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
A Mohammed West Indies 2008 2021 117 113 395.3 6 2206 125 17.65 5.58 18.98 5/10 4 3
Nida Dar Pakistan 2010 2023 128 121 410.2 10 2231 123 18.14 5.44 20.02 5/21 1 1
EA Perry Australia 2008 2023 136 128 392.5 8 2297 121 18.98 5.85 19.48 4/12 4 0
M Schutt Australia 2013 2023 93 92 309.3 7 1916 121 15.83 6.19 15.35 5/15 4 1
S Ismail South Africa 2007 2023 109 108 381.5 20 2191 117 18.73 5.74 19.58 5/12 0 2
KH Brunt England 2005 2023 109 108 381.1 17 2102 112 18.77 5.51 20.42 4/15 1 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
RR Pant 2018 2022 33 65 133 119 0 119 14 6
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 WMN Perth 20 60 38 2 0 52.63 2 caught 1 FALSE
2011-01-07 2 ENG WMN Perth 104 148 118 8 1 88.13 2 not out 2 TRUE
2011-06-14 2 NZ WMN Brisbane 11 15 14 2 0 78.57 2 bowled 2 FALSE
2011-06-16 1 NZ WMN Brisbane 5 8 8 1 0 62.50 2 caught 1 FALSE
2011-06-30 1 NZ WMN 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 rows containing missing values (`geom_point()`).