The Impact of Selecting a Validation Method in Machine Learning on Predicting Basketball Game Outcomes

Abstract

Interest in sports predictions as well as the public availability of large amounts of structured and unstructured data are increasing every day. As sporting events are not completely independent events, but characterized by the influence of the human factor, the adequate selection of the analysis process is very important. In this paper, seven different classification machine learning algorithms are used and validated with two validation methods: Train&Test and cross-validation. Validation methods were analyzed and critically reviewed. The obtained results are analyzed and compared. Analyzing the results of the used machine learning algorithms, the best average prediction results were obtained by using the nearest neighbors algorithm and the worst prediction results were obtained by using decision trees. The cross-validation method obtained better results than the Train&Test validation method. The prediction results of the Train&Test validation method by using disjoint datasets and up-to-date data were also compared. Better results were obtained by using up-to-date data. In addition, directions for future research are also explained.

Keywords

• classification
• cross-validation
• machine learning
• validation methods
• predicting outcomes
• train&amp
• amp
• test