Sporting Activity Classification Using Player Location Data
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Solution Overview
Problem
Current systems for monitoring and analyzing team sports data generate vast amounts of data, making it time-consuming and difficult for coaches to use effectively in real-time, as they lack automated classification of player skills and team performance based on pre-configured parameters.
Innovation Solution
A method and system that analyze location data from players and game objects within a coordinate system to determine relative locations and classify activities using threshold criteria, enabling real-time analysis of physical, physiological, technical, and tactical aspects of team sports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple video cameras and sensors are used to track player location and biometric data, then measurement precision and data quantity are improved, but device complexity and data processing difficulty increase
Solution Approach 1:
The patent segments the complex monitoring system into distinct functional modules: location tracking devices (GPS, video cameras), biometric sensors (heart rate monitors), and data processing components. Each module independently collects specific types of data, which are then integrated through standardized protocols. This segmentation allows the system to maintain high measurement precision while managing device complexity through modular architecture.
2Measurement precision
If location data is collected at high frequency (25 frames per second), then measurement precision is improved, but productivity and ease of operation worsen due to overwhelming data volume
Solution Approach 1:
The patent extracts only the essential and relevant data points from the high-frequency location data streams. Instead of processing all 25 frames per second, the system identifies and extracts key events such as player positioning during critical game moments, transition phases, and skill execution instances. This extraction approach maintains measurement precision for important events while dramatically reducing the overall data volume that requires processing, thereby improving productivity.
3Measurement precision
If comprehensive player tracking data is collected, then measurement precision is improved, but loss of time increases due to time-consuming analysis
Solution Approach 1:
The patent implements preliminary classification and filtering of location data as it is being collected, rather than performing comprehensive analysis after data collection. The system pre-identifies and tags data points that meet specific criteria for skill classification (such as distance from goal, player positioning, movement patterns) during the game itself. This preliminary action allows coaches to receive pre-processed, classified performance information immediately after games, maintaining high measurement precision while eliminating time-consuming post-game analysis.
4Measurement precision
If manual video logging and expert coaching analysis are used, then measurement precision is improved, but loss of time and productivity worsen
Solution Approach 1:
The patent replaces the manual mechanical process of expert coaching analysis with an automated electronic classification system. The system uses pre-configured classification rules and algorithms to automatically analyze location data, identify skill events, and generate performance reports without requiring manual video review or expert interpretation. This substitution maintains measurement precision through consistent application of classification criteria while dramatically reducing the time loss associated with manual analysis, allowing coaches to receive processed information in real-time or near-real-time.
Data Source
AI summary
The technology provides a method and system of analyzing a sporting activity. An example of the technology involves one or more processors receiving location data associated with a first player and location data associated with a second player, wherein the location data of the first player and the location data of the second player are derived from one or more sensor devices; detecting a game event associated with the first player; determining, in view of the received location data, a plurality of locations of the first player and at least one location of the second player; and classifying the game event based on the plurality of locations of the first player and the at least one location of the second player.