Explorando a eficiência da aprendizagem de máquina na previsão de resultados do brasileirão da série A: uma análise de dados e algoritmos

Abstract

With the advancement of the technological revolution, machine learning has become increasingly relevant in various fields of knowledge, bringing significant benefits to sectors such as healthcare, education, fraud detection, and big data analysis. In sports, particularly football, this technology has been applied to understand complex patterns and predict results more accurately. In this scenario, this work investigates the efficiency of machine learning algorithms in predicting the Brazilian Serie A Championship results. The goal is to understand how these computational models can adjust and learn from historical match data, analyzing statistics to generate predictions for future matchups. Furthermore, the research proposes a comparison between the predictive capabilities of algorithms and human analysis, evaluating whether machine learning techniques can surpass traditional prediction methods and the intuition of experts. To achieve this, a review of previous works was carried out along with a detailed analysis of the errors made in other predictive approaches, aiming to improve the strategies used. The study encompasses all stages of the proposed predictive process, from data collection and processing to implementing Machine Learning models and performance evaluation. Different algorithms will be tested and compared to identify which ones exhibit the highest accuracy rate and which characteristics directly influence the effectiveness of predictions. Our approach contributes to a better understanding of the applicability of machine learning in the sports field, providing insights that may be useful both for scholars in the area and for professionals who use statistical predictions, such as bettors and sports analysts.