Dataset: T20I Men's Cricket Match Data (2003 - 2023)

About this Dataset

T20I Men's Cricket Match Data (2003 - 2023)

The data was downloaded from the extensive cricket data website cricsheet.org in JSON format. I used the pandas Python library to transform the match data into ball-by-ball data with several relevant fields. This allows for the data to be used to train regression models etc

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). The entire project materials can be found at https://github.com/jamiewelsh25/Cricket_Data_Project/

Notebooks can be found below where I delve into predicting second innings chase success as well as first innings scores. Furthermore, I build a model to evaluate batters, bowlers and all-rounders using a Runs Added Over Average Player metric.

Tables

Ball By Ball It20

@kaggle.jamiewelsh2_ball_by_ball_it20.ball_by_ball_it20

7.02 MB
425119 rows
35 columns


CREATE TABLE ball_by_ball_it20 (
  "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
);