Athlete Performance Prediction Using Historical Training Parameters
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Solution Overview
Problem
Conventional fitness tracking devices fail to optimize individual and team performance by providing user-dependent data analysis, lacking the ability to predict future performance, identify important parameters, and adjust training schedules based on team dynamics.
Innovation Solution
A method and system that collects and analyzes data from past events to determine statistically significant training parameters, allowing for the prediction and optimization of future performance by adjusting training programs to conform to optimal performance ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fitness tracking devices provide user-dependent data analysis with normal ranges based on population averages, then data collection and tracking capabilities are comprehensive, but the data cannot provide complete picture for particular individual and fail to optimize future performance
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical performance data and training parameters from multiple past events before the future event occurs. This advance analysis establishes baseline performance levels and identifies optimal training parameter ranges, enabling accurate predictions and optimized training programs before the actual performance event takes place.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing actual performance data and training parameters against predicted outcomes and optimal ranges. This feedback loop allows the system to refine predictions, identify deviations from optimal performance, and adjust training recommendations to improve future performance based on actual results.
2Loss of information
If the system collects and analyzes abundant data from multiple sources, then comprehensive performance information is available, but sorting and identifying important parameters becomes complicated
Solution Approach 1:
The system extracts and isolates statistically significant training parameters and performance indicators from the abundant collected data by comparing training parameters against performance outcomes. This extraction process identifies the specific subset of parameters that have meaningful relationships with performance, separating important signals from noise in the comprehensive data set.
Solution Approach 2:
The system applies local quality by determining event-specific and individual-specific importance weights for different parameters. Rather than treating all parameters uniformly, the system identifies which parameters are most critical for particular event types and individual athletes, allowing targeted analysis and optimization focused on the most relevant local characteristics.
3Loss of time
If conventional products focus on displaying immediate feedback during training, then real-time monitoring is provided, but information for optimizing future performance over days or weeks is not provided
Solution Approach 1:
The system performs preliminary analysis of historical data and establishes optimized training programs in advance, before the performance event occurs. By analyzing past performance patterns and training responses, the system prepares forward-looking recommendations that optimize training schedules and parameters for future events, rather than only providing reactive feedback.
Solution Approach 2:
The system implements dynamic training program optimization by adjusting training recommendations based on evolving performance trends, individual responses to training, and changing conditions leading up to future events. The optimization adapts over time as new data becomes available, allowing the training program to evolve dynamically rather than remaining static.
4Ease of operation
If generalized normal ranges are used for performance evaluation, then comparison is simplified, but individual variability is not accounted for and complete picture is not provided
Solution Approach 1:
The system performs preliminary individualization by establishing personalized baseline performance levels and optimal parameter ranges for each individual based on their historical data before evaluation occurs. This advance personalization creates individual-specific reference standards that account for natural variability while maintaining systematic evaluation approaches.
Solution Approach 2:
The system applies local quality by creating individualized performance evaluation criteria rather than using uniform standardized ranges. Each individual receives personalized performance thresholds and optimal parameter ranges based on their unique characteristics, training history, and response patterns, allowing accurate assessment that respects individual variability.
Data Source
AI summary
Systems and methods for predicting/optimizing physical performance including: receiving a selection of one or more individual performance parameters for a future instance of an event, the individual performance parameters indicative of a desired physical performance; receiving a selection of one or more team performance parameters, the team performance parameters indicative of a physical performance of a team; collecting data related to the individual performance parameters and the team performance parameters from past occurrences of the particular event; identifying a subset of training parameters, from within a collection of training parameter data associated with past occurrences; and providing a training program.