Live Possession Value Modeling for Real-Time Scoring Prediction
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Solution Overview
Problem
Conventional sports analytics systems are limited to post-hoc analyses and fail to provide real-time or near real-time predictions of a team's likelihood to score after an event, lacking the ability to process noisy live data effectively.
Innovation Solution
A machine learning-based model that processes real-time or near real-time data to predict the likelihood of a team scoring within a specified time frame after an event, generating possession values and momentum metrics for both teams and individual players, using features derived from event data and trained on historical game data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sports analytics systems perform post-hoc analyses, then they can process data comprehensively, but they fail to provide real-time predictions and cannot process noisy live data effectively
Solution Approach 1:
The system performs preliminary actions by pre-processing and cleaning historical game data to create a robust training dataset before live events occur. The machine learning model is trained in advance on cleaned and transformed data, enabling it to make real-time predictions during actual games without delays for data processing or cleaning.
Solution Approach 2:
The patent replaces conventional mechanical data processing approaches with machine learning algorithms that can automatically learn patterns from noisy live data. The ML model substitutes for manual or batch processing systems, enabling real-time inference without the time delays associated with traditional post-hoc analysis methodologies.
2Productivity
If the system processes noisy live data in real-time, then it provides timely predictions, but the complexity of processing and cleaning data increases
Solution Approach 1:
The system performs all data cleaning, transformation, and feature engineering operations in advance during the training phase. By pre-processing historical data to create a standardized training dataset, the system eliminates the need for complex real-time data cleaning operations during live events, thereby reducing processing complexity while maintaining real-time capability.
Solution Approach 2:
The machine learning model is designed to automatically handle noisy data and learn relevant patterns without requiring manual intervention or complex preprocessing during inference. The model self-adjusts to handle variations in live data quality, reducing the operational complexity of processing noisy real-time data.
3Loss of information
If the system generates granular analytics for teams and players, then it provides detailed performance insights, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary feature extraction and transformation during the training phase, creating a optimized model structure that captures essential patterns. This pre-computed feature representation allows the model to generate granular analytics quickly during inference without requiring intensive real-time computational resources for data processing or analysis.
Data Source
AI summary
A computing system receives a plurality of game files corresponding to a plurality of games across a plurality of seasons. The computing system generates a prediction model configured to generate a possession value for an event. The computing system receives a target event, in real-time or near real-time, from a tracking system monitoring a target game. The computing system generates target features for the target event based on target event data associated with the target event. The computing system generates, via the prediction model, a target possession value for the target event based on the target event data and the target features. The target possession value represents a likelihood that a team with possession will score within a following x-seconds after the target event.