Estudo sobre a evolução da eficiência das Odds no mercado de apostas de futebol

Abstract

This study aims to analyze and compare the degree of informational efficiency in football betting markets for the English Premier League and the Brazilian Championship (Série A), with the purpose of identifying the presence, magnitude, and possible causes of pricing inefficiencies in the odds. The methodology was based on building a database through automated extraction (web scraping) and the integration of weather variables. The predictive modeling, focused on binary outcome classification, compared five machine learning algorithms, optimized via stochastic search and validated by the intertemporal walk-forward method to prevent data leakage. The prediction testing occurred in a simulated financial backtest environment. Statistical results demonstrated the superiority of Logistic Regression with L2 regularization (60.17% accuracy and 0.6202 AUC in the test set), which outperformed complex architectures based on decision trees. In the financial application guided by Expected Value (+EV) thresholds, a market asymmetry was observed: the algorithm achieved a positive Return on Investment (ROI) of 0.78% in the Brazilian Championship, but recorded a loss of -38.35% in the Premier League. It is concluded, further supported by Monte Carlo simulations, that the English market validates the Efficient Market Hypothesis in its semi-strong form due to its high liquidity, nullifying basic statistical advantages, whereas the Brazilian scenario, in its consolidation phase, still presents marginal inefficiencies that can be quantitatively exploited.