Multi-Task Learning with Mixture Density Networks for Sports Prediction
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Solution Overview
Problem
Conventional sports analytics rely on a single metric to explain performance, which is limiting and fails to capture the complexity and nuances of sports like rugby, where multiple predictors are needed for different contexts and temporal resolutions.
Innovation Solution
A multi-task learning approach using a mixture density network that generates multi-modal predictions by simultaneously training predictors for various game outcomes, incorporating spatial and contextual information to provide probabilistic distributions and uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single metric is used to explain sports performance, then the analysis is simple and easy to interpret, but the analysis fails to capture the complexity and nuances of sports
Solution Approach 1:
The patent segments the performance analysis into multiple independent prediction tasks (next event type, event outcome, temporal patterns) rather than using a single metric. Each task is handled by dedicated neural network components that process specific features, allowing comprehensive analysis while maintaining interpretability through separate functional modules.
Solution Approach 2:
The patent implements a multi-functional prediction system where a single integrated model performs multiple analysis functions simultaneously - predicting event types, outcomes, temporal patterns, and player performance across different contexts. This universal approach captures sports complexity while providing unified insights.
2Adaptability or versatility
If multiple predictors are used for different contexts and temporal resolutions, then the analysis captures comprehensive game dynamics, but the system complexity increases
Solution Approach 1:
The patent merges multiple prediction tasks and temporal resolutions into a single integrated neural network architecture. Different prediction functions share common feature extraction layers and processing pipelines, reducing redundancy and managing system complexity while maintaining comprehensive analytical capabilities.
Solution Approach 2:
The patent employs a nested architecture where simpler prediction models handle specific tasks (e.g., next event type prediction) while more complex models handle broader tasks (e.g., outcome prediction with temporal patterns). This hierarchical nesting allows manageable complexity at each level while achieving comprehensive analysis at the top level.
3Reliability
If probabilistic distributions and uncertainties are incorporated, then the prediction accuracy and reliability improve, but the computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary computations to establish baseline probabilities and uncertainty estimates during training and feature extraction, rather than computing full probabilistic distributions in real-time during prediction. This preliminary action reduces computational burden during actual prediction while maintaining reliable uncertainty quantification.
Data Source
AI summary
A method of generating a multi-modal prediction is disclosed herein. A computing system retrieves event data from a data store. The event data includes information for a plurality of events across a plurality of seasons. Computing system generates a predictive model using a mixture density network, by generating an input vector from the event data learning, by the mixture density network, a plurality of values associated with a next play following each play in the event data. The mixture density network is trained to output the plurality of values near simultaneously. Computing system receives a set of event data directed to an event in a match. The set of event data includes information directed to at least playing surface position and current score. Computing system generates, via the predictive model, a plurality of values associated with a next event following the event based on the set of event data.