ESPNCricinfo data

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 row containing missing values or values outside the scale range
#> (`geom_point()`).