Deep Imitation Learning Ghosting Model for Player Movement Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current sports analytics metrics, such as Expected Point Value (EPV), are limited in providing fine-grain analysis of how teams create scoring opportunities, as they are tied to discrete outcomes and require substantial manual annotation, making it cumbersome to analyze and compare movement patterns effectively.
Innovation Solution
Deep imitation learning is used to train a neural network associated with a ghosting model to predict player movements, allowing for automatic data-driven ghosting by determining ghosted movement paths based on tracking data and features, reducing the need for manual annotation and enabling real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to analyze player movement patterns, then fine-grain analysis of scoring opportunities can be achieved, but the analysis process becomes cumbersome and time-consuming
Solution Approach 1:
The patent replaces manual annotation (mechanical human analysis) with an automated machine learning system that uses tracking data to generate ghosting visualizations. The system automatically processes player movement data through trained models to produce fine-grain analysis without manual intervention, thereby maintaining measurement precision while eliminating time loss.
2Extent of automation
If deep imitation learning is used to train neural networks for predicting player movements, then automatic data-driven ghosting can be achieved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by training the neural network models in advance using historical tracking data before actual game analysis. The ghosting models and player movement predictors are pre-trained offline, so that during live or post-game analysis, the system can automatically generate ghosting visualizations without performing complex training computations in real-time, thus achieving automation while managing computational complexity.
3Loss of information
If traditional sports statistics metrics are used, then league average performance comparison is provided, but insights into specific player movement strategies are limited
Solution Approach 1:
The patent segments the analysis into multiple specialized components: tracking data processing, feature extraction, ghosting model prediction, and visualization generation. Each component handles a specific aspect of movement analysis, allowing the system to capture detailed strategic information while organizing complexity into manageable modular units rather than requiring a monolithic complex system.
Data Source
AI summary
One embodiment provides a method, comprising: training, using deep imitation learning, a neural network associated with a predetermined ghosting model to predict player movements for at least one player during at least one sequence in a game; receiving, at an information handling device, tracking data associated with a player movement path for at least one player during the at least one sequence; analyzing, using a processor, the tracking data to determine at least one feature associated with the at least one player at a plurality of predetermined time points during the at least one sequence; and determining, using the predetermined ghosting model and the at least one feature, a ghosted movement path for the at least one player beginning from one of the plurality of predetermined time points. Other aspects are described and claimed.