a standard policy requiring all potential head baseball coaches to have a high school diploma. Certainly, high school dropouts would encounter more difficulties in obtaining high school head-coaching jobs than a high school graduate.

An interestingly supplemental finding revealed that there were only two nationally-certified high school strength and conditioning coaches. No HHSAA league athletic director required his or her respective high school to have a certified strength and conditioning coach on staff.

In Hawaii, 92 high schools compete in men and women interscholastic sports. In 2005, 34,758 student-athletes participated in Hawaii’s 24 state high school sports programs (K. Amemiya, personal communication, January 15, 2007). Not one head coach had a national strength and conditioning certification credential. Yet in any of these 92 high schools, if the athletes participated in any on-campus formal off-season or in-season strength and conditioning programs, unqualified sport-coaches conducted these regiments, thereby increasing the risk of injury to these 34,758 student-athletes. In a progressive state, like Hawaii, which was the first and only state to require every athletic high school to have on staff two-full time nationally certified athletic trainers, the HHSAA should recognize the need to require and fund one full-time certified strength and conditioning coach on every high school staff.

Conclusions and Recommendations:

In conclusion, there is a national movement toward high school coaching certification. To date, there are 40 states that have adopted, recommended, or required one of two national certification programs — ASEP or PACE. There seems to be a disparity in Hawaii’s high school athletic departments. There is no movement to adopt, recommend, or require national certification for Hawaii’s coaches, yet Hawaii is the only state to require two athletic trainers in every high school. Therefore, Hawaii’s athletic departments should seriously consider the following recommendations: (1) HHSAA should adopt either the American Sport Education Program’s (ASEP) or the Program for Athletic Coaches Education (PACE) national coaching certification requirements for their head baseball coaches; and (2) The National Standards for Athletic Coaches created by the National Association for Sport and Physical Education (NASPE) should be incorporated into HHSAA’s ASEP or PACE national accreditation-coaching program. The national standards should be used as a basis or framework for design of selection, evaluation, and education programs.

References:

American Sport Education Program (2006). ASEP’s Beginnings. Retrieved November 20, 2006, from http://www.asep.com/about.cfm

Brylinsky, J., (2002). National standards for athletic coaches. ERIC Digest, Oct.2002. Retrieved Nov. 12, 2005, from http://www.ericdigests.org/2004-1/coaches.htm

Burgeson, C., Wechsler, H., Brener, N.D., Young, J.C., & Spain, C.G. (2001). Physical education and activity: Results from the school health policies and programs study 2000. Journal of School Health, 71, 279-293.

Gilbert, W. & Trudel, P., (1999). An evaluation strategy for coach education programs. Journal of Sport Behavior, 22, 234-250.

Jackowiak, L., (2003). Developing an athletic program based on sound principles. Incorporating the national federation coaching education program. Retrieved Sept.23, 2006, from http://www.nfhs.org

Martens, R., Flannery, T., & Retort, P. (2003). The future of coaching Education in America, Retrieved May 11, 2006, from http://www.nfhs.org/cep/articles/future_coaching.htm

National Association for Sport and Physical Education (1995). National Standards for Athletic Coaches. Dubuque: Kendall/Hunt.

National Association of State Boards of Education (NASBE) (2003). Education requirements for athletic coaches. NASBE policy update, 11 (4). Retrieved May 12, 2006, from http://www.nasbe.org/Educational_Issues/Policy_Updates/11_4.html

National Council for Accreditation of Coaching Education (NCACE) (2006). NCACE program registry and approved program list. NASPE. Retrieved May 10, 2006, from http://www.aahperd.org/naspe/template.cfm?template=programs-html

National Federation of High School Association (NFHSA) (n.d.). Coaching education in America: A white paper. Retrieved May 13, 2006 from http://www.nfhs.org/staticContent/PDFs/cep/cep_whitepaper.pdf.

Seefeldt, V. & Brown, E. (1991). Program for athletic coaches’ education. Carmel: Benchmark Press, Inc.

College Sport Management Student Perceptions Regarding Special Olympics Curriculum and Service Learning

U.S. Sports Academy2013-11-26T14:29:53-06:00March 14th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|

Abstract:

This pre-test/post-test study evaluated college sport management student (N = 21) perceptions of Special Olympics North America curriculum/field experience. Pre-event and post-event values indicate that students had positive perceptions. Significant individual effects were found for General Orientation, Facilities and Safety, and Event Management. The strongest correlate relationships were for General Orientation with Volunteerism (52% predictive), Event Management (50%), and Athletes (53%), and Volunteerism with Event Management (54%) and Athletes (62%). Overall, results indicate that service learning can be implemented successfully into a sport management curriculum, field experience is an effective practical experience, and feedback from students should be used to improve teaching.

Introduction:

The service-learning/practical-experience student is an emerging professional who must guide the course of his/her own career. This is the opportunity for a student to apply professional knowledge and expertise in the field under the direction and supervision of a qualified practitioner. Such a student should receive varied experiences ranging from leadership to program development. The variety and intensity of experiences should allow the student to assess knowledge and skills in relation to career goals. The student should be challenged in a manner in which both strengths and weaknesses are evident. Such experiences can only be assured through careful planning by the student and by the agency supervisor (Overton, 2005).

The Special Olympics North America University (SONA) has developed a curriculum which consists of Special Olympics courses that can be incorporated into university curricula. These include General Orientation and Event Management courses within the Special Olympics Coach Education System and Games Management Training program. Through the Special Olympics University Curriculum, universities play a renewed role in training coaches and sport managers. The SONA curriculum is endorsed by the American Alliance of Health, Physical Education, Recreation, and Dance (AAHPERD) and the National Association for Sport and Physical Education (NASPE). Currently, nine universities have adopted this unique curriculum concept.

While service-learning implementation has increased in higher education curricula throughout the United States, the concept has been around for quite a while (Dewey, 1938). A sport management curriculum could benefit from incorporating service-learning based Special Olympic programs into university curricula. Event management, budgeting, concessions, and personnel management are examples of areas of experience that an organization such as the Special Olympics could provide a student volunteer. Equally important is that this cooperative program would provide sport management students with insight into how a service-oriented agency such as the Special Olympics is managed (Daughtrey, Gillentine, & Hunt, 2002). Many professors utilize academic-based service learning in their classes to provide students with practical experience. In fact, service learning has increased in popularity in higher education due to the many perceived benefits of the method.

Perceptions of the sport management students participating in the SONA curriculum are paramount in the success of the program. Feedback can assist in improving the overall structure of the service-learning experience. Thus, the research questions under investigation seek to first determine college sport management student perceptions regarding sport management Special Olympics training, and Special Olympics field experience. Second, they identify individual effects (individual differences) and determine whether perceptions differ in pre- versus post-event (situation effects) with regard to selected areas of sport management, including general orientation of the SONA training, volunteerism, adequacy of facilities and athlete safety, event management, and event contribution to competition and the well being of athletes, at a specific field-experience event. Third, they identify correlate relationships between perceptions of the selected areas of sport management.

Methods:

Design

The study utilizes a pre-test/post-test design. Participants were recruited from two facilities management courses, one for undergraduate students, the other for graduate students, during the 2006 spring term at a historically-black college and university in south central Virginia. Sport management majors are the only students eligible to take these courses. The pre-test was administered before students had Special Olympics North America University curriculum (SONA) training. The post-test was administered to those who had completed the pre-test, all modules of the 3-module SONA curriculum, and the sport management field-experience requirement (Module 3) at a specific Special Olympics regional track meet. Pre- and post-test surveys were administered to all participants by a sport management professor.

SONA curriculum description and administration

The Special Olympics of North America, in conjunction with selected higher education institutions, collaborate with sport management faculty to implement the SONA curriculum within selected sport management courses. Currently, there are nine universities throughout the United States that participate in this program. Module 1 of the SONA curriculum is a Special Olympics orientation, Module 2 is application of sport management principles to Special Olympics events, and Module 3 is a Special Olympics field experience. The sport management professors coordinated their teaching efforts and standardized their delivery of Module 2 to ensure that the content delivery was the same between the classes participating in the study. Completing the SONA curriculum and a sport management field experience at a Special Olympics event were course requirements of respective undergraduate and graduate courses.

Survey development and administration

The study survey was developed by one of the sport management professors who taught Module 2 and who had directed Module 3. The survey was reviewed for content validity by the Special Olympics administrator who taught Module 1. Section 1 of the survey assessed demographic characteristics, Section 2 was composed of six subscales that assessed perceptions regarding General Orientation (e.g., student preparedness, sport management knowledge, and organization of training staff), Volunteerism (e.g., “making a difference” as a volunteer, whether volunteer efforts would be appreciated), Facilities and Safety (adequacy of facilities and athlete safety), Event Management (e.g., job descriptions, operating plans, games meeting schedule), and Athletes (e.g., fair competition for athletes, whether event participation contributes to health and wellness of athletes). The content of the subscales was adapted from the SONA curriculum and from the mission statement of the Special Olympics of North America Curriculum Guide, 2005). Each subscale score was a mean score of five questions that were individually scored using a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree). The university-affiliated institutional review board approved the project. Signed informed consent was obtained from all participants. The survey was pilot tested by four graduate sport management students. Based on their responses, modifications to the survey were not necessary.

Statistical analyses

Analyses were performed using JMP IN® software, version 5.0 (Sall, Creighton, & Lehman, 2005). The descriptive analysis included means, standard deviations, and frequency distributions. Item reliability was evaluated using Cronbach’s α. Subsequent data analysis involved Pearson x² and one-way analysis of variance with a comparison for each pair using Student’s t. Multivariate pairwise correlations were used to evaluate relationships between subscales. A significant level of .05 was used for statistical analysis.

Results:

The total sample recruited included 45 students, which represented all students from the undergraduate and graduate students enrolled in one of two facility management courses. Overall, 21 students completed the study, which was a 47% participation rate. The average (M ± SD) age of the participants was 22.3 ± 3.2 years, 71% of participants (n = 15) were male, all were African American, and academic classification was fairly evenly distributed between undergraduate (58%, n = 12) and graduate students.

Cronbach’s α was used to evaluate item reliability for each subscale score in Section 2. The subscales internal consistency ranged from .73 to .90, indicating an acceptable correlation of ranked values among subscale parameters.

Regarding research question 1, participant perceptions of sport management Special Olympics training and a Special Olympics field experience, are reported in Table 1. Pre- and post-event values indicate that students had positive perceptions for all sport management subscales evaluated; mean scores ranged from 4.0 to 4.6 for all subscale scores. A score of 4 indicated participants “agreed” with statements, whereas a score of 5 indicated participants “strongly agreed.”

Table 1: College Sport Management Student Perceptions Regarding Special Olympics Training and a Special Olympics Field ExperienceNote: M ± SD. N = 21. * Each subscale was a mean score based on responses to five questions that were individually scored using a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree).

In reference to question 2, individual and situation effects of student perceptions are reported in Table 2. Significant individual effects indicate that there were significant differences between perceptions of different participants, whereas significant situation effects indicate that perceptions of participants, as a group, differed from pre- to post-test. Significant individual effects were found for General Orientation, Facilities and Safety, and Event Management. The researchers identified no significant situation effects for any of the subscales.

Table 2: Individual and Situational Effects for College Sport Management Student Perceptions Regarding Special Olympics Training and a Special Olympics Field ExperienceNote: N = 21. Each subscale was a mean score based on responses to five questions that were individually scored using a 5-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree).

Concerning question 3, Special Olympics training and field experience perceptions correlate relationships; all subscales assessed had statistically significant direct correlate relationships with all other subscales (see Table 3). The strongest correlate relationships (r_S² x 10) were for General Orientation with Volunteerism (52% predictive of each other), Event Management (50%), and Athletes (53%), and for Volunteerism with Event Management (54%) and Athletes (62%).

Table 3: Correlate Relationships Regarding Perceptions of Special Olympics Training and a Special Olympics Field Experience Among College Sport Management StudentsNote: N = 21. Multivariate pairwise correlations were used to evaluate relationships between sport management subscales.

Discussion:

The primary purpose of a service-learning project is to provide a work-study-learning program to further the professional development of students. Agencies electing to accept students in a service-learning environment have an obligation to maintain reputations for professional service. Thus, the general orientation to a service-learning project is critical. Results from this study indicate that the general-orientation portion of a service-learning project sets the tone in preparing the student for volunteer work. It is evident that this module in the curriculum is important in creating a solid base and understanding of the overall service-learning experience. Furthermore, the general orientation portion of the curriculum had positive correlate relationships with several of the subscale areas included in the Special Olympics curriculum modules. Particularly, the Event Management, Athlete, and Volunteerism subscales had direct correlate relationships with General Orientation. Similarly, Volunteerism had positive correlate relationships with the Athlete and Event Management subscales. Thus, these results suggest that the sport management student will recognize that volunteer, philanthropic organizational events are viable career options in the sport industry. In addition, this study indicates that future sport management professionals will be aware of the importance of event-management planning and the significant role volunteerism plays in the development of a successful service-learning event.

The present study allowed the students the opportunity to participate in a sport management event and to evaluate sport management concepts, including event management, volunteerism, athlete mentoring, and facility planning. From an academician’s viewpoint, the significant individual effects indicate areas a faculty member may concentrate in so as to improve the overall effectiveness of the service-learning project. Because students have varied perceptions, an instructor may use open-ended discussions to educate students regarding different sport management areas that are paramount to the success of a service-learning project.

Service learning has been a popular pedagogical tool in academic programs for years. Recently, the concept has gained popularity in other forms, such as class projects and internships (Petkus, 2000). The implications of this study are that, because students have positive perceptions regarding service-learning projects, the projects can be implemented successfully into a sport management curriculum. In fact, specialized internship/field experiences with organizations such as the Special Olympics can work efficiently within an already existing sport management program. When implementing service-learning components into existing sport management curriculums, it is important to receive feedback from the students. This will assist the instructor in evaluating the effectiveness of the curriculum and subsequent service-learning experiences.

References:

Daughtrey, C., Gillentine, A., & Hunt, B. (2002). Student collaboration in community sporting activities. American Alliance for Health, Physical Education, Recreation and Dance, 15, 33-36.

Dewey, J. (1938) Experience and Education, New York: Collier Books.

Overton, R. (2005). Sport Management Internship Manual. Ettrick, Virginia: Virginia State University.

Petkus, E. (2000). A theoretical and practical framework for service-learning in marketing: Kolb’s experiential learning cycle. Journal of Marketing Education, 22(1), 64-70.

Sall, J., Creighton L., & Lehman A. (2005). JMP IN® Start Statistics. Southbank, Australia: Thomson Brooks/Cole.

Special Olympics Special Olympics of North America Curriculum Guide (2005). Washington, D.C.

The Effects of Promotions on Attendance in Professional Baseball

U.S. Sports Academy2016-10-19T10:51:50-05:00March 14th, 2008|Contemporary Sports Issues, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|

Abstract:

Professional baseball organizations use many types of promotions to increase attendance. The purpose of the study was to determine whether or not different types of promotions effected attendance in professional baseball. Promotions were categorized into price, non-price, and a combination of price and non-price. Attendance and promotion data were collected from four professional baseball organizations located in the Ohio River Area. The results indicated significant increases in attendance in two of the four teams when any promotion was used. Two teams also revealed attendance increases when non-price promotions were present, as well as when combination of price and non-price promotions were employed. Finally, this study supports previous research, which has found higher attendance at games with promotions than games without promotions and when non-price promotions are used rather than price promotions.

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More than Just the Ryder Cup: An Examination of Relevant Natural Characteristics in Professional Golf

U.S. Sports Academy2016-10-12T14:52:28-05:00March 14th, 2008|Contemporary Sports Issues, Sports Studies and Sports Psychology|

Abstract:

The researcher examined the two major professional golf associations, the Professional Golfer’s Association (PGA) and the Ladies Professional Golfer’s Association (LPGA), to determine physical characteristics relevant for success. The researcher found that those players born outside of the U.S. consistently earn more money and have lower average scores in the most recent professional season. These results are consistent across both tours. The researcher attempted to uncover individual statistical categories that influence this finding. He found that players born outside of the U.S. have significantly superior putting averages, while there appears to be no significant difference in other categories, such as driving and hitting greens in regulation. The superior performance of players born outside of the U.S. remains after controlling for these statistical areas.

Introduction:

Many studies have examined the skills necessary to succeed at the game of golf. Among the first were Davidson and Templin (1986), who found hitting greens in regulation and putting to be the most important determinants of success. These results have been consistent in numerous studies, including Jones (1990), Shmanske (1992), Belkin, Gansneder, Pickens, Rotella, and Striegel (1994), Nero (2001), Dorsel and Rotunda (2001), and Engelhardt (2002).

This paper took a different approach. The researcher examined the impact of professional golfers’ physical characteristics on performance, measured by the money earned and the scoring average for the 2006 season. The researcher examined both the Professional Golfers Association (PGA) and the Ladies Professional Golfers Association (LPGA) tours. There has been considerable evidence that talent and skill level in professional golf have been increasing rapidly over the past decade (e.g. Chatterjee, Wiseman, and Perez, 2001). This is likely attributable to the rapid improvement in equipment, but just as much, if not more, is attributable to increased participant abilities. The researcher examined whether natural characteristics such as size influence professional success in both tours.

Further, the researcher examined the effect of the individual’s origin, specifically segmenting those born in the U.S. and those that were not. Following the two most recent Ryder Cups, which the European teams won handily, there has been much discussion on the “advantages” they must have. Those advantages appear to be more than statistical, as the American team fielded the top three players in the world during the most recent Cup and were still unable to threaten the lesser-known European team. It has been suggested this is potentially due to the team camaraderie the international team enjoys and the American team lacks. While this is a question the researcher cannot answer, he attempted to determine whether this “international effect” is evident in settings other than these group competitions.

This work makes numerous contributions to the literature related to this subject. First, to his knowledge, the researcher is among the first to directly examine the individual physical characteristics of professional golfers. The researcher took an opposite approach than most studies do. Characteristics such as putting and driving are explanatory variables, which the researcher used in an attempt to explain the variation found in relation to the physical characteristics.

Second, the researcher contributed to the literature (e.g. Wiseman, Chatterjee, Wiseman, and Chatterjee, 2004) that examines gender differences in professional golf. While the researcher primarily examined both tours separately, he found the results to be consistent for both tours, suggesting an overall effect, rather than a gender-specific anomaly. In addition, the researcher examined the 2006 professional season, which has just recently concluded. Given the increased quality of equipment, as well as the improved performance of the participants, it is important to examine the most recent data.

The researcher found relations to be consistent with those documented in past literature. In addition, the most interesting finding dealt with the nationality variable. The researcher found those individuals born outside the U.S. scored lower and earned more money than those born within, a result consistent across both professional tours. Further, by examining the primary performance-predicting variables, the researcher found players born outside of the U.S. have lower putting averages, while the other performance-predicting variables are statistically equal. Therefore, it appears that putting explains some of the variation between U.S. born and non-U.S. born players. However, the researcher also examined end-performance controlling for the predictive statistics (including putting average) and found the significant relation remains. Therefore, there appears to be undefined influence. The researcher briefly discussed possible reasons for this, for example, prior experience on international tours.

Literature Review:

Davidson and Templin (1986) were among the first to examine the characteristics that are important in golf success. Examining 1983 PGA data, they concluded that relative to driving, skills of finesse, such as putting and hitting greens in regulation (GIR) were more important statistical areas in relation to performance, as measured by money earned and scoring average. The results suggest that golfers who possess proficiency in many shot-making areas have a higher probability of success than those players with proficiency in a few.

Numerous studies have examined the same topic and have concluded the same, that putting and GIR are the most important determinants of success. Among these are Jones (1990), Shmanske (1992), Belkin, Gansneder, Pickens, Rotella, and Striegel (1994), Wiseman, Chatterjee, (1994), Engelhardt (1995,1997), Moy and Liaw (1998), and more recently Nero (2001), Dorsel and Rotunda (2001), and Engelhardt (2002). This finding is not exclusive to professional golf, as Callen and Thomas (2004) found that amateur golfers must possess a wide array of shot-making skills to be successful, particularly putting and hitting GIR.

Several studies have also examined the incremental significance of certain statistical areas for male golfers in comparison to female golfers. For example, Wiseman, Chatterjee, Wiseman, Chatterjee (1994) examined the PGA, LPGA, and SPGA tours and found that males drive the ball farther and hit more GIR. Consistent with the above results, they also found the most important characteristics for LPGA golfers are putting and greens in regulation. Moy and Liaw (1998) found the same, adding that PGA participants perform better in sand saves relative to the LPGA tour. Shmankse (2000) noted that the PGA tour yielded a superior putting average relative to the LPGA.

Further, Nero (2001) estimated golfers earnings based upon driving distance, driving accuracy, putting average, and sand saves. He concluded that professional golfers would benefit by improved putting more than increased driving distance. Callen and Thomas (2006) extended their previous study by examining male and female NCAA amateur golfers. They reached two primary conclusions: (1) males and females possess different levels of shot-making skills, and (2) these disparate skills influence tournament performance differently across genders. These disparate skills are consistent with those found in professional golf.

Moy and Liaw (1998) asserted that men’s larger physical size and superior strength explained the advantage enjoyed by professional male golfers over their female counterparts, as they can drive the ball farther. However, others have argued that successfully driving the ball requires more than just strength. For example, Hume, Keogh, and Reid (2005) analyzed both driving and putting and found that strength is certainly important in both areas, but flexibility and timing are also critical for success. The related hypothesis is that gender-related differences are therefore related to one or more of those physiological areas. Myers, Gebhardt, Crump, and Fleishman (1993) found statistical support for this; male golfers score higher in strength and stamina, while females have superior flexibility.

Data and Methods:

All data in this study are available online. The researcher obtained PGA and LPGA statistics from the official websites, www.pgatour.com and www.lpgatour.com, respectively. For each tour, the researcher obtained end-performance measures and performance-predicting measures. The researcher’s primary measures of end performance were total money earned during the year (Money) and scoring average (Scoring). Money is defined as the sum total earnings due to end tournament placement throughout the entire season.¹ Scoring is defined as the average score (i.e. number of strokes) obtained through each round (i.e. 18 holes).

Also, the researcher examined four performance-predicting statistics. The first, driving distance (DrivingDist), measured the total length of each participant’s average drive. During each round, two holes were selected to be measured, with special care taken to ensure the holes face in opposite directions to counteract the effects of wind. Drives were measured at the point they come to rest. The researcher also examined driving accuracy (DrivingAcc), which is the percentage of time a player hit the fairway with his/her drive, the first stroke taken on par 4 and par 5 holes. These two variables are included to examine the overall “power game” of each player. Past studies have typically found these variables to be less important than the finesse areas of the game. However, there have been studies (e.g. Engelhardt, 1995) that suggest a reversal in recent seasons, as the game of golf has become a more distance-demanding sport. The researcher attempted to see if this was indeed the case.

The researcher examined each player’s percentage of GIR, defined as having any part of the ball touching the putting surface in two or less strokes than par for each hole. Finally, the researcher examined putting average (PuttAv) for each participant. PuttAv is the average number of putts used only on those greens hit in regulation. Using this measure eliminated biasing the results due to chipping the ball close to the hole and having a relatively short putt. Both of these variables have been found to be significant in relation to performance in almost all studies. Therefore, the researcher attempted to see if this relationship still existed.²

Rather than using the value of each of these performance variables, the researcher chose to use the ranking. In each tour, the participants are ranked based upon each statistical category. In fact, those are the numbers most often quoted when commenting on the various statistics. The researcher chose to use rankings in order to have a consistent relationship between the coefficient signs and each variable. Otherwise, each would have to have its own interpretation. For example, a lower putting average is a positive statistic, while a higher percentage of greens in regulation is a positive. By using the respective rankings, the researcher could consistently say that a higher ranking is a positive signal, regardless of the variable.³

The researcher’ primary contribution was to extend the analysis to control for personal characteristics. Therefore, the researcher also identified several natural physical characteristics. He identified the age (age) of each participant, defined as the number of whole years from the individual’s birth date to the end of the 2006 professional year.^4,5 Also, he defined height (height) and weight (weight) to control for the player’s physical structure. Height is measured in inches; weight is measured in pounds. The researcher did not have data on the LPGA player’s weight; therefore, this variable was defined only for the PGA sample. Finally, the researcher identified each individual’s birth place. Using this, the researcher created USA, which is a dummy variable equal to one if an individual was born in any of the 50 states, zero otherwise. As such, the researcher could examine the difference between performance of USA-born players, both in end performance and in performance-predicting variables. All personal characteristics are available online. After excluding those players for which complete data was unavailable, the final sample consisted of 196 PGA professionals and 166 LPGA professionals.

The researcher initially examined summary statistics of both sub-samples. Consistent with Chaterjee, Wiseman, Chaterjee, and Wiseman (1994), the researcher found the unsurprising result that PGA players drive the ball farther than LPGA players. This is consistent with physiological studies, such as Myers, et al. (1993.) However, the researcher found no significant difference between the two tours in relation to greens hit in regulation. He found the putting average on the PGA tour to be significantly lower than on the LPGA tour, consistent with Schmanske (2000). PGA tour players underperformed LPGA players in driving accuracy, also consistent with Myers et al. (1993.) More important to the study, the researcher found PGA players are older, on average. It appears that there is a higher percentage of American born players on the PGA tour relative to the LPGA tour.

Table 1 – Summary Statistics:

The following table represents summary statistics for the sample, segmented by observations from the Professional Golfers Association (PGA) tour and the Ladies Professional Golfers Association (LPGA) tour. For the PGA tour, Age is defined as number of whole years from the individual’s birth date to November 6, 2006, the day after the end of the Tour Championship. For the LPGA tour, Age is defined as the number of whole years from the individual’s birth date to November 15, 2006 the date following the ADT Championships. Height is the individual’s height in inches. Weight is the individual’s weight in pounds. EventNumb is the number of events each individual participated in during the 2006 year. USA is a dummy variable equal to one if the player was born in any of the 50 United States, zero otherwise. Money is the total amount of prize money awarded to each individual during the 2006 tour year in each respective tour. Scoring is the average 18-round score for each individual. DrivingDist is the average number of yards for each drive. During each round, two holes are selected to be measured, with special care taken to ensure the holes face in opposite directions to counteract the effects of wind. Drives are measured at the point they come to rest. DrivingAcc is the percentage of time the player hits the fairway with their drive. GIR is the percentage of the time the player hits the green in regulation (i.e. when the ball is on the green and the number of strokes taken is two or less than par.) PuttAv is the average number of putts used on those greens hit in regulation.

Results:

The researcher began by estimating the following regression model via traditional Ordinary Least Squares:

Dep_i = α + β₁EventNumb + β₂Height + β₃Weight + β₄USA + β₅Age + ε_i (1)

where Dep_i is either Money, Scoring, DrivingDist, DrivingAcc, GIR, or PuttAv. Each variable is the rank of the golfer in the respective tour in each statistical category. Therefore, he could interpret each positive coefficient as a negative effect on end performance, as it indicated a higher value for independent variable will result in a higher ranking value for the performance measure. EventNumb is the number of full-field events the participant entered into during the 2006 season on each tour, and it was used to control for variation that is a result of frequency of play. The results are presented in Tables 2 and 3. Table 2 presents the results for the end performance variables, Money and Scoring. By first examining the PGA results, he found that older players make less money. More interesting, American born players score higher and make less money on average than their counterparts. The coefficient indicates that, on average, non-American born players rank 32 and 34 places higher than American born players in money earned and scoring average, respectively.

Table 2 – Multivariate Analyses: Cumulative Performance:

The following table presents results from the following model:

Dep_i = α + β₁EventNumb + β₁Height + β₂Weight + β₃USA + β₅Age + β₆LPGAdum+ ε_i

where the dependent variable is either Money (in columns 1, 3, and 5) or Scoring (in columns 2, 4, and 6). For columns 1 through 6, each dependent variable is the rank of the individual observation in each of the pre-mentioned categories, as defined in Table 1. In columns 7 and 8, the dependent variable is an adjusted rank, segmented by quintiles, where the rank is 1 through 5. LPGAdum is dummy variable equal to one if the individual is on the LPGA tour, zero otherwise. All other variables are as defined in Table 1.

The results for the LPGA were consistent with the results for the PGA tour in that non-American born players outperformed their counterparts in both end performance measures. The average increase in ranking was 20 and 19 places for money earned and scoring, respectively. Neither age nor height had any significant relation to end performance.

Although their primary focus was on the two tours separately, the researcher also combined the two tours in a total sample. The difference in the numbers of golfers would create problematic model estimations if the researcher were to simply use the ranking as the dependent variable. He adjusted the rankings by creating quintiles for each performance measure. Therefore, for the total sample, the dependent variable only had 5 values, 1 through 5, where 1 represented those individuals who ranked in the top quintile in that respective category and 5 represented the bottom quintile. In doing this, the researcher assured the rankings were consistent across the two tours. The researcher included LPGAdum, a dummy variable equal to 1 for those individuals on the LPGA tour, zero otherwise. This variable is designed to control for systematic differences between characteristics on the two tours.

The results for the total sample confirmed those found individually in both tours. Specifically, the positive coefficient on USA indicated that across both tours, American born golfers have higher ranking values in both Money and Scoring, which indicates inferior performance. A negative relationship between age and end performance was found in the PGA rankings, but the significance was only marginal, a product of the insignificant relation in the LPGA tour. However, the highly significant and negative relation between USA and end performance was consistent across the two tours and provided an interesting question. It appeared that, of all the physical characteristics the researcher examined, the most important is nationality. This could be a product of many things, some of which the researcher could not examine. For example, it is well known that many players, particularly on the PGA tour, are successful, established players on tours in their native countries prior to participating in the United States. However, the researcher was unaware of any way to fully capture the increased ability attributable to this prior experience.

Regardless, if non-American born players are outperforming, it could simply be due to superior performance in individual areas, which the researcher called performance-predicting characteristics. It was not the researcher’ intent to examine where these skills are obtained, but rather to determine whether evidence supported the existence of superior skill in each statistical area. Therefore, the researcher examined these variables in an effort to “explain” the results of Table 2. Those results are presented in Table 3.

Table 3 – Multivariate Results:

The following table presents results from the following model:

Dep_i = α + β₁EventNumb + β₁Height + β₂Weight + β₃USA + β₅Age + β₆LPGAdum+ ε_i

where the dependent variable is either DrivingDist, DrivingAcc, GIR, or PuttAv. For Panels A and B, each dependent variable is the rank of the individual observation in each of the previously-mentioned categories, as defined in Table 1. For Panel C, the dependent variable is an adjusted rank, segmented by quintiles, where the rank is 1 through 5. All other variables are as defined in Tables 1 and 3.

Panel A examines the PGA tour, while Panel B examines the LPGA tour. Panel C examines the combined total sample. The researcher found, unsurprisingly, that younger, heavier, and taller players on the PGA tour hit longer drives. The researcher also found that younger players had less accuracy in their drives, as did taller players (although the significance is marginal.) In column 3, he sought to determine whether or not any of the personal characteristics help explain the percentage of greens hit in regulation. However, the researcher found the natural characteristics have no significance to GIR. In the last column of Panel A, the researcher found non-USA born players have lower (better) putting averages that their U.S. counterparts. Since this is a variable consistently found to be greatly important in golfing success (e.g. Davidson and Templing, 1986), this could explain, at least partially, the variation of USA in end performance.

Turning to Panel B, the researcher examined the performance-predicting variables for the LPGA tour. He found that taller, younger players hit longer drives, again consistent with expectations. However, shorter players drive more accurately. Most interesting, he also found a positive relation between U.S. born status and putting average rank, indicating non-American born players putt more efficiently. Again, this could explain some of the variation found in Table 2. In Panel 3, he examined the total sample. As expected, given the results in the first two panels, taller, younger players hit longer drives than their counterparts. However, taller players hit fewer fairways than shorter players. Also, as expected, the total sample results confirmed that non-U.S. born players have superior ranked putting averages than U.S. born players.

It appears some of the variation in performance unexplained by individual statistical categories may be due to physical characteristics. In regards to nationality, it may be that the superior performance is due to superior putting abilities, as he found no relation to other performance-predicting characteristics. However, he needed to examine the influence of natural physical characteristics in congruence with traditional predictors of end performance. In order to do this, the researcher estimated the following OLS model:

Dep_i = α + β₁DrivingDist + β₂DrivingAcc + β₃GIR + β₄PuttAv + β₄EventNumb + β₅LPGAdum + β₆Height + β₇Weight + β₈ USA + (2)β₉age + ε_I

where Dep_i is either Money or Scoring. The results are presented in Table 4. Panel A presents the results for Money, while Panel B presents the results for Scoring. To be consistent with previous studies, the researcher first examined only the performance-predicting variables. Those are presented in columns 1, 3, and 5 of each panel. The results were wholly consistent with those found in the majority of previous studies in that the two most important statistical categories are putting average and percentage of greens hit in regulation. In fact, the researcher found no significance at all in relation to the two driving measures on the PGA tour. However, driving (particularly driving accuracy) appears to be predictive of superior performance on the LPGA tour.

More important to this study, the researcher wanted to see if all of the variation in end performance can be determined by these performance-predicting variables. In other words, does the significance identified in the previous analyses disappear when combined with these more established measures? The researcher examined this in columns 2, 4, and 6 of each panel. The researcher found the results for the PGA tour to be consistent even when controlling for these variables. Specifically, USA maintained significance while GIR and PuttAv also remained highly significant. Therefore, successful players must be proficient at putting and hitting greens in regulation. However, there still seems to be an unexplained contribution from the individual’s nationality that comes from some undefined factor, perhaps prior experience (and success) on a tour in their native country.

The LPGA results are consistent in that the two most important variables for LPGA golfers are also putting and greens hit in regulation. However, there also seems to be a significant effect of driving accuracy on end performance. In both the LPGA sample and the total sample, USA maintains significance.

Table 4 – Multivariate Results with Both Physical and Performance Characteristics:

The following table presents results from the following model:

Dep_i = α + β₁DrivingDist + β₂DrivingAcc + β₃GIR + β₄PuttAv + β₄LPGAdum + β₅EventNumb + β₆Height + β₇Weight + β₈USA + β₉Age + ε_i

where the dependent variable is either Money (in Panel A) or Scoring (in Panel B). For each statistical category, the variable is the rank of the individual observation. In columns 5 and 6, the statistical variables are an adjusted rank, segmented by quintiles where the rank is adjusted to take on a value of 1 through 5. All other variables are as defined in Tables 1 and 3.

Conclusions:

The researcher examined natural physical characteristics of professional golfers on the PGA and LPGA tours. He controlled for performance-predicting statistical measures, namely driving distance, driving accuracy, percentage of greens hit in regulation, and putting average. The researcher found the percentage of greens hit in regulation and putting average to be the most important characteristics of end performance (i.e. success) in professional golf. This is consistent with numerous prior studies. Driving, particularly driving accuracy, appears also to be important on the LPGA tour, but not on the PGA tour.

More important to this study, the researcher found a strong relationship between nationality and end performance. Specifically, U.S. born players have inferior end performance relative to their counterparts. One explanation for this is perhaps the superior putting averages enjoyed by the non-U.S. golfers.

The researcher’ results have interesting implications for professional golf. It is obvious that golf is an international game more now than ever before, particularly in the United States, where the professional prizes are higher than any other country. Recent domination in Ryder Cup has led many to comment on the unexplained advantage European golfers seem to enjoy during those events. While this work takes a broader approach by examining all international born golfers (and not just European ones ), it provides a good starting point in investigating whether superior performance is contingent on nationality. The researcher’ primary objective was to identify whether such a relationship exists and not necessarily to describe its origin. Therefore, future research could be designed to examine the cause, for example training methods or coaching practices.

Endnotes:

¹ During the 2006 season, the PGA tour had a total of 48 tournaments, while the LPGA had only 33.

² In unreported results, the researcher examined numerous statistics, such as sand saves. However, the researcher found no significance in relation to those variables. In order to remain consistent with the previous literature, the researcher chose to examine only the variables that have been consistently used in similar studies.
The obvious assumption is that the incremental difference between each ranking category carries the same weight. In other words, the difference between rankings 1 and 2 is the same as the difference between 2 and 3.

³ While this is a restriction, there is no reason to believe it would bias the result as the pertinent question in an individual sport is performance relative to other competitors. In order to be absolutely sure, the researcher conducted all statistical analyses using the actual number rather than the ranking. All results were qualitatively identical. Results are available upon request.

⁴ For the PGA tour, the last event concluded on November 6, 2006 while the last event for the LPGA tour concluded on November 19, 2006. There are events on both tours that are not full-field events, meaning that not all players had the opportunity to participate. While there is no reason to believe this would bias the results, for completeness, the researcher eliminated tournaments in unreported results. The final conclusions are unchanged.

⁵ The researcher also identified a variable labeled experience, defined as the number of years the player has been a professional golfer. However, the variables age and experience were highly correlated (p = .94), therefore the researcher chose to examine only age. However, in unreported results he repeated all analyses replacing age with experience and find the results qualitatively unchanged.

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