Ranking Prediction Model Using the Competition Record of Ladies Professional Golf Association Players

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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:






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.

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