Golf Ball Resting Position Prediction from Impact and Surface Data
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Solution Overview
Problem
Existing methods for determining the final resting position of a golf ball during tournament play are inadequate, lacking precision and accuracy in predicting bounce and roll behavior due to insufficient consideration of environmental factors and surface properties.
Innovation Solution
A system utilizing predictive analytics with a prediction model that incorporates metadata on ball physics, course environment, and historical shot data to predict bounce and roll behavior, employing detailed surface models and environmental sensors to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional laser tracking and visual location methods are used to determine ball position, then the system is simple to operate, but the measurement precision and predictive accuracy of bounce and roll behavior are insufficient
Solution Approach 1:
The system segments the prediction process into multiple independent modules: environmental data collection (surface properties, moisture, temperature), ball impact data collection (velocity, angle, spin), predictive modeling (bounce prediction, roll prediction), and integration with existing laser tracking. Each module can be developed, tested, and maintained independently while contributing to the overall prediction accuracy.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing environmental data (surface moisture, temperature, firmness) and historical shot data before the actual ball shot occurs. This pre-processing of data enables the predictive model to account for varying course conditions and provide more accurate predictions of bounce and roll behavior in advance.
2Reliability
If detailed surface models and environmental sensors are integrated into the prediction system, then the predictive accuracy of bounce and roll behavior improves, but the device complexity and data processing requirements increase
Solution Approach 1:
The prediction system is designed with multi-functionality to handle diverse data types and prediction scenarios. The same predictive model framework processes both bounce prediction (using impact angle, velocity, surface firmness) and roll prediction (using surface moisture, grass type, slope) through unified algorithms that adapt to different course conditions and ball types, reducing the need for separate specialized systems.
Solution Approach 2:
The system introduces intermediary components including a data integration layer that harmonizes inputs from environmental sensors, historical databases, and real-time ball tracking, and a predictive modeling layer that translates raw data into reliable predictions. These intermediaries buffer the complexity between data collection and final prediction output, making the system more manageable and reliable.
3Adaptability or versatility
If historical shot data and real-time environmental data are continuously integrated, then the adaptability to changing course conditions improves, but the loss of time for data processing and computation increases
Solution Approach 1:
The system implements periodic action by updating environmental parameters (surface moisture, temperature, firmness) at scheduled intervals rather than continuously, and by processing historical shot data in batches. This approach maintains adaptability to changing course conditions while significantly reducing computational overhead and data processing time compared to continuous real-time analysis of all available data.
Solution Approach 2:
The predictive model dynamically changes parameters based on detected course conditions. When environmental sensors detect significant changes in surface moisture, temperature, or firmness, the system adjusts the relevant parameters in the predictive model and triggers targeted data processing only for affected areas, rather than reprocessing all historical data, thus maintaining adaptability while minimizing time loss.
Data Source
AI summary
A system for predicting a resting position of a golf ball may use ball tracking sensor data to identify an impact location of the ball. Modeling of ball movement following impact may be performed that incorporates measured ball impact physics, impact coordinates, and material properties of ground or objects to which the impact coordinates correspond and, together with historical shot data corresponding to the impact coordinates or an encompassing zone, bounce and roll behavior and/or final resting position predictions may be generated.