Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Application of Machine Learning in Cricket and Predictive Analytics of IPL 2020

Version 1 : Received: 20 October 2020 / Approved: 21 October 2020 / Online: 21 October 2020 (12:34:00 CEST)

How to cite: Sinha, A. Application of Machine Learning in Cricket and Predictive Analytics of IPL 2020. Preprints 2020, 2020100436 (doi: 10.20944/preprints202010.0436.v1). Sinha, A. Application of Machine Learning in Cricket and Predictive Analytics of IPL 2020. Preprints 2020, 2020100436 (doi: 10.20944/preprints202010.0436.v1).

Abstract

In cricket, particularly the twenty20 format is most watched and loved by the people, where no one can guess who will win the match until the last ball of the last over. In India, The Indian Premier League (IPL) started in 2008 and now it is the most popular T20 league in the world. So we decided to develop a machine learning model for predicting the outcome of its matches. Winning in a Cricket Match depends on many key factors like a home ground advantage, past performances on that ground, records at the same venue, the overall experience of the players, record with a particular opposition, and the overall current form of the team and also the individual player. This paper briefs about the key factors that affect the result of the cricket match and the regression model that best fits this data and gives the best predictions. Cricket, the mainstream and widely played sport across India which has the most noteworthy fan base. Indian Premier League follows 20-20 format which is very unpredictable. IPL match predictor is a ML based prediction approach where the data sets and previous stats are trained in all dimensions covering all important factors such as: Toss, Home Ground, Captains, Favorite Players, Opposition Battle, Previous Stats etc, with each factor having different strength with the help of KNIME Tool and with the added intelligence of Naive Bayes network and Eulers strength calculation formula.

Subject Areas

Naïve Bayes Classification; Eulers Strength Formula; Cricket Prediction; Supervised Learning; KNIME Tool; Cricket prediction; sports analytics; multivariate regression; neural network

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