yorkr analyzes women’s One Day International and International T20
Tinniam V Ganesh
07/05/2020
“Life is not measured by the number of breaths we take, but by the moments that take our breath away.” Maya Angelou
“Life shrinks or expands in proportion to one’s courage.” Anais Nin
“Devotion to the truth is the hallmark of morality; there is no greater, nobler, more heroic form of devotion than the act of a man who assumes the responsibility of thinking.” Ayn Rand in Atlas Shrugged
Introduction
yorkr, this time, rocks women’s cricket!!! In this post, my R package yorkr analyzes women’s One Day International and International T20. The latest changes in my R package yorkr, as mentioned in my last post Revitalizing R package yorkr, included the modifications for the segregation men’s and women’s ODI and T20 matches into separate folders while converting them from YAML to R data frames. As the data was already converted I could just use the yorkr functions 45+ to analyze the women’s ODI and women’s T20. The data for this post is taken from Cricsheet
My R package yorkr has 4 classes of functions
ODI Functions
- Class 1: Analysis of ODI matches - See ODI-Part 1
- Class 2: Analysis of all ODI matches between 2 ODI teams - See ODI Part 2
- Class 3 : Analysis of all matches played by a ODI team againsta all other ODI teams - See ODI Part 3
- Class 4 : Analysis of ODI batsmen and bowlers - See ODI Part 4
The converted data is available at yorkrData
This RMarkdown file has been published at RPubs at yorkrAnalyzesWomensODIT20
You can download this as a PDF at yorkrAnalyzesWomensODIT20
install.packages("../../../yorkrgit/yorkr_0.0.9.tar.gz",repos = NULL, type="source")##1. Analysis of women’s ODI matches
library(yorkr)Save all matches between 2 teams
#saveAllMatchesBetweenTeams("../../../yorkrData2020/odi/odiWomenMatches/","../../../yorkrData2020/odi/odiWomenMatches2Teams/")Save all matches played by an ODI team against all other ODI teams
#saveAllMatchesAllOpposition("../../../yorkrData2020/odi/odiWomenMatches/","../../../yorkrData2020/odi/odiWomenAllMatchesAllTeams/")Since there are several functions in each class, I have randomly selected a few functions to demonstrate yorkr’s analysis ## ODI Match Analysis (Class 1) In the functions below ODI women matches are analyzed as in the India-Australia ODI in 7 Feb 2016.
load("../../../yorkrData2020/odi/odiWomenMatches/Australia-India-2016-02-07.RData")
aus_ind <- overs
teamBattingScorecardMatch(aus_ind,'India')## Total= 223## # A tibble: 7 x 5
## batsman ballsPlayed fours sixes runs
## <chr> <int> <int> <dbl> <dbl>
## 1 H Kaur 42 2 0 22
## 2 J Goswami 4 1 0 4
## 3 M Raj 113 12 0 89
## 4 PG Raut 31 2 0 24
## 5 S Mandhana 52 7 0 55
## 6 S Pandey 18 2 0 17
## 7 V Krishnamurthy 21 2 0 12The partnerships in this match between India and Australia. Mithali Raj tops the list, with partnerships with Smriti Mandhana, Harmanpreet Kaur and Punam Raut. The next highest partnership is Smriti Mandhana
teamBatsmenPartnershipMatch(aus_ind,"India","Australia")
Analyze bowling in the women’s ODI England-New Zealand match on 15 Feb 2013
load("../../../yorkrData2020/odi/odiWomenMatches/England-New Zealand-2013-02-15.RData")
eng_nz <- overs
teamBowlingWicketKindMatch(eng_nz,"England","New Zealand")
Match worm graph
Plot the match worm graph for Pakistan-South Africa women’s ODI 25 Jun 2017
load("../../../yorkrData2020/odi/odiWomenMatches/Pakistan-South Africa-2017-06-25.RData")
pak_sa <-overs
matchWormGraph(pak_sa,'Pakistan',"South Africa")
Analysis of team in all matches against another team (Class 2)
The functions below analyze all matches between South Africa and Sri Lanka.
load("../../../yorkrData2020/odi/odiWomenMatches2Teams/South Africa-Sri Lanka-allMatches.RData")
sa_sl_matches <- matches
m <-teamBatsmenPartnershiOppnAllMatches(sa_sl_matches,'South Africa',report="summary")
m## # A tibble: 16 x 2
## batsman totalRuns
## <chr> <dbl>
## 1 M du Preez 241
## 2 M Kapp 194
## 3 L Wolvaardt 168
## 4 D van Niekerk 138
## 5 L Lee 138
## 6 T Chetty 136
## 7 A Steyn 118
## 8 L Goodall 89
## 9 S Luus 71
## 10 N de Klerk 35
## 11 CL Tryon 15
## 12 F Tunnicliffe 15
## 13 S Ismail 9
## 14 M Klaas 2
## 15 Y Fourie 1
## 16 B Bezuidenhout 0teamBatsmenPartnershipOppnAllMatchesChart(sa_sl_matches,"Sri Lanka","South Africa")
The plot below gives the performance if women Indian ODI bowlers in all ODI matches against England. The top wicket takers are Jhulan Goswami, Ekta Bisht, Gouher Sultana
load("../../../yorkrData2020/odi/odiWomenMatches2Teams/India-England-allMatches.RData")
ind_eng_matches <- matches
teamBowlersWicketsOppnAllMatches(ind_eng_matches,"India","England")
Performance of women ODI teams against all other teams in all matches (Class 3)
West Indies top scorers in ODI in all matches. The top scorers in West Indies are 1. Stafanie Taylor 2. Deandra Dottin 3. Hayley Matthews
load("../../../yorkrData2020/odi/odiWomenAllMatchesAllteams/allMatchesAllOpposition-West Indies.RData")
wi_matches <- matches
m <-teamBattingScorecardAllOppnAllMatches(wi_matches,theTeam="West Indies")## Total= 4629m## # A tibble: 31 x 5
## batsman ballsPlayed fours sixes runs
## <chr> <int> <int> <int> <dbl>
## 1 SR Taylor 1087 83 7 766
## 2 DJS Dottin 778 69 21 641
## 3 HK Matthews 734 71 4 527
## 4 SA Campbelle 649 39 4 396
## 5 Kycia A Knight 517 35 2 284
## 6 CN Nation 554 31 1 274
## 7 Kyshona A Knight 578 35 NA 264
## 8 MR Aguilleira 481 20 3 252
## 9 B Cooper 289 19 3 176
## 10 NY McLean 230 18 2 155
## # … with 21 more rowsIndividual batsman and bowler performances (Class 4)
The functions below perform individual batsman and bowler analysis. I chose the top women ODI batsman
- Mithali Raj (Ind) has the highest ODI runs with a career average of 50.64
- Charlotte Edwards (Eng)
- Suzie Bates (NX)
#india_details <- getTeamBattingDetails("India",dir="../../../yorkrData2020/odi/odiWomenMatches", save=TRUE,odir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")
#eng_details <- getTeamBattingDetails("England",dir="../../../yorkrData2020/odi/odiWomenMatches", save=TRUE,odir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")
#nz_details <- getTeamBattingDetails("New Zealand",dir="../../../yorkrData2020/odi/odiWomenMatches", save=TRUE,odir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")
mithali <- getBatsmanDetails(team="India",name="M Raj",dir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")## [1] "../../../yorkrData2020/odi/odiWomenBattingBowlingDetails/India-BattingDetails.RData"charlotte <- getBatsmanDetails(team="England",name="CM Edwards",dir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")## [1] "../../../yorkrData2020/odi/odiWomenBattingBowlingDetails/England-BattingDetails.RData"suzie<- getBatsmanDetails(team="New Zealand",name="SW Bates",dir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")## [1] "../../../yorkrData2020/odi/odiWomenBattingBowlingDetails/New Zealand-BattingDetails.RData"Plot Runs vs Strike Rate
library(grid)
library(gridExtra)
p1 <-batsmanRunsVsStrikeRate(mithali,"Mithali Raj")
p2 <- batsmanRunsVsStrikeRate(charlotte, "Charlotte E")
p3 <- batsmanRunsVsStrikeRate(suzie, "Suzie Bates")
grid.arrange(p1,p2,p3, ncol=2)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Plot the moving average
p1 <-batsmanMovingAverage(mithali,"Mithali Raj")
p2 <- batsmanMovingAverage(charlotte, "Charlotte E")
p3 <- batsmanMovingAverage(suzie, "Suzie Bates")
grid.arrange(p1,p2,p3, ncol=2)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
p1 <-batsmanCumulativeAverageRuns(mithali,"Mithali Raj")
p2 <- batsmanCumulativeAverageRuns(charlotte, "Charlotte E")
p3 <- batsmanCumulativeAverageRuns(suzie, "Suzie Bates")
grid.arrange(p1,p2,p3, ncol=2)
Analyze ODI bowler performances
The following 3 bowlers have been chosen for analysis
- Jhulan Goswami (Ind) is the highest overwall wicket taker with 225 wicket
- Anisa Mohammed (WI)
- Sana Mir (Pak)
#india_details <- getTeamBowlingDetails("India",dir="../../../yorkrData2020/odi/odiWomenMatches", save=TRUE,odir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")
#wi_details <- getTeamBowlingDetails("West Indies",dir="../../../yorkrData2020/odi/odiWomenMatches", save=TRUE,odir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")
#pak_details <- getTeamBowlingDetails("Pakistan",dir="../../../yorkrData2020/odi/odiWomenMatches", save=TRUE,odir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")
jhulan <- getBowlerWicketDetails(team="India",name="J Goswami",dir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")
anisa <- getBowlerWicketDetails(team="West Indies",name="A Mohammed",dir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")
sana <- getBowlerWicketDetails(team="Pakistan",name="Sana Mir",dir="../../../yorkrData2020/odi/odiWomenBattingBowlingDetails")Plot the bowler Mean Economy Rate
p1<-bowlerMeanEconomyRate(jhulan,"Jhulan G")
p2<-bowlerMeanEconomyRate(anisa, "Anisa M")
p3<-bowlerMeanEconomyRate(sana, "Sana Mir")
grid.arrange(p1,p2,p3, ncol=2)
Plot the cumulative average wickets taken by the bowlers
p1<-bowlerCumulativeAvgWickets(jhulan,"Jhulan G")
p2<-bowlerCumulativeAvgWickets(anisa, "Anisa M")
p3<-bowlerCumulativeAvgWickets(sana, "Sana Mir")
grid.arrange(p1,p2,p3, ncol=2)
##2. Analysis of women’s International Twenty 20 matches
I have chosen some random yorkr functions to show the analysis of T20 players and matches
T20 Functions
There are the following class of T20 functions
- Class 1: Analysis of T20 matches - See T20-Part 1
- Class 2: Analysis of all T20 matches between 2 T20 teams - See T20 Part 2
- Class 3 : Analysis of all matches played by a T20 team againsta All other T20 teams - See T20 Part 3
- Class 4 : Analysis of T20 batsmen and bowlers - See T20 Part 4
You can also refer to the yorkr template that I created Analysis of International T20 matches with yorkr templates
Save all matches between teams
#saveAllMatchesBetweenTeams("../../../yorkrData2020/t20/t20WomenMatches/","../../../yorkrData2020/t20/t20WomenMatches2Teams/")Save all T20 matches played by a team against all other teams
#saveAllMatchesAllOpposition("../../../yorkrData2020/t20/t20WomenMatches/","../../../yorkrData2020/t20/t20WomenAllMatchesAllTeams/")T20 Match Analysis (Class 1)
Print the scorecard for the Bangladesh- Ireland match played on 3 Apr 2014
load("../../../yorkrData2020/t20/t20WomenMatches/Bangladesh-Ireland-2014-04-03.RData")
ban_ire <- overs
teamBattingScorecardMatch(ban_ire,'Bangladesh')## Total= 95## # A tibble: 9 x 5
## batsman ballsPlayed fours sixes runs
## <chr> <int> <dbl> <dbl> <dbl>
## 1 Ayasha Rahman 19 2 0 12
## 2 Fahima Khatun 2 0 0 0
## 3 Lata Mondal 12 1 0 8
## 4 Panna Ghosh 3 0 0 4
## 5 Rumana Ahmed 14 3 0 16
## 6 Salma Khatun 6 1 0 7
## 7 Shaila Sharmin 7 0 0 6
## 8 Shamima Sultana 11 0 0 7
## 9 Sharmin Akhter 46 3 0 35Plot the performance of T20 batsmen against in bowlers in Germany - Netherlands.
load("../../../yorkrData2020/t20/t20WomenMatches/Germany-Netherlands-2019-06-27.RData")
ger_net <- overs
teamBatsmenVsBowlersMatch(ger_net,'Netherlands',"Germany",plot=TRUE)
Print the bowling scorecard of Hong Kong-Kuwait T20 match played on 25 Feb 2019
load("../../../yorkrData2020/t20/t20WomenMatches/Hong Kong-Kuwait-2019-02-25.RData")
hk_kuw <-overs
teamBowlingScorecardMatch(hk_kuw,'Hong Kong')## # A tibble: 5 x 5
## bowler overs maidens runs wickets
## <chr> <int> <int> <dbl> <int>
## 1 Chan Ka Man 2 0 5 1
## 2 KY Chan 3 1 2 4
## 3 M Hill 2 0 6 1
## 4 M Wai Siu 2 0 11 1
## 5 M Yousaf 1 1 0 3Head to head between 2 women’s T20 teams (Class 2)
Print the partnership among Indian T20 women in all matches against England
load("../../../yorkrData2020/t20/t20WomenMatches2Teams/India-England-allMatches.RData")
ind_eng_matches <- matches
m <-teamBatsmenPartnershiOppnAllMatches(ind_eng_matches,'India',report="detailed")
m[1:30,]## batsman nonStriker partnershipRuns totalRuns
## 1 M Raj A Sharma 2 233
## 2 M Raj BS Fulmali 25 233
## 3 M Raj DB Sharma 16 233
## 4 M Raj H Kaur 18 233
## 5 M Raj J Goswami 6 233
## 6 M Raj KV Jain 5 233
## 7 M Raj L Kumari 5 233
## 8 M Raj N Niranjana 3 233
## 9 M Raj N Tanwar 17 233
## 10 M Raj PG Raut 41 233
## 11 M Raj R Malhotra 5 233
## 12 M Raj S Mandhana 17 233
## 13 M Raj S Naik 10 233
## 14 M Raj S Pandey 19 233
## 15 M Raj SK Naidu 37 233
## 16 M Raj V Krishnamurthy 7 233
## 17 S Mandhana H Deol 20 145
## 18 S Mandhana JI Rodrigues 47 145
## 19 S Mandhana M Raj 32 145
## 20 S Mandhana Shafali Verma 46 145
## 21 H Kaur A Sharma 1 137
## 22 H Kaur AA Patil 8 137
## 23 H Kaur DB Sharma 14 137
## 24 H Kaur E Bisht 3 137
## 25 H Kaur J Goswami 11 137
## 26 H Kaur JI Rodrigues 12 137
## 27 H Kaur M Raj 19 137
## 28 H Kaur MR Meshram 33 137
## 29 H Kaur N Tanwar 2 137
## 30 H Kaur PG Raut 0 137Plot the batting partnetship of Indian T20 womern against England
The best batsmen are Mithali Raj, Smriti Mandhana and Harmanpreet Kaur in that order
teamBatsmenPartnershipOppnAllMatchesChart(ind_eng_matches,"India","England")
Plot the wicket kind taken by the bowlers of Scotland against USA
load("../../../yorkrData2020/t20/t20WomenMatches2Teams/Scotland-United States of America-allMatches.RData")
sco_usa_matches <- matches
teamBowlersWicketsOppnAllMatches(sco_usa_matches,"Scotalnd","USA")
Performance of teams against all other teams in all T20 matches (Class 3)
Print the batting scorecard of Zimbabwe against all other teams
load("../../../yorkrData2020/t20/t20WomenAllMatchesAllTeams/allMatchesAllOpposition-Zimbabwe.RData")
zim_matches <- matches
m <-teamBattingScorecardAllOppnAllMatches(zim_matches,theTeam="Zimbabwe")## Total= 571m## # A tibble: 7 x 5
## batsman ballsPlayed fours sixes runs
## <chr> <int> <int> <int> <dbl>
## 1 SM Mayers 181 20 3 216
## 2 M Mupachikwa 139 9 NA 125
## 3 CS Mugeri 88 9 2 119
## 4 M Musonda 38 2 1 46
## 5 J Nkomo 25 3 NA 34
## 6 A Ndiraya 14 3 NA 18
## 7 AC Mushangwe 13 NA NA 13Pring the batting partnership of West Indies. The best performances are by 1. Stafanie Taylor 2. Deandra Dottin 3. Hayley Matthews
load("../../../yorkrData2020/t20/t20WomenAllMatchesAllTeams/allMatchesAllOpposition-West Indies.RData")
wi_matches <- matches
m <- teamBatsmenPartnershipAllOppnAllMatches(wi_matches,theTeam='West Indies')
m## # A tibble: 29 x 2
## batsman totalRuns
## <chr> <dbl>
## 1 SR Taylor 1199
## 2 DJS Dottin 912
## 3 HK Matthews 458
## 4 SA Campbelle 407
## 5 B Cooper 300
## 6 SACA King 287
## 7 MR Aguilleira 250
## 8 CN Nation 243
## 9 Kycia A Knight 240
## 10 NY McLean 142
## # … with 19 more rowsThe plot below shows the women T20 bowlers who have performed the best against India namely 1. Katherine Brunt (Eng) 2. Elysse Perry (Aus) 3. Anya Shrubsole
load("../../../yorkrData2020/t20/t20WomenAllMatchesAllTeams/allMatchesAllOpposition-India.RData")
ind_matches <- matches
teamBowlingWicketKindAllOppnAllMatches(ind_matches,t1="India",t2="All")
Analyze women T20 batsmen & bowlers (Class 4)
The following 4 players were chosen
- Harmanpreet Kaur (Ind)
- Suzie Bates (NZ)
- Meg Lanning (Aus)
- Stafanie Tay;or (WI)
#india_details <- getTeamBattingDetails("India",dir="../../../yorkrData2020/t20/t20WomenMatches", save=TRUE,odir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
#eng_details <- getTeamBattingDetails("England",dir="../../../yorkrData2020/t20/t20WomenMatches", save=TRUE,odir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
#aus_details <- getTeamBattingDetails("Australia",dir="../../../yorkrData2020/t20/t20WomenMatches", save=TRUE,odir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
#wi_details <- getTeamBattingDetails("West Indies",dir="../../../yorkrData2020/t20/t20WomenMatches", save=TRUE,odir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
#nz_details <- getTeamBattingDetails("New Zealand",dir="../../../yorkrData2020/t20/t20WomenMatches", save=TRUE,odir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
harmanpreet <- getBatsmanDetails(team="India",name="H Kaur",dir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")## [1] "../../../yorkrData2020/t20/t20WomenBattingBowlingDetails/India-BattingDetails.RData"suzie <- getBatsmanDetails(team="New Zealand",name="SW Bates",dir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")## [1] "../../../yorkrData2020/t20/t20WomenBattingBowlingDetails/New Zealand-BattingDetails.RData"meg <- getBatsmanDetails(team="Australia",name="MM Lanning",dir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")## [1] "../../../yorkrData2020/t20/t20WomenBattingBowlingDetails/Australia-BattingDetails.RData"stafanie <- getBatsmanDetails(team="West Indies",name="SR Taylor",dir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")## [1] "../../../yorkrData2020/t20/t20WomenBattingBowlingDetails/West Indies-BattingDetails.RData"Plot the performance of the players against opposition.
batsmanRunsAgainstOpposition(harmanpreet,"Harmanpreet")
batsmanRunsAgainstOpposition(suzie,"Suzie Bates")
batsmanRunsAgainstOpposition(stafanie,"Stafanie Taylor")
batsmanRunsAgainstOpposition(meg,"Meg Lanning")
Plot the cumulative strike rate of the players. Meg Lanning has the best strike rate of the lot. Stafanie and Suzie also touch a strike rate of 100
p1<-batsmanCumulativeStrikeRate(harmanpreet,"Harmanpreet")
p2<-batsmanCumulativeStrikeRate(suzie,"Suzie Bates")
p3<-batsmanCumulativeStrikeRate(stafanie,"Stafanie Taylor")
p4 <-batsmanCumulativeStrikeRate(meg,"Meg Lanning")
grid.arrange(p1,p2,p3,p4, ncol=2)
Analyze women’s T20 bowlers.
The following bowlers were chosen for analysis
- Poonam Yadav (Ind)
- Anisa Mohammed (WI)
- Ellyse Perry (Aus)
- Anya Shrubsole (England)
#india_details <- getTeamBowlingDetails("India",dir="../../../yorkrData2020/t20/t20WomenMatches", save=TRUE,odir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
#wi_details <- getTeamBowlingDetails("West Indies",dir="../../../yorkrData2020/t20/t20WomenMatches", save=TRUE,odir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
#aus_details <- getTeamBowlingDetails("Australia",dir="../../../yorkrData2020/t20/t20WomenMatches", save=TRUE,odir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
#eng_details <- getTeamBowlingDetails("England",dir="../../../yorkrData2020/t20/t20WomenMatches", save=TRUE,odir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
poonam <- getBowlerWicketDetails(team="India",name="Poonam Yadav",dir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
anisa <- getBowlerWicketDetails(team="West Indies",name="A Mohammed",dir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
ellyse <- getBowlerWicketDetails(team="Australia",name="EA Perry",dir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")
anya <- getBowlerWicketDetails(team="England",name="A Shrubsole",dir="../../../yorkrData2020/t20/t20WomenBattingBowlingDetails")Plot the bowler’s moving average
p1<-bowlerMovingAverage(poonam,"Poonam Yadav")
p2<-bowlerMovingAverage(anisa,"Anisa M")
p3 <-bowlerMovingAverage(ellyse,"Ellyse Perry")
p4 <-bowlerMovingAverage(anya,"Anya Shrubsole")
grid.arrange(p1,p2,p3,p4, ncol=2)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Plot the bowlers Cumulative Average
p1<-bowlerCumulativeAvgWickets(poonam,"Poonam Yadav")
p2<-bowlerCumulativeAvgWickets(anisa,"Anisa M")
p3 <-bowlerCumulativeAvgWickets(ellyse,"Ellyse Perry")
p4 <-bowlerCumulativeAvgWickets(anya,"Anya Shrubsole")
grid.arrange(p1,p2,p3,p4, ncol=2)
3a. Rank women ODI batsmen
library(yorkr)
dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/odi/odiWomenMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/odi/odiWomenBattingBowlingDetails"
rankODIBatsmen(dir=dir,odir=odir,minMatches=30)## [1] "Australia"
## [1] "India"
## [1] "Pakistan"
## [1] "West Indies"
## [1] "Sri Lanka"
## [1] "England"
## [1] "Bangladesh"
## [1] "Netherlands"
## [1] "No data"
## [1] "Scotland"
## [1] "No data"
## [1] "Afghanistan"
## [1] "No data"
## [1] "Zimbabwe"
## [1] "No data"
## [1] "Ireland"
## [1] "New Zealand"
## [1] "South Africa"
## [1] "Canada"
## [1] "No data"
## [1] "Bermuda"
## [1] "No data"
## [1] "Kenya"
## [1] "No data"
## [1] "Hong Kong"
## [1] "No data"
## [1] "Nepal"
## [1] "No data"
## [1] "Oman"
## [1] "No data"
## [1] "Papua New Guinea"
## [1] "No data"
## [1] "United Arab Emirates"
## [1] "No data"
## [1] "Namibia"
## [1] "No data"
## [1] "Cayman Islands"
## [1] "No data"
## [1] "Singapore"
## [1] "No data"
## [1] "United States of America"
## [1] "No data"
## [1] "Bhutan"
## [1] "No data"
## [1] "Maldives"
## [1] "No data"
## [1] "Botswana"
## [1] "No data"
## [1] "Nigeria"
## [1] "No data"
## [1] "Denmark"
## [1] "No data"
## [1] "Germany"
## [1] "No data"
## [1] "Jersey"
## [1] "No data"
## [1] "Norway"
## [1] "No data"
## [1] "Qatar"
## [1] "No data"
## [1] "Malaysia"
## [1] "No data"
## [1] "Vanuatu"
## [1] "No data"
## [1] "Thailand"
## [1] "No data"
## [1] "Australia-BattingDetails.RData"
## [1] "India-BattingDetails.RData"
## [1] "Pakistan-BattingDetails.RData"
## [1] "West Indies-BattingDetails.RData"
## [1] "Sri Lanka-BattingDetails.RData"
## [1] "England-BattingDetails.RData"
## [1] "Bangladesh-BattingDetails.RData"
## [1] "Ireland-BattingDetails.RData"
## [1] "New Zealand-BattingDetails.RData"
## [1] "South Africa-BattingDetails.RData"## # A tibble: 24 x 4
## batsman matches meanRuns meanSR
## <chr> <int> <dbl> <dbl>
## 1 AE Satterthwaite 32 61.5 81.2
## 2 MM Lanning 47 49.5 85.0
## 3 TT Beaumont 35 45.8 68.5
## 4 EA Perry 42 45.7 74.3
## 5 SW Bates 42 44.0 70.9
## 6 NR Sciver 35 43.0 94.7
## 7 M Raj 35 42.8 64.1
## 8 AC Jayangani 48 38.6 59.9
## 9 NE Bolton 32 36.5 60.5
## 10 T Chetty 34 33.1 70.3
## # … with 14 more rows3b. Rank women ODI bowlers
library(yorkr)
dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/odi/odiWomenMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/odi/odiWomenBattingBowlingDetails"
rankODIBowlers(dir=dir,odir=odir,minMatches=30)## [1] "Australia"
## [1] "India"
## [1] "Pakistan"
## [1] "West Indies"
## [1] "Sri Lanka"
## [1] "England"
## [1] "Bangladesh"
## [1] "Netherlands"
## [1] "No data"
## [1] "Scotland"
## [1] "No data"
## [1] "Afghanistan"
## [1] "No data"
## [1] "Zimbabwe"
## [1] "No data"
## [1] "Ireland"
## [1] "New Zealand"
## [1] "South Africa"
## [1] "Canada"
## [1] "No data"
## [1] "Bermuda"
## [1] "No data"
## [1] "Kenya"
## [1] "No data"
## [1] "Hong Kong"
## [1] "No data"
## [1] "Nepal"
## [1] "No data"
## [1] "Oman"
## [1] "No data"
## [1] "Papua New Guinea"
## [1] "No data"
## [1] "United Arab Emirates"
## [1] "No data"
## [1] "Namibia"
## [1] "No data"
## [1] "Cayman Islands"
## [1] "No data"
## [1] "Singapore"
## [1] "No data"
## [1] "United States of America"
## [1] "No data"
## [1] "Bhutan"
## [1] "No data"
## [1] "Maldives"
## [1] "No data"
## [1] "Botswana"
## [1] "No data"
## [1] "Nigeria"
## [1] "No data"
## [1] "Denmark"
## [1] "No data"
## [1] "Germany"
## [1] "No data"
## [1] "Jersey"
## [1] "No data"
## [1] "Norway"
## [1] "No data"
## [1] "Qatar"
## [1] "No data"
## [1] "Malaysia"
## [1] "No data"
## [1] "Vanuatu"
## [1] "No data"
## [1] "Thailand"
## [1] "No data"
## [1] "Australia-BowlingDetails.RData"
## [1] "India-BowlingDetails.RData"
## [1] "Pakistan-BowlingDetails.RData"
## [1] "West Indies-BowlingDetails.RData"
## [1] "Sri Lanka-BowlingDetails.RData"
## [1] "England-BowlingDetails.RData"
## [1] "Bangladesh-BowlingDetails.RData"
## [1] "Ireland-BowlingDetails.RData"
## [1] "New Zealand-BowlingDetails.RData"
## [1] "South Africa-BowlingDetails.RData"## # A tibble: 19 x 4
## bowler matches totalWickets meanER
## <chr> <int> <dbl> <dbl>
## 1 JL Jonassen 44 76 3.90
## 2 M Kapp 49 70 3.80
## 3 S Ismail 44 65 3.82
## 4 KH Brunt 42 62 3.57
## 5 EA Perry 43 58 4.44
## 6 A Shrubsole 41 58 4.07
## 7 J Goswami 33 58 3.59
## 8 S Luus 41 54 4.82
## 9 D van Niekerk 40 53 3.84
## 10 ML Schutt 35 48 4.46
## 11 A Khaka 33 47 4.08
## 12 JL Gunn 30 43 4.23
## 13 I Ranaweera 35 42 4.89
## 14 Sana Mir 32 41 4.29
## 15 LA Marsh 30 40 4.16
## 16 NR Sciver 36 37 4.64
## 17 NR Sciver 36 37 4.64
## 18 NR Sciver 36 37 4.64
## 19 NR Sciver 36 37 4.644a. Rank women T20 batsman
library(yorkr)
dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20WomenMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20WomenBattingBowlingDetails"
rankT20Batsmen(dir=dir,odir=odir,minMatches=30)## [1] "Australia"
## [1] "India"
## [1] "Pakistan"
## [1] "West Indies"
## [1] "Sri Lanka"
## [1] "England"
## [1] "Bangladesh"
## [1] "Netherlands"
## [1] "Scotland"
## [1] "Afghanistan"
## [1] "No data"
## [1] "Zimbabwe"
## [1] "Ireland"
## [1] "New Zealand"
## [1] "South Africa"
## [1] "Canada"
## [1] "Bermuda"
## [1] "No data"
## [1] "Kenya"
## [1] "Hong Kong"
## [1] "Nepal"
## [1] "Oman"
## [1] "Papua New Guinea"
## [1] "United Arab Emirates"
## [1] "Namibia"
## [1] "Cayman Islands"
## [1] "No data"
## [1] "Singapore"
## [1] "United States of America"
## [1] "Bhutan"
## [1] "No data"
## [1] "Maldives"
## [1] "Botswana"
## [1] "Nigeria"
## [1] "Denmark"
## [1] "No data"
## [1] "Germany"
## [1] "Jersey"
## [1] "No data"
## [1] "Norway"
## [1] "No data"
## [1] "Qatar"
## [1] "No data"
## [1] "Malaysia"
## [1] "Vanuatu"
## [1] "Thailand"
## [1] "Australia-BattingDetails.RData"
## [1] "India-BattingDetails.RData"
## [1] "Pakistan-BattingDetails.RData"
## [1] "West Indies-BattingDetails.RData"
## [1] "Sri Lanka-BattingDetails.RData"
## [1] "England-BattingDetails.RData"
## [1] "Bangladesh-BattingDetails.RData"
## [1] "Netherlands-BattingDetails.RData"
## [1] "Scotland-BattingDetails.RData"
## [1] "Zimbabwe-BattingDetails.RData"
## [1] "Ireland-BattingDetails.RData"
## [1] "New Zealand-BattingDetails.RData"
## [1] "South Africa-BattingDetails.RData"
## [1] "Canada-BattingDetails.RData"
## [1] "Kenya-BattingDetails.RData"
## [1] "Hong Kong-BattingDetails.RData"
## [1] "Nepal-BattingDetails.RData"
## [1] "Oman-BattingDetails.RData"
## [1] "Papua New Guinea-BattingDetails.RData"
## [1] "United Arab Emirates-BattingDetails.RData"
## [1] "Namibia-BattingDetails.RData"
## [1] "Singapore-BattingDetails.RData"
## [1] "United States of America-BattingDetails.RData"
## [1] "Maldives-BattingDetails.RData"
## [1] "Botswana-BattingDetails.RData"
## [1] "Nigeria-BattingDetails.RData"
## [1] "Germany-BattingDetails.RData"
## [1] "Malaysia-BattingDetails.RData"
## [1] "Vanuatu-BattingDetails.RData"
## [1] "Thailand-BattingDetails.RData"## # A tibble: 30 x 4
## batsman matches meanRuns meanSR
## <chr> <int> <dbl> <dbl>
## 1 SR Taylor 39 33.1 96.7
## 2 MM Lanning 53 29.3 102.
## 3 EJ Villani 32 28.2 94.8
## 4 D van Niekerk 41 27.3 88.2
## 5 SJ Taylor 46 26.7 100.
## 6 SW Bates 35 26.1 99.8
## 7 AC Jayangani 41 25.5 94.7
## 8 Bismah Maroof 52 24.5 83.0
## 9 DJS Dottin 38 24 109.
## 10 CM Edwards 44 23.7 94.1
## # … with 20 more rows4b. Rank women T20 bowlers
library(yorkr)
dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20WomenMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20WomenBattingBowlingDetails"
rankT20Bowlers(dir=dir,odir=odir,minMatches=30)## [1] "Australia"
## [1] "India"
## [1] "Pakistan"
## [1] "West Indies"
## [1] "Sri Lanka"
## [1] "England"
## [1] "Bangladesh"
## [1] "Netherlands"
## [1] "Scotland"
## [1] "Afghanistan"
## [1] "No data"
## [1] "Zimbabwe"
## [1] "Ireland"
## [1] "New Zealand"
## [1] "South Africa"
## [1] "Canada"
## [1] "Bermuda"
## [1] "No data"
## [1] "Kenya"
## [1] "Hong Kong"
## [1] "Nepal"
## [1] "Oman"
## [1] "Papua New Guinea"
## [1] "United Arab Emirates"
## [1] "Namibia"
## [1] "Cayman Islands"
## [1] "No data"
## [1] "Singapore"
## [1] "United States of America"
## [1] "Bhutan"
## [1] "No data"
## [1] "Maldives"
## [1] "Botswana"
## [1] "Nigeria"
## [1] "Denmark"
## [1] "No data"
## [1] "Germany"
## [1] "Jersey"
## [1] "No data"
## [1] "Norway"
## [1] "No data"
## [1] "Qatar"
## [1] "No data"
## [1] "Malaysia"
## [1] "Vanuatu"
## [1] "Thailand"
## [1] "Australia-BowlingDetails.RData"
## [1] "India-BowlingDetails.RData"
## [1] "Pakistan-BowlingDetails.RData"
## [1] "West Indies-BowlingDetails.RData"
## [1] "Sri Lanka-BowlingDetails.RData"
## [1] "England-BowlingDetails.RData"
## [1] "Bangladesh-BowlingDetails.RData"
## [1] "Netherlands-BowlingDetails.RData"
## [1] "Scotland-BowlingDetails.RData"
## [1] "Zimbabwe-BowlingDetails.RData"
## [1] "Ireland-BowlingDetails.RData"
## [1] "New Zealand-BowlingDetails.RData"
## [1] "South Africa-BowlingDetails.RData"
## [1] "Canada-BowlingDetails.RData"
## [1] "Kenya-BowlingDetails.RData"
## [1] "Hong Kong-BowlingDetails.RData"
## [1] "Nepal-BowlingDetails.RData"
## [1] "Oman-BowlingDetails.RData"
## [1] "Papua New Guinea-BowlingDetails.RData"
## [1] "United Arab Emirates-BowlingDetails.RData"
## [1] "Namibia-BowlingDetails.RData"
## [1] "Singapore-BowlingDetails.RData"
## [1] "United States of America-BowlingDetails.RData"
## [1] "Maldives-BowlingDetails.RData"
## [1] "Botswana-BowlingDetails.RData"
## [1] "Nigeria-BowlingDetails.RData"
## [1] "Germany-BowlingDetails.RData"
## [1] "Malaysia-BowlingDetails.RData"
## [1] "Vanuatu-BowlingDetails.RData"
## [1] "Thailand-BowlingDetails.RData"## # A tibble: 20 x 4
## bowler matches totalWickets meanER
## <chr> <int> <dbl> <dbl>
## 1 A Shrubsole 50 76 5.95
## 2 Nida Dar 50 59 5.99
## 3 KH Brunt 49 57 5.93
## 4 JL Jonassen 50 55 5.31
## 5 EA Perry 51 52 5.67
## 6 S Ismail 50 52 5.40
## 7 ML Schutt 39 50 6.17
## 8 D van Niekerk 39 47 5.45
## 9 D Hazell 35 44 4.95
## 10 NR Sciver 44 43 6.30
## 11 JL Gunn 30 41 6.14
## 12 A Mohammed 43 41 5.80
## 13 M Kapp 31 39 5.08
## 14 Asmavia Iqbal 33 36 6.39
## 15 Sana Mir 46 36 5.85
## 16 HASD Siriwardene 35 33 6.31
## 17 EA Osborne 30 31 5.62
## 18 S Luus 37 29 7.13
## 19 KDU Prabodhani 33 25 4.87
## 20 Bismah Maroof 35 22 6.49Conclusion
While I have just shown how to use a small subset of functions, you can use the entire set of yorkr functions to analyze individual matches, head-2-head confrontation of two teams, performance of a teams against all other teams and finally performance of individual batsmen and bowlers in women’s ODI and T20 games.
You may also like
- Understanding Neural Style Transfer with Tensorflow and Keras
- Using Reinforcement Learning to solve Gridworld
- Big Data-4: Webserver log analysis with RDDs, Pyspark, SparkR and SparklyR
- Cricpy takes a swing at the ODIs
- GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables
- Cricketr adds team analytics to its repertoire!!!
- Deep Learning from first principles in Python, R and Octave – Part 8
- Natural language processing: What would Shakespeare say?
- Simulating an Edge Shape in Android
To see all posts click Index of posts