The purpose of this study was to suggest a ranking prediction model using the competition record of the Ladies Professional Golf Association (LPGA) players. The top 100 players on the tour money list from the 2013–2016 US Open were analyzed in this model. Stepwise regression analysis was conducted to examine the effect of performance and independent variables (i.e., driving accuracy, green in regulation, putts per round, driving distance, percentage of sand saves, par-3 average, par-4 average, par-5 average, birdies average, and eagle average) on dependent variables (i.e., scoring average, official money, top-10 finishes, winning percentage, and 60-strokes average). The following prediction model was suggested:
Y (Scoring average) = 55.871 - 0.947 (Birdies average) + 4.576 (Par-4 average) - 0.028 (Green in regulation) - 0.012 (Percentage of sand saves) + 2.088 (Par-3 average) - 0.026 (Driving accuracy) - 0.017 (Driving distance) + 0.085 (Putts per round)
Y (Official money) = 6628736.723 + 528557.907 (Birdies average) - 1831800.821 (Par-4 average) + 11681.739 (Green in regulation) + 6476.344 (Percentage of sand saves) - 688115.074 (Par-3 average) + 7375.971 (Driving accuracy)
Y (Top-10 finish%) = 204.462 + 12.562 (Birdies average) - 47.745 (Par-4 average) + 1.633 (Green in regulation) - 5.151 (Putts per round) + 0.132 (Percentage of sand saves)
Y (Winning percentage) = 49.949 + 3.191 (Birdies average) - 15.023 (Par-4 average) + 0.043 (Percentage of sand saves)
Y (60-strokes average) = 217.649 + 13.978 (Birdies average) - 44.855 (Par-4 average) - 22.433 (Par-3 average) + 0.16 (Green in regulation)
Scoring of the above five prediction models and the prediction of golf ranking in the 2016 Women’s Golf Olympic competition in Rio revealed a significant correlation between the predicted and real ranking (r = 0.689, p < 0.001) and between the predicted and the real average score (r = 0.653, p < 0.001). Our ranking prediction model using LPGA data may help coaches and players to identify which players are likely to participate in Olympic and World competitions, based on their performance.
1Measurement and Evaluation in Physical Education and Sports Science, Seoul National University of Science and Technology, Seoul, Republic of Korea
2Department of Human Movement Science, Seoul Women's University, Seoul, Republic of Korea
3Sports and Health Care Major, College of Humanities and Arts, Korea National University of Transportation, Chungju-si, Republic of Korea.
Corresponding author: Wi-Young So, PhD, Associate Professor, Sports and Health Care Major, College of Humanities and Arts, Korea National University of Transportation, 50 Daehak-ro, Chungju-si, Chungbuk 27469, Republic of Korea, Office: 82-43-841-5993, Fax: 82-43-841-5990, E-mail: email@example.com