Tactics analysis and evaluation of women football team based on convolutional neural network
A summary of the research:
The article "Tactics Analysis and Evaluation of Women's Football Team Based on Convolutional Neural Network" introduces a novel method using Convolutional Neural Networks (CNN) to analyze and evaluate tactical play in women's football. The study uses CNN to process and classify video footage of matches, allowing for quicker and more accurate tactical assessments compared to traditional manual methods. The model analyzes players' hidden abilities, such as their mistakes in different positions and their effectiveness in specific tactical scenarios. Data from the 2021–2022 UEFA Women's Champions League was used to train the CNN, which demonstrated a classification accuracy of over 95% for various football actions. The CNN also provided real-time evaluations of player errors, aiding coaches in making more informed decisions about player substitutions and tactical adjustments during matches.
The results showed that CNN technology is highly effective for identifying and evaluating players' performances, making it a valuable tool for pre-match tactical training and in-game analysis. By reducing the time needed to assess player mistakes and tactical decisions, the use of CNN has the potential to improve the winning rate of teams by providing faster, more precise feedback.
Key Takeaways:
High accuracy in tactical analysis: The CNN model achieved over 95% accuracy in classifying different football actions and player abilities.
Real-time performance evaluation: The model helps identify player mistakes in real-time, enabling coaches to make timely substitutions and tactical changes.
Efficient pre-match training: By analyzing players' hidden abilities and tactical errors, the model allows for more efficient pre-match preparations and game-time decisions
Authors: Lechuan Shen, Zhongquan Tan, Zekun Li, Qikun Li & Guoqin Jiang
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