Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kulkarni, Parag
- Context Based Cricket Player Evaluation Using Statistical Analysis
Abstract Views :261 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Engineering and Information Technology, College of Engineering, Pune, Maharashtra, IN
1 Department of Computer Engineering and Information Technology, College of Engineering, Pune, Maharashtra, IN
Source
International Journal of Knowledge Based Computer System, Vol 7, No 1 (2019), Pagination: 1-9Abstract
In business, making decisions on the spot is a critical task since data is available online. Same applies for sports. For managers and coaches selection of best fit player in the team has to be made. Many times, team managers have to bid for the players. Many statistical methods are applied in order to evaluate a player for sports, specially, baseball, and football. This paper focuses on Cricket since it has been deprived of good statistical methods. Typically, for cricket, Deep Performance Index (DPI) method is used to evaluate a player. But it has some limitations. This work aims to assimilate the best features of the previous approaches and losing the drawbacks. An algorithm is devised for statistical analysis of cricket data and particularly Indian Premier League (IPL) data which can be later extended to other sports with minor customizations. The algorithm considers context of the player in terms of indices like most valuable player index for batsman and for bowler. The important context introduced is winning contribution ratio of the player. The algorithm is tested on IPL and proves to be comparable with DPI. Hypothesis testing is performed to validate the results.Keywords
Context, Cricket Data Mining, Hypothesis Testing, Indian Premier League (IPL), Statistical Analysis.
References
- “PECOTA,” En.wikipedia.org, [Online]. Available: https://en.wikipedia.org/wiki/PECOTA
- N. Hirotsu, and M. Wright, “A markov chain approach to optimal pinch hitting strategies in a designated hitter rule baseball game,” Journal of the Operations Research, vol. 46, no. 3, pp. 353-371, 2003.
- J. Davis, H. Perera, and T. B. Swartz, “A simulator for twenty-20 cricket,” Australian and New Zealand Journal of Statistics, vol. 57, no. 1, pp. 55-71, 2015.
- Method and apparatus for detecting selected absence of digital logic synchronism, by J. L. Williams. (1991, May 28). Patent US5020024A [Online]. Available: https://www.google.si/patents/US5020024
- C. D. Prakash, C. Patvardhan, and S. Singh, “A new machine learning based deep performance index for ranking IPL t-20 cricketers,” International Journal of Computer Applications, vol. 137, no. 10, pp. 42-49, March 2016.
- “Indian premier league twenty-twenty cricket matches.” Iplt20.com. [Online]. Available: https://www.iplt20.com
- “Cricket rankings,” Rediff.com, [Online]. Available: https://www.rediff.com/cricket/report/icc-world-cup-de-villiers-maintains-big-lead20150320.htm
- P. K. Dey, D. N. Ghosh, and A. C. Mondal, “An MCDM approach for evaluating bowlers performance in IPL,” Journal of Emerging Trends in Computing and Information Sciences, vol. 2, no. 11, pp. 563-573, November 2011.
- A. B. W. Manage, and S. M. Scariano, “An introductory application of principal components to cricket data,” Journal of Statistics Education, vol. 21, no. 3, 2013.
- ESPNcricinfo, [Online]. Available: https://www.espncricinfo.com
- V. Khatavkar, and P. Kulkarni, “Document context identification using latent semantic analysis,” 3rd Int. Conf. on Data Management, Analytics and Innovation, Springer Conference, January 2017.
- V. Khatavkar, and P. Kulkarni, “Context vector machine for information retrieval,” Advances in Intelligent Systems Research, vol. 137, pp. 375-379, Atlantis Press, 2017.
- D. Nagarale, V. Khatavkar, and P. Kulkarni, “Document theme extraction using NER,” ICCASP, Springer, January 2018.
- V. Khatavkar, and P. Kulkarni, “Usage of context vectors in document analysis,” Proc. of 3rd Int. Conf. on Computing, Communication, Control and Automation, Pune, Maharashtra, India, 17-18 August 2017.
- V. Khatavkar, and P. Kulkarni, “Trends in document analysis,” in Int. Conf. on Data Management, Analytics & Innovation, Springer, January 2018.
- H. Ahmad, A. Daud, L. Wang, H. Hong, H. Dawoods, and Y. Yang, “Prediction of rising stars in the game of cricket,” IEEE Access, vol. 5, pp. 4104-4124, 2017.