SPRING 2026
Performance Analysis in the Brazilian Soccer League: Applying Machine Learning Techniques to Team Evaluation
AUTHOR
José Vinicius Boaventura Barbeiro, Universidade Tecnológica Federal do Paraná (UTFPR), Londrina, Brazil
ABSTRACT
This study presents a method for analyzing the sports performance of teams through artificial intelligence, using data from the Série A of the Brazilian Championship. Machine learning methods were applied, highlighting K-Means clustering for identifying patterns among teams, Random Forest for pointing out the most relevant variables, and SHapley Additive exPlanations (SHAP) analysis to interpret the importance of these variables in each profile.
The clustering revealed six distinct groups of teams, ranging from aged and less competitive squads to young, offensive, and disciplined teams. Variables such as the number of goals, average age of the starters, betting market odds, and number of fouls stood out as determinants for performance.
Overall, the results demonstrate how applying artificial intelligence can enhance game analysis, providing deeper insights and a stronger foundation for strategic decision-making. This approach contributes to a clearer understanding of the factors influencing team performance and strengthens sports analysis through objective data and advanced methods.
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