A comparison between different classifiers for tennis match result prediction

Abstract

Tennis is a very popular sport in the world. Many researchers have worked in the fields of forecasting the outcome of tennis matches using past statistical data records. This paper mainly investigates the comparison between three different classifiers namely decision tree, learning vector quantization and support vector machine. The research study aims to predict the result of tennis singles matches using eight UCI databases of grand slam tennis tournaments and evaluate the classification performance using various measures such as the root-mean-square error, accuracy, false positive rate, true positive rate, kappa statistic, recall, precision, and f-measure. All these performance measures confirm the supremacy of the decision tree classification algorithm compared to the others.