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IPL Player Performance Analysis and Auction Decision Support using Data Analytics and Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Sports Analytics & Auction Decision Systems
DOI: https://doi.org/10.64388/IREV9I11-1717854
Abstract
The player auction process in the Indian Premier League is one of the significant factors that impact team building and performance. Auctioning decisions, however, are taken on the basis of limited statistics or judgment. This project is focused on applying concepts related to data analytics in evaluating player performance in IPL, including player vs player statistics and using historical IPL data. Simple machine learning models will be applied to help in making auctioning decisions in IPL in an objective manner.
Keywords
Indian Premier League, Player Performance Analysis, Data Analytics, Auction Decision Support System, Sports Analytics, Venue Classification.
References
[1] N. V. Chawla, “Data mining for sports analytics: A review,” IEEE Data Engineering Bulletin, vol. 37, no. 3, pp. 45–52, 2014.
[2] A. Bunker and S. Thabtah, “A machine learning framework for sport result prediction,” Applied Computing and Informatics, vol. 15, no. 1, pp. 27–33, 2019.
[3] R. S. Oliveira et al., “Sports analytics in cricket: Predicting player performance,” Procedia Computer Science, vol. 112, pp. 1–10, 2017.
[4] S. Sankaranarayanan, J. Sattar, and L. V. S. Lakshmanan, “Auto-play: A data mining approach to cricket strategy,” in Proc. IEEE ICDM, 2014, pp. 1065–1070.
[5] P. Kampakis and T. Thomas, “Using machine learning to predict cricket match outcomes,” arXiv preprint arXiv:1506.02732, 2015.
[6] M. Bailey and S. Clarke, “Predicting the match outcome in cricket,” Journal of Sports Science & Medicine, vol. 5, pp. 480–487, 2006.
[7] A. Pathak and S. Wadhwa, “Application of machine learning in IPL analytics,” International Journal of Computer Applications, vol. 178, no. 39, pp. 20–25, 2019.
[8] J. Lewis, “Moneyball: The art of winning an unfair game,” W. W. Norton & Company, 2003.
[9] F. Provost and T. Fawcett, “Data science for business,” O’Reilly Media, 2013.
[10] T. Hastie, R. Tibshirani, and J. Friedman, “The elements of statistical learning,” Springer, 2009.
[11] C. M. Bishop, “Pattern recognition and machine learning,” Springer, 2006.
[12] S. Raschka and V. Mirjalili, “Python machine learning,” Packt Publishing, 2017.
[13] A. Géron, “Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow,” O’Reilly Media, 2019.
[14] Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[15] W. McKinney, “Data structures for statistical computing in Python,” in Proc. Python in Science Conf., 2010, pp. 51–56.
[16] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. ACM SIGKDD, 2016.
[17] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
[18] J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, pp. 81–106, 1986.
[19] D. W. Hosmer and S. Lemeshow, “Applied logistic regression,” Wiley, 2000.
[20] T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
[21] Cricsheet, “Cricsheet IPL dataset,” [Online]. Available: https://cricsheet.org\
[22] J. Han, M. Kamber, and J. Pei, “Data mining: Concepts and techniques,” Morgan Kaufmann, 2011.
How to cite this paper
@article{1717854,
author = {Sanjay Prabhakaran S, Dr. G Sumathi, B. Sivapandi, Subash T.},
title = {IPL Player Performance Analysis and Auction Decision Support using Data Analytics and Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {2054-2065},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717854.pdf},
abstract = {The player auction process in the Indian Premier League is one of the significant factors that impact team building and performance. Auctioning decisions, however, are taken on the basis of limited statistics or judgment. This project is focused on applying concepts related to data analytics in evaluating player performance in IPL, including player vs player statistics and using historical IPL data. Simple machine learning models will be applied to help in making auctioning decisions in IPL in an objective manner.},
keywords = {Indian Premier League, Player Performance Analysis, Data Analytics, Auction Decision Support System, Sports Analytics, Venue Classification. },
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717854}
}