Prediction of IPL Match Outcome Using Machine Learning Techniques

TL;DR

This paper develops machine learning models to predict IPL match outcomes, demonstrating that Random Forest achieves the highest accuracy of 88.10% by considering various match and team factors.

Contribution

The paper introduces a predictive model for IPL match outcomes using multiple machine learning algorithms, with a focus on identifying the most accurate approach.

Findings

Random Forest outperforms other algorithms with 88.10% accuracy.

Multiple factors like team composition and previous performance influence predictions.

Machine learning can effectively forecast cricket match results.

Abstract

India's most popular sport is cricket and is played across all over the nation in different formats like T20, ODI, and Test. The Indian Premier League (IPL) is a national cricket match where players are drawn from regional teams of India, National Team and also from international team. Many factors like live streaming, radio, TV broadcast made this league as popular among cricket fans. The prediction of the outcome of the IPL matches is very important for online traders and sponsors. We can predict the match between two teams based on various factors like team composition, batting and bowling averages of each player in the team, and the team's success in their previous matches, in addition to traditional factors such as toss, venue, and day-night, the probability of winning by batting first at a specified match venue against a specific team. In this paper, we have proposed a model for predicting outcome of the IPL matches using Machine learning Algorithms namely SVM, Random Forest Classifier (RFC), Logistic Regression and K-Nearest Neighbor. Experimental results showed that the Random Forest algorithm outperforms other algorithms with an accuracy of 88.10%.