IPL Score Prediction & Analysis

Author(s)	Priyanka Kumbhar, Gaurav Patil, Gaurav Gawarguru, Tejas Nirmal, Nagesh Panchling
Country	India
Abstract	Cricket, particularly the Indian Premier League (IPL), is known for its unpredictability. In this context, this project tackles the challenge of predicting the total score of an inning in IPL matches using machine learning techniques. By leveraging historical match data, team dynamics, player statistics, and environmental variables, a predictive model is constructed. The system not only delivers score predictions but also offers insights into the critical factors influencing these predictions. The goal is to empower cricket enthusiasts, teams, and broadcasters with a tool that enhances their understanding of match dynamics and aids in making informed predictions. This project represents an exciting intersection of sports, data science, and predictive analytics, with the potential to reshape how cricket fans and professionals engage with the IPL.
Keywords	IPL Prediction, CNN, Classification, Deep Learning, LSTM
Field	Computer > Data / Information
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-11-03
Cite This	IPL Score Prediction & Analysis - Priyanka Kumbhar, Gaurav Patil, Gaurav Gawarguru, Tejas Nirmal, Nagesh Panchling - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.8241
DOI	https://doi.org/10.36948/ijfmr.2023.v05i06.8241
Short DOI	https://doi.org/gs38m9

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