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, failing to provide real-time or near real-time predictions of a team's likelihood to score after an event, and lack detailed metrics on team and player performance.
Innovation Solution
A machine learning-based model that processes live or 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 teams and players, using features derived from event data and trained on historical game data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional post-hoc analysis methods are used, then implementation simplicity is maintained, but real-time prediction capability is lost
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical game data before live events occur. The model training phase prepares the system in advance, creating a trained model that can then make real-time predictions during actual games without requiring complex processing during the event itself.
Solution Approach 2:
The system transitions from static post-hoc analysis to dynamic real-time prediction. The machine learning model adapts to different game situations, teams, and contexts, providing dynamic predictions that change based on current game state, player statistics, and event conditions rather than using fixed predetermined values.
2Measurement precision
If detailed granular metrics are generated for every event, then measurement precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system replaces complex manual or rule-based mechanical analysis with machine learning-based predictive modeling. Instead of using predetermined formulas or manual evaluation, the system uses trained models that automatically process event data and generate precise metrics, reducing processing time while maintaining or improving measurement accuracy.
Solution Approach 2:
The system changes parameters by using machine learning models that can process multiple input features (player statistics, team performance, contextual factors) and transform them into precise prediction outputs. The model learns optimal parameter relationships from historical data, enabling efficient computation of detailed metrics without linear increases in processing time.
3Productivity
If real-time data processing is implemented, then immediacy of analytics is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the analytics process into distinct phases: historical data collection and model training (offline), and real-time prediction (online). This segmentation allows complex computational work to be performed in advance during training, while real-time processing only requires inference operations, significantly reducing the computational complexity needed during live events.
Solution Approach 2:
The trained machine learning model acts as an intermediary between raw event data and final analytics outputs. The model pre-processes and learns from historical data, then serves as a computational bridge that efficiently transforms real-time event inputs into predicted outcomes, reducing the need for complex real-time calculations.
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.