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“It was a puzzling thing. The truth knocks on the door and you say ‘Go away, I ’m looking for the truth,’ and so it goes away. Puzzling.”
“But even though Quality cannot be defined, you know what Quality is!”
“The Buddha, the Godhead, resides quite comfortably in the circuits of a digital computer or the gears of a cycle transmission as he does at the top of a mountain or in the petals of the flower. To think otherwise is to demean the Buddha – which is to demean oneself.”
Zen and the Art of Motorcycle maintenance - Robert M Pirsig
Introduction
If we were to to extend the last quote from Zen and the Art of Motorcycle Maintenance, by Robert M Pirsig, I think it would be fair to say that the Buddha also comfortably resides in the exquisite backhand cross-court return of Bjorn Borg, to the the graceful arc of the football in a Lionel Messi’s free kick to the smashing cover drive of Sunil Gavaskar.
In this post I continue to introduce my latest cricket package yorkr. This post is a continuation of my earlier post – Introducing cricket package yorkr-Part1:Beaten by sheer pace!. This post deals with Class 2 functions namely the performances of a team in all matches against a single opposition for e.g all matches of India-Australia, Pakistan-West Indies etc. You can clone/fork the code for my package yorkr from Github atyorkr
This post has also been published at RPubs [yorkr-Part2]9http://rpubs.com/tvganesh/yorkr-Part2 and can also be downloaded as a PDF document from yorkr-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
install.packages("yorkr_0.0.2.tar.gz",repos = NULL, type="source")
library(yorkr)
library(plotly)
rm(list=ls())
2. Get data for all matches between 2 teams
We can get all matches between any 2 teams using the function below. The dir parameter should point to the folder which RData files of the individual matches. This function creates a data frame of all the matches and also saves the dataframe as RData
setwd("C:/software/cricket-package/york-test/yorkrData/ODI/ODI-matches")
matches <- getAllMatchesBetweenTeams("Australia","India",dir=".")
dim(matches)
## [1] 67428 25
I have however already saved the 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 ODI-allmatches-between-two-teams
Note: The dataframe for the different head-to-head matches can be loaded directly into your code. The datframes are 15000+ rows x 25 columns. While I have 10 functions to process the details between teams, feel free to let loose any statistical or machine learning algorithms on the dataframe. So go ahead with any insights that can be gleaned from random forests, ridge regression,SVM classifiers and so on. If you do come up with something interesting, I would appreciate if you could drop me a note. Also please do attribute source to Cricsheet (http://cricsheet.org), the package york and my blog Giga thoughts
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 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 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
setwd("C:/software/cricket-package/york-test/yorkrData/ODI/ODI-allmatches-between-two-teams")
load("India-Australia-allMatches.RData")
aus_ind_matches <- matches
dim(aus_ind_matches)
## [1] 21909 25
load("England-New Zealand-allMatches.RData")
eng_nz_matches <- matches
dim(eng_nz_matches)
## [1] 15343 25
load("Pakistan-South Africa-allMatches.RData")
pak_sa_matches <- matches
dim(pak_sa_matches)
## [1] 17083 25
load("Sri Lanka-West Indies-allMatches.RData")
sl_wi_matches <- matches
dim(sl_wi_matches)
## [1] 4869 25
load("Bangladesh-Ireland-allMatches.RData")
ban_ire_matches <-matches
dim(ban_ire_matches)
## [1] 1668 25
load("Kenya-Bermuda-allMatches.RData")
ken_ber_matches <- matches
dim(ken_ber_matches)
## [1] 1518 25
load("Scotland-Canada-allMatches.RData")
sco_can_matches <-matches
dim(sco_can_matches)
## [1] 1061 25
load("Netherlands-Afghanistan-allMatches.RData")
nl_afg_matches <- matches
dim(nl_afg_matches)
## [1] 402 25
5. Team Batsmen partnership (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 Ricky Ponting from Australia and Mahendra Singh Dhoni of India.
m<- teamBatsmenPartnershiOppnAllMatches(aus_ind_matches,'Australia',report="summary") m ## Source: local data frame [47 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 RT Ponting 876 ## 2 MEK Hussey 753 ## 3 GJ Bailey 614 ## 4 SR Watson 609 ## 5 MJ Clarke 607 ## 6 ML Hayden 573 ## 7 A Symonds 536 ## 8 AJ Finch 525 ## 9 SPD Smith 467 ## 10 DA Warner 391 ## .. ... ... m <-teamBatsmenPartnershiOppnAllMatches(aus_ind_matches,'India',report="summary") m ## Source: local data frame [44 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 MS Dhoni 1156 ## 2 RG Sharma 918 ## 3 SR Tendulkar 910 ## 4 V Kohli 902 ## 5 G Gambhir 536 ## 6 Yuvraj Singh 524 ## 7 SK Raina 509 ## 8 S Dhawan 471 ## 9 V Sehwag 289 ## 10 RV Uthappa 283 ## .. ... ... m <-teamBatsmenPartnershiOppnAllMatches(aus_ind_matches,'Australia',report="detailed") m <-teamBatsmenPartnershiOppnAllMatches(pak_sa_matches,'Pakistan',report="summary") m ## Source: local data frame [40 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 Misbah-ul-Haq 727 ## 2 Younis Khan 657 ## 3 Shahid Afridi 558 ## 4 Mohammad Yousuf 539 ## 5 Mohammad Hafeez 477 ## 6 Shoaib Malik 452 ## 7 Ahmed Shehzad 348 ## 8 Abdul Razzaq 246 ## 9 Kamran Akmal 241 ## 10 Umar Akmal 215 ## .. ... ... m <-teamBatsmenPartnershiOppnAllMatches(eng_nz_matches,'England',report="summary") m ## Source: local data frame [47 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 IR Bell 654 ## 2 JE Root 612 ## 3 PD Collingwood 514 ## 4 EJG Morgan 479 ## 5 AN Cook 464 ## 6 IJL Trott 362 ## 7 KP Pietersen 358 ## 8 JC Buttler 287 ## 9 OA Shah 274 ## 10 RS Bopara 222 ## .. ... ... m <-teamBatsmenPartnershiOppnAllMatches(sl_wi_matches,'Sri Lanka',report="summary") m[1:50,] ## Source: local data frame [50 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 DPMD Jayawardene 288 ## 2 KC Sangakkara 238 ## 3 TM Dilshan 224 ## 4 WU Tharanga 220 ## 5 AD Mathews 161 ## 6 ST Jayasuriya 160 ## 7 ML Udawatte 87 ## 8 HDRL Thirimanne 67 ## 9 MDKJ Perera 64 ## 10 CK Kapugedera 57 ## .. ... ... m <- teamBatsmenPartnershiOppnAllMatches(ban_ire_matches,"Ireland",report="summary") m ## Source: local data frame [16 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 WTS Porterfield 111 ## 2 KJ O'Brien 99 ## 3 NJ O'Brien 75 ## 4 GC Wilson 60 ## 5 AR White 38 ## 6 DT Johnston 36 ## 7 JP Bray 31 ## 8 JF Mooney 28 ## 9 AC Botha 23 ## 10 EC Joyce 16 ## 11 PR Stirling 15 ## 12 GH Dockrell 9 ## 13 WB Rankin 9 ## 14 D Langford-Smith 6 ## 15 EJG Morgan 5 ## 16 AR Cusack 0
6. Team batsmen partnership (all matches with opposition)
This is plotted graphically in the charts below
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 KS Williamson LRPL Taylor 354 ## 2 BB McCullum MJ Guptill 275 ## 3 LRPL Taylor KS Williamson 273 ## 4 MJ Guptill BB McCullum 227 ## 5 BB McCullum JD Ryder 212 ## 6 MJ Guptill KS Williamson 196 ## 7 KS Williamson MJ Guptill 179 ## 8 JD Ryder BB McCullum 175 ## 9 JDP Oram SB Styris 153 ## 10 LRPL Taylor GD Elliott 147 ## 11 GD Elliott LRPL Taylor 143 ## 12 LRPL Taylor MJ Guptill 140 ## 13 JM How BB McCullum 128 ## 14 MJ Guptill LRPL Taylor 125 ## 15 BB McCullum JM How 117 ## 16 BB McCullum LRPL Taylor 116 ## 17 SB Styris JDP Oram 100 ## 18 LRPL Taylor JM How 98 ## 19 JM How LRPL Taylor 98 ## 20 JDP Oram BB McCullum 84 ## 21 LRPL Taylor L Vincent 71 ## 22 JDP Oram DL Vettori 70 ## 23 LRPL Taylor BB McCullum 61 ## 24 SB Styris JM How 55 ## 25 DR Flynn SB Styris 54 ## 26 DL Vettori JDP Oram 53 ## 27 L Vincent LRPL Taylor 53 ## 28 MJ Santner LRPL Taylor 53 ## 29 SP Fleming L Vincent 52 ## 30 JM How SB Styris 50 teamBatsmenPartnershipOppnAllMatchesChart(sl_wi_matches,"Sri Lanka","West Indies")
teamBatsmenPartnershipOppnAllMatchesChart(ban_ire_matches,"Bangladesh","Ireland")
7. Team batsmen versus bowler (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 [157 x 3] ## Groups: batsman [1] ## ## batsman bowler runs ## (fctr) (fctr) (dbl) ## 1 IR Bell JEC Franklin 63 ## 2 IR Bell SE Bond 13 ## 3 IR Bell MR Gillespie 33 ## 4 IR Bell NJ Astle 0 ## 5 IR Bell JS Patel 20 ## 6 IR Bell DL Vettori 28 ## 7 IR Bell JDP Oram 48 ## 8 IR Bell SB Styris 12 ## 9 IR Bell KD Mills 124 ## 10 IR Bell TG Southee 84 ## .. ... ... ... teamBatsmenVsBowlersOppnAllMatches(sl_wi_matches,"Sri Lanka","West Indies")
teamBatsmenVsBowlersOppnAllMatches(ban_ire_matches,"Bangladesh","Ireland")
8. Team batsmen versus bowler (all matches with opposition)
The following tables gives the overall performances of the country’s batsmen against the opposition. For India-Australia matches Dhoni, Rohit Sharma and Tendulkar lead the way. For Australia it is Ricky Ponting, M Hussey and GJ Bailey. In South Africa- Pakistan matches it is AB Devilliers, Hashim Amla etc.
a <-teamBattingScorecardOppnAllMatches(aus_ind_matches,main="India",opposition="Australia") ## Total= 8331 a ## Source: local data frame [44 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 MS Dhoni 1406 78 22 1156 ## 2 RG Sharma 1015 73 24 918 ## 3 SR Tendulkar 1157 103 6 910 ## 4 V Kohli 961 87 6 902 ## 5 G Gambhir 677 44 2 536 ## 6 Yuvraj Singh 664 52 11 524 ## 7 SK Raina 536 43 11 509 ## 8 S Dhawan 470 55 6 471 ## 9 V Sehwag 305 42 4 289 ## 10 RV Uthappa 295 29 7 283 ## .. ... ... ... ... ... teamBattingScorecardOppnAllMatches(aus_ind_matches,"Australia","India") ## Total= 9995 ## Source: local data frame [47 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 RT Ponting 1107 86 8 876 ## 2 MEK Hussey 816 56 5 753 ## 3 GJ Bailey 578 51 13 614 ## 4 SR Watson 653 81 10 609 ## 5 MJ Clarke 786 45 5 607 ## 6 ML Hayden 660 72 8 573 ## 7 A Symonds 543 43 15 536 ## 8 AJ Finch 617 52 9 525 ## 9 SPD Smith 431 44 7 467 ## 10 DA Warner 385 40 6 391 ## .. ... ... ... ... ... teamBattingScorecardOppnAllMatches(pak_sa_matches,"South Africa","Pakistan") ## Total= 6657 ## Source: local data frame [36 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 AB de Villiers 1533 128 23 1423 ## 2 HM Amla 864 88 3 815 ## 3 GC Smith 726 68 3 597 ## 4 JH Kallis 710 40 8 543 ## 5 JP Duminy 620 35 3 481 ## 6 CA Ingram 388 32 1 305 ## 7 F du Plessis 363 30 4 278 ## 8 Q de Kock 336 28 2 270 ## 9 DA Miller 329 20 2 250 ## 10 HH Gibbs 252 33 2 228 ## .. ... ... ... ... ... teamBattingScorecardOppnAllMatches(sl_wi_matches,"West Indies","Sri Lanka") ## Total= 1800 ## Source: local data frame [36 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 DM Bravo 353 20 6 265 ## 2 RR Sarwan 315 11 3 205 ## 3 MN Samuels 209 19 5 188 ## 4 CH Gayle 198 18 8 176 ## 5 S Chanderpaul 181 6 7 152 ## 6 AB Barath 162 9 2 125 ## 7 DJ Bravo 139 7 2 102 ## 8 CS Baugh 102 5 NA 78 ## 9 LMP Simmons 78 5 4 67 ## 10 JO Holder 33 5 3 55 ## .. ... ... ... ... ... teamBattingScorecardOppnAllMatches(eng_nz_matches,"England","New Zealand") ## Total= 6472 ## Source: local data frame [47 x 5] ## ## batsman ballsPlayed fours sixes runs ## (fctr) (int) (int) (int) (dbl) ## 1 IR Bell 871 74 7 654 ## 2 JE Root 651 54 5 612 ## 3 PD Collingwood 619 34 15 514 ## 4 EJG Morgan 445 35 22 479 ## 5 AN Cook 616 49 3 464 ## 6 IJL Trott 421 26 1 362 ## 7 KP Pietersen 481 30 6 358 ## 8 JC Buttler 199 28 11 287 ## 9 OA Shah 323 17 6 274 ## 10 RS Bopara 350 21 NA 222 ## .. ... ... ... ... ... teamBatsmenPartnershiOppnAllMatches(sco_can_matches,"Scotland","Canada") ## Source: local data frame [20 x 2] ## ## batsman totalRuns ## (fctr) (dbl) ## 1 CS MacLeod 177 ## 2 MW Machan 68 ## 3 CJO Smith 43 ## 4 FRJ Coleman 40 ## 5 RR Watson 14 ## 6 JH Stander 12 ## 7 MA Leask 12 ## 8 RML Taylor 10 ## 9 KJ Coetzer 8 ## 10 GM Hamilton 7 ## 11 RM Haq 7 ## 12 PL Mommsen 6 ## 13 CM Wright 5 ## 14 JD Nel 5 ## 15 MH Cross 4 ## 16 SM Sharif 4 ## 17 JAR Blain 2 ## 18 NFI McCallum 1 ## 19 RD Berrington 1 ## 20 NS Poonia 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 Ishant Sharma leads, in Pakistan-South Africa matches Shahid Afridi tops and so on.
teamBowlingPerfOppnAllMatches(aus_ind_matches,"India","Australia") ## Source: local data frame [36 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 I Sharma 44 1 739 20 ## 2 Harbhajan Singh 40 0 926 15 ## 3 RA Jadeja 39 0 867 14 ## 4 IK Pathan 42 1 702 11 ## 5 UT Yadav 37 2 606 10 ## 6 P Kumar 27 0 501 10 ## 7 Z Khan 33 1 500 10 ## 8 S Sreesanth 34 0 454 10 ## 9 R Ashwin 43 0 684 9 ## 10 R Vinay Kumar 31 1 380 9 ## .. ... ... ... ... ... teamBowlingPerfOppnAllMatches(pak_sa_matches,main="Pakistan",opposition="South Africa") ## Source: local data frame [24 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 Shahid Afridi 38 0 1053 17 ## 2 Saeed Ajmal 39 0 658 14 ## 3 Mohammad Hafeez 38 0 774 13 ## 4 Mohammad Irfan 29 0 467 13 ## 5 Iftikhar Anjum 29 1 257 12 ## 6 Wahab Riaz 31 0 534 11 ## 7 Junaid Khan 32 0 429 10 ## 8 Sohail Tanvir 26 1 409 9 ## 9 Shoaib Akhtar 22 1 313 9 ## 10 Umar Gul 25 2 365 7 ## .. ... ... ... ... ... teamBowlingPerfOppnAllMatches(eng_nz_matches,"New Zealand","England") ## Source: local data frame [33 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 TG Southee 40 0 684 19 ## 2 KD Mills 36 1 742 17 ## 3 DL Vettori 35 0 561 16 ## 4 MJ McClenaghan 34 0 515 14 ## 5 SE Bond 17 1 205 11 ## 6 GD Elliott 20 0 194 10 ## 7 JEC Franklin 24 0 418 7 ## 8 KS Williamson 21 1 225 7 ## 9 TA Boult 18 2 195 7 ## 10 NL McCullum 30 0 425 6 ## .. ... ... ... ... ... teamBowlingPerfOppnAllMatches(sl_wi_matches,"Sri Lanka","West Indies") ## Source: local data frame [24 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 SL Malinga 28 1 280 11 ## 2 BAW Mendis 15 0 267 8 ## 3 KMDN Kulasekara 13 1 185 7 ## 4 AD Mathews 14 0 191 6 ## 5 M Muralitharan 20 1 157 6 ## 6 MF Maharoof 9 2 14 6 ## 7 WPUJC Vaas 7 2 82 5 ## 8 RAS Lakmal 7 0 55 4 ## 9 ST Jayasuriya 1 0 38 4 ## 10 HMRKB Herath 10 1 124 3 ## .. ... ... ... ... ... teamBowlingPerfOppnAllMatches(ken_ber_matches,"Kenya","Bermuda") ## Source: local data frame [9 x 5] ## ## bowler overs maidens runs wickets ## (fctr) (int) (int) (dbl) (dbl) ## 1 JK Kamande 16 0 122 5 ## 2 HA Varaiya 13 1 64 5 ## 3 AS Luseno 6 0 32 4 ## 4 PJ Ongondo 7 0 39 3 ## 5 TM Odoyo 7 0 36 3 ## 6 LN Onyango 7 0 37 2 ## 7 SO Tikolo 18 0 81 1 ## 8 NN Odhiambo 14 1 76 1 ## 9 CO Obuya 4 0 20 0
10. Team bowler’s wickets (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 JM Anderson 20 ## 2 SCJ Broad 13 ## 3 ST Finn 12 ## 4 PD Collingwood 11 ## 5 GP Swann 10 ## 6 RJ Sidebottom 8 ## 7 CR Woakes 8 ## 8 A Flintoff 7 ## 9 LE Plunkett 6 ## 10 AU Rashid 6 ## 11 BA Stokes 6 ## 12 MS Panesar 5 ## 13 LJ Wright 4 ## 14 TT Bresnan 4 ## 15 DJ Willey 4 ## 16 JC Tredwell 3 ## 17 CT Tremlett 2 ## 18 RS Bopara 2 ## 19 CJ Jordan 2 ## 20 J Lewis 1 teamBowlersWicketsOppnAllMatches(ban_ire_matches,"Bangladesh","Ireland",top=7)
11. Team bowler vs batsmen (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 (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 GD McGrath caught SR Tendulkar 69 ## 2 SR Watson caught D Mongia 532 ## 3 MG Johnson lbw V Sehwag 1020 ## 4 B Lee caught R Dravid 671 ## 5 B Lee bowled M Kaif 671 ## 6 NW Bracken caught SK Raina 429 ## 7 GD McGrath caught IK Pathan 69 ## 8 NW Bracken lbw MS Dhoni 429 ## 9 MG Johnson lbw SR Tendulkar 1020 ## 10 MG Johnson bowled G Gambhir 1020 ## 11 SR Clark caught SR Tendulkar 254 ## 12 JR Hopes caught Yuvraj Singh 346 ## 13 SR Clark lbw RV Uthappa 254 ## 14 GB Hogg caught R Dravid 427 ## 15 MJ Clarke run out IK Pathan 212 ## 16 MJ Clarke stumped Harbhajan Singh 212 ## 17 MJ Clarke bowled RR Powar 212 ## 18 GB Hogg caught Z Khan 427 ## 19 GB Hogg caught MS Dhoni 427 ## 20 B Lee lbw G Gambhir 671 ## 21 MG Johnson lbw RV Uthappa 1020 ## 22 B Lee caught R Dravid 671 ## 23 GB Hogg bowled SR Tendulkar 427 ## 24 B Lee caught MS Dhoni 671 ## 25 JR Hopes caught RG Sharma 346 ## 26 GB Hogg lbw IK Pathan 427 ## 27 MG Johnson bowled Yuvraj Singh 1020 ## 28 GB Hogg caught and bowled Z Khan 427 ## 29 SR Clark bowled S Sreesanth 254 ## 30 JR Hopes caught SC Ganguly 346 teamBowlersWicketKindOppnAllMatches(sl_wi_matches,"Sri Lanka",'West Indies',plot=TRUE)
13. Team bowler’s wicket taken and runs conceded (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 Umar Gul 25 2 365 7 ## 2 Iftikhar Anjum 29 1 257 12 ## 3 Yasir Arafat 5 0 33 1 ## 4 Abdul Razzaq 16 0 290 4 ## 5 Mohammad Hafeez 38 0 774 13 ## 6 Shahid Afridi 38 0 1053 17 ## 7 Shoaib Malik 18 0 219 4 ## 8 Sohail Tanvir 26 1 409 9 ## 9 Abdur Rehman 25 0 301 4 ## 10 Mohammad Asif 10 1 204 2 ## .. ... ... ... ... ...
14. Plot of wins vs losses between teams.
setwd("C:/software/cricket-package/york-test/yorkrData/ODI/ODI-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 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.
There are 2 more posts required for the introduction of MY yorkr package.So, Hasta la vista, baby! I’ll be back!
Also see
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