ESPNCricinfo data

Rob J Hyndman

library(cricketdata)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
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.

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
M Schutt Australia 2013 2025 122 121 413.1 10 2632 149 17.66 6.37 16.64 5/15 4 1
Nida Dar Pakistan 2010 2024 160 152 510.2 13 2910 144 20.21 5.70 21.26 5/21 1 1
DB Sharma India 2016 2024 124 121 436.5 12 2642 138 19.14 6.05 18.99 4/10 1 0
S Ecclestone England 2016 2025 96 95 355.2 10 2089 137 15.25 5.88 15.56 4/18 2 0
N Boochatham THA 2018 2025 99 98 331.4 23 1363 126 10.82 4.11 15.79 5/5 5 1
EA Perry Australia 2008 2025 167 137 407.5 8 2385 126 18.93 5.85 19.42 4/12 4 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
2023-11-07 GJ Maxwell 201 TRUE 181 128 21 10 157.0312 2 B Afghanistan Wankhede
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 W.A.C.A
2023-11-11 MR Marsh 177 TRUE 177 132 17 9 134.0909 2 B Bangladesh Pune 
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
RR Pant 2018 2025 43 85 164 149 0 149 15 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 
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))
Date Innings Opposition Ground Runs Mins BF X4s X6s SR Pos Dismissal Inns NotOut
2011-01-05 1 ENG Women W.A.C.A 20 60 38 2 0 52.63 2 caught 1 FALSE
2011-01-07 2 ENG Women W.A.C.A 104 148 118 8 1 88.13 2 not out 2 TRUE
2011-06-14 2 NZ Women Brisbane 11 15 14 2 0 78.57 2 bowled 2 FALSE
2011-06-16 1 NZ Women Brisbane 5 8 8 1 0 62.50 2 caught 1 FALSE
2011-06-30 1 NZ Women Chesterfield 17 24 20 3 0 85.00 2 caught 1 FALSE
2011-07-02 2 India Women Chesterfield 23 40 32 3 0 71.87 2 run out 2 FALSE 
# 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))
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).