ESPNCricinfo data

Rob J Hyndman

library(cricketdata)
library(tidyverse)
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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.

Women’s T20 bowling data

# Fetch all Women's T20 data
wt20 <- fetch_cricinfo("T20", "Women", "Bowling")
Player Country Start End Matches Innings Overs Maidens Runs Wickets Average Economy StrikeRate BestBowlingInnings FourWickets FiveWickets
A Mohammed West Indies 2008 2021 117 113 395.3 6 2206 125 17.65 5.58 18.98 5/10 4 3
EA Perry Australia 2008 2021 126 119 380.5 6 2237 115 19.45 5.87 19.87 4/12 4 0
S Ismail South Africa 2007 2021 98 97 343.5 14 1990 110 18.09 5.79 18.75 5/12 0 2
Nida Dar Pakistan 2010 2021 108 103 347.2 8 1871 103 18.17 5.39 20.23 5/21 1 1
A Shrubsole England 2008 2020 79 79 266.2 10 1587 102 15.56 5.96 15.67 5/11 2 1
KH Brunt England 2005 2022 96 95 340.5 15 1888 98 19.27 5.54 20.87 3/6 0 0 
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")
Date Player Runs NotOut Minutes BallsFaced Fours Sixes StrikeRate Innings Participation Opposition Ground
2011-04-11 SR Watson 185 TRUE 113 96 15 15 192.7083 2 B Bangladesh Mirpur
2007-02-20 ML Hayden 181 TRUE 227 166 11 10 109.0361 1 B New Zealand Hamilton
2017-01-26 DA Warner 179 FALSE 186 128 19 5 139.8438 1 B Pakistan Adelaide
2015-03-04 DA Warner 178 FALSE 164 133 19 5 133.8346 1 B Afghanistan Perth
2001-02-09 ME Waugh 173 FALSE 199 148 16 3 116.8919 1 B West Indies Melbourne
2016-10-12 DA Warner 173 FALSE 218 136 24 0 127.2059 2 B South Africa Cape Town 
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")
Player Start End Matches Innings Dismissals Caught CaughtFielder CaughtBehind Stumped MaxDismissalsInnings
MS Dhoni 2005 2014 90 166 294 256 0 256 38 6
R Dravid 1996 2012 163 299 209 209 209 0 0 3
SMH Kirmani 1976 1986 88 151 198 160 0 160 38 6
VVS Laxman 1996 2012 134 248 135 135 135 0 0 4
KS More 1986 1993 49 90 130 110 0 110 20 5
SR Tendulkar 1989 2013 200 366 115 115 115 0 0 3 
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"))
Date Innings Opposition Ground Runs Mins BF X4s X6s SR Pos Dismissal Inns Start_Date NotOut
2011-01-05 1 ENG Women Perth 20 60 38 2 0 52.63 2 caught 1 5 Jan 2011 FALSE
2011-01-07 2 ENG Women Perth 104 148 118 8 1 88.13 2 not out 2 7 Jan 2011 TRUE
2011-06-14 2 NZ Women Brisbane 11 15 14 2 0 78.57 2 bowled 2 14 Jun 2011 FALSE
2011-06-16 1 NZ Women Brisbane 5 8 8 1 0 62.50 2 caught 1 16 Jun 2011 FALSE
2011-06-30 1 NZ Women Chesterfield 17 24 20 3 0 85.00 2 caught 1 30 Jun 2011 FALSE
2011-07-02 2 India Women Chesterfield 23 40 32 3 0 71.87 2 run out 2 2 Jul 2011 FALSE 
# Compute batting average
MLave <- MegLanning %>%
  summarise(
    Innings = sum(!is.na(Runs)),
    Average = sum(Runs, na.rm = TRUE) / (Innings - 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))