Dataset: IPL Cricket Analysis Dataset

About this Dataset

IPL Cricket Analysis Dataset

This dataset is ideal for cricket enthusiasts, data scientists, and analysts looking to derive insights into player performance trends, match outcomes, and predictive modeling in the sport of cricket.

This dataset has been meticulously scraped from ESPN's cricket match data. It includes comprehensive match data, player performance statistics, and game outcomes, which are essential for conducting in-depth analyses. The dataset has been cleaned and pre-processed to ensure accuracy, making it ready for data exploration, visualization, analytics and machine learning applications. This dataset is available in both csv and json format.

This dataset contains the following files:

df_batting.csv : Contains detailed batting summary data, including player stats like runs, balls, and strike rate.
df_bowling.csv : Includes bowling stats such as overs, runs conceded, wickets, and economy rate.
df_match.csv : Captures match details including results, teams, winner, margin, venue, date, and match ID.
df_players.csv : Contains player profiles with team, batting/bowling style, role, and a brief description.

Tables

Df Batting

@kaggle.diishasiing_cricket_analysis_dataset.df_batting

17.03 KB
699 rows
11 columns


CREATE TABLE df_batting (
  "match" VARCHAR,
  "teaminnings" VARCHAR,
  "battingpos" BIGINT,
  "batsmanname" VARCHAR,
  "runs" BIGINT,
  "balls" BIGINT,
  "n_4s" BIGINT,
  "n_6s" BIGINT,
  "sr" VARCHAR,
  "out_not_out" VARCHAR,
  "match_id" VARCHAR
);

Df Bowling

@kaggle.diishasiing_cricket_analysis_dataset.df_bowling

16.45 KB
500 rows
14 columns


CREATE TABLE df_bowling (
  "match" VARCHAR,
  "bowlingteam" VARCHAR,
  "bowlername" VARCHAR,
  "overs" DOUBLE,
  "maiden" BIGINT,
  "runs" BIGINT,
  "wickets" BIGINT,
  "economy" DOUBLE,
  "n_0s" BIGINT,
  "n_4s" BIGINT,
  "n_6s" BIGINT,
  "wides" BIGINT,
  "noballs" BIGINT,
  "match_id" VARCHAR
);

Df Match

@kaggle.diishasiing_cricket_analysis_dataset.df_match

6.26 KB
45 rows
7 columns


CREATE TABLE df_match (
  "team1" VARCHAR,
  "team2" VARCHAR,
  "winner" VARCHAR,
  "margin" VARCHAR,
  "ground" VARCHAR,
  "matchdate" TIMESTAMP,
  "match_id" VARCHAR
);

Df Players

@kaggle.diishasiing_cricket_analysis_dataset.df_players

46.43 KB
219 rows
7 columns


CREATE TABLE df_players (
  "name" VARCHAR,
  "team" VARCHAR,
  "image" VARCHAR,
  "battingstyle" VARCHAR,
  "bowlingstyle" VARCHAR,
  "playingrole" VARCHAR,
  "description" VARCHAR
);