Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning

by   Rabindra Lamsal, et al.

Cricket, especially the twenty20 format, has maximum uncertainty, where a single over can completely change the momentum of the game. With millions of people following the Indian Premier League, therefore developing a model for predicting the outcome of its matches beforehand is a real-world problem. A cricket match depends upon various factors, and in this work, various feature selection methods were used to reduce the number of features to 5 from 15. Player's performance in the field is considered to find out the overall weightage (relative strength) of the team. A Linear Regression based solution is proposed to calculate the weightage of a team based on the past performance of its players who have appeared most for the team. Finally, a dataset with the features: home team, away team, stadium, toss winner, toss decision, home-team-weightage and away-team-weightage is fed to a Random Forest Classifier to train the model and make a prediction on unseen matches. Classification results are satisfactory. The problems in the dataset and how the accuracy of the classifier can be improved is discussed.


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