Multimodal Soccer Tracking for Accurate Agentic Match Analysis
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Solution Overview
Problem
Existing solutions are unable to generate accurate information relating to sports events, particularly in generating sports tracking data and performing agentic actions based on user inputs such as text, audio, or video.
Innovation Solution
A multimodal sports learning language model (LLM) receives user inputs in the form of text, audio, or video and maps corresponding metadata to generate sports tracking data, which is used by agents to perform actions like generating highlight reels, narrating events, or simulating player movements, utilizing preprocessed event streams and sport-specific orchestrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing solutions are used for generating sports event data, then the system is simple, but the accuracy and performance of generating sports tracking data is insufficient
Solution Approach 1:
The system is divided into multiple specialized components: superior orchestrator for high-level coordination, sport-specific orchestrators for domain expertise, and multiple agents (video analyst, stats analyst, graphics generator) for specific tasks. Each component focuses on a particular aspect of sports data generation, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The system dynamically selects and activates specific agents and orchestrators based on the type of sports event and required analysis. The superior orchestrator routes queries to appropriate sport-specific orchestrators, which then activate relevant agents based on real-time needs, allowing the system to adapt its complexity to the specific task at hand.
2Reliability
If multiple agents and orchestrators are used to improve sports data generation, then the accuracy improves, but the device complexity increases
Solution Approach 1:
The superior orchestrator serves as a universal coordinator that handles multiple sport types and query types through a single interface. Sport-specific orchestrators are trained on general sports concepts but specialized for particular sports, allowing them to handle various tasks within their domain. This multi-functionality reduces the need for completely separate systems for different sports.
Solution Approach 2:
The orchestrators act as intermediaries between user queries and the agent team. The superior orchestrator translates general queries into sport-specific tasks, and sport-specific orchestrators further refine these into agent-specific instructions. This intermediary layer manages the complexity by providing clear interfaces and coordination protocols.
3Manufacturing precision
If contextual and intentional information extraction is performed, then the precision of sports data output improves, but the processing time increases
Solution Approach 1:
The superior orchestrator performs preliminary extraction of contextual information (event type, sport category, key entities) and intentional information (analysis goals, required metrics) from user queries before routing to sport-specific orchestrators. This preliminary processing prepares the query in advance, reducing the processing time needed at subsequent stages while maintaining high precision.
Solution Approach 2:
The system dynamically adjusts the depth of contextual and intentional information extraction based on the query type and sport category. For simple queries, minimal extraction is performed; for complex analytical queries, more extensive extraction occurs. This dynamic approach balances precision requirements with processing time constraints.
Data Source
AI summary
Disclosed techniques relate to using one or more of soccer match statistics, textual insights, predictions (e.g., team and player at the match level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.