• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.543-555
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• 2013

This study is to analyze effectiveness of golf skills (driving distance, rating of fairway, green in regulation, sand save ratio, recovery ratio, putting average) to average score using records of PGA, LPGA, KPGA, KLPGA. Independent variables were driving distance, rating of fairway, green in regulation, sand save ratio or recovery ratio, putting average. Dependent variable was the scoring average in this study. To analyze these variables, multi-group (PGA vs LPGA, KPGA vs KLPGA, PGA vs KPGA, LPGA vs KLPGA) path analysis was used through AMOS 18.0 program and significance level was set at 0.05. As the result, the variables that show significant differences of path coefficient between PGA model and LPGA model were driving distance and green in regulation to average score. The variables that show significant differences of path coefficient between KPGA model and KLPGA model were driving distance, recovery ratio, and putting average to average score. The variables that show significant differences of path coefficient between PGA model and KPGA model were driving distance, recovery ratio, and putting average to average score. There was not significant difference of path coefficient between LPGA model and KLPGA model.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.41-55
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• 2017

This study predicts the average scores of top 150 PGA golf players on 132 PGA Tour tournaments (2013-2015) using data mining techniques and statistical analysis. This study also aims to predict the Top 10 and Top 25 best players in 4 different playoffs. Linear and nonlinear regression methods were used to predict average scores. Stepwise regression, all best subset, LASSO, ridge regression and principal component regression were used for the linear regression method. Tree, bagging, gradient boosting, neural network, random forests and KNN were used for nonlinear regression method. We found that the average score increases as fairway firmness or green height or average maximum wind speed increases. We also found that the average score decreases as the number of one-putts or scrambling variable or longest driving distance increases. All 11 different models have low prediction error when predicting the average scores of PGA Tournaments in 2015 which is not included in the training set. However, the performances of Bagging and Random Forest models are the best among all models and these two models have the highest prediction accuracy when predicting the Top 10 and Top 25 best players in 4 different playoffs.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.526-542
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• 2020

The purpose of this study was to examine contents through inductive analytic method and make systematic improvement method on satisfaction & dissatisfaction of university students participating in stipulated PGA pro-golf competitions as volunteers. The objects of this study were university students participating in 2019 The CJ Cup@Nine Bridges as volunteers. Excluding 76 copies judged to be disloyal out of 300 collected copies, the researcher analyzed the contents of 224 copies. To begin with, the contents were categorized in 10 detailed areas & 5 general areas by implementing the 2nd inductive categoric analysis mainly on the basis of 408 raw materials, the overall detailed areas of satisfaction on participation. Checking specifically, first, "satisfaction on experience in golf competition" was divided into golf competition, golf information, golf course. Second, "satisfaction on player" was divided into direct watching and famous player. Third, "satisfaction on environment" was divided into satisfaction on place & satisfaction on facilities. Fourth, "satisfaction on participation" was divided into satisfaction on service & satisfaction on competence. Finally, "satisfaction on interpersonal relation" was divided with application same to that of detailed area. In the case of dissatisfaction on participation, the contents were categorized in 10 detailed areas & 3 general areas by implementing the 2nd inductive categoric analysis mainly on the basis of 369 raw materials, the overall detailed areas. Checking specifically, first, dissatisfaction on competition operating system was detailedly divided into dissatisfaction on job system, dissatisfaction on operating system, dissatisfaction on delivery system. Second, dissatisfaction on treatment for volunteers was divided into dissatisfaction on treatment for volunteers & dissatisfaction on work hours. Third, dissatisfaction on welfare for volunteers was divided into dissatisfaction on food & beverage, dissatisfaction on costumes and dissatisfaction on incidental facilities.