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

Abstract

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.

Victory Prediction of Ladies Professional Golf Association Players: Influential Factors and Comparison of Prediction Models

“…We can conclude that the artificial neural network model was superior when comparing the classification accuracy rates of the predicting models. This is consistent with the results of another study using neural networks when the sports disciplines considered were basketball, soccer, and tennis ( Chae et al, 2018 ). Future research can supplement the data for predicting variables and quantify the mental strength and teamwork that are difficult to quantify for achieving an optimum harmony of predicting variables.…”

“…This requirement has reached a level wherein scholars statistically provide winner and rank possibilities employing prediction models on accumulated data ( Hayes et al, 2015 ; Jida and Jie, 2015 ; Neeley et al, 2009). Chae et al (2018) used multiple regression analysis, which is a statistical analytical model, for the rank prediction of LPGA players based on the fact that the medal rank of the 2016 Rio Olympic female golf tournament was predicted by multiple regression analysis ( Mercuri et al, 2017 ). The methods of analysis for this type of prediction are usually linear regression analysis, curve estimation, discriminant function analysis, logistic regression analysis, principal component regression analysis, classification tree analysis, and more recently, the frequently used artificial neural network analysis.…”

Investigating factors that improve golf scores by comparing statistics of amateur golfers in repeat scramble strokes and one-ball conditions

“…Nevertheless, there is a lack of fundamental data on how scores and statistics (fairway keep rate, par on rate, putting rate, etc.) vary when amateur golfers play just a single stroke versus when they are subjected to course management and strict rules but have the ability to perform VOLUME --| ISSUE -| 2020 | 3 multiple hits (Broadie and Ko, 2009;Chae et al, 2018;Dorsel and Rotunda, 2001;Fried et al, 2004;Pfitzner and Rishel, 2005).…”

Comparative Analysis of Performance Factors between Ladies Professional Golf Association and Korea Ladies Professional Golf Association Golfers

“…To analyze the performance skill factors of the top 60 golfers in the LPGA and KLPGA, the differences in the coefficients were verified using the seemingly unrelated estimation(SUE) method independently from an ordinary least squares (OLS) analysis for estimating the marginal effect of each variable. The popularity of SUE is related to its applicability to a large class of modeling and testing problems and also the relative ease of estimation [ 27 ]. The OLS is useful when the parameters are unknown and the relationship between the dependent variable and the explanatory variable is a hypothesis that must be tested [ 28 ].This was done to analyze two sets of data from two nations at identical points in time using separate models.The equation of basic SUE is as follows: …”