IPL Data Analysis and Prediction Using Machine Learning

Authors

  • Dr. Vilas Joshi  Assistant Professor, Department of Computer Engineering, ISBM College of Engineering, Pune, Maharashtra, India
  • Yash Padol  Student, Department of Computer Engineering, ISBM College of Engineering, Pune, Maharashtra, India
  • Kaiwalya Sevekar  Student, Department of Computer Engineering, ISBM College of Engineering, Pune, Maharashtra, India
  • Chandan Sinha  Student, Department of Computer Engineering, ISBM College of Engineering, Pune, Maharashtra, India
  • Rizwan Shaikh  Student, Department of Computer Engineering, ISBM College of Engineering, Pune, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT2390359

Keywords:

Abstract

The Indian Premier League (IPL) is a highly attended T20 cricket league founded by the Board of Control for Cricket in India (BCCI) in 2008. Valued at $6.7 billion in 2019, the IPL remains the most popular cricketing event in the world, even amidst the COVID-19 pandemic. Cricket is a beloved sport in India and enjoys significant media attention and financial investment. With the rise of technology, audiences can access affordable live streaming of matches from anywhere. Cricket analytics, powered by tools like NumPy, allow for the use of data to improve performance, study business opportunities, and provide insights and predictive intelligence on game outcomes.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-06-30

Issue

Volume 9, Issue 3, May-June-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. Vilas Joshi, Yash Padol, Kaiwalya Sevekar, Chandan Sinha, Rizwan Shaikh, " IPL Data Analysis and Prediction Using Machine Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.205-208, May-June-2023. Available at doi : https://doi.org/10.32628/CSEIT2390359