Browsing by Author "MD.ROBEL"

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    CRICKET PLAYERS SELECTION FOR NATIONAL TEAM AND FRANCHISE LEAGUE USING MACHINE LEARNIG ALGORITHMS
    (2023-03-20) MD.ROBEL
    Cricket player selection is a crucial task for both national teams and franchise leagues. Traditionally, selectors rely on their experience and knowledge to evaluate a player's physical fitness, batting, and bowling performance. However, with the advancements in machine learning algorithms, it is possible to automate and improve the selection process. In this study, we propose a machine learning-based approach for cricket player selection. The proposed approach uses a combination of physical fitness data, batting and bowling statistics, and other relevant metrics to create a comprehensive player profile. We then use machine learning algorithms, such as decision trees, random forests, and linear regression, to identify the most promising players. To evaluate the proposed approach, we collect data on a large number of cricket players and their performance in national and franchise leagues. We then train and test several machine learning models on this data, comparing their accuracy and performance. Our results demonstrate that the proposed approach can significantly improve the selection process and identify players with high potential. Overall, this study highlights the potential of machine learning algorithms for cricket player selection. By leveraging the power of data and automation, selectors can make more informed decisions and improve the performance of national teams and franchise leagues. The selection of cricket players for national teams and franchise leagues involves considering various factors such as physical fitness, batting and bowling performance. In this study, we propose a machine learning-based approach to assist in the selection process. We collected data on physical fitness measures, batting and bowling performance of players from past matches and tournaments. We then applied various machine learning algorithms such as logistic regression, decision trees, and random forests to analyze the data and predict player selection. Our results show that physical fitness measures such as speed, agility, and endurance play a crucial role in player selection. Additionally, players with high batting and bowling performance are also more likely to be selected. We also found that the random forest algorithm outperformed other machine learning models in predicting player selection with an accuracy of 87%. These findings suggest that machine learning algorithms can effectively assist in the selection of cricket players for national teams and franchise leagues, based on physical fitness, batting and bowling performance