The use of data mining for basketball matches outcomes prediction

Abstract

Sport result prediction is nowadays very popular among fans around the world, which particularly contributed to the expansion of sports betting. This makes the problem of predicting the results of sporting events, a new and interesting challenge. Consequently systems dealing with this problem are developed every day. This paper presents one such system, which uses data mining techniques in order to predict the outcomes of basketball games in NBA (National Basketball Association) league. The problem of predicting the game result is formalized as a classification problem, where the Naive Bayes method is used. Besides actual result, for each game system calculates the spread, by using multivariate linear regression. The MVC Model 2 pattern based software system is implemented. The system was evaluated on the dataset comprising 778 games from the regular part of the 2009/2010 NBA season and it correctly predicted the winners of about 67% of matches.

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