Heuristics for Winner Prediction in International Cricket Matches

  • Vetukuri Sivaramaraju Biju Patnaik University of Technology, Odisha, India
  • Nilambar Sethi GIET, Gunupur Odisha-765022, INDIA
  • Renugunta Rajender NSRIT, Sontyam Visakhapatnam-531173, A.P, INDIA
Keywords: Cricket, T-20, ODI cricket, Data mining, Winner prediction


Cricket is popularly known as the game of gentlemen. The game of cricket has been introduced to the World by England. Since the introduction till date, it has become the second most ever popular game. In this context, few a data mining and analytical techniques have been proposed for the same. In this work, two different scenario have been considered for the prediction of winning team based on several parameters. These scenario are taken for two different standard formats for the game namely, one day international (ODI) cricket and twenty-twenty cricket (T-20). The prediction approaches differ from each other based on the types of parameters considered and the corresponding functional strategies. The strategies proposed here adopts two different approaches. One approach is for the winner prediction for one-day matches and the other is for predicting the winner for a T-20 match. The approaches have been proposed separately for both the versions of the game pertaining to the intra-variability in the strategies adopted by a team and individuals for each. The proposed strategies for each of the two scenarios have been individually evaluated against existing benchmark works, and for each of the cases the duo of approaches have outperformed the rest in terms of the prediction accuracy. The novel heuristics proposed herewith reflects efficiency and accuracy with respect to prediction of cricket data.

Author Biographies

Vetukuri Sivaramaraju, Biju Patnaik University of Technology, Odisha, India
Research Scholar, Dept. of CSE
Nilambar Sethi, GIET, Gunupur Odisha-765022, INDIA
Professor, Department of Computer Science and Engineering
Renugunta Rajender, NSRIT, Sontyam Visakhapatnam-531173, A.P, INDIA
Professor, Department of Computer Science and Engineering


Al-Zahrani and M A Ali, Recurrence Relations for Moments of Order Statistics from the Lindley Distribution with General Multiply Type-II Censored Sample Bander, Statistics, Optimization & Information Computing, Vol. 2, no. 2, pp. 147 - 160, 2014.

C. Parpoula, C. Koukouvinos, D.E. Simos and S. Stylianou, Supersaturated plans for variable selection in large databases, Statistics, Optimization & Information Computing, Vol. 2, pp. 161 - 175, 2014.

Noryanti Muhammad, Tahani Coolen-Maturi, Frank P.A. Coolen, Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests, Statistics, Optimization & Information Computing, Vol. 6, pp 398C408, 2018.

M. Nilashi, O. bin Ibrahim, H. Ahmadi, and L. Shahmoradi, An analytical method for diseases prediction using machine learning techniques, Computers & Chemical Engineering, vol. 106, pp. 212 C 223, 2017.

S. A and A. T. T, Prediction of heart disease complication for diabetic patient using data mining techniques, International Journal of Pure and Applied Mathematics, pp. 13 869C13 879, 119 2018.

F. Kunihiko, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, vol. 36, no. 04, pp. 193C202, 1980.

Shubham Agarwal, Lavish Yadav and Shikha Mehta, Cricket Team Prediction with Hadoop: Statistical Modeling Approach, Procedia Computer Science, vol. 122, pp. 525 - 532, 2017.

Subramanian Rama Iyer and Ramesh Sharda, Prediction of athletes performance using neural networks: An application in cricket team selection, Expert Systems with Applications, vol. 36, no. 3, Part 1, pp. 5510 - 5522, 2009.

Rajitha M. Silva, Ananda B.W. Manage and Tim B. Swartz, A study of the powerplay in one-day cricket, European Journal of Operational Research, vol. 244, no. 3, pp. 931 - 938, 2015.

Neeraj Pathak and Hardik Wadhwa, Applications of Modern Classification Techniques to Predict the Outcome of ODI Cricket, Procedia Computer Science, vol. 244, pp. 55 - 60, 2016.

Muhammad Asif and Ian G. McHale, In-play forecasting of win probability in One-Day International cricket: A dynamic logistic regression model, International Journal of Forecasting, vol. 32, no. 1, pp. 34 - 43, 2016.

Hugh Norton, Steve Gray and Robert Faff, Yes, one-day international cricket in-play trading strategies can be profitable!, Journal of Banking & Finance, vol. 61, pp. S164 - S176, 2015.

Sohail Akhtar and Philip Scarf, Forecasting test cricket match outcomes in play, International Journal of Forecasting, vol. 28, no. 3, pp. 632 - 643, 2012.

H. Ahmad, A. Daud, L. Wang, H. Hong, H. Dawood and Y. Yang, Prediction of Rising Stars in the Game of Cricket, IEEE Access, vol. 5, pp. 4104 - 4124, 2017.

T. Singh, V. Singla and P. Bhatia, Score and winning prediction in cricket through data mining, 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), pp. 60 - 66, 2015.





Carson K. Leung, Kyle W. Joseph, Sports data mining: predicting results for the college football games, 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES 2014, pp. 710 - 719, 2014.

A. Bialkowski, P. Lucey, P. Carr, Y. Yue, S. Sridharan and I. Matthews, Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data, 2014 IEEE International Conference on Data Mining, pp. 725 - 730, 2014.

H. Janetzko, D. Sacha, M. Stein and T. Schreck, D. A. Keim and O. Deussen, Feature-driven visual analytics of soccer data, 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 13 - 22, 2014.

P. UmaMaheswari and M. Rajaram, A Novel Approach for Mining Association Rules on Sports Data using Principal Component Analysis: For Cricket match perspective, 2009 IEEE International Advance Computing Conference, pp. 1074 - 1080, 2009.

S. Bhattacherjee, J. Sahoo and A. Goswami, Association Rule Mining Approach in Strategy Planning for Team India in ICC World Cup 2015, 2015 Second International Conference on Advances in Computing and Communication Engineering, pp. 616 - 621, 2015.

R. K. Khan, I. Manarvi and Mohay-ud-din, Evaluating performance of Blackcaps of New Zealand vs. global cricket teams, 2009 International Conference on Computers Industrial Engineering, pp. 1500 - 1504, 2009.

Neeraj Pathak, HardikWadhwa, Applications of Modern Classification Techniques to Predict the Outcome of ODI Cricket, Procedia Computer Science, no. 87, pp. 55 - 60, 2016.

R. Bryll, R. Gutierrez-Osuna, and F. Quek, Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets, Pattern Recognition, Vol. 36, no. 6, pp. 1291 - 1302, 2003.

http://mines.humanoriented.com/classes/2010/fall/csci568/portfolio exports/lguo/similarity.html

Szekely, G.J. and Rizzo, M.L., Data mining and knowledge discovery, Springer, The Netherlands, The Annals of Statistics, Vol. 42, no. 6, pp. 121 - 167, 2014.

How to Cite
Sivaramaraju, V., Sethi, N., & Rajender, R. (2020). Heuristics for Winner Prediction in International Cricket Matches. Statistics, Optimization & Information Computing, 8(2), 602-609. https://doi.org/10.19139/soic-2310-5070-648
Research Articles