Identification of Performance Factors and Derivation of Prediction Regression for Tour Players Participating in the 2024 KLPGA

PURPOSE: This study aimed to identify key performance indicators influencing average strokes among Korean Ladies Professional golfers participating in the 2024 Korea Ladies Professional Golf Association (KLPGA) Tour and to develop a regression model to predict performance outcomes based on multiple technical factors. In Korea, despite the growing global recognition of the KLPGA Tour, systematic research identifying key indicators of performance and developing predictive models remains scarce. Considering that women’s professional golf may involve different determinants of success compared to men’s golf, there is a pressing need for empirical research that reflects the specific characteristics of KLPGA players.METHODS: Data from 119 players in the 2024 KLPGA Tour were analyzed. Fifteen performance variables were examined using Pearson’s correlation and stepwise multiple regression to identify predictors of average strokes. Group differences among the top 10, the top 11–60, and the top 61–119 players were assessed using One-way ANOVA.RESULTS: Significant group differences were found in key performance variables such as Drive distance (yards), On green (%), Putting success on GIR (%), Sand save (%), Average birdie (%), and Par break (%). The final regression models included Par save (%), Par break (%), Fairway (%), and Recovery (%) with a high explanatory power (Adjusted r<sup>2</sup>=.990).CONCLUSIONS: This study identified key performance factors associated with Average strokes (#) among KLPGA players. Although Drive distance showed a weak direct correlation with average strokes, its strong association with other core variables suggests its indirect relevance to performance. These performance factors constitute critical determinants of individual athletic performance enhancement and offer substantial value in formulating effective training strategies. Further studies using multi-season and environmental data are recommended to enhance generalizability.