Current Volume 7
Abstract
Cricket is one of the most popular sports worldwide, and Twenty20 (T20) cricket has gained immense popularity due to its fast-paced and exciting nature. Predicting the final score in a T20 match is a challenging task, as it involves multiple variables, including the current score, overs played, wickets have fallen, team strengths, and venue conditions. In this project, we present a T20 Cricket Score Predictor powered by machine learning. The project comprises three main components: data extraction, feature extraction, and a user-friendly application. The data extraction component collects and preprocesses historical cricket match data, while the feature extraction component engineers relevant features to be used in our predictive model. The heart of our project is the user-friendly Streamlined application, which allows cricket enthusiasts and analysts to predict the final score of a T20 match in real time. Users can input various match parameters, such as batting team, bowling team, current score, overs played, wickets have fallen, and runs scored in the last five overs. The application then employs a machine learning model, trained on historical match data, to predict the final score. The predictive model is based on the XGBoost algorithm, which has demonstrated excellent performance in regression tasks. It takes into account factors such as team strength, venue conditions, and recent performance to provide an accurate estimate of the expected score. Our T20 Cricket Score Predictor is a valuable tool for cricket fans, coaches, and analysts seeking insights into match outcomes. It can aid in making informed decisions during live matches and provide a deeper understanding of the dynamics that influence T20 cricket scores.By harnessing the power of machine learning and data analysis, our project contributes to the ever-evolving field of sports analytics, making it more accessible to cricket enthusiasts and professionals alike. Whether used for strategic planning or for enhancing the viewing experience, our T20 Cricket Score Predictor adds an exciting dimension to the world of cricket.
Keywords
T20 Cricket, Score Prediction, Machine Learning, XGBoost Algorithm, Cricket Analytics
Citations
IRE Journals:
Amit Kumar Pandey , Sherilyn Kevin , Bipin Yadav , Gopal Rajbhar
"T20 Cricket Score Prediction Using Machine Learning" Iconic Research And Engineering Journals Volume 7 Issue 6 2023 Page 49-57
IEEE:
Amit Kumar Pandey , Sherilyn Kevin , Bipin Yadav , Gopal Rajbhar
"T20 Cricket Score Prediction Using Machine Learning" Iconic Research And Engineering Journals, 7(6)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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