Chae Jin Seok, Park Jin, So Wi-Young
Measurement and Evaluation in Physical Education and Sports Science, Yongin University, Yongin-si, Republic of Korea.
Department of Human Movement Science, Seoul Women's University, Seoul, Republic of Korea.
J Hum Kinet. 2021 Jan 30;77:245-259. doi: 10.2478/hukin-2021-0023. eCollection 2021 Jan.
This study aims to identify the most accurate prediction model for the possibility of victory from the annual average data of 25 seasons (1993-2017) of the Ladies Professional Golf Association (LPGA), and to determine the importance of the predicting factors. The four prediction models considered in this study were a decision tree, discriminant analysis, logistic regression, and artificial neural network analysis. The mean difference in the classification accuracy of these models was analyzed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA) and the one-way analysis of variance (ANOVA). When the prediction was based on technical variables, the most important predicting variables for determining victory were greens in regulation (GIR) and putting average (PA) in all four prediction models. When the prediction was based on the output of the technical variables, the most important predicting variable for determining victory was birdies in all four prediction models. When the prediction was based on the season outcome, the most important predicting variables for determining victory were the top 10 finish% (T10) and official money. A significant mean difference in classification accuracy was observed while performing the one-way ANOVA, and the least significant difference post-hoc test showed that artificial neural network analysis exhibited higher accuracy than the other models, especially, for larger data sizes. From the results of this study, it can be inferred that the player who wants to win the LPGA should aim to increase GIR, reduce PA, and improve driving distance and accuracy through training to increase the birdies chance at each hole, which can lead to lower average strokes and increased possibility of being within T10.
本研究旨在根据女子职业高尔夫协会（LPGA）25个赛季（1993 - 2017年）的年度平均数据，确定预测获胜可能性的最准确模型，并确定预测因素的重要性。本研究考虑的四个预测模型为决策树、判别分析、逻辑回归和人工神经网络分析。使用SPSS 22.0软件（美国纽约州阿蒙克市IBM公司）和单因素方差分析（ANOVA）对这些模型分类准确率的平均差异进行了分析。当基于技术变量进行预测时，在所有四个预测模型中，决定胜负的最重要预测变量是上果岭率（GIR）和平均推杆数（PA）。当基于技术变量的输出进行预测时，在所有四个预测模型中，决定胜负的最重要预测变量是小鸟球数。当基于赛季成绩进行预测时，决定胜负的最重要预测变量是前10名完赛百分比（T10）和奖金。在进行单因素方差分析时，观察到分类准确率存在显著的平均差异，最小显著差异事后检验表明，人工神经网络分析的准确率高于其他模型，特别是对于较大的数据量。从本研究结果可以推断，想要赢得LPGA比赛的选手应旨在提高上果岭率、降低平均推杆数，并通过训练提高击球距离和准确性，以增加每一洞打出小鸟球的机会，这可以导致平均杆数降低以及进入前10名的可能性增加。
