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Team Selection Strategy in IPL 9 using Random Forests Algorithm

by C. Deep Prakash, C. Patvardhan, C. Vasantha Lakshmi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 12
Year of Publication: 2016
Authors: C. Deep Prakash, C. Patvardhan, C. Vasantha Lakshmi
10.5120/ijca2016909516

C. Deep Prakash, C. Patvardhan, C. Vasantha Lakshmi . Team Selection Strategy in IPL 9 using Random Forests Algorithm. International Journal of Computer Applications. 139, 12 ( April 2016), 42-48. DOI=10.5120/ijca2016909516

@article{ 10.5120/ijca2016909516,
author = { C. Deep Prakash, C. Patvardhan, C. Vasantha Lakshmi },
title = { Team Selection Strategy in IPL 9 using Random Forests Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 12 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 42-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume139/number12/24546-2016909516/ },
doi = { 10.5120/ijca2016909516 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:40:48.371098+05:30
%A C. Deep Prakash
%A C. Patvardhan
%A C. Vasantha Lakshmi
%T Team Selection Strategy in IPL 9 using Random Forests Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 12
%P 42-48
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

IPL 9 is scheduled to be held in April 2016. T20 cricket is relatively new and the strategies and techniques are evolving. This is evident in the better performances by both bowlers and batsmen in successive IPL seasons. This paper presents a detailed analysis of the data of IPL upto season 8 and overall T20 career data of players upto January 2016 to design performance indices for batsmen and bowlers in IPL 9. Categorization of players is done based on their roles in the team and the indices are determined separately for each category using Random Forests Algorithm. A heuristic is designed to enable selection of the best playing 11 out of the available team using the performance indices designed in this work. The algorithm is effective in enabling the best 11 to be selected within the constraints of the rules in the IPL tournament.

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. C. Deep Prakash, C.Patvardhan and Sushobhit Singh, “A new Machine Learning based Deep Performance Index for Ranking IPL T20 Cricketers”, International Journal of Computer Applications (0975 – 8887) Volume 137 – No.10, March 2016
  17. C. Deep Prakash, C.Patvardhan and Sushobhit Singh,” A new Category based Deep Performance Index using Machine Learning for ranking IPL Cricketers”, International Journal of Electronics, Electrical and Computational System IJEECS ISSN 2348-117X Volume 5, Issue 2 February 2016
  18. Leo Breiman. Random forests. Machine Learning, 45(1): 5–32, 2001
Index Terms

Computer Science
Information Sciences

Keywords

IPL 9 Team selection Random Forests Heuristic

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