Sensor-Based Sports Tracking Probability Assignment
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Solution Overview
Problem
Current methods for evaluating sporting activities rely heavily on discrete, measurable events and human observation, which are limited in capturing essential skills like strength and speed, and involve too many variables for practical monitoring.
Innovation Solution
A sensor-based tracking system that uses processors and memory to determine probabilities of sporting outcomes based on data from sensors like accelerometers, gyroscopes, and geo-spatial sensors, assigning credit or fault to participants for actions affecting the outcome, such as possession of a tracked object or successful completions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor-based tracking technologies are used to monitor participants, then measurement precision and comprehensiveness of evaluation are improved, but device complexity and cost increase
Solution Approach 1:
The system divides participant evaluation into multiple independent metric dimensions (speed, distance, acceleration, positional changes) that can be measured and processed separately, then aggregated to form comprehensive performance assessments. This segmentation allows precise measurement of each parameter while managing system complexity through modular data handling.
Solution Approach 2:
The sensor-based tracking system is designed to serve multiple evaluation functions simultaneously - tracking positional data, measuring physical exertion, analyzing movement patterns, and generating performance metrics across different sports and participant types. This multi-functionality improves measurement comprehensiveness while amortizing the complexity cost across diverse applications.
2Measurement precision
If traditional human observation methods are used, then ease of operation is maintained, but measurement precision and ability to capture essential skills are insufficient
Solution Approach 1:
The sensor-based system automatically collects, processes, and analyzes performance data without requiring continuous human observation or intervention. Participants wear sensors that self-record metrics, and the system autonomously computes performance evaluations, eliminating the need for manual tracking while achieving superior measurement precision for skills like speed and strength.
3Loss of information
If comprehensive variable monitoring is attempted, then measurement completeness is improved, but the number of variables becomes unmanageable for practical monitoring
Solution Approach 1:
The system extracts and isolates the most critical performance variables from the full set of possible measurements, focusing on key metrics that directly impact participant evaluation. By selectively extracting essential data points (core movement parameters, critical performance indicators) rather than attempting to monitor all possible variables, the system maintains information completeness for decision-making while reducing management complexity to practical levels.
Data Source
AI summary
A system for sensor-based tracking of participants of a sporting activity is provided. In some implementations, the system performs operations comprising determining, based on sensor data (e.g., information indicative of at least a location of a plurality of tracked participants and a location of a tracked object), a first probability of a team successfully scoring. The operations further comprise determining, in response to detecting an action which at least changes the location of the tracked object, a second probability of the team (or an opposing team (successfully scoring, and assigning, based on a difference between the first probability and the second probability, at least a portion of the difference among one or more of the plurality of participants. Related systems, methods, and articles of manufacture are also described.