Abstract
FIFA is the world’s most popular association football regulating body. Football is one of the most popular sports in the world owing credit to primarily FIFA itself. In this regard, understanding the credibility of the player’s rating plays act as a major factor for the performance evaluation criteria. This paper mainly seeks to predict the credibility of the professional football player’s rating analytically by making use of various skills and traits of the football players. The effectiveness of using machine learning models namely Support vector machine, Random Forest, Decision Tree, K nearest neighbour and XGBoost for evaluating performance is used for further analysis. We performed the testing using both external testing and cross validation. The best result is obtained by decision tree and support vector machine for both 10 fold cross validation and external testing.
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Datta, M., Rudra, B. (2022). Prediction of Credibility of Football Player Rating Using Data Analytics. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_72
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