Super Predictor of Indian Premier League (IPL) using Various ML techniques with help of IBM Cloud

Abstract

Abstract—Indian Premier League matches are one in every of the foremost important events in India. it's professional cricket league in India contested by eight teams representing the various cities in India. The paper focuses on the performance analysis of the eight contesting IPL teams supported the runs of the team, wickets, decisions usurping winning the toss and Duckworth Lewis rule analysis. The IPL data from 2008 to 2019 is used for the player analysis. The team performances are visualized graphically using data analytics to render the interpretation in an exceedingly good manner. The performance data using visual analytics helps in selecting players for future matches and provides additional information on player yet as team profiles. Almost every IPL team’s Management use Analytics for better games. Not only team’s owners, there are several betting and fantasy cricket platform, which are highly rely upon analytics for his or her success. Analytics can help all of them for his or her success. The research paper tries to predict the IPL matches using machine learning models with variables like match id, inning, batting team, bowling team, over, ball, batsman, non-striker, bowler is super over, wide runs, bye runs, leg by runs, bowling runs, penalty runs, batsman runs, extra runs, total runs, player dismissed, dismissal kind fielder. to appear out the result it uses different machine learning models like statistical method model, Random Forest. The results of the study shows that for IPL game, Teams, Venue, Winning Toss, Venue of the Match and Decision after winning the toss are important influencers to win a match. Different Machine Learning helps to predict outcome of a match. Right selection of Machine Learning Model helps to extend Accuracy of Prediction. From Different Classification Models, Support Vector Machine, Decision Tree and Random Forest are best to predict outcome of an IPL games. All of the subsequent gives almost 88% accuracy Level. The study has been conducted from data of Kaggle. Secondary data has been used for the analysis.