Técnicas de mineração de dados para partidas de futebol com viés nas apostas esportivas

Abstract

Predicting football match results is a significant challenge due to the complexity and unpredictability of the sport. The large volume of factors involved in a match demands the use of advanced technologies to achieve the intended objectives. In the last decade, with the evolution in the techniques of acquisition, storage, and processing of large volumes of data, several online data sources appeared. These sources contain information on played matches and information on betting market movements. In such a scenario, data mining and machine learning techniques can be a viable alternative for discovering patterns in historical data and, consequently, for the prediction of results. This study explores the application of data mining techniques to identify patterns in a large set of football matches and consequently predict "Double Chance"(Betting on two of the three possible outcomes) results and analyze them in relation to sports betting. This type of prediction has aroused growing interest in recent years, due to the growth of the sports betting market. A set of experiments was carried out to evaluate different techniques in terms of accuracy and profitability in the betting market. The results obtained are important in several aspects, ranging from evaluating the factors that influence the outcome of a match to the analysis of the efficiency of machine learning techniques in the sports betting market.