A Personal Odyssey to Greece and the 2004 Olympic Games


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.


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

“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.



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.

Sample of Schools Included in the Study

Number of Golfers
Golfweek/Sagarin Ranking
Clemson University
Atlantic Coast
University of Arizona
Pacific 10
University of Southern CA
Pacific 10
Duke University
Atlantic Coast
Vanderbilt University
California State -Fresno
Western Athletic
University of Kentucky
Georgia State University
Atlantic Sun
Texas A&M University
Big 12
Southeastern Louisiana Univ.
Coastal Carolina University
Big South

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).


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.


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.

Basic Descriptive Statistics

Mean Std. Dev
Average Score (AS)
Fairways Hit
Greens in Regulation (GIR)
Putts per round
GIR Putts
Sand Saves (SS)
Short Game
Academic Age Dummy Variable
Mean Std. Dev
Team Dummy Variables
Mean Std. Dev
University of Arizona
Clemson University
Duke University
California State -Fresno
Georgia State University
University of Kentucky
Southeastern Louisiana University
University of Southern CA
Texas A& M University
Vanderbilt University
Coastal Carolina University

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

Pearson Correlation Coefficients

MEASURES Correlation with Average Score (AS)
Fairways Hit
Greens in Regulation (GIR)
Putts per Round
GIR Putts
Sand Saves (SS)
Short Game
Academic Age Dummy Variables
Team Dummy Variables
University of Arizona
Clemson University
Duke University
California State – Fresno
Georgia State University
University of Kentucky
Southeastern Louisiana University
University of Southern CA
Texas A& M University
Vanderbilt University
Coastal Carolina University

* 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.

Regression Analysis (Standardized Beta Coefficients in parentheses)

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

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).


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.


  1. 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.
  2. Davidson, J. D. & Templin, T. J. (1986) “Determinants of
    Success Among Professional Golfers.” Research Quarterly for Exercise
    and Sport, 57, 60-67.
  3. Dirks, K. T. (2000) “Trust in Leadership and Team Performance:
    Evidence from NCAA Basketball.” Journal of Applied Psychology,
    85, 1004-1012.
  4. 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.
  5. Engelhardt, G. M. (1995) “‘It’s Not How You Drive,
    It’s How You Arrive’: The Myth.” Perceptual and Motor
    Skills, 80, 1135-1138.
  6. 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.
  7. Engelhardt, G. M. (2002) “Driving Distance and Driving Accuracy
    Equals Total Driving: Reply to Dorsel and Rotunda.” Perceptual
    and Motor Skills, 95, 423-424.
  8. 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.
  9. Golfstat, Inc. (2003) “Customized Team Pages-Men.” www.golfstat.com/2003-2004/men/mstop10.htm
    (accessed June 16, 2003), various teams.
  10. Golfweek. (2003) “Golfweek/Sagarin Performance Index- Men’s
    Team Ratings” www.golfweek.com/college/mens1/teamrankings.asp,
    (accessed July 1, 2003).
  11. 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.
  12. Moy, R. L. and Liaw, T. (1998) “Determinants of Professional
    Golf Tournament Earnings.” The American Economist, 42, 65-70.
  13. Nero, P. (2001) “Relative Salary Efficiency of PGA Tour Golfers.”
    The American Economist, 45, 51-56.
  14. National Collegiate Athletic Association (2003) “Sports Sponsorship
  15. www1.ncaa.org/membership/membership_svcs/sponssummary, (accessed
    July 1, 2003).
  16. Royal and Ancient Golf Club of St. Andrews (2003) “Amateur Status.”
    (accessed August 16, 2003)
  17. Shmanske, S. (1992) “Human Capital Formation in Professional
    Sports: Evidence from the PGA Tour.” Atlantic Economic Journal,
    20, 66-80.
  18. 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).
  19. 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.