Golf Club Component Selection Using Swing Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to efficiently recommend golf club components, such as heads and shafts, to individual golfers due to the variety of characteristics and indices used to evaluate them, necessitating a more effective selection process.
Innovation Solution
A selection support apparatus that acquires swing characteristic data from test shots, calculates recommended values for golf club components based on this data, and selects suitable components from stored information using a CPU and communication interface, facilitating efficient component fitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of characteristic data and component information are used to provide personalized recommendations, then the accuracy of component fitting is improved, but the complexity of the selection process increases
Solution Approach 1:
The system segments the component selection process into distinct functional modules: a characteristic data acquisition unit that collects swing data, a calculation unit that processes the data to determine recommended values, and a selection unit that matches recommendations with available components. This segmentation allows each module to handle specific tasks independently, improving fitting accuracy while managing complexity through modular design.
Solution Approach 2:
The calculation unit serves as an intermediary between the characteristic data acquisition unit and the selection unit. It processes raw swing characteristic data, calculates recommended component values, and translates these recommendations into selectable component options. This intermediary function simplifies the overall system by handling the complex data processing and matching logic in a dedicated intermediate stage.
2Reliability
If comprehensive characteristic data is collected and analyzed, then the quality of component recommendation is improved, but the time required for the selection process increases
Solution Approach 1:
The system performs preliminary actions by pre-storing component information and characteristic values in databases before the actual selection process. When a golfer undergoes testing, the selection unit can quickly retrieve and compare component options based on pre-calculated recommended values, significantly reducing the time required for the selection process while maintaining comprehensive data analysis for high recommendation quality.
3Measurement precision
If multiple indices and characteristics are used to evaluate components, then the precision of matching golfer needs is improved, but the difficulty of detecting and measuring appropriate components increases
Solution Approach 1:
The calculation unit transforms multiple swing characteristic parameters into a simplified set of recommended values that directly correspond to component characteristic values. This parameter transformation converts complex multi-dimensional swing data into straightforward recommendation metrics, improving matching precision while reducing the difficulty of component evaluation and selection.
Data Source
AI summary
A selection support apparatus acquires a plurality of types of characteristic data representing a swing characteristic of a testing golfer based on a test shot result of a golf club. The apparatus calculates each of recommended values for the testing golfer in association with a plurality of types of characteristic values that characterize a component of a golf club based on the plurality of types of characteristic data. The apparatus selects, based on the recommended values and component information representing a correspondence between components and the plurality of types of characteristic values, a recommended component from components listed in the component information.