Analysis and Prediction of Soccer Games: An Application to the Kaggle European Soccer Database
Abstract
The study of soccer game data has many applications for both fans and teams. The effective analytical work can not only help the teams to improve their offensive and defensive skills and strategies, but also could assist the fans to make a bet. In this work, the authors study the European League Dataset with statistical methods to analyze the game data. Moreover, machine learning techniques are designed to predict the game results based on in-game performance and pre-game odds provided by bookmakers. With rational feature engineering and model selection, our model results in an overall 95% accuracy.
References
Ogunseye AA. Artificial neural network approach to football score prediction. Journal of Artificial Intelligence 2019; 1.
Alfredo YF, Sani MI. Football match prediction with tree based model classification. International Journal of Intelligent Systems and Applications 2019; 11(7): 20–28.
Copyright (c) 2020 Wuhuan Deng, Eric Zhong
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