Goalkeeper Performance Prediction with Personalized Body-Pose Embeddings
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Solution Overview
Problem
Conventional methods for evaluating goalkeeper performance are inadequate as they fail to account for the diverse contexts and situations faced by goalkeepers, leading to inconsistent and inaccurate predictions of their performance when transferred between teams.
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 shot and goalkeeper locations, player form, and body pose, to simulate and compare goalkeeper performance in similar 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 prediction accuracy is low and fails to account for diverse contexts
Solution Approach 1:
The evaluation system segments goalkeeper performance into multiple contextual factors including shot location, goalkeeper position, player form, and body pose. Each factor is independently captured and processed by the neural network to provide a comprehensive evaluation that accounts for diverse game situations.
Solution Approach 2:
The system transitions from traditional 2D video analysis to 3D spatial understanding by incorporating body pose estimation and spatial relationships between players, shot, and goalkeeper. This dimensional enhancement enables more accurate prediction of goal outcomes by considering the full spatial context.
2Measurement precision
If personalized prediction using deep learning is implemented, then prediction accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary processing of game data by pre-extracting relevant features such as shot location, goalkeeper position, and body pose before feeding them to the neural network. This preliminary action reduces the computational burden during actual prediction by preparing data in advance in the required format.
Solution Approach 2:
The neural network acts as an intermediary that transforms complex multi-dimensional input data (including body pose, spatial positions, and game context) into simplified prediction outputs. This intermediary processing layer manages computational complexity by systematically integrating multiple data sources.
3Adaptability or versatility
If multiple contextual factors are incorporated into evaluation, then the comprehensiveness of analysis improves, but the difficulty of data collection and processing increases
Solution Approach 1:
The system replaces manual data collection and analysis methods with automated computer vision and machine learning technologies. The neural network automatically detects and processes multiple contextual factors including body pose, spatial relationships, and game events, eliminating the need for manual annotation and reducing data collection difficulty.
Data Source
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.