Progressivistic Metadata Layer for Sports Analytics
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Solution Overview
Problem
Current sports analytics systems lack the capability to collect and process progressivistic metadata, which includes events that have not occurred during a sport game, limiting their ability to provide comprehensive insights for decision-making and analysis.
Innovation Solution
The system integrates a progressivistic metadata layer synchronized with a timecode or keykode of a video file, allowing for the logging and processing of events that have not occurred during a sport game, including associations with specific frames and players, and enables correlation with performance data for enhanced analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current sports analytics systems only collect and process historical event data that occurred during games, then data collection is straightforward and systematic, but the systems lack capability to provide predictive insights and comprehensive analysis
Solution Approach 1:
The patent segments the metadata system into two distinct layers: event data layer (historical events during games) and progressivistic metadata layer (predictive and associative events). This segmentation allows the system to maintain the simplicity of traditional event data collection while adding predictive capabilities through a separate, structured metadata layer that can be independently managed and processed.
Solution Approach 2:
The patent introduces a new dimension to sports analytics by adding the progressivistic metadata layer that operates alongside the traditional event data layer. This dimensional expansion enables the system to handle both historical event data and predictive/associative metadata simultaneously, transforming the system from purely reactive analysis to include proactive predictive insights without completely restructuring the existing data collection infrastructure.
2Measurement precision
If the system integrates a progressivistic metadata layer synchronized with video files, then comprehensive insights and predictive analysis are enabled, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-synchronizing the progressivistic metadata layer with video file timecodes and keykodes before analysis. This pre-synchronization establishes a structured framework where predictive and associative metadata are already aligned with specific video frames and events, enabling precise analysis without requiring complex real-time synchronization processing during query execution.
Solution Approach 2:
The patent introduces timecodes and keykodes as intermediary elements that mediate between the progressivistic metadata layer and the video file content. These intermediaries provide a standardized reference system that simplifies the correlation between predictive metadata and actual video events, reducing processing complexity by establishing clear mapping relationships without requiring direct complex analysis between all metadata elements and video content.
3Loss of information
If events in the progressivistic metadata layer are correlated with performance data, then deeper insights into player and team performance are achieved, but data collection and processing requirements increase
Solution Approach 1:
The patent applies universality by designing the progressivistic metadata layer to serve multiple functions simultaneously: it stores predictive events, associative metadata, performance correlations, and strategic information in a unified structure. This multi-functional metadata layer reduces the need for separate data collection systems for different types of analytics, consolidating diverse data requirements into a single integrated framework that manages data volume more efficiently.
Data Source
AI summary
Systems, methods and computer readable storage media for collecting and processing progressivistic metadata are described; a structure of files comprising a progressivistic metadata and implementational aspects of various uses thereof are further described.