Abstract
Exploratory data analysis for Indian Premier League (IPL) data is widely covered in the field of data analytics and machine learning problem and specifically based for match prediction and team prediction IPL. IPL is a global tournament with over 1200 players participating in the auction every year, and it draws the attention of many cricketing fans around the globe. So, the proposed system predicts the team and winner with an accuracy of over 50% and engages many cricketing fans. The goal of this paper is to develop a system for predicting the Dream11 team and the possible team to win the match every day. The common attributes to be included while choosing a team involves a player’s batting average, batting strike rate, bowling’s economy rate, match ratings, and his experience; similarly, while predicting match winner, the machine will see different factors like toss outcome, venue of the match, team track record in that venue, and team to play that match which are stored in the datasets and generated according to the conditions given to the machine and the criteria’s generated for the team selection. The objective of the paper is to develop a model which will give the accuracy of more than 50%, and it satisfies all the necessary conditions for team selection. And, the best part is that it will create a buzz around many cricketing fans around the globe and keep them engaged throughout the course of the tournament and it helps in increasing its fan base.
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Mohapatra, S., Goswami, A., Singh, A., Singh, V.K., Sen, B., Sharma, K. (2022). Exploratory Data Analysis on IPL Data. In: Sarma, H.K.D., Balas, V.E., Bhuyan, B., Dutta, N. (eds) Contemporary Issues in Communication, Cloud and Big Data Analytics. Lecture Notes in Networks and Systems, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-4244-9_26
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