Spatial Sports Prediction Features for Edge-Case Scoring Accuracy
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Solution Overview
Problem
Existing machine learning models for sports predictions, particularly in soccer, fail to accurately capture edge-case scenarios due to insufficient incorporation of granular data, leading to inaccurate predictions when goalkeepers are out of position or other players obstruct the view, resulting in low reliability in determining the likelihood of scoring events.
Innovation Solution
Implementing a machine learning model that utilizes a feature set including detailed positional data of players, angles, and additional granular information such as goalkeeper location, defensive pressure, and shot context to modify initial projected probabilities, using a generative adversarial network (GAN) with monotonic constraints to enhance prediction accuracy in outlier scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for sports predictions, then the model complexity is low and ease of operation is maintained, but the prediction accuracy in edge-case scenarios deteriorates due to insufficient incorporation of granular data
Solution Approach 1:
The prediction system is segmented into multiple specialized components: a base machine learning model for general predictions, a GAN module specifically for generating edge-case scenarios, and a combination module that integrates both. This segmentation allows each component to specialize in specific tasks, improving overall prediction accuracy without requiring the entire system to be excessively complex.
Solution Approach 2:
The patent introduces a new dimension to the prediction space by generating synthetic edge-case scenarios through the GAN. Instead of only analyzing real historical data, the system now operates in an expanded dimension that includes artificially generated outlier scenarios, enabling better preparation for rare events without significantly increasing the base model complexity.
2Reliability
If more granular data features are incorporated into the model, then the reliability of scoring probability assessments improves, but the difficulty of detecting and measuring increases due to the complexity of processing positional data, angles, and contextual information
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing granular data features (positional data, angles, distances, player orientations) into structured formats before they reach the main prediction models. The GAN also pre-generates edge-case scenarios in advance, so that during actual prediction, the models can directly utilize these prepared features without real-time processing complexity.
3Measurement precision
If a GAN with monotonic constraints is implemented to enhance prediction accuracy in outlier scenarios, then the prediction reliability in edge-cases improves, but the device complexity increases due to the additional architectural components and constraints
Solution Approach 1:
The GAN is equipped with monotonic constraints that apply local quality control to specific aspects of the generated data. Rather than making the entire GAN architecture complex, the constraints are applied locally to ensure that generated edge-case scenarios maintain realistic relationships (e.g., if distance to goal increases, scoring probability should monotonically decrease), improving accuracy without excessive complexity.
4Measurement precision
If detailed positional data and granular features are used to modify initial projected probabilities, then the prediction accuracy in edge-case scenarios improves, but the loss of information increases due to the complexity of integrating multiple data sources
Solution Approach 1:
The system implements feedback mechanisms where the GAN-generated edge-case scenarios are combined with base model predictions through a combination module. This feedback loop allows the system to continuously refine predictions by comparing base predictions with edge-case adjusted predictions, reducing information loss through iterative improvement rather than single-pass integration.
Data Source
AI summary
A method for generating a probability for a first action of a sporting event by implementing a feature set, the method including: obtaining an initial set of data relating to the first action of a sporting event, the initial set of data including at least a position of a first player on a surface and a position of a target area on the surface; generating, by a machine learning model, an initial projected scoring probability based on the initial set of data; generating a feature set relating to the sporting event; and modifying, by the machine learning model, the initial projected scoring probability to an updated scoring probability using the feature set.