Novel Method for Cricket Match Outcome Prediction using Data Mining Techniques
S.A.D.P Subasingha1, S. C. Premaratne2, K. L. Jayaratne3, P. Sellappan4
1S.A.D.P Subasingha,  Department of Information Technology, University of Moratuwa.
2S. C. Premaratne, Department of Information Technology, University of Moratuwa.
3K. L. Jayaratne, Department of Computing, University of Colombo Sri Lanka.
4P. Sellappan, Department of Science and Technology, Malaysia University of Science and Technology, MUST.
Manuscript received on 27 September 2019 | Revised Manuscript received on 09 November 2019 | Manuscript Published on 22 November 2019 | PP: 15-21 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F10040986S319/19©BEIESP | DOI: 10.35940/ijeat.F1004.0986S319
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Abstract: Cricket is one of the most popular games in many countries. As many as 19 countries play cricket as their main game, and the number is likely to increase in the future. However, there are no suitable tools for analyzing pre-outcome of the match from beginning to end. Existing tools do not support simulating match using batting partnerships. The ultimate goal of predicting pre-outcome of a cricket match is to identify key players and their batting performances. It is also to prevent wrong players from selecting and toss decision by making statistical predictions. This research focuses on One Day International (ODI) cricket match and predicts the outcome of a particular match. Our solution consists of three major modules, namely, Web UI Module, CRIC-Win Analytic Engine, and Backend Data Module. CRIC-Win Analytic Engine has two sub data models, one for predicting the overall match outcome based on a given pre-match data, and the other for predicting match outcome based on batting partnership of both home and rival teams. All sub-models in the CRIC-Win Analytic Engine are developed using the Naïve Bayes algorithm for generating the classifier model, which is used to predict the outcome of the cricket match.
Keywords: Classification, Data Mining, Cricket Match, Prediction.
Scope of the Article: Data Mining