{"id":177,"date":"2008-03-03T15:23:07","date_gmt":"2008-03-03T15:23:07","guid":{"rendered":""},"modified":"2015-10-30T13:26:19","modified_gmt":"2015-10-30T18:26:19","slug":"determinants-of-success-among-amateur-golfers-an-examination-of-ncaa-division-i-male-golfers","status":"publish","type":"post","link":"https:\/\/thesportjournal.org\/article\/determinants-of-success-among-amateur-golfers-an-examination-of-ncaa-division-i-male-golfers\/","title":{"rendered":"Determinants of Success Among Amateur Golfers: An Examination of NCAA Division I Male Golfers"},"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
\nshot-making skills to the player\u2019s overall performance, where performance
\nis measured as either tournament money winnings or average score per round
\nof golf. Independent of the performance measure, existing studies find
\nthat a player\u2019s shot-making skills contribute significantly to explaining
\nthe variability in a golfer\u2019s performance. To date, this research
\nhas focused exclusively on the professional golfer. This study attempts
\nto extend the findings in the literature by examining the performance
\ndeterminants of amateur golfers. Using a sample of NCAA Division I male
\ngolfers, various shot-making skills are analyzed and correlated with average
\nscore per round of golf. Overall, the findings validate those dealing
\nwith professional golfers. In particular, the results suggest that, like
\nprofessional golfers, amateurs must possess a variety of shot-making skills
\nto be successful. Moreover, relative to driving ability, putting skills
\nand reaching greens in regulation contribute more to explaining the variability
\nin a player\u2019s success.<\/p>\n

<\/p>\n

Introduction<\/h2>\n

Davidson and Templin (1986) present one of the first statistical investigations
\nof the major determinants of a professional golfer\u2019s success. Using
\nU.S. Professional Golf Association (PGA) data, these researchers find
\nthat a player\u2019s shot-making skills explain approximately 86 percent
\nof the variability in a player\u2019s average score and about 59 percent
\nof the variance in a player\u2019s earnings. Based on these results,
\nDavidson and Templin conclude that a professional golfer must possess
\na variety of shot-making skills to be successful as a tournament player.
\nThey further offer strong empirical support that hitting greens in regulation
\nand putting were the two most important factors in explaining scoring
\naverage variability across players, with driving ability showing up as
\na distant third.<\/p>\n

Following Davidson and Templin (1986), a number of researchers have
\ncontinued to investigate the determinants of a professional golfer\u2019s
\noverall performance. Examples include Jones (1990), Shmanske (1992), Belkin,
\nGansneder, Pickens, Rotella, and Striegel (1994), Wiseman, Chatterjee,
\nWiseman, and Chatterjee (1994), Engelhardt (1995, 1997), Moy and Liaw
\n(1998), and more recently Nero (2001), Dorsel and Rotunda (2001), and
\nEngelhardt (2002). Overall, these studies support the major conclusion
\npresented by Davidson and Templin (1986), which is that a professional
\ngolfer must exhibit a variety of shot-making skills to be successful as
\na touring professional. While the relative importance of these skills
\nto player performance is not uniform across these studies, there is a
\ndeveloping consensus that shot-making skills like putting and hitting
\ngreens in regulation are more important to a player\u2019s success than
\ndriving distance.<\/p>\n

Interestingly, while there is an accumulating literature investigating
\nprofessional golfers, no analogous studies have examined the amateur player,
\ndespite the fact that Davidson and Templin (1986) explicitly state that
\nthis avenue of investigation would be a useful direction for future research.
\nMore recently, Belkin, et al. (1994) specifically raise this point, suggesting
\nthat:<\/p>\n

\u201cIt would also be intriguing to examine whether the same
\nskills which differentiate successful professionals also contribute
\nin the same manner to the fortunes of amateurs of differing capabilities.\u201d
\n(p. 1280).<\/em><\/p><\/blockquote>\n

By way of response, this study fills that particular void in the literature
\nby empirically estimating the relationship between an amateur golfer\u2019s
\noverall performance and various shot-making skills. To facilitate direct
\ncomparisons to the existing literature on the determinants of professional
\ngolfers\u2019 performance, we employ the basic approach used by Davidson
\nand 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
\ngolfers who participated in at least one tournament during the 2002\u20132003
\nseason. Table 1 presents a listing of the colleges and universities represented
\nin the study and the number of players from each institution. The specific
\ndata on these collegiate golfers are obtained from Golfstat, Inc. (2003)
\n(accessible on the Internet at www.golfstat.com), and\/or from the respective
\ncolleges and universities directly. The colleges and universities included
\nin the analysis are a subset of the college teams participating in National
\nCollegiate Athletic Association (NCAA) Division I Men\u2019s Golf. While
\nit would be preferable to examine all Division I teams, the individual
\nplayer statistics needed to perform the analysis are not available. However,
\nsince it is reasonable to assume that the schools listed in Table 1 are
\na representative sample of all Division I men\u2019s teams, the data
\nsample is appropriate for this study.<\/p>\n

TABLE 1
\nSample 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.”
\nwww.golfstat.com\/2003-2004\/men\/mstop10.htm, (accessed June 16, 2003),
\nvarious teams; Golfweek. (2003) “Golfweek\/Sagarin Performance Index –
\nMen’s Team Ratings.” www.golfweek.com\/college\/mens1\/teamrankings.asp,
\n(accessed July 1, 2003). <\/em><\/p>\n

Measures<\/h3>\n

For the schools represented in this study, Golfstat, Inc. collects and
\nreports individual player statistics necessary to complete a performance
\nanalysis. For this study we used statistics for the 2002 \u2013 2003
\nNCAA Division I tournament season. Among the available data are the average
\nscore per round (AS) for each amateur player in the sample. This statistic
\nprovides the performance measure needed for the dependent variable in
\nthis study, since earnings are not relevant to amateurs. Specifically,
\naccording to the United States Golf Association (2003, p. 1) and the Royal
\nand Ancient Golf Club of St. Andrews (2003, p.1), an amateur golfer is
\ndefined as:<\/p>\n

\u201c\u2026one who plays the game as a non-remunerative and
\nnon-profit-making sport and who does not receive remuneration for teaching
\ngolf or for other activities because of golf skill or reputation, except
\nas provided in the Rules.\u201d <\/em><\/p><\/blockquote>\n

Although studies of professional golfers examine scoring average and\/or
\nearnings as performance measures, Wiseman et al. (1994) argue that correlation
\nresults are stronger when scoring average is used. Hence, the use of scoring
\naverage for this study of amateurs is soundly supported by the literature
\nexamining professional golfers.<\/p>\n

Statistics for the primary shot-making skills typically used in the
\nliterature are collected and reported by Golfstat, Inc. and by some colleges
\nand universities. These include measures of driving accuracy, greens in
\nregulation, putting average, sand saves, and short game.<\/p>\n

To capture amateurs\u2019 long game skills, we use one of the classic
\nmeasures, which is driving accuracy. Specifically, we use the variable
\nFairways Hit, which is defined as the percentage of fairways hit on par
\n4 and par 5 holes during a round of golf. Data on driving distance for
\nthe amateur sample are not available. However, Dorsel and Rotunda (2001)
\npresent evidence suggesting that the number of eagles (i.e., two strokes
\nunder par on any hole) a player makes is positively correlated with the
\nplayer\u2019s average driving distance. Hence, we use the variable Eagles,
\nthe total number of eagles a player makes during the season, to control
\nfor each player\u2019s average driving distance. Following the literature,
\nwe also include the variable Greens in Regulation (GIR) to measure the
\npercentage of greens a player reaches in regulation for the season. This
\nis defined as one stroke for a par three, two strokes or less for a par
\nfour, and three strokes or less for a par five. As discussed in Belkin
\net al. (1994), this GIR variable captures a player\u2019s iron play and
\ntheir 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.
\nIn keeping with the literature, we use two measures of putting skill \u2013
\nPutts per Round, defined as the average number of putts per round, and
\nGIR Putts, which is the average number of putts measured only on greens
\nreached in regulation. Belkin, et al. (1994) is one study that uses the
\nformer measure, while Dorsel and Rotunda (2001) is an example of a study
\nusing the latter. Interestingly, Shmanske (1992) argues that the latter
\nstatistic, GIR Putts, is superior because it correctly accounts for the
\nlonger putting distances associated with a player who achieves a higher
\nnumber of greens in regulation. By including one of these measures in
\ndifferent regression models, we can assess the validity of that argument.
\nWe also include the variable Sand Saves (SS), which measures the percentage
\nof time a golfer makes par or better when hitting from a sand bunker.
\nIn certain specifications of our regression analysis, we experiment with
\nthe variable Short Game as an alternative measure to Sand Saves. Short
\nGame measures the percentage of time a player makes par or better when
\nnot reaching the green in the regulation number of strokes.<\/p>\n

In addition to a player\u2019s shot-making skills, Belkin, et al. (1994)
\nand others note the importance of experience in determining a player\u2019s
\nsuccess. To control for this factor, two experience measures are used.
\nFirst, we define the variable Rounds as the number of tournament rounds
\ncompleted by each player during the 2002\u20132003 season. In a sense,
\nthis measure captures a player\u2019s short-term experience, in that
\nit measures how each additional round played in a season increases the
\nexperience that a player can call upon in subsequent rounds. Second, to
\ncontrol for longer-term cumulative experience, we construct a set of dummy
\nvariables to reflect the player\u2019s academic age, (i.e., Freshman,
\nSophomore, Junior, or Senior). It is hypothesized that the higher a player\u2019s
\nacademic age, the more collegiate golfing experience has been gained,
\nand therefore the lower the expected average score.<\/p>\n

Finally, since golf at the collegiate level is a team sport, it is important
\nto capture any associated team effects. That is, a player\u2019s performance
\nmight be affected by the team with which they are associated. At least
\ntwo plausible explanations for this team effect are viable \u2013 one
\nrelating to the team\u2019s coach and the other relating to the courses
\nplayed. With regard to the former, each team\u2019s coach is expected
\nto uniquely affect the success of each team member through mentoring,
\nleadership, instruction, and overall direction. In fact, Dirks (2000)
\nand Giacobbi, Roper, Whitney, and Butryn (2002) present evidence supporting
\nthe importance of a coach\u2019s influence on the performance of a collegiate
\nathlete. Primarily, the coach acts as the team leader and instructor.
\nAs a leader, the coach is responsible for the overall team strategy and
\nfor ultimately determining a player\u2019s tournament participation.
\nAs an instructor, the more experienced coach may be better able to teach
\nplayers and to motivate them to improve their play.<\/p>\n

As for courses played, we expect a player\u2019s scoring average to
\nbe affected by the specific golf courses played, which in turn are not
\nconsistent across collegiate teams. Indeed, it is highly plausible that
\nsome teams might, for example, play easier courses throughout a given
\ntournament season, which may lower a team member\u2019s score. To account
\nfor these team effects, dummy variables are constructed, whereby each
\ndummy variable identifies the team to which each player belongs.<\/p>\n

Procedure<\/h3>\n

Following the literature, multiple regression analysis is used to estimate
\nthe relationship between an amateur golfer\u2019s average score and various
\nshot-making skills. In addition, each regression model is specified to
\ncontrol for player experience and team factors. Ordinary least squares
\n(OLS) is used to derive the regression estimates for four different models.
\nThese models are distinguished by the selection of shot-making skill statistics
\nused for certain variables. Specifically, each model is distinguished
\nby its use of Sand Saves (SS) versus Short Game and Putts per Round versus
\nGIR putts. We also generate simple Pearson correlation coefficients between
\nthe measure of player performance and each of the independent variables
\nin the study.<\/p>\n

Results and Discussion<\/h2>\n

Basic descriptive statistics for the sample of 93 golfers are presented
\nin Table 2. At the collegiate level, most tournaments consist of three
\nrounds of golf, and, like the professionals, each round comprises eighteen
\nholes. The average NCAA Division I male golfer in the sample participated
\nin approximately nine tournaments, played slightly less than 26 rounds
\nof golf, and had an average score per round of approximately 75 strokes
\nduring the 2002 \u2013 2003 season.<\/p>\n

TABLE 2
\nBasic 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
\napproximately 68 percent of the fairways and reaches the green in the
\nregulation number of strokes 60 percent of the time. Of the greens reached
\nin regulation, the average player needs 1.88 putts to finish a hole, and
\nover the course of a round, each needs to take slightly more than 31 putts.
\nOn average, an amateur golfer makes par or better when hitting from a
\nsand bunker 42 percent of the time and makes par or better when not on
\na green in regulation 51 percent of the time. Over the course of the 2002
\n\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
\naverage score (AS) and various shot-making skills, experience, and team
\neffects. Notice that all shot-making skills are significantly correlated
\nwith a player\u2019s average score. Somewhat predictably, GIR is the
\nvariable that is most highly correlated with an amateur golfer\u2019s
\naverage score. This finding is analogous to what has been found for professional
\ngolfers by Davidson and Templin (1986) and others. We also find that the
\nShort Game variable and GIR Putts rank second and third respectively in
\nterms of the strength of correlation among shot-making skills. Notice
\nthat across the two putting measures \u2013 GIR Putts and Putts per Round,
\nthe correlation for GIR Putts is higher, which may support Shmanske\u2019s
\n(1992) assertion that this is a more accurate measure of putting skill.
\nWe also find that both the short-term and long-term experience measures
\nare statistically correlated with a player\u2019s performance. With regard
\nto the Rounds variable, the correlation shows a significant negative relationship
\nwith a player\u2019s average score, which follows our expectations. Also,
\nas anticipated, the dummy variable for academic age is positively correlated
\nwith the player\u2019s average score for freshmen and negatively correlated
\nfor seniors. Lastly, for certain colleges and universities, there is a
\nsignificant correlation between a team effect and a player\u2019s average
\nscore.<\/p>\n

TABLE 3
\nPearson 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
\n** significant at the 0.05 level
\n*** significant at the 0.01 level<\/p>\n

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

TABLE 4
\nRegression 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
\ntest of hypothesis
\n** significant at the 0.05 level, assuming a one-tailed test of hypothesis
\n*** 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
\nteam effects collectively explain a large proportion of the variability
\nin an amateur\u2019s scoring average independent of the model specified.
\nSpecifically, the adjusted R2 statistics across the four models range
\nfrom 0.81 to 0.90, values that are similar to those reported in Davidson
\nand Templin (1986) and Belkin, et al. (1994).<\/p>\n

Of the specific shot-making skills, GIR and putting (either Putts per
\nRound or GIR Putts), are the most consistent predictors of an amateur\u2019s
\naverage score across the four models. In each case, GIR is significant
\nat the 1 percent level, as are both putting variables. However, the standardized
\nbeta coefficients show that GIR is the most important predictor, as was
\nthe case for the models estimated by Davidson and Templin (1986) and Belkin,
\net al. (1994). Both putting variables also are significant at the 1 percent
\nlevel, though the standardized beta coefficients suggest that Putts per
\nRound might be a superior measure of amateur putting, which runs counter
\nto Shmanske\u2019s (1992) view of these variable definitions, as noted
\npreviously.<\/p>\n

Interestingly, Short Game is a significant predictor of average score,
\nbut only when the variable GIR Putts is included in the model, which is
\nModel 4 specifically. With regard to Sand Saves (SS), we find that it
\nis not a significant factor in predicting a player\u2019s performance
\nin either Model 1 or Model 3. Davidson and Templin (1986) and, more recently,
\nMoy and Liaw (1998) find analogous results for their respective samples
\nof professional golfers. One explanation put forth by Moy and Liaw is
\nthat all golfers have similar abilities in this skill category. Another
\nmore likely justification is one presented by Dorsal and Rotunda (2001),
\nwhich is that bunker play is less frequent and, as a result, has a negligible
\neffect on a player\u2019s overall performance.<\/p>\n

To the extent that the number of eagles over the season captures driving
\ndistance, the results indicate that driving distance is not a major factor
\nin determining a player\u2019s performance. In general, this conclusion
\nagrees with the findings of Davidson and Templin (1986), Belkin, et al.
\n(1994), and Dorsel and Rotunda (2001). Hence, this finding seems to be
\nindependent of whether the golfer is an NCAA amateur or a professional
\nplayer. However, such an assertion has to be made with caution, since
\nno direct measure of driving distance was available to include in this
\namateur study.<\/p>\n

In addition to a player\u2019s shot-making skills, experience and team
\neffects appear to have an influence on an NCAA golfer\u2019s performance.
\nWith regard to the experience measures, the total number of rounds played
\nin the 2002-2003 season improves a player\u2019s overall performance.
\nThis assertion is based on the consistently negative coefficient on Rounds
\nacross models, though the result is statistically significant only in
\nModel 3. As for longer-term experience, sophomore players consistently
\nachieve a lower average score than their freshman counterparts, and this
\neffect is statistically significant across the four models. Juniors and
\nseniors are found to enjoy the same performance effect linked to experience,
\nbut the influence is found to be statistically significant only in Models
\n1 and 2.<\/p>\n

As for individual team effects, the results suggest that a statistically
\nsignificant influence exists for certain collegiate programs. For example,
\nholding all else constant, all four models indicate that players on the
\nUniversity of Kentucky team have higher and statistically significant
\naverage scores relative to players on the Clemson team (the suppressed
\ndummy variable), who are the 2002-2003 NCAA Division I Champions. Conversely,
\nplayers at Georgia State University achieve lower average scores than
\nplayers at Clemson, independent of individual shot-making skills or experience,
\nand three of the four models show this finding to be statistically significant.
\nThe absence of statistical significance for the other teams might be attributable
\nto 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
\nconducted across each model specification, where the reduced model assumes
\nthat the academic age and team effects are jointly zero. As noted in Table
\n4, the null hypothesis was rejected across all four models, indicating
\nthat these two experience variables collectively help to explain the variability
\nof an amateur player\u2019s performance. This outcome validates the belief
\nof 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
\nsuccess has been well documented in the literature. In general, research
\nstudies point to the fact that a variety of shot-making skills are important
\nto a player\u2019s overall performance. More specifically, four shot-making
\nskills \u2013 GIR, putting, driving accuracy, and driving distance \u2013
\nare responsible for the majority of variation in a professional golfer\u2019s
\nscoring performance. Of these four, GIR and putting have consistently
\nbeen found to be the more important factors. On occasion, driving accuracy
\nand driving distance have been found to statistically impact a professional
\ngolfer\u2019s average score, but typically the influence is weaker than
\nfor GIR and putting skills.<\/p>\n

Despite an accumulating literature seeking to validate or refine these
\nresults, we know of no study that has extended this analysis beyond the
\nrealm of professional golfers. To that end, we attempt to fill this void
\nin the literature by empirically identifying performance determinants
\nfor amateur golfers. Using a sample of NCAA Division I male golfers, we
\nhypothesize that a variety of shot-making skills along with player experience
\nand team membership are expected to influence an amateur golfer\u2019s
\nperformance measured as average score per round. Using multiple regression
\nanalysis, our results indicate that all these factors collectively explain
\na large percentage of the variability in an NCAA golfer\u2019s average
\nscore. This is evidenced by R-squared values ranging from 0.81 to 0.90
\nacross four different models distinguished by varying variable definitions.<\/p>\n

We further find that the amateur golfer\u2019s shot-making skills measured
\nthrough GIR and putting are the most important factors to explaining average
\nscore per round. These findings offer an important contribution to the
\ngrowing literature on professional golfer performance in that they validate
\nand extend much of what has been shown in existing studies. Future research
\nshould attempt to further extend these findings to other amateur data,
\nas they become available.<\/p>\n

References<\/h2>\n
    \n
  1. Belkin, D.S., Gansneder, B., Pickens, M., Rotella, R.J., & Striegel,
    \nD. (1994) \u201cPredictability and Stability of Professional Golf Association
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  2. Davidson, J. D. & Templin, T. J. (1986) \u201cDeterminants of
    \nSuccess Among Professional Golfers.\u201d Research Quarterly for Exercise
    \nand Sport, 57, 60-67.<\/li>\n
  3. Dirks, K. T. (2000) \u201cTrust in Leadership and Team Performance:
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  4. Dorsel, T. N. & Rotunda, R. J. (2001) \u201cLow Scores, Top 10
    \nFinishes, and Big Money: An Analysis of Professional Golf Association
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  5. Engelhardt, G. M. (1995) \u201c\u2018It\u2019s Not How You Drive,
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  6. Engelhardt, G. M. (1997) \u201cDifferences in Shot-Making Skills
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  7. Engelhardt, G. M. (2002) \u201cDriving Distance and Driving Accuracy
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  8. Giacobbi, P.R., Roper, E., Whitney, J. and Butryn, T. (2002) \u201cCollege
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  9. Golfstat, Inc. (2003) \u201cCustomized Team Pages-Men.\u201d www.golfstat.com\/2003-2004\/men\/mstop10.htm
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  10. Golfweek. (2003) \u201cGolfweek\/Sagarin Performance Index- Men\u2019s
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  11. Jones, R.E. (1990) \u201cA Correlation Analysis of the Professional
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  12. Moy, R. L. and Liaw, T. (1998) \u201cDeterminants of Professional
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  13. Nero, P. (2001) \u201cRelative Salary Efficiency of PGA Tour Golfers.\u201d
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  14. National Collegiate Athletic Association (2003) \u201cSports Sponsorship
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  15. www1.ncaa.org\/membership\/membership_svcs\/sponssummary, (accessed
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  16. Royal and Ancient Golf Club of St. Andrews (2003) \u201cAmateur Status.\u201d
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  18. United States Golf Association. (2003) \u201cRules of Amateur Status
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  19. Wiseman, F., Chatterjee, S. Wiseman, D. and Chatterjee, N. (1994)
    \n\u201cAn Analysis of 1992 Performance Statistics for Players on the
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    \nR. Farrally (Eds.), Science and Golf: II. Proceedings of the World Scientific
    \nCongress 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":[290,295,296],"tags":[60,8,23,62],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4btio-2R","jetpack-related-posts":[{"id":182,"url":"https:\/\/thesportjournal.org\/article\/a-personal-odyssey-to-greece-and-the-2004-olympic-games\/","url_meta":{"origin":177,"position":0},"title":"A Personal Odyssey to Greece and the 2004 Olympic Games","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":177,"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":177,"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. No conclusive answer has been found despite the efforts of numerous researchers who have investigated\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":277,"url":"https:\/\/thesportjournal.org\/article\/more-than-just-the-ryder-cup-an-examination-of-relevant-natural-characteristics-in-professional-golf\/","url_meta":{"origin":177,"position":3},"title":"More than Just the Ryder Cup: An Examination of Relevant Natural Characteristics in Professional Golf","date":"March 14, 2008","format":false,"excerpt":"Submitted by: Mark K. Pyles, Ph.D. Abstract: The researcher examined the two major professional golf associations, the Professional Golfer's Association (PGA) and the Ladies Professional Golfer's Association (LPGA), to determine physical characteristics relevant for success. The researcher found that those players born outside of the U.S. consistently earn more money\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":261,"url":"https:\/\/thesportjournal.org\/article\/a-new-method-for-ranking-total-driving-performance-on-the-pga-tour\/","url_meta":{"origin":177,"position":4},"title":"A New Method for Ranking Total Driving Performance on the PGA Tour","date":"March 14, 2008","format":false,"excerpt":"Submitted by: Frederick Wiseman, Ph.D., Mohamed Habibullah, Ph.D. & Mustafa Yilmaz, Ph.D. Abstract The Professional Golf Association Tour (PGA Tour) currently ranks its players according to their overall Total Driving performance by adding together individual ranks for their average driving distance and for their driving accuracy percentage. However, this widely\u2026","rel":"","context":"In "Contemporary Sports Issues"","img":{"alt_text":"Figure 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2008\/03\/Figure1.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":54,"url":"https:\/\/thesportjournal.org\/article\/analysis-of-selected-physical-and-performance-attributes-of-the-united-states-olympic-team-handball-players-preliminary-study\/","url_meta":{"origin":177,"position":5},"title":"Analysis of Selected Physical and Performance Attributes of the United States Olympic Team Handball Players: Preliminary Study","date":"February 11, 2008","format":false,"excerpt":"Submitted by: Brian Bergemann, Ph.D. During the Spring of 1995, prior to the Olympic Games in Atlanta, the United States Team Handball team and coaches came to the United States Sports Academy in Daphne, AL for testing. Dr. Thomas P. 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