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Improving women football tactics analysis by using extreme learning and accumulated optimization algorithm.

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This study introduces a new deep-learning framework with the Accumulated Chimp Optimization Algorithm (ACHOA) to analyze women

Keywords:
Convolutional neural networksExtreme learning machineRefined chimp optimizationWomen’s football

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Area of Science:

  • Sports Science and Analytics
  • Artificial Intelligence in Sports
  • Machine Learning for Performance Analysis

Background:

  • Traditional methods for analyzing women's football strategies, such as Deep Convolutional Neural Networks (DCNN), are computationally intensive and unsuitable for real-time applications.
  • Existing Deep Extreme Learning Machines (DELM) models suffer from unreliable forecasting due to random factor selection, impacting accuracy.
  • There is a need for advanced analytical frameworks to accurately assess player performance and team tactics in women's football.

Purpose of the Study:

  • To introduce a novel deep-learning framework for analyzing women's football strategies.
  • To develop the Accumulated Chimp Optimization Algorithm (ACHOA) to enhance the reliability and accuracy of predictive models.
  • To enable real-time performance monitoring and tactical analysis in women's soccer.

Main Methods:

  • Development of the Accumulated Chimp Optimization Algorithm (ACHOA) to stabilize parameters in Deep Extreme Learning Machines (DELM).
  • Implementation of an ACHOA-enhanced DELM model for analyzing player effectiveness, skill estimation, and positional errors from video frames.
  • Training and validation of the model using real-world video data from the 2021-2022 UEFA Women's Champions League.

Main Results:

  • The ACHOA-enhanced DELM model achieved over 95% classification accuracy on women's football video data.
  • The proposed method demonstrated improved agreement between model-derived factors and expert evaluations.
  • The model's real-time monitoring capabilities were validated, showing potential for tactical adjustments.

Conclusions:

  • The ACHOA algorithm significantly enhances the reliability and performance of DELM models for sports analytics.
  • The developed framework provides a robust tool for detailed player and team strategy analysis in women's football.
  • This research offers a pathway for improved tactical decision-making, performance metrics, and pre-game strategy development in women's soccer.