I conducted a comprehensive case study on a dataset that contained information about IPL match played between year 2021 to 2023 using SQL and Python . Python for loading the file in the SQL and SQL for data anlysis . The dataset includes Four tables: dim_match_summary , dim_players , fact_bating_summary , fact_bowling_summary
dim_match_summary : Have information of the match
dim_players : Have detail information about the player
fact_bating_summary : Data of the batsman
fact_bowling_summary : Data of the Bowler
1.Top 10 Batsman with highest strike_rate
2.Batsman with the highest strike rate at every position
3.Top 2 batsmn from each team in a match
4.Top 10 Bowler with most number of wicket
5.Top 10 Bowlers with the lowest and the higest economy rate
6.Best 2 Bowler from each team in a match with less economy and most wicket
7.Total Wicket By the Bowling Style
8.Team winning percentage while Bowling_1 and Batting_1
9.Top 10 bowlers based on past 3 years bowling average
10.Top 10 Batsman with highest Batting average
11.On which Day of the week do virat and rohit are more charged up to hit more six
12.On which Day of the week Bumrah and Shami are more thirsty for taking wicket
13.In Super Kings which player has contribute more in winning the match
14.Which team has has the worst bowling average against Royal