Sequence Prediction Model for Sports Object Tracking
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Solution Overview
Problem
Current technologies for predicting sports outcomes, such as player actions in soccer or tennis, lack the accuracy and power needed for real-time analysis and strategic planning.
Innovation Solution
A data processing apparatus and method using a sequence-prediction machine learning model, trained with both generic and specific data, to predict the behavior of sports players and simulate gameplay, incorporating object tracking data and artificially generated data for enhanced training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current prediction technologies are used for sports outcomes, then basic prediction capability is provided, but accuracy and power are insufficient for real-time analysis
Solution Approach 1:
The system segments the prediction task into multiple components: object tracking (separating players and ball), sequence prediction (predicting future positions), and break event detection (identifying significant moments). This segmentation allows each component to be optimized independently, improving overall prediction accuracy while maintaining computational reliability for real-time analysis.
Solution Approach 2:
The system performs preliminary actions by continuously tracking object positions and predicting future states before actual events occur. The sequence prediction model anticipates player and ball movements in advance, enabling real-time analysis and strategic decision-making before break events happen, thereby enhancing both accuracy and predictive power.
2Productivity
If sequence prediction is continued without interruption, then continuous prediction coverage is achieved, but computational resources are wasted during break events
Solution Approach 1:
The system implements periodic action by detecting break events (goals, fouls, substitutions) that naturally segment the sports event into distinct phases. The sequence prediction process is activated during active play and paused during break events, creating a periodic pattern of computation that maintains prediction coverage while conserving computational resources during non-critical periods.
Solution Approach 2:
The system extracts and identifies break events from the continuous stream of sports data, separating them from regular play. By taking out these break events, the system can suspend unnecessary computations during these periods while maintaining prediction capability during active play, thus improving productivity without excessive resource consumption.
3Measurement precision
If only real tracking data is used for training, then data accuracy is maintained, but training dataset size is limited
Solution Approach 1:
The system merges real tracking data from actual sports events with artificially generated data to create a comprehensive training dataset. This combination preserves the accuracy of real data while augmenting it with additional synthetic scenarios, thereby increasing the overall dataset size and improving model training without sacrificing data quality.
Solution Approach 2:
The system creates copies of real tracking data through artificial generation, producing synthetic sports event data that mimics real scenarios. These copied and synthesized datasets expand the training volume while maintaining the statistical properties and accuracy characteristics of authentic sports data, enabling more robust model training.
Data Source
AI summary
A data processing apparatus comprising circuitry receives a sequence of first data elements from object tracking data indicating position of objects in a sports event at a first set of time steps; receives at least one of (i) adjustment information for adjusting the first data elements and (ii) second data elements based on user input indicating position information of the objects at a second set of time steps; generates an adjusted sequence having at least one of (i) adjusted first data elements adjusted using the adjustment information and (ii) the second data elements; performs a sequence prediction process using the adjusted sequence to predict a predicted data element at a future time step, the predicted data element indicating predicted position information of the objects at the future time step.