DNN-based multi-output model for predicting soccer team tactics.

Area of Science:

• Sports Analytics
• Machine Learning
• Computational Science

Background:

• Modern sports heavily rely on strategy and tactics for game outcomes.
• Coaching decisions are often based on experience and intuition rather than data-driven insights.
• Existing machine learning techniques have limitations in predicting complex soccer tactics.

Purpose of the Study:

• To predict soccer tactics such as formations, game styles, and game outcomes using a soccer dataset.
• To propose a Deep Neural Network (DNN) based model for enhanced tactical prediction.
• To overcome limitations of previous machine learning approaches in analyzing soccer data.

Main Methods:

• Utilized Deep Neural Networks (DNN) with Multi-Layer Perceptron (MLP), wide inputs, and residual connections.
• Employed feature selection to identify crucial player attributes and clustering for positional segmentation.
• Developed a Multi-Output model for Soccer (MOS) trained on segmented positions and game data.

Main Results:

• The proposed DNN model demonstrated superior performance in predicting core soccer tactics.
• Significant improvements were observed compared to baseline machine learning models.
• The model effectively learned sparse rules and generalized features from the soccer dataset.

Conclusions:

• The developed DNN-based model offers a powerful tool for data-driven tactical prediction in soccer.
• This approach provides a more accurate and reliable method for analyzing and predicting game strategies.
• The findings suggest a shift towards advanced machine learning in sports strategy and coaching.