Predicting BPL Match Winners: An Empirical Study Using Machine Learning Approach

Abstract

With the evolution of computer science, every company is implementing the newest technologies to survive in market with better decision-making capabilities, better communication and customer satisfaction. The only means of fulfilling all these criteria’s is to perform data analysis that is more accurate and pure. In cricket, where no one can guess which team will win until the last ball of the last over, machine learning can help by predicting the results of the games. Match outcome prediction models have a lot of financial incentive because cricket is a multi-billion-dollar industry. The goal of this study is to identify the most accurate machine learning model that can accurately predict the winner given the data from the Bangladesh Premier League. For this analysis five ML models XGBoost, Gradient Boosting, KNN, Decision Tree, Random Forest has been tested for the purpose of model building despite that our proposed model is XGBoost. To get access to BPL dataset web scrapping has been done, the dataset contains 15 columns and 3239 values and 8 team was available in each season from 2018 to 2023. We use cutting-edge machine learning techniques based on the use of numerous models, feature selection, and data separation techniques. Finally, by structuring every line of action, the forecast accuracy is attained.