Authors :
Rayini Arun Kumar Reddy; Sahith kumar Reddy; T Sushmitha
Volume/Issue :
Volume 10 - 2025, Issue 1 - January
Google Scholar :
https://tinyurl.com/mr37fptp
Scribd :
https://tinyurl.com/4rs4y7fb
DOI :
https://doi.org/10.5281/zenodo.14603646
Abstract :
In our present world, sports produce a very
large amount of statistical data. What makes cricket
different from other sports is the number of variables
involved in it right from the pitch to conditions playing
under, a breeze to the length of the boundary line and
likewise many other which makes every game has its
prominence maybe that’s the reason haven’t got bored
of the game though it is more than 100 years old. From
the day we started using analytics in cricket to today the
game evolved massively, it changed the way the players,
coaches look at the game and it brought a new dimension
to the game. IPL has been a carnival of cricket and
showing the potential of cricket to the world and acted as
a bridge in carrying the game to a wider range of
audiences. In present-day IPL we are using every
statistic that’s available because of the high
competitiveness of the tournament. This project is a
sincere effort to find hidden insights in the IPL by using
the data of previous seasons.
References :
- Haghighat, M., Rastegari, H. and Nourafza, N., 2013. A review of data mining techniques for result prediction in sports. Advances in Computer Science: an International Journal, 2(5), pp.7-12.
- Vistro, D.M., Rasheed, F. and David, L.G., The Cricket Winner Prediction With Application Of Machine Learning And Data Analytics.
- Dey, P.K., Chakraborty, G., Ruj, P. and Sarkar, S., 2012. A Data Mining Approach on Cluster Analysis of IPL. International Journal of Machine Learning and Computing, 2(4), p.351.
- Rastogi, S.K. and Deodhar, S.Y., 2009. Player pricing and valuation of cricketing attributes: exploring the IPL Twenty20 vision. Vikalpa, 34(2), pp.15-24.
- Dey, P.K., Banerjee, A., Ghosh, D.N. and Mondal, A.C., 2014. AHP-neural network based player price estimation in IPL. International Journal of Hybrid Information Technology, 7(3), pp.15-24.
- Dey, P.K., Ghosh, D.N. and Mondal, A.C., 2011. A MCDM approach for evaluating bowlers performance in the IPL. Journal of emerging trends in Computing and Information Sciences, 2(11), pp.563-573.
- D. Thenmozhi, P. Mirunalini, S. M. Jaisakthi, S. Vasudevan, V. Veeramani Kannan and S. Sagubar Sadiq, "MoneyBall - Data Mining on Cricket Dataset," 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2019, pp. 1-5.
- K. Jain, M. N. Murty and P. J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, Vol. 31, No. 3, September 1999, pp. 264-323.
- Kimber, Alan C., and Alan R. Hansford. “A Statistical Analysis of Batting in Cricket.” Journal of the Royal Statistical Society. Series A (Statistics in Society), vol. 156, no. 3, 1993, pp. 443–455. JSTOR, www.jstor.org/stable/2983068. Accessed 28 Feb. 2020.
- Manage, Ananda & Butar Butar, Ferry. (2007). Statistical analysis in one-day cricket. Proc. Amer. Statist. Assoc.. 2600-2605.
In our present world, sports produce a very
large amount of statistical data. What makes cricket
different from other sports is the number of variables
involved in it right from the pitch to conditions playing
under, a breeze to the length of the boundary line and
likewise many other which makes every game has its
prominence maybe that’s the reason haven’t got bored
of the game though it is more than 100 years old. From
the day we started using analytics in cricket to today the
game evolved massively, it changed the way the players,
coaches look at the game and it brought a new dimension
to the game. IPL has been a carnival of cricket and
showing the potential of cricket to the world and acted as
a bridge in carrying the game to a wider range of
audiences. In present-day IPL we are using every
statistic that’s available because of the high
competitiveness of the tournament. This project is a
sincere effort to find hidden insights in the IPL by using
the data of previous seasons.