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Leandro Stival¹, Allan Pinto², Felipe Dos Santos Pinto de Andrade³

¹School of Technology, University of Campinas, Limeira, São Paulo, Brazil.

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Summary

Analyzing the first five seconds of ball possession in soccer can predict offensive plays. This approach uses graph modeling and deep learning to identify goal-scoring chances early in the game.

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Area of Science:

Sports Science
Computational Analytics
Performance Prediction

Background:

Modern sports science relies heavily on data analysis for performance insights.
Soccer performance hinges on scoring, necessitating offensive strategies and ball possession.

Purpose of the Study:

To determine if the initial five seconds of ball control can predict reaching the opponent's final quarter.
To identify conditions that enhance strategic advantage in soccer.

Main Methods:

Modeling player interactions as graph structures and extracting performance metrics.
Encoding metrics into visual rhythm representations for deep convolutional networks.
Utilizing a classifier to predict the likelihood of reaching the final quarter for goal-scoring opportunities.

Main Results:

Offensive plays near the opponent's penalty area are predictable within the first five seconds of ball possession.
The developed model accurately forecasts the progression of offensive plays.

Conclusions:

Early game analysis (first five seconds) is sufficient for predicting significant offensive advancements in soccer.
Explainable AI models highlight key metrics, validating findings with existing soccer analytics literature.