Cricket Score Prediction Using Machine Learning
Author(s):
Preetham HK, Prajwal R, Prince Kumar, Naveen Kumar
Keywords:
Cricket Prediction, Cricket analysis, Lasso Regression, Naïve Bayes, Logistic Regression, Random Forest Classifier.
Abstract
Cricket is the most popular sport in India, and it is played there in all of its regions in various formats such T20, ODI, and Test. Indian regional teams, the national squad, and international teams all field players in the Indian Premier League (IPL), a national cricket competition. This league became well-known among cricket enthusiasts due to numerous factors, including live streaming, radio, and TV broadcasts. For internet traders and sponsors, the outcome predictions of IPL matches are crucial. In addition to more conventional variables like the toss, venue, and day-night, we may anticipate a match between two teams based on a variety of characteristics like the team's composition, the batting and bowling averages of each player on the team, and the team's success in prior matches, Cricket is the most popular sport in India, and it is played there in all of its regions in various formats such T20, ODI, and Test. The Indian Premier calculates the likelihood of winning by batting first against a certain team at a specific match location. In this research, utilising machine learning algorithms such as SVM, Random Forest Classifier (RFC), Logistic Regression, and we have suggested a model for predicting the results of the IPL matches. The accuracy of the Random Forest algorithm, which is 88.10%, exceeds other algorithms, according to experimental results.
Article Details
Unique Paper ID: 157821

Publication Volume & Issue: Volume 9, Issue 8

Page(s): 109 - 114
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