In ropenscilabs/cricketdata: International Cricket Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  echo = TRUE
)
# Okabi-Ito colours
options(
  ggplot2.discrete.colour = c("#D55E00", "#0072B2", "#009E73", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442")
)

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

The fetch_cricinfo() function will fetch data on all international cricket matches provided by ESPNCricinfo. Please respect the ESPNCricinfo terms of use when using this function.

Here are some examples of its use.

# Avoid downloading the data when the package is checked by CRAN.
# This only needs to be run once to store the data locally
wt20 <- fetch_cricinfo("T20", "Women", "Bowling")
menODI <- fetch_cricinfo("ODI", "Men", "Batting", type = "innings", country = "Australia")
Indfielding <- fetch_cricinfo("Test", "Men", "Fielding", country = "India")
meg_lanning_id <- find_player_id("Meg Lanning")$ID
MegLanning <- fetch_player_data(meg_lanning_id, "ODI") %>%
  mutate(NotOut = (Dismissal == "not out")) %>%
  mutate(NotOut = tidyr::replace_na(NotOut, FALSE))

saveRDS(wt20, "inst/extdata/wt20.rds")
saveRDS(menODI, "inst/extdata/menODI.rds")
saveRDS(Indfielding, "inst/extdata/Indfielding.rds")
saveRDS(MegLanning, "inst/extdata/MegLanning.rds")

wt20 <- readRDS("../inst/extdata/wt20.rds")
menODI <- readRDS("../inst/extdata/menODI.rds")
Indfielding <- readRDS("../inst/extdata/Indfielding.rds")
MegLanning <- readRDS("../inst/extdata/MegLanning.rds")

Women's T20 bowling data

# Fetch all Women's T20 data
wt20 <- fetch_cricinfo("T20", "Women", "Bowling")

wt20 %>%
  head() %>%
  knitr::kable(digits = 2)

wt20 %>%
  filter(Wickets > 20, !is.na(Country)) %>%
  ggplot(aes(y = StrikeRate, x = Country)) +
  geom_boxplot() +
  geom_point(alpha = 0.3, col = "blue") +
  ggtitle("Women T20: Strike Rates") +
  ylab("Balls per wicket") +
  coord_flip()

Australian men's ODI data by innings

# Fetch all Australian Men's ODI data by innings
menODI <- fetch_cricinfo("ODI", "Men", "Batting", type = "innings", country = "Australia")

menODI %>%
  head() %>%
  knitr::kable()

menODI %>%
  ggplot(aes(y = Runs, x = Date)) +
  geom_point(alpha = 0.2, col = "#D55E00") +
  geom_smooth() +
  ggtitle("Australia Men ODI: Runs per Innings")

Indian test fielding data

Indfielding <- fetch_cricinfo("Test", "Men", "Fielding", country = "India")

Indfielding %>%
  head() %>%
  knitr::kable()

Indfielding %>%
  mutate(wktkeeper = (CaughtBehind > 0) | (Stumped > 0)) %>%
  ggplot(aes(x = Matches, y = Dismissals, col = wktkeeper)) +
  geom_point() +
  ggtitle("Indian Men Test Fielding")

Meg Lanning's ODI batting

meg_lanning_id <- find_player_id("Meg Lanning")$ID
MegLanning <- fetch_player_data(meg_lanning_id, "ODI") %>%
  mutate(NotOut = (Dismissal == "not out")) %>%
  mutate(NotOut = tidyr::replace_na(NotOut, FALSE))

MegLanning %>%
  head() %>%
  knitr::kable()

# Compute batting average
MLave <- MegLanning %>%
  summarise(
    Innings = sum(!is.na(Runs)),
    Average = sum(Runs, na.rm = TRUE) / (Innings - sum(NotOut, na.rm=TRUE))
  ) %>%
  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))