CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A new Machine Learning based Deep Performance Index for Ranking IPL T20 Cricketers

by C. Deep Prakash, C. Patvardhan, Sushobhit Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 10
Year of Publication: 2016
Authors: C. Deep Prakash, C. Patvardhan, Sushobhit Singh
10.5120/ijca2016908903

C. Deep Prakash, C. Patvardhan, Sushobhit Singh . A new Machine Learning based Deep Performance Index for Ranking IPL T20 Cricketers. International Journal of Computer Applications. 137, 10 ( March 2016), 42-49. DOI=10.5120/ijca2016908903

@article{ 10.5120/ijca2016908903,
author = { C. Deep Prakash, C. Patvardhan, Sushobhit Singh },
title = { A new Machine Learning based Deep Performance Index for Ranking IPL T20 Cricketers },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 10 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 42-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number10/24313-2016908903/ },
doi = { 10.5120/ijca2016908903 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:38:02.361091+05:30
%A C. Deep Prakash
%A C. Patvardhan
%A Sushobhit Singh
%T A new Machine Learning based Deep Performance Index for Ranking IPL T20 Cricketers
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 10
%P 42-49
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

T20 cricket has brought about a revolution in cricket. The Indian Premier League (IPL) tournament organized every year by the Board of Cricket Control of India has become very popular with a huge fan following. It is based on franchises bidding for acquiring players to play for their side. Huge amounts of money are involved in the auction. Ranking of players in IPL according to their performance is an important step that would allow franchises and team managers to take better informed decisions in choosing their sides. In this paper, a machine learning based approach is used to create a new index, named as Deep Performance Index (DPI), that reflects the performance of the batsmen and bowlers on a deeper analysis of the requirements of T20 cricket. The Recursive Feature elimination algorithm based on machine learning is used for extracting meaningful features and their relative importance towards designing the DPI. It is shown that DPI is able to better capture performance related data for both batsmen and bowlers when compared to some other well-known ranking schemes for T20 cricket.

References
  1. Indian Premier League, https://en.wikipedia.org/wiki/Indian_Premier_League
  2. Clarke, S R, “Dynamic programming in one day cricket - optimal scoring rates,” Journal of the Operational Research Society, 50, 1988, pp 536 – 545.
  3. Kimber, A C and Hansford, A R, “A Statistical Analysis of Batting in Cricket,” Journal of Royal Statistical Society, 156, 1993, pp 443 – 455.
  4. Damodaran, U, “Stochastic Dominance and Analysis of ODI Batting Performance: The Indian Cricket Team, 1989-2005,” Journal of Sports Science and Medicine, 5, 2006, pp 503 – 508.
  5. Barr, G. D. I., and Kantor, B.S., “A Criterion for Comparing and Selecting Batsmen in Limited Overs Cricket,” Journal of the Operational Research Society, 55, 2004, pp 1266-1274.
  6. Borooah, V. K., and Mangan, J E, “The ‘Bradman Class’: An Exploration of Some Issues in the Evaluation of Batsmen for Test Matches, 1877–2006.”, Journal of Quantitative Analysis in Sports, 6 (3), Article 14, 2010.
  7. Norman, J and Clarke, S R, “Dynamic programming in cricket: Batting on sticky wicket,” Proceedings of the 7th Australasian Conference on Mathematics and Computers in Sport, 2004, pp 226 – 232.
  8. Ovens, M and Bukeit, B, “A mathematical modeling approach to one day cricket batting orders,” Journal of Sports Science and Medicine, 5, 2006, pp 495-502.
  9. Lewis, A., “Extending the Range of Player-Performance Measures in One-Day Cricket,” Journal of Operational Research Society, 59, 2008, pp 729-742.
  10. Van Staden, P., “Comparison of Cricketers' Bowling and Batting Performance using Graphical Displays,” Current Science, 96, 2009, pp 764-766.
  11. Lakkaraju, P., and Sethi, S., “Correlating the Analysis of Opinionated Texts Using SAS® Text Analytics with Application of Sabermetrics to Cricket Statistics,” Proceedings of SAS Global Forum 2012, 136-2012, pp 1-10.
  12. Lemmer, H., “A Measure for the Batting performance of Cricket Players,” South African Journal for Research in Sport, Physical Education and Recreation, 26, 2004, pp 55-64.
  13. Lemmer, H., “An Analysis of Players' Performances in the First Cricket Twenty20 World Cup Series,” South African Journal for Research in Sport, Physical Education and Recreation 30, 2008, pp 71-77.
  14. Lemmer, H., “The Single Match Approach to Strike Rate Adjustments in Batting Performance Measures in Cricket,” Journal of Sports Science and Medicine, 10, 2012, pp 630-634.
  15. Saikia, Hemanta and Bhattacharjee Dibojyoti, “A Bayesian Classification Model for Predicting the Performance of All-Rounders in the Indian Premier League, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=16220 60.
  16. http://www.espncricinfo.com/india/content/player/28081.html, T20 statistics of each player
  17. http://www.iplt20.com/teams/royal-challengers-bangalore/squad/236/chris-gayle , IPL statistics of each player
  18. http://www.rediff.com/cricket/report/icc-world-cup-de-villiers-maintains-big-lead-shami-rises-to-7th-in-most-valuable-player-table/20150320.htm
  19. Amit Kumar and Ritu Sindhu, “Reflection against perception: Data Analysis of IPL Batsmen”, International Journal of Engineering Science Invention, Vol. 3, Issue 6, June 2014, pp 7 – 11. ISSN No. 2319-6734.
  20. Ahmad F, Kalyanmoy Deb and Abhilash Jindal, “Multi-objective Optimization and decision making approaches to cricket team selection”, Applied Soft Computing, Vol 13, 2013, pp 402 – 414.
  21. Dey PK and Ghosh DN, “An MCDM approach for evaluating Bowlers performance in IPL”, Journal of emerging trends in Computing and Information Sciences, Vol 2, No. 11,November, 2011, ISSN 2079-8407, pp 663-573
  22. Manage ABW and Scariano SM, “An Introductory Application of Principal Components to Cricket Data”, Journal of Statistics Education, Volume 21, Number 3, 2013.
  23. thecricketcouch.com\blog\2012\06\15
Index Terms

Computer Science
Information Sciences

Keywords

IPL Cricket T20 Performance Index Player Evaluation

Powered by PhDFocusTM