Introduction
He will win, who knows when to fight and when not to fight.
He will win, who knows how to handle both superior and inferior forces
If you know neither the enemy nor yourself, you will succumb in every battle.
Hence the skilful fighter puts himself in a position which makes defeat impossible, and does not miss the moment for defeating the enemy.
Hence that general is skillful in attack whose opponent does not know what to defend; and he is skilled in defense whose opponent does know what to attack.
The Art of War - Sun Tzu
This post is a continuation of my introduction to my latest cricket package yorkr. This is the 3rd part of the introduction, the 2 earlier ones were
- Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
- Introducing cricket package yorkr: Part 2-Trapped leg before wicket!
This post deals with Class 3 functions, namely the performances of a team in all matches against all oppositions for e.g India/Australia/South Africa against all oppositions in all matches. In other words it is the performance of the team against the rest of the world.
If you are passionate about cricket, and love analyzing cricket performances, then check out my 2 racy books on cricket! In my books, I perform detailed yet compact analysis of performances of both batsmen, bowlers besides evaluating team & match performances in Tests , ODIs, T20s & IPL. You can buy my books on cricket from Amazon at $12.99 for the paperback and $4.99/$6.99 respectively for the kindle versions. The books can be accessed at Cricket analytics with cricketr and Beaten by sheer pace-Cricket analytics with yorkr A must read for any cricket lover! Check it out!!
This post has also been published at RPubs yorkr-Part3 and can also be downloaded as a PDF document from yorkr-Part3.pdf.
You can clone/fork the code for the package yorkr from Github at yorkr-package
Checkout my interactive Shiny apps GooglyPlus (plots & tables) and Googly (only plots) which can be used to analyze IPL players, teams and matches.
Important note 1: Do check out all the posts on the python avatar of yorkr, namely ‘yorkpy’ in my post ‘Pitching yorkpy … short of good length to IPL – Part 1
The list of functions in Class 3 are
- teamBattingScorecardAllOppnAllMatches()
- teamBatsmenPartnershipAllOppnAllMatches()
- teamBatsmenPartnershipAllOppnAllMatchesPlot()
- teamBatsmenVsBowlersAllOppnAllMatchesRept()
- teamBatsmenVsBowlersAllOppnAllMatchesPlot()
- teamBowlingScorecardAllOppnAllMatchesMain()
- teamBowlersVsBatsmenAllOppnAllMatchesRept()
- teamBowlersVsBatsmenAllOppnAllMatchesPlot()
- teamBowlingWicketKindAllOppnAllMatches()
- teamBowlingWicketRunsAllOppnAllMatches()
Note 1: The yorkr package in its current avatar supports ODI, T20 and IPL T20 matches.
Note 2: As in the previous parts the plots usually have the plot=TRUE/FALSE parameter. This is to allow the user to get a return value of the desired dataframe. The user can choose to plot this, in any way he/she likes for e.g in interactive charts using rcharts, ggvis,googleVis,plotly etc
1. Install the package from CRAN
The yorkr package can be installed directly from CRAN now! Install the yorkr package.
if (!require("yorkr")) {
install.packages("yorkr")
library("yorkr")
}
rm(list=ls())
2. Get data for all matches against all oppositions for a team
We can get all matches against all oppositions for a team/country using the function below. The dir parameter should point to the folder in which the RData files of the individual matches exist. This function creates a data frame of all the matches and also saves the resulting dataframe as RData
setwd("C:/software/cricket-package/york-test/yorkrData/ODI/ODI-team-allmatches-allOppositions")
matches <-getAllMatchesAllOpposition("India",dir=".",save=TRUE)
dim(matches)
## [1] 140655 25
“`
3. Save data for all matches against all oppositions
This can be done locally using the function below. This function gets all the matches of the country/team against all other countrioes//teams and combines them into a single dataframe and saves it in the current folder. The current implementation expects 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
4. Load data directly for all matches between 2 teams
As in my earlier posts (yorkr-Part1 & yorkr-Part2) I have however already saved the data, for all matches of the individual countries, against all oppositons. The data for these matches for the individual teams/countries can be downloaded directly from Github folder at ODI-team-allmatches-allOppositions
Note: The dataframe for the different for all the matches of a country agaisnt all oppositons can be loaded directly into your code. As can be seen in the calls below the datframes are ~100,000+ rows x 25 columns. While I have 10+ functions to process these dataframes, for a particular team, feel free to download these data frames and perform your own analysis. The data frames include ball-by-ball details, details on non-striker, bowler, runs, extras, venue,date etc. Certainly these data frames are a gold-mine of interesting insights. So do go ahead and unleash your bagging/boosting algorithms, SVM classifiers or Random Forest algorithm on them.
I plan to try out some algorithms of statistical/machine learning in the months to come. If you do come up with interesting insights, I would appreciate if attribute the source to Cricsheet(http://cricsheet.org), and my package yorkr and my blog Giga thoughts, besides dropping me a note.*
As in my earlier post I will be directly loading the saved files. For the illustration of the functions, I will use India in all the functions, (for obvious reasons) and will randomly use the data from the rest of the top 8 teams
setwd("C:/software/cricket-package/york-test/yorkrData/ODI/ODI-team-allmatches-allOppositions")
load("allMatchesAllOpposition-India.RData")
ind_matches <- matches
dim(ind_matches)
## [1] 140655 25
load("allMatchesAllOpposition-Australia.RData")
aus_matches <- matches
dim(aus_matches)
## [1] 128148 25
load("allMatchesAllOpposition-New Zealand.RData")
nz_matches <- matches
dim(nz_matches)
## [1] 98573 25
load("allMatchesAllOpposition-Pakistan.RData")
pak_matches <- matches
dim(pak_matches)
## [1] 117947 25
load("allMatchesAllOpposition-England.RData")
eng_matches <- matches
dim(eng_matches)
## [1] 118859 25
load("allMatchesAllOpposition-Sri Lanka.RData")
sl_matches <- matches
dim(sl_matches)
## [1] 125893 25
load("allMatchesAllOpposition-West Indies.RData")
wi_matches <- matches
dim(wi_matches)
## [1] 92716 25
load("allMatchesAllOpposition-South Africa.RData")
sa_matches <- matches
dim(sa_matches)
## [1] 100916 25
5. Team Batting Scorecard (all matches with opposition)
The following functions shows the batting scorecards in each country. It returns a dataframe with the top batsmen in each country
m <-teamBattingScorecardAllOppnAllMatches(ind_matches,theTeam="India")
## Total= 58079
## Source: local data frame [68 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 V Kohli 7774 663 67 7039
## 2 MS Dhoni 7878 515 129 6885
## 3 SK Raina 5076 429 114 4964
## 4 G Gambhir 5138 472 15 4503
## 5 RG Sharma 5245 372 89 4385
## 6 SR Tendulkar 4708 504 43 4196
## 7 Yuvraj Singh 4472 403 96 3976
## 8 V Sehwag 3106 494 74 3681
## 9 S Dhawan 2956 314 37 2694
## 10 AM Rahane 2490 195 24 2009
## .. ... ... ... ... ...
m <-teamBattingScorecardAllOppnAllMatches(aus_matches,theTeam="Australia")
## Total= 54736
## Source: local data frame [70 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 MJ Clarke 7060 440 39 5485
## 2 SR Watson 5435 519 114 5035
## 3 RT Ponting 5301 447 43 4440
## 4 MEK Hussey 4990 286 60 4286
## 5 BJ Haddin 3308 266 69 2858
## 6 DA Warner 2701 264 43 2537
## 7 GJ Bailey 2805 176 43 2392
## 8 SPD Smith 2303 174 19 2082
## 9 CL White 2471 142 44 2018
## 10 ML Hayden 2276 219 37 2002
## .. ... ... ... ... ...
m <-teamBattingScorecardAllOppnAllMatches(pak_matches,theTeam="Pakistan")
## Total= NA
## Source: local data frame [74 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 Mohammad Hafeez 5714 471 71 4574
## 2 Younis Khan 4561 306 24 3465
## 3 Shahid Afridi 2316 264 132 3125
## 4 Shoaib Malik 3472 240 40 2897
## 5 Umar Akmal 3272 241 47 2843
## 6 Ahmed Shehzad 3386 259 18 2491
## 7 Mohammad Yousuf 2933 191 11 2241
## 8 Kamran Akmal 2533 247 25 2104
## 9 Salman Butt 2037 206 6 1653
## 10 Nasir Jamshed 1862 150 19 1418
## .. ... ... ... ... ...
m <-teamBattingScorecardAllOppnAllMatches(nz_matches,theTeam="New Zealand")
## Total= 39993
## Source: local data frame [68 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 LRPL Taylor 6153 418 103 5120
## 2 BB McCullum 4321 446 159 4489
## 3 MJ Guptill 5205 462 100 4460
## 4 KS Williamson 4044 325 25 3418
## 5 SB Styris 2324 167 23 1944
## 6 GD Elliott 2274 149 26 1889
## 7 JD Ryder 1232 139 33 1223
## 8 JDP Oram 1174 81 48 1195
## 9 DL Vettori 1238 97 8 1130
## 10 L Ronchi 927 108 32 1070
## .. ... ... ... ... ...
m <-teamBattingScorecardAllOppnAllMatches(eng_matches,theTeam="England")
## Total= 48152
## Source: local data frame [72 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 IR Bell 6401 488 31 5051
## 2 EJG Morgan 4249 323 98 3927
## 3 KP Pietersen 3828 315 44 3231
## 4 AN Cook 4052 360 10 3163
## 5 PD Collingwood 3693 213 48 2992
## 6 IJL Trott 3418 205 3 2653
## 7 RS Bopara 3326 202 32 2624
## 8 AJ Strauss 3062 276 20 2566
## 9 JE Root 2983 200 26 2543
## 10 JC Buttler 1467 155 54 1777
## .. ... ... ... ... ...
m <-teamBattingScorecardAllOppnAllMatches(wi_matches,theTeam="West Indies")
## Total= 34622
## Source: local data frame [65 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 CH Gayle 3839 386 144 3635
## 2 MN Samuels 4057 294 72 3062
## 3 S Chanderpaul 3521 188 28 2469
## 4 DJ Bravo 2804 193 49 2390
## 5 DM Bravo 2916 174 41 2051
## 6 RR Sarwan 2682 172 20 1960
## 7 KA Pollard 2064 127 92 1947
## 8 LMP Simmons 2538 157 46 1863
## 9 DJG Sammy 1799 143 83 1835
## 10 D Ramdin 1817 115 23 1516
## .. ... ... ... ... ...
m <-teamBattingScorecardAllOppnAllMatches(sl_matches,theTeam="Sri Lanka")
## Total= NA
## Source: local data frame [60 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 KC Sangakkara 10449 852 64 8778
## 2 TM Dilshan 8838 914 45 7981
## 3 DPMD Jayawardene 7482 599 43 6260
## 4 WU Tharanga 5690 483 24 4232
## 5 AD Mathews 4383 288 59 3764
## 6 ST Jayasuriya 2266 297 61 2396
## 7 HDRL Thirimanne 3286 192 17 2371
## 8 LD Chandimal 3026 165 27 2308
## 9 KMDN Kulasekara 1406 83 37 1204
## 10 NLTC Perera 1007 90 42 1137
## .. ... ... ... ... ...
6. Team Batting Scorecard
The following functions show the best batsmen from the opposition ‘theTeam’ in the ‘matches’. For e.g. when the matches=ind_matches and theTeam=“England” then the returned dataframe shows the best English batsmen against India
m <-teamBattingScorecardAllOppnAllMatches(matches=ind_matches,theTeam="England")
## Total= 7620
## Source: local data frame [43 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 IR Bell 1238 110 9 1085
## 2 KP Pietersen 990 89 10 847
## 3 AN Cook 1049 103 2 822
## 4 RS Bopara 632 42 8 534
## 5 PD Collingwood 450 39 6 397
## 6 OA Shah 394 40 7 385
## 7 IJL Trott 410 33 2 349
## 8 JE Root 408 32 4 336
## 9 SR Patel 336 25 10 329
## 10 C Kieswetter 309 34 13 313
## .. ... ... ... ... ...
m <-teamBattingScorecardAllOppnAllMatches(matches=ind_matches,theTeam="Australia")
## 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
## .. ... ... ... ... ...
m <-teamBattingScorecardAllOppnAllMatches(aus_matches,theTeam="New Zealand")
## Total= 6106
## Source: local data frame [44 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 LRPL Taylor 1012 71 13 804
## 2 BB McCullum 768 71 25 761
## 3 MJ Guptill 618 50 17 485
## 4 PG Fulton 526 35 9 425
## 5 GD Elliott 469 29 4 405
## 6 SB Styris 415 36 5 369
## 7 DL Vettori 334 24 2 291
## 8 L Vincent 338 27 5 272
## 9 CD McMillan 227 28 10 266
## 10 JDP Oram 181 13 7 193
## .. ... ... ... ... ...
m <-teamBattingScorecardAllOppnAllMatches(wi_matches,theTeam="Sri Lanka")
## Total= 1851
## Source: local data frame [28 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (int) (int) (dbl)
## 1 DPMD Jayawardene 330 26 2 288
## 2 KC Sangakkara 326 16 2 238
## 3 TM Dilshan 173 18 7 224
## 4 WU Tharanga 349 22 NA 220
## 5 AD Mathews 171 10 3 161
## 6 ST Jayasuriya 146 19 4 160
## 7 ML Udawatte 138 8 1 87
## 8 HDRL Thirimanne 144 6 NA 67
## 9 MDKJ Perera 63 4 2 64
## 10 CK Kapugedera 68 2 NA 57
## .. ... ... ... ... ...
7. Team Batting Partnerships
This gives the top batting partnerships in each team in all its matches against all oppositions. The report can either be a ‘summary’ or a ‘detailed’ breakup of the batting partnerships.
m <- teamBatsmenPartnershipAllOppnAllMatches(ind_matches,theTeam='India')
m
## Source: local data frame [68 x 2]
##
## batsman totalRuns
## (fctr) (dbl)
## 1 V Kohli 7039
## 2 MS Dhoni 6885
## 3 SK Raina 4964
## 4 G Gambhir 4503
## 5 RG Sharma 4385
## 6 SR Tendulkar 4196
## 7 Yuvraj Singh 3976
## 8 V Sehwag 3681
## 9 S Dhawan 2694
## 10 AM Rahane 2009
## .. ... ...
m <- teamBatsmenPartnershipAllOppnAllMatches(matches,theTeam='India',report="detailed")
head(m,30)
## batsman nonStriker partnershipRuns totalRuns
## 1 V Kohli S Dhawan 661 7039
## 2 V Kohli AM Rahane 502 7039
## 3 V Kohli RG Sharma 1073 7039
## 4 V Kohli KD Karthik 139 7039
## 5 V Kohli SR Tendulkar 278 7039
## 6 V Kohli R Dravid 132 7039
## 7 V Kohli V Sehwag 255 7039
## 8 V Kohli Yuvraj Singh 420 7039
## 9 V Kohli SK Raina 1072 7039
## 10 V Kohli MS Dhoni 534 7039
## 11 V Kohli Harbhajan Singh 13 7039
## 12 V Kohli IK Pathan 1 7039
## 13 V Kohli G Gambhir 962 7039
## 14 V Kohli RV Uthappa 10 7039
## 15 V Kohli RA Jadeja 91 7039
## 16 V Kohli R Ashwin 71 7039
## 17 V Kohli AT Rayudu 345 7039
## 18 V Kohli Gurkeerat Singh 1 7039
## 19 V Kohli YK Pathan 68 7039
## 20 V Kohli STR Binny 4 7039
## 21 V Kohli MK Tiwary 111 7039
## 22 V Kohli AR Patel 39 7039
## 23 V Kohli PA Patel 180 7039
## 24 V Kohli M Vijay 33 7039
## 25 V Kohli KM Jadhav 10 7039
## 26 V Kohli AM Nayar 25 7039
## 27 V Kohli S Badrinath 9 7039
## 28 MS Dhoni S Dhawan 49 6885
## 29 MS Dhoni AM Rahane 50 6885
## 30 MS Dhoni RG Sharma 300 6885
9. More Team Batting Partnerships
When we use the dataframe ind_matches (matches of India against all opoositions) and choose another country in the theTeam then we will get the names of those top batsmen against India.
m <- teamBatsmenPartnershipAllOppnAllMatches(ind_matches,theTeam='England')
m
## Source: local data frame [43 x 2]
##
## batsman totalRuns
## (fctr) (dbl)
## 1 IR Bell 1085
## 2 KP Pietersen 847
## 3 AN Cook 822
## 4 RS Bopara 534
## 5 PD Collingwood 397
## 6 OA Shah 385
## 7 IJL Trott 349
## 8 JE Root 336
## 9 SR Patel 329
## 10 C Kieswetter 313
## .. ... ...
m <- teamBatsmenPartnershipAllOppnAllMatches(ind_matches,theTeam='South Africa', report="detailed")
m[1:30,]
## batsman nonStriker partnershipRuns totalRuns
## 1 AB de Villiers MN van Wyk 30 1179
## 2 AB de Villiers JH Kallis 207 1179
## 3 AB de Villiers HH Gibbs 20 1179
## 4 AB de Villiers JP Duminy 168 1179
## 5 AB de Villiers MV Boucher 37 1179
## 6 AB de Villiers JM Kemp 5 1179
## 7 AB de Villiers AN Petersen 8 1179
## 8 AB de Villiers WD Parnell 56 1179
## 9 AB de Villiers DW Steyn 5 1179
## 10 AB de Villiers CK Langeveldt 19 1179
## 11 AB de Villiers HM Amla 26 1179
## 12 AB de Villiers GC Smith 106 1179
## 13 AB de Villiers F du Plessis 133 1179
## 14 AB de Villiers Q de Kock 113 1179
## 15 AB de Villiers DA Miller 103 1179
## 16 AB de Villiers F Behardien 64 1179
## 17 AB de Villiers CH Morris 32 1179
## 18 AB de Villiers AM Phangiso 37 1179
## 19 AB de Villiers SM Pollock 10 1179
## 20 HM Amla MN van Wyk 66 704
## 21 HM Amla AB de Villiers 9 704
## 22 HM Amla JH Kallis 88 704
## 23 HM Amla HH Gibbs 10 704
## 24 HM Amla JP Duminy 79 704
## 25 HM Amla LE Bosman 43 704
## 26 HM Amla RE van der Merwe 17 704
## 27 HM Amla GC Smith 92 704
## 28 HM Amla F du Plessis 45 704
## 29 HM Amla RJ Peterson 2 704
## 30 HM Amla Q de Kock 211 704
10. Team Batting partnerships of other countries
m <- teamBatsmenPartnershipAllOppnAllMatches(eng_matches,theTeam='India',report="detailed")
head(m,30)
## batsman nonStriker partnershipRuns totalRuns
## 1 MS Dhoni G Gambhir 6 1083
## 2 MS Dhoni R Dravid 59 1083
## 3 MS Dhoni PP Chawla 1 1083
## 4 MS Dhoni Z Khan 4 1083
## 5 MS Dhoni RP Singh 26 1083
## 6 MS Dhoni Yuvraj Singh 157 1083
## 7 MS Dhoni RR Powar 15 1083
## 8 MS Dhoni RV Uthappa 29 1083
## 9 MS Dhoni AM Rahane 1 1083
## 10 MS Dhoni V Kohli 28 1083
## 11 MS Dhoni SK Raina 372 1083
## 12 MS Dhoni P Kumar 42 1083
## 13 MS Dhoni R Vinay Kumar 12 1083
## 14 MS Dhoni R Ashwin 27 1083
## 15 MS Dhoni RA Jadeja 238 1083
## 16 MS Dhoni AT Rayudu 17 1083
## 17 MS Dhoni STR Binny 41 1083
## 18 MS Dhoni YK Pathan 8 1083
## 19 SK Raina G Gambhir 23 918
## 20 SK Raina R Dravid 1 918
## 21 SK Raina MS Dhoni 450 918
## 22 SK Raina Yuvraj Singh 56 918
## 23 SK Raina AM Rahane 17 918
## 24 SK Raina V Kohli 144 918
## 25 SK Raina RG Sharma 58 918
## 26 SK Raina MK Tiwary 28 918
## 27 SK Raina R Ashwin 15 918
## 28 SK Raina RA Jadeja 59 918
## 29 SK Raina AT Rayudu 61 918
## 30 SK Raina V Sehwag 6 918
m <- teamBatsmenPartnershipAllOppnAllMatches(sa_matches,theTeam='South Africa', report="detailed")
head(m,30)
## batsman nonStriker partnershipRuns totalRuns
## 1 AB de Villiers GC Smith 957 7693
## 2 AB de Villiers JH Kallis 897 7693
## 3 AB de Villiers HH Gibbs 295 7693
## 4 AB de Villiers MV Boucher 143 7693
## 5 AB de Villiers JM Kemp 8 7693
## 6 AB de Villiers SM Pollock 16 7693
## 7 AB de Villiers CK Langeveldt 19 7693
## 8 AB de Villiers HM Amla 1437 7693
## 9 AB de Villiers JP Duminy 1123 7693
## 10 AB de Villiers JA Morkel 169 7693
## 11 AB de Villiers J Botha 27 7693
## 12 AB de Villiers Q de Kock 248 7693
## 13 AB de Villiers F du Plessis 667 7693
## 14 AB de Villiers DA Miller 571 7693
## 15 AB de Villiers R McLaren 120 7693
## 16 AB de Villiers DW Steyn 32 7693
## 17 AB de Villiers AM Phangiso 37 7693
## 18 AB de Villiers M Morkel 21 7693
## 19 AB de Villiers WD Parnell 83 7693
## 20 AB de Villiers F Behardien 223 7693
## 21 AB de Villiers VD Philander 12 7693
## 22 AB de Villiers RR Rossouw 90 7693
## 23 AB de Villiers RJ Peterson 5 7693
## 24 AB de Villiers AN Petersen 132 7693
## 25 AB de Villiers MN van Wyk 89 7693
## 26 AB de Villiers CH Morris 32 7693
## 27 AB de Villiers KJ Abbott 21 7693
## 28 AB de Villiers D Elgar 54 7693
## 29 AB de Villiers RE van der Merwe 1 7693
## 30 AB de Villiers CA Ingram 138 7693
m <- teamBatsmenPartnershipAllOppnAllMatches(sl_matches,theTeam='Sri Lanka',report="summary")
m
## Source: local data frame [60 x 2]
##
## batsman totalRuns
## (fctr) (dbl)
## 1 KC Sangakkara 8778
## 2 TM Dilshan 7981
## 3 DPMD Jayawardene 6260
## 4 WU Tharanga 4232
## 5 AD Mathews 3764
## 6 ST Jayasuriya 2396
## 7 HDRL Thirimanne 2371
## 8 LD Chandimal 2308
## 9 KMDN Kulasekara 1204
## 10 NLTC Perera 1137
## .. ... ...
m <- teamBatsmenPartnershipAllOppnAllMatches(eng_matches,theTeam='England',report="summary")
m
## Source: local data frame [72 x 2]
##
## batsman totalRuns
## (fctr) (dbl)
## 1 IR Bell 5051
## 2 EJG Morgan 3927
## 3 KP Pietersen 3231
## 4 AN Cook 3163
## 5 PD Collingwood 2992
## 6 IJL Trott 2653
## 7 RS Bopara 2624
## 8 AJ Strauss 2566
## 9 JE Root 2543
## 10 JC Buttler 1777
## .. ... ...
m <- teamBatsmenPartnershipAllOppnAllMatches(wi_matches,theTeam='Australia',report="summary")
m
## Source: local data frame [39 x 2]
##
## batsman totalRuns
## (fctr) (dbl)
## 1 SR Watson 851
## 2 MEK Hussey 630
## 3 RT Ponting 503
## 4 MJ Clarke 435
## 5 GJ Bailey 341
## 6 A Symonds 252
## 7 SE Marsh 245
## 8 BJ Haddin 220
## 9 DJ Hussey 211
## 10 AC Voges 209
## .. ... ...
m <- teamBatsmenPartnershipAllOppnAllMatches(nz_matches,theTeam='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
## .. ... ...
11. Team Batting Partnership plots
Graphical plot of batting partnerships for the countries
teamBatsmenPartnershipAllOppnAllMatchesPlot(ind_matches,"India",main="India")
![]()
teamBatsmenPartnershipAllOppnAllMatchesPlot(pak_matches,"Pakistan",main="Pakistan")
![]()
teamBatsmenPartnershipAllOppnAllMatchesPlot(aus_matches,"Australia",main="Australia")
![]()
12. Top opposition batting partnerships.
This gives the best performance of the team against a specified country Indian partnetships against Australia
New Zealand Partnetship against South Africa
teamBatsmenPartnershipAllOppnAllMatchesPlot(ind_matches,"India",main="West Indies")
![]()
teamBatsmenPartnershipAllOppnAllMatchesPlot(sl_matches,"Sri Lanka",main="India")
![]()
teamBatsmenPartnershipAllOppnAllMatchesPlot(nz_matches,"New Zealand",main="South Africa")
![]()
13. Batsmen vs Bowlers
The function below gives the top performance of batsmen against the opposition countries
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"India",rank=0)
m
## Source: local data frame [68 x 2]
##
## batsman runsScored
## (fctr) (dbl)
## 1 V Kohli 7039
## 2 MS Dhoni 6885
## 3 SK Raina 4964
## 4 G Gambhir 4503
## 5 RG Sharma 4385
## 6 SR Tendulkar 4196
## 7 Yuvraj Singh 3976
## 8 V Sehwag 3681
## 9 S Dhawan 2694
## 10 AM Rahane 2009
## .. ... ...
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"India",rank=1,dispRows=30)
m
## Source: local data frame [30 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 V Kohli NLTC Perera 242
## 2 V Kohli KMDN Kulasekara 196
## 3 V Kohli SL Malinga 175
## 4 V Kohli AD Mathews 155
## 5 V Kohli BAW Mendis 132
## 6 V Kohli R Rampaul 127
## 7 V Kohli JW Dernbach 121
## 8 V Kohli JP Faulkner 118
## 9 V Kohli DJG Sammy 116
## 10 V Kohli HMRKB Herath 113
## .. ... ... ...
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"India",rank=2,dispRows=50)
m
## Source: local data frame [50 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 MS Dhoni M Muralitharan 195
## 2 MS Dhoni ST Jayasuriya 183
## 3 MS Dhoni SL Malinga 144
## 4 MS Dhoni SR Watson 135
## 5 MS Dhoni ST Finn 130
## 6 MS Dhoni MG Johnson 128
## 7 MS Dhoni JP Faulkner 125
## 8 MS Dhoni Shahid Afridi 120
## 9 MS Dhoni TT Bresnan 111
## 10 MS Dhoni AD Mathews 111
## .. ... ... ...
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(matches=ind_matches,theTeam="England",rank=1,dispRows=25)
m
## Source: local data frame [25 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 IR Bell Z Khan 127
## 2 IR Bell PP Chawla 111
## 3 IR Bell RA Jadeja 94
## 4 IR Bell B Kumar 78
## 5 IR Bell MM Patel 77
## 6 IR Bell R Ashwin 71
## 7 IR Bell AB Agarkar 66
## 8 IR Bell I Sharma 57
## 9 IR Bell RP Singh 51
## 10 IR Bell Yuvraj Singh 51
## .. ... ... ...
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"Australia",rank=0)
m
## Source: local data frame [47 x 2]
##
## batsman runsScored
## (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
## .. ... ...
14. Batsmen vs Bowlers (continued)
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(eng_matches,"India",rank=1,dispRows=30)
m
## Source: local data frame [28 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 MS Dhoni ST Finn 130
## 2 MS Dhoni TT Bresnan 111
## 3 MS Dhoni GP Swann 101
## 4 MS Dhoni JW Dernbach 95
## 5 MS Dhoni SCJ Broad 92
## 6 MS Dhoni JM Anderson 89
## 7 MS Dhoni SR Patel 83
## 8 MS Dhoni JC Tredwell 40
## 9 MS Dhoni CR Woakes 38
## 10 MS Dhoni MS Panesar 37
## .. ... ... ...
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(aus_matches,"Sri Lanka",rank=0)
m
## Source: local data frame [31 x 2]
##
## batsman runsScored
## (fctr) (dbl)
## 1 KC Sangakkara 888
## 2 DPMD Jayawardene 846
## 3 TM Dilshan 799
## 4 WU Tharanga 464
## 5 LD Chandimal 413
## 6 AD Mathews 404
## 7 HDRL Thirimanne 290
## 8 KMDN Kulasekara 232
## 9 ST Jayasuriya 117
## 10 SL Malinga 91
## .. ... ...
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(sa_matches,"England",rank=0)
m
## Source: local data frame [39 x 2]
##
## batsman runsScored
## (fctr) (dbl)
## 1 IJL Trott 424
## 2 JE Root 372
## 3 IR Bell 362
## 4 EJG Morgan 335
## 5 PD Collingwood 319
## 6 AD Hales 271
## 7 KP Pietersen 192
## 8 A Flintoff 192
## 9 OA Shah 177
## 10 JC Buttler 154
## .. ... ...
15. Batsmen vs Bowlers Plot
The following functions plot the performances of the batsman based on the rank chosen against opposition bowlers. Note: The rank has to be >0
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"India",rank=1,dispRows=50)
d
## Source: local data frame [50 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 V Kohli NLTC Perera 242
## 2 V Kohli KMDN Kulasekara 196
## 3 V Kohli SL Malinga 175
## 4 V Kohli AD Mathews 155
## 5 V Kohli BAW Mendis 132
## 6 V Kohli R Rampaul 127
## 7 V Kohli JW Dernbach 121
## 8 V Kohli JP Faulkner 118
## 9 V Kohli DJG Sammy 116
## 10 V Kohli HMRKB Herath 113
## .. ... ... ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
![]()
e <- teamBatsmenVsBowlersAllOppnAllMatchesPlot(d,plot=FALSE)
e
## Source: local data frame [50 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 V Kohli NLTC Perera 242
## 2 V Kohli KMDN Kulasekara 196
## 3 V Kohli SL Malinga 175
## 4 V Kohli AD Mathews 155
## 5 V Kohli BAW Mendis 132
## 6 V Kohli R Rampaul 127
## 7 V Kohli JW Dernbach 121
## 8 V Kohli JP Faulkner 118
## 9 V Kohli DJG Sammy 116
## 10 V Kohli HMRKB Herath 113
## .. ... ... ...
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"India",rank=2,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
![]()
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"South Africa",rank=1,dispRows=30)
d
## Source: local data frame [30 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 AB de Villiers Harbhajan Singh 133
## 2 AB de Villiers B Kumar 93
## 3 AB de Villiers RA Jadeja 90
## 4 AB de Villiers A Mishra 77
## 5 AB de Villiers MM Sharma 68
## 6 AB de Villiers Z Khan 65
## 7 AB de Villiers S Sreesanth 61
## 8 AB de Villiers A Nehra 58
## 9 AB de Villiers R Ashwin 55
## 10 AB de Villiers IK Pathan 45
## .. ... ... ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
![]()
d <-teamBatsmenVsBowlersAllOppnAllMatchesRept(matches=ind_matches,"England",rank=1,dispRows=50)
d
## Source: local data frame [28 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 IR Bell Z Khan 127
## 2 IR Bell PP Chawla 111
## 3 IR Bell RA Jadeja 94
## 4 IR Bell B Kumar 78
## 5 IR Bell MM Patel 77
## 6 IR Bell R Ashwin 71
## 7 IR Bell AB Agarkar 66
## 8 IR Bell I Sharma 57
## 9 IR Bell RP Singh 51
## 10 IR Bell Yuvraj Singh 51
## .. ... ... ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
![]()
15. Batsmen vs Bowlers Plot (continued)
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(sa_matches,"South Africa",rank=1,dispRows=50)
d
## Source: local data frame [50 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 AB de Villiers Shahid Afridi 227
## 2 AB de Villiers Saeed Ajmal 174
## 3 AB de Villiers Mohammad Hafeez 151
## 4 AB de Villiers JO Holder 138
## 5 AB de Villiers Harbhajan Singh 133
## 6 AB de Villiers Wahab Riaz 130
## 7 AB de Villiers MG Johnson 129
## 8 AB de Villiers P Utseya 128
## 9 AB de Villiers DJG Sammy 110
## 10 AB de Villiers DJ Bravo 107
## .. ... ... ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
![]()
e <- teamBatsmenVsBowlersAllOppnAllMatchesPlot(d,plot=FALSE)
e
## Source: local data frame [50 x 3]
## Groups: batsman [1]
##
## batsman bowler runs
## (fctr) (fctr) (dbl)
## 1 AB de Villiers Shahid Afridi 227
## 2 AB de Villiers Saeed Ajmal 174
## 3 AB de Villiers Mohammad Hafeez 151
## 4 AB de Villiers JO Holder 138
## 5 AB de Villiers Harbhajan Singh 133
## 6 AB de Villiers Wahab Riaz 130
## 7 AB de Villiers MG Johnson 129
## 8 AB de Villiers P Utseya 128
## 9 AB de Villiers DJG Sammy 110
## 10 AB de Villiers DJ Bravo 107
## .. ... ... ...
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(sl_matches,"Sri Lanka",rank=1,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
![]()
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(eng_matches,"West Indies",rank=1,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
![]()
16 Team bowling scorecard against all opposition
The functions lists the top bowlers of each country in ODI matches. This function returns a dataframe when ‘matches’ is the matches of the country and ‘theTeam’ is the same country as in the functions below
teamBowlingScorecardAllOppnAllMatchesMain(matches=ind_matches,theTeam="India")
## Source: local data frame [57 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 RA Jadeja 43 0 4749 153
## 2 R Ashwin 49 0 4225 146
## 3 Z Khan 47 0 3692 141
## 4 Harbhajan Singh 45 0 4040 123
## 5 I Sharma 51 0 3216 113
## 6 MM Patel 49 1 2400 92
## 7 P Kumar 50 2 2752 84
## 8 UT Yadav 51 0 2442 80
## 9 Mohammed Shami 43 0 1806 80
## 10 Yuvraj Singh 38 0 2588 77
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatchesMain(matches=aus_matches,theTeam="Australia")
## Source: local data frame [54 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 MG Johnson 51 0 5635 245
## 2 B Lee 50 0 3400 147
## 3 SR Watson 45 NA NA 136
## 4 NW Bracken 51 0 2763 114
## 5 CJ McKay 49 NA NA 103
## 6 MA Starc 48 1 1769 97
## 7 JP Faulkner 44 0 2004 75
## 8 JR Hopes 43 0 2098 69
## 9 SW Tait 50 0 1461 66
## 10 DE Bollinger 51 0 1482 65
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatchesMain(eng_matches,"England")
## Source: local data frame [52 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 JM Anderson 51 0 5688 202
## 2 SCJ Broad 51 0 5160 198
## 3 TT Bresnan 51 0 3730 117
## 4 ST Finn 49 0 2839 106
## 5 GP Swann 39 0 2760 106
## 6 PD Collingwood 40 1 2517 77
## 7 A Flintoff 45 0 1260 68
## 8 JC Tredwell 42 0 1614 62
## 9 CR Woakes 47 0 1859 57
## 10 RS Bopara 34 0 1508 42
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatchesMain(pak_matches,"Pakistan")
## Source: local data frame [55 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 Shahid Afridi 45 0 6674 212
## 2 Saeed Ajmal 44 0 4089 184
## 3 Umar Gul 49 0 4127 151
## 4 Wahab Riaz 50 0 2954 111
## 5 Mohammad Hafeez 51 0 3502 109
## 6 Mohammad Irfan 49 0 2523 86
## 7 Sohail Tanvir 48 1 2534 75
## 8 Junaid Khan 48 1 2056 75
## 9 Iftikhar Anjum 49 2 1674 62
## 10 Shoaib Malik 41 1 2206 59
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatchesMain(sa_matches,"South Africa")
## Source: local data frame [41 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 DW Steyn 51 0 4294 179
## 2 M Morkel 51 0 4012 172
## 3 LL Tsotsobe 42 0 2231 100
## 4 Imran Tahir 39 0 2124 93
## 5 R McLaren 41 1 1983 80
## 6 JH Kallis 44 0 2075 77
## 7 WD Parnell 44 0 1957 74
## 8 J Botha 44 0 2311 69
## 9 RJ Peterson 47 1 1872 68
## 10 CK Langeveldt 49 0 1829 65
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatchesMain(nz_matches,"New Zealand")
## Source: local data frame [51 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 KD Mills 50 1 3918 160
## 2 DL Vettori 43 1 3767 147
## 3 TG Southee 51 0 3996 134
## 4 MJ McClenaghan 49 0 2252 85
## 5 JDP Oram 46 0 2064 78
## 6 NL McCullum 46 0 2840 67
## 7 SE Bond 37 1 1449 62
## 8 TA Boult 40 3 1324 58
## 9 CJ Anderson 41 0 1297 52
## 10 MJ Henry 41 0 1098 47
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatchesMain(sl_matches,"Sri Lanka")
## Source: local data frame [54 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 SL Malinga 51 0 7214 281
## 2 KMDN Kulasekara 51 0 5481 179
## 3 BAW Mendis 47 0 2979 135
## 4 NLTC Perera 48 0 3624 129
## 5 M Muralitharan 45 0 2471 114
## 6 AD Mathews 51 0 3394 113
## 7 TM Dilshan 50 0 3049 73
## 8 CRD Fernando 51 1 2067 73
## 9 HMRKB Herath 41 0 2027 71
## 10 MF Maharoof 48 0 1860 70
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatchesMain(wi_matches,"West Indies")
## Source: local data frame [45 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 DJ Bravo 51 0 4239 153
## 2 JE Taylor 50 0 2530 103
## 3 R Rampaul 46 1 2608 102
## 4 KAJ Roach 49 0 2500 98
## 5 SP Narine 47 0 1924 82
## 6 DJG Sammy 51 1 3584 79
## 7 AD Russell 48 0 1987 63
## 8 CH Gayle 38 0 1955 53
## 9 JO Holder 44 0 1542 50
## 10 MN Samuels 38 0 2209 48
## .. ... ... ... ... ...
17 Team bowling scorecard against all opposition (continued)
The function lists the top bowlers of a country (‘matches’) against the opposition country
teamBowlingScorecardAllOppnAllMatches(ind_matches,'Australia')
## Source: local data frame [36 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 I Sharma 44 1 739 26
## 2 Harbhajan Singh 40 0 926 25
## 3 IK Pathan 42 1 702 22
## 4 UT Yadav 37 2 606 18
## 5 S Sreesanth 34 0 454 18
## 6 RA Jadeja 39 0 867 16
## 7 Z Khan 33 1 500 15
## 8 R Ashwin 43 0 684 14
## 9 P Kumar 27 0 501 14
## 10 R Vinay Kumar 31 1 380 14
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatches(aus_matches,'India')
## Source: local data frame [39 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 MG Johnson 47 0 1020 44
## 2 B Lee 41 3 671 28
## 3 SR Watson 36 1 532 18
## 4 CJ McKay 37 1 403 18
## 5 GB Hogg 33 0 427 17
## 6 JP Faulkner 26 0 598 16
## 7 JR Hopes 31 0 346 14
## 8 NW Bracken 35 1 429 13
## 9 JW Hastings 27 2 259 13
## 10 MA Starc 26 0 251 13
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatches(nz_matches,'England')
## Source: local data frame [33 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 TG Southee 39 2 684 33
## 2 DL Vettori 27 1 561 28
## 3 KD Mills 27 0 742 24
## 4 MJ McClenaghan 25 1 515 20
## 5 JEC Franklin 23 0 418 12
## 6 SE Bond 16 0 205 12
## 7 GD Elliott 10 3 194 12
## 8 SB Styris 8 0 296 9
## 9 NL McCullum 24 0 425 7
## 10 MJ Santner 18 0 230 7
## .. ... ... ... ... ...
teamBowlingScorecardAllOppnAllMatches(sl_matches,"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 14
## 2 BAW Mendis 15 0 267 9
## 3 KMDN Kulasekara 13 1 185 8
## 4 AD Mathews 14 0 191 7
## 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 5
## 9 HMRKB Herath 10 1 124 4
## 10 ST Jayasuriya 1 0 38 4
## .. ... ... ... ... ...
18. Team Bowlers versus Batsmen (against all oppositions)
The functions below give the peformance of bowlers versus batsman. They give the best bowlers and the total runs conceded and against whom were the runs conceded
teamBowlersVsBatsmenAllOppnAllMatchesMain(ind_matches,theTeam="India",rank=0)
## Source: local data frame [10 x 2]
##
## bowler runs
## (fctr) (dbl)
## 1 RA Jadeja 4691
## 2 R Ashwin 4111
## 3 Harbhajan Singh 3858
## 4 Z Khan 3514
## 5 I Sharma 3100
## 6 P Kumar 2646
## 7 Yuvraj Singh 2542
## 8 IK Pathan 2359
## 9 UT Yadav 2343
## 10 MM Patel 2314
(rank=1)
## [1] 1
m <-teamBowlersVsBatsmenAllOppnAllMatchesMain(ind_matches,theTeam="India",rank=1)
m
## Source: local data frame [207 x 3]
## Groups: bowler [1]
##
## bowler batsman runsConceded
## (fctr) (fctr) (dbl)
## 1 RA Jadeja KC Sangakkara 172
## 2 RA Jadeja DPMD Jayawardene 117
## 3 RA Jadeja TM Dilshan 108
## 4 RA Jadeja LD Chandimal 103
## 5 RA Jadeja GJ Bailey 99
## 6 RA Jadeja LRPL Taylor 95
## 7 RA Jadeja IR Bell 94
## 8 RA Jadeja KS Williamson 92
## 9 RA Jadeja AB de Villiers 90
## 10 RA Jadeja SR Watson 85
## .. ... ... ...
m <-teamBowlersVsBatsmenAllOppnAllMatchesMain(ind_matches,theTeam="India",rank=2)
m
## Source: local data frame [177 x 3]
## Groups: bowler [1]
##
## bowler batsman runsConceded
## (fctr) (fctr) (dbl)
## 1 R Ashwin GJ Bailey 132
## 2 R Ashwin KC Sangakkara 117
## 3 R Ashwin AN Cook 115
## 4 R Ashwin KS Williamson 114
## 5 R Ashwin DM Bravo 111
## 6 R Ashwin AD Mathews 100
## 7 R Ashwin LD Chandimal 98
## 8 R Ashwin LRPL Taylor 93
## 9 R Ashwin DPMD Jayawardene 93
## 10 R Ashwin KP Pietersen 81
## .. ... ... ...
18. Team Bowlers versus Batsmen (against all oppositions continued)
teamBowlersVsBatsmenAllOppnAllMatchesMain(sa_matches,theTeam="South Africa",rank=0)
## Source: local data frame [10 x 2]
##
## bowler runs
## (fctr) (dbl)
## 1 DW Steyn 4116
## 2 M Morkel 3808
## 3 J Botha 2244
## 4 LL Tsotsobe 2147
## 5 JP Duminy 2111
## 6 Imran Tahir 2087
## 7 JH Kallis 2014
## 8 WD Parnell 1864
## 9 R McLaren 1863
## 10 RJ Peterson 1842
teamBowlersVsBatsmenAllOppnAllMatchesMain(pak_matches,theTeam="Pakistan",rank=0)
## Source: local data frame [10 x 2]
##
## bowler runs
## (fctr) (dbl)
## 1 Shahid Afridi 6444
## 2 Saeed Ajmal 3956
## 3 Umar Gul 3901
## 4 Mohammad Hafeez 3434
## 5 Wahab Riaz 2755
## 6 Mohammad Irfan 2399
## 7 Sohail Tanvir 2337
## 8 Shoaib Malik 2105
## 9 Junaid Khan 1974
## 10 Iftikhar Anjum 1626
teamBowlersVsBatsmenAllOppnAllMatchesMain(sl_matches,theTeam="Sri Lanka",rank=1)
## Source: local data frame [314 x 3]
## Groups: bowler [1]
##
## bowler batsman runsConceded
## (fctr) (fctr) (dbl)
## 1 SL Malinga Mohammad Hafeez 191
## 2 SL Malinga V Kohli 175
## 3 SL Malinga G Gambhir 170
## 4 SL Malinga MS Dhoni 144
## 5 SL Malinga Umar Akmal 142
## 6 SL Malinga V Sehwag 140
## 7 SL Malinga IR Bell 134
## 8 SL Malinga SR Tendulkar 133
## 9 SL Malinga Ahmed Shehzad 121
## 10 SL Malinga AN Cook 120
## .. ... ... ...
m <-teamBowlersVsBatsmenAllOppnAllMatchesMain(ind_matches,theTeam="India",rank=2)
m
## Source: local data frame [177 x 3]
## Groups: bowler [1]
##
## bowler batsman runsConceded
## (fctr) (fctr) (dbl)
## 1 R Ashwin GJ Bailey 132
## 2 R Ashwin KC Sangakkara 117
## 3 R Ashwin AN Cook 115
## 4 R Ashwin KS Williamson 114
## 5 R Ashwin DM Bravo 111
## 6 R Ashwin AD Mathews 100
## 7 R Ashwin LD Chandimal 98
## 8 R Ashwin LRPL Taylor 93
## 9 R Ashwin DPMD Jayawardene 93
## 10 R Ashwin KP Pietersen 81
## .. ... ... ...
19. Team bowlers versus batsmen report (all oppositions)
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=ind_matches,theTeam="India",rank=0)
## Source: local data frame [10 x 2]
##
## bowler runs
## (fctr) (dbl)
## 1 KMDN Kulasekara 1448
## 2 SL Malinga 1319
## 3 NLTC Perera 959
## 4 JM Anderson 954
## 5 MG Johnson 939
## 6 SCJ Broad 877
## 7 BAW Mendis 783
## 8 AD Mathews 776
## 9 ST Finn 751
## 10 TT Bresnan 741
a <- teamBowlersVsBatsmenAllOppnAllMatchesRept(ind_matches,theTeam="India",rank=1)
a
## Source: local data frame [31 x 3]
## Groups: bowler [1]
##
## bowler batsman runsConceded
## (fctr) (fctr) (dbl)
## 1 KMDN Kulasekara V Sehwag 199
## 2 KMDN Kulasekara V Kohli 196
## 3 KMDN Kulasekara G Gambhir 157
## 4 KMDN Kulasekara SR Tendulkar 127
## 5 KMDN Kulasekara Yuvraj Singh 118
## 6 KMDN Kulasekara RG Sharma 114
## 7 KMDN Kulasekara SK Raina 104
## 8 KMDN Kulasekara MS Dhoni 80
## 9 KMDN Kulasekara KD Karthik 56
## 10 KMDN Kulasekara SC Ganguly 51
## .. ... ... ...
20. Team bowlers versus batsmen report (all oppositions continued)
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=ind_matches,theTeam="Sri Lanka",rank=0)
## Source: local data frame [10 x 2]
##
## bowler runs
## (fctr) (dbl)
## 1 Z Khan 1141
## 2 RA Jadeja 882
## 3 I Sharma 855
## 4 Harbhajan Singh 805
## 5 P Kumar 758
## 6 R Ashwin 740
## 7 IK Pathan 678
## 8 A Nehra 584
## 9 UT Yadav 544
## 10 MM Patel 488
teamBowlersVsBatsmenAllOppnAllMatchesRept(ind_matches,"England",rank=0)
## Source: local data frame [10 x 2]
##
## bowler runs
## (fctr) (dbl)
## 1 R Ashwin 777
## 2 RA Jadeja 735
## 3 Z Khan 507
## 4 MM Patel 463
## 5 RP Singh 410
## 6 I Sharma 396
## 7 PP Chawla 375
## 8 Yuvraj Singh 370
## 9 B Kumar 353
## 10 AB Agarkar 336
21. Team bowlers versus batsmen report (all oppositions coninued-1)
teamBowlersVsBatsmenAllOppnAllMatchesRept(nz_matches,theTeam="New Zealand",rank=0)
## Source: local data frame [10 x 2]
##
## bowler runs
## (fctr) (dbl)
## 1 JM Anderson 889
## 2 MG Johnson 828
## 3 Shahid Afridi 751
## 4 KMDN Kulasekara 728
## 5 SCJ Broad 638
## 6 NW Bracken 626
## 7 SL Malinga 601
## 8 DW Steyn 556
## 9 ST Finn 482
## 10 SR Watson 468
teamBowlersVsBatsmenAllOppnAllMatchesRept(aus_matches,"Australia",rank=0)
## Source: local data frame [10 x 2]
##
## bowler runs
## (fctr) (dbl)
## 1 JM Anderson 1211
## 2 TT Bresnan 1087
## 3 SL Malinga 1078
## 4 SCJ Broad 948
## 5 Harbhajan Singh 890
## 6 DL Vettori 883
## 7 KMDN Kulasekara 875
## 8 DW Steyn 872
## 9 RA Jadeja 853
## 10 DJ Bravo 830
teamBowlersVsBatsmenAllOppnAllMatchesRept(sl_matches,"Sri Lanka",rank=0)
## Source: local data frame [10 x 2]
##
## bowler runs
## (fctr) (dbl)
## 1 Shahid Afridi 1177
## 2 Z Khan 1141
## 3 RA Jadeja 882
## 4 I Sharma 855
## 5 Saeed Ajmal 814
## 6 Harbhajan Singh 805
## 7 Mohammad Hafeez 774
## 8 P Kumar 758
## 9 R Ashwin 740
## 10 Umar Gul 718
22. Team bowlers versus batsmen report (all oppositions) plot
This function can only be used for rank>0 (rank=1,2,3..)
Giga thoughts ... 