Dataset: Indian Premier League (IPL) Ball-by-Ball Data

About this Dataset

Indian Premier League (IPL) Ball-by-Ball Data

IPL match data from 2008-2023 was downloaded from cricsheet.org in JSON format. I used the pandas Python library to transform this data into ball-by-ball data with a number of relevant and useful columns. After this, the data was saved as a csv file.

This dataset was created as part of a project where I created metrics to rank players for T20 Internationals and the Indian Premier League (IPL). In the IPL Data Analysis notebook found below, I perform some exploratory data analysis before creating metrics to rank batters, bowlers and all-rounders based on the Runs Added/Reduced Compared to the Average Player. The entire project materials can be found at https://github.com/jamiewelsh25/Cricket_Data_Project/.

Furthermore, If you are interested in how I used this data to train predictive models for second innings chase success and first innings scores, then check out the notebooks below!

Tables

Ball By Ball Ipl

@kaggle.jamiewelsh2_ball_by_ball_ipl.ball_by_ball_ipl

3.72 MB
239693 rows
35 columns


CREATE TABLE ball_by_ball_ipl (
  "unnamed_0" BIGINT,
  "match_id" BIGINT,
  "date" TIMESTAMP,
  "venue" VARCHAR,
  "bat_first" VARCHAR,
  "bat_second" VARCHAR,
  "innings" BIGINT,
  "over" BIGINT,
  "ball" BIGINT,
  "batter" VARCHAR,
  "non_striker" VARCHAR,
  "bowler" VARCHAR,
  "batter_runs" BIGINT,
  "extra_runs" BIGINT,
  "runs_from_ball" BIGINT,
  "ball_rebowled" BIGINT,
  "extra_type" VARCHAR,
  "wicket" BIGINT,
  "method" VARCHAR,
  "player_out" VARCHAR,
  "innings_runs" BIGINT,
  "innings_wickets" BIGINT,
  "target_score" BIGINT,
  "runs_to_get" DOUBLE,
  "balls_remaining" BIGINT,
  "winner" VARCHAR,
  "chased_successfully" BIGINT,
  "total_batter_runs" BIGINT,
  "total_non_striker_runs" BIGINT,
  "batter_balls_faced" BIGINT,
  "non_striker_balls_faced" BIGINT,
  "player_out_runs" DOUBLE,
  "player_out_balls_faced" DOUBLE,
  "bowler_runs_conceded" BIGINT,
  "valid_ball" BIGINT
);