Golf Club Fitting via Regression Analysis of Impact Variables
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Solution Overview
Problem
Current golf club fitting methods struggle to accurately determine the recommended loft angle for individual golf players, leading to significant variations in hit ball characteristics such as flight distance, which affects scoring and player performance.
Innovation Solution
A method involving multiple linear regression analysis using impact conditions like head speed, face angle, and dynamic loft to select a specific explanation variable that contributes most to hit ball arrival point data, allowing for the determination of a recommended club specification that suppresses variation, thereby improving fitting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fitting methods use average flight distance data, then the fitting process is simple, but the reliability of data is insufficient and variation in hit ball results cannot be properly evaluated
Solution Approach 1:
The patent changes the evaluation parameter from average flight distance to standard deviation of flight distance. This parameter transformation allows proper evaluation of hit ball result variation, enabling more reliable fitting decisions while maintaining practical applicability through standardized statistical measures.
Solution Approach 2:
The patent replaces conventional simple averaging methods with multiple linear regression analysis. This substitution introduces a more sophisticated statistical mechanism that can evaluate the relationship between impact conditions and hit ball results, providing more reliable fitting recommendations despite increased computational complexity.
2Measurement precision
If multiple linear regression analysis is performed with multiple explanation variables, then fitting accuracy is improved, but the complexity of analysis increases
Solution Approach 1:
The patent extracts and focuses on the most significant explanation variable from multiple candidates using multiple linear regression analysis. By identifying and concentrating on the variable with the highest contribution to hit ball arrival point data, the system achieves high fitting accuracy while managing analysis complexity through selective focus on key parameters.
Solution Approach 2:
The patent applies different levels of analysis complexity to different variables. Instead of treating all impact conditions equally, the regression analysis identifies which variables locally have the greatest influence on hit ball results, allowing precise fitting recommendations based on the specific characteristics of each golfer's swing.
Data Source
AI summary
This fitting method includes, for example, the following steps A1 to E1:(A1) a step of measuring a plurality of impact conditions using a reference club;(B1) a step of obtaining hit ball arrival point data;(C1) a step of selecting two or more of the plurality of impact conditions as explanation variables and performing multiple linear regression analysis with the hit ball arrival point data as an objective variable;(D1) a step of selecting a specific explanation variable from the two or more explanation variables based on a result of the multiple linear regression analysis; and(E1) a step of determining a recommended club including a specification capable of suppressing variation in the specific explanation variable.