Sports Formation Prediction Using Gumbel-Sinkhorn Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems struggle to accurately predict a team's formation in sports due to inherent permutation disorder and limited context-specific data, making it difficult to identify formations in specific scenarios or unobserved contexts.
Innovation Solution
A neural network framework incorporating a Gumbel-Sinkhorn (GS) layer and mixture density network is used to learn optimal player permutations and predict distribution of positions, enabling end-to-end formation prediction in various contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems are used to predict team formation, then the system structure is simple, but the prediction accuracy is low due to inherent permutation disorder
Solution Approach 1:
The patent replaces conventional mechanical/statistical methods with a deep neural network system that uses Gumbel-Sinkhorn layers and mixture density networks to model player position distributions, enabling accurate formation prediction despite permutation disorder
Solution Approach 2:
The system transforms the formation prediction problem by changing parameters to represent probability distributions of player positions rather than fixed positions, allowing the model to handle permutation variability through learned distributions
2Measurement precision
If more context-specific data is collected to improve prediction accuracy, then the prediction precision improves, but the data processing complexity increases
Solution Approach 1:
The mixture density network automatically learns optimal feature representations and position distributions from the data without requiring manual feature engineering or complex preprocessing, allowing the system to handle context-specific data efficiently
Solution Approach 2:
The system replaces manual data processing and feature extraction with automated neural network learning, where the Gumbel-Sinkhorn layers and mixture density networks jointly optimize representation learning and formation prediction
Data Source
AI summary
A system and method of predicting a team's formation on a playing surface are disclosed herein. A computing system retrieves one or more sets of event data for a plurality of events. Each set of event data corresponds to a segment of the event. A deep neural network, such as a mixture density network, learns to predict an optimal permutation of players in each segment of the event based on the one or more sets of event data. The deep neural network learns a distribution of players for each segment based on the corresponding event data and optimal permutation of players. The computing system generates a fully trained prediction model based on the learning. The computing system receives target event data corresponding to a target event. The computing system generates, via the trained prediction model, an expected position of each player based on the target event data.