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TechStack: Numpy, Pandas, Matplotlib, Seaborn, Python. Analysed IPL data and found out some interesting facts about playing teams, players etc. and visualized findings for effectively communicating data insights.

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IPL Data Analysis

  • Conducted IPL data analysis by cleaning, manipulating, and visualizing data using Python libraries.
  • Transformed and manipulated data to uncover some intersting facts.
  • Visualized data with line plots, bar plots, heatmaps etc.
  • Libraries Used: Numpy, Pandas, Matplotlib, Seaborn, Python.

About Data:

  • The dataset you used for IPL analysis consists of 3 CSV files, which contain information about matches throughout the seasons and data for each ball delivered in all of the matches.

Some Interesting Facts based on data:

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  • There is a higher tendency of batsman to hit six (100+) in the final overs.

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  • In 2017, KKR scored on an average 56 runs during the powerplay overs followed by Gujarat Lions and Mumbai Indians.

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  • Virat Kohli faced the highest number of dot balls around 1347 followed by Shikhar Dhawan (1320).

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  • Virat Kohli is the highest run maker scoring about 5434 runs throughout the IPL seasons followed by Suresh Raina (5415) and Rohit Sharma (4914).

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  • DJ Bravo was the purple cap holder for two years (2013 and 2015) while Bhuvaneshwar Kumar was during 2016 and 2017.

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  • There is a 46.15% chance of winning if a team bats first at Wankhede Stadium while there is a 40.5% chance at Eden Gardens.

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TechStack: Numpy, Pandas, Matplotlib, Seaborn, Python. Analysed IPL data and found out some interesting facts about playing teams, players etc. and visualized findings for effectively communicating data insights.

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