Hierarchical Sports Data Aggregation for Strategic Pattern Recognition
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Solution Overview
Problem
Current sports data analysis technologies are inadequate for improving game strategy, as they focus more on player technique rather than strategic decision-making, and lack effective visualization tools to help experts recognize complex patterns and trends in real-time game data.
Innovation Solution
A computer-automated method and system that analyzes sports performance data by aggregating game events into patterns and strategies, allowing for multi-level hierarchical analysis and visualization, enabling experts to recognize trends and patterns, even in incomplete data sets, and facilitating the use of machine learning for further pattern detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex analytics solutions are applied to raw sports data, then measurement precision and data granularity are improved, but device complexity and difficulty of detecting patterns increase
Solution Approach 1:
The patent segments complex sports data analysis into multiple hierarchical levels: individual events, event patterns, event strategies, and performance metrics. Each level processes specific types of data and produces structured outputs that feed into the next level, making the overall complex system manageable through modular organization of analysis functions.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers between raw sensor data and final analytics results. Event patterns serve as intermediaries that aggregate individual events, event strategies aggregate patterns, and performance metrics aggregate strategies. These intermediaries simplify the detection of complex patterns by breaking down the analysis into manageable steps.
2Loss of information
If multiple levels of event aggregation are created, then information completeness is improved, but loss of time for processing increases
Solution Approach 1:
The patent performs preliminary aggregation of events into patterns and strategies during data collection phases. By pre-processing data into structured hierarchical formats before final analysis, the system reduces the computational burden during real-time processing and minimizes information loss while maintaining efficient processing speeds.
Solution Approach 2:
The patent implements continuous processing pipelines where events are aggregated into patterns, patterns into strategies, and strategies into performance metrics in an ongoing manner. This continuous action ensures that information is preserved across all aggregation levels without requiring complete re-processing, thereby reducing overall processing time while maintaining information completeness.
3Ease of operation
If hierarchical aggregation of events into patterns and strategies is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent adds hierarchical dimensions to the data structure, organizing events into patterns, patterns into strategies, and strategies into performance metrics. This dimensional organization transforms complex multi-dimensional sports data into a structured hierarchy that is easier to interpret and analyze, while the systematic architecture manages the complexity through clear hierarchical relationships.
Data Source
AI summary
A computer system and computer-automated method for analyzing sports performance data from a game. A data set is provided that contains a log of a first time sequence of game data events which are classified into event types. A second time sequence is generated from the first time sequence by aggregating the events into game patterns, and a third time sequence is generated by aggregating the game patterns into game strategies. A multi-level time sequence event hierarchy is thus created. These multiple time sequence levels are rendered into a visualization in which the different types at each level are visually distinct from each other. The visualization reveals to a game expert player or team behavior in the data set which can be used for player and team improvement.