This paper is published in Volume 3, Issue 3, 2018
Area
Data Mining
Author
Akhil Nimmagadda
Co-authors
Nidamanuri Venkata Kalyan, Manigandla Venkatesh, Nuthi Naga Sai Teja, Chavali Gopi Raju
Org/Univ
Vasireddy Venkatadri Institute of Technology, Namburu, Andhra Pradesh, India
Pub. Date
30 March, 2018
Paper ID
V3I3-1230
Publisher
IJARnD
Edition
Volume 3, Issue 3, 2018
Keywords
Data Mining, Prediction, T20, IPL, Logistic Regression, Random Forest.

Citationsacebook

IEEE
Akhil Nimmagadda, Nidamanuri Venkata Kalyan, Manigandla Venkatesh, Nuthi Naga Sai Teja, Chavali Gopi Raju. Cricket score and winning prediction using data mining, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARnD.com.

APA
Akhil Nimmagadda, Nidamanuri Venkata Kalyan, Manigandla Venkatesh, Nuthi Naga Sai Teja, Chavali Gopi Raju (2018). Cricket score and winning prediction using data mining. International Journal of Advance Research, Ideas and Innovations in Technology, 3(3) www.IJARnD.com.

MLA
Akhil Nimmagadda, Nidamanuri Venkata Kalyan, Manigandla Venkatesh, Nuthi Naga Sai Teja, Chavali Gopi Raju. "Cricket score and winning prediction using data mining." International Journal of Advance Research, Ideas and Innovations in Technology 3.3 (2018). www.IJARnD.com.

Give proper credits, use Citation.

Abstract

Data Mining and Machine Learning in sports analytics is a brand-new research field in computer science with a lot of challenge. In this research, the goal is to design a result prediction system for a T20 cricket match, in particular for an IPL match while the match is in progress. Different Machine Learning and statistical approach were taken to find out the best possible outcome. A very popular mathematical technique named Multiple Linear Regression is used in order to make a comparison of results found. This model is very much popular in predictive modeling. Currently, in Twenty-Twenty(T20) cricket matches first innings score is predicted on the basis of a current run rate which can be calculated as the number of runs scored per the number of overs bowled. It does not include factors like the number of wickets fallen, venue of the match, toss. Furthermore, in second innings there is no method to predict the outcome of the match. In this paper a model has been proposed that predicts the score in each of the innings using Multiple Variable Linear Regression along with Logistic regression and finally the winner of the match using Random Forest algorithm.

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