{"id":182,"date":"2008-03-03T15:41:08","date_gmt":"2008-03-03T15:41:08","guid":{"rendered":""},"modified":"2016-04-01T09:45:27","modified_gmt":"2016-04-01T14:45:27","slug":"a-personal-odyssey-to-greece-and-the-2004-olympic-games","status":"publish","type":"post","link":"https:\/\/thesportjournal.org\/article\/a-personal-odyssey-to-greece-and-the-2004-olympic-games\/","title":{"rendered":"A Personal Odyssey to Greece and the 2004 Olympic Games"},"content":{"rendered":"
Submitted by: Scott J. Callan, Ph.D. & Janet M. Thomas, Ph.D.<\/div>\n

Abstract<\/h2>\n

An extensive body of research examines the importance of a golfer\u2019s
shot-making skills to the player\u2019s 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\u2019s shot-making skills contribute significantly to explaining
the variability in a golfer\u2019s 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\u2019s success.<\/p>\n

<\/p>\n

Introduction<\/h2>\n

Davidson and Templin (1986) present one of the first statistical investigations
of the major determinants of a professional golfer\u2019s success. Using
U.S. Professional Golf Association (PGA) data, these researchers find
that a player\u2019s shot-making skills explain approximately 86 percent
of the variability in a player\u2019s average score and about 59 percent
of the variance in a player\u2019s 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.<\/p>\n

Following Davidson and Templin (1986), a number of researchers have
continued to investigate the determinants of a professional golfer\u2019s
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\u2019s success than
driving distance.<\/p>\n

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:<\/p>\n

\n

\u201cIt 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.\u201d
(p. 1280).<\/em><\/p>\n<\/blockquote>\n

By way of response, this study fills that particular void in the literature
by empirically estimating the relationship between an amateur golfer\u2019s
overall performance and various shot-making skills. To facilitate direct
comparisons to the existing literature on the determinants of professional
golfers\u2019 performance, we employ the basic approach used by Davidson
and Templin (1986) and Belkin, et al. (1994), among others.<\/p>\n

Method<\/h2>\n

Sample<\/h3>\n

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\u20132003
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\u2019s 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\u2019s teams, the data
sample is appropriate for this study.<\/p>\n

TABLE 1
Sample of Schools Included in the Study <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
School<\/th>\n\n
Number of Golfers<\/div>\n<\/th>\n
\n
Conference<\/div>\n<\/th>\n
\n
Golfweek\/Sagarin Ranking<\/div>\n<\/th>\n<\/tr>\n
Clemson University<\/td>\n\n
5<\/div>\n<\/td>\n
\n
Atlantic Coast<\/div>\n<\/td>\n
\n
1<\/div>\n<\/td>\n<\/tr>\n
University of Arizona<\/td>\n\n
11<\/div>\n<\/td>\n
\n
Pacific 10<\/div>\n<\/td>\n
\n
7<\/div>\n<\/td>\n<\/tr>\n
University of Southern CA<\/td>\n\n
9<\/div>\n<\/td>\n
\n
Pacific 10<\/div>\n<\/td>\n
\n
23<\/div>\n<\/td>\n<\/tr>\n
Duke University<\/td>\n\n
8<\/div>\n<\/td>\n
\n
Atlantic Coast<\/div>\n<\/td>\n
\n
25<\/div>\n<\/td>\n<\/tr>\n
Vanderbilt University<\/td>\n\n
7<\/div>\n<\/td>\n
\n
Southeastern<\/div>\n<\/td>\n
\n
31<\/div>\n<\/td>\n<\/tr>\n
California State -Fresno<\/td>\n\n
9<\/div>\n<\/td>\n
\n
Western Athletic<\/div>\n<\/td>\n
\n
33<\/div>\n<\/td>\n<\/tr>\n
University of Kentucky<\/td>\n\n
9<\/div>\n<\/td>\n
\n
Southeastern<\/div>\n<\/td>\n
\n
45<\/div>\n<\/td>\n<\/tr>\n
Georgia State University<\/td>\n\n
8<\/div>\n<\/td>\n
\n
Atlantic Sun<\/div>\n<\/td>\n
\n
51<\/div>\n<\/td>\n<\/tr>\n
Texas A&M University<\/td>\n\n
9<\/div>\n<\/td>\n
\n
Big 12<\/div>\n<\/td>\n
\n
60<\/div>\n<\/td>\n<\/tr>\n
Southeastern Louisiana Univ.<\/td>\n\n
8<\/div>\n<\/td>\n
\n
Southland<\/div>\n<\/td>\n
\n
71<\/div>\n<\/td>\n<\/tr>\n
Coastal Carolina University<\/td>\n\n
10<\/div>\n<\/td>\n
\n
Big South<\/div>\n<\/td>\n
\n
76<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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). <\/em><\/p>\n

Measures<\/h3>\n

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 \u2013 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:<\/p>\n

\n

\u201c\u2026one 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.\u201d <\/em><\/p>\n<\/blockquote>\n

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.<\/p>\n

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.<\/p>\n

To capture amateurs\u2019 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\u2019s 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\u2019s 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\u2019s iron play and
their success at reading a green within the regulation number of strokes.<\/p>\n

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 \u2013
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.<\/p>\n

In addition to a player\u2019s shot-making skills, Belkin, et al. (1994)
and others note the importance of experience in determining a player\u2019s
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\u20132003 season. In a sense,
this measure captures a player\u2019s 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\u2019s academic age, (i.e., Freshman,
Sophomore, Junior, or Senior). It is hypothesized that the higher a player\u2019s
academic age, the more collegiate golfing experience has been gained,
and therefore the lower the expected average score.<\/p>\n

Finally, since golf at the collegiate level is a team sport, it is important
to capture any associated team effects. That is, a player\u2019s performance
might be affected by the team with which they are associated. At least
two plausible explanations for this team effect are viable \u2013 one
relating to the team\u2019s coach and the other relating to the courses
played. With regard to the former, each team\u2019s 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\u2019s 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\u2019s tournament participation.
As an instructor, the more experienced coach may be better able to teach
players and to motivate them to improve their play.<\/p>\n

As for courses played, we expect a player\u2019s 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\u2019s score. To account
for these team effects, dummy variables are constructed, whereby each
dummy variable identifies the team to which each player belongs.<\/p>\n

Procedure<\/h3>\n

Following the literature, multiple regression analysis is used to estimate
the relationship between an amateur golfer\u2019s 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.<\/p>\n

Results and Discussion<\/h2>\n

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 \u2013 2003 season.<\/p>\n

TABLE 2
Basic Descriptive Statistics <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n
MEASURES<\/div>\n<\/th>\n
Mean<\/th>\nStd. Dev<\/th>\n<\/tr>\n
<\/td>\n<\/tr>\n
Tournaments<\/td>\n\n
8.72043<\/div>\n<\/td>\n
\n
4.22818<\/div>\n<\/td>\n<\/tr>\n
Rounds<\/td>\n\n
25.78495<\/div>\n<\/td>\n
\n
12.62318<\/div>\n<\/td>\n<\/tr>\n
Average Score (AS)<\/td>\n\n
75.04548<\/div>\n<\/td>\n
\n
2.20730<\/div>\n<\/td>\n<\/tr>\n
Fairways Hit<\/td>\n\n
0.68033<\/div>\n<\/td>\n
\n
0.08356<\/div>\n<\/td>\n<\/tr>\n
Greens in Regulation (GIR)<\/td>\n\n
0.60471<\/div>\n<\/td>\n
\n
0.07985<\/div>\n<\/td>\n<\/tr>\n
Putts per round<\/td>\n\n
31.02602<\/div>\n<\/td>\n
\n
1.23018<\/div>\n<\/td>\n<\/tr>\n
GIR Putts<\/td>\n\n
1.87653<\/div>\n<\/td>\n
\n
0.07043<\/div>\n<\/td>\n<\/tr>\n
Sand Saves (SS)<\/td>\n\n
0.41998<\/div>\n<\/td>\n
\n
0.12239<\/div>\n<\/td>\n<\/tr>\n
Short Game<\/td>\n\n
0.51377<\/div>\n<\/td>\n
\n
0.08947<\/div>\n<\/td>\n<\/tr>\n
Eagles<\/td>\n\n
1.50538<\/div>\n<\/td>\n
\n
1.80352<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
\n
Academic Age Dummy Variable<\/div>\n<\/th>\n
Mean<\/th>\nStd. Dev<\/th>\n<\/tr>\n
<\/td>\n<\/tr>\n
Senior<\/td>\n\n
0.19355<\/div>\n<\/td>\n
\n
0.39722<\/div>\n<\/td>\n<\/tr>\n
Junior<\/td>\n\n
0.23656<\/div>\n<\/td>\n
\n
0.42727<\/div>\n<\/td>\n<\/tr>\n
Sophomore<\/td>\n\n
0.31183<\/div>\n<\/td>\n
\n
0.46575<\/div>\n<\/td>\n<\/tr>\n
Freshman<\/td>\n\n
0.25806<\/div>\n<\/td>\n
\n
0.43994<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
\n
Team Dummy Variables<\/div>\n<\/th>\n
Mean<\/th>\nStd. Dev<\/th>\n<\/tr>\n
<\/td>\n<\/tr>\n
University of Arizona<\/td>\n\n
0.11828<\/div>\n<\/td>\n
\n
0.32469<\/div>\n<\/td>\n<\/tr>\n
Clemson University<\/td>\n\n
0.05376<\/div>\n<\/td>\n
\n
0.22677<\/div>\n<\/td>\n<\/tr>\n
Duke University<\/td>\n\n
0.08602<\/div>\n<\/td>\n
\n
0.28192<\/div>\n<\/td>\n<\/tr>\n
California State -Fresno<\/td>\n\n
0.09677<\/div>\n<\/td>\n
\n
0.29725<\/div>\n<\/td>\n<\/tr>\n
Georgia State University<\/td>\n\n
0.08602<\/div>\n<\/td>\n
\n
0.28192<\/div>\n<\/td>\n<\/tr>\n
University of Kentucky<\/td>\n\n
0.09677<\/div>\n<\/td>\n
\n
0.29725<\/div>\n<\/td>\n<\/tr>\n
Southeastern Louisiana University<\/td>\n\n
0.08602<\/div>\n<\/td>\n
\n
0.28192<\/div>\n<\/td>\n<\/tr>\n
University of Southern CA<\/td>\n\n
0.09677<\/div>\n<\/td>\n
\n
0.29725<\/div>\n<\/td>\n<\/tr>\n
Texas A& M University<\/td>\n\n
0.09677<\/div>\n<\/td>\n
\n
0.29725<\/div>\n<\/td>\n<\/tr>\n
Vanderbilt University<\/td>\n\n
0.07527<\/div>\n<\/td>\n
\n
0.26525<\/div>\n<\/td>\n<\/tr>\n
Coastal Carolina University<\/td>\n\n
0.10753<\/div>\n<\/td>\n
\n
0.31146<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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
\u2013 2003 season, the average player made 1.5 eagles.<\/p>\n

Table 3 presents the results of the correlation analysis among an amateur\u2019s
average score (AS) and various shot-making skills, experience, and team
effects. Notice that all shot-making skills are significantly correlated
with a player\u2019s average score. Somewhat predictably, GIR is the
variable that is most highly correlated with an amateur golfer\u2019s
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 \u2013 GIR Putts and Putts per Round,
the correlation for GIR Putts is higher, which may support Shmanske\u2019s
(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\u2019s performance. With regard
to the Rounds variable, the correlation shows a significant negative relationship
with a player\u2019s average score, which follows our expectations. Also,
as anticipated, the dummy variable for academic age is positively correlated
with the player\u2019s 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\u2019s average
score.<\/p>\n

TABLE 3
Pearson Correlation Coefficients <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
MEASURES<\/th>\nCorrelation with Average Score (AS)<\/th>\n<\/tr>\n
<\/td>\n<\/tr>\n
Fairways Hit<\/td>\n\n
-0.42884***<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Greens in Regulation (GIR)<\/td>\n\n
-0.77499***<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Putts per Round<\/td>\n\n
0.35983***<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
GIR Putts<\/td>\n\n
0.58234***<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Sand Saves (SS)<\/td>\n\n
-0.32141***<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Short Game<\/td>\n\n
-0.61039***<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/tr>\n
Eagles<\/td>\n\n
-0.48784***<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Rounds<\/td>\n\n
-0.68418***<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
\n
Academic Age Dummy Variables<\/div>\n<\/th>\n<\/tr>\n
<\/td>\n<\/tr>\n
Senior<\/td>\n\n
-0.22301**<\/div>\n<\/td>\n<\/tr>\n
Junior<\/td>\n\n
-0.12563<\/div>\n<\/td>\n<\/tr>\n
Sophomore<\/td>\n\n
0.07899<\/div>\n<\/td>\n<\/tr>\n
Freshman<\/td>\n\n
0.23974**<\/div>\n<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
\n
Team Dummy Variables<\/div>\n<\/th>\n<\/tr>\n
<\/td>\n<\/tr>\n
University of Arizona<\/td>\n\n
-0.14242<\/div>\n<\/td>\n<\/tr>\n
Clemson University<\/td>\n\n
-0.29896***<\/div>\n<\/td>\n<\/tr>\n
Duke University<\/td>\n\n
-0.02609<\/div>\n<\/td>\n<\/tr>\n
California State – Fresno<\/td>\n\n
-0.01887<\/div>\n<\/td>\n<\/tr>\n
Georgia State University<\/td>\n\n
-0.02679<\/div>\n<\/td>\n<\/tr>\n
University of Kentucky<\/td>\n\n
0.15855<\/div>\n<\/td>\n<\/tr>\n
Southeastern Louisiana University<\/td>\n\n
-0.10522<\/div>\n<\/td>\n<\/tr>\n
University of Southern CA<\/td>\n\n
-0.10022<\/div>\n<\/td>\n<\/tr>\n
Texas A& M University<\/td>\n\n
0.18837*<\/div>\n<\/td>\n<\/tr>\n
Vanderbilt University<\/td>\n\n
-0.03283<\/div>\n<\/td>\n<\/tr>\n
Coastal Carolina University<\/td>\n\n
0.31977***<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

* significant at the 0.10 level
** significant at the 0.05 level
*** significant at the 0.01 level<\/p>\n

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.<\/p>\n

TABLE 4
Regression Analysis (Standardized Beta Coefficients in parentheses)<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n
MEASURE<\/div>\n<\/th>\n
\n
Model 1<\/div>\n<\/th>\n
\n
Model 2<\/div>\n<\/th>\n
\n
Model 3<\/div>\n<\/th>\n
\n
Model 4<\/div>\n<\/th>\n<\/tr>\n
Fairways Hit<\/td>\n-0.28<\/td>\n-0.43<\/td>\n-0.99<\/td>\n-0.53<\/td>\n<\/tr>\n
<\/td>\n(-0.01)<\/td>\n(-0.02)<\/td>\n(-0.04)<\/td>\n(-0.02)<\/td>\n<\/tr>\n
Greens in Regulation (GIR)<\/td>\n-22.34***<\/td>\n-21.60***<\/td>\n-15.73***<\/td>\n-14.97***<\/td>\n<\/tr>\n
<\/td>\n(-0.81)<\/td>\n(-0.78)<\/td>\n(-0.57)<\/td>\n(-0.54)<\/td>\n<\/tr>\n
Putts per Round<\/td>\n1.00***<\/td>\n0.94***<\/td>\n—–<\/td>\n——<\/td>\n<\/tr>\n
<\/td>\n(0.56)<\/td>\n(0.52)<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
GIR Putts<\/td>\n—–<\/td>\n—–<\/td>\n13.27***<\/td>\n8.92***<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n(0.42)<\/td>\n(0.28)<\/td>\n<\/tr>\n
Sand Saves (SS)<\/td>\n0.67<\/td>\n—–<\/td>\n-0.32<\/td>\n—–<\/td>\n<\/tr>\n
<\/td>\n(0.04)<\/td>\n<\/td>\n(-0.02)<\/td>\n<\/td>\n<\/tr>\n
Short Game<\/td>\n—-<\/td>\n-0.70<\/td>\n—–<\/td>\n-7.09***<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n(-0.03)<\/td>\n<\/td>\n(-0.29)<\/td>\n<\/tr>\n
Eagles<\/td>\n0.01<\/td>\n0.01<\/td>\n-0.01<\/td>\n-0.02<\/td>\n<\/tr>\n
<\/td>\n(0.01)<\/td>\n(0.01)<\/td>\n(-0.01)<\/td>\n(-0.02)<\/td>\n<\/tr>\n
Rounds<\/td>\n-0.01<\/td>\n-0.01<\/td>\n-0.02**<\/td>\n-0.01<\/td>\n<\/tr>\n
<\/td>\n(-0.04)<\/td>\n(-0.04)<\/td>\n(-0.12)<\/td>\n(-0.07)<\/td>\n<\/tr>\n
\n
Academic Age Dummy Variables<\/div>\n<\/th>\n<\/tr>\n
Senior<\/td>\n-0.40*<\/td>\n-0.42*<\/td>\n-0.20<\/td>\n-0.19<\/td>\n<\/tr>\n
Junior<\/td>\n-0.33*<\/td>\n-0.36*<\/td>\n-0.22<\/td>\n-0.20<\/td>\n<\/tr>\n
Sophomore<\/td>\n-0.48**<\/td>\n-0.50**<\/td>\n-0.46*<\/td>\n-0.51**<\/td>\n<\/tr>\n
\n
Team Dummy Variables<\/div>\n<\/th>\n<\/tr>\n
University of Arizona<\/td>\n-0.02<\/td>\n0.01<\/td>\n-0.23<\/td>\n-0.11<\/td>\n<\/tr>\n
Duke University<\/td>\n-0.06<\/td>\n-0.01<\/td>\n-0.33<\/td>\n-0.17<\/td>\n<\/tr>\n
California State -Fresno<\/td>\n-0.11<\/td>\n-0.10<\/td>\n-0.11<\/td>\n0.00<\/td>\n<\/tr>\n
Georgia State University<\/td>\n-0.79**<\/td>\n-0.71*<\/td>\n-1.25**<\/td>\n-0.66<\/td>\n<\/tr>\n
University of Kentucky<\/td>\n1.44***<\/td>\n1.43***<\/td>\n0.85*<\/td>\n1.18**<\/td>\n<\/tr>\n
Southeastern Louisiana University<\/td>\n-0.11<\/td>\n0.04<\/td>\n-0.50<\/td>\n0.40<\/td>\n<\/tr>\n
University of Southern CA<\/td>\n-0.13<\/td>\n-0.15<\/td>\n-0.45<\/td>\n-0.29<\/td>\n<\/tr>\n
Texas A& M University<\/td>\n-0.26<\/td>\n-0.20<\/td>\n-0.49<\/td>\n-0.14<\/td>\n<\/tr>\n
Vanderbilt University<\/td>\n0.28<\/td>\n0.25<\/td>\n-0.37<\/td>\n-0.27<\/td>\n<\/tr>\n
Coastal Carolina University<\/td>\n0.78**<\/td>\n0.79**<\/td>\n0.42<\/td>\n0.84*<\/td>\n<\/tr>\n
F-Statistic<\/td>\n46.73***<\/td>\n46.23***<\/td>\n21.78***<\/td>\n32.09***<\/td>\n<\/tr>\n
R-Square<\/td>\n0.92<\/td>\n0.92<\/td>\n0.85<\/td>\n0.89<\/td>\n<\/tr>\n
Adjusted R-Square<\/td>\n0.90<\/td>\n0.90<\/td>\n0.81<\/td>\n0.87<\/td>\n<\/tr>\n
F-Statistic (full versus reduced)<\/td>\n4.38***<\/td>\n4.16***<\/td>\n1.93**<\/td>\n2.78***<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

* 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<\/p>\n

Overall, we observe that shot-making skills, player experience, and
team effects collectively explain a large proportion of the variability
in an amateur\u2019s 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).<\/p>\n

Of the specific shot-making skills, GIR and putting (either Putts per
Round or GIR Putts), are the most consistent predictors of an amateur\u2019s
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\u2019s (1992) view of these variable definitions, as noted
previously.<\/p>\n

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\u2019s 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\u2019s overall performance.<\/p>\n

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\u2019s 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.<\/p>\n

In addition to a player\u2019s shot-making skills, experience and team
effects appear to have an influence on an NCAA golfer\u2019s performance.
With regard to the experience measures, the total number of rounds played
in the 2002-2003 season improves a player\u2019s 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.<\/p>\n

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.<\/p>\n

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\u2019s performance. This outcome validates the belief
of other researchers, including Belkin et al. (1994) and Shmanske (1992).<\/p>\n

Conclusions<\/h2>\n

The importance of shot-making skills to a professional golfer\u2019s
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\u2019s overall performance. More specifically, four shot-making
skills \u2013 GIR, putting, driving accuracy, and driving distance \u2013
are responsible for the majority of variation in a professional golfer\u2019s
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\u2019s average score, but typically the influence is weaker than
for GIR and putting skills.<\/p>\n

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\u2019s
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\u2019s 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.<\/p>\n

We further find that the amateur golfer\u2019s 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.<\/p>\n

References<\/h2>\n
    \n
  1. Belkin, D.S., Gansneder, B., Pickens, M., Rotella, R.J., & Striegel,
    D. (1994) \u201cPredictability and Stability of Professional Golf Association
    Tour Statistics.\u201d Perceptual and Motor Skills, 78, 1275-1280.<\/li>\n
  2. Davidson, J. D. & Templin, T. J. (1986) \u201cDeterminants of
    Success Among Professional Golfers.\u201d Research Quarterly for Exercise
    and Sport, 57, 60-67.<\/li>\n
  3. Dirks, K. T. (2000) \u201cTrust in Leadership and Team Performance:
    Evidence from NCAA Basketball.\u201d Journal of Applied Psychology,
    85, 1004-1012.<\/li>\n
  4. Dorsel, T. N. & Rotunda, R. J. (2001) \u201cLow Scores, Top 10
    Finishes, and Big Money: An Analysis of Professional Golf Association
    Tour Statistics and How These Relate to Overall Performance.\u201d
    Perceptual and Motor Skills, 92, 575-585.<\/li>\n
  5. Engelhardt, G. M. (1995) \u201c\u2018It\u2019s Not How You Drive,
    It\u2019s How You Arrive\u2019: The Myth.\u201d Perceptual and Motor
    Skills, 80, 1135-1138.<\/li>\n
  6. Engelhardt, G. M. (1997) \u201cDifferences in Shot-Making Skills
    among High and Low Money Winners on the PGA Tour.\u201d Perceptual
    and Motor Skills, 84, 1314.<\/li>\n
  7. Engelhardt, G. M. (2002) \u201cDriving Distance and Driving Accuracy
    Equals Total Driving: Reply to Dorsel and Rotunda.\u201d Perceptual
    and Motor Skills, 95, 423-424.<\/li>\n
  8. Giacobbi, P.R., Roper, E., Whitney, J. and Butryn, T. (2002) \u201cCollege
    Coaches\u2019 Views About the Development of Successful Athletes: A
    Descriptive Exploratory Investigation.\u201d Journal of Sport Behavior,
    25, 164-180.<\/li>\n
  9. Golfstat, Inc. (2003) \u201cCustomized Team Pages-Men.\u201d www.golfstat.com\/2003-2004\/men\/mstop10.htm
    (accessed June 16, 2003), various teams.<\/li>\n
  10. Golfweek. (2003) \u201cGolfweek\/Sagarin Performance Index- Men\u2019s
    Team Ratings\u201d www.golfweek.com\/college\/mens1\/teamrankings.asp,
    (accessed July 1, 2003).<\/li>\n
  11. Jones, R.E. (1990) \u201cA Correlation Analysis of the Professional
    Golf Association (PGA) Statistical Ranking for 1988.\u201d In A.J.
    Cochran (Ed.), Science and Golf: Proceedings of the First World Scientific
    Conference of Golf. London: E & FN Spon. 165-167.<\/li>\n
  12. Moy, R. L. and Liaw, T. (1998) \u201cDeterminants of Professional
    Golf Tournament Earnings.\u201d The American Economist, 42, 65-70.<\/li>\n
  13. Nero, P. (2001) \u201cRelative Salary Efficiency of PGA Tour Golfers.\u201d
    The American Economist, 45, 51-56.<\/li>\n
  14. National Collegiate Athletic Association (2003) \u201cSports Sponsorship
    Summary.\u201d<\/li>\n
  15. www1.ncaa.org\/membership\/membership_svcs\/sponssummary, (accessed
    July 1, 2003).<\/li>\n
  16. Royal and Ancient Golf Club of St. Andrews (2003) \u201cAmateur Status.\u201d
    www.randa.org\/index.cfm?cfid=1066700&cftoken=78999628&action=rules.amateur.home,
    (accessed August 16, 2003)<\/li>\n
  17. Shmanske, S. (1992) \u201cHuman Capital Formation in Professional
    Sports: Evidence from the PGA Tour.\u201d Atlantic Economic Journal,
    20, 66-80.<\/li>\n
  18. United States Golf Association. (2003) \u201cRules of Amateur Status
    and the Decisions on the Rules of Amateur Status.\u201d www.usga.org\/rules\/am_status\/,
    (accessed August 16, 2003).<\/li>\n
  19. Wiseman, F., Chatterjee, S. Wiseman, D. and Chatterjee, N. (1994)
    \u201cAn Analysis of 1992 Performance Statistics for Players on the
    U.S. PGA, Senior PGA, and LPGA Tours.\u201d 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.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
    Submitted by: Scott J. Callan, Ph.D. & Janet M. Thomas, Ph.D.<\/div>\n

    Abstract<\/h2>\n

    An extensive body of research examines the importance of a golfer’s
    \n shot-making skills to the player’s overall performance, where performance
    \n is measured as either tournament money winnings or average score per round
    \n of golf. Independent of the performance measure, existing studies find
    \n that a player’s shot-making skills contribute significantly to explaining
    \n the variability in a golfer’s performance. To date, this research
    \n has focused exclusively on the professional golfer. This study attempts
    \n to extend the findings in the literature by examining the performance
    \n determinants of amateur golfers. Using a sample of NCAA Division I male
    \n golfers, various shot-making skills are analyzed and correlated with average
    \n score per round of golf. Overall, the findings validate those dealing
    \n with professional golfers. In particular, the results suggest that, like
    \n professional golfers, amateurs must possess a variety of shot-making skills
    \n to be successful. Moreover, relative to driving ability, putting skills
    \n and reaching greens in regulation contribute more to explaining the variability
    \n in a player’s success. <\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_options":[]},"categories":[290,296],"tags":[60,8,23,62],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4btio-2W","jetpack-related-posts":[{"id":177,"url":"https:\/\/thesportjournal.org\/article\/determinants-of-success-among-amateur-golfers-an-examination-of-ncaa-division-i-male-golfers\/","url_meta":{"origin":182,"position":0},"title":"Determinants of Success Among Amateur Golfers: An Examination of NCAA Division I Male Golfers","date":"March 3, 2008","format":false,"excerpt":"Submitted by: Scott J. Callan, Ph.D. & Janet M. Thomas, Ph.D. Abstract An extensive body of research examines the importance of a golfer\u2019s shot-making skills to the player\u2019s overall performance, where performance is measured as either tournament money winnings or average score per round of golf. Independent of the performance\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":242,"url":"https:\/\/thesportjournal.org\/article\/gender-skill-and-performance-in-amateur-golf-an-examination-of-ncaa-division-i-golfers\/","url_meta":{"origin":182,"position":1},"title":"Gender, Skill, and Performance in Amateur Golf: An Examination of NCAA Division I Golfers","date":"June 3, 2006","format":false,"excerpt":"Submitted by: Scott J. Callan, Ph.D. & Janet M. Thomas, Ph.D. Abstract In a previous study, it was found that male amateur golfers must possess a variety of shot-making skills to be successful and that relative to driving ability, putting skills and reaching greens in regulation contribute more to explaining\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":410,"url":"https:\/\/thesportjournal.org\/article\/the-importance-of-driving-distance-and-driving-accuracy-on-the-pga-and-champions-tours\/","url_meta":{"origin":182,"position":2},"title":"The Importance of Driving Distance and Driving Accuracy on the PGA and Champions Tours","date":"March 16, 2011","format":false,"excerpt":"Frederick Wiseman, Mohamed Habibullah, John Friar ### Abstract The question of whether driving distance or driving accuracy is more important to a golfer\u2019s overall level of performance is a question that has long been debated. 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