Measurement and Evaluation in Physical Education and Sports Science, Seoul National University of Science and Technology, Seoul, Republic of Korea.
Department of Human Movement Science, Seoul Women's University, Seoul, Republic of Korea.
J Strength Cond Res. 2018 Aug;32(8):2363-2374. doi: 10.1519/JSC.0000000000002018.
Chae, JS, Park, J, and So, W-Y. Ranking prediction model using the competition record of ladies professional golf association players. J Strength Cond Res 32(8): 2363-2374, 2018-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:(Equation is included in full-text article.)(Equation is included in full-text article.)(Equation is included in full-text article.)(Equation is included in full-text article.)(Equation is included in full-text article.)Scoring of the above 5 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.
蔡、JS、朴、J 和苏，WY。使用女子职业高尔夫协会球员的比赛记录进行排名预测模型。J 强实力康 res 32(8)：2363-2374，2018 年-本研究的目的是提出一种使用女子职业高尔夫协会（LPGA）球员比赛记录的排名预测模型。该模型分析了 2013-2016 年美国公开赛奖金榜前 100 名选手。逐步回归分析用于检验表现和独立变量（即开球准确性、果岭上的球数、每轮推杆数、开球距离、沙保成功率、标准杆 3 杆平均成绩、标准杆 4 杆平均成绩、标准杆 5 杆平均成绩、小鸟平均成绩和老鹰平均成绩）对因变量（即平均得分、官方奖金、前十完成次数、胜率和 60 杆平均成绩）的影响。提出了以下预测模型：（方程式包含在全文文章中。）（方程式包含在全文文章中。）（方程式包含在全文文章中。）（方程式包含在全文文章中。）（方程式包含在全文文章中。）以上 5 个预测模型的得分和 2016 年里约热内卢女子高尔夫奥运比赛的高尔夫排名预测显示，预测排名与实际排名之间（r = 0.689，p < 0.001）和预测平均得分与实际平均得分之间（r = 0.653，p < 0.001）存在显著相关性。我们使用 LPGA 数据的排名预测模型可以帮助教练和球员根据球员的表现，确定哪些球员有可能参加奥运会和世界比赛。
