Interactive Sports Formation Analysis Using Segmented Player Tracking
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Solution Overview
Problem
Existing sports analytics technologies struggle to accurately and efficiently map team formations in real-time, especially in dynamic sports like soccer, failing to provide semantic labels and temporal analysis of formation changes.
Innovation Solution
A computing system divides player tracking data into segments based on event information, learns formations for both teams, and maps them to known formation clusters, using Gaussian Mixture Models and the Hungarian algorithm to align player positions with role distributions and templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If player tracking data is analyzed in real-time to identify team formations, then the speed of formation identification is improved, but the complexity of the system increases due to the need to process and map dynamic player positions to formation clusters
Solution Approach 1:
The player tracking data is divided into discrete time segments or frames, allowing the system to process formation data in manageable intervals rather than continuously. This segmentation enables real-time analysis by breaking down the complex continuous stream into discrete analytical units, reducing computational overload while maintaining speed.
Solution Approach 2:
The patent introduces formation clusters as an intermediary layer between raw player tracking data and formation identification. These pre-defined clusters serve as reference templates that simplify the mapping process, acting as a mediator that translates complex coordinate positions into recognizable formation patterns, thereby reducing system complexity.
2Measurement precision
If the system maps player positions to semantic formation labels, then the precision of formation analysis is improved, but the time required for processing increases due to the computational complexity of matching positions to formation templates
Solution Approach 1:
The system pre-computes and stores formation cluster templates representing ideal formation patterns before actual match analysis. This preliminary preparation of reference data allows the matching process during games to be faster, as the system only needs to compare live positions against pre-defined templates rather than computing formations from scratch, thus reducing processing time while maintaining precision.
Solution Approach 2:
The formation identification system dynamically adjusts the matching process by considering the temporal context and flow of the game. Rather than rigidly matching every instantaneous position, the system adapts its analysis to account for natural movements and transitions, allowing for more efficient processing that maintains semantic accuracy while reducing unnecessary computational overhead.
3Measurement precision
If the system analyzes formations for both teams in possession and not in possession separately, then the accuracy of formation mapping is improved, but the computational load increases due to processing dual team formations simultaneously
Solution Approach 1:
The analysis is segmented by possession state, with separate formation cluster templates and matching processes for the team in possession and the team not in possession. This segmentation allows the system to apply context-specific formation models to each team, improving accuracy by accounting for different tactical behaviors, while processing can be managed sequentially or with optimized resource allocation to control computational energy consumption.
Data Source
AI summary
A computing system identifies player tracking data and event data corresponding to a match. The match includes a first team and a second team. The player tracking data includes coordinate positions of each player during the event. The event data defines events that occur during the match. The computing system divides the player tracking data into a plurality of segments based on the event information. For each segment of the plurality of segments, the computing system learns a first formation associated with a respective team in possession. For each segment of the plurality of segments, the computing system learns a second formation associated with a respective team not in possession. The computing system maps each first formation to a first class of known formation clusters. The computing system maps each second formation to a second class of known formation clusters.