Bowling Ball Motion Prediction Using Multivariable Regression
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Solution Overview
Problem
The complexity of numerous variables affecting a bowling ball's motion makes it difficult for manufacturers and testers to accurately predict its behavior without physically throwing it down a lane, leading to costly and time-consuming testing processes.
Innovation Solution
A system comprising an automatic precision ball thrower, computer-aided tracking system, and computing device that records and analyzes the ball's physical and dynamic characteristics using multivariable regression analysis to predict its path, reducing the need for physical throws.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical testing of bowling ball motion is performed by throwing the ball down a lane, then accurate prediction of ball behavior is achieved, but the process becomes expensive and time consuming
Solution Approach 1:
The system performs preliminary measurements of ball properties (weight, radius of gyration, differential, coverstock characteristics, finger hole positions) before the actual motion prediction. These preliminary data collections enable the computational model to predict ball motion without requiring extensive physical testing, thus reducing time loss while maintaining prediction accuracy
Solution Approach 2:
The system creates a computational copy or virtual model of the bowling ball that replicates its physical properties. This digital twin allows for virtual testing and prediction of ball motion through computer simulations, eliminating the need for repeated physical throws while maintaining accurate prediction of ball behavior
2Measurement precision
If physical testing of bowling ball motion is performed by throwing the ball down a lane, then accurate prediction of ball behavior is achieved, but the process becomes expensive
Solution Approach 1:
The system creates a computational copy or virtual model of the bowling ball that replicates its physical properties. This digital twin allows for virtual testing and prediction of ball motion through computer simulations, eliminating the need for repeated physical throws while maintaining accurate prediction of ball behavior
Solution Approach 2:
The system replaces the mechanical physical testing process with a computational modeling approach. Instead of physically throwing balls down lanes to test performance, the system uses computer algorithms to simulate ball motion based on measured physical properties, substituting mechanical experimentation with digital computation to reduce costs
3Measurement precision
If multiple variables affecting bowling ball motion are considered, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex prediction problem into distinct measurement and computation components. Physical properties (weight, radius of gyration, differential) are measured separately, coverstock characteristics are characterized independently, and finger hole positions are recorded as discrete parameters. This segmentation allows the complex multi-variable system to be managed through modular data collection and processing steps
Data Source
AI summary
A system and method for graphically and statistically analyzing and predicting the motion of a bowling ball. In one embodiment, the system includes an automatic precision ball thrower, a computer aided tracking system (“C.A.T.S.”), and a computing device. Certain static properties of the bowling ball are recorded as independent variables. The automatic precision ball thrower is used to throw the bowling ball down the lane a number of times and the C.A.T.S. records various dynamic characteristics of its path. This data is received by the computing device which uses it to calculate a plurality of dependent variables associated with the path of the bowling ball. The computing device relates the independent variables to the dependent variables using multivariable regression analysis, yielding a set of equations which can be used to predict the dependent variables (or dynamic characteristics) of a second bowling ball given a set of independent variables (or static characteristics of the second bowling ball).