Axial Transformer Predictions for Racer and Team Interactions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions fail to accurately predict outcomes in position-based sporting events, such as vehicular and animal races, due to inadequate capture of correlations between team-mates, other racers, course layout, and contextual features.
Innovation Solution
A transformer neural network, specifically an axial transformer neural network, is used to generate predictions by incorporating super features like track layout and weather, applying self-attention with autoregressive masks, and mapping output embeddings to target metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prediction solutions are used, then computational simplicity is maintained, but prediction accuracy deteriorates due to inadequate capture of correlations between team-mates, racers, course layout, and contextual features
Solution Approach 1:
The model segments the prediction problem into multiple independent embedding layers, each handling specific feature types (player strength, team strength, game context, live features, game events, and super features). This segmentation allows complex correlations to be captured through dedicated processing for each feature category while maintaining modular architecture that manages computational complexity.
Solution Approach 2:
The axial transformer architecture introduces dimensional separation by applying self-attention mechanisms independently along different axes (player dimension and game event dimension). This dimensional decomposition enables the model to capture complex correlations between multiple entities without requiring a single monolithic attention matrix, thus improving prediction accuracy while controlling computational complexity.
2Reliability
If axial transformer neural network with multiple embedding layers is used, then correlation capture between entities is improved, but computational complexity increases
Solution Approach 1:
The axial transformer layers serve multiple functions simultaneously: they perform self-attention computation, capture temporal dynamics, model interactions between entities, and generate predictions for multiple target metrics. This multi-functionality reduces the need for separate specialized components, thereby improving prediction consistency without proportionally increasing architectural complexity.
Solution Approach 2:
The model incorporates dynamic feature processing through live team features and live player features that update during the game, allowing the system to adapt to changing conditions. The autoregressive attention mask enables sequential processing that dynamically captures temporal dependencies, improving prediction reliability while maintaining a structured architecture.
3Adaptability or versatility
If comprehensive features including super features are incorporated, then prediction coverage is improved, but data processing complexity increases
Solution Approach 1:
The comprehensive feature set is segmented into distinct embedding layers, with super features (track layout, weather conditions, tire types) processed separately from player and team features. This segmentation allows each feature type to be processed with appropriate transformations while maintaining overall system manageability and enabling versatile prediction coverage across different racing conditions.
Data Source
AI summary
A method of generating a set of predictions associated with position-based sporting events using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network; mapping output embeddings from the axial transformer layers to target layers; and generating a set of target metric predictions for each racer, team, and overall for the position-based sporting events, based on the output embeddings from the target layers.