Yi Jae Hyun, Lee Soo Won

KIPS Transactions on Software and Data Engineering, Vol. 9, No. 5, pp. 161-168, May. 2020
https://doi.org/10.3745/KTSDE.2020.9.5.161,   PDF Download:
Keywords: Machine Learning, Artificial intelligence, Sports Game Prediction, Ensemble Technique, Data Analysis
Abstract

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

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