Using machine learning models for predicting the U-19 world cup (men) match outcomes
Using machine learning models for predicting the U-19 world cup (men) match outcomes
Author(s): Dr. B Navaneethan
Abstract: This paper presents a method aimed at predicting the outcome of the U-19 WORLD CUP matches by implementing machine learning algorithms. The proposed model consists of statistical data from the U-19 World Cup matches which has been collected from trusted sports websites. Machine learning algorithms such as linear regression, decision tree regressor, random forest, XG Boost has been used to predict the match results, and their performance was compared using metrics such as accuracy, precision, recall and F1 score. The performance metrics of the train and test predictions has been compared to evaluate the overfitting and underfitting models. The model that is neither overfitting or underfitting the unseen data and with higher performance metrics shall be chosen to predict the future U-19 WORLD CUP match outcomes. To implement the proposed model, the data is preprocessed into numerical values to implement the algorithms. The experimental setup demonstrates that the model gives up to 73.35% accuracy.
Dr. B Navaneethan. Using machine learning models for predicting the U-19 world cup (men) match outcomes. J Sports Sci Nutr 2024;5(2):167-171. DOI: 10.33545/27077012.2024.v5.i2c.288