Performance Projection Platform Using Component Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems fail to predict a person's performance effectively due to their inability to consider both inherent attributes and dynamic external factors, and they are computationally inefficient in handling the large volume of data required for accurate predictions, especially for long-term and real-time predictions.
Innovation Solution
A performance projection platform that aggregates attributes of a person of interest, their team, and event properties into component vectors, which are then input into machine learning models to generate real-time and long-term performance metrics, accounting for dynamic conditions such as injuries and career trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use traditional prediction models, then they are simple to implement, but they cannot accurately predict performance over long time periods or in real-time due to inability to consider dynamic external factors
Solution Approach 1:
The prediction system is divided into multiple independent machine learning models, each trained on specific aspects of performance data (inherent attributes, team attributes, event properties, dynamic conditions). This segmentation allows each model to specialize in particular prediction tasks while maintaining overall system manageability and accuracy.
Solution Approach 2:
The system incorporates dynamic external factors and conditions that change over time, allowing predictions to adapt to current states rather than relying on static historical averages. This enables accurate real-time and long-term predictions by considering evolving circumstances.
2Measurement precision
If the system processes comprehensive data including inherent attributes and external factors, then prediction accuracy improves, but computational resources and processing time increase significantly
Solution Approach 1:
Data from third-party servers is pre-aggregated into component vectors before being input to machine learning models. This preliminary processing organizes raw data into structured formats, reducing the computational burden during real-time prediction and improving processing efficiency.
Solution Approach 2:
Component vectors serve as intermediary representations between raw performance data and machine learning models. These vectors consolidate multiple attributes into compact numerical representations, enabling efficient processing while preserving the essential information needed for accurate predictions.
3Reliability
If conventional systems gather and process large volumes of performance data, then they can capture more factors affecting performance, but the processing becomes computationally impractical and inefficient
Solution Approach 1:
The system extracts only the most relevant features and attributes from large volumes of raw performance data, rather than processing all available data. This extraction focuses computational resources on the most predictive elements, maintaining reliability while reducing energy consumption.
Solution Approach 2:
The system transforms raw performance data into different parameter representations (component vectors) that are more suitable for machine learning processing. This parameter transformation reduces data dimensionality and complexity, enabling efficient computation while preserving predictive information.
Data Source
AI summary
A system and a method are disclosed for a platform that predicts performance metrics for a person of interest (POI) in real-time and over an extended time period. The platform extracts attributes of the POI, attributes of a team corresponding to the POI, and inherent properties of an event corresponding to the POI. The platform generates a live component vector comprising the extracted attributes and inherent properties and inputs the live component vector to a first machine learning model to determine a live metric characterizing the performance of the POI during a current event. The platform generates a career component vector by updating the live component vector with one or more live metrics determined by the first machine learning model. The platform inputs the career component vector to a second machine learning model to determine a career metric characterizing the long-term performance of the POI during an extended time period.