Research Article
Data Analytics based Deep Mayo Predictor for IPL-9
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 152 - Issue 6 |
| Published: Oct 2016 |
| Authors: C. Deep Prakash, C. Patvardhan, C. Vasantha Lakshmi |
10.5120/ijca2016911875
|
C. Deep Prakash, C. Patvardhan, C. Vasantha Lakshmi . Data Analytics based Deep Mayo Predictor for IPL-9. International Journal of Computer Applications. 152, 6 (Oct 2016), 6-11. DOI=10.5120/ijca2016911875
@article{ 10.5120/ijca2016911875,
author = { C. Deep Prakash,C. Patvardhan,C. Vasantha Lakshmi },
title = { Data Analytics based Deep Mayo Predictor for IPL-9 },
journal = { International Journal of Computer Applications },
year = { 2016 },
volume = { 152 },
number = { 6 },
pages = { 6-11 },
doi = { 10.5120/ijca2016911875 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A C. Deep Prakash
%A C. Patvardhan
%A C. Vasantha Lakshmi
%T Data Analytics based Deep Mayo Predictor for IPL-9%T
%J International Journal of Computer Applications
%V 152
%N 6
%P 6-11
%R 10.5120/ijca2016911875
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents a Deep Mayo Predictor model for predicting the outcomes of the matches in IPL 9 being played in April – May, 2016. The model has three components which are based on multifarious considerations emerging out of a deeper analysis of T20 cricket. The models are created using Data Analytics methods from machine learning domain. The prediction accuracy obtained is high as the Mayo Predictor Model is able to correctly predict the outcomes of 39 matches out of the 56 matches played in the league stage of the IPL IX tournament. Further improvement in the model can be attempted by using a larger training data set than the one that has been utilized in this work. No such effort at creating predictor models for cricket matches has been reported in the literature.
References
- Indian Premier League, https://en.wikipedia.org/wiki/Indian_Premier_League
- Parker, David, Phil Burns, and Harish Natarajan. "Player valuations in the indian premier league." Frontier Economics 116 (2008).
- Singh, Sanjeet. "Measuring the performance of teams in the Indian Premier League." American Journal of Operations Research 1.03 (2011): 180.
- Saikia, Hemanta, and Dibyojyoti Bhattacharjee. "On classification of all-rounders of the Indian premier league (IPL): a Bayesian approach." Vikalpa36.4 (2011): 25-40.
- Lenten, Liam JA, Wayne Geerling, and László Kónya. "A hedonic model of player wage determination from the Indian Premier League auction: Further evidence." Sport Management Review 15.1 (2012): 60-71.
- Rastogi, Siddhartha K., and Satish Y. Deodhar. "Player pricing and valuation of cricketing attributes: exploring the IPL Twenty20 vision." Vikalpa 34.2 (2009): 15-23.
- Petersen, C., et al. "Analysis of Twenty/20 cricket performance during the 2008 Indian Premier League." International Journal of Performance Analysis in Sport 8.3 (2008): 63-69.
- http://www.espncricinfo.com/india/content/player/28081.html, T20 statistics of each player
- http://www.iplt20.com/teams/royal-challengers-bangalore/squad/236/chris-gayle , IPL statistics of each player
- 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
- 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
- C. Deep Prakash, C.Patvardhan and Sushobhit Singh,” A new Category based Deep Performance Index using Machine Learning for ranking IPL Cricketers”, Int. Jl. of Electronics, Electrical and Computational System IJEECS ISSN 2348-117X Volume 5, Issue 2 February 2016
- Leo Breiman. Random forests. Machine Learning, 45(1): 5–32, 2001
- https://en.wikipedia.org/wiki/Support_vector_machine
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