Goalkeeper Performance Prediction Using Body-Pose Embeddings
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Solution Overview
Problem
Conventional methods for evaluating goalkeeper performance are inadequate as they do not account for the diverse situations and contexts in which goalkeepers operate, and they fail to accurately predict a goalkeeper's performance when transferred to a new team due to lack of granular data and changing form.
Innovation Solution
A personalized prediction approach using a deep learning framework with a feed-forward neural network that incorporates fixed and dynamically updated embeddings and features, including body pose information, to simulate goalkeeper performance in specific situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional evaluation methods are used for goalkeeper performance, then the evaluation process is simple, but the accuracy and personalization of performance prediction deteriorates
Solution Approach 1:
The goalkeeper's performance is segmented into multiple independent body parts (torso, left arm, right arm, left leg, right leg) with each segment tracked separately using computer vision. This segmentation allows for granular analysis of each body part's position and movement, enabling personalized performance prediction while maintaining computational feasibility through modular processing of individual segments.
Solution Approach 2:
The system transitions from conventional 2D video analysis to 3D spatial tracking by inferring three-dimensional positions of goalkeeper body parts from multiple camera angles. This dimensional enhancement provides comprehensive spatial context for performance evaluation, capturing the full range of motion and positioning without proportionally increasing system complexity.
2Measurement precision
If granular body pose data is collected and analyzed, then performance prediction accuracy improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The system creates simplified digital representations (skeleton models) that copy only the essential geometric relationships and positions of goalkeeper body parts, rather than processing complete raw video data. These simplified models retain the critical spatial information needed for performance prediction while dramatically reducing computational complexity by eliminating redundant visual details.
Solution Approach 2:
The system extracts only the essential geometric features (positions and orientations of body parts) from complex video data, discarding unnecessary visual information. This extraction process isolates the critical data elements needed for performance analysis, reducing processing complexity while maintaining prediction accuracy.
3Adaptability or versatility
If dynamic form changes are accounted for in performance prediction, then the personalization and accuracy improve, but the complexity of modeling and simulation increases
Solution Approach 1:
The system implements dynamic tracking of goalkeeper body positions throughout the save attempt, continuously updating the spatial coordinates of each body part as the goalkeeper moves. This dynamic approach captures form changes in real-time, allowing the model to adapt to the goalkeeper's actual movements rather than relying on static averages, thereby improving personalization without requiring overly complex simulations.
Data Source
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AI summary
A method of generating a player prediction is disclosed herein. A computing system retrieves data from a data store. The computing system generates a predictive model using an artificial neural network. The artificial neural network generates one or more personalized embeddings that include player-specific information based on historical performance. The computing system selects, from the data, one or more features related to each shot attempt captured in the data. The artificial neural network learns an outcome of each shot attempt based at least on the one or more personalized embeddings and the one or more features related to each shot attempt.