Abstract

In this paper, a new MAYO Index is presented for deeper analytics of the price and performance of IPL players in IPL season IX. The MAYO index is comprehensive in terms of including both price and performance in one index. This is in contrast to the popular indices like batting and bowling averages and MVPI that only measure performance. The index is created with the help of machine learning technique called Random Forests. The analytics provide deeper insight into the complex problem of understanding how the performance of the players of different franchises and countries was and provides clues for better management practices in terms of player acquisition. The players to watch for in future are clearly identified and so are those who did not perform according to expectations.

References

• Indian Premier League, https://en.wikipedia.org/wiki/Indian_Premier_League
• Clarke, S R, “Dynamic programming in one day cricket - optimal scoring rates,” Journal of the Operational Research Society, 50, 1988, pp 536 – 545.
• Kimber, A C and Hansford, A R, “A Statistical Analysis of Batting in Cricket,” Journal of Royal Statistical Society, 156, 1993, pp 443 – 455.
• 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.
• 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.
• Lewis, A., “Extending the Range of Player-Performance Measures in One-Day Cricket,” Journal of Operational Research Society, 59, 2008, pp 729-742.
• 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.
• 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.
• C.Deep Prakash , “A New Team Selection Methodology using Machine Learning and Memetic Genetic algorithm for IPL-9”, Int. Jl. of Electronics, Electrical and Computational System IJEECS ISSN 2348-117X Volume 5, Issue 4 April 2016.
• C. Deep Prakash, C. Patvardhan and C. Vasantha Lakshmi, “Team Selection Strategy in IPL-9 using Random Forests Algorithm”, International Journal of Computer Applications (0975 – 8887) Volume 139 – No.12, April 2016.
• 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
• 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
• Leo Breiman. Random forests. Machine Learning, 45(1): 5–32, 2001