Predicting Outcome of Live Cricket Match Using Duckworth- Lewis Par Score
International Journal of Systems Science and Applied Mathematics
Volume 2, Issue 5, September 2017, Pages: 83-86
Received: May 12, 2017; Accepted: Jun. 3, 2017; Published: Oct. 5, 2017
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Parag Shah, Department of Statistics, H L College of Commerce, Ahmedabad, India
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Cricket is the second most watched sport in the world after soccer, and enjoys a multi-million dollar industry. There is remarkable interest in simulating cricket and more importantly in predicting the outcome of cricket match which is played in three formats namely test match, one day international and T20 match. The complex rules prevailing in the game, along with the various natural parameters affecting the outcome of a cricket match present significant challenges for accurate prediction. Several diverse parameters, including but not limited to cricketing skills and performances, match venues and even weather conditions can significantly affect the outcome of a game. There are number of research paper on pre-match prediction of cricket match. Many papers on building a prediction model that takes in historical match data as well as the instantaneous state of a match, and predict match results. We know in the cricket match with shorter version match result keep on changing every ball. So, it is important to predict the outcome of the match on every ball. In this paper, I have developed a model that predicts match result on every ball played. Using Duckworth- Lewis formula match outcome will be predicted for live match. For every ball bowled a probability is calculated and probability figure is plotted. For betting industry this model and the probability figure will be very useful for bettor in deciding which team to on and how much to bet.
Simulating, Duckworth-Lewis, Prediction Model, Probability Figure, Betting
To cite this article
Parag Shah, Predicting Outcome of Live Cricket Match Using Duckworth- Lewis Par Score, International Journal of Systems Science and Applied Mathematics. Vol. 2, No. 5, 2017, pp. 83-86. doi: 10.11648/j.ijssam.20170205.11
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Akhtar S and Scarf PA (2012): “Forecasting test cricket match outcomes in play”. International Journal of Forecasting, 28(3), 632–643.
Bailey & Clarke (2006): “Predicting the match outcome in one day international cricket matches, while the match is in progress”. Journal of Science and Sports Medicine, 5, 480–487.
De Silva, B. M., and Swartz, T. B. (2001): “Estimation of the magnitude of the victory in one-day cricket”. Australian & New Zealand Journal of Statistics, 43, 1369-1373.
Duckworth, F. and Lewis, T. (1998): “A fair method for resetting the target in interrupted one-day cricket matches”. Journal of Operation Research Society, 49, 22-28.
Ganeshapillai G, Guttag J (2013): “A data-driven method for in game decision making in MLB: When to pull a starting pitcher”. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 973–979.
Kaluarachchi, Amal, and S. Varde Aparna (2010): “CricAI: A classification based tool to predict the outcome in ODI cricket”. 2010 Fifth International Conference on Information and Automation for Sustainability. IEEE.
Kimber, A. C. and Hansford A. R. (1993): “A statistics analysis of batting in cricket”. Journal of the Royal Statistical Society Series A, 156, 443-455.
Madan Gopal Jhawar, Vikram Pudi (2016): “Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach”. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2016).
Sankaranarayanan VV, Sattar J. and Lakshmanan LVS (2014): “Autoplay - A data mining approach to ODI cricket simulation and prediction”. In: Proceedings of the 2014 SIAM International Conference on Data Mining, 1064–1072.
Theja Tulabandhula and Cynthia Rudin (2014): “Tire Changes, Fresh Air, And Yellow Flags: Challenges in Predictive Analytics For Professional Racing”. Massachusetts Institute of Technology, Cambridge, Massachusetts.
Wood, G. H. (1945): “Cricket scores and geometrical progression”. Journal of the Royal Statistical Society Series A, 108, 12-22.
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