yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams
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Alice:“How long is forever”? White Rabbit:“Sometimes, just one second.”
Alice :“Where should I go?” The Cheshire Cat: “That depends on where you want to end up.”
“I’m not strange, weird, off, nor crazy, my reality is just different from yours.”
Alice through the looking glass - Lewis Caroll
Introduction
In this post, my R package ‘yorkr’, continues to bat in the Twenty20s. This post is a continuation of my earlier post – yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance. This post deals with Class 2 functions namely the performances of a team in all T20 matches against a single opposition for e.g all T20 matches of India-Australia, Pakistan-West Indies etc. You can clone/fork the code for my package yorkr from Github at yorkr
This post has also been published at RPubs yorkrT20-Part2 and can also be downloaded as a PDF document from yorkrT20-Part2.pdf
The list of function in Class 2 are
- teamBatsmenPartnershiOppnAllMatches()
- teamBatsmenPartnershipOppnAllMatchesChart()
- teamBatsmenVsBowlersOppnAllMatches()
- teamBattingScorecardOppnAllMatches()
- teamBowlingPerfOppnAllMatches()
- teamBowlersWicketsOppnAllMatches()
- teamBowlersVsBatsmenOppnAllMatches()
- teamBowlersWicketKindOppnAllMatches()
- teamBowlersWicketRunsOppnAllMatches()
- plotWinLossBetweenTeams()
1. Install the package from CRAN
library(yorkr) rm(list=ls())
2. Get data for all T20 matches between 2 teams
We can get all T20 matches between any 2 teams using the function below. The dir parameter should point to the folder which has the T20 RData files of the individual matches. This function creates a data frame of all the T20 matches and also saves the dataframe as RData. The function below gets all matches between India and Australia
setwd("C:/software/cricket-package/york-test/yorkrData/Twenty20/T20-matches")
matches <- getAllMatchesBetweenTeams("Australia","India",dir=".")
dim(matches)
## [1] 2829 25
I have however already saved the Twenty20 matches for all possible combination of opposing countries. The data for these matches for the individual teams/countries can be obtained from Github at in the folder T20-allmatches-between-two-teams
3. Save data for all matches between all combination of 2 teams
This can be done locally using the function below. You could use this function to combine all Twenty20 matches between any 2 teams into a single dataframe and save it in the current folder. The current implementation expectes that the the RData files of individual matches are in ../data folder. Since I already have converted this I will not be running this again
#saveAllMatchesBetweenTeams()
4. Load data directly for all matches between 2 teams
As in my earlier post I pick all Twenty20 matches between 2 random teams. I load the data directly from the stored RData files. When we load the Rdata file a “matches” object will be created. This object can be stored for the apporpriate teams as below
# Load T20 matches between teams
setwd("C:/software/cricket-package/york-test/yorkrData/Twenty20/T20-allmatches-between-two-teams")
load("India-Australia-allMatches.RData")
aus_ind_matches <- matches
dim(aus_ind_matches)
## [1] 2829 25
load("England-New Zealand-allMatches.RData")
eng_nz_matches <- matches
dim(eng_nz_matches)
## [1] 2760 25
load("Pakistan-South Africa-allMatches.RData")
pak_sa_matches <- matches
dim(pak_sa_matches)
## [1] 2308 25
load("Sri Lanka-West Indies-allMatches.RData")
sl_wi_matches <- matches
dim(sl_wi_matches)
## [1] 1909 25
load("Bangladesh-Ireland-allMatches.RData")
ban_ire_matches <-matches
dim(ban_ire_matches)
## [1] 479 25
load("Scotland-Canada-allMatches.RData")
sco_can_matches <-matches
dim(sco_can_matches)
## [1] 250 25
load("Netherlands-Afghanistan-allMatches.RData")
nl_afg_matches <- matches
dim(nl_afg_matches)
## [1] 927 25
5. Team Batsmen partnership in Twenty20 (all matches with opposition)
This function will create a report of the batting partnerships in the teams. The report can be brief or detailed depending on the parameter ‘report’. The top batsmen in India-Australia clashes are Shane Watson & AJ Finch from Australia and Virat Kohli & Yuvraj Singh of India.
m<- teamBatsmenPartnershiOppnAllMatches(aus_ind_matches,'Australia',report="summary") m ## Source: local data frame [40 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 SR Watson 284 ## 2 AJ Finch 249 ## 3 DA Warner 204 ## 4 MS Wade 125 ## 5 DJ Hussey 101 ## 6 ML Hayden 79 ## 7 RT Ponting 76 ## 8 MJ Clarke 65 ## 9 A Symonds 63 ## 10 AC Gilchrist 59 ## .. ... ... m <-teamBatsmenPartnershiOppnAllMatches(aus_ind_matches,'India',report="summary") m ## Source: local data frame [23 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 V Kohli 319 ## 2 Yuvraj Singh 262 ## 3 RG Sharma 252 ## 4 MS Dhoni 213 ## 5 G Gambhir 198 ## 6 SK Raina 160 ## 7 S Dhawan 105 ## 8 RV Uthappa 70 ## 9 IK Pathan 57 ## 10 V Sehwag 41 ## .. ... ... m <-teamBatsmenPartnershiOppnAllMatches(aus_ind_matches,'Australia',report="detailed") m[1:30,] ## batsman nonStriker partnershipRuns totalRuns ## 1 SR Watson AJ Finch 21 284 ## 2 SR Watson GJ Maxwell 3 284 ## 3 SR Watson DA Warner 127 284 ## 4 SR Watson SE Marsh 41 284 ## 5 SR Watson TM Head 63 284 ## 6 SR Watson CA Lynn 23 284 ## 7 SR Watson UT Khawaja 2 284 ## 8 SR Watson CT Bancroft 4 284 ## 9 AJ Finch BJ Haddin 15 249 ## 10 AJ Finch NJ Maddinson 21 249 ## 11 AJ Finch SR Watson 25 249 ## 12 AJ Finch GJ Maxwell 12 249 ## 13 AJ Finch MC Henriques 21 249 ## 14 AJ Finch DA Warner 44 249 ## 15 AJ Finch DJ Hussey 25 249 ## 16 AJ Finch MS Wade 1 249 ## 17 AJ Finch SE Marsh 66 249 ## 18 AJ Finch SPD Smith 16 249 ## 19 AJ Finch TM Head 0 249 ## 20 AJ Finch CA Lynn 3 249 ## 21 DA Warner AJ Finch 30 204 ## 22 DA Warner SR Watson 110 204 ## 23 DA Warner GJ Maxwell 11 204 ## 24 DA Warner DJ Hussey 22 204 ## 25 DA Warner CL White 6 204 ## 26 DA Warner MS Wade 25 204 ## 27 MS Wade AJ Finch 2 125 ## 28 MS Wade JP Faulkner 6 125 ## 29 MS Wade DA Warner 12 125 ## 30 MS Wade DJ Hussey 54 125 m <-teamBatsmenPartnershiOppnAllMatches(pak_sa_matches,'Pakistan',report="summary") m ## Source: local data frame [24 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 Umar Akmal 255 ## 2 Mohammad Hafeez 205 ## 3 Shahid Afridi 165 ## 4 Ahmed Shehzad 85 ## 5 Shoaib Malik 80 ## 6 Nasir Jamshed 69 ## 7 Misbah-ul-Haq 63 ## 8 Kamran Akmal 62 ## 9 Abdul Razzaq 62 ## 10 Sohaib Maqsood 41 ## .. ... ... m <-teamBatsmenPartnershiOppnAllMatches(eng_nz_matches,'England',report="summary") m ## Source: local data frame [35 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 LJ Wright 273 ## 2 AD Hales 194 ## 3 MJ Lumb 188 ## 4 EJG Morgan 152 ## 5 JC Buttler 140 ## 6 KP Pietersen 112 ## 7 OA Shah 91 ## 8 PD Collingwood 86 ## 9 IR Bell 73 ## 10 JE Root 68 ## .. ... ... m <-teamBatsmenPartnershiOppnAllMatches(sl_wi_matches,'Sri Lanka',report="summary") m[1:20,] ## Source: local data frame [20 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 TM Dilshan 334 ## 2 DPMD Jayawardene 202 ## 3 KC Sangakkara 135 ## 4 ST Jayasuriya 111 ## 5 AD Mathews 98 ## 6 MDKJ Perera 78 ## 7 DSNFG Jayasuriya 66 ## 8 HDRL Thirimanne 48 ## 9 LD Chandimal 41 ## 10 KMDN Kulasekara 30 ## 11 LPC Silva 18 ## 12 J Mubarak 15 ## 13 TAM Siriwardana 15 ## 14 CK Kapugedera 8 ## 15 SL Malinga 7 ## 16 S Prasanna 6 ## 17 BMAJ Mendis 3 ## 18 NLTC Perera 3 ## 19 SMSM Senanayake 3 ## 20 PVD Chameera 3 m <- teamBatsmenPartnershiOppnAllMatches(ban_ire_matches,"Ireland",report="summary") m ## Source: local data frame [11 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 GC Wilson 51 ## 2 WTS Porterfield 49 ## 3 NJ O'Brien 48 ## 4 KJ O'Brien 39 ## 5 JF Mooney 18 ## 6 MC Sorensen 12 ## 7 EC Joyce 11 ## 8 DT Johnston 7 ## 9 PR Stirling 4 ## 10 JP Bray 2 ## 11 AR Cusack 1
6. Team batsmen partnership in Twenty20 (all matches with opposition)
This is plotted graphically in the charts below. Kohli & Yuvraj top the list for India.
teamBatsmenPartnershipOppnAllMatchesChart(aus_ind_matches,"India","Australia")
teamBatsmenPartnershipOppnAllMatchesChart(pak_sa_matches,main="South Africa",opposition="Pakistan")
m<- teamBatsmenPartnershipOppnAllMatchesChart(eng_nz_matches,"New Zealand",opposition="England",plot=FALSE) m[1:30,] ## batsman nonStriker runs ## 1 HD Rutherford MJ Guptill 69 ## 2 HD Rutherford BB McCullum 61 ## 3 BB McCullum MJ Guptill 53 ## 4 MJ Guptill HD Rutherford 52 ## 5 BB McCullum KS Williamson 51 ## 6 BB McCullum HD Rutherford 49 ## 7 LRPL Taylor BB McCullum 49 ## 8 BB McCullum LRPL Taylor 46 ## 9 MJ Guptill BB McCullum 41 ## 10 SB Styris CD McMillan 40 ## 11 CD McMillan JDP Oram 38 ## 12 JEC Franklin LRPL Taylor 33 ## 13 LRPL Taylor KS Williamson 32 ## 14 KS Williamson LRPL Taylor 32 ## 15 SB Styris LRPL Taylor 31 ## 16 LRPL Taylor SB Styris 30 ## 17 BB McCullum JD Ryder 29 ## 18 JDP Oram JS Patel 28 ## 19 JD Ryder BB McCullum 27 ## 20 BB McCullum JEC Franklin 26 ## 21 DR Flynn SB Styris 22 ## 22 TWM Latham LRPL Taylor 22 ## 23 KS Williamson MJ Santner 21 ## 24 JEC Franklin NL McCullum 21 ## 25 C Munro MJ Guptill 21 ## 26 LRPL Taylor JM How 19 ## 27 LRPL Taylor MJ Guptill 19 ## 28 CD McMillan SB Styris 19 ## 29 MJ Guptill JEC Franklin 19 ## 30 BB McCullum SB Styris 18 teamBatsmenPartnershipOppnAllMatchesChart(sl_wi_matches,"Sri Lanka","West Indies")
teamBatsmenPartnershipOppnAllMatchesChart(ban_ire_matches,"Bangladesh","Ireland")
7. Team batsmen versus bowler in Twenty20 (all matches with opposition)
The plots below provide information on how each of the top batsmen fared against the opposition bowlers
teamBatsmenVsBowlersOppnAllMatches(aus_ind_matches,"India","Australia")
teamBatsmenVsBowlersOppnAllMatches(pak_sa_matches,"South Africa","Pakistan",top=3)
m <- teamBatsmenVsBowlersOppnAllMatches(eng_nz_matches,"England","New Zealnd",top=10,plot=FALSE) m ## Source: local data frame [113 x 3] ## Groups: batsman [1] ## ## batsman bowler runs ## (fctr) (fctr) (dbl) ## 1 LJ Wright SE Bond 1 ## 2 LJ Wright MR Gillespie 17 ## 3 LJ Wright JDP Oram 4 ## 4 LJ Wright CS Martin 19 ## 5 LJ Wright DL Vettori 18 ## 6 LJ Wright SB Styris 14 ## 7 LJ Wright KD Mills 23 ## 8 LJ Wright MJ Mason 4 ## 9 LJ Wright NL McCullum 42 ## 10 LJ Wright IG Butler 15 ## .. ... ... ... teamBatsmenVsBowlersOppnAllMatches(sl_wi_matches,"Sri Lanka","West Indies")
teamBatsmenVsBowlersOppnAllMatches(ban_ire_matches,"Bangladesh","Ireland")
8. Team batsmen versus bowler in Twenty20(all matches with opposition)
The following tables gives the overall performances of the country’s batsmen against the opposition. For India-Australia matches Virat Kohli, Yuvraj Singh and Rohit Sharma lead the way. For Australia it is Shane Watson, AJ Finch and DA Warner. In South Africa- Pakistan matches it is JP Duminy & De Kock respectively
a <-teamBattingScorecardOppnAllMatches(aus_ind_matches,main="India",opposition="Australia") ## Total= 1787 a ## Source: local data frame [23 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 V Kohli 225 27 7 319 ## 2 Yuvraj Singh 151 21 18 262 ## 3 RG Sharma 175 20 12 252 ## 4 MS Dhoni 189 15 7 213 ## 5 G Gambhir 174 25 1 198 ## 6 SK Raina 117 17 3 160 ## 7 S Dhawan 65 12 3 105 ## 8 RV Uthappa 54 7 3 70 ## 9 IK Pathan 58 2 1 57 ## 10 V Sehwag 38 5 1 41 ## .. ... ... ... ... ... teamBattingScorecardOppnAllMatches(aus_ind_matches,"Australia","India") ## Total= 1767 ## Source: local data frame [40 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 SR Watson 173 16 20 284 ## 2 AJ Finch 164 33 5 249 ## 3 DA Warner 134 14 14 204 ## 4 MS Wade 93 6 5 125 ## 5 DJ Hussey 81 5 6 101 ## 6 ML Hayden 63 5 6 79 ## 7 RT Ponting 52 13 NA 76 ## 8 MJ Clarke 54 3 1 65 ## 9 A Symonds 43 4 2 63 ## 10 AC Gilchrist 38 7 3 59 ## .. ... ... ... ... ... teamBattingScorecardOppnAllMatches(pak_sa_matches,"South Africa","Pakistan") ## Total= 1265 ## Source: local data frame [27 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 JP Duminy 178 14 7 214 ## 2 Q de Kock 110 21 2 147 ## 3 HM Amla 114 17 2 146 ## 4 AB de Villiers 116 10 5 144 ## 5 F du Plessis 121 6 4 129 ## 6 JH Kallis 92 9 2 98 ## 7 CA Ingram 55 8 3 77 ## 8 GC Smith 78 9 NA 74 ## 9 DA Miller 54 7 2 73 ## 10 RK Kleinveldt 7 1 3 22 ## .. ... ... ... ... ... teamBattingScorecardOppnAllMatches(sl_wi_matches,"West Indies","Sri Lanka") ## Total= 1017 ## Source: local data frame [20 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 DJ Bravo 173 17 9 218 ## 2 MN Samuels 132 9 8 157 ## 3 ADS Fletcher 74 10 7 109 ## 4 CH Gayle 91 9 2 76 ## 5 KA Pollard 61 6 2 65 ## 6 RR Sarwan 66 2 NA 61 ## 7 D Ramdin 30 3 2 47 ## 8 J Charles 51 3 3 46 ## 9 DJG Sammy 34 4 NA 45 ## 10 AD Russell 32 NA 4 44 ## 11 LMP Simmons 29 5 NA 33 ## 12 JE Taylor 23 2 NA 24 ## 13 SP Narine 15 2 1 23 ## 14 S Chanderpaul 28 1 1 19 ## 15 DR Smith 14 1 1 17 ## 16 XM Marshall 12 2 NA 14 ## 17 SJ Benn 8 1 NA 6 ## 18 D Bishoo 5 1 NA 6 ## 19 WW Hinds 7 1 NA 5 ## 20 JO Holder 4 NA NA 2 teamBattingScorecardOppnAllMatches(eng_nz_matches,"England","New Zealand") ## Total= 1943 ## Source: local data frame [35 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 LJ Wright 167 28 12 273 ## 2 AD Hales 125 22 7 194 ## 3 MJ Lumb 129 15 11 188 ## 4 EJG Morgan 141 12 5 152 ## 5 JC Buttler 83 16 5 140 ## 6 KP Pietersen 83 13 2 112 ## 7 OA Shah 68 6 4 91 ## 8 PD Collingwood 61 6 4 86 ## 9 IR Bell 60 11 1 73 ## 10 JE Root 45 8 1 68 ## .. ... ... ... ... ... teamBatsmenPartnershiOppnAllMatches(sco_can_matches,"Scotland","Canada") ## Source: local data frame [8 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 RD Berrington 47 ## 2 KJ Coetzer 22 ## 3 JH Stander 21 ## 4 DF Watts 18 ## 5 R Flannigan 15 ## 6 CS MacLeod 2 ## 7 RM Haq 2 ## 8 PL Mommsen 0
9. Team performances of bowlers (all matches with opposition)
Like the function above the following tables provide the top bowlers of the countries in the matches against the oppoition. In India-Australia matches RA Jadeja leads, in Pakistan-South Africa matches Saeed Ajmal tops and so on.
teamBowlingPerfOppnAllMatches(aus_ind_matches,"India","Australia") ## Source: local data frame [26 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 RA Jadeja 13 0 219 8 ## 2 R Ashwin 12 0 232 7 ## 3 JJ Bumrah 5 0 103 6 ## 4 R Vinay Kumar 6 0 79 6 ## 5 R Sharma 5 0 56 5 ## 6 A Nehra 9 0 127 4 ## 7 Yuvraj Singh 5 0 72 4 ## 8 B Kumar 5 0 42 4 ## 9 IK Pathan 5 0 115 3 ## 10 Harbhajan Singh 9 1 83 3 ## .. ... ... ... ... ... teamBowlingPerfOppnAllMatches(pak_sa_matches,main="Pakistan",opposition="South Africa") ## Source: local data frame [17 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 Saeed Ajmal 8 1 202 10 ## 2 Mohammad Hafeez 10 0 178 9 ## 3 Shahid Afridi 11 0 200 6 ## 4 Umar Gul 3 0 93 6 ## 5 Sohail Tanvir 6 0 103 3 ## 6 Junaid Khan 4 0 75 3 ## 7 Shoaib Akhtar 1 0 65 3 ## 8 Mohammad Amir 1 0 63 2 ## 9 Bilawal Bhatti 5 0 54 2 ## 10 Abdur Rehman 1 0 53 2 ## 11 Yasir Arafat 3 0 25 2 ## 12 Abdul Razzaq 2 0 69 1 ## 13 Mohammad Irfan 3 0 46 1 ## 14 Anwar Ali 2 0 22 0 ## 15 Shoaib Malik 3 0 17 0 ## 16 Fawad Alam 1 0 15 0 ## 17 Raza Hasan 3 1 12 0 teamBowlingPerfOppnAllMatches(eng_nz_matches,"New Zealand","England") ## Source: local data frame [26 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 KD Mills 8 0 199 5 ## 2 MJ McClenaghan 10 0 189 5 ## 3 TG Southee 13 0 183 5 ## 4 DL Vettori 1 0 91 5 ## 5 JEC Franklin 2 0 53 5 ## 6 NL McCullum 9 0 281 4 ## 7 CS Martin 6 0 116 4 ## 8 SE Bond 1 0 49 4 ## 9 IG Butler 1 0 95 3 ## 10 SB Styris 4 0 80 3 ## .. ... ... ... ... ... teamBowlingPerfOppnAllMatches(sl_wi_matches,"Sri Lanka","West Indies") ## Source: local data frame [16 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 BAW Mendis 8 1 82 10 ## 2 SL Malinga 7 0 217 9 ## 3 AD Mathews 7 0 87 6 ## 4 TAM Siriwardana 4 0 58 5 ## 5 SMSM Senanayake 4 0 90 4 ## 6 M Muralitharan 1 0 76 4 ## 7 KMDN Kulasekara 7 0 158 2 ## 8 PVD Chameera 4 0 66 2 ## 9 I Udana 1 0 56 1 ## 10 DSNFG Jayasuriya 4 0 38 1 ## 11 BMAJ Mendis 2 0 32 1 ## 12 A Dananjaya 3 0 16 1 ## 13 S Prasanna 1 0 15 1 ## 14 HMRKB Herath 3 0 43 0 ## 15 ST Jayasuriya 1 0 34 0 ## 16 NLTC Perera 1 0 13 0
10. Team bowler’s wickets in Twenty20 (all matches with opposition)
This provided a graphical plot of the tables above
teamBowlersWicketsOppnAllMatches(aus_ind_matches,"India","Australia")
teamBowlersWicketsOppnAllMatches(aus_ind_matches,"Australia","India")
teamBowlersWicketsOppnAllMatches(pak_sa_matches,"South Africa","Pakistan",top=10)
m <-teamBowlersWicketsOppnAllMatches(eng_nz_matches,"England","Zealand",plot=FALSE) m ## Source: local data frame [20 x 2] ## ## bowler wickets ## (fctr) (int) ## 1 SCJ Broad 12 ## 2 JM Anderson 7 ## 3 JW Dernbach 7 ## 4 GP Swann 6 ## 5 LJ Wright 5 ## 6 RJ Sidebottom 4 ## 7 ST Finn 4 ## 8 MA Wood 4 ## 9 AD Mascarenhas 3 ## 10 PD Collingwood 3 ## 11 DJ Willey 3 ## 12 DL Maddy 2 ## 13 TT Bresnan 2 ## 14 BA Stokes 2 ## 15 JC Tredwell 2 ## 16 A Flintoff 1 ## 17 DR Briggs 1 ## 18 WB Rankin 1 ## 19 AU Rashid 1 ## 20 JE Root 1 teamBowlersWicketsOppnAllMatches(ban_ire_matches,"Bangladesh","Ireland",top=3)
11. Team bowler vs batsmen in Twenty20(all matches with opposition)
These plots show how the bowlers fared against the batsmen. It shows which of the opposing teams batsmen were able to score the most runs
teamBowlersVsBatsmenOppnAllMatches(aus_ind_matches,'India',"Australia",top=5)
teamBowlersVsBatsmenOppnAllMatches(pak_sa_matches,"Pakistan","South Africa",top=3)
teamBowlersVsBatsmenOppnAllMatches(eng_nz_matches,"England","New Zealand")
teamBowlersVsBatsmenOppnAllMatches(eng_nz_matches,"New Zealand","England")
12. Team bowler’s wicket kind in Twenty20(caught,bowled,etc) (all matches with opposition)
The charts below show the wicket kind taken by the bowler (caught, bowled, lbw etc)
teamBowlersWicketKindOppnAllMatches(aus_ind_matches,"India","Australia",plot=TRUE)
m <- teamBowlersWicketKindOppnAllMatches(aus_ind_matches,"Australia","India",plot=FALSE) m[1:30,] ## bowler wicketKind wicketPlayerOut runs ## 1 B Lee caught V Sehwag 133 ## 2 MJ Clarke caught RV Uthappa 27 ## 3 BW Hilfenhaus caught G Gambhir 28 ## 4 CJ McKay caught RG Sharma 75 ## 5 NM Coulter-Nile caught SK Raina 44 ## 6 XJ Doherty stumped S Dhawan 76 ## 7 CJ McKay caught V Kohli 75 ## 8 MG Johnson caught V Sehwag 54 ## 9 MG Johnson caught G Gambhir 54 ## 10 MG Johnson run out RV Uthappa 54 ## 11 MJ Clarke caught Yuvraj Singh 27 ## 12 MG Johnson run out MS Dhoni 54 ## 13 B Lee run out V Sehwag 133 ## 14 NW Bracken caught G Gambhir 68 ## 15 B Lee bowled KD Karthik 133 ## 16 NW Bracken caught RV Uthappa 68 ## 17 JR Hopes bowled RG Sharma 10 ## 18 DJ Hussey caught MS Dhoni 24 ## 19 AA Noffke caught P Kumar 23 ## 20 AC Voges caught Harbhajan Singh 5 ## 21 AC Voges caught S Sreesanth 5 ## 22 NW Bracken caught IK Pathan 68 ## 23 DP Nannes caught M Vijay 25 ## 24 DP Nannes caught G Gambhir 25 ## 25 SW Tait caught SK Raina 112 ## 26 DP Nannes bowled Yuvraj Singh 25 ## 27 SPD Smith caught MS Dhoni 34 ## 28 MG Johnson caught YK Pathan 54 ## 29 SR Watson run out RA Jadeja 201 ## 30 SR Watson caught Harbhajan Singh 201 teamBowlersWicketKindOppnAllMatches(sl_wi_matches,"Sri Lanka",'West Indies',plot=TRUE)
13. Team bowler’s wicket taken and runs conceded in Twenty20(all matches with opposition)
teamBowlersWicketRunsOppnAllMatches(aus_ind_matches,"India","Australia")
m <-teamBowlersWicketRunsOppnAllMatches(pak_sa_matches,"Pakistan","South Africa",plot=FALSE) m[1:30,] ## Source: local data frame [30 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 Abdul Razzaq 2 0 69 1 ## 2 Mohammad Amir 1 0 63 2 ## 3 Shahid Afridi 11 0 200 6 ## 4 Saeed Ajmal 8 1 202 10 ## 5 Shoaib Malik 3 0 17 0 ## 6 Umar Gul 3 0 93 6 ## 7 Fawad Alam 1 0 15 0 ## 8 Abdur Rehman 1 0 53 2 ## 9 Mohammad Hafeez 10 0 178 9 ## 10 Shoaib Akhtar 1 0 65 3 ## .. ... ... ... ... ...
14. Plot of wins vs losses between teams in Twenty20.
setwd("C:/software/cricket-package/york-test/yorkrData/Twenty20/T20-matches")
plotWinLossBetweenTeams("India","Sri Lanka")
plotWinLossBetweenTeams('Pakistan',"South Africa",".")
plotWinLossBetweenTeams('England',"New Zealand",".")
plotWinLossBetweenTeams("Australia","West Indies",".")
plotWinLossBetweenTeams('Bangladesh',"Zimbabwe",".")
plotWinLossBetweenTeams('Scotland',"Ireland",".")
Conclusion
This post included all functions for all Twenty20 matches between any 2 opposing countries. As before the data frames are already available. You can load the data and begin to use them. If more insights from the dataframe are possible do go ahead. But please do attribute the source to Cricheet (http://cricsheet.org), my package yorkr and my blog. Do give the functions a spin for yourself!
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