The Olympic Odyssey

Submitted by: Dr. Thomas P. Rosandich – USSA President and CEO

Athens, Greece – I am starting the writing of this President’s Column from a small island in the Aegean Sea, an hour out of Athens, and am enjoying the magnificent villa home of Joe and Mina Valyraki. Joe has served in the Greek government for more than 25 years. He was the Minister of Sport when they signed the agreement to bring the Athens Olympiad 2004 to its original home in Greece. He then served twice as a Minister of the Interior – security is a specialty of his. His beautiful wife, Mina, was the Academy’s Sport Artist of the Year in 2002 (see picture above).

This is my first stop in a world sports tour to view Academy programs throughout the world. Currently, I am here as an observer of the Games. But this is far from my first visit to Athens as the Academy has had various projects in Greece in the past and several in the last eight years.

I feel like my travels are an ongoing “Odyssey” not unlike Homer’s tale of Odysseus after the Trojan War. Webster describes an odyssey as “a long wandering trek marked by many changes of fortune.”

My odyssey has been one of sport that has taken me to every Olympiad since Melbourne 1956, when I was a U.S. Marine Corps Officer and the All Service Coach. At that period of time, the majority of the athletes on the U.S. Track and Field Team were from the military because the draft was very much a part of life in America. Since then, during the past 50 years, I have had the privilege of visiting over 100 countries, and the Academy has developed sport programs in one form or another in more than half of them.

This has been an exciting Olympics in Greece. Each day, we have driven from Eretria on the island of Evia to take in a variety of Olympic events, e.g. water polo,

volleyball, and of course, track and field, the centerpiece of all Olympiads. (Incidentally, for anyone interested in what the original games were really all about, I recommend “The Naked Olympics” by Tony Perrottet).

I believe this to be the best Olympics I have seen in the last 48 years and probably the best in modern times. In many ways it was a miracle. I have been coming to Athens continuously over the last eight years, and I thought that Jacques Rogge, the President of the IOC, was correct when he almost took the Games away from the Greeks, fearing that they would not be ready. However, apparently if you tell the Greeks they can’t do something, they will go out and prove that they can indeed do it – and they did it in spades with these Games. I rate them A-plus – even better than the Seoul Olympics of 1988, which I thought was the best to date, except for the Korean language problem.

The Greeks made it all come together in the very end. I have never traveled so easily around Athens! Not long ago, it was nothing short of a nightmare just getting from Athens to their beautiful new airport. The underground trains were not useable except for small segments within the city, and many ring roads led to nowhere. But by magic, it all hooked up with the kind of “discipline” you usually only find in Asian cultures like Japan.

The ring roads around Athens cleared the gridlock, a trademark of the city. These roads were built with private money, which will be repaid through tolls in the coming years. This is a classic example of the private sector working with the government to achieve a common goal. Incidentally, all of these new roads lead from a beautifully built Olympic village, designed like a city – complete with shops, hospitals and all the normal city services; certainly one of the biggest and best ever built. The roads through the stadiums have a lane marked off with orange paint for Olympic vehicles only, and any violation of that policy carries a stiff $157 fine. A real coup by the Olympic committee is that, if you have an Olympic ticket, you can get on all public transport free of charge.

The Olympic complex, particularly the main stadium, is spectacular and architecturally brilliant, displaying the artistic hand of the Spanish architect, Santiago Clatrava. The stadium grounds are immaculate. They are set off by reflecting pools and a Spanish art piece, called the “WAVING WALL,” 100 meters long, that chimes throughout the night and serves as the backdrop for endless projected Olympic competitions, like a giant outdoor movie theater.

The grounds surrounding the sport complex are impeccable. At midnight, after a track and field event, I watched as 72,000 spectators (basically Greeks) carried their trash and bottles (from vendors like McDonald’s and Coca Cola – the major sponsors of the Games) and put them into the bins provided outside the stadium. Where else have you seen this?

A diverse group of some 65,000 volunteers, including the disabled in wheelchairs, was organized to help everyone and anyone attending the Games. It was one of the best-trained and most helpful “Corps of Volunteers” I have ever seen at a Games. Originally, the goal was for 45,000 volunteers but the foreign volunteers increased the total to some 65,000. All were dressed in an attractive common uniform, including some 15,000 “extras.” As spectators left the stadium and the Olympic grounds, dozens of well-groomed and cordial ladies called out from judges chairs “good night,” “goodbye,” “sweet dreams,” “travel safely” and other such hospitable farewells.

Before I leave the topic of the Olympic complex and the grounds, I would like to congratulate the Greeks on how they laid out and installed their shopping centers – again, some of the best that I have seen. Major sponsors paid millions to use the Olympic Rings and the remarkable thing was that there was no “ambush marketing.” The prices were standardized for all the Olympic clothing and mementos. They were the same whether they were sold on the Olympic grounds, in the city of Athens, or indeed on the outer islands. I particularly was aware of this as I shopped for family, staff and friends. Even more important, bottled water, for instance, was cheaper on the Olympic grounds than in the normal grocery store.

Unfortunately, this was a total reverse of what happened in Atlanta in 1996, where vendors were selling the same items at different prices five feet from each other down every side street. I rated the Atlanta Olympics as a C-minus, at best, as so did the rest of the world, I believe.

Throughout the streets of Athens there were continuous athletic and cultural programs late into the night for weeks, and there was a mass of well-behaved crowds. Again, this was not only throughout Athens but in the suburbs and on the outer islands, all well run without rowdy crowds.

What Athens did was rebuild itself for years to come. I call this the “Barcelona Model.” I watched Barcelona during the early 90’s and certainly during the Games, as it built new roads, airports, hotels, streets and apartments; while eliminating slums and the factory district, and recapturing the polluted Mediterranean, much like Sydney rebuilt itself in 2000. The only city that was not able to take the great opportunity of the Olympics to rebuild its inner structure was Atlanta. In fact, they ended up as probably the only Olympic city that lost their Olympic stadium, which in this case is now Turner’s Field for the Atlanta Braves baseball team.

I thought that Athens not only did a remarkable job of rebuilding itself but it did so without destroying its great antiquities, such as the Acropolis. (I, for one, hope the British give back the marble facings they took at the time of the Turkish occupation.)

Incidentally, I was in Barcelona earlier this year for Olympic meetings with the IOC Culture and Olympic Education Commission, on which I am privileged to serve. The reconstruction and the development of Barcelona that was done for the 1992 Olympic Games has not stopped. I hope that will be true with Athens.

Lastly, the greatest miracle of the Olympiad was the security. Guards and special electronic equipment were everywhere at an estimated cost of $1.6 billion. Security was everywhere, from helicopters above to cameras sliding on cable over every stadium, with checkpoints throughout the Olympic sites. It was subtle but with a touch of class. Such a touch of class is needed with our TSA people managing airport security throughout the USA.

The greatest problem in this Olympiad was drugs, as the Greeks lost some of their best sprinters at the beginning of the Games. Performance enhancing drugs could destroy the Games, along with violence and corruption.

There is no question that these were the best Games ever. It didn’t come cheap! The estimated cost was $12 billion – the most costly Games ever and a debt the Greek people will pay for generations. But from my perspective, the Greeks are prepared to do so.

In the Closing Ceremonies, Gianna Angelopaulos-Daskalaki, President of the Athens 2004 Organizing Committee, told of the achievements of the Greek people in bringing these Games together, which did in fact conclude once again with one of the most spectacular closing ceremonies that the world has ever seen. The opening ceremonies were equally spectacular. Rogge said at the end of the Games: “The Greek people have won!” and indeed they had!

Most importantly for us, after the Games, the Academy will have ongoing sport education programs in Greece with both the Greek Olympic Committee and some of the country’s better colleges and universities via distance learning.

I left Greece the following day for Cyprus, a Greek-speaking island nation some 45 minutes south of Greece by air. As we traveled to the airport everyone forgot the orange line on the highway, and we were back to driving like the Greek people of old. Some things will never change.

U.S. Sports Academy2013-11-26T19:27:43-06:00March 3rd, 2008|Contemporary Sports Issues, Sports Facilities, Sports Management|Comments Off

A Personal Odyssey to Greece and the 2004 Olympic Games

Submitted by: Scott J. Callan, Ph.D. & Janet M. Thomas, Ph.D.

Abstract

An extensive body of research examines the importance of a golfer’s
shot-making skills to the player’s overall performance, where performance
is measured as either tournament money winnings or average score per round
of golf. Independent of the performance measure, existing studies find
that a player’s shot-making skills contribute significantly to explaining
the variability in a golfer’s performance. To date, this research
has focused exclusively on the professional golfer. This study attempts
to extend the findings in the literature by examining the performance
determinants of amateur golfers. Using a sample of NCAA Division I male
golfers, various shot-making skills are analyzed and correlated with average
score per round of golf. Overall, the findings validate those dealing
with professional golfers. In particular, the results suggest that, like
professional golfers, amateurs must possess a variety of shot-making skills
to be successful. Moreover, relative to driving ability, putting skills
and reaching greens in regulation contribute more to explaining the variability
in a player’s success.

Introduction

Davidson and Templin (1986) present one of the first statistical investigations
of the major determinants of a professional golfer’s success. Using
U.S. Professional Golf Association (PGA) data, these researchers find
that a player’s shot-making skills explain approximately 86 percent
of the variability in a player’s average score and about 59 percent
of the variance in a player’s earnings. Based on these results,
Davidson and Templin conclude that a professional golfer must possess
a variety of shot-making skills to be successful as a tournament player.
They further offer strong empirical support that hitting greens in regulation
and putting were the two most important factors in explaining scoring
average variability across players, with driving ability showing up as
a distant third.

Following Davidson and Templin (1986), a number of researchers have
continued to investigate the determinants of a professional golfer’s
overall performance. Examples include Jones (1990), Shmanske (1992), Belkin,
Gansneder, Pickens, Rotella, and Striegel (1994), Wiseman, Chatterjee,
Wiseman, and Chatterjee (1994), Engelhardt (1995, 1997), Moy and Liaw
(1998), and more recently Nero (2001), Dorsel and Rotunda (2001), and
Engelhardt (2002). Overall, these studies support the major conclusion
presented by Davidson and Templin (1986), which is that a professional
golfer must exhibit a variety of shot-making skills to be successful as
a touring professional. While the relative importance of these skills
to player performance is not uniform across these studies, there is a
developing consensus that shot-making skills like putting and hitting
greens in regulation are more important to a player’s success than
driving distance.

Interestingly, while there is an accumulating literature investigating
professional golfers, no analogous studies have examined the amateur player,
despite the fact that Davidson and Templin (1986) explicitly state that
this avenue of investigation would be a useful direction for future research.
More recently, Belkin, et al. (1994) specifically raise this point, suggesting
that:

“It would also be intriguing to examine whether the same
skills which differentiate successful professionals also contribute
in the same manner to the fortunes of amateurs of differing capabilities.”
(p. 1280).

By way of response, this study fills that particular void in the literature
by empirically estimating the relationship between an amateur golfer’s
overall performance and various shot-making skills. To facilitate direct
comparisons to the existing literature on the determinants of professional
golfers’ performance, we employ the basic approach used by Davidson
and Templin (1986) and Belkin, et al. (1994), among others.

Method

Sample

The sample used for this analysis is a subset of NCAA Division I male
golfers who participated in at least one tournament during the 2002–2003
season. Table 1 presents a listing of the colleges and universities represented
in the study and the number of players from each institution. The specific
data on these collegiate golfers are obtained from Golfstat, Inc. (2003)
(accessible on the Internet at www.golfstat.com), and/or from the respective
colleges and universities directly. The colleges and universities included
in the analysis are a subset of the college teams participating in National
Collegiate Athletic Association (NCAA) Division I Men’s Golf. While
it would be preferable to examine all Division I teams, the individual
player statistics needed to perform the analysis are not available. However,
since it is reasonable to assume that the schools listed in Table 1 are
a representative sample of all Division I men’s teams, the data
sample is appropriate for this study.

TABLE 1
Sample of Schools Included in the Study

School	Number of Golfers	Conference	Golfweek/Sagarin Ranking
Clemson University	5	Atlantic Coast	1
University of Arizona	11	Pacific 10	7
University of Southern CA	9	Pacific 10	23
Duke University	8	Atlantic Coast	25
Vanderbilt University	7	Southeastern	31
California State -Fresno	9	Western Athletic	33
University of Kentucky	9	Southeastern	45
Georgia State University	8	Atlantic Sun	51
Texas A&M University	9	Big 12	60
Southeastern Louisiana Univ.	8	Southland	71
Coastal Carolina University	10	Big South	76

Sources: Golfstat, Inc. (2003) “Customized Team Pages-Men.”
www.golfstat.com/2003-2004/men/mstop10.htm, (accessed June 16, 2003),
various teams; Golfweek. (2003) “Golfweek/Sagarin Performance Index –
Men’s Team Ratings.” www.golfweek.com/college/mens1/teamrankings.asp,
(accessed July 1, 2003).

Measures

For the schools represented in this study, Golfstat, Inc. collects and
reports individual player statistics necessary to complete a performance
analysis. For this study we used statistics for the 2002 – 2003
NCAA Division I tournament season. Among the available data are the average
score per round (AS) for each amateur player in the sample. This statistic
provides the performance measure needed for the dependent variable in
this study, since earnings are not relevant to amateurs. Specifically,
according to the United States Golf Association (2003, p. 1) and the Royal
and Ancient Golf Club of St. Andrews (2003, p.1), an amateur golfer is
defined as:

“…one who plays the game as a non-remunerative and
non-profit-making sport and who does not receive remuneration for teaching
golf or for other activities because of golf skill or reputation, except
as provided in the Rules.”

Although studies of professional golfers examine scoring average and/or
earnings as performance measures, Wiseman et al. (1994) argue that correlation
results are stronger when scoring average is used. Hence, the use of scoring
average for this study of amateurs is soundly supported by the literature
examining professional golfers.

Statistics for the primary shot-making skills typically used in the
literature are collected and reported by Golfstat, Inc. and by some colleges
and universities. These include measures of driving accuracy, greens in
regulation, putting average, sand saves, and short game.

To capture amateurs’ long game skills, we use one of the classic
measures, which is driving accuracy. Specifically, we use the variable
Fairways Hit, which is defined as the percentage of fairways hit on par
4 and par 5 holes during a round of golf. Data on driving distance for
the amateur sample are not available. However, Dorsel and Rotunda (2001)
present evidence suggesting that the number of eagles (i.e., two strokes
under par on any hole) a player makes is positively correlated with the
player’s average driving distance. Hence, we use the variable Eagles,
the total number of eagles a player makes during the season, to control
for each player’s average driving distance. Following the literature,
we also include the variable Greens in Regulation (GIR) to measure the
percentage of greens a player reaches in regulation for the season. This
is defined as one stroke for a par three, two strokes or less for a par
four, and three strokes or less for a par five. As discussed in Belkin
et al. (1994), this GIR variable captures a player’s iron play and
their success at reading a green within the regulation number of strokes.

With regard to the short game, several variables are used in the analysis.
In keeping with the literature, we use two measures of putting skill –
Putts per Round, defined as the average number of putts per round, and
GIR Putts, which is the average number of putts measured only on greens
reached in regulation. Belkin, et al. (1994) is one study that uses the
former measure, while Dorsel and Rotunda (2001) is an example of a study
using the latter. Interestingly, Shmanske (1992) argues that the latter
statistic, GIR Putts, is superior because it correctly accounts for the
longer putting distances associated with a player who achieves a higher
number of greens in regulation. By including one of these measures in
different regression models, we can assess the validity of that argument.
We also include the variable Sand Saves (SS), which measures the percentage
of time a golfer makes par or better when hitting from a sand bunker.
In certain specifications of our regression analysis, we experiment with
the variable Short Game as an alternative measure to Sand Saves. Short
Game measures the percentage of time a player makes par or better when
not reaching the green in the regulation number of strokes.

In addition to a player’s shot-making skills, Belkin, et al. (1994)
and others note the importance of experience in determining a player’s
success. To control for this factor, two experience measures are used.
First, we define the variable Rounds as the number of tournament rounds
completed by each player during the 2002–2003 season. In a sense,
this measure captures a player’s short-term experience, in that
it measures how each additional round played in a season increases the
experience that a player can call upon in subsequent rounds. Second, to
control for longer-term cumulative experience, we construct a set of dummy
variables to reflect the player’s academic age, (i.e., Freshman,
Sophomore, Junior, or Senior). It is hypothesized that the higher a player’s
academic age, the more collegiate golfing experience has been gained,
and therefore the lower the expected average score.

Finally, since golf at the collegiate level is a team sport, it is important
to capture any associated team effects. That is, a player’s performance
might be affected by the team with which they are associated. At least
two plausible explanations for this team effect are viable – one
relating to the team’s coach and the other relating to the courses
played. With regard to the former, each team’s coach is expected
to uniquely affect the success of each team member through mentoring,
leadership, instruction, and overall direction. In fact, Dirks (2000)
and Giacobbi, Roper, Whitney, and Butryn (2002) present evidence supporting
the importance of a coach’s influence on the performance of a collegiate
athlete. Primarily, the coach acts as the team leader and instructor.
As a leader, the coach is responsible for the overall team strategy and
for ultimately determining a player’s tournament participation.
As an instructor, the more experienced coach may be better able to teach
players and to motivate them to improve their play.

As for courses played, we expect a player’s scoring average to
be affected by the specific golf courses played, which in turn are not
consistent across collegiate teams. Indeed, it is highly plausible that
some teams might, for example, play easier courses throughout a given
tournament season, which may lower a team member’s score. To account
for these team effects, dummy variables are constructed, whereby each
dummy variable identifies the team to which each player belongs.

Procedure

Following the literature, multiple regression analysis is used to estimate
the relationship between an amateur golfer’s average score and various
shot-making skills. In addition, each regression model is specified to
control for player experience and team factors. Ordinary least squares
(OLS) is used to derive the regression estimates for four different models.
These models are distinguished by the selection of shot-making skill statistics
used for certain variables. Specifically, each model is distinguished
by its use of Sand Saves (SS) versus Short Game and Putts per Round versus
GIR putts. We also generate simple Pearson correlation coefficients between
the measure of player performance and each of the independent variables
in the study.

Results and Discussion

Basic descriptive statistics for the sample of 93 golfers are presented
in Table 2. At the collegiate level, most tournaments consist of three
rounds of golf, and, like the professionals, each round comprises eighteen
holes. The average NCAA Division I male golfer in the sample participated
in approximately nine tournaments, played slightly less than 26 rounds
of golf, and had an average score per round of approximately 75 strokes
during the 2002 – 2003 season.

TABLE 2
Basic Descriptive Statistics

MEASURES	Mean	Std. Dev

Tournaments	8.72043	4.22818
Rounds	25.78495	12.62318
Average Score (AS)	75.04548	2.20730
Fairways Hit	0.68033	0.08356
Greens in Regulation (GIR)	0.60471	0.07985
Putts per round	31.02602	1.23018
GIR Putts	1.87653	0.07043
Sand Saves (SS)	0.41998	0.12239
Short Game	0.51377	0.08947
Eagles	1.50538	1.80352

Academic Age Dummy Variable	Mean	Std. Dev

Senior	0.19355	0.39722
Junior	0.23656	0.42727
Sophomore	0.31183	0.46575
Freshman	0.25806	0.43994

Team Dummy Variables	Mean	Std. Dev

University of Arizona	0.11828	0.32469
Clemson University	0.05376	0.22677
Duke University	0.08602	0.28192
California State -Fresno	0.09677	0.29725
Georgia State University	0.08602	0.28192
University of Kentucky	0.09677	0.29725
Southeastern Louisiana University	0.08602	0.28192
University of Southern CA	0.09677	0.29725
Texas A& M University	0.09677	0.29725
Vanderbilt University	0.07527	0.26525
Coastal Carolina University	0.10753	0.31146

With regard to specific shot-making skills, the average amateur hits
approximately 68 percent of the fairways and reaches the green in the
regulation number of strokes 60 percent of the time. Of the greens reached
in regulation, the average player needs 1.88 putts to finish a hole, and
over the course of a round, each needs to take slightly more than 31 putts.
On average, an amateur golfer makes par or better when hitting from a
sand bunker 42 percent of the time and makes par or better when not on
a green in regulation 51 percent of the time. Over the course of the 2002
– 2003 season, the average player made 1.5 eagles.

Table 3 presents the results of the correlation analysis among an amateur’s
average score (AS) and various shot-making skills, experience, and team
effects. Notice that all shot-making skills are significantly correlated
with a player’s average score. Somewhat predictably, GIR is the
variable that is most highly correlated with an amateur golfer’s
average score. This finding is analogous to what has been found for professional
golfers by Davidson and Templin (1986) and others. We also find that the
Short Game variable and GIR Putts rank second and third respectively in
terms of the strength of correlation among shot-making skills. Notice
that across the two putting measures – GIR Putts and Putts per Round,
the correlation for GIR Putts is higher, which may support Shmanske’s
(1992) assertion that this is a more accurate measure of putting skill.
We also find that both the short-term and long-term experience measures
are statistically correlated with a player’s performance. With regard
to the Rounds variable, the correlation shows a significant negative relationship
with a player’s average score, which follows our expectations. Also,
as anticipated, the dummy variable for academic age is positively correlated
with the player’s average score for freshmen and negatively correlated
for seniors. Lastly, for certain colleges and universities, there is a
significant correlation between a team effect and a player’s average
score.

TABLE 3
Pearson Correlation Coefficients

MEASURES	Correlation with Average Score (AS)

Fairways Hit	-0.42884***

Greens in Regulation (GIR)	-0.77499***

Putts per Round	0.35983***

GIR Putts	0.58234***

Sand Saves (SS)	-0.32141***

Short Game	-0.61039***

Eagles	-0.48784***

Rounds	-0.68418***

Academic Age Dummy Variables

Senior	-0.22301**
Junior	-0.12563
Sophomore	0.07899
Freshman	0.23974**

Team Dummy Variables

University of Arizona	-0.14242
Clemson University	-0.29896***
Duke University	-0.02609
California State – Fresno	-0.01887
Georgia State University	-0.02679
University of Kentucky	0.15855
Southeastern Louisiana University	-0.10522
University of Southern CA	-0.10022
Texas A& M University	0.18837*
Vanderbilt University	-0.03283
Coastal Carolina University	0.31977***

* significant at the 0.10 level
** significant at the 0.05 level
*** significant at the 0.01 level

In Table 4, we present the multiple regression results for four alternative
models. As previously noted, these models vary by which putting statistic
is used and by whether Short Game or Sand Saves is used in the estimation.
Model 1 uses Putts per Round and Sand Saves (SS), Model 2 uses Putts per
Round and Short Game, Model 3 uses GIR Putts and Sand Saves (SS), and
Model 4 uses GIR Putts and Short Game.

TABLE 4
Regression Analysis (Standardized Beta Coefficients in parentheses)

MEASURE	Model 1	Model 2	Model 3	Model 4
Fairways Hit	-0.28	-0.43	-0.99	-0.53
	(-0.01)	(-0.02)	(-0.04)	(-0.02)
Greens in Regulation (GIR)	-22.34***	-21.60***	-15.73***	-14.97***
	(-0.81)	(-0.78)	(-0.57)	(-0.54)
Putts per Round	1.00***	0.94***	—–	——
	(0.56)	(0.52)
GIR Putts	—–	—–	13.27***	8.92***
			(0.42)	(0.28)
Sand Saves (SS)	0.67	—–	-0.32	—–
	(0.04)		(-0.02)
Short Game	—-	-0.70	—–	-7.09***
		(-0.03)		(-0.29)
Eagles	0.01	0.01	-0.01	-0.02
	(0.01)	(0.01)	(-0.01)	(-0.02)
Rounds	-0.01	-0.01	-0.02**	-0.01
	(-0.04)	(-0.04)	(-0.12)	(-0.07)
Academic Age Dummy Variables
Senior	-0.40*	-0.42*	-0.20	-0.19
Junior	-0.33*	-0.36*	-0.22	-0.20
Sophomore	-0.48**	-0.50**	-0.46*	-0.51**
Team Dummy Variables
University of Arizona	-0.02	0.01	-0.23	-0.11
Duke University	-0.06	-0.01	-0.33	-0.17
California State -Fresno	-0.11	-0.10	-0.11	0.00
Georgia State University	-0.79**	-0.71*	-1.25**	-0.66
University of Kentucky	1.44***	1.43***	0.85*	1.18**
Southeastern Louisiana University	-0.11	0.04	-0.50	0.40
University of Southern CA	-0.13	-0.15	-0.45	-0.29
Texas A& M University	-0.26	-0.20	-0.49	-0.14
Vanderbilt University	0.28	0.25	-0.37	-0.27
Coastal Carolina University	0.78**	0.79**	0.42	0.84*
F-Statistic	46.73***	46.23***	21.78***	32.09***
R-Square	0.92	0.92	0.85	0.89
Adjusted R-Square	0.90	0.90	0.81	0.87
F-Statistic (full versus reduced)	4.38***	4.16***	1.93**	2.78***

* significant at the 0.10 level, assuming a one-tailed
test of hypothesis
** significant at the 0.05 level, assuming a one-tailed test of hypothesis
*** significant at the 0.01 level, assuming a one-tailed test of hypothesis

Overall, we observe that shot-making skills, player experience, and
team effects collectively explain a large proportion of the variability
in an amateur’s scoring average independent of the model specified.
Specifically, the adjusted R2 statistics across the four models range
from 0.81 to 0.90, values that are similar to those reported in Davidson
and Templin (1986) and Belkin, et al. (1994).

Of the specific shot-making skills, GIR and putting (either Putts per
Round or GIR Putts), are the most consistent predictors of an amateur’s
average score across the four models. In each case, GIR is significant
at the 1 percent level, as are both putting variables. However, the standardized
beta coefficients show that GIR is the most important predictor, as was
the case for the models estimated by Davidson and Templin (1986) and Belkin,
et al. (1994). Both putting variables also are significant at the 1 percent
level, though the standardized beta coefficients suggest that Putts per
Round might be a superior measure of amateur putting, which runs counter
to Shmanske’s (1992) view of these variable definitions, as noted
previously.

Interestingly, Short Game is a significant predictor of average score,
but only when the variable GIR Putts is included in the model, which is
Model 4 specifically. With regard to Sand Saves (SS), we find that it
is not a significant factor in predicting a player’s performance
in either Model 1 or Model 3. Davidson and Templin (1986) and, more recently,
Moy and Liaw (1998) find analogous results for their respective samples
of professional golfers. One explanation put forth by Moy and Liaw is
that all golfers have similar abilities in this skill category. Another
more likely justification is one presented by Dorsal and Rotunda (2001),
which is that bunker play is less frequent and, as a result, has a negligible
effect on a player’s overall performance.

To the extent that the number of eagles over the season captures driving
distance, the results indicate that driving distance is not a major factor
in determining a player’s performance. In general, this conclusion
agrees with the findings of Davidson and Templin (1986), Belkin, et al.
(1994), and Dorsel and Rotunda (2001). Hence, this finding seems to be
independent of whether the golfer is an NCAA amateur or a professional
player. However, such an assertion has to be made with caution, since
no direct measure of driving distance was available to include in this
amateur study.

In addition to a player’s shot-making skills, experience and team
effects appear to have an influence on an NCAA golfer’s performance.
With regard to the experience measures, the total number of rounds played
in the 2002-2003 season improves a player’s overall performance.
This assertion is based on the consistently negative coefficient on Rounds
across models, though the result is statistically significant only in
Model 3. As for longer-term experience, sophomore players consistently
achieve a lower average score than their freshman counterparts, and this
effect is statistically significant across the four models. Juniors and
seniors are found to enjoy the same performance effect linked to experience,
but the influence is found to be statistically significant only in Models
1 and 2.

As for individual team effects, the results suggest that a statistically
significant influence exists for certain collegiate programs. For example,
holding all else constant, all four models indicate that players on the
University of Kentucky team have higher and statistically significant
average scores relative to players on the Clemson team (the suppressed
dummy variable), who are the 2002-2003 NCAA Division I Champions. Conversely,
players at Georgia State University achieve lower average scores than
players at Clemson, independent of individual shot-making skills or experience,
and three of the four models show this finding to be statistically significant.
The absence of statistical significance for the other teams might be attributable
to limited variability of team effects within a single NCAA division.

Finally, an F-test comparing the full model to a reduced version was
conducted across each model specification, where the reduced model assumes
that the academic age and team effects are jointly zero. As noted in Table
4, the null hypothesis was rejected across all four models, indicating
that these two experience variables collectively help to explain the variability
of an amateur player’s performance. This outcome validates the belief
of other researchers, including Belkin et al. (1994) and Shmanske (1992).

Conclusions

The importance of shot-making skills to a professional golfer’s
success has been well documented in the literature. In general, research
studies point to the fact that a variety of shot-making skills are important
to a player’s overall performance. More specifically, four shot-making
skills – GIR, putting, driving accuracy, and driving distance –
are responsible for the majority of variation in a professional golfer’s
scoring performance. Of these four, GIR and putting have consistently
been found to be the more important factors. On occasion, driving accuracy
and driving distance have been found to statistically impact a professional
golfer’s average score, but typically the influence is weaker than
for GIR and putting skills.

Despite an accumulating literature seeking to validate or refine these
results, we know of no study that has extended this analysis beyond the
realm of professional golfers. To that end, we attempt to fill this void
in the literature by empirically identifying performance determinants
for amateur golfers. Using a sample of NCAA Division I male golfers, we
hypothesize that a variety of shot-making skills along with player experience
and team membership are expected to influence an amateur golfer’s
performance measured as average score per round. Using multiple regression
analysis, our results indicate that all these factors collectively explain
a large percentage of the variability in an NCAA golfer’s average
score. This is evidenced by R-squared values ranging from 0.81 to 0.90
across four different models distinguished by varying variable definitions.

We further find that the amateur golfer’s shot-making skills measured
through GIR and putting are the most important factors to explaining average
score per round. These findings offer an important contribution to the
growing literature on professional golfer performance in that they validate
and extend much of what has been shown in existing studies. Future research
should attempt to further extend these findings to other amateur data,
as they become available.

References

Belkin, D.S., Gansneder, B., Pickens, M., Rotella, R.J., & Striegel,
D. (1994) “Predictability and Stability of Professional Golf Association
Tour Statistics.” Perceptual and Motor Skills, 78, 1275-1280.
Davidson, J. D. & Templin, T. J. (1986) “Determinants of
Success Among Professional Golfers.” Research Quarterly for Exercise
and Sport, 57, 60-67.
Dirks, K. T. (2000) “Trust in Leadership and Team Performance:
Evidence from NCAA Basketball.” Journal of Applied Psychology,
85, 1004-1012.
Dorsel, T. N. & Rotunda, R. J. (2001) “Low Scores, Top 10
Finishes, and Big Money: An Analysis of Professional Golf Association
Tour Statistics and How These Relate to Overall Performance.”
Perceptual and Motor Skills, 92, 575-585.
Engelhardt, G. M. (1995) “‘It’s Not How You Drive,
It’s How You Arrive’: The Myth.” Perceptual and Motor
Skills, 80, 1135-1138.
Engelhardt, G. M. (1997) “Differences in Shot-Making Skills
among High and Low Money Winners on the PGA Tour.” Perceptual
and Motor Skills, 84, 1314.
Engelhardt, G. M. (2002) “Driving Distance and Driving Accuracy
Equals Total Driving: Reply to Dorsel and Rotunda.” Perceptual
and Motor Skills, 95, 423-424.
Giacobbi, P.R., Roper, E., Whitney, J. and Butryn, T. (2002) “College
Coaches’ Views About the Development of Successful Athletes: A
Descriptive Exploratory Investigation.” Journal of Sport Behavior,
25, 164-180.
Golfstat, Inc. (2003) “Customized Team Pages-Men.” www.golfstat.com/2003-2004/men/mstop10.htm
(accessed June 16, 2003), various teams.
Golfweek. (2003) “Golfweek/Sagarin Performance Index- Men’s
Team Ratings” www.golfweek.com/college/mens1/teamrankings.asp,
(accessed July 1, 2003).
Jones, R.E. (1990) “A Correlation Analysis of the Professional
Golf Association (PGA) Statistical Ranking for 1988.” In A.J.
Cochran (Ed.), Science and Golf: Proceedings of the First World Scientific
Conference of Golf. London: E & FN Spon. 165-167.
Moy, R. L. and Liaw, T. (1998) “Determinants of Professional
Golf Tournament Earnings.” The American Economist, 42, 65-70.
Nero, P. (2001) “Relative Salary Efficiency of PGA Tour Golfers.”
The American Economist, 45, 51-56.
National Collegiate Athletic Association (2003) “Sports Sponsorship
Summary.”
www1.ncaa.org/membership/membership_svcs/sponssummary, (accessed
July 1, 2003).
Royal and Ancient Golf Club of St. Andrews (2003) “Amateur Status.”
www.randa.org/index.cfm?cfid=1066700&cftoken=78999628&action=rules.amateur.home,
(accessed August 16, 2003)
Shmanske, S. (1992) “Human Capital Formation in Professional
Sports: Evidence from the PGA Tour.” Atlantic Economic Journal,
20, 66-80.
United States Golf Association. (2003) “Rules of Amateur Status
and the Decisions on the Rules of Amateur Status.” www.usga.org/rules/am_status/,
(accessed August 16, 2003).
Wiseman, F., Chatterjee, S. Wiseman, D. and Chatterjee, N. (1994)
“An Analysis of 1992 Performance Statistics for Players on the
U.S. PGA, Senior PGA, and LPGA Tours.” In A. J. Cochran and M.
R. Farrally (Eds.), Science and Golf: II. Proceedings of the World Scientific
Congress of Golf. London: E & FN Spon. 199-204.

U.S. Sports Academy2016-04-01T09:45:27-05:00March 3rd, 2008|Contemporary Sports Issues, Sports Studies and Sports Psychology|Comments Off

Soccer Hooliganism in England Between the Wars

Submitted by: Gerald Griggs

Hooliganism has long been associated with soccer in England and has been
a common occurrence from the late nineteenth century onwards. Yet following
the end of the First World War, incidents of crowd disorder appeared to
fall resulting in a period of calm and orderly behavior up until the
Second World War. The purpose of this study is to focus upon the inter-war
period, examining the theories proposed that explain the apparent calm
amongst the spectators of English soccer.

INTRODUCTION

Prior to the introduction of the organized and professional game in the
latter half of the nineteenth century, English soccer had been something
of a savage affair, involving large unruly mobs indulging in mass violence.
Although the codification of soccer and the establishment of the Football
Association (FA) in 1863 brought a sense of order to the game, crowd disorder
remained prevalent throughout the late nineteenth and early twentieth
century. However, following the end of the First World War in 1918, incidents
of crowd disorder and hooliganism appeared to fall, resulting in a period
of calm and orderly behavior right up until the Second World War in 1939
(Dunning et al., 1993). Post-war Britain once again witnessed crowd trouble
with the re-emergence of disorder, which was to continue until the present
day (Sleap, 1998).

The intention of this paper is to therefore focus upon the inter-war
period, examining the theories proposed that explain the apparent calm
amongst the spectators of English soccer. First, issues relating to the
social composition of the crowd will be discussed. This will be followed
by considering how crowd disorder was reported upon by both official and
media sources. Lastly, consideration will be given to how unruly behavior
was dealt with by the different parties concerned.

SOCIAL COMPOSITION OF THE CROWD

The incorporation of the working class into mainstream respectable society
has been offered by Figurational Sociologists as a significant reason
why soccer spectators behaved in a more civilized way between the wars
(Dunning et al., 1988, Maguire 1986, Murphy et al., 1990). The idea is
posited that the working class between the wars wished to convey to higher
class members of society (and presumably show each other) that they could
collectively interact at a large social gathering without disorder being
created. Maguire (1986) points out that the FA actually believed that
soccer was especially capable of achieving civilized and orderly behavior
among the working classes, particularly in difficult social climates.
During the General Strike of 1926 for instance, the “FA committee
argued that the playing of soccer would prove helpful in the present unsettled
condition of industrial affairs of the country” (Maguire, 1986, p.
230).

In respect to the class structure, another main theme that becomes apparent,
is the idea that soccer spectatorship was becoming increasingly respectable
as a result of the re-emergence of the middle classes attending soccer
matches. Both Walvin (1986) and Mason (1979), in particular, refer to
mixed classes being apparent at soccer matches during the inter-war period.
These are significant observations, as before the First World War, middle
class men would mostly watch rugby during the traditional soccer season
(Lowerson, 1995). The appearance of women at soccer matches also indicates
too that crowds were becoming more middle class (Hayward, 1995). Evidence
indicates that the women present would most likely have been middle class,
as during the inter-war period, working class women did not spend their
limited leisure time at sporting occasions (Jones, 1992).

Although little else can be derived from the specific composition of
inter-war crowds, not least because of the lack of recorded data (Holt,
1990), it is possible to consider how spectators were organized. In respect
of where and how a soccer fan would spectate, a factor that became more
evident in the 1920s and 1930s was not so much the social class of an
individual but their ability to pay. What resulted according to Bale (1993)
was the first case of physical segregation determined by prices, with
seating and shelter demanding a higher price. Hargreaves (1986) suggests
that such segregation was a necessary demarcation of social position that
existed as much within classes as between them. It is argued that the
visible social hierarchy which was evident in the later part of the nineteenth
century within soccer, needed to be re-established, particularly by the
‘petit bourgeois’ in order that their new found social status be acknowledged.
Whether the new fashion of segregation somehow pacified and ordered the
crowd would be a contentious suggestion but Hutchinson (1982) certainly
considers that such physical features as turnstiles and fences helped
to control such large numbers.

THE REPORTING OF CROWD DISORDER

In examining how incidents of crowd disorder were reported between the
wars most research concerns itself with the examination of FA minutes
and press reports. During the inter-war period, FA records show a marked
fall in hooliganism (Dunning et al., 1988). Between 1921 and 1939 there
were a total of seventy one incidents of crowd misconduct recorded by
the FA (an average of just under four per season). Moreover, between 1930
and 1934 there were merely five cases, none of which resulted in ground
closure (ground closure was a common punishment by the FA after violence
at matches). In total there were in fact eight ground closures in the
twenty years after the First World War, whereas there is evidence to suggest
that there could have been as many as forty six in the twenty years preceding
it. Post-war statistics again show recorded incidents rising steadily,
up to as many as twenty five cases per season (Dunning et al., 1988, p.
134). It can be assumed perhaps, that the FA took a softer line on crowd
disorder during the inter-war period, again perhaps in a bid to make soccer
appear more respectable, given the poor reputation it was trying to shed.
However, it must be said that the incidents recorded are ‘sketchy’ at
best (Dunning et al., 1988).

According to Murphy et al. (1990) the press too under reported incidents
of crowd disorder between the wars, though this was less to do with becoming
more civilized but more to do with the new commercial pressures being
placed upon editors. As the 1920s and 1930s heralded a new era of consumption
and consumerism, advertising became an increasingly significant means
of revenue for newspapers. As a result, headlines and print grew in size
and more photographs were included. As Murphy et al. (1990, p. 110) point
out “under the twin constraints of lessened space and the emerging,
competition-induced desire for a more attractive presentation, editors
seem to have become more sensitive to the issue of ‘newsworthiness’ and
the need for selectivity”. Therefore, given that soccer hooliganism
was not seen to be a social problem at that time, it would therefore have
been deemed to hold little or no interest to a newspaper reader.

DEALING WITH UNRULY behavior

According to Williams et al. (1991), at a time of soaring attendances
the “patterns of spectating of the period were indicative of considerably
more self policing and internal discipline within soccer crowds compared
with those of twenty years or more later and, indeed, those in the early
years of the century” (Williams et al., 1991, p. 164).

This is supported by Maguire (1986) who makes reference to a number of
FA minutes recorded in the 1920s which indicate that ‘respectable’ people
should exercise self control and aid in the controlling of fellow spectators,
allowing what was agreed upon, to be ‘permissible’. Maguire (1986, p.
230) suggests that “attempts to promote self regulation and increasing
agreement over what was considered permissible may well have reflected
the continuing successful endeavours of the middle classes to impose their
values on society as a whole”.

When self regulation failed however, the police themselves restored law
and order, with Walvin (1986) indicating that stricter and more rigorous
policing methods were employed during the inter-war period. This raises
a number of interesting questions. First, were the police reacting to
an apparently more uncontrollable crowd? Secondly, did the implementation
of such strategies represent a shift in police policies during the inter-war
period? Thirdly, did the action taken during this period in fact result
in there being less spectator disorder?

Although, as mentioned in the introduction that crowd disorder always
existed there is little evidence to suggest that the police were unduly
concerned. Hooliganism was not the social phenomenon that it later became.
However, it would be reasonable to suggest that more effective methods
of general crowd control indicated by Walvin (1986) were probably more
to do with personal safety than outbreaks of violence. Whether or not
the action taken by the police in any way quieted crowd disturbances is
questionable, though they may have contributed through their presence,
as relations between the police and the public were considered to be at
there most harmonious during the inter-war period (Reiner, 1985).

Relations between the fans and the club itself between 1919 and 1939
were also considered to be closer than they had ever been. Taylor (1971)
proposes that this is based upon the perceptions of the sub culture of
the working class that would be most likely to create trouble. His theory
of ‘Participatory Democracy’ details that “in the inter-war
years, the illusion persisted that power – over the future of the club
and particularly over the possibility of victory was distributed between
management, directors, players and the sub culture, all of whom were seen
as standing in some kind of unambiguous relationship to the working class
of the area as a whole” (Taylor, 1971, p. 362). It must be remembered
however that those that administered the club were markedly middle class
and had only the watching of soccer in common with the working class on
the terraces. After the Second World War, as soccer became more professional
and affluent (Bourgeoisification), more overt and frequent hooliganism
resulted, which was considered a working class reaction to not being consulted
over the new direction of soccer (Taylor, 1971).

Clarke (1978) too believes that the subsequent professionalisation, along
with the transformation of the social situation experienced by young working
class people, together resulted in the breaking of ties between members
of the same family or community which were strong amongst the pre-war
working class. Consequently as Clarke (1978, p. 25) points out “working
class boys before the Second World War typically went to soccer with their
fathers, uncles, older brothers or neighbours; in that context, their
behavior was subject to relatively effective control”. Working class
youth, the most likely group to engage in hooliganism, were therefore
effectively babysat for most, if not all of the inter-war period. It was
only later in the century when they went to matches in gangs with their
peers that control from elders ceased to be exercised effectively.

CONCLUSION

In summary, after examining the theories proposed that explain the apparent
calm amongst the spectators of English soccer during the inter-war period,
it would appear to be somewhat naïve to suggest that one overriding
idea could be held accountable. An interplay and evolution of a great
number of social factors such as Clarke’s (1978) idea of the ‘family on
the terrace’, coupled with a general willingness to implement more effective
regulation by all parties concerned, would seem to offer a more plausible
but less clear cut explanation.

BIBLIOGRAPHY

Bale, J. (1993) Sport, Space and the City. London: Routledge.
Clarke, J. (1978) Football and Working Class Fans: Tradition and Change.
In Ingham, R. (Ed.) Football Hooliganism. London: Inter-Action.
Dunning, E., Murphy, P., Willaims, J. (1988) The Roots of Football
Hooliganism. London: Routledge.
Dunning, E., Maguire, J., Pearton, R. (Eds.) (1993) The Sports Process.
Leeds: Human Kinetics.
Hargreaves, J. (1986) Sport, Power and Culture. Cambridge: Polity
Press.
Hayward, T. (1995) Women and Football Factsheet: A History of Female
Football Fans. Leicester: Leicester University.
Holt, R. (1990) Sport and the British. Oxford: Clarendon Press.
Hutchinson, J. (1982) The Football Industry. Glasgow: RD.
Jones, S (1992) Sport, Politics and the Working Class. Manchester:
Manchester University Press.
Lowerson, J. (1995) Sport and the English Middle Classes 1870 – 1914.
Manchester: Manchester University Press.
Maguire, J. (1986) ‘The Emergence of Football Spectating as a Social
Problem 1880 – 1985: A Figurational and Developmental Perspective’.
Sociology of Sport Journal, Volume 3, (pp.217-244).
Mason, A. (1979) Association football and English Society 1863 – 1915.
Sussex: Harvester Press.
Murphy, P., Williams, J., Dunning, E. (1990) Football on Trial. London:
Routledge.
Reiner, R. (1985) The Politics of the Police. Brighton: Wheatsheaf.
Sleap, M. (1998) Social Issues in Sport. London: Macmillan.
Taylor, I. (1971) Football Mad: A Speculative Sociology of Football
Hooliganism. In Dunning, E. (Ed.) The Sociology of Sport. London:
Frank Cass & Co.
Walvin, J. (1986) Football and the Decline of Britain. London: Macmillan.
Williams, J., Wagg, S. (1991) British Football and Social Change.
Leicester: Leicester University Press.

U.S. Sports Academy2015-11-06T20:23:17-06:00March 3rd, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off

The Importance of Expectations on Participatory Sport Event Satisfaction

Submitted by: Matthew J. Bernthal & Laura L. Sawyer

Abstract

Prior research on service quality in the sport industry has focused
almost exclusively on the satisfaction of sport spectators. The
current study expands this literature by beginning exploration into service
quality issues related to sport event participants. Specifically, we examine
the effect of participant skill level on the expectations that event participants
place on various service quality dimensions applicable to a participatory
sport event. Specifically, we propose that relatively lower skilled players
will place greater importance on peripheral event service dimensions (those
attributes of an event that fall outside the actual competitive play of
the sport and do not directly influence the athlete’s performance, such
as event parties, promotional giveaways to participants, and general ambiance
surrounding the event). We also propose that relatively higher
skilled players will place greater importance on play-related event service
dimensions (those attributes that are directly associated with the competitive
play of the sport and can directly influence athlete performance).
Tests of these hypotheses are performed through survey data collected
from participants at the United States Tennis Association’s Southern Sectional
Championships. Results indicate that lower skilled players indeed
place greater emphasis on peripheral event service dimensions than do
higher skilled players. However, lower skilled players did not
place less emphasis on play-related event service dimensions than did
higher skilled players. The importance of understanding the expectations
of participatory sport event consumers is discussed, and directions for
future research are provided.

The Importance of Expectations on Participatory Sport
Event Satisfaction: An Exploration into the Effect of Athlete Skill
Level on Service Expectations

Introduction

The emergence of research related to service quality in the sport industry
has only recently gained moderate attention. Given the enormity
of the sport industry in the United States, and indeed across the globe,
this is somewhat surprising. Moreover, the stream of sport service
quality research that has emerged in recent years has been somewhat limited,
focusing almost entirely on understanding fan satisfaction at spectator
events. For example, Kelley and Turley (2001) find that the importance
of nine different service quality factors at a sport spectating event
(e.g., concessions, price, fan comfort, facility access) differs across
a variety of demographic and fan identification characteristics.
As another example, the “sportscape” model has been an important relatively
recent contribution to the sport service quality literature, yet it too
focuses solely on spectator service encounters (e.g., Hightower, Brady,
& Baker 2002). The sportscape (e.g., the physical environment
where a spectator event occurs, primarily the arena/stadium) has been
shown to influence fans’ excitement and satisfaction with the experience
(Wakefield & Blodgett, 1994), their desire to stay through the event
(Wakefield & Sloan, 1995), and their likelihood of repatronizing events
at the facility (Wakefield, Blodgett, & Sloan, 1996). This
stream of research geared toward a better understanding of service quality
related to spectator events is invaluable, yet service quality research
geared toward a better understanding of service quality issues related
specifically to participatory sport events (i.e., events for which the
primary customers are the event participants, such as recreational golf
tournaments, tennis tournaments, softball tournaments, etc.) has been
largely unstudied and is much needed. It is toward this end that
the current study is addressed.

Chang, Chen, and Hsu (2002) provide an overview of service quality literature
to be considered in examining sport industry quality issues. One
of the models they touch on, and indeed one of the most influential models
in the service quality literature is the Gap model of service quality.
According to the Gap model, a customer’s satisfaction with a service is
largely driven by the extent to which his or her perceptions of received
service meet or exceed his or her expectations (Parasuraman, Zeithaml,
& Bitner, 1985). Customer expectations, in turn, can be defined
as beliefs about service delivery that function as standards or reference
points against which performance is judged (Zeithaml & Bitner, 2000).
It is critically important, therefore, that in order for participatory
sport events to be judged in a favorable light by participants, event
managers must pay particular attention to participant expectations during
event organization and management.

A key element for event managers in meeting or hopefully exceeding event
participant expectations is the careful consideration of the various sources
from which such expectations can arise. Zeithaml and Bitner (2000)
classify various sources of customer expectations, including enduring
service intensifiers, transitory service intensifiers, perceived service
alternatives, and explicit and implicit service promises. While
we contend that each is relevant to participatory sport event managers
(for example, an event participant’s satisfaction with an event would
logically depend on the number of competing events from which the participant
can choose), the current study focuses on enduring service intensifiers
and their ability to influence sport event participant’s satisfaction
with an event. Enduring service intensifiers are defined as stable
personal factors that lead to higher service sensitivity (Zeithaml &
Bitner, 2000). We propose that one such enduring service intensifier
relevant to participatory sport events is athlete skill level.
Event managers should consider that the skill level of the athletes participating
in their event could potentially influence the athletes’ expectations
for various event attributes. We posit that event attributes can
fall into two distinct categories, play-related attributes and peripheral
attributes. We define play-related attributes are those attributes
that are directly associated with the competitive play of the sport at
an event. Peripheral attributes are those attributes of an event
that fall outside the actual competitive play of the sport and do not
directly influence the athlete’s performance, such as event parties, promotional
giveaways to participants, and general ambiance surrounding the event
(e.g., play-site attractiveness). We hypothesize a direct, positive
relationship between skill level and play-related expectations, such that
as the skill level of the athlete rises, so do expectations regarding
play-related attributes. In turn, we hypothesize a direct, negative
relationship between skill level and peripheral expectations, such that
as the skill level of the athlete declines, expectations regarding peripheral
event attributes increase.

To illustrate the rationale behind these hypotheses, consider United
States Tennis Association (USTA) League Tennis. Players are grouped
according to skill level in categories ranging from 2.0 through 5.0, and
destination events are often held that hold competitions for players of
multiple skill levels. For example, the USTA holds state and regional
events in which one site hosts tournaments and the peripheral (e.g., banquets)
events surrounding them for players of multiple skill levels; in other
words, multiple skill level participants are participants in their own
skill level tournament, but are participants at the same overall event.
It is proposed that participants with a high skill level (e.g.,
a 5.0 USTA rating), given their competitive drive and focus related to
tennis (necessary in achieving their high skill level), are likely to
have relatively high expectations on play-related attributes such as the
match schedule, officiating, and court conditions. What we have
defined as play-related attributes are conceptually similar to what Gronroos
(1983) has defined as “technical quality”, or the core service that the
buyer receives from the seller. On the other hand, we propose that
players with lower skill levels (e.g., a USTA 2.5 rating) are often playing
as much for the “experience” and social aspects of the event as they are
for the competition, and are therefore likely to have relatively higher
expectations on peripheral attributes, such as event apparel offered for
sale or as a premium, food, and social “events (e.g., nightly parties/banquets)
within the event”. These hypotheses are stated formally as follows:

H1: Sporting event participant skill level is positively related to expectations
on play-related event service dimensions, such that higher skilled participants
will have higher expectations than will lower skilled participants on
service attributes related to the competitive play of the event.

H2: Sporting event participant skill level is negatively related to expectations
on peripheral event service dimensions, such that higher skilled participants
will have lower expectations than will lower skilled participants on service
attributes with are part of the event but unrelated to the competitive
play of the event.

Method

To test these hypotheses, we collected data from 487 participants at
the 2003 USTA Southern Sectional Championships, an event with tennis players
ranging in USTA skill rating from 2.5 (novice) to 5.0 (expert).
Prior to play, players were asked to rate the importance of multiple items
which could affect their overall satisfaction with a multiple-day tennis
tournament. The survey items were generated prior to the event
by asking ten tennis players (not participating in the event surveyed
in this study) to list items which might influence their satisfaction
when participating in a tennis tournament. Items receiving more
than one mention were included in the final survey used in this study,
resulting in 33 items. The items included those which were both
play-related and peripheral. The 33 items are provided in Appendix
A.

Formally stated, the survey question asked players “When evaluating your
satisfaction with a multiple-day tennis event to which you travel, how
important is each of the following items?” Players rated each of
the 33 items on a seven-point likert-type scale, with one being very unimportant
and seven being very important. Importance was used as a proxy
measure for expectations, as respondents will logically place more importance
on the dimensions for which they have higher expectations. Following
the importance ratings, respondents were asked to indicate their USTA
skill rating, gender, and age.

Results

Exploratory factor analysis was performed on the 33 items (Kaiser-Meyer-Olkin
= .929, suggesting the data were highly appropriate for factor analysis).
Using a varimax rotation and a loading cutoff value of 0.5, four
factors were retained (eigenvalues ranging from 11.78 to 1.07) and labeled
as follows: Play (court condition, sufficient practice
courts available, draw continually updated/readily available, courts conducive
to spectating, all matches played on same surface type, well-equipped
area for changeover (water, chairs, etc.), extent to which match/draw
schedule runs on time, quality of officiating, tournament officials readily
available at all sites; a = .84); Souvenirs (quality of souvenir
merchandise (t-shirts, hats, etc.), selection of souvenir merchandise,
price of souvenir merchandise, attractiveness of awards offered, free
souvenirs offered to participants; a = .90); Hotel (proximity
of hotel to play sites, directions to tourist attractions/restaurants
provided, availability of reasonably priced hotels, availability of high
quality, attractive hotels, cleanliness of hotels; a =.85); Tournament
Destination (tourist attractiveness of host city, physical attractiveness
of play sites, wide selection of restaurants in host city; a = .77); and
Concessions (selection of concessions at play sites, price of
concessions at play sites; a =.72). Cronbach’s alphas for all five
factors indicate that the five retained factors demonstrate strong internal
consistency. Further, the five retained factors explained the majority
(58.43%) of the variance. Factor structure, loadings, percent of
explained variance, and eigenvalues are provided in Table 1. The
Play dimension represents a service dimension directly related to a participant’s
competitive play in the event, while the four remaining dimensions of
Souvenirs, Hotel, Tournament Destination, and Concessions represent what
we have referred to as peripheral service dimensions. Nine items
did not load on any of the five factors and were dropped; these items
are noted in bold in Appendix A.

In order to analyze differences in importance by participant skill level,
a one-way MANOVA with skill level (relatively lower skilled = 2.5, 3.0,
3.5 USTA rating, n = 281; higher skilled = 4.0, 4.5, 5.0 USTA rating,
n = 206) as the independent variable and the mean of the summed score
of each service dimension factor (Play, Souvenirs, Hotel, Tournament Destination,
Concessions) as the multivariate dependent variables was performed.
MANOVA revealed a significant between-subjects skill level main effect
(Wilks’ Lambda = .97; F(5, 481) = 3.35; p<.005). Given multivariate
significance, we examined the univariate F-tests on each of the five service
dimension factors, which indicated significant differences between skill
level on four of the five service dimensions. Results of the univariate
tests are provided in Table 2. Note that all tests are one-tailed
due to directional hypotheses. These results indicate that lower
skilled players placed greater importance on each of the four peripheral
event dimensions (Souvenirs, Hotel, Tournament Destination, and Concessions)
than did higher skilled players, providing support for H2. As added
support for H2, we analyzed one item which did not load highly on any
of the four peripheral dimensions, yet represents a peripheral attribute.
Specifically, lower skill level players placed greater importance
on the item “quality of event social functions (banquets/parties)” than
did higher skill level players (lower skilled M = 4.96, higher skilled
M = 4.67; t = 2.12, p = .017). However, there was no difference
between lower skilled players and higher skilled players on the play-related
dimension. Therefore, H1 was not supported.

Discussion

It is pertinent that managers of participant events pay particular attention
to participant expectations and the various factors that might influence
such expectations. This study is an initial step toward this end.
Thoughtful analysis of participant expectations is especially important
for managers of grassroots or local events. According to Graham,
Goldblatt and Delpy (1995), special events have continued to increase
exponentially both across the country and around the world. City
officials and officers of other entities are drawn to the idea of hosting
special events not only to create positive publicity, but also for city
branding purposes and to create economic impact. Special events
are perceived to be economic catalysts for future growth, and the increased
number of special events has created competition for consumers’ discretionary
time and income. These events include not only sport events, but
any of the special event types as categorized by the International Events
Group (IEG), including 1) sport, 2) festivals, fairs and annual events,
3) cause-related, 4) arts, and 5) entertainment, tours and attractions.
Within sport events alone, on any given day or weekend a consumer
may choose between any number of events. However, given that most
people do not have unlimited discretionary time or income, it is important
to understand as much as possible about the expectations of attendees
in order to maximize branding and economic opportunities.

The following definitions apply to types of special events:

Mega event – Mega events by way of their size or significance, are those
that yield extraordinarily high levels of tourism, media coverage, prestige,
or economic impact for the host community or destination. Their
volume should exceed one million visits, their capital costs should be
at least $500 million and their reputation should be of a “must see”
event (Getz, 1997, p.6).

Hallmark event – a recurring event that possesses such significance,
in terms of tradition, attractiveness, image, or publicity, that the event
provides the host venue, community, or destination with a competitive
advantage. Over time, the event and destination become inseparable
(Getz, 1997, p.7).

Major events – events that by their scale and media interest, are capable
of attracting significant visitor numbers, media coverage and economic
benefit (Allen, O’Toole, McDonnell, & Harris, 2002, p. 14)

Given these definitions, there is no doubt that branding opportunities
and economic impact are more easily achieved for a mega event such as
the Olympics or for hallmark events such as New Orleans’ Mardi Gras, Omaha’s
College World Series or Times Square’s New Year’s Eve celebration.
Events such as these have achieved a level of prestige and have generally
garnered significant corporate and municipal sponsorship, and have increased
media coverage, specifically from television. However, it is generally
much more difficult for managers of local events, and particularly participant
events, to garner financial support and media coverage. Furthermore,
because these local participatory events have relatively small budgets,
lower sponsorship prices, and less media coverage than do events falling
in the other categories, it is particularly important that local event
managers know and understand the expectations of their patrons in order
to be efficient and effective in event production. These events
simply do not have the resources to spend on service dimensions that participants
consider relatively unimportant.

From a management perspective, participatory event directors should become
familiar with the Gap model of service quality, and in particular, realize
that participant expectations are a key component in participants’ event
satisfaction. Our results indicate that participant skill level
is one variable which can affect participant expectations, and thus a
variable which event directors should consider when planning an event.
Clearly, understanding participant expectations will allow an event
manager to more effectively establish long-term commitment from participants,
direct event production efforts, and inform event budget allocation.
Participatory event managers are often of the mindset that offering more
amenities makes for a more satisfying event. However, given that
few managers have unlimited event budgets, knowing the relative value
of various service dimensions such as (but not limited to) those analyzed
in this study will help managers better direct expenditures, whether this
be increasing spending on important dimensions or decreasing or eliminating
spending on relatively unimportant dimensions. For example, if
a manager is holding a USTA league tournament and has a large number of
high-level athletes (4.5, 5.0), excessive expenditures on banquets and
merchandise would not prove as beneficial as spending resources developing
an optimal tournament draw and schedule (for example, holding the events
at multiple play sites in order to avoid a significant number of matches
running behind schedule) or repairing courts and ensuring the presence
of qualified officials.

Most event managers must be concerned with corporate and municipal fundraising
to supplement the cost of event production. Furthermore, evaluation
of sponsorship relationships and accomplishing return on investment is
crucial as both corporations and municipalities that fund events are seeking
tangible results (Irwin, Asimakopoulos, & Sutton, 1994; Kuzma, Shanklin,
& McCally,1993; Meagher, 1992; Schlossberg, 1992; Stotlar, 1996).
Understanding the participants’ expectations can help event managers
to better match which sponsors will be more successful and can, in turn,
increase the relationships and longevity of sponsor relationships.
For example, if event participants place a relatively strong importance
on peripheral event dimensions, event managers can target hospitality
organizations as likely sponsors whose association with the event would
prove beneficial to both sponsor and event. Additionally, as competition
for both municipal and corporate sponsorship dollars increases, a thorough
understanding of participant expectations becomes increasingly significant.

In this study, the USTA’s Southern sectionals hosted players from beginning
skill level to advanced skill level as participants. This study
hypothesized that 1) players of higher skill (4.0, 4.5, and 5.0) levels
had higher expectations where play-related dimensions were concerned and
2) that players of lower skill levels (2.5, 3.0, and 3.5) had higher expectations
where peripheral dimensions were concerned. Although the first
hypothesis was not supported, one possible explanation pertains to the
championship level of this event. For a team to participate in
a sectional event, it would be necessary for the team to finish in the
top two in their league standings, and subsequently win both their city
and state playoffs. Therefore, even a lower skilled participant
or team would have to be highly competitive to achieve this standing,
and thus place significant importance on play-related service dimensions.
For instance, the Southern sectional tournament in this study was
the first event to have senior 2.5 teams. The implication from
this issue is that event directors who are managing an event of this stature
should consider that all participants will have certain expectations of
the play-related or more technical aspects of the event given their efforts
expended to earn eligibility to participate. Therefore, future
event directors of the USTA’s sectional event should pay particular attention
to play-related dimensions.

Future Research

Athlete skill level is only one factor that can influence event participant
expectations. Future research should be directed toward identifying
and analyzing other factors which might influence such expectations.
For example, the gender of the participant could be hypothesized to influence
their event expectations. It might be hypothesized that relative to male
participants, female participants would generally be more concerned with
souvenirs/merchandise, the tourism attractiveness of the host city, and
hotel/accommodations. This knowledge could help inform decisions such
as the type of hotel used and arranging the city attractions that may
be most attractive in order to meet the female participants’ expectations
more thoroughly. In a similar vein, the age of the participant
might also play a significant role in influencing their expectations.
As enduring service intensifiers such as gender and age are outside
the scope of the current study, this avenue proves ripe for further research.

Future research should also use existing marketing theory on service
quality to springboard into a deeper understanding of participant expectations.
As an example stemming from the current study, consider the zone
of tolerance, used by marketing scholars to explain the difference between
desired service, which represents what the service customer hopes to receive,
and adequate service, which represents the level of service that the customer
will accept as adequate or sufficient. According to service literature,
zones of tolerance are narrower for more important service dimensions
(e.g., Berry, Parasuraman, & Zeithaml, 1993). It could be posited
that for play-related attributes, the zone of tolerance will narrow as
athlete skill level increases. Conversely, for peripheral attributes,
the zone of tolerance will narrow as athlete skill level decreases.
The tolerance zones should narrow primarily due to the effect of skill
level on adequate expectations. For example, while both a 2.5 and
5.0 tennis player would likely desire similar quality in play-related
attributes, the quality that a 5.0 player will accept as adequate
, given his or her competitive focus, is likely to be higher than
that of a 2.5 player. Conversely, while both a 2.5 and 5.0 tennis
player would likely desire similar quality in peripheral attributes,
the quality that a 2.5 player will accept as adequate , given
his or her focus on the “overall event experience”, is likely to be higher
than that of a 5.0 player. Future research addressing propositions
such as these would prove both theoretically and practically interesting.

References

Allen, J., O’Toole, W., McDonnell, I., & Harris, R. (2002).
Festival and special event management . Australia: John Wiley
& Sons.
Berry, Leonard L., Parasuraman, A., & Zeithaml, Valarie A. (1993).
Ten lessons for improving service quality (Marketing Science
Institute Rep. No. 93-104, May). Cambridge, MA: Marketing Science
Institute.
Chang, C, Chen, C., & Hsu, C. (2002). A review of service quality
in corporate and recreational sport/fitness programs. The Sport
Journal , 5(3). Retrieved February 10, 2004 from http://www.thesportjournal.org/2002Journal/Vol5-No3/service-quality.asp.
Getz, D. (1997). Event management and event tourism . NY:
Cognizant Communication Corporation.
Graham, S., Goldblatt, J., & Delpy, L. (1995). The ultimate
guide to sport event management and marketing . Chicago: Richard
D Irwin.
Gronroos, C. (1983). Strategic management and marketing in the
service sector . Cambridge, MA: Marketing Science Institute.
Hightower, R., Brady, M., & Baker, T. (2002). Investigating
the role of the physical environment in hedonic service consumption:
An exploratory study of sporting events. Journal of Business Research
, 55(9), 697-707.
Irwin, R., Asimakopoulos, M., & Sutton, W. (1994). A model for
screening sport sponsorship opportunities. Journal of Promotion
Management , 2(314), 53-69.
Kelley, S. & Turley, L. (2001). Consumer perceptions of service
quality attributes at sporting events. Journal of Business Research
, 54(2), 161-166.
Kuzma, J., Shanklin, W., & McCally, J. (1993). Number one principle
for sporting events seeking corporate sponsors: Meet benefactor’s
objectives. Sport Marketing Quarterly , 2(3), 27-32.
Meagher, J. (1992, May). And now, a word from our sponsor. Athletic
Business , 14.
Parasuraman, A., Zeithaml, Valarie A., & Bitner, Mary J. (1985).
A conceptual model of service quality and its implications for future
research. Journal of Marketing, 49(Fall), 41-50.
Schlossberg, H. (1992, October 26). Firms using research to assess
sponsorship value. Marketing News , 13-15.
Stotlar, D. (1996, October). Trends in US sport sponsorship:
from philanthropy to retails sales . Paper presented at the meeting
of the European Association of Sport Management, Montpellier, France.
Wakefield, K., & Blodgett, J. (1994). The importance of servicescapes
in leisure service settings. Journal of Service Marketing ,
8(3), 66-76.
Wakefield, K., Blodgett, J., & Sloan, H. (1996). Measurement
and management of the sportscape. Journal of Sport Management
, 10(1), 15-31.
Wakefield, K., & Sloan, H. (1995). The effects of team loyalty
and selected stadium factors on spectator attendance. Journal
of Sport Management , 9(2), 153-172.
Zeithaml, Valarie A., & Bitner, Mary J. (2000). Services
marketing: Integrating customer focus across the firm (2 nd
ed.). Boston: McGraw-Hill.

Table 1

Factor Analysis of Event Service Dimensions

	Factor
	Play	Souvenirs	Hotel	Tournament Destination	Concessions
Eigenvalue	11.78	3.15	1.74	1.55	1.07
Percent Variance Explained	35.67	9.53	5.28	4.71	3.23
Factor Loadings
Court Condition	.638
Sufficient Practice Courts Avail.	.503
Draw Continually Updated/ Readily Available	.674
Courts Conducive to Spectating	.551
All Matches Played on Same Surface	.564
Well-Equipped Area for Changeover	.686
Extent to Which Match Schedule/Draw Runs on Time	.620
Quality of Officiating	.628
Tournament Officials Readily Available at All Sites	.622
Quality of Souvenir Merchandise		.868
Selection of Souvenir Merchandise		.882
Price of Souvenir Merchandise		.856
Attractiveness of Awards Offered		.683
Free Souvenirs Offered to Participants		.754
Proximity of Hotel to Play Sites			.711
Directions to Tourist Attractions/Restaurants Provided			.500
Avail. of Reasonably Priced Hotels			.760
Avail. of High Quality, Attractive Hotels			.682
Cleanliness of Hotels			.756
Tourist Attractiveness of Host City				.787
Physical Attractiveness of Play Sites				.629
Wide Selection of Restaurants in Host City				.543
Selection of Concessions at Play Sites					.677
Price of Concessions at Play Sites					.692

Table 2

Univariate F-tests (Mean Comparisons) on Service Dimension Factors

Factor	Lower Skilled	Higher Skilled	F
Play	53.73	52.85	1.69
Souvenirs	23.09	20.80	11.95**
Hotel	30.24	28.94	8.62**
Tournament Destination	16.22	15.68	3.39*
Concessions	9.12	8.46	6.09**

Note. **p<.01, *p<.05

Appendix A

Court Condition (court surface, lines, nets)
Sufficient Practice Courts Available
Draw is Continually Updated and Readily Viewable
Courts Conducive to Spectating
All Matches Played on Same Surface Type
Well-Equipped Area for Changeover (water, chairs, etc.)
Extent to which Match/Draw Schedule Runs On-Time
Quality of Officiating
Tournament Officials Readily Available at All Sites
Well-Equipped Locker-Rooms at Play Sites
On-Site Racquet Stringing
Quality of Competition
Medical Staff Present at All Play Sites
Event Results Reported in Local Media
Quality of Souvenir Merchandise (t-shirts, hats, etc.)
Selection of Souvenir Merchandise
Price of Souvenir Merchandise
Attractiveness of Awards Offered
Free Souvenirs Offered to Participants
Quality of Event Social Functions (banquets/parties)
Tourist Attractiveness of Host City
Physical Attractiveness of Play Sites
Wide Selection of Restaurants in Host City
Play-Related Food/Beverage at Play Sites (Fruit, Energy Bars/Drinks)
Selection of Other Concessions at Play Sites (Burgers, Chips, Soft
Drinks, etc.)
Price of Concessions at Play Sites
Friendliness and Courtesy of Host Site Staff
Host Site Staff Knowledgeable about Host City (restaurants, tourist
destinations, etc.)
Proximity of Hotels to Play Sites
Directions to Tourist Attractions/Restaurants Provided
Availability of Reasonably Priced Hotels
Availability of High Quality, Attractive Hotels
Cleanliness of Hotels

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U.S. Sports Academy2017-11-02T13:56:29-05:00March 3rd, 2008|Contemporary Sports Issues, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off

The Effect of a Plyometrics Program Intervention on Skating Speed in Junior Hockey Players

Submitted by: Kelly L. Lockwood & Patrick Brophey

Abstract

Few studies have been conducted to examine the effects of plyometrics on skating speed in junior hockey players. The present study was designed to look at the effects of a 4-week, eight session, plyometric training program intervention on skating speed. Six male subjects (18.8 ± .98 years) that engaged in the training program completed pre and post 40 meter on-ice sprinting tests. The training group showed significant time improvements (p<.05) in the 40 meter skating distance. The results suggested that plyometric training has a positive effect on skating speed in junior hockey players such that a reduction in on-ice sprinting times is evident.

(more…)

U.S. Sports Academy2016-10-24T11:45:30-05:00March 3rd, 2008|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off

The Olympic Odyssey

A Personal Odyssey to Greece and the 2004 Olympic Games

Abstract

Introduction

Method

Sample

Measures

Procedure

Results and Discussion

Conclusions

References

Soccer Hooliganism in England Between the Wars

INTRODUCTION

SOCIAL COMPOSITION OF THE CROWD

THE REPORTING OF CROWD DISORDER

DEALING WITH UNRULY behavior

CONCLUSION

BIBLIOGRAPHY

The Importance of Expectations on Participatory Sport Event Satisfaction

Abstract

The Importance of Expectations on Participatory Sport Event Satisfaction: An Exploration into the Effect of Athlete Skill Level on Service Expectations

Introduction

Method

Results

Discussion

Future Research

Table 1

Factor Analysis of Event Service Dimensions

Table 2

Univariate F-tests (Mean Comparisons) on Service Dimension Factors

Appendix A

The Effect of a Plyometrics Program Intervention on Skating Speed in Junior Hockey Players

Abstract

The Importance of Expectations on Participatory Sport
Event Satisfaction: An Exploration into the Effect of Athlete Skill
Level on Service Expectations