engleski

Importance of the training dataset length in basketball game outcome prediction by using naive classification machine learning methods

The focus of the paper is on using naive machine learning algorithms for predicting the NBA game outcomes. In order to complete a convincing result, the data of nine full NBA seasons are scraped for the proposed model training and result evaluation. The aim of the paper is to present the possibilities of naive machine learning methods and to define the length of the training phase as well as of the evaluation phase to be optimal for predicting the NBA games outcome. The research serves as an initial stage in the development of a doctoral dissertation on the outcome prediction in sport. The proposed supervised classification machine learning methods is used and two possible outcomes (win or loss) are predicted. The data segmentation is used as an evaluation method for a training dataset occurring chronologically prior to the testing dataset. The best results are achieved by using a single training season and one to three evaluation seasons and all the played games during the training phase.

basketball, classification, machine learning, NBA, outcome prediction

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