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Background/Objectives: The IPL (Indian Premier League) is one of the most viewed cricketmatches in theworld.With a perpetual increase in the popularity and advertising associated with it, forecasting the IPL matches is becoming a need for the advertisers and the sponsors. This paper is centered on the implementation of machine learning to foretell the winner of an IPL match. Methods/Statistical analysis: The cricket in the T-20 format is highly unpredictable many features contribute to the result of a cricket match, and each attribute feature has a weighted impact on the outcome of a game. In this paper, first, a meaningful dataset through data mining was defined; next, essential features using various methods like feature engineering and Analytic Hierarchy Processwere derived. Besides, a key issue on data symmetry and the inability of models to handle it was identified, which extends to all types of classification models that compare two or more classes using similar features for both the classes. This concept in the paper is termed as model ambiguity that occurs due to themodel’s asymmetric nature. Alongside, different machine learning classification algorithms like Naïve Bayes, SVM, kNearest Neighbor, Random Forest, Logistic Regression, ExtraTreesClassifier, XGBoost were adopted to train themodels for predicting the winner. Findings: As per the investigation, tree-based classifiers provided better results with the derived model. The highest accuracy of 60.043% with Random Forest, with a standard deviation of 6.3% and an ambiguity of 1.4%, was observed. Novelty/Applications: Apart from reporting a more accurate result, the derived model has also solved the problem of multicollinearity and identified the issue of data symmetry (termed as model ambiguity). It can be leveraged by brands, sponsors, and advertisers to keep up their marketing strategies.

Prediction of IPL matches using Machine Learning while tackling ambiguity in results