Abstract
Cricket is the most popular sport after football. Indian Premier League or the IPL is the most popular T20 domestic league in the world. Cricket involves lots of data and statistics. In the game of cricket, several parameters can be used to predict the outcome of the game. The factors affecting a cricket match can be combined with Machine Learning to predict the outcome of a match. This research has focused on analysing the features of the cricket matches in IPL. Moving further towards the analysis of the Indian Premier League, this paper has rated the Batsmen and Bowlers in a unique way based on their performance. A few crucial factors like team form and team strength in predicting the match outcome apart from the conventional features like the toss, venue of the games etc., have been added. Further, a novel analysis of Batting and Bowling has been proposed based on Batting Index and Bowling Index. Machine Learning algorithms like SVM, Logistic Regression, Random Tree, Random Forest and Naive Bayes have been applied, for match predictions. Lastly, the results, based on which algorithm gives the best accuracy, have been plotted. Decision Tree and Logistic Regression algorithms have given an accuracy over 87% and 95% respectively.
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