Axial Transformer Forecasting for Live Player-Team Interactions
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Solution Overview
Problem
Existing solutions fail to accurately predict player and team performance in striking-based sporting events due to inadequate capture of correlations between team-mates, opposing players, and contextual features, leading to inaccurate predictions, especially in inefficient markets and situations with changing game states.
Innovation Solution
A transformer neural network, specifically an axial transformer neural network, is employed to generate predictions by incorporating a super feature embedding that accounts for environmental and surface conditions, and applying self-attention with an autoregressive mask to handle dynamic interactions between players and teams, enabling consistent and real-time forecasting of player and team actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prediction solutions are used, then computational resources are conserved, but prediction accuracy deteriorates due to inadequate capture of correlations between players and contextual features
Solution Approach 1:
The model segments the prediction task into multiple embedding layers, each handling specific aspects: player strength embedding, team strength embedding, game context embedding, live features embedding, and super feature embedding. This segmentation allows each layer to specialize in capturing specific correlations while maintaining overall model efficiency
Solution Approach 2:
The model introduces a new dimension through the axial transformer architecture that processes embeddings along both sequence length and feature dimensions simultaneously. This dual-dimensional approach enables comprehensive capture of player-team-opposition correlations without proportionally increasing computational complexity
2Reliability
If traditional models are used, then model simplicity is maintained, but ability to capture temporal dynamics and interactions deteriorates
Solution Approach 1:
The model performs preliminary encoding of all relevant features into embeddings before the main prediction process. Player strength, team strength, game context, and live features are pre-processed into standardized embeddings that capture temporal dynamics and interactions in advance, ensuring consistent predictions during actual gameplay
Solution Approach 2:
The axial transformer layers act as intermediaries between the various embedding layers and the final prediction output. These transformer layers facilitate the interaction and integration of multiple feature dimensions, enabling reliable capture of complex player-team-opposition correlations
3Measurement precision
If comprehensive feature embedding is implemented, then prediction accuracy improves, but computational latency increases
Solution Approach 1:
The model implements partial embedding by focusing computational resources on the most critical features: player strength, team strength, and super features. Rather than processing all possible features equally, the model selectively embeds the most impactful variables, achieving high accuracy while controlling latency
Solution Approach 2:
The model dynamically adjusts embedding dimensions and transformer layer configurations based on the specific prediction task and available computational resources. This parameter optimization allows comprehensive feature processing when needed while maintaining lower latency for time-critical predictions
Data Source
AI summary
A method of generating a set of predictions associated with a striking-based sporting event 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 of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.