Name: IPL 2022 Player Statistics
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

IPL 2022 Player Statistics

Context

Many of us have watched the movie Moneyball. The film summarizes that with proper scouting and believing in the statistics of players, a great team can be built. However, this analysis can be done with an excellent dataset to help analyze the players, strengths, and weaknesses.
So with the new season of IPL starting very soon and with the squad finalized, this dataset is a concise dataset to get statistics of all the players. All you need now is to get this data and start analyzing it to make your dream team, which can also help you play all the fantasy leagues coming your way.

Content

This datasheet has a single CSV file with all players in the list. It contains details of each player's all-time batting, bowling, and fielding figures in IPL and T20 Matches.
File IPL_Data.csv contains details of all players with their all-time IPL stats.
File T20_Data.csv contains details of all players with their all-time T20 stats, either international or domestic, apart from IPL.

Acknowledgements

Datasource: NDTV SPORTS
Image Credits

How was the dataset created

The dataset is created with the help of Web Scraping in Python. BeautifulSoup package helps out to scrap any public website and fetch data.

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Tables

Ipl Data

@kaggle.vora1011_ipl_2022_player_statistics.ipl_data

61.12 kB
237 rows
39 columns

CREATE TABLE ipl_data (
  "name" VARCHAR,
  "team" VARCHAR,
  "url" VARCHAR,
  "type" VARCHAR,
  "valueincr" DOUBLE,
  "full_name" VARCHAR,
  "born" VARCHAR,
  "age" VARCHAR,
  "national_side" VARCHAR,
  "batting_style" VARCHAR,
  "bowling" VARCHAR,
  "sport" VARCHAR,
  "matchplayed" DOUBLE,
  "inningsbatted" DOUBLE,
  "notouts" DOUBLE,
  "runsscored" DOUBLE,
  "highestinnscore" VARCHAR,
  "n_100s" DOUBLE  -- 100s,
  "n_50s" DOUBLE  -- 50s,
  "n_4s" DOUBLE  -- 4s,
  "n_6s" DOUBLE  -- 6s,
  "battingavg" DOUBLE,
  "battings_r" DOUBLE,
  "catchestaken" DOUBLE,
  "stumpingsmade" DOUBLE,
  "ducks" DOUBLE,
  "r_o" DOUBLE,
  "inningsbowled" DOUBLE,
  "overs" DOUBLE,
  "maidens" DOUBLE,
  "runsconceded" DOUBLE,
  "wickets" DOUBLE,
  "best" VARCHAR,
  "n_3s" DOUBLE  -- 3s,
  "n_5s" DOUBLE  -- 5s,
  "bowlingavg" DOUBLE,
  "economyrate" DOUBLE,
  "s_r" DOUBLE,
  "mtc" DOUBLE
);

T20 Data

@kaggle.vora1011_ipl_2022_player_statistics.t20_data

60.07 kB
237 rows
39 columns

CREATE TABLE t20_data (
  "name" VARCHAR,
  "team" VARCHAR,
  "url" VARCHAR,
  "type" VARCHAR,
  "valueincr" DOUBLE,
  "full_name" VARCHAR,
  "born" VARCHAR,
  "age" VARCHAR,
  "national_side" VARCHAR,
  "batting_style" VARCHAR,
  "bowling" VARCHAR,
  "sport" VARCHAR,
  "matchplayed" DOUBLE,
  "inningsbatted" DOUBLE,
  "notouts" DOUBLE,
  "runsscored" DOUBLE,
  "highestinnscore" VARCHAR,
  "n_100s" DOUBLE  -- 100s,
  "n_50s" DOUBLE  -- 50s,
  "n_4s" DOUBLE  -- 4s,
  "n_6s" DOUBLE  -- 6s,
  "battingavg" DOUBLE,
  "battings_r" DOUBLE,
  "catchestaken" DOUBLE,
  "stumpingsmade" DOUBLE,
  "ducks" DOUBLE,
  "r_o" DOUBLE,
  "inningsbowled" DOUBLE,
  "overs" DOUBLE,
  "maidens" DOUBLE,
  "runsconceded" DOUBLE,
  "wickets" DOUBLE,
  "best" VARCHAR,
  "n_3s" DOUBLE  -- 3s,
  "n_5s" DOUBLE  -- 5s,
  "bowlingavg" DOUBLE,
  "economyrate" DOUBLE,
  "s_r" DOUBLE,
  "mtc" DOUBLE
);