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cricketdata: An Open Source R package

Hassan Rafique and Jacquie Tran

18 October 2022

Abstract

Open and accessible data streams are crucial for reproducible research and further development. Cricket data sources are limited and are usually not in a format ready for analysis. cricketdata R package allows the users to download the data as a tibble ready for analysis from two primary sources: ESPNCricinfo and Cricsheet. fetch_cricinfo() and fetch_player_data() functions allow the user to download the data from ESPNCricinfo for different formats of international cricket (tests, odis, T20), player position (batter, bowler, fielding), and whole career or innings wise. Cricsheet is another data source, primarily for ball-by-ball data. fetch_cricsheet() function downloads the ball-by-ball, match, and player data for different competitions/formats (tests, odis, T20 internationals, T20 leagues). The T20 data is further processed by adding more features (columns) using the raw data. Some other functions provide access to the individual players’ playing career data and information about their playing style, country of origin, etc. The package essentially provides (almost) all publicly available cricket data ready for analysis. The package saves the user significant time in building the data pipeline, which may now be used for analysis. Here’s an example of project built using cricketdata: https://dazzalytics.shinyapps.io/cricwar/ 
library(cricketdata)
library(dplyr)
library(ggplot2)

Introduction

The coverage of cricket as a sport has been limited compared to other global sports. ESPN Cricinfo is the major and one of the few online platforms dedicated to cricket coverage. It started as Cricinfo in the late 90s, and it was maintained by students and cricket fans who had immigrated to North America but were eager to keep tabs on the cricket activity around the globe. ESPN acquired Cricinfo in 2007, becoming ESPN Cricinfo. It is the most extensive repository of open cricket data with the caveat that data is not in an accessible format to be downloaded easily. You would have to copy-paste (tables) or write programming scripts to access the data in a format suitable for analysis. Recently they have added a search tool, Statsguru, that lets you parse through their database, presenting results usually in a table format.

Cricsheet is another open data source for ball-by-ball data maintained by a great fan of the game, Stephen Rushe. The cricsheet provides raw ball-by-ball data for all formats (tests, odis, T20) and both Men’s and Women’s games. It is an extensive project to produce ball-by-ball data, and we hugely appreciate Stephen Rushe’s work. The data is available in different formats, such as JSON, YAML, and CSV.

Why cricketdata

The cricketdata (open-source) package aims to be a one-stop shop for most cricket data from all primary sources, available in an accessible form and ready for analysis. Different functions in the package allow us to download the data from Cricinfo and cricsheet as a data frame (tibble) in R. The user can access data from different formats of the game, e,g, tests, odis, international T20, league T20, etc. In particular, the

cricWAR https://dazzalytics.shinyapps.io/cricwar/ is an example of sports analytic project based on cricketdata resources.

cricketdata as an open-source project is inspired primarily from the open-source work done by Rstats community and sports analytics projects such as nflfastR (Carl and Baldwin, n.d.), sportsdataverse (Gilani, n.d.).

In the following sections, we will show how to install the package and take full advantage of the package functionality with numerous examples.

Installation

cricketdata is available on CRAN and the stable version can be installed.

install.packages("cricketdata", dependencies = TRUE)

You may also download the development version from Github

install.packages("devtools")
devtools::install_github("robjhyndman/cricketdata")

Functions

There are six main functions,

and a data file containing the player meta data.

We show the use of each function with examples below.

fetch_cricinfo()

Fetch team data on international cricket matches provided by ESPNCricinfo. It downloads data for international T20, ODI or Test matches, for men or women, and for batting, bowling or fielding. By default, it downloads career-level statistics for individual players.

Arguments

Women’s T20 Bowling Data

# Fetch all Women's Bowling data for T20 format
wt20 <- fetch_cricinfo("T20", "Women", "Bowling")
# Looking at data
wt20 %>%
  glimpse()
#> Rows: 1,977
#> Columns: 16
#> $ Player             <chr> "A Mohammed", "Nida Dar", "EA Perry", "M Schutt", "…
#> $ Country            <chr> "West Indies", "Pakistan", "Australia", "Australia"…
#> $ Start              <int> 2008, 2010, 2008, 2013, 2007, 2005, 2006, 2008, 201…
#> $ End                <int> 2021, 2023, 2023, 2023, 2023, 2023, 2023, 2020, 202…
#> $ Matches            <int> 117, 128, 136, 93, 109, 109, 118, 79, 89, 72, 113, …
#> $ Innings            <int> 113, 121, 128, 92, 108, 108, 103, 79, 87, 72, 87, 6…
#> $ Overs              <dbl> 395.3, 410.2, 392.5, 309.3, 381.5, 381.1, 302.3, 26…
#> $ Maidens            <int> 6, 10, 8, 7, 20, 17, 6, 10, 11, 5, 4, 10, 7, 9, 6, …
#> $ Runs               <int> 2206, 2231, 2297, 1916, 2191, 2102, 1920, 1587, 190…
#> $ Wickets            <int> 125, 123, 121, 121, 117, 112, 110, 102, 100, 98, 98…
#> $ Average            <dbl> 17.64800, 18.13821, 18.98347, 15.83471, 18.72650, 1…
#> $ Economy            <dbl> 5.577750, 5.437043, 5.847263, 6.190630, 5.738106, 5…
#> $ StrikeRate         <dbl> 18.98400, 20.01626, 19.47934, 15.34711, 19.58120, 2…
#> $ BestBowlingInnings <chr> "5/10", "5/21", "4/12", "5/15", "5/12", "4/15", "4/…
#> $ FourWickets        <int> 4, 1, 4, 4, 0, 1, 1, 2, 1, 3, 2, 1, 3, 4, 1, 1, 4, …
#> $ FiveWickets        <int> 3, 1, 0, 1, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, …

# Table showing certain features of the data
wt20 %>%
  select(Player, Country, Matches, Runs, Wickets, Economy, StrikeRate) %>%
  head() %>%
  knitr::kable(
    digits = 2, align = "c",
    caption = "Women Player career profile for international T20"
  )
Women Player career profile for international T20
Player Country Matches Runs Wickets Economy StrikeRate
A Mohammed West Indies 117 2206 125 5.58 18.98
Nida Dar Pakistan 128 2231 123 5.44 20.02
EA Perry Australia 136 2297 121 5.85 19.48
M Schutt Australia 93 1916 121 6.19 15.35
S Ismail South Africa 109 2191 117 5.74 19.58
KH Brunt England 109 2102 112 5.51 20.42 
# Plotting Data
wt20 %>%
  filter(Wickets >= 50) %>%
  ggplot(aes(y = StrikeRate, x = Average)) +
  geom_point(alpha = 0.3, col = "blue") +
  ggtitle("Women International T20 Bowlers") +
  ylab("Balls bowled per wicket") +
  xlab("Runs conceded per wicket")
Strike Rate (balls bowled per wicket) Vs Average (runs conceded per wicket) for Women international T20 bowlers. Each observation represents one player, who has taken at least 50 international wickets.
Strike Rate (balls bowled per wicket) Vs Average (runs conceded per wicket) for Women international T20 bowlers. Each observation represents one player, who has taken at least 50 international wickets.

USA men’s ODI data by innings

# Fetch all USA Men's ODI data by innings
menODI <- fetch_cricinfo("ODI", "Men", "Batting",
  type = "innings",
  country = "United States of America"
)
# Table of USA player who have scored a century
menODI %>%
  filter(Runs >= 100) %>%
  select(Player, Runs, BallsFaced, Fours, Sixes, Opposition) %>%
  knitr::kable(digits = 2)
Player Runs BallsFaced Fours Sixes Opposition
JS Malhotra 173 124 4 16 Papau New Guinea
MD Patel 130 101 11 6 Oman
Aaron Jones 123 87 9 6 Scotland
SR Taylor 114 123 11 3 Nepal
SJ Modani 111 133 9 0 Oman
MD Patel 100 114 9 1 Nepal

fetch_player_id

Each player has a player id on ESPNCricinfo, which is useful to access a individual player’s data. This function given a string of players name or part of the name would return the name of corresponding player(s), their cricinfo id(s), and some other information.

Argument

# Fetching a player, Meg Lanning's, ID
meg_lanning_id <- find_player_id("Meg Lanning")$ID
meg_lanning_id
#> [1] 329336

fetch_player_data

Fetch individual player data from all matches played. The function will scrape the data from ESPNCricinfo and return a tibble with one line per innings for all games a player has played. To identify a player, use their Cricinfo player ID. The simplest way to find this is to look up their Cricinfo Profile page. The number at the end of the URL is the ID. For example, Meg Lanning’s profile page is https://www.espncricinfo.com/cricketers/meg-lanning-329336, so her ID is 329336. Or you may use the find_player_id function.

Argument

# Fetching the player Meg Lanning's playing data
MegLanning <- fetch_player_data(meg_lanning_id, "ODI") %>%
  mutate(NotOut = (Dismissal == "not out"))
dim(MegLanning)
#> [1] 103  14
names(MegLanning)
#>  [1] "Date"       "Innings"    "Opposition" "Ground"     "Runs"      
#>  [6] "Mins"       "BF"         "X4s"        "X6s"        "SR"        
#> [11] "Pos"        "Dismissal"  "Inns"       "NotOut"

# Compute batting average
MLave <- MegLanning %>%
  filter(!is.na(Runs)) %>%
  summarise(Average = sum(Runs) / (n() - sum(NotOut))) %>%
  pull(Average)
names(MLave) <- paste("Average =", round(MLave, 2))

# Plot ODI scores
ggplot(MegLanning) +
  geom_hline(aes(yintercept = MLave), col = "gray") +
  geom_point(aes(x = Date, y = Runs, col = NotOut)) +
  ggtitle("Meg Lanning ODI Scores") +
  scale_y_continuous(sec.axis = sec_axis(~., breaks = MLave))
Meg Lanning, Australian captain, has shown amazing consistency over her career, with centuries scored in every year of her career except for 2021, when her highest score from 6 matches was 53.
Meg Lanning, Australian captain, has shown amazing consistency over her career, with centuries scored in every year of her career except for 2021, when her highest score from 6 matches was 53.

fetch_cricsheet()

Cricsheet is the only open accessible source for cricket ball-by-ball data. fetch_cricsheet() download csv data from cricsheet. Data must be specified by three factors: (a) type of data: bbb (ball-by-ball), match or player. (b) gender; (c) competition. See https://cricsheet.org/downloads/ for what the competition character codes mean.

The raw T20 data from cricsheet is further processed to add more columns (features) to facilitate analysis.

Arguments

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

# Fetch all IPL ball-by-ball data
ipl_bbb <- fetch_cricsheet("bbb", "male", "ipl")
ipl_bbb %>%
  glimpse()
#> Rows: 225,954
#> Columns: 33
#> $ match_id               <int> 335982, 335982, 335982, 335982, 335982, 335982,…
#> $ season                 <chr> "2007/08", "2007/08", "2007/08", "2007/08", "20…
#> $ start_date             <chr> "2008-04-18", "2008-04-18", "2008-04-18", "2008…
#> $ venue                  <chr> "M Chinnaswamy Stadium", "M Chinnaswamy Stadium…
#> $ innings                <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ over                   <dbl> 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3,…
#> $ ball                   <int> 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 1, 2, 3,…
#> $ batting_team           <chr> "Kolkata Knight Riders", "Kolkata Knight Riders…
#> $ bowling_team           <chr> "Royal Challengers Bangalore", "Royal Challenge…
#> $ striker                <chr> "SC Ganguly", "BB McCullum", "BB McCullum", "BB…
#> $ non_striker            <chr> "BB McCullum", "SC Ganguly", "SC Ganguly", "SC …
#> $ bowler                 <chr> "P Kumar", "P Kumar", "P Kumar", "P Kumar", "P …
#> $ runs_off_bat           <int> 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 6, 4, 0, 0, 0, 0,…
#> $ extras                 <int> 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,…
#> $ ball_in_over           <int> 1, 2, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3,…
#> $ extra_ball             <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,…
#> $ balls_remaining        <dbl> 119, 118, 118, 117, 116, 115, 114, 113, 112, 11…
#> $ runs_scored_yet        <int> 1, 1, 2, 2, 2, 2, 3, 3, 7, 11, 17, 21, 21, 21, …
#> $ wicket                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
#> $ wickets_lost_yet       <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ innings1_total         <int> 222, 222, 222, 222, 222, 222, 222, 222, 222, 22…
#> $ innings2_total         <int> 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82,…
#> $ target                 <dbl> 223, 223, 223, 223, 223, 223, 223, 223, 223, 22…
#> $ wides                  <int> NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ noballs                <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ byes                   <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ legbyes                <int> 1, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, N…
#> $ penalty                <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ wicket_type            <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
#> $ player_dismissed       <chr> "", "", "", "", "", "", "", "", "", "", "", "",…
#> $ other_wicket_type      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ other_player_dismissed <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ .groups                <chr> "drop", "drop", "drop", "drop", "drop", "drop",…
# Top 20 batters wrt Boundary and Dot % in IPL 2022 season
ipl_bbb %>%
  filter(season == "2022") %>%
  group_by(striker) %>%
  summarize(
    Runs = sum(runs_off_bat), BallsFaced = n() - sum(!is.na(wides)),
    StrikeRate = Runs / BallsFaced, DotPercent = sum(runs_off_bat == 0) * 100 / BallsFaced,
    BoundaryPercent = sum(runs_off_bat %in% c(4, 6)) * 100 / BallsFaced
  ) %>%
  arrange(desc(Runs)) %>%
  rename(Batter = striker) %>%
  slice(1:20) %>%
  ggplot(aes(y = BoundaryPercent, x = DotPercent, size = BallsFaced)) +
  geom_point(color = "red", alpha = 0.3) +
  geom_text(aes(label = Batter),
    vjust = -0.5, hjust = 0.5, color = "#013369",
    position = position_dodge(0.9), size = 3
  ) +
  ylab("Boundary Percent") +
  xlab("Dot Percent") +
  ggtitle("IPL 2022: Top 20 Batters")
Top 20 prolific batters in IPL 2022. We show what percentage of balls they hit for a boundary (4 or 6) against percentage of how many balls they do not score off of (dot percent). Ideally we want to be in top left quadrant, high boundary % and low dot %.
Top 20 prolific batters in IPL 2022. We show what percentage of balls they hit for a boundary (4 or 6) against percentage of how many balls they do not score off of (dot percent). Ideally we want to be in top left quadrant, high boundary % and low dot %.
# Top 10 prolific batters in IPL 2022 season.
ipl_bbb %>%
  filter(season == "2022") %>%
  group_by(striker) %>%
  summarize(
    Runs = sum(runs_off_bat), BallsFaced = n() - sum(!is.na(wides)),
    StrikeRate = Runs / BallsFaced,
    DotPercent = sum(runs_off_bat == 0) * 100 / BallsFaced,
    BoundaryPercent = sum(runs_off_bat %in% c(4, 6)) * 100 / BallsFaced
  ) %>%
  arrange(desc(Runs)) %>%
  rename(Batter = striker) %>%
  slice(1:10) %>%
  knitr::kable(digits = 1, align = "c")
Batter Runs BallsFaced StrikeRate DotPercent BoundaryPercent
JC Buttler 863 579 1.5 42.7 22.3
KL Rahul 616 455 1.4 36.9 16.5
Q de Kock 508 341 1.5 36.4 20.5
HH Pandya 487 371 1.3 36.4 16.4
Shubman Gill 483 365 1.3 34.5 17.0
DA Miller 481 337 1.4 31.5 16.3
F du Plessis 468 367 1.3 42.2 16.9
S Dhawan 460 375 1.2 42.9 15.7
SV Samson 458 312 1.5 44.2 22.1
DJ Hooda 451 330 1.4 34.5 16.4

player_meta

It is a data set containing player’s and cricket officials meta data such as full name, country of representation, data of birth, bowling and batting hand, bowling style, and playing role. More than 11,000 player’s and officials data is available. This data was scraped from ESPNCricinfo website.

player_meta %>%
  filter(!is.na(playing_role)) %>%
  select(-cricinfo_id, -unique_name) %>%
  head() %>%
  knitr::kable(
    digits = 1, align = "c", format = "pipe",
    col.names = c(
      "ID", "FullName", "Country", "DOB", "BirthPlace",
      "BattingStyle", "BowlingStyle", "PlayingRole"
    )
  )
ID FullName Country DOB BirthPlace BattingStyle BowlingStyle PlayingRole
9dbc77b3 Aaftab Alam Khan Malta 1986-01-31 NA Right hand Bat Right arm Medium fast Wicketkeeper Batter
797f52cc Aahan Gopinath Achar Singapore 1999-03-30 NA Left hand Bat Slow Left arm Orthodox Bowler
e249fdaa Aakash Chopra India 1977-09-19 Agra, Uttar Pradesh Right hand Bat Right arm Medium, Right arm Offbreak Batter
4b0e3049 Aaliyah Alicia Alleyne West Indies 1994-11-11 NA Right hand Bat Right arm Medium Bowler
f1733e13 Aaliyah Williams West Indies 1998-02-28 NA Right hand Bat Right arm Medium Allrounder
a8e54ef4 Aamer Jamal Pakistan 1996-07-05 Mianwali Right hand Bat Right arm Medium Allrounder

fetch_player_meta()

Fetch the player’s meta data such as full name, country of representation, data of birth, bowling and batting hand, bowling style, and playing role. This meta data is useful for advance modeling, e,g, age curves, batter profile against bowling types etc.

Argument

The cricinfo player ids can be accessed in multiple ways, e.g. use fetch_player_id() function, get the id from the player’s cricinfo page or consult the player_meta data frame which has player meta data of more than 11,000 players.

# Download meta data on Meg Lanning and Ellyse Perry
aus_women <- fetch_player_meta(c(329336, 275487))
aus_women %>%
  knitr::kable(
    digits = 1, align = "c", format = "pipe",
    col.names = c(
      "ID", "FullName", "Country", "DOB", "BirthPlace", "BattingStyle",
      "BowlingStyle", "PlayingRole"
    )
  )
ID FullName Country DOB BirthPlace BattingStyle BowlingStyle PlayingRole
329336 Meghann Moira Lanning Australia 1992-03-25 Singapore Right hand Bat Right arm Medium Top order Batter
275487 Ellyse Alexandra Perry Australia 1990-11-03 Wahroonga, Sydney, New South Wales Right hand Bat Right arm Fast medium Allrounder

update_player_meta()

This function is supposed to consult the directory of all players available on cricsheet website and include the meta data of new players into the player_meta data frame. The data for new players will be scraped from the ESPNCricinfo.

References

Carl, Sebastian, and Ben Baldwin. n.d. “nflfastR: Functions to Efficiently Access NFL Play by Play Data.” R Package. https://CRAN.R-project.org/package=nflfastR.
Gilani, Saiem. n.d. “Sports Dataverse.” https://sportsdataverse.org/.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.