Abstract
Football is one of the dominant sports in the world and has become a trillion-dollar industry. The top professional football league in China, the Chinese Super League (CSL) with tons of room to grow, is responsible for improving the competitive level of Chinese football and promoting the development of the football industry under the background of big data era. This study tries to predict the result of the game. Among the complete dateset, 1920 team matches of CSL from 2014 to 2017, this study selects 64 variables related to the prediction. The data is fitted with logistic lasso model to analysis and select the variables in the prediction. Then the data set is randomly divided into training set and test set according to the research. With those machine learning classification models trained in the training set and some of them adjusted, this paper finds support-vector machine (SVM) model performing best in the test set, and long-term short-term memory (LSTM) model is applied to predict the outcome of games depending on the data of several previous matches. Then this paper uses the cross-validation method to check and give the validation results. In SVM, the accuracy of prediction reaches 84.54% in the test set, which turns out to be effective. Also, LSTM model gets 63.2% results of the test set and has realistic value for teams. The model in this study is of high credibility for the data from CSL provided by Champion®.
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Yu, G., Yang, J., Chen, X., Qian, Z., Sun, B., **, Q. (2022). Prediction of Game Result in Chinese Football Super League. In: Fan, W., Zhang, L., Li, N., Song, X. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2022. Communications in Computer and Information Science, vol 1713. Springer, Singapore. https://doi.org/10.1007/978-981-19-9195-0_49
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DOI: https://doi.org/10.1007/978-981-19-9195-0_49
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