Dynamic Athlete Performance Tracking System with Adaptive Metrics
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Solution Overview
Problem
Existing athlete performance tracking systems are limited to a predetermined set of performance factors and cannot accommodate new factors that may emerge after deployment, making them inadequate for comprehensive prediction and evaluation.
Innovation Solution
A system that includes a device processor and a non-transitory computer readable medium with instructions for collecting, compiling, and comparing data across multiple sources, allowing for the consideration of new performance metrics and conversion of similar metrics to a common scale for universal comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system uses a predetermined set of performance factors, then the system structure remains simple and deployment is straightforward, but the system cannot accommodate new performance factors that emerge after deployment
Solution Approach 1:
The system dynamically adjusts its performance factors through automated machine learning processes. New performance factors can be discovered and incorporated without manual reconfiguration, allowing the system to evolve its structure based on emerging data patterns and organizational needs.
Solution Approach 2:
The system performs self-configuration through automated machine learning algorithms that automatically identify, evaluate, and integrate new performance factors. This eliminates the need for manual system reconfiguration when new factors emerge, enabling the system to adapt autonomously.
2Loss of information
If the system collects and processes multiple data sources with different metrics, then the comprehensiveness of performance evaluation improves, but the complexity of data standardization and comparison increases
Solution Approach 1:
The system automatically transforms diverse performance metrics from multiple data sources into a standardized evaluation framework. Machine learning algorithms dynamically adjust parameter weights and normalize different metric types, enabling comprehensive comparison while managing processing complexity through automated parameter transformation.
Solution Approach 2:
The system creates a universal performance evaluation framework that can process and compare multiple types of data sources simultaneously. The automated machine learning engine provides a multi-functional processing capability that handles various metric types through a single standardized interface.
3Speed
If the system makes predictions based on limited historical data, then the system responds quickly to performance changes, but the accuracy of future performance predictions decreases
Solution Approach 1:
The system performs preliminary analysis of available data using machine learning algorithms to identify patterns and trends before making predictions. This preliminary processing enables the system to extract maximum predictive value from limited historical data while maintaining quick response times to performance changes.
Solution Approach 2:
The system continuously refines its predictions through feedback mechanisms that learn from actual performance outcomes. Machine learning algorithms adjust prediction models based on prediction accuracy feedback, improving prediction precision over time while maintaining responsive performance tracking.
Data Source
AI summary
An athlete performance tracking and prediction system may include a device processor and a non-transitory computer readable medium including instructions stored thereon, and executable by the processor, for performing the following steps: collecting data related to performance of a player into a database; compiling data and reporting performance attribute scores; performing comparisons between the reported performance attribute scores for the player to scores of other players in the database; and projecting future performance of the player based on the performed comparisons.