CNN Set-Piece Alignment Detection From Soccer Tracking Data
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Solution Overview
Problem
Existing methods for analyzing defensive and offensive alignments in soccer set-pieces fail to capture the nuanced behaviors and fine-grained movements of players, leading to inefficiencies in exploiting market inefficiencies in goal scoring strategies.
Innovation Solution
A system utilizing a convolutional neural network (CNN) and machine learning algorithms to analyze tracking data, combining deep-learning methods with hand-crafted features to identify and classify defensive and offensive alignments in set-pieces, enabling a hybrid approach for robust set-piece analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to analyze defensive and offensive alignments, then the analysis process is simple, but the measurement precision of player behaviors and movements is insufficient
Solution Approach 1:
The patent replaces traditional mechanical analysis methods with a convolutional neural network (CNN)-based machine learning system. The CNN automatically extracts features from tracking data representing player positions, enabling precise detection of defensive and offensive alignments without manual intervention or simple computational methods.
Solution Approach 2:
The patent introduces a CNN model as an intermediary between raw tracking data and alignment classification. This intermediary layer processes complex player movement patterns and positional relationships, transforming them into meaningful alignment categories while maintaining high measurement precision.
2Measurement precision
If a convolutional neural network is used to extract features from set-piece data, then the measurement precision of alignment types is improved, but the device complexity increases
Solution Approach 1:
The patent segments the analysis process into distinct stages: feature extraction using CNN, feature scanning using machine learning algorithms, and alignment classification. This segmentation allows the complex task of analyzing player alignments to be broken down into manageable components, each handled by specialized methods.
Solution Approach 2:
The patent combines multiple methodologies (CNN-based deep learning and traditional machine learning algorithms) into a hybrid system. This composite approach leverages the strengths of both methods: CNN provides precise feature extraction from tracking data, while traditional algorithms handle pattern recognition and classification efficiently.
3Adaptability or versatility
If deep-learning methods are combined with hand-crafted features, then the adaptability of the system to different set-piece scenarios is improved, but the device complexity increases
Solution Approach 1:
The patent creates a universal analysis system that can handle various set-piece scenarios (corners, free-kicks, penalties) through a single integrated CNN-based framework. The system automatically adapts to different alignment types and playing conditions without requiring separate specialized models for each scenario.
Solution Approach 2:
The patent utilizes parameter changes in the CNN model architecture and training process to adapt to different set-piece scenarios. By adjusting network parameters, layer configurations, and feature extraction parameters, the system can effectively analyze diverse alignment patterns while maintaining a unified computational framework.
Data Source
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AI summary
A method of identifying a defensive alignment and an offensive alignment in a set-piece is disclosed herein. A computing system receives one or more streams of tracking data. The computing system identifies a set-piece contained in the one or more streams of tracking data. The computing system identifies a defensive alignment of a first team and an offensive alignment of a second team. The computing system extracts, via a convolutional neural network, one or more features corresponding to a type of defensive alignment implemented by the first team by passing the set-piece through the convolutional neural network. The computing system scans the set-piece, via a machine learning algorithm, to identify one or more features indicative of a type of offensive alignment implemented by the second team. The computing system infers the type of defensive alignment implemented by the first team.