cricketdata: An Open Source R package

In cricketdata: International Cricket Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  echo = TRUE,
  cache = TRUE,
  warning = FALSE
)

library(cricketdata)
library(dplyr)
library(ggplot2)

# Avoid downloading the data when the package is checked by CRAN.
# This only needs to be run once to store the data locally
ipl_bbb <- fetch_cricsheet("bbb", "male", "ipl")
wt20 <- fetch_cricinfo("T20", "Women", "Bowling")
menODI <- fetch_cricinfo("ODI", "Men", "Batting",
  type = "innings",
  country = "United States of America"
)
meg_lanning_id <- find_player_id("Meg Lanning")$ID
MegLanning <- fetch_player_data(meg_lanning_id, "ODI") %>%
  mutate(NotOut = (Dismissal == "not out"))
aus_women <- fetch_player_meta(c(329336, 275487))

saveRDS(wt20, here::here("inst/extdata/wt20.rds"))
saveRDS(menODI, here::here("inst/extdata/usmenODI.rds"))
saveRDS(MegLanning, here::here("inst/extdata/MegLanning.rds"))
saveRDS(meg_lanning_id, here::here("inst/extdata/meg_lanning_id.rds"))
saveRDS(ipl_bbb, here::here("inst/extdata/ipl_bbb.rds"))
saveRDS(aus_women, here::here("inst/extdata/aus_women.rds"))

ipl_bbb <- readRDS(here::here("inst/extdata/ipl_bbb.rds"))
wt20 <- readRDS(here::here("inst/extdata/wt20.rds"))
menODI <- readRDS(here::here("inst/extdata/usmenODI.rds"))
MegLanning <- readRDS(here::here("inst/extdata/MegLanning.rds"))
meg_lanning_id <- readRDS(here::here("inst/extdata/meg_lanning_id.rds"))
aus_women <- readRDS(here::here("inst/extdata/aus_women.rds"))

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

• ball-by-ball data,
• individual player play by innings data,
• player play by team wrt career or innings data,
• player id, dob, batting/bowling hand, bowling type.

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 [@nflfastR], sportsdataverse [@dataverse].

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,

• fetch_cricinfo()
• find_player_id()
• fetch_player_data()
• fetch_cricsheet()
• fetch_player_meta()
• update_player_meta()

and a data file containing the player meta data.

• player_meta

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

• matchtype: Character indicating test (default), odi, or t20.
• sex: Character indicating men (default) or women.
• activity: Character indicating batting (default), bowling or fielding.
• type: Character indicating innings-by-innings or career (default) data.
• country: Character indicating country. The default is to fetch data for all countries.

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()

# 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"
  )

# 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")

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"
)

#| tbl-cap: Centuries, 100 runs or more in a single innings, scored by USA Batters
# Table of USA player who have scored a century
menODI %>%
  filter(Runs >= 100) %>%
  select(Player, Runs, BallsFaced, Fours, Sixes, Opposition) %>%
  knitr::kable(digits = 2)

# menODI %>%
#   filter(Runs >= 50) %>%
#   ggplot(aes(y = Runs, x = BallsFaced) ) +
#   geom_point(size = 2) +
#   geom_text(aes(label= Player), vjust=-0.5, color="#013369",
#             position = position_dodge(0.9), size=2) +
#   ylab("Runs Scored") + xlab("Balls Faced")

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

• searchstring: string of a player's name or part of the name

# Fetching a player, Meg Lanning's, ID
meg_lanning_id <- find_player_id("Meg Lanning")$ID

meg_lanning_id

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

• playerid
• matchtype: Character indicating test (default), odi, or t20.
• activity: Character indicating batting (default), bowling or fielding.

# Fetching the player Meg Lanning's playing data
MegLanning <- fetch_player_data(meg_lanning_id, "ODI") %>%
  mutate(NotOut = (Dismissal == "not out"))

dim(MegLanning)
names(MegLanning)

# 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))

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

• type: Character string giving type of data: ball-by-ball, match info or player info.

• gender: Character string giving player gender: female or male.

• competition: Character string giving name of competition. e.g. ipl for Indiana Premier League, psl for Pakistan Super League, tests for international test matches, etc.

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()

# 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")

#| tbl-cap: Top 10 prolific batters of IPL 2022 season. JC Butler scored the most runs in total and scored at the highest strike rate (runs per ball). His boundary percent (percentage of balls faced hit for 4s or 6s) is also the highest, while his dot percent (percentage of balls not scored of) is also among the highest.
# 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")

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.

#| tbl-cap: Player and officials meta data.
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"
    )
  )

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

• playerid: A vector of player IDs as given in Cricinfo profiles. Integer or character.

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))

#| tbl-cap: Australian Women player meta data.
aus_women %>%
  knitr::kable(
    digits = 1, align = "c", format = "pipe",
    col.names = c(
      "ID", "FullName", "Country", "DOB", "BirthPlace", "BattingStyle",
      "BowlingStyle", "PlayingRole"
    )
  )

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

cricketdata documentation built on Aug. 29, 2023, 5:10 p.m.