• 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.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.65-71
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• 2011

Path analysis is a useful method to find out direct or indirect effects between variables. Compared to regression analysis for studying the casual relationship, this method has a good advantage. In this study, I want to figure out direct or indirect relationships between golf skills and adjust average scores using path analysis. To analyze data, I applied AMOS in SPSS and collected data in 2010 PGA.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.369-377
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• 2013

The purpose of this study was to explore which factors mostly influenced players' tour results employing tour skill factors provided by LPGA. For this study, Top 10 LPGA players' stats during 9 years (2004 2012) were used. As matter of fact, 10 variables were used like average score, top 10 finish, average putt, average birdies, average eagles, driving distance, driving accuracy, greens in regulation, sand saves, putts per GIR. and prize money earning. Stepwise multiple regression was conducted using SPSS win 20.0. Results indicated that the most influential tour skill factor to 9 seasons' results was average score, second influential factor was average putt, and the third factor was driving distance, and then top 10 finish was the fourth. Also on a year on year basis, 2004 was average score, 2005 was GIR., 2006 was average eagles, 2007 was top 10 finish, 2008 was average score, 2009 was average putt, 2010 were average score, GIR. and putt per GIR, 2011 was average birdies and 2012 was putt per GIR.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.505-514
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• 2012

This study is for effectiveness of golf skills to average score using path analysis in Professional golf association. The variables in this study were that seven independent variable were driving accuracy, green in regulation, driving distance, sand save ratio, scrambling, putting average, and two endogenous variables were birdie average, bogey average, and dependent variable was the scoring average. To analyze these variables, path analysis was used through AMOS 18.0 program and Alpha level sets at.05. As the result, the final model had significant goodness-of-fit (GFI=.989, RMSEA=.044, TLI=.991, CFI=.998) and showed that green in regulation, driving distance, sand save ratio, scrambling, and putting average significantly affected average score directly. Especially, the scrambling was the highest affectation to average score and the sand save ratio was the lowest affectation to the average score.