Regression based Machine Learning Model to Predict IPL Score after Power Play

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Sreekanth Reddy. B, Padma. S


In Cricket, particularly the 20-20 over format and especially Indian Premier League (IPL) is most watched and loved by the people, even where no one can guess who will win the match until the last ball of the last over of the match. In India, the IPL have started in the year 2008 and now it is the most popular T20 league in the world. So, it is decided to develop a basic Machine Learning (ML) model for predicting the scores at the end of powerplay (at the end of sixth over) in every innings for every match. Predicting a score in a Cricket Match depends on various key factors like a home ground advantage, past performances on that ground, records at the same venue. And also, the overall experience of the players, record with a particular opposition, correlation between ever bowler to the ever batsmen 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. IPL Score predictor is a ML based prediction approach with the help of Python Programming where the data sets namely IPL Ball-by-Ball 2008-2020 and all matches are used like Previous stats data are cleaned and 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 Regression Model, Consideration of all these strengthening factors helps in predicting the accurate power play scores.

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