yorkr is generic!


The features and functionality in my yorkr package is now complete. My R package yorkr, is totally generic, which means that the R package  can be used for all ODI, T20 matches. Hence yorkr can be used for professional or amateur ODI and T20 matches. The R package can be used for both men and women ODI, T20 international or domestic matches. The main requirement is, that the match data  be created as a Yaml file in the format Cricsheet (Required yaml format for the match data).

I have successfully used my R functions for the Indian Premier League (IPL) matches with changes only to the convertAllYamlFiles2RDataFramesXX (please see posts below)

The convertAllYamlFiles2RDataframes &convertAllYamlFiles2RDataFramesT20 will have to be customized for the names of the teams playing in the domestic professional or amateur matches. All other classes of functions namely Class1, Class2, Class 3 and Class 4 as discussed in my post Introducing cricket package yorkr-Part 1: Beaten by sheer pace

There are numerous professional & amateur T20 matches that are played around the world. Here are a list of domestic T20 tournaments that are played around the world (from Wikipedia). The yorkr package can be used for any of these matches once the match data is saved as yaml as mentioned above.

So do go ahead and have fun, analyzing cricket performances with yorkr!

Please take a look at my posts on how to use yorkr for ODI, Twenty20 matches.

  1. Introducing cricket package yorkr:Part 1- Beaten by sheer pace!
    2. Introducing cricket package yorkr:Part 2- Trapped leg before wicket!
    3.  Introducing cricket package yorkr:Part 3- foxed by flight!
    4. Introducing cricket package yorkr:Part 4-In the block hole!
    5. yorkr pads up for the Twenty20s: Part 1- Analyzing team”s match performance
    6. yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams
    7. yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions!
    8. yorkr pads up for Twenty20s:Part 4- Individual batting and bowling performances!
    9. yorkr crashes the IPL party ! – Part 1
    10. yorkr crashes the IPL party! – Part 2
    11. yorkr crashes the IPL party! – Part 3
    12. yorkr crashes the IPL party! – Part 4
    13. yorkr ranks IPL batsmen and bowlers
    14. yorkr ranks T20 batsmen and bowlers
    15. yorkr ranks ODI batsmen and bowlers

yorkr ranks ODI batsmen and bowlers


This is the last and final post in which yorkr ranks ODI batsmen and bowlers. These are based on match data from Cricsheet. The ranking is done on

  1. average runs and average strike rate for batsmen and
  2. average wickets and average economy rate for bowlers.

This post has also been published in RPubs RankODIPlayers. You can download this as a pdf file at RankODIPlayers.pdf.

You can take a look at the code at rankODIPlayers (available in yorkr_0.0.5)

rm(list=ls())
library(yorkr)
library(dplyr)
source("rankODIBatsmen.R")
source("rankODIBowlers.R")

Rank ODI batsmen

The top 3 ODI batsmen are hashim Amla (SA), Matther Hayden(Aus) & Virat Kohli (Ind) . Note: For ODI a a cutoff of at least 50 matches played was chosen.

ODIBatsmanRank <- rankODIBatsmen()
as.data.frame(ODIBatsmanRank[1:30,])
##            batsman matches meanRuns    meanSR
## 1          HM Amla     185 51.96216  84.15508
## 2        ML Hayden      79 50.08861  81.20646
## 3          V Kohli     279 48.51971  78.55197
## 4   AB de Villiers     253 47.93676  95.05561
## 5     SR Tendulkar     151 45.82119  79.62311
## 6         S Dhawan     116 45.03448  81.54043
## 7         V Sehwag     167 44.49102 106.27563
## 8          JE Root     111 43.64865  81.66054
## 9        Q de Kock      85 43.61176  82.55235
## 10       IJL Trott     113 43.36283  70.69761
## 11   KC Sangakkara     293 42.81911  75.10420
## 12      TM Dilshan     283 41.76678  89.70360
## 13   KS Williamson     146 41.24658  73.49267
## 14   S Chanderpaul      93 40.07527  70.59613
## 15        HH Gibbs      75 40.00000  79.03813
## 16     Salman Butt      57 39.85965  59.29807
## 17    Anamul Haque      58 39.72414  56.45224
## 18      RT Ponting     238 38.88235  71.94294
## 19       JH Kallis     136 38.77941  67.17794
## 20        MS Dhoni     328 38.57927  90.30555
## 21      MJ Guptill     199 38.54774  73.88090
## 22       DA Warner     138 38.52174  87.24978
## 23 Mohammad Yousuf      94 38.44681  72.69851
## 24        JD Ryder      66 38.40909  91.29667
## 25       GJ Bailey     133 38.38346  75.74519
## 26       G Gambhir     209 37.83254  75.15483
## 27      AJ Strauss     122 37.80328  71.54844
## 28       MJ Clarke     301 37.67442  69.78415
## 29       SR Watson     274 37.08029  83.46489
## 30        AJ Finch     103 36.36893  79.49845

Rank ODI bowlers

The top 3 ODI bowlers are R J Harris (Aus), MJ Henry(NZ) and MA Starc(Aus). Mohammed Shami is 4th and Amit Mishra is 8th A cutoff of 20 matches was considered for bowlers

ODIBowlersRank <- rankODIBowlers()
## [1] 35072     3
## [1] "C:/software/cricket-package/york-test/yorkrData/ODI/ODI-matches"
as.data.frame(ODIBowlersRank[1:30,])
##               bowler matches meanWickets   meanER
## 1  Mustafizur Rahman      56    4.000000 4.293214
## 2           JH Davey      53    3.528302 4.455094
## 3          RJ Harris      94    3.276596 4.361489
## 4           MA Starc     208    3.144231 4.425865
## 5           MJ Henry      88    3.125000 4.961250
## 6         A Flintoff     139    2.956835 4.283022
## 7           A Mishra     106    2.886792 4.365849
## 8     Mohammed Shami     144    2.777778 5.609306
## 9     MJ McClenaghan     165    2.751515 5.640424
## 10          CJ McKay     230    2.704348       NA
## 11       MF Maharoof     114    2.701754 4.427018
## 12       Imran Tahir     156    2.660256 4.461923
## 13        BAW Mendis     234    2.641026 4.532308
## 14     RK Kleinveldt      54    2.629630 4.306667
## 15      Arafat Sunny      62    2.612903 4.103226
## 16         JE Taylor     156    2.602564 5.115192
## 17           AJ Hall      55    2.600000 3.879091
## 18        WD Parnell     129    2.596899 5.477597
## 19         CR Woakes     129    2.596899 5.340620
## 20      DE Bollinger     152    2.592105 4.282763
## 21        Wahab Riaz     206    2.567961 5.431748
## 22        PJ Cummins     148    2.567568 5.715405
## 23         R Rampaul     173    2.549133 4.726590
## 24      Taskin Ahmed      56    2.535714 5.325357
## 25          DW Steyn     292    2.534247 4.534007
## 26      JR Hazlewood      64    2.531250 4.392500
## 27        Abdur Rauf      84    2.523810 4.786667
## 28           SW Tait     141    2.517730 5.173191
## 29      Hamid Hassan     106    2.509434 4.686038
## 30        SL Malinga     419    2.498807 4.968974

Hope you have fun with my yorkr package.!

yorkr ranks T20 batsmen and bowlers


Here is another short post which ranks T20 batsmen and bowlers. These are based on match data from Cricsheet. The ranking is done on

  1. average runs and average strike rate for batsmen and
  2. average wickets and average economy rate for bowlers.

This post has also been published in RPubs RankT20Players. You can download this as a pdf file at RankT20Players.pdf.

You can take a look at the code at rankT20Players (available in yorkr_0.0.5)

rm(list=ls())
library(yorkr)
library(dplyr)
source("rankT20Batsmen.R")
source("rankT20Bowlers.R")

Rank T20 batsmen

Virat Kohli (Ind), Chris Gayle (WI) and Kevin Pietersen (Eng) top the T20 rankings. Virat Kohli stands tall among the batsmen with a average of 39.1935, followed by Chris Gayle who has an average of 32.69 and finally Kevin Pietersen.

Note: For T20 a cutoff of at least 30 matches played was chosen.

T20BatsmanRank <- rankT20Batsmen()
as.data.frame(T20BatsmanRank[1:30,])
##             batsman matches meanRuns   meanSR
## 1           V Kohli      31 39.19355 128.8371
## 2          CH Gayle      43 32.69767 119.6467
## 3      KP Pietersen      37 32.43243 138.6732
## 4     KS Williamson      31 32.25806 130.1255
## 5  Mohammad Shahzad      33 31.66667 115.4582
## 6       BB McCullum      69 30.98551 126.0610
## 7        MJ Guptill      54 30.83333 120.0669
## 8          AD Hales      37 30.75676 115.3511
## 9       H Masakadza      38 29.26316 109.6182
## 10         GC Smith      32 27.59375 114.1831
## 11        DA Warner      56 27.53571 123.2209
## 12        JP Duminy      58 26.84483 117.3952
## 13 DPMD Jayawardene      51 26.47059 112.4257
## 14        SR Watson      50 26.30000 118.9464
## 15    KC Sangakkara      52 26.23077 112.4665
## 16       TM Dilshan      66 26.18182 102.5683
## 17         SK Raina      43 25.90698 124.3044
## 18        RG Sharma      41 25.68293 123.3983
## 19        G Gambhir      36 25.66667 117.5764
## 20     Yuvraj Singh      41 25.12195 119.5846
## 21    Misbah-ul-Haq      32 25.09375 106.6762
## 22       EJG Morgan      52 24.71154 121.1462
## 23       MN Samuels      40 24.35000 105.8547
## 24       MEK Hussey      30 24.03333 129.1250
## 25    Ahmed Shehzad      41 23.82927 100.8805
## 26  Shakib Al Hasan      40 23.35000 109.3798
## 27          HM Amla      30 23.33333 111.2513
## 28         CL White      45 22.73333       NA
## 29      LMP Simmons      33 22.54545       NA
## 30       Umar Akmal      69 22.20290 108.3590

Rank T20 bowlers

 

The top 3 T20 bowlers are BAW Mendis (SL) Umar Gul (Pak) and Steyn(SA). R Ashwin is 13th. As with batsmen, a minimum of 30 matches played was taken into consideration.

T20BowlersRank <- rankT20Bowlers()
as.data.frame(T20BowlersRank[1:30,])
##             bowler matches meanWickets   meanER
## 1       BAW Mendis      36   1.6944444 6.581111
## 2         Umar Gul      57   1.5964912 7.306842
## 3         DW Steyn      38   1.5526316 6.407632
## 4      Saeed Ajmal      63   1.4920635 6.316190
## 5       SL Malinga      59   1.4576271 7.163898
## 6       TG Southee      37   1.4054054 8.840000
## 7       MG Johnson      30   1.4000000 7.080667
## 8         GP Swann      38   1.3947368 6.576842
## 9      JW Dernbach      33   1.3636364 8.550303
## 10        M Morkel      39   1.3333333 7.384872
## 11 Shakib Al Hasan      37   1.2972973 6.648649
## 12       SP Narine      32   1.2500000 5.757812
## 13        R Ashwin      33   1.2424242 7.247273
## 14 KMDN Kulasekara      42   1.2380952 6.938095
## 15       SCJ Broad      55   1.2363636 7.832182
## 16      WD Parnell      34   1.2058824 8.227941
## 17        KD Mills      41   1.1951220 8.077317
## 18      DL Vettori      34   1.1470588 5.708235
## 19   Shahid Afridi      85   1.1294118 6.748000
## 20       SR Watson      44   1.1136364 8.015227
## 21   Sohail Tanvir      48   1.1041667 7.354167
## 22   Sohail Tanvir      48   1.1041667 7.354167
## 23     NL McCullum      56   1.0535714 7.246964
## 24     NLTC Perera      34   1.0294118 8.916471
## 25         J Botha      39   1.0256410 6.647436
## 26        DJ Bravo      45   1.0222222 8.630000
## 27   Mohammad Nabi      32   0.9687500 7.208437
## 28       DJG Sammy      55   0.8909091 7.899818
## 29 Mohammad Hafeez      56   0.8392857 6.996964
## 30      AD Mathews      44   0.7954545 6.827727

Conclusion

Conclusion

As expected Virat Kohli stands head and shoulders above the rest. Hamid Hasan and Mohammed Shami figuring the top T20 bowlers was a bit of a surprise to me.

Watch this space!

  1. Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
  2. yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
  3. yorkr crashes the IPL party !Part 1
  4. Introducing cricketr! : An R package to analyze performances of cricketers
  5. Cricket analytics with cricketr in paperback and Kindle versions

yorkr ranks IPL batsmen and bowlers


Here is a short post which ranks IPL batsmen and bowlers. These are based on match data from Cricsheet. Ranking batsmen and bowlers in IPL is more challenging as the players can belong to different teams in different years. Hence I create a combined data frame of the batsmen and bowlers regardless of their IPL teams and calculate a) average runs and average strike rate for batsmen and c) average wickets and d) average economy rate for bowlers.

I will be doing this ranking for T20 and ODI batting and bowling performances shortly.

This post has also been published in RPubs RankIPLPlayers. You can download this as a pdf file at RankIPLPlayers.pdf.

You can take a look at the code at rankIPLPlayers (should be available in yorkr_0.0.5)

The results are slightly surprising

rm(list=ls())
library(yorkr)
library(dplyr)
setwd("C:/software/cricket-package/cricsheet/cleanup/IPL/rank")
source("rankIPLBatsmen.R")
source("rankIPLBowlers.R")

Rank IPL batsmen

Chris Gayle, MEK Hussey and Shane Watson are top 3 IPL batsmen. Gayle towers over the others in mean runs and mean strike rate. Surprisingly Ajinkya Rahane is the top Indian T20 batsman, if we leave out Sachin Tendulkar (who tops India yet again!). The other top IPL T20 batsmen are Raina, Gambhir, Rohit Sharma in that order. Virat Kohli comes a distant 14th.

iplBatsmanRank <- rankIPLBatsmen()
as.data.frame(iplBatsmanRank[1:30,])
##             batsman matches meanRuns    meanSR
## 1          CH Gayle     128 40.00781 144.92188
## 2        MEK Hussey      64 33.57812 107.23500
## 3         SR Watson      75 31.46667 129.97733
## 4      SR Tendulkar     127 29.74803 108.86622
## 5         AM Rahane      77 29.14286 101.40065
## 6         DA Warner     134 29.10448 118.38313
## 7         JP Duminy      94 28.77660 124.61702
## 8          SK Raina     128 28.62500 122.12656
## 9         G Gambhir     210 28.13810 108.78090
## 10        RG Sharma     181 28.07182 118.57801
## 11         DR Smith      78 27.82051 119.64462
## 12      BB McCullum      98 27.81633 114.91255
## 13         S Dhawan     109 27.74312 112.21000
## 14          V Kohli     188 27.56915 113.81261
## 15   AB de Villiers     150 27.46000 136.70860
## 16         R Dravid     104 27.02885 107.78923
## 17        JH Kallis     167 26.54491  94.65641
## 18         V Sehwag     174 26.39655 140.29011
## 19       RV Uthappa     166 26.27711 120.48506
## 20       SC Ganguly      86 25.98837  96.39849
## 21     AC Gilchrist      81 25.77778 122.69074
## 22    KC Sangakkara      70 25.67143 112.97529
## 23         MS Dhoni     119 25.29412 130.99832
## 24       TM Dilshan      82 24.13415 101.12634
## 25          M Vijay      96 23.92708 102.01771
## 26        AT Rayudu     146 23.63014 117.91000
## 27 DPMD Jayawardene     109 22.95413 110.73862
## 28        MK Pandey     105 22.71429        NA
## 29     Yuvraj Singh     112 22.48214 114.51018
## 30      S Badrinath      66 22.22727 114.97061

Rank IPL bowlers

The top 3 IPL T20 bowlers are SL Malinga,SP Narine and DJ Bravo.

Don’t get hung up on the decimals in the average wickets for the bowlers. All it implies is that if 2 bowlers have average wickets of 1.0 and 1.5, it implies that in 2 matches the 1st bowler will take 2 wickets and the 2nd bowler will take 3 wickets.

iplBowlersRank <- rankIPLBowlers()
as.data.frame(iplBowlersRank[1:30,])
##             bowler matches meanWickets   meanER
## 1       SL Malinga      96    1.645833 6.545208
## 2        SP Narine      54    1.555556 5.967593
## 3         DJ Bravo      58    1.517241 7.929310
## 4         M Morkel      37    1.405405 7.626216
## 5        IK Pathan      40    1.400000 7.579250
## 6         RP Singh      42    1.357143 7.966429
## 7         MM Patel      31    1.354839 7.282581
## 8  Shakib Al Hasan      32    1.343750 6.911250
## 9    R Vinay Kumar      63    1.317460 8.342540
## 10       MM Sharma      46    1.304348 7.740652
## 11         P Awana      33    1.303030 8.325758
## 12        MM Patel      30    1.300000 7.569667
## 13          Z Khan      41    1.292683 7.735854
## 14        A Mishra      43    1.255814 7.226512
## 15         PP Ojha      53    1.245283 7.268679
## 16     JP Faulkner      40    1.225000 8.502250
## 17     DS Kulkarni      32    1.156250 8.372188
## 18        UT Yadav      46    1.152174 8.394783
## 19        A Kumble      41    1.146341 6.567073
## 20       JA Morkel      73    1.136986 8.131370
## 21        SK Warne      53    1.132075 7.277170
## 22 Harbhajan Singh     107    1.102804 7.014953
## 23        L Balaji      34    1.088235 7.186176
## 24        R Ashwin      92    1.065217 6.812391
## 25        AR Patel      31    1.064516 7.137097
## 26  M Muralitharan      39    1.051282 6.470256
## 27         P Kumar      36    1.027778 8.148056
## 28       PP Chawla      85    1.023529 8.017765
## 29       SR Watson      67    1.014925 7.695224
## 30        DJ Bravo      30    1.000000 7.966333

Conclusion: The results are somewhat surprising. The ranking was based on data from Cricsheet. The data in this site are available from 2008-2015. I hope to do this ranking for T20 and ODIs shortly

Watch this space!

  1. Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
  2. yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
  3. yorkr crashes the IPL party !Part 1
  4. Introducing cricketr! : An R package to analyze performances of cricketers
  5. Cricket analytics with cricketr in paperback and Kindle versions

yorkr crashes the IPL party! – Part 4


Introduction

I’ve missed more than 9000 shots in my career. I’ve lost almost 300 games. 26 times, I’ve been trusted to take the game winning shot and missed. I’ve failed over and over and over again in my life. And that is why I succeed.

                      Michael Jordan

Success is where preparation and opportunity meet.

                      Bobby Unser

It is not whether you get knocked down. It is whether you get up.

                      Vince Lombardi

Make sure your worst enemy doesn’t live between your own two ears.

                      Laird Hamilton

This post should be the last post for “yorkr crashes the IPL party!”. In fact it is final post for the whole ‘yorkr’ series. I have now covered the use of yorkr for ODIs, Twenty20s and IPL T20 formats. I will not be including functionality in yorkr to handle Test cricket from Cricsheet. I would recommend that you use my R package cricketr. Please see my post Introducing cricketr! : An R package to analyze performances of cricketers

In this last post on IPL T20 I look at the top individual batting and bowling performances in the IPL Twenty20s. Also please take a look at my 3 earlier post on yorkr’s handling of IPL Twenty20 matches

  1. yorkr crashes the IPL party ! – Part 1
  2. yorkr crashes the IPL party ! – Part 2
  3. yorkr crashes the IPL party ! – Part 3

This post has also been published at RPubs IPLT20-Part4 and can also be downloaded as a PDF document from IPLT20-Part4.pdf.

You can clone/fork the code for the package yorkr from Github at yorkr-package

The list of Class 4 functions are shown below.The Twenty20 features will be available from yorkr_0.0.4

Batsman functions

  1. batsmanRunsVsDeliveries
  2. batsmanFoursSixes
  3. batsmanDismissals
  4. batsmanRunsVsStrikeRate
  5. batsmanMovingAverage
  6. batsmanCumulativeAverageRuns
  7. batsmanCumulativeStrikeRate
  8. batsmanRunsAgainstOpposition
  9. batsmanRunsVenue
  10. batsmanRunsPredict

Bowler functions

  1. bowlerMeanEconomyRate
  2. bowlerMeanRunsConceded
  3. bowlerMovingAverage
  4. bowlerCumulativeAvgWickets
  5. bowlerCumulativeAvgEconRate
  6. bowlerWicketPlot
  7. bowlerWicketsAgainstOpposition
  8. bowlerWicketsVenue
  9. bowlerWktsPredict
library(yorkr)
library(gridExtra)
library(rpart.plot)
library(dplyr)
library(ggplot2)
rm(list=ls())

A. Batsman functions

1. Get IPL Team Batting details

The function below gets the overall IPL team batting details based on the RData file available in IPL T20 matches. This is currently also available in Github at [IPL-T20-matches] (https://github.com/tvganesh/yorkrData/tree/master/IPL/IPL-T20-matches). The batting details of the IPL team in each match is created and a huge data frame is created by rbinding the individual dataframes. This can be saved as a RData file

setwd("C:/software/cricket-package/york-test/yorkrData/IPL/IPL-T20-matches")
csk_details <- getTeamBattingDetails("Chennai Super Kings",dir=".", save=TRUE)
dd_details <- getTeamBattingDetails("Delhi Daredevils",dir=".",save=TRUE)
kkr_details <- getTeamBattingDetails("Kolkata Knight Riders",dir=".",save=TRUE)
mi_details <- getTeamBattingDetails("Mumbai Indians",dir=".",save=TRUE)
rcb_details <- getTeamBattingDetails("Royal Challengers Bangalore",dir=".",save=TRUE)

2. Get IPL batsman details

This function is used to get the individual IPL T20 batting record for a the specified batsman of the team as in the functions below. For analyzing the batting performances I have chosen the top IPL T20 batsmen from the teams. This was based to a large extent on batting scorecard functions from yorkr crashes the IPL party!:Part 3 The top IPL batsmen chosen are the ones below

  1. Suresh Raina (CSK)
  2. MS Dhoni (CSK)
  3. Virendar Sehwag (DD)
  4. Rohit Sharma (MI)
  5. Gautham Gambhir (KKR)
  6. Virat Kohli (RCB)
setwd("C:/software/cricket-package/cricsheet/cleanup/IPL/part4")
raina <- getBatsmanDetails(team="Chennai Super Kings",name="SK Raina",dir=".")
## [1] "./Chennai Super Kings-BattingDetails.RData"
dhoni <- getBatsmanDetails(team="Chennai Super Kings",name="MS Dhoni")
## [1] "./Chennai Super Kings-BattingDetails.RData"
sehwag <-  getBatsmanDetails(team="Delhi Daredevils",name="V Sehwag",dir=".")
## [1] "./Delhi Daredevils-BattingDetails.RData"
gambhir <-  getBatsmanDetails(team="Kolkata Knight Riders",name="G Gambhir",dir=".")
## [1] "./Kolkata Knight Riders-BattingDetails.RData"
rsharma <-  getBatsmanDetails(team="Mumbai Indians",name="RG Sharma",dir=".")
## [1] "./Mumbai Indians-BattingDetails.RData"
kohli <-  getBatsmanDetails(team="Royal Challengers Bangalore",name="V Kohli",dir=".")
## [1] "./Royal Challengers Bangalore-BattingDetails.RData"

3. Runs versus deliveries (in IPL matches)

Sehwag has a superb strike rate. It can be seen that Sehwag averages around 80 runs for around 40 deliveries followed by Rohit Sharma. Raina and Dhoni average around 60 runs

p1 <-batsmanRunsVsDeliveries(raina, "SK Raina")
p2 <-batsmanRunsVsDeliveries(dhoni,"MS Dhoni")
p3 <-batsmanRunsVsDeliveries(sehwag,"V Sehwag")
p4 <-batsmanRunsVsDeliveries(gambhir,"G Gambhir")
p5 <-batsmanRunsVsDeliveries(rsharma,"RG Sharma")
p6 <-batsmanRunsVsDeliveries(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

runsVsDeliveries-1

4. Batsman Total runs, Fours and Sixes (in IPL matches)

Dhoni leads in the runs made from sixes in comparison to the others

raina46 <- select(raina,batsman,ballsPlayed,fours,sixes,runs)
p1 <-batsmanFoursSixes(raina46, "SK Raina")
dhoni46 <- select(dhoni,batsman,ballsPlayed,fours,sixes,runs)
p2 <-batsmanFoursSixes(dhoni46,"MS Dhoni")
sehwag46 <- select(sehwag,batsman,ballsPlayed,fours,sixes,runs)
p3 <-batsmanFoursSixes(sehwag46,"V Sehwag")
gambhir46 <- select(gambhir,batsman,ballsPlayed,fours,sixes,runs)
p4 <-batsmanFoursSixes(gambhir46,"G Gambhir")
rsharma46 <- select(rsharma,batsman,ballsPlayed,fours,sixes,runs)
p5 <-batsmanFoursSixes(rsharma46,"RG Sharma")
kohli46 <- select(kohli,batsman,ballsPlayed,fours,sixes,runs)
p6 <-batsmanFoursSixes(kohli46,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

foursSixes-1

5. Batsman dismissals (in IPL matches)

The type of dismissal for each batsman is shown below

p1 <-batsmanDismissals(raina, "SK Raina")
p2 <-batsmanDismissals(dhoni,"MS Dhoni")
p3 <-batsmanDismissals(sehwag,"V Sehwag")
p4 <-batsmanDismissals(gambhir,"G Gambhir")
p5 <-batsmanDismissals(rsharma,"RG Sharma")
p6 <-batsmanDismissals(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

dismissal-1

6. Runs versus Strike Rate (in IPL matches)

Raina, Dhoni and Kohli have an increasing strike rate with more runs scored

p1 <-batsmanRunsVsStrikeRate(raina, "SK Raina")
p2 <-batsmanRunsVsStrikeRate(dhoni,"MS Dhoni")
p3 <-batsmanRunsVsStrikeRate(sehwag,"V Sehwag")
p4 <-batsmanRunsVsStrikeRate(gambhir,"G Gambhir")
p5 <-batsmanRunsVsStrikeRate(rsharma,"RG Sharma")
p6 <-batsmanRunsVsStrikeRate(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

runsSR-1

7. Batsman moving average (in IPL matches)

Rohit Sharma seems to maintain an average of almost 30 runs, while Dhoni and Kohli average around 25.

p1 <-batsmanMovingAverage(raina, "SK Raina")
p2 <-batsmanMovingAverage(dhoni,"MS Dhoni")
p3 <-batsmanMovingAverage(sehwag,"V Sehwag")
p4 <-batsmanMovingAverage(gambhir,"G Gambhir")
p5 <-batsmanMovingAverage(rsharma,"RG Sharma")
p6 <-batsmanMovingAverage(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

ma-1

8. Batsman cumulative average (in IPL matches)

The cumulative runs average of Raina, Gambhir, Kohli and Rohit Sharma are around 28-30 runs.Dhoni drops to 25

p1 <-batsmanCumulativeAverageRuns(raina, "SK Raina")
p2 <-batsmanCumulativeAverageRuns(dhoni,"MS Dhoni")
p3 <-batsmanCumulativeAverageRuns(sehwag,"V Sehwag")
p4 <-batsmanCumulativeAverageRuns(gambhir,"G Gambhir")
p5 <-batsmanCumulativeAverageRuns(rsharma,"RG Sharma")
p6 <-batsmanCumulativeAverageRuns(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cAvg-1

9. Cumulative Average Strike Rate (in IPL matches)

As seen above Sehwag has a phenomenal cumulative strike rate of around 150, followed by Dhoni around 130, then we Raina and finally Kohli.

p1 <-batsmanCumulativeStrikeRate(raina, "SK Raina")
p2 <-batsmanCumulativeStrikeRate(dhoni,"MS Dhoni")
p3 <-batsmanCumulativeStrikeRate(sehwag,"V Sehwag")
p4 <-batsmanCumulativeStrikeRate(gambhir,"G Gambhir")
p5 <-batsmanCumulativeStrikeRate(rsharma,"RG Sharma")
p6 <-batsmanCumulativeStrikeRate(kohli,"V Kohli")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cSR-1

10. Batsman runs against opposition (in IPL matches)

The following charts show the performance of te batsmen against opposing IPL teams


batsmanRunsAgainstOpposition(raina, "SK Raina")

runsOppn1-1

batsmanRunsAgainstOpposition(dhoni,"MS Dhoni")

runsOppn2-1

batsmanRunsAgainstOpposition(sehwag,"V Sehwag")

runsOppn3-1

batsmanRunsAgainstOpposition(gambhir,"G Gambhir")

runsOppn4-1

batsmanRunsAgainstOpposition(rsharma,"RG Sharma")

runsOppn5-1

batsmanRunsAgainstOpposition(kohli,"V Kohli")

runsOppn6-1

11. Runs at different venues (in IPL matches)

The plots below give the performances of the batsmen at different grounds.

batsmanRunsVenue(raina, "SK Raina")

runsVenue1-1

batsmanRunsVenue(dhoni,"MS Dhoni")

runsVenue2-1

batsmanRunsVenue(sehwag,"V Sehwag")

runsVenue3-1

batsmanRunsVenue(gambhir,"G Gambhir")

runsVenue4-1

batsmanRunsVenue(rsharma,"RG Sharma")

runsVenue5-1

batsmanRunsVenue(kohli,"V Kohli")

runsVenue6-1

12. Predict number of runs to deliveries (in IPL matches)

The plots below use rpart classification tree to predict the number of deliveries required to score the runs in the leaf node.

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsPredict(raina, "SK Raina")
batsmanRunsPredict(dhoni,"MS Dhoni")
batsmanRunsPredict(sehwag,"V Sehwag")

runsPredict1,runsVenue1-1

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsPredict(gambhir,"G Gambhir")
batsmanRunsPredict(rsharma,"RG Sharma")
batsmanRunsPredict(kohli,"V Kohli")

runsPredict2,runsVenue1-1

B. Bowler functions

13. Get bowling details in IPL matches

The function below gets the overall team IPL T20 bowling details based on the RData file available in IPL T20 matches. This is currently also available in Github at [yorkrData] (https://github.com/tvganesh/yorkrData/tree/master/IPL/IPL-T20-matches). The IPL T20 bowling details of the IPL team in each match is created, and a huge data frame is created by rbinding the individual dataframes. This can be saved as a RData file

setwd("C:/software/cricket-package/york-test/yorkrData/IPL/IPL-T20-matches")
kkr_bowling <- getTeamBowlingDetails("Kolkata Knight Riders",dir=".",save=TRUE)
csk_bowling <- getTeamBowlingDetails("Chennai Super Kings",dir=".",save=TRUE)
kxip_bowling <- getTeamBowlingDetails("Kings XI Punjab",dir=".",save=TRUE)
mi_bowling <- getTeamBowlingDetails("Mumbai Indians",dir=".",save=TRUE)
rcb_bowling <- getTeamBowlingDetails("Royal Challengers Bangalore",dir=".",save=TRUE)
rr_bowling <- getTeamBowlingDetails("Rajasthan Royals",dir=".",save=TRUE)
fl <- list.files(".","BowlingDetails.RData")
file.copy(fl, "C:/software/cricket-package/cricsheet/cleanup/IPL/part4")

14. Get bowling details of the individual IPL bowlers

This function is used to get the individual bowling record for a specified bowler of the country as in the functions below. For analyzing the bowling performances the following cricketers have been chosen based on the bowling scorecard from my post yorkr crashes the IPL party ! – Part 3

  1. Ravichander Ashwin (CSK)
  2. DJ Bravo (CSK)
  3. PP Chawla (KXIP)
  4. Harbhajan Singh (MI)
  5. R Vinay Kumar (RCB)
  6. SK Trivedi (RR)
setwd("C:/software/cricket-package/cricsheet/cleanup/IPL/part4")
ashwin <- getBowlerWicketDetails(team="Chennai Super Kings",name="R Ashwin",dir=".")
bravo <-  getBowlerWicketDetails(team="Chennai Super Kings",name="DJ Bravo",dir=".")
chawla <-  getBowlerWicketDetails(team="Kings XI Punjab",name="PP Chawla",dir=".")
harbhajan <-  getBowlerWicketDetails(team="Mumbai Indians",name="Harbhajan Singh",dir=".")
vinay <-  getBowlerWicketDetails(team="Royal Challengers Bangalore",name="R Vinay Kumar",dir=".")
sktrivedi <-  getBowlerWicketDetails(team="Rajasthan Royals",name="SK Trivedi",dir=".")

15. Bowler Mean Economy Rate (in IPL matches)

Ashwin & Chawla have the best economy rates of in the IPL teams, followed by Harbhajan Singh

p1<-bowlerMeanEconomyRate(ashwin,"R Ashwin")
p2<-bowlerMeanEconomyRate(bravo, "DJ Bravo")
p3<-bowlerMeanEconomyRate(chawla, "PP Chawla")
p4<-bowlerMeanEconomyRate(harbhajan, "Harbhajan Singh")
p5<-bowlerMeanEconomyRate(vinay, "R Vinay")
p6<-bowlerMeanEconomyRate(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

meanER-1

16. Bowler Mean Runs conceded (in IPL matches)

p1<-bowlerMeanRunsConceded(ashwin,"R Ashwin")
p2<-bowlerMeanRunsConceded(bravo, "DJ Bravo")
p3<-bowlerMeanRunsConceded(chawla, "PP Chawla")
p4<-bowlerMeanRunsConceded(harbhajan, "Harbhajan Singh")
p5<-bowlerMeanRunsConceded(vinay, "R Vinay")
p6<-bowlerMeanRunsConceded(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

meanRunsConceded-1

17. Bowler Moving average (in IPL matches)

Harbhajan’s moving average is the best hovering around 2 wickets

p1<-bowlerMovingAverage(ashwin,"R Ashwin")
p2<-bowlerMovingAverage(bravo, "DJ Bravo")
p3<-bowlerMovingAverage(chawla, "PP Chawla")
p4<-bowlerMovingAverage(harbhajan, "Harbhajan Singh")
p5<-bowlerMovingAverage(vinay, "R Vinay")
p6<-bowlerMovingAverage(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

bowlerMA-1

17. Bowler cumulative average wickets (in IPL matches)

The cumulative average tells a different story. DJ Bravo and R Vinay have a cumulative average of 2 wickets. All others are around 1.5

p1<-bowlerCumulativeAvgWickets(ashwin,"R Ashwin")
p2<-bowlerCumulativeAvgWickets(bravo, "DJ Bravo")
p3<-bowlerCumulativeAvgWickets(chawla, "PP Chawla")
p4<-bowlerCumulativeAvgWickets(harbhajan, "Harbhajan Singh")
p5<-bowlerCumulativeAvgWickets(vinay, "R Vinay")
p6<-bowlerCumulativeAvgWickets(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cumWkts-1

18. Bowler cumulative Economy Rate (ER) (in IPL matches)

Ashwin & Harbhajan have the best cumulative economy rate

p1<-bowlerCumulativeAvgEconRate(ashwin,"R Ashwin")
p2<-bowlerCumulativeAvgEconRate(bravo, "DJ Bravo")
p3<-bowlerCumulativeAvgEconRate(chawla, "PP Chawla")
p4<-bowlerCumulativeAvgEconRate(harbhajan, "Harbhajan Singh")
p5<-bowlerCumulativeAvgEconRate(vinay, "R Vinay")
p6<-bowlerCumulativeAvgEconRate(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cumER-1

19. Bowler wicket plot (in IPL matches)

The plot below gives the average wickets versus number of overs

p1<-bowlerWicketPlot(ashwin,"R Ashwin")
p2<-bowlerWicketPlot(bravo, "DJ Bravo")
p3<-bowlerWicketPlot(chawla, "PP Chawla")
p4<-bowlerWicketPlot(harbhajan, "Harbhajan Singh")
p5<-bowlerWicketPlot(vinay, "R Vinay")
p6<-bowlerWicketPlot(sktrivedi, "SK Trivedi")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

wktPlot-1

20. Bowler wicket against opposing IPL teams

bowlerWicketsAgainstOpposition(ashwin,"R Ashwin")

wktsOppn1-1

bowlerWicketsAgainstOpposition(bravo, "DJ Bravo")

wktsOppn2-1

bowlerWicketsAgainstOpposition(chawla, "PP Chawla")

wktsOppn3-1

bowlerWicketsAgainstOpposition(harbhajan, "Harbhajan Singh")

wktsOppn4-1

bowlerWicketsAgainstOpposition(vinay, "R Vinay")

wktsOppn5-1

bowlerWicketsAgainstOpposition(sktrivedi, "SK Trivedi")

wktsOppn6-1

21. Bowler wicket at cricket grounds in IPL

bowlerWicketsVenue(ashwin,"R Ashwin")

wktsAve1-1

bowlerWicketsVenue(bravo, "DJ Bravo")

wktsAve2-1

bowlerWicketsVenue(chawla, "PP Chawla")

wktsAve3-1

bowlerWicketsVenue(harbhajan, "Harbhajan Singh")

wktsAve4-1

bowlerWicketsVenue(vinay, "R Vinay")

wktsAve5-1

bowlerWicketsVenue(sktrivedi, "SK Trivedi")

wktsAve6-1

22. Get Delivery wickets for IPL bowlers

This function creates a dataframe of deliveries and the wickets taken

setwd("C:/software/cricket-package/york-test/yorkrData/IPL/IPL-T20-matches")
ashwin1 <- getDeliveryWickets(team="Chennai Super Kings",dir=".",name="R Ashwin",save=FALSE)
bravo1 <- getDeliveryWickets(team="Chennai Super Kings",dir=".",name="DJ Bravo",save=FALSE)
chawla1 <- getDeliveryWickets(team="Kings XI Punjab",dir=".",name="PP Chawla",save=FALSE)
harbhajan1 <- getDeliveryWickets(team="Mumbai Indians",dir=".",name="Harbhajan Singh",save=FALSE)
vinay1 <- getDeliveryWickets(team="Royal Challengers Bangalore",dir=".",name="R Vinay",save=FALSE)
sktrivedi1 <- getDeliveryWickets(team="Rajasthan Royals",dir=".",name="SK Trivedi",save=FALSE)

23. Predict number of deliveries to wickets in IPL T20

#Ashwin takes <6.5 deliveries for a wicket while Bravo takes around 5.5 deliveries. Don't get hung up on the .5 delivery. We can just take it that Bravo will provide a breakthrough quicker than Ashwin
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))

bowlerWktsPredict(ashwin1,"R Ashwin")
bowlerWktsPredict(bravo1, "DJ Bravo")

wktsPred1-1

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(chawla1, "PP Chawla")
bowlerWktsPredict(harbhajan1, "Harbhajan Singh")

wktsPred2-1

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(vinay1, "R Vinay")
bowlerWktsPredict(sktrivedi1, "SK Trivedi")

wktsPred3-1

Conclusion

This concludes the 4 part writeup of yorkr’s handling of IPL Twenty20’s. You can fork/clone the code from Github at yorkr.

As I mentioned earlier, this brings to a close to all my posts based on my R cricket package yorkr. I do have a couple of more ideas, but this will take some time I think.

Hope you have a great time with my yorkr package!

Till next time, adieu!

Also see

  1. Introducing cricket package yorkr: Part 3-Foxed by flight!
  2. Introducing cricketr! : An R package to analyze performances of cricketers
  3. Cricket analytics with cricketr in paperback and Kindle versions
  4. Bend it like Bluemix, MongoDB with auto-scaling – Part 1
  5. The dark side of the Internet
  6. Modeling a Car in Android
  7. yorkr pads up for the Twenty20s: Part 1- Analyzing team”s match performance
  8. Cricket analytics with cricketr

yorkr crashes the IPL party! – Part 3!


Introduction

“I’m sorry, if you were right, I’d agree with you.”

                 Robin Williams

Get your facts first. Then you can distort them as you please.

                 Mark Twain
                 

Do not take life too seriously. You will never get out of it alive.

                 Elbert Hubbard

This is the 3rd post in the “yorkr crashes the IPL party!” series. The 2 earlier ones were

  1. yorkr crashes the IPL party ! – Part 1
  2. yorkr crashes the IPL party ! – Part 2

This post deals with Class 3 functions, namely the performances of an IPL team in all matches against all other IPL teams matches for e.g Chennai Super Kings against all other IPL teams or Delhi Daredevils against all other teams.

This post has also been published at RPubs IPLT20-Part3 and can also be downloaded as a PDF document from IPLT20-Part3.pdf.

You can clone/fork the code for the package yorkr from Github at yorkr-package

The list of functions in Class 3 are

  1. teamBattingScorecardAllOppnAllMatches()
  2. teamBatsmenPartnershipAllOppnAllMatches()
  3. teamBatsmenPartnershipAllOppnAllMatchesPlot()
  4. teamBatsmenVsBowlersAllOppnAllMatchesRept()
  5. teamBatsmenVsBowlersAllOppnAllMatchesPlot()
  6. teamBowlingScorecardAllOppnAllMatchesMain()
  7. teamBowlersVsBatsmenAllOppnAllMatchesRept()
  8. teamBowlersVsBatsmenAllOppnAllMatchesPlot()
  9. teamBowlingWicketKindAllOppnAllMatches()
  10. teamBowlingWicketRunsAllOppnAllMatches()

Note: 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 IPL matches against all other IPL teams using the function below. The dir parameter should point to the folder in which the RData files where the individual IPL T20 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/IPL/IPL-T20-team-allMatches-allOppostions")
# Get all matches against all oppositions for India and save as RData
matches <-getAllMatchesAllOpposition("Royal Challengers Bangalore",dir=".",save=TRUE)
dim(matches)
## [1] 28199    25

“`

3. Save data for all matches against all oppositions

This can be done locally using the function below. This function gets all the IPL T20 matches of the IPL team against all other IPL 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. However you will need to use this function for future matches!

# To be available in yorkr_0.0.5. You can install from Github though.
#saveAllMatchesAllOppositionIPLT20()

4. Load data directly for all matches between 2 teams

As in my earlier posts (IPLT20-Part1 & IPLT20-Part2) I have however already saved the data, for all IPL matches of the individual IPL teams, against all other IPL teams. The data for these matches for the individual IPL teams can be downloaded directly from Github folder at IPL-T20-team-allmatches-allOppositions

Note: The dataframe for the different for all the matches of a IPL team against all other IPL teams can be loaded directly into your code.Feel free to download the zip of the data and to perform any data mining on them.

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 any random IPL team for the funtions for illustration

setwd("C:/software/cricket-package/york-test/yorkrData/IPL/IPL-T20-team-allMatches-allOppostions")
load("allMatchesAllOpposition-Chennai Super Kings.RData")
csk_Allmatches <- matches
load("allMatchesAllOpposition-Deccan Chargers.RData")
dc_Allmatches <- matches
load("allMatchesAllOpposition-Delhi Daredevils.RData")
dd_Allmatches <- matches
load("allMatchesAllOpposition-Kings XI Punjab.RData")
kxip_Allmatches <- matches
load("allMatchesAllOpposition-Kochi Tuskers Kerala.RData")
ktk_Allmatches <- matches
load("allMatchesAllOpposition-Kolkata Knight Riders.RData")
kkr_Allmatches <- matches
load("allMatchesAllOpposition-Mumbai Indians.RData")
mi_Allmatches <- matches
load("allMatchesAllOpposition-Pune Warriors.RData")
pw_Allmatches <- matches
load("allMatchesAllOpposition-Rajasthan Royals.RData")
rr_Allmatches <- matches
load("allMatchesAllOpposition-Royal Challengers Bangalore.RData")
rcb_Allmatches <- matches
load("allMatchesAllOpposition-Sunrisers Hyderabad.RData")
sh_Allmatches <- matches

5. Team IPL T20 Batting Scorecard (all matches with all opposing IPL teams)

The following functions shows the batting scorecards for each IPL team. It returns a dataframe with the top batsmen in each IPL team

#Top IPL Twenty20 batsmen for Chennai Super Kings
m <-teamBattingScorecardAllOppnAllMatches(csk_Allmatches,theTeam="Chennai Super Kings")
## Total= 19312
m
## Source: local data frame [46 x 5]
## 
##         batsman ballsPlayed fours sixes  runs
##          (fctr)       (int) (int) (int) (dbl)
## 1      SK Raina        2513   312   144  3567
## 2      MS Dhoni        2036   206   121  2887
## 3    MEK Hussey        1361   178    43  1721
## 4       M Vijay        1220   140    64  1574
## 5   S Badrinath        1171   152    28  1427
## 6  F du Plessis         837    92    29  1081
## 7     ML Hayden         725   118    43  1077
## 8      DR Smith         673    98    42   886
## 9     JA Morkel         549    50    47   812
## 10  BB McCullum         562    78    42   809
## ..          ...         ...   ...   ...   ...
#Top IPL Twenty20 batsmen for Deccan Chargers
m <-teamBattingScorecardAllOppnAllMatches(dc_Allmatches,theTeam="Deccan Chargers")
## Total= 10885
m
## Source: local data frame [58 x 5]
## 
##            batsman ballsPlayed fours sixes  runs
##             (fctr)       (int) (int) (int) (dbl)
## 1     AC Gilchrist         802   136    64  1220
## 2        RG Sharma         866    96    51  1170
## 3         S Dhawan         737   106    25   969
## 4        A Symonds         600    65    38   839
## 5         HH Gibbs         695    75    29   805
## 6         CL White         425    47    22   583
## 7    KC Sangakkara         458    65     9   558
## 8         TL Suman         437    41    20   544
## 9        JP Duminy         360    22    19   449
## 10 Y Venugopal Rao         357    32    19   446
## ..             ...         ...   ...   ...   ...
#Top IPL Twenty20 batsmen for Delhi Daredevils
m <-teamBattingScorecardAllOppnAllMatches(dd_Allmatches,theTeam="Delhi Daredevils")
## Total= 16324
m
## Source: local data frame [76 x 5]
## 
##             batsman ballsPlayed fours sixes  runs
##              (fctr)       (int) (int) (int) (dbl)
## 1          V Sehwag        1290   257    84  2127
## 2         DA Warner        1050   146    58  1435
## 3         G Gambhir         828   126    12  1058
## 4        KD Karthik         762    82    27   979
## 5         JP Duminy         585    44    40   796
## 6  DPMD Jayawardene         600    79     9   666
## 7    AB de Villiers         541    50    13   650
## 8      KP Pietersen         431    56    27   599
## 9        TM Dilshan         452    62    16   562
## 10        KM Jadhav         394    42    19   537
## ..              ...         ...   ...   ...   ...
#Top IPL Twenty20 batsmen for Kolkata Knight Riders
m <-teamBattingScorecardAllOppnAllMatches(kkr_Allmatches,theTeam="Kolkata Knight Riders")
## Total= 16001
m
## Source: local data frame [69 x 5]
## 
##        batsman ballsPlayed fours sixes  runs
##         (fctr)       (int) (int) (int) (dbl)
## 1    G Gambhir        1583   235    33  2024
## 2    YK Pathan         937   100    64  1317
## 3    JH Kallis        1193   128    23  1294
## 4   SC Ganguly         909   105    36  1031
## 5   RV Uthappa         739   118    25  1024
## 6    MK Tiwary         878    86    23  1002
## 7  BB McCullum         709    92    32   882
## 8    MK Pandey         518    58    17   626
## 9     MS Bisla         470    60    16   542
## 10   DJ Hussey         389    31    28   511
## ..         ...         ...   ...   ...   ...
#Top IPL Twenty20 batsmen for Kochi Tuskers Kerala
m <-teamBattingScorecardAllOppnAllMatches(ktk_Allmatches,theTeam="Kochi Tuskers Kerala")
## Total= 1758
m
## Source: local data frame [19 x 5]
## 
##             batsman ballsPlayed fours sixes  runs
##              (fctr)       (int) (int) (int) (dbl)
## 1       BB McCullum         265    35    16   357
## 2  DPMD Jayawardene         249    35     5   299
## 3          BJ Hodge         230    26     9   285
## 4         RA Jadeja         225    20    14   283
## 5          PA Patel         181    25     2   202
## 6         M Klinger          74     9    NA    73
## 7     R Vinay Kumar          47     4     1    50
## 8          RV Gomez          47     4     1    46
## 9        VVS Laxman          38     3     2    44
## 10          OA Shah          14     1     2    26
## 11      NLTC Perera          27     4    NA    23
## 12 Y Gnaneswara Rao          17     3    NA    19
## 13        KM Jadhav          23     1    NA    18
## 14          B Akhil           9    NA     1    13
## 15         RR Powar          13    NA    NA    11
## 16   M Muralitharan           6    NA    NA     5
## 17         RP Singh           3    NA    NA     2
## 18      S Sreesanth           8    NA    NA     1
## 19   P Parameswaran           1    NA    NA     1
#Top IPL Twenty20 batsmen for Kings XI Punjab
m <-teamBattingScorecardAllOppnAllMatches(kxip_Allmatches,theTeam="Kings XI Punjab")
## Total= 17333
m
## Source: local data frame [74 x 5]
## 
##             batsman ballsPlayed fours sixes  runs
##              (fctr)       (int) (int) (int) (dbl)
## 1          SE Marsh        1444   211    65  1973
## 2         DA Miller         865    89    70  1319
## 3     KC Sangakkara         755   117    18  1000
## 4      Yuvraj Singh         643    67    46   859
## 5      AC Gilchrist         636   103    28   849
## 6  DPMD Jayawardene         546    81    24   792
## 7     Mandeep Singh         616    96     8   763
## 8        GJ Maxwell         388    61    44   697
## 9         DJ Hussey         567    51    26   695
## 10          WP Saha         437    49    28   611
## ..              ...         ...   ...   ...   ...
#Top IPL Twenty20 batsmen for Mumbai Indians
m <-teamBattingScorecardAllOppnAllMatches(mi_Allmatches,theTeam="Mumbai Indians")
## Total= NA
m
## Source: local data frame [73 x 5]
## 
##          batsman ballsPlayed fours sixes  runs
##           (fctr)       (int) (int) (int) (dbl)
## 1   SR Tendulkar        1806   277    29  2221
## 2      RG Sharma        1657   176    96  2201
## 3      AT Rayudu        1507   169    63  1963
## 4     KA Pollard        1117   116   106  1707
## 5    LMP Simmons         720    99    35   934
## 6  ST Jayasuriya         504    84    37   748
## 7     KD Karthik         591    82    16   727
## 8       DR Smith         449    62    26   600
## 9      JP Duminy         437    39    16   523
## 10     SS Tiwary         332    32    19   458
## ..           ...         ...   ...   ...   ...
#Top IPL Twenty20 batsmen for Pune Warriors
m <-teamBattingScorecardAllOppnAllMatches(pw_Allmatches,theTeam="Pune Warriors")
## Total= 5871
m
## Source: local data frame [43 x 5]
## 
##         batsman ballsPlayed fours sixes  runs
##          (fctr)       (int) (int) (int) (dbl)
## 1    RV Uthappa         880   106    30  1078
## 2  Yuvraj Singh         427    38    30   551
## 3      JD Ryder         396    60    19   548
## 4     SPD Smith         388    39    18   521
## 5     MK Pandey         421    44    14   459
## 6      AJ Finch         293    52    13   408
## 7    SC Ganguly         321    33     6   318
## 8    AD Mathews         246    10    14   299
## 9      M Manhas         234    19     8   247
## 10     MR Marsh         153     8    13   190
## ..          ...         ...   ...   ...   ...
#Top Twenty20 batsmen for Rajasthan Royals
m <-teamBattingScorecardAllOppnAllMatches(rr_Allmatches,theTeam="Rajasthan Royals")
## Total= 16359
m
## Source: local data frame [80 x 5]
## 
##      batsman ballsPlayed fours sixes  runs
##       (fctr)       (int) (int) (int) (dbl)
## 1  SR Watson        1609   237   107  2342
## 2  AM Rahane        1637   218    38  2031
## 3   R Dravid        1098   171    10  1247
## 4  YK Pathan         601    89    59   990
## 5  SV Samson         596    59    30   740
## 6   GC Smith         601    88     9   697
## 7   BJ Hodge         423    41    24   592
## 8  STR Binny         438    43    21   572
## 9    KK Nair         369    56    14   511
## 10   NV Ojha         385    49    25   501
## ..       ...         ...   ...   ...   ...
#Top IPL Twenty20 batsmen for Royal Challengers Bangalore
m <-teamBattingScorecardAllOppnAllMatches(rcb_Allmatches,theTeam="Royal Challengers Bangalore")
## Total= 17288
m
## Source: local data frame [85 x 5]
## 
##           batsman ballsPlayed fours sixes  runs
##            (fctr)       (int) (int) (int) (dbl)
## 1         V Kohli        2470   274   111  3125
## 2        CH Gayle        1704   217   204  2736
## 3  AB de Villiers        1170   167    90  1899
## 4       JH Kallis         952   123    20  1093
## 5        R Dravid         706    96    17   889
## 6      TM Dilshan         510    78     8   587
## 7      RV Uthappa         380    39    30   549
## 8       SS Tiwary         456    26    17   487
## 9     LRPL Taylor         318    33    28   464
## 10     MA Agarwal         332    41    22   433
## ..            ...         ...   ...   ...   ...
#Top IPL Twenty20 batsmen for Sunrisers Hyderabad
m <-teamBattingScorecardAllOppnAllMatches(sh_Allmatches,theTeam="Sunrisers Hyderabad")
## Total= 6117
m
## Source: local data frame [32 x 5]
## 
##         batsman ballsPlayed fours sixes  runs
##          (fctr)       (int) (int) (int) (dbl)
## 1     DA Warner         715   104    45  1090
## 2      S Dhawan         835   131    18  1040
## 3       NV Ojha         277    26    21   369
## 4      AJ Finch         251    31     9   309
## 5      KL Rahul         284    20     8   308
## 6  MC Henriques         220    20    13   296
## 7      PA Patel         244    37     4   292
## 8     GH Vihari         281    23     1   261
## 9     DJG Sammy         198    14    17   259
## 10    KV Sharma         191    10    11   227
## ..          ...         ...   ...   ...   ...

6. Team Batting Scorecard in IPL Twenty20 matches against all opposing IPL teams

The following functions show the best batsmen from the opposition ‘theTeam’ in the ‘matches’. For e.g. when the matches=csk_Allmatches and theTeam=“Royal Challengers Bangalore” then the returned dataframe shows the best Royal Challengers Bangalore batsmen against CSK

#Top IPL T20  Royal Challengers Bangalore batsmen against Chennai Super Kings
m <-teamBattingScorecardAllOppnAllMatches(matches=csk_Allmatches,theTeam="Royal Challengers Bangalore")
## Total= 2485
m
## Source: local data frame [54 x 5]
## 
##           batsman ballsPlayed fours sixes  runs
##            (fctr)       (int) (int) (int) (dbl)
## 1         V Kohli         542    49    30   694
## 2        CH Gayle         204    12    23   270
## 3  AB de Villiers         147    26     9   241
## 4        R Dravid         111    18    NA   133
## 5      MA Agarwal          96    15     4   120
## 6      RV Uthappa          71     7     8   115
## 7       JH Kallis          95    19    NA   109
## 8       SS Tiwary          84     4     3    86
## 9       MK Pandey          67    10    NA    73
## 10    LRPL Taylor          62     2     3    64
## ..            ...         ...   ...   ...   ...
#Top IPL T20 Mumbai Indians batsmen against Chennai Super Kings
m <-teamBattingScorecardAllOppnAllMatches(matches=csk_Allmatches,theTeam="Mumbai Indians")
## Total= 3223
m
## Source: local data frame [43 x 5]
## 
##            batsman ballsPlayed fours sixes  runs
##             (fctr)       (int) (int) (int) (dbl)
## 1        RG Sharma         343    37    14   444
## 2       KA Pollard         209    25    26   351
## 3     SR Tendulkar         259    38     5   320
## 4        AT Rayudu         243    17    14   299
## 5      LMP Simmons         219    23    16   281
## 6    ST Jayasuriya          91    21    13   190
## 7  Harbhajan Singh         115    13    12   170
## 8         DR Smith          90    16    10   159
## 9         AM Nayar          59    10     6   113
## 10      KD Karthik         100    13     2   109
## ..             ...         ...   ...   ...   ...
#Top IPL T20 Rajasthan Royals batsmen against Pune Warriors
m <-teamBattingScorecardAllOppnAllMatches(pw_Allmatches,theTeam="Rajasthan Royals")
## Total= 743
m
## Source: local data frame [13 x 5]
## 
##        batsman ballsPlayed fours sixes  runs
##         (fctr)       (int) (int) (int) (dbl)
## 1    AM Rahane         134    18     2   173
## 2    SR Watson         100    17     9   165
## 3     R Dravid         140    25     1   165
## 4   AL Menaria          42     1     3    47
## 5  LRPL Taylor          33     4     2    47
## 6     BJ Hodge          37     3    NA    40
## 7    STR Binny          22     2     2    39
## 8  JP Faulkner          16     1     1    22
## 9      J Botha          20     2    NA    19
## 10   DH Yagnik           8     2    NA    12
## 11   SV Samson           6     2    NA    10
## 12     OA Shah           7    NA    NA     4
## 13 MDKJ Perera           1    NA    NA     0
#Top IPL T20 Sunrisers Hyderabad batsmen against West Indies
m <-teamBattingScorecardAllOppnAllMatches(kkr_Allmatches,theTeam="Sunrisers Hyderabad")
## Total= 814
m
## Source: local data frame [27 x 5]
## 
##            batsman ballsPlayed fours sixes  runs
##             (fctr)       (int) (int) (int) (dbl)
## 1         S Dhawan         138    19     3   159
## 2        DA Warner          76    14     6   133
## 3         PA Patel          65     9     1    74
## 4          NV Ojha          56     7     5    66
## 5        DJG Sammy          44     1     5    53
## 6     MC Henriques          38     3     2    48
## 7        KV Sharma          35     1     3    46
## 8      NLTC Perera          34     2     1    40
## 9         CL White          33     3     1    36
## 10 Y Venugopal Rao          24     4    NA    27
## ..             ...         ...   ...   ...   ...

7. Team Batting Partnerships of an IL T20 matches against all opposing teams

This gives the top batting partnerships in each IPL team in all its matches against all opposing teams. The report can either be a ‘summary’ or a ‘detailed’ breakup of the batting partnerships.

# The function gives the names of highest IPL T20 partnership for Chennai Super Kings. The default report parameter is "summary"
m <- teamBatsmenPartnershipAllOppnAllMatches(csk_Allmatches,theTeam='Chennai Super Kings')
m
## Source: local data frame [46 x 2]
## 
##         batsman totalRuns
##          (fctr)     (dbl)
## 1      SK Raina      3567
## 2      MS Dhoni      2887
## 3    MEK Hussey      1721
## 4       M Vijay      1574
## 5   S Badrinath      1427
## 6  F du Plessis      1081
## 7     ML Hayden      1077
## 8      DR Smith       886
## 9     JA Morkel       812
## 10  BB McCullum       809
## ..          ...       ...
# When the report parameter is 'detailed' then the detailed break up of the T20 partnership is returned as a data frame
m <- teamBatsmenPartnershipAllOppnAllMatches(csk_Allmatches,theTeam='Chennai Super Kings',report="detailed")
head(m,30)
##     batsman   nonStriker partnershipRuns totalRuns
## 1  SK Raina   SP Fleming              20      3567
## 2  SK Raina   S Anirudha              33      3567
## 3  SK Raina  S Badrinath             413      3567
## 4  SK Raina     MS Dhoni             546      3567
## 5  SK Raina    JA Morkel             142      3567
## 6  SK Raina      MS Gony              10      3567
## 7  SK Raina    ML Hayden             219      3567
## 8  SK Raina     JDP Oram               6      3567
## 9  SK Raina     DR Smith             239      3567
## 10 SK Raina      M Vijay             325      3567
## 11 SK Raina      JM Kemp              10      3567
## 12 SK Raina     R Ashwin               0      3567
## 13 SK Raina   MEK Hussey             617      3567
## 14 SK Raina F du Plessis             336      3567
## 15 SK Raina    RA Jadeja              50      3567
## 16 SK Raina     DJ Bravo              87      3567
## 17 SK Raina     PA Patel             166      3567
## 18 SK Raina     S Vidyut               1      3567
## 19 SK Raina   A Flintoff              30      3567
## 20 SK Raina      WP Saha              16      3567
## 21 SK Raina  BB McCullum             186      3567
## 22 SK Raina    GJ Bailey              10      3567
## 23 SK Raina    DJ Hussey              95      3567
## 24 SK Raina      M Ntini               2      3567
## 25 SK Raina  C Ganapathy               8      3567
## 26 MS Dhoni   SP Fleming              11      2887
## 27 MS Dhoni   S Anirudha              29      2887
## 28 MS Dhoni     SK Raina             488      2887
## 29 MS Dhoni  S Badrinath             341      2887
## 30 MS Dhoni    JA Morkel             209      2887
#Top IPL T20 Rajasthan Royal partnerships 
teamBatsmenPartnershipAllOppnAllMatches(rr_Allmatches,theTeam='Rajasthan Royals',report="summary")
## Source: local data frame [80 x 2]
## 
##      batsman totalRuns
##       (fctr)     (dbl)
## 1  SR Watson      2342
## 2  AM Rahane      2031
## 3   R Dravid      1247
## 4  YK Pathan       990
## 5  SV Samson       740
## 6   GC Smith       697
## 7   BJ Hodge       592
## 8  STR Binny       572
## 9    KK Nair       511
## 10   NV Ojha       501
## ..       ...       ...
#Top IPL T20 Mumbai Indians batting partnerships 
m <- teamBatsmenPartnershipAllOppnAllMatches(mi_Allmatches,theTeam='Mumbai Indians',report="detailed")
m[1:30,]
##         batsman        nonStriker partnershipRuns totalRuns
## 1  SR Tendulkar     ST Jayasuriya             254      2221
## 2  SR Tendulkar        RV Uthappa              10      2221
## 3  SR Tendulkar          DJ Bravo              66      2221
## 4  SR Tendulkar          AM Nayar              41      2221
## 5  SR Tendulkar   Harbhajan Singh              50      2221
## 6  SR Tendulkar          S Dhawan             183      2221
## 7  SR Tendulkar         JP Duminy             107      2221
## 8  SR Tendulkar            Z Khan               2      2221
## 9  SR Tendulkar           PR Shah              19      2221
## 10 SR Tendulkar         SS Tiwary              81      2221
## 11 SR Tendulkar         AT Rayudu             261      2221
## 12 SR Tendulkar        KA Pollard              65      2221
## 13 SR Tendulkar         R Sathish              62      2221
## 14 SR Tendulkar         R McLaren              33      2221
## 15 SR Tendulkar           RE Levi              14      2221
## 16 SR Tendulkar         RG Sharma             226      2221
## 17 SR Tendulkar      JEC Franklin              82      2221
## 18 SR Tendulkar          DR Smith             187      2221
## 19 SR Tendulkar        KD Karthik             104      2221
## 20 SR Tendulkar        RT Ponting              63      2221
## 21 SR Tendulkar           AP Tare              36      2221
## 22 SR Tendulkar       DJ Thornely              21      2221
## 23 SR Tendulkar          HH Gibbs              13      2221
## 24 SR Tendulkar          TL Suman              19      2221
## 25 SR Tendulkar         A Symonds              13      2221
## 26 SR Tendulkar         DJ Jacobs              68      2221
## 27 SR Tendulkar         AM Rahane              36      2221
## 28 SR Tendulkar Mohammad Ashraful               1      2221
## 29 SR Tendulkar       RJ Peterson               9      2221
## 30 SR Tendulkar       AC Blizzard              77      2221
#Top IPL T20 Royal Challengers Bangalore batting partnerships 
m <-teamBatsmenPartnershipAllOppnAllMatches(rcb_Allmatches,theTeam='Royal Challengers Bangalore',report="detailed")
m[1:30,]
##    batsman      nonStriker partnershipRuns totalRuns
## 1  V Kohli      SP Goswami               7      3125
## 2  V Kohli       JH Kallis             181      3125
## 3  V Kohli        R Dravid              72      3125
## 4  V Kohli   Misbah-ul-Haq              13      3125
## 5  V Kohli      MV Boucher              39      3125
## 6  V Kohli         B Akhil               0      3125
## 7  V Kohli         P Kumar              17      3125
## 8  V Kohli        PA Patel              33      3125
## 9  V Kohli       JA Morkel               2      3125
## 10 V Kohli       MK Pandey              19      3125
## 11 V Kohli      RV Uthappa              32      3125
## 12 V Kohli    KP Pietersen              71      3125
## 13 V Kohli        CL White              25      3125
## 14 V Kohli      TM Dilshan              89      3125
## 15 V Kohli      MA Agarwal              74      3125
## 16 V Kohli  AB de Villiers             582      3125
## 17 V Kohli       SS Tiwary             154      3125
## 18 V Kohli       CA Pujara              56      3125
## 19 V Kohli      DL Vettori              12      3125
## 20 V Kohli        CH Gayle             855      3125
## 21 V Kohli   LA Pomersbach              39      3125
## 22 V Kohli J Syed Mohammad               1      3125
## 23 V Kohli      RR Bhatkal               0      3125
## 24 V Kohli   R Vinay Kumar              21      3125
## 25 V Kohli KB Arun Karthik               2      3125
## 26 V Kohli    Yuvraj Singh             126      3125
## 27 V Kohli          S Rana              23      3125
## 28 V Kohli    NJ Maddinson               6      3125
## 29 V Kohli   Mandeep Singh              23      3125
## 30 V Kohli      KD Karthik              92      3125

8. Batting Partnerships of opposing teams in IPL T20 matches with IPL team

When we use the dataframe rcb_Allmatches (matches of Royal Challengers Bangalore against all opposing teams) and choose another IPL team in the theTeam for e.g Rajasthan Royals then we will get the names of those top Rajasthan Royals batsmen against RCB.

# Top T20 Rajasthan Royals batting partnerships against Royal Challengers Bangalore (report="summary")
m <- teamBatsmenPartnershipAllOppnAllMatches(rcb_Allmatches,theTeam='Rajasthan Royals')
m
## Source: local data frame [50 x 2]
## 
##      batsman totalRuns
##       (fctr)     (dbl)
## 1  SR Watson       271
## 2  AM Rahane       256
## 3   R Dravid       177
## 4   GC Smith       146
## 5    KK Nair        92
## 6  YK Pathan        91
## 7  SPD Smith        91
## 8  SV Samson        87
## 9  STR Binny        80
## 10   NV Ojha        75
## ..       ...       ...
# Top T20 Kolkata Knight Riders  batting partnerships against Sunrisers Hyderabad (report="detailed")
m <- teamBatsmenPartnershipAllOppnAllMatches(sh_Allmatches,theTeam='Kolkata Knight Riders', report="detailed")
m[1:30,]
##             batsman       nonStriker partnershipRuns totalRuns
## 1         YK Pathan          J Botha              14       199
## 2         YK Pathan        PP Chawla              13       199
## 3         YK Pathan        JH Kallis              26       199
## 4         YK Pathan       RV Uthappa               3       199
## 5         YK Pathan        MK Pandey              28       199
## 6         YK Pathan RN ten Doeschate              22       199
## 7         YK Pathan  Shakib Al Hasan              36       199
## 8         YK Pathan         SA Yadav              32       199
## 9         YK Pathan          P Dogra              10       199
## 10        YK Pathan        SP Narine               9       199
## 11        YK Pathan    Iqbal Abdulla               6       199
## 12       RV Uthappa        G Gambhir              65       145
## 13       RV Uthappa        YK Pathan              19       145
## 14       RV Uthappa        MK Pandey              61       145
## 15        G Gambhir         MS Bisla              33       132
## 16        G Gambhir        JH Kallis              30       132
## 17        G Gambhir       RV Uthappa              69       132
## 18        MK Pandey        YK Pathan              31       103
## 19        MK Pandey       RV Uthappa              37       103
## 20        MK Pandey RN ten Doeschate              14       103
## 21        MK Pandey         SA Yadav               5       103
## 22        MK Pandey       AD Russell              16       103
## 23        JH Kallis       EJG Morgan              24        65
## 24        JH Kallis        G Gambhir              27        65
## 25        JH Kallis        YK Pathan              14        65
## 26       EJG Morgan        JH Kallis              56        56
## 27         MS Bisla        G Gambhir              43        43
## 28 RN ten Doeschate        YK Pathan              29        34
## 29 RN ten Doeschate        MK Pandey               5        34
## 30         SA Yadav        YK Pathan              11        20
#Top IPL T20 Chennai Super Kings batting partnerships  against Mumbai Indians
m <- teamBatsmenPartnershipAllOppnAllMatches(mi_Allmatches,theTeam='Chennai Super Kings',report="detailed")
head(m,30)
##     batsman      nonStriker partnershipRuns totalRuns
## 1  SK Raina       ML Hayden             104       537
## 2  SK Raina        PA Patel               5       537
## 3  SK Raina        MS Dhoni              23       537
## 4  SK Raina        DJ Bravo              59       537
## 5  SK Raina     S Badrinath              78       537
## 6  SK Raina         M Vijay              22       537
## 7  SK Raina      MEK Hussey              97       537
## 8  SK Raina    F du Plessis              43       537
## 9  SK Raina        DR Smith              51       537
## 10 SK Raina     BB McCullum               8       537
## 11 SK Raina       DJ Hussey              46       537
## 12 SK Raina      SP Fleming               1       537
## 13 MS Dhoni       ML Hayden              83       466
## 14 MS Dhoni        SK Raina               8       466
## 15 MS Dhoni        JDP Oram              10       466
## 16 MS Dhoni        DJ Bravo              63       466
## 17 MS Dhoni     S Badrinath              55       466
## 18 MS Dhoni Joginder Sharma              19       466
## 19 MS Dhoni         M Vijay               1       466
## 20 MS Dhoni      MEK Hussey               8       466
## 21 MS Dhoni    F du Plessis              24       466
## 22 MS Dhoni       JA Morkel              13       466
## 23 MS Dhoni       RA Jadeja              37       466
## 24 MS Dhoni        R Ashwin              52       466
## 25 MS Dhoni      B Laughlin              16       466
## 26 MS Dhoni      AS Rajpoot               9       466
## 27 MS Dhoni       CH Morris               1       466
## 28 MS Dhoni       MM Sharma              23       466
## 29 MS Dhoni     BB McCullum              14       466
## 30 MS Dhoni          P Negi              30       466
#Top IPL T20 Kochi Tusker Kerala batting partnerships  against Kings XI Punjab
m <- teamBatsmenPartnershipAllOppnAllMatches(kxip_Allmatches,theTeam='Kochi Tuskers Kerala',report="detailed")
head(m,30)
##             batsman       nonStriker partnershipRuns totalRuns
## 1  DPMD Jayawardene        RA Jadeja              12        76
## 2  DPMD Jayawardene      BB McCullum              42        76
## 3  DPMD Jayawardene         BJ Hodge               3        76
## 4  DPMD Jayawardene          OA Shah               9        76
## 5  DPMD Jayawardene         RV Gomez              10        76
## 6       BB McCullum DPMD Jayawardene              32        32
## 7           OA Shah DPMD Jayawardene              23        23
## 8         RA Jadeja DPMD Jayawardene              17        17
## 9          BJ Hodge DPMD Jayawardene               4         4
## 10         RV Gomez DPMD Jayawardene               2         2
## 11    R Vinay Kumar DPMD Jayawardene               1         1
## 12         PA Patel DPMD Jayawardene               0         0

9. Team Batting Partnership plots in Twenty20 matches against all opposing teams

Graphical plot of batting partnerships for the IPL teams

# Plot of IPL T20 batting partnerships of Chennai Super Kings (Suresh Raina  and MS Dhoni have the best IPL T20 partnerships for CSK)
teamBatsmenPartnershipAllOppnAllMatchesPlot(csk_Allmatches,"Chennai Super Kings",main="Chennai Super Kings")

batsmenPartnership1-1

# Plot of T20 batting partnerships of Royal Challengers Bangalore (Virat Kohli  and Chris Gayle lead)
teamBatsmenPartnershipAllOppnAllMatchesPlot(rcb_Allmatches,"Royal Challengers Bangalore",main="Royal Challengers Bangalore")

batsmenPartnership1-2

# Plot of IPL T20 batting partnerships of Kolkata Knight Riders (Gautham Gambhir and Yusuf Pathan head the list)
teamBatsmenPartnershipAllOppnAllMatchesPlot(kkr_Allmatches,"Kolkata Knight Riders",main="Kolkata Knight Riders")

batsmenPartnership1-3

10. Top opposition batting partnerships in IPL Twenty20 matches against all opposing teams

This gives the best performance of the team against a specified IPL team

# Top Sunrisers Hyderabad IPL T20 partnerships against Kings XI Punjab
teamBatsmenPartnershipAllOppnAllMatchesPlot(sh_Allmatches,"Sunrisers Hyderabad",main="Kings XI Punjab")

batsmenPartnership2-1

# Top Delhi Daredevils T20 partnerships against Deccan Chargers
teamBatsmenPartnershipAllOppnAllMatchesPlot(dd_Allmatches,"Delhi Daredevils",main="Deccan Chargers")

batsmenPartnership2-2

# Top Rajasthan Royals T20 partnerships against Chennai Super Kings 
teamBatsmenPartnershipAllOppnAllMatchesPlot(rr_Allmatches,"Rajasthan Royals",main="Chennai Super Kings")

batsmenPartnership2-3

11. Batsmen vs Bowlers in IPL Twenty20 matches against all opposing teams

The function below gives the top performance of batsmen against the opposing teams

# Top IPL T20 Chennai Super Kings batsmen against bowlers when rank=0
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(csk_Allmatches,"Chennai Super Kings",rank=0)
m
## Source: local data frame [46 x 2]
## 
##         batsman runsScored
##          (fctr)      (dbl)
## 1      SK Raina       3567
## 2      MS Dhoni       2887
## 3    MEK Hussey       1721
## 4       M Vijay       1574
## 5   S Badrinath       1427
## 6  F du Plessis       1081
## 7     ML Hayden       1077
## 8      DR Smith        886
## 9     JA Morkel        812
## 10  BB McCullum        809
## ..          ...        ...
# Performance of IPL T20  Rajasthan Royals batsman in T20 with rank=1 against all other IPL teams. This is Shane Watson for RR
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(rr_Allmatches,"Rajasthan Royals",rank=1,dispRows=30)
m
## Source: local data frame [30 x 3]
## Groups: batsman [1]
## 
##      batsman          bowler  runs
##       (fctr)          (fctr) (dbl)
## 1  SR Watson        M Kartik    94
## 2  SR Watson       RA Jadeja    73
## 3  SR Watson      SL Malinga    68
## 4  SR Watson         P Kumar    66
## 5  SR Watson       PP Chawla    63
## 6  SR Watson        R Ashwin    59
## 7  SR Watson Shakib Al Hasan    56
## 8  SR Watson  M Muralitharan    54
## 9  SR Watson       MM Sharma    50
## 10 SR Watson        DJ Bravo    49
## ..       ...             ...   ...
# Performance of Kolkata Knight Riders batsman in IPL T20 with rank=2 with all other IPL teams. This is Yusuf Pathan with
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(kkr_Allmatches,"Kolkata Knight Riders",rank=2,dispRows=30)
m
## Source: local data frame [30 x 3]
## Groups: batsman [1]
## 
##      batsman          bowler  runs
##       (fctr)          (fctr) (dbl)
## 1  YK Pathan        DW Steyn    75
## 2  YK Pathan        A Mishra    63
## 3  YK Pathan      SL Malinga    57
## 4  YK Pathan   R Vinay Kumar    39
## 5  YK Pathan Harbhajan Singh    36
## 6  YK Pathan       KV Sharma    33
## 7  YK Pathan         B Kumar    32
## 8  YK Pathan     Imran Tahir    27
## 9  YK Pathan       S Aravind    26
## 10 YK Pathan        M Morkel    25
## ..       ...             ...   ...

12. Batsmen vs Bowlers in IPL Twenty20 matches against all other IPL teams (continued)

# The RCB IPL T20 batsmen who has the best performance against Sunrisers Hyderabad bowlers
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(matches=sh_Allmatches,theTeam="Royal Challengers Bangalore",rank=1,dispRows=25)
m
## Source: local data frame [18 x 3]
## Groups: batsman [1]
## 
##    batsman          bowler  runs
##     (fctr)          (fctr) (dbl)
## 1  V Kohli       KV Sharma    48
## 2  V Kohli        A Mishra    35
## 3  V Kohli     NLTC Perera    31
## 4  V Kohli         B Kumar    26
## 5  V Kohli        I Sharma    25
## 6  V Kohli        DW Steyn    23
## 7  V Kohli         P Kumar    21
## 8  V Kohli       IK Pathan    16
## 9  V Kohli        CL White    13
## 10 V Kohli        TA Boult    11
## 11 V Kohli  A Ashish Reddy    10
## 12 V Kohli    Ankit Sharma    10
## 13 V Kohli       DJG Sammy     7
## 14 V Kohli   Parvez Rasool     7
## 15 V Kohli       RS Bopara     3
## 16 V Kohli    MC Henriques     2
## 17 V Kohli       GH Vihari     2
## 18 V Kohli Y Venugopal Rao     1
# All the top IPL T20 Delhi Daredevils batsmen against Kings XI Punjab in all of Indian matches
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(kxip_Allmatches,"Delhi Daredevils",rank=0)
m
## Source: local data frame [52 x 2]
## 
##             batsman runsScored
##              (fctr)      (dbl)
## 1          V Sehwag        250
## 2         DA Warner        245
## 3         G Gambhir        179
## 4        KD Karthik        175
## 5        MA Agarwal        128
## 6  DPMD Jayawardene        107
## 7      KP Pietersen        107
## 8           NV Ojha         86
## 9          M Manhas         83
## 10  Y Venugopal Rao         72
## ..              ...        ...
# The best Chennai Super Kings IPL T20 batsman(rank=0) against Delhi Daredevils and his performance against England bowlers
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(dd_Allmatches,"Chennai Super Kings",rank=1,dispRows=30)
m
## Source: local data frame [30 x 3]
## Groups: batsman [1]
## 
##     batsman        bowler  runs
##      (fctr)        (fctr) (dbl)
## 1  MS Dhoni     IK Pathan    33
## 2  MS Dhoni    AB Agarkar    30
## 3  MS Dhoni       A Nehra    29
## 4  MS Dhoni Mohammad Asif    21
## 5  MS Dhoni    JD Unadkat    19
## 6  MS Dhoni      M Morkel    17
## 7  MS Dhoni      UT Yadav    16
## 8  MS Dhoni    GD McGrath    14
## 9  MS Dhoni      V Sehwag    14
## 10 MS Dhoni      S Nadeem    14
## ..      ...           ...   ...
# All the top Deccan Chargers IPL T20 batsmen (rank=0) against Kolkata Knight Riders and performances against Australian bowlers
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(kkr_Allmatches,"Deccan Chargers",rank=0)
m
## Source: local data frame [29 x 2]
## 
##            batsman runsScored
##             (fctr)      (dbl)
## 1     AC Gilchrist        166
## 2         HH Gibbs        145
## 3        RG Sharma        116
## 4         S Dhawan        111
## 5        A Symonds        100
## 6  Y Venugopal Rao         92
## 7         B Chipli         60
## 8     DB Ravi Teja         54
## 9         TL Suman         53
## 10      VVS Laxman         32
## ..             ...        ...

13. IPL Batsmen vs Bowlers Plot in Twenty20 matches against all other IPL teams

The following functions plot the performances of the IPL batsman based on the rank chosen against all other IPL team’s bowlers. Note: The rank has to be >0

#The following plot displays the performance of the top Royal Challengers Bangalore IPL T20 batsman (rank=1) against all opposition IPL bowlers. This is Virat Kohli for India

d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(rcb_Allmatches,"Royal Challengers Bangalore",rank=1,dispRows=50)
d
## Source: local data frame [50 x 3]
## Groups: batsman [1]
## 
##    batsman          bowler  runs
##     (fctr)          (fctr) (dbl)
## 1  V Kohli        UT Yadav   129
## 2  V Kohli        R Ashwin   115
## 3  V Kohli        A Mishra   106
## 4  V Kohli       IK Pathan    92
## 5  V Kohli        DJ Bravo    79
## 6  V Kohli       RA Jadeja    78
## 7  V Kohli       JA Morkel    73
## 8  V Kohli       PP Chawla    64
## 9  V Kohli       SB Jakati    62
## 10 V Kohli Harbhajan Singh    61
## ..     ...             ...   ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler1-1

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        UT Yadav   129
## 2  V Kohli        R Ashwin   115
## 3  V Kohli        A Mishra   106
## 4  V Kohli       IK Pathan    92
## 5  V Kohli        DJ Bravo    79
## 6  V Kohli       RA Jadeja    78
## 7  V Kohli       JA Morkel    73
## 8  V Kohli       PP Chawla    64
## 9  V Kohli       SB Jakati    62
## 10 V Kohli Harbhajan Singh    61
## ..     ...             ...   ...
# The following plot displays the performance of the Chennai Super Kings IPL T20 batsman (rank=2) against all opposition IPL bowlers. This is M S Dhoni for India
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(csk_Allmatches,"Chennai Super Kings",rank=2,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler1-2

# Best IPL T20 batsman of Delhi Daredevils against all other IPL  bowlers
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(dd_Allmatches,"Delhi Daredevils",rank=1,dispRows=30)
d
## Source: local data frame [30 x 3]
## Groups: batsman [1]
## 
##     batsman          bowler  runs
##      (fctr)          (fctr) (dbl)
## 1  V Sehwag        RP Singh    88
## 2  V Sehwag Harbhajan Singh    54
## 3  V Sehwag        MM Patel    52
## 4  V Sehwag       JA Morkel    47
## 5  V Sehwag   R Vinay Kumar    47
## 6  V Sehwag  AD Mascarenhas    47
## 7  V Sehwag         P Kumar    46
## 8  V Sehwag       PP Chawla    45
## 9  V Sehwag        R Sharma    43
## 10 V Sehwag         MS Gony    42
## ..      ...             ...   ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler1-3

# Best IPL T20 batsman of Deccan Chargers against all other IPL  bowlers
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(dc_Allmatches,"Deccan Chargers",rank=1,dispRows=30)
d
## Source: local data frame [30 x 3]
## Groups: batsman [1]
## 
##         batsman      bowler  runs
##          (fctr)      (fctr) (dbl)
## 1  AC Gilchrist     A Nehra    67
## 2  AC Gilchrist   DP Nannes    45
## 3  AC Gilchrist S Sreesanth    40
## 4  AC Gilchrist   JH Kallis    39
## 5  AC Gilchrist    MM Patel    39
## 6  AC Gilchrist   JA Morkel    34
## 7  AC Gilchrist   IK Pathan    34
## 8  AC Gilchrist    A Kumble    34
## 9  AC Gilchrist  AB Agarkar    31
## 10 AC Gilchrist    I Sharma    30
## ..          ...         ...   ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler1-4

14. Team bowling IPL T20 scorecard against all opposing IPL steams

The functions lists the top IPL T20 bowlers of each IPL team. This function returns a dataframe when ‘matches’ is the matches of the IPL and ‘theTeam’ is the same IPL team as in the functions below

teamBowlingScorecardAllOppnAllMatchesMain(matches=kkr_Allmatches,theTeam="Kolkata Knight Riders")
## Source: local data frame [52 x 5]
## 
##             bowler overs maidens  runs wickets
##             (fctr) (int)   (int) (dbl)   (dbl)
## 1        SP Narine    18       0  1289      84
## 2        JH Kallis    20       0  1105      46
## 3  Shakib Al Hasan    18       0   827      43
## 4         R Bhatia    15       0   905      38
## 5         L Balaji    21       0   879      37
## 6    Iqbal Abdulla    20       0   674      32
## 7         AB Dinda    17       0   605      29
## 8        PP Chawla    15       0   572      26
## 9         I Sharma    17       0   862      25
## 10        M Morkel    14       0   573      25
## ..             ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(matches=csk_Allmatches,theTeam="Chennai Super Kings")
## Source: local data frame [44 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1        R Ashwin    20       0  2127      98
## 2        DJ Bravo    17       0  1542      88
## 3       JA Morkel    20       0  2000      83
## 4       MM Sharma    19       0  1208      60
## 5       RA Jadeja    17       0  1346      58
## 6       SB Jakati    18       0  1154      46
## 7  M Muralitharan    16       0   977      41
## 8    DE Bollinger    13       0   662      41
## 9        L Balaji    21       0   809      35
## 10        A Nehra    12       0   591      33
## ..            ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(dc_Allmatches,"Deccan Chargers")
## Source: local data frame [43 x 5]
## 
##           bowler overs maidens  runs wickets
##           (fctr) (int)   (int) (dbl)   (dbl)
## 1        PP Ojha    18       0  1328      66
## 2       RP Singh    18       0  1229      57
## 3       A Mishra    14       0   735      32
## 4       DW Steyn    13       0   584      32
## 5      A Symonds    18       0   633      22
## 6     WPUJC Vaas     8       0   355      22
## 7      RJ Harris    15       0   463      21
## 8   DT Christian    17       0   583      20
## 9  Harmeet Singh    18       0   419      17
## 10     RG Sharma    13       0   339      15
## ..           ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(dd_Allmatches,"Delhi Daredevils")
## Source: local data frame [61 x 5]
## 
##         bowler overs maidens  runs wickets
##         (fctr) (int)   (int) (dbl)   (dbl)
## 1     A Mishra    20       0  1105      54
## 2     UT Yadav    19       0  1314      53
## 3     M Morkel    19       0  1113      52
## 4      A Nehra    16       0   758      41
## 5    IK Pathan    21       0  1191      34
## 6   PJ Sangwan    19       0   782      34
## 7    DP Nannes    17       0   596      31
## 8     S Nadeem    19       0  1046      30
## 9  MF Maharoof    18       0   507      29
## 10 Imran Tahir    13       0   485      26
## ..         ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(kxip_Allmatches,"Kings XI Punjab")
## Source: local data frame [52 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1       PP Chawla    15       0  2188      87
## 2       IK Pathan    18       0  1112      56
## 3         P Awana    21       0  1029      43
## 4         P Kumar    19       0  1152      35
## 5        AR Patel    19       0   797      33
## 6  Sandeep Sharma    14       0   704      32
## 7   Azhar Mahmood    18       0   659      30
## 8     S Sreesanth    18       0   750      28
## 9      MG Johnson    19       0   780      26
## 10      RJ Harris    15       0   584      26
## ..            ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(ktk_Allmatches,"Kochi Tuskers Kerala")
## Source: local data frame [13 x 5]
## 
##              bowler overs maidens  runs wickets
##              (fctr) (int)   (int) (dbl)   (dbl)
## 1     R Vinay Kumar    15       0   335      17
## 2          RP Singh    11       0   345      15
## 3         RA Jadeja    12       0   305       9
## 4          BJ Hodge     7       0    77       8
## 5       S Sreesanth     9       1   206       7
## 6          RV Gomez     7       0   124       5
## 7       NLTC Perera    10       0   111       5
## 8    P Parameswaran     9       0   137       4
## 9    M Muralitharan     5       0   141       2
## 10         RR Powar     7       0   112       2
## 11          B Akhil     2       0    22       0
## 12       P Prasanth     1       0    18       0
## 13 Y Gnaneswara Rao     1       0     7       0
teamBowlingScorecardAllOppnAllMatchesMain(mi_Allmatches,"Mumbai Indians")
## Source: local data frame [59 x 5]
## 
##             bowler overs maidens  runs wickets
##             (fctr) (int)   (int) (dbl)   (dbl)
## 1       SL Malinga    21       0  2478     158
## 2  Harbhajan Singh    20       0  2668     118
## 3       KA Pollard    16       0  1614      66
## 4         MM Patel    19       0   862      42
## 5      DS Kulkarni    19       0   825      37
## 6          PP Ojha    16       0   968      32
## 7         DJ Bravo    17       0   719      30
## 8           Z Khan    14       0   630      30
## 9       MG Johnson    11       0   436      22
## 10  MJ McClenaghan    12       0   386      18
## ..             ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(pw_Allmatches,"Pune Warriors")
## Source: local data frame [34 x 5]
## 
##          bowler overs maidens  runs wickets
##          (fctr) (int)   (int) (dbl)   (dbl)
## 1      R Sharma    17       0   854      35
## 2      AB Dinda    17       0   634      28
## 3       B Kumar    20       0   697      24
## 4    WD Parnell    16       1   444      21
## 5     AC Thomas    14       0   406      16
## 6  Yuvraj Singh    10       0   333      15
## 7      MR Marsh    13       0   281      14
## 8    AD Mathews    16       0   407      12
## 9       A Nehra    17       0   402      12
## 10   MN Samuels    14       0   250      10
## ..          ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(rcb_Allmatches,"Royal Challengers Bangalore")
## Source: local data frame [57 x 5]
## 
##           bowler overs maidens  runs wickets
##           (fctr) (int)   (int) (dbl)   (dbl)
## 1  R Vinay Kumar    21       0  1822      83
## 2         Z Khan    15       0  1237      53
## 3       A Kumble    18       0  1040      47
## 4       MA Starc    18       0   693      39
## 5        P Kumar    18       0  1078      37
## 6      YS Chahal    14       0   801      36
## 7       HV Patel    15       0   781      32
## 8       DW Steyn    15       0   654      32
## 9      S Aravind    11       0   548      32
## 10     JH Kallis    16       0  1088      26
## ..           ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(rr_Allmatches,"Rajasthan Royals")
## Source: local data frame [56 x 5]
## 
##           bowler overs maidens  runs wickets
##           (fctr) (int)   (int) (dbl)   (dbl)
## 1     SK Trivedi    19       0  1862      70
## 2      SR Watson    21       0  1633      68
## 3       SK Warne    16       0  1405      60
## 4    JP Faulkner    20       0  1224      49
## 5       MM Patel    20       0   779      39
## 6      KK Cooper    18       0   691      32
## 7        A Singh    14       0   620      31
## 8        SW Tait    19      NA    NA      26
## 9  Sohail Tanvir    12       0   266      24
## 10     YK Pathan    17       0   693      23
## ..           ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(sh_Allmatches,"Sunrisers Hyderabad")
## Source: local data frame [21 x 5]
## 
##          bowler overs maidens  runs wickets
##          (fctr) (int)   (int) (dbl)   (dbl)
## 1       B Kumar    16       0   761      40
## 2     KV Sharma    17       0   905      38
## 3      DW Steyn    17       0   968      33
## 4      A Mishra    16       0   726      29
## 5      I Sharma    18       0   704      20
## 6   NLTC Perera    13       0   453      19
## 7  MC Henriques    15       0   254      15
## 8     DJG Sammy    14       0   321      14
## 9      TA Boult    12       0   236      10
## 10    RS Bopara     7       0   160       8
## ..          ...   ...     ...   ...     ...

15. Team bowling IPL T20 scorecard against all opposing teams (continued)

The function lists the top bowlers of a IPL team (‘matches’) against the opposing teams

# Best Sunrisers Hyderabad bowlers in matches against CSK
teamBowlingScorecardAllOppnAllMatches(sh_Allmatches,'Chennai Super Kings')
## Source: local data frame [14 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1       KV Sharma    10       1   151       4
## 2         B Kumar     8       0   122       4
## 3        I Sharma     9       0   183       3
## 4        A Mishra     9       0    94       3
## 5     NLTC Perera     1       0    45       3
## 6        TA Boult     5       0    78       2
## 7  A Ashish Reddy     3       0    34       2
## 8       RS Bopara     1       0    29       2
## 9    MC Henriques     4       0    20       2
## 10       DW Steyn     7       0   125       1
## 11      DJG Sammy     4       0    75       0
## 12  Parvez Rasool     1       0    46       0
## 13      IK Pathan     1       0    34       0
## 14        P Kumar     4       0    33       0
# Best Kolkata Knight Riders bowlers in matches against Kings XI Punjab
teamBowlingScorecardAllOppnAllMatches(ktk_Allmatches,'Kings XI Punjab')
## Source: local data frame [7 x 5]
## 
##           bowler overs maidens  runs wickets
##           (fctr) (int)   (int) (dbl)   (dbl)
## 1       RP Singh     4       0    25       4
## 2    S Sreesanth     4       0    38       0
## 3  R Vinay Kumar     3       0    28       0
## 4      RA Jadeja     3       0    27       0
## 5       BJ Hodge     2       0    26       0
## 6 P Parameswaran     2       0    23       0
## 7       RV Gomez     1       0    12       0
# Best Mumbai Indian bowlers in matches against Delhi Daredevils
teamBowlingScorecardAllOppnAllMatches(mi_Allmatches,'Delhi Daredevils')
## Source: local data frame [37 x 5]
## 
##             bowler overs maidens  runs wickets
##             (fctr) (int)   (int) (dbl)   (dbl)
## 1       SL Malinga    14       0   271      21
## 2  Harbhajan Singh    17       1   318      17
## 3       KA Pollard    10       0   185       6
## 4       AG Murtaza     5       0    39       6
## 5      DS Kulkarni     9       0   116       5
## 6          A Nehra     6       0    55       4
## 7          PP Ojha    12       0   112       3
## 8    ST Jayasuriya     7       0    93       3
## 9         DJ Bravo     6       0    66       3
## 10      SM Pollock     4       0    49       3
## ..             ...   ...     ...   ...     ...
# Best Royal Challengers Bangalore bowlers in matches against Pune Warriors
teamBowlingScorecardAllOppnAllMatches(rcb_Allmatches,"Pune Warriors")
## Source: local data frame [16 x 5]
## 
##             bowler overs maidens  runs wickets
##             (fctr) (int)   (int) (dbl)   (dbl)
## 1    R Vinay Kumar    12       0   121      10
## 2           Z Khan     6       0    88       4
## 3         CH Gayle     3       0    18       4
## 4         M Kartik     6       0    54       3
## 5         HV Patel     5       0    65       2
## 6       DL Vettori     5       0    57       2
## 7   M Muralitharan     6       0    48       2
## 8         RP Singh     5       0    47       2
## 9     MC Henriques     4       0    44       2
## 10      JD Unadkat     4       0    37       2
## 11       R Rampaul     4       0    21       2
## 12      KP Appanna     5       0    53       1
## 13       S Aravind     4       0    35       1
## 14 J Syed Mohammad     2       0    26       1
## 15        A Mithun     4       0    26       0
## 16      TM Dilshan     1       0    24       0

16. Team Bowlers versus Batsmen (in T20 against all oppositions)

The functions below give the peformance of IPL bowlers versus opposing IPL batsman. They give the best bowlers and the total runs conceded and against whom were the runs conceded

# Best Chennai Super Kings IPL T20 bowlers overall  against all other IPL (rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(csk_Allmatches,theTeam="Chennai Super Kings",rank=0)
## Source: local data frame [10 x 2]
## 
##            bowler  runs
##            (fctr) (dbl)
## 1        R Ashwin  2063
## 2       JA Morkel  1917
## 3        DJ Bravo  1486
## 4       RA Jadeja  1320
## 5       MM Sharma  1192
## 6       SB Jakati  1134
## 7  M Muralitharan   928
## 8        SK Raina   888
## 9        L Balaji   781
## 10        MS Gony   654
# Top Chennai Super Kings IPL T20 bowler of India and runs conceded against different opposition batsmen 
(rank=1)
## [1] 1
m <-teamBowlersVsBatsmenAllOppnAllMatchesMain(csk_Allmatches,theTeam="Chennai Super Kings",rank=1)
m
## Source: local data frame [148 x 3]
## Groups: bowler [1]
## 
##      bowler      batsman runsConceded
##      (fctr)       (fctr)        (dbl)
## 1  R Ashwin      V Kohli          115
## 2  R Ashwin   GJ Maxwell           72
## 3  R Ashwin   RV Uthappa           68
## 4  R Ashwin    RG Sharma           59
## 5  R Ashwin    SR Watson           59
## 6  R Ashwin  LMP Simmons           58
## 7  R Ashwin     MS Bisla           52
## 8  R Ashwin     CH Gayle           49
## 9  R Ashwin    DA Warner           48
## 10 R Ashwin AC Gilchrist           46
## ..      ...          ...          ...
# Top Kolkata Knight Riders IPL T20 bowler and runs conceded  against different opposing IPL  batsmen (rank=1)
m <-teamBowlersVsBatsmenAllOppnAllMatchesMain(kkr_Allmatches,theTeam="Kolkata Knight Riders",rank=1)
m
## Source: local data frame [132 x 3]
## Groups: bowler [1]
## 
##       bowler    batsman runsConceded
##       (fctr)     (fctr)        (dbl)
## 1  SP Narine  RG Sharma           62
## 2  SP Narine   SK Raina           58
## 3  SP Narine    WP Saha           52
## 4  SP Narine  JP Duminy           50
## 5  SP Narine  DA Warner           44
## 6  SP Narine MEK Hussey           37
## 7  SP Narine  AM Rahane           33
## 8  SP Narine    V Kohli           29
## 9  SP Narine KD Karthik           27
## 10 SP Narine KA Pollard           26
## ..       ...        ...          ...
# Top IPL T20 bowlers versus batsmen of Delhi Daredevils(rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(dd_Allmatches,theTeam="Delhi Daredevils",rank=0)
## Source: local data frame [10 x 2]
## 
##         bowler  runs
##         (fctr) (dbl)
## 1     UT Yadav  1261
## 2    IK Pathan  1156
## 3     M Morkel  1075
## 4     A Mishra  1072
## 5     S Nadeem  1026
## 6   PJ Sangwan   744
## 7      A Nehra   728
## 8    DP Nannes   558
## 9  MF Maharoof   481
## 10 Imran Tahir   461

17. Team bowlers versus batsmen report (in IPL T20 matches against all opposing team)

#The best IPL T20 bowlers of other IPL teams against Chennai Super Kings
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=csk_Allmatches,theTeam="Chennai Super Kings",rank=0)
## Source: local data frame [10 x 2]
## 
##             bowler  runs
##             (fctr) (dbl)
## 1  Harbhajan Singh   461
## 2    R Vinay Kumar   435
## 3          P Kumar   421
## 4        PP Chawla   410
## 5        IK Pathan   399
## 6       SL Malinga   380
## 7         RP Singh   353
## 8        JH Kallis   348
## 9          PP Ojha   333
## 10       YK Pathan   293
# Best T20 performer against Mumbai Indians is A Mishra
a <- teamBowlersVsBatsmenAllOppnAllMatchesRept(mi_Allmatches,theTeam="Mumbai Indians",rank=1)
a
## Source: local data frame [24 x 3]
## Groups: bowler [1]
## 
##      bowler      batsman runsConceded
##      (fctr)       (fctr)        (dbl)
## 1  A Mishra   KA Pollard           91
## 2  A Mishra    AT Rayudu           71
## 3  A Mishra SR Tendulkar           55
## 4  A Mishra    RG Sharma           51
## 5  A Mishra    SS Tiwary           26
## 6  A Mishra    AM Rahane           23
## 7  A Mishra     DR Smith           16
## 8  A Mishra  LMP Simmons           16
## 9  A Mishra    JP Duminy           13
## 10 A Mishra     DJ Bravo           11
## ..      ...          ...          ...

18. Team bowlers versus batsmen report (in T20s against all opposing teams continued)

#Top Royal Challengers Bangalore T20 Indian bowlers against Rajasthan Royals
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=rcb_Allmatches,theTeam="Rajasthan Royals",rank=0)
## Source: local data frame [10 x 2]
## 
##           bowler  runs
##           (fctr) (dbl)
## 1  R Vinay Kumar   252
## 2         Z Khan   134
## 3        P Kumar   115
## 4       A Kumble   106
## 5      S Aravind   101
## 6       MA Starc   100
## 7      JH Kallis    96
## 8      YS Chahal    86
## 9       HV Patel    69
## 10      M Kartik    67
#Top Pune Warriors T20 Indian bowlers against Kings XI Punjab
teamBowlersVsBatsmenAllOppnAllMatchesRept(pw_Allmatches,"Kings XI Punjab",rank=0)
## Source: local data frame [10 x 2]
## 
##          bowler  runs
##          (fctr) (dbl)
## 1      R Sharma   128
## 2      AB Dinda    93
## 3       B Kumar    61
## 4    MN Samuels    50
## 5      MR Marsh    48
## 6      JD Ryder    43
## 7       A Nehra    42
## 8    BAW Mendis    38
## 9     LJ Wright    35
## 10 Yuvraj Singh    32

19. Team bowlers versus batsmen report (in T20s against all opposing teams) plot

This function can only be used for rank > 0 (rank=1,2,3..)

# Top IPL T20 bowler against Chennai Super Kings (This is Harbhajan Singh)
df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(csk_Allmatches,theTeam="Chennai Super Kings",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"Chennai Super Kings","Chennai Super Kings")

bowlerVsbatsmen1-1

# Top IPL T20 Indian bowler versus Delhi Daredevils (R Vinay Kumar)
df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(dd_Allmatches,theTeam="Delhi Daredevils",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"Delhi Daredevils","Delhi Daredevils")

bowlerVsbatsmen1-2

20. Team Bowler Wicket Kind in Twenty20 matches against all opposing IPL teams

# Top opposition IPL T20  bowlers against Chennai Super Kings and the kind of wickets
teamBowlingWicketKindAllOppnAllMatches(csk_Allmatches,t1="Chennai Super Kings",t2="All")

bowlingWicketkind1-1

# Top opposition IPL T20  bowlers against Royal Challengers Bangalore and the kind of 
# wickets. Get the data frame. Do not plot
m <-teamBowlingWicketKindAllOppnAllMatches(rcb_Allmatches,t1="Royal Challengers Bangalore",t2="All",plot=FALSE)
m
## Source: local data frame [34 x 3]
## Groups: bowler [?]
## 
##       bowler wicketKind     m
##       (fctr)      (chr) (int)
## 1   L Balaji     bowled     6
## 2   L Balaji     caught    10
## 3   L Balaji    run out     2
## 4  JA Morkel     bowled     4
## 5  JA Morkel     caught     6
## 6  JA Morkel        lbw     4
## 7    A Nehra     bowled     5
## 8    A Nehra     caught    14
## 9    A Nehra        lbw     1
## 10   A Nehra    run out     1
## ..       ...        ...   ...
# Top opposition IPL T20  bowlers against Delhi Daredevils and the kind of wickets
teamBowlingWicketKindAllOppnAllMatches(dd_Allmatches,t1="Delhi Daredevils",t2="All")

bowlingWicketkind1-2

21. Team Bowler Wicket Runs in IPL Twenty20 matches against all opposing teams

# Opposition T20 bowlers against Chennai Super Kings and runs conceded
teamBowlingWicketRunsAllOppnAllMatches(csk_Allmatches,t1="Chennai Super Kings",t2="All",plot=TRUE)

bowlingWicketRuns1-1

# Opposition T20 bowlers against Mumbai Indians and runs conceded returned as dataframe
m <-teamBowlingWicketRunsAllOppnAllMatches(mi_Allmatches,t1="Mumbai Indians",t2="All",plot=FALSE)
m
## Source: local data frame [10 x 3]
## 
##           bowler runsConceded wickets
##           (fctr)        (dbl)   (dbl)
## 1       DJ Bravo          299      24
## 2      PP Chawla          348      18
## 3        A Nehra          296      16
## 4       DW Steyn          326      16
## 5      IK Pathan          297      16
## 6      MM Sharma          293      15
## 7  R Vinay Kumar          402      15
## 8      SP Narine          193      14
## 9       RP Singh          182      13
## 10      AB Dinda          292      13
# Top T20 Indian bowlers against Kolkata Knight Riders and runs conceded
teamBowlingWicketRunsAllOppnAllMatches(kkr_Allmatches,t1="Kolkata Knight Riders",t2="All",plot=TRUE)

bowlingWicketRuns1-2

22. Team Bowler Wicket Runs in IPL Twenty20 matches against all opposing teams(continued)

#Top opposition IPL T20 bowlers against Sunrisers Hyderabad
teamBowlingWicketRunsAllOppnAllMatches(sh_Allmatches,t1="Sunrisers Hyderabad",t2="All",plot=TRUE)

bowlingWicketRuns2-1

#Top opposition IPL T20 bowlers against Pune Warriorss
teamBowlingWicketRunsAllOppnAllMatches(pw_Allmatches,t1="Pune Warriors",t2="All",plot=TRUE)

bowlingWicketRuns2-2bowlingWicketRuns2-3

#Top opposition IPL T20 bowlers against Kings XI Punjab
teamBowlingWicketRunsAllOppnAllMatches(kxip_Allmatches,t1="Kings XI Punjab",t2="All",plot=TRUE)

Conclusion

This post included all functions for an IPL team in all IPL T20 matches against all opposing IPL teams. As before the data frames for the IPL T20 matches 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.

The 4th and final part of the IPL yorkr package’s handling of IPL T20 will follow soon.

Watch this space!

You may also like

  1. Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
  2. yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance
  3. Literacy in India: A deepR dive
  4. Introducing cricketr! : An R package to analyze performances of cricketers
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yorkr crashes the IPL party! – Part 2


Most people say that it is the intellect which makes a great scientist. They are wrong: it is character.

                 Albert Einstein

*Science is organized knowledge. Wisdom is organized life.“*

                 Immanuel Kant

If I have seen further, it is by standing on the shoulders of giants

                 Isaac Newton
                 

Valid criticism does you a favor.

                 Carl Sagan

Introduction

In this post, my R package ‘yorkr’, continues to bat in the IPL Twenty20s. This post is a continuation of my earlier post – yorkr crashes the IPL party ! – Part 1. This post deals with Class 2 functions namely the performances of an IPL team in all T20 matches against another IPL team for e.g all T20 matches of Chennai Super Kings vs Royal Challengers Bangalore or Kochi Tuskers Kerala vs Mumbai Indians etc.

You can clone/fork the code for my package yorkr from Github at yorkr

This post has also been published at RPubs IPLT20-Part2 and can also be downloaded as a PDF document from IPLT20-Part2.pdf

The list of function in Class 2 are

  1. teamBatsmenPartnershiOppnAllMatches()
  2. teamBatsmenPartnershipOppnAllMatchesChart()
  3. teamBatsmenVsBowlersOppnAllMatches()
  4. teamBattingScorecardOppnAllMatches()
  5. teamBowlingPerfOppnAllMatches()
  6. teamBowlersWicketsOppnAllMatches()
  7. teamBowlersVsBatsmenOppnAllMatches()
  8. teamBowlersWicketKindOppnAllMatches()
  9. teamBowlersWicketRunsOppnAllMatches()
  10. 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 IPL T20 matches between any 2 teams using the function below. The dir parameter should point to the folder which has the IPL T20 RData files of the individual matches. This function creates a data frame of all the IPL 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/IPL/IPL-T20-matches")
matches <- getAllMatchesBetweenTeams("Sunrisers Hyderabad","Royal Challengers Bangalore",dir=".")
dim(matches)
## [1] 1320   25

I have however already saved the IPL Twenty20 matches for all possible combinations of opposing IPL Teams. The data for these matches for the individual teams/countries can be obtained from Github at in the folder IPL-T20-allmatches-between-two-teams

Note: You will need to use the function below for future matches! The data in Cricsheet are from 2008 -2015

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 IPL Twenty20 matches between any 2 IPL teams into a single dataframe and save 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

# Available in yorkr_0.0.5. Can be installed from Github though!
#saveAllMatchesBetween2IPLTeams()

4. Load data directly for all matches between 2 IPL teams

As in my earlier post I pick all IPL Twenty20 matches between 2 random IPL 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 2 IPL teams
setwd("C:/software/cricket-package/york-test/yorkrData/IPL/IPL-T20-allmatches-between-two-teams")
load("Chennai Super Kings-Delhi Daredevils-allMatches.RData")
csk_dd_matches <- matches
load("Deccan Chargers-Kolkata Knight Riders-allMatches.RData")
dc_kkr_matches <- matches
load("Mumbai Indians-Pune Warriors-allMatches.RData")
mi_pw_matches <- matches
load("Rajasthan Royals-Sunrisers Hyderabad-allMatches.RData")
rr_sh_matches <- matches
load("Kings XI Punjab-Royal Challengers Bangalore-allMatches.RData")
kxip_rcb_matches <-matches
load("Chennai Super Kings-Kochi Tuskers Kerala-allMatches.RData")
csk_ktk_matches <-matches

5. Team Batsmen partnership in Twenty20 (all matches with opposing IPL team)

This function will create a report of the batting partnerships in the IPL teams for the matches between the teams. The report can be brief or detailed depending on the parameter ‘report’. As can be seen M S Dhoni tops the list for CSK, followed by Raina and then Murali Vijay for matches against Delhi Daredevils. For the Delhi Daredevils it is V Sehawag followed by Gambhir.

m<- teamBatsmenPartnershiOppnAllMatches(csk_dd_matches,'Chennai Super Kings',report="summary")
m
## Source: local data frame [29 x 2]
## 
##         batsman totalRuns
##          (fctr)     (dbl)
## 1      MS Dhoni       364
## 2      SK Raina       335
## 3       M Vijay       290
## 4   S Badrinath       185
## 5     ML Hayden       181
## 6    MEK Hussey       169
## 7  F du Plessis       100
## 8      S Vidyut        94
## 9      DR Smith        81
## 10    JA Morkel        80
## ..          ...       ...
m<- teamBatsmenPartnershiOppnAllMatches(csk_dd_matches,'Delhi Daredevils',report="summary")
m
## Source: local data frame [53 x 2]
## 
##             batsman totalRuns
##              (fctr)     (dbl)
## 1          V Sehwag       233
## 2         G Gambhir       200
## 3         DA Warner       134
## 4    AB de Villiers       133
## 5        KD Karthik       129
## 6  DPMD Jayawardene        89
## 7         JA Morkel        81
## 8        TM Dilshan        79
## 9          S Dhawan        78
## 10          SS Iyer        77
## ..              ...       ...
m <-teamBatsmenPartnershiOppnAllMatches(dc_kkr_matches,'Deccan Chargers',report="summary")
m
## Source: local data frame [29 x 2]
## 
##            batsman totalRuns
##             (fctr)     (dbl)
## 1     AC Gilchrist       166
## 2         HH Gibbs       145
## 3        RG Sharma       116
## 4         S Dhawan       111
## 5        A Symonds       100
## 6  Y Venugopal Rao        92
## 7         B Chipli        60
## 8     DB Ravi Teja        54
## 9         TL Suman        53
## 10      VVS Laxman        32
## ..             ...       ...
m <-teamBatsmenPartnershiOppnAllMatches(mi_pw_matches,'Mumbai Indians',report="detailed")
m[1:30,]
##         batsman   nonStriker partnershipRuns totalRuns
## 1  SR Tendulkar JEC Franklin              24       152
## 2  SR Tendulkar    AT Rayudu              46       152
## 3  SR Tendulkar    RG Sharma               2       152
## 4  SR Tendulkar   KD Karthik              20       152
## 5  SR Tendulkar   RT Ponting              39       152
## 6  SR Tendulkar  AC Blizzard              12       152
## 7  SR Tendulkar  RJ Peterson               9       152
## 8     RG Sharma SR Tendulkar               3       135
## 9     RG Sharma JEC Franklin               0       135
## 10    RG Sharma    AT Rayudu              34       135
## 11    RG Sharma    A Symonds              19       135
## 12    RG Sharma   KD Karthik              19       135
## 13    RG Sharma   KA Pollard              47       135
## 14    RG Sharma     TL Suman               7       135
## 15    RG Sharma   GJ Maxwell               6       135
## 16   KD Karthik SR Tendulkar               8       108
## 17   KD Karthik JEC Franklin              32       108
## 18   KD Karthik    AT Rayudu               3       108
## 19   KD Karthik    RG Sharma              50       108
## 20   KD Karthik   SL Malinga              10       108
## 21   KD Karthik      PP Ojha               0       108
## 22   KD Karthik  RJ Peterson               4       108
## 23   KD Karthik  NLTC Perera               1       108
## 24    AT Rayudu SR Tendulkar              54        92
## 25    AT Rayudu    RG Sharma              37        92
## 26    AT Rayudu   KD Karthik               1        92
## 27 JEC Franklin SR Tendulkar              31        63
## 28 JEC Franklin    RG Sharma               1        63
## 29 JEC Franklin   KD Karthik              15        63
## 30 JEC Franklin     SA Yadav              10        63
m <-teamBatsmenPartnershiOppnAllMatches(rr_sh_matches,'Sunrisers Hyderabad',report="summary")
m
## Source: local data frame [23 x 2]
## 
##         batsman totalRuns
##          (fctr)     (dbl)
## 1      S Dhawan       168
## 2     DJG Sammy        95
## 3    EJG Morgan        90
## 4     DA Warner        83
## 5       NV Ojha        50
## 6      KL Rahul        40
## 7     RS Bopara        40
## 8      DW Steyn        31
## 9      CL White        31
## 10 MC Henriques        29
## ..          ...       ...
m <-teamBatsmenPartnershiOppnAllMatches(kxip_rcb_matches,'Kings XI Punjab',report="summary")
m
## Source: local data frame [47 x 2]
## 
##          batsman totalRuns
##           (fctr)     (dbl)
## 1       SE Marsh       246
## 2      DA Miller       224
## 3      RS Bopara       203
## 4   AC Gilchrist       191
## 5   Yuvraj Singh       126
## 6       MS Bisla       103
## 7  Mandeep Singh       100
## 8      DJ Hussey        99
## 9  Azhar Mahmood        96
## 10 KC Sangakkara        88
## ..           ...       ...
m <-teamBatsmenPartnershiOppnAllMatches(csk_ktk_matches,'Kochi Tuskers Kerala',report="summary")
m
## Source: local data frame [8 x 2]
## 
##            batsman totalRuns
##             (fctr)     (dbl)
## 1      BB McCullum        80
## 2         BJ Hodge        70
## 3         PA Patel        40
## 4        RA Jadeja        35
## 5 Y Gnaneswara Rao        19
## 6 DPMD Jayawardene        16
## 7          OA Shah         3
## 8        KM Jadhav         1

6. Team batsmen partnership in Twenty20 (all matches with opposing IPL team)

This is plotted graphically in the charts below. The partnerships are shown. Note: All functions which create a plot also include a parameter plot=TRUE/FALSE. If you set this as FALSE then a data frame is returned. You can use the dataframe to create an interactive plot for the partnerships (mouse over) using packages like plotly,rcharts, googleVis or ggvis.

teamBatsmenPartnershipOppnAllMatchesChart(csk_dd_matches,'Chennai Super Kings',"Delhi Daredevils")

teamBatsmenPartnership-1

teamBatsmenPartnershipOppnAllMatchesChart(dc_kkr_matches,main="Kolkata Knight Riders",opposition="Deccan Chargers")

teamBatsmenPartnership-2

teamBatsmenPartnershipOppnAllMatchesChart(kxip_rcb_matches,"Royal Challengers Bangalore",opposition="Kings XI Punjab")

teamBatsmenPartnership-3

teamBatsmenPartnershipOppnAllMatchesChart(mi_pw_matches,"Mumbai Indians","Pune Warriors")

teamBatsmenPartnership-4

m <- teamBatsmenPartnershipOppnAllMatchesChart(rr_sh_matches,"Rajasthan Royals","Sunrisers Hyderabad",plot=FALSE)
m[1:30,]
##        batsman  nonStriker runs
## 1    SR Watson   STR Binny   60
## 2    AM Rahane   STR Binny   59
## 3    STR Binny   AM Rahane   45
## 4    SR Watson    R Dravid   42
## 5    AM Rahane   SV Samson   41
## 6     BJ Hodge   SV Samson   36
## 7    CH Morris   STR Binny   34
## 8    AM Rahane   SR Watson   31
## 9     R Dravid   SR Watson   30
## 10   SV Samson   AM Rahane   29
## 11   SR Watson   AM Rahane   27
## 12   SPD Smith    DJ Hooda   25
## 13   SPD Smith JP Faulkner   24
## 14   SPD Smith   STR Binny   20
## 15    R Dravid   AM Rahane   18
## 16    BJ Hodge JP Faulkner   18
## 17 JP Faulkner   SPD Smith   18
## 18   SV Samson     KK Nair   14
## 19 JP Faulkner   STR Binny   14
## 20   SV Samson   STR Binny   13
## 21   SPD Smith   AM Rahane   13
## 22   SR Watson   SPD Smith   12
## 23   STR Binny JP Faulkner   12
## 24   STR Binny   SPD Smith   12
## 25 JP Faulkner   SV Samson   12
## 26     KK Nair   SV Samson   12
## 27 JP Faulkner    BJ Hodge   11
## 28   SPD Smith   SR Watson   10
## 29   STR Binny   SR Watson    9
## 30   SV Samson    BJ Hodge    9

7. Team batsmen versus bowler in Twenty20 (all matches with opposing IPL team)

The plots below provide information on how each of the top batsmen of the IPL teams fared against the opposition bowlers

# Adam Gilchrist was the top performer for Deccan Chargers
teamBatsmenVsBowlersOppnAllMatches(dc_kkr_matches,"Deccan Chargers","Kolkata Knight Riders")

batsmenvsBowler-1

teamBatsmenVsBowlersOppnAllMatches(csk_dd_matches,"Delhi Daredevils","Chennai Super Kings",top=3)

batsmenvsBowler-2

m <- teamBatsmenVsBowlersOppnAllMatches(csk_ktk_matches,"Chennai Super Kings","Kochi Tuskers Kerala",top=10,plot=FALSE)
m
## Source: local data frame [37 x 3]
## Groups: batsman [1]
## 
##     batsman         bowler  runs
##      (fctr)         (fctr) (dbl)
## 1  SK Raina       RP Singh     6
## 2  SK Raina    S Sreesanth    18
## 3  SK Raina M Muralitharan     1
## 4  SK Raina  R Vinay Kumar     4
## 5  SK Raina    NLTC Perera    11
## 6  SK Raina       RR Powar    13
## 7  SK Raina       RV Gomez    16
## 8   WP Saha       RP Singh    15
## 9   WP Saha M Muralitharan    11
## 10  WP Saha       BJ Hodge     1
## ..      ...            ...   ...
teamBatsmenVsBowlersOppnAllMatches(rr_sh_matches,"Sunrisers Hyderabad","Rajasthan Royals")

batsmenvsBowler-3

8. Team batsmen versus bowler in Twenty20(all matches with opposing IPL team)

The following tables gives the overall performances of the IPL team’s batsmen against the opposition.

#Chris Gayle followed by Virat Kohli tops for RCB
a <-teamBattingScorecardOppnAllMatches(kxip_rcb_matches,main="Royal Challengers Bangalore",opposition="Kings XI Punjab")
## Total= 2444
a
## Source: local data frame [55 x 5]
## 
##           batsman ballsPlayed fours sixes  runs
##            (fctr)       (int) (int) (int) (dbl)
## 1        CH Gayle         313    45    41   561
## 2         V Kohli         296    39     8   344
## 3  AB de Villiers         183    23    16   301
## 4       JH Kallis         133    18     7   187
## 5        R Dravid          90    11     1   105
## 6      RV Uthappa          47     7     6    92
## 7       CA Pujara          66    11    NA    70
## 8       MK Pandey          50     5     3    67
## 9    KP Pietersen          43     7     1    66
## 10     MV Boucher          36     4     1    41
## ..            ...         ...   ...   ...   ...
#Tendulkar & Rohit Sharma lead for Mumbai Indians
teamBattingScorecardOppnAllMatches(mi_pw_matches,"Mumbai Indians","Pune Warriors")
## Total= 756
## Source: local data frame [20 x 5]
## 
##            batsman ballsPlayed fours sixes  runs
##             (fctr)       (int) (int) (int) (dbl)
## 1     SR Tendulkar         134    21     1   152
## 2        RG Sharma         121     7     6   135
## 3       KD Karthik         107    10     3   108
## 4        AT Rayudu          93     8     1    92
## 5     JEC Franklin          70     5     2    63
## 6       KA Pollard          43     3     3    55
## 7         TL Suman          16     3     3    36
## 8  Harbhajan Singh          22     3     1    29
## 9       SL Malinga          16     2     1    19
## 10       A Symonds          18     2    NA    19
## 11      RT Ponting          17     2    NA    14
## 12      GJ Maxwell           7     1     1    13
## 13     RJ Peterson          13     1    NA    13
## 14     AC Blizzard           6     1    NA     6
## 15         PP Ojha           2    NA    NA     1
## 16        MM Patel           2    NA    NA     1
## 17         RE Levi           2    NA    NA     0
## 18        SA Yadav           4    NA    NA     0
## 19     NLTC Perera           4    NA    NA     0
## 20        DR Smith           1    NA    NA     0
teamBattingScorecardOppnAllMatches(mi_pw_matches,"Pune Warriors","Mumbai Indians")
## Total= 714
## Source: local data frame [28 x 5]
## 
##         batsman ballsPlayed fours sixes  runs
##          (fctr)       (int) (int) (int) (dbl)
## 1    RV Uthappa         131    13     4   151
## 2     MK Pandey          80     5     4    88
## 3  Yuvraj Singh          62     3     6    77
## 4      M Manhas          36     5    NA    42
## 5     SPD Smith          38     4    NA    41
## 6      MR Marsh          26     2     2    38
## 7      M Kartik          21     2     1    25
## 8      R Sharma          22     2     1    23
## 9      TL Suman          15     5    NA    23
## 10   WD Parnell          24     3    NA    22
## ..          ...         ...   ...   ...   ...
teamBattingScorecardOppnAllMatches(csk_dd_matches,"Delhi Daredevils","Chennai Super Kings")
## Total= 1983
## Source: local data frame [53 x 5]
## 
##             batsman ballsPlayed fours sixes  runs
##              (fctr)       (int) (int) (int) (dbl)
## 1          V Sehwag         147    27     9   233
## 2         G Gambhir         155    23     2   200
## 3         DA Warner         130    11     2   134
## 4    AB de Villiers          80     7     6   133
## 5        KD Karthik          99    15     1   129
## 6  DPMD Jayawardene          77     7     2    89
## 7         JA Morkel          63     8     2    81
## 8        TM Dilshan          65     8     3    79
## 9          S Dhawan          58     8     2    78
## 10          SS Iyer          56    11     1    77
## ..              ...         ...   ...   ...   ...
teamBattingScorecardOppnAllMatches(rr_sh_matches,"Rajasthan Royals","Sunrisers Hyderabad")
## Total= 808
## Source: local data frame [17 x 5]
## 
##          batsman ballsPlayed fours sixes  runs
##           (fctr)       (int) (int) (int) (dbl)
## 1      SR Watson          97    22     4   148
## 2      AM Rahane         145    17     1   148
## 3      SPD Smith          81    11     2   103
## 4      STR Binny          83     6     1    90
## 5      SV Samson          83     3     4    76
## 6    JP Faulkner          41     7     2    59
## 7       BJ Hodge          37     2     5    55
## 8       R Dravid          44     7     1    48
## 9      CH Morris          11     2     3    34
## 10       KK Nair          23     3    NA    17
## 11      R Bhatia          10     1    NA     8
## 12   DS Kulkarni           6     1    NA     7
## 13      DJ Hooda           9    NA    NA     7
## 14      AM Nayar           3     1    NA     4
## 15      PV Tambe           7    NA    NA     3
## 16 KW Richardson           2    NA    NA     1
## 17     DH Yagnik           4    NA    NA     0

9. Team performances of IPL bowlers (all matches with opposing IPL team)

Like the function above the following tables provide the top IPL bowlers of the respective teams in the matches against the opposition.

#Piyush Chawla has the most wickets for KXIP against RCB
teamBowlingPerfOppnAllMatches(kxip_rcb_matches,"Kings XI Punjab","Royal Challengers Bangalore")
## Source: local data frame [38 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1       PP Chawla    14       0   311      12
## 2       IK Pathan    12       0   159       9
## 3      YA Abdulla     9       1   103       8
## 4       RJ Harris     5       0    87       7
## 5         P Awana    11       0   149       6
## 6     S Sreesanth     6       0   101       5
## 7   Azhar Mahmood     8       0    74       5
## 8  Sandeep Sharma     8       1   101       4
## 9        AR Patel     5       0    94       4
## 10      VRV Singh     6       0    70       4
## ..            ...   ...     ...   ...     ...
#Ashwin is the highest wicket takes for CSK against DD
teamBowlingPerfOppnAllMatches(csk_dd_matches,main="Chennai Super Kings",opposition="Delhi Daredevils")
## Source: local data frame [26 x 5]
## 
##           bowler overs maidens  runs wickets
##           (fctr) (int)   (int) (dbl)   (dbl)
## 1       R Ashwin     9       0   233      17
## 2      JA Morkel    11       0   338      10
## 3       DJ Bravo     5       0   135       8
## 4      SB Jakati     4       0   140       6
## 5       L Balaji    10       0   117       6
## 6      MM Sharma     1       0    99       6
## 7      RA Jadeja     2       0    85       4
## 8      IC Pandey     1       0    80       4
## 9  BW Hilfenhaus     5       0    53       4
## 10       A Nehra     1       0    25       4
## ..           ...   ...     ...   ...     ...
teamBowlingPerfOppnAllMatches(dc_kkr_matches,"Deccan Chargers","Kolkata Knight Riders")
## Source: local data frame [26 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1        RP Singh    11       0   161       7
## 2         PP Ojha    11       0   196       6
## 3      WPUJC Vaas     4       0    67       5
## 4       A Symonds    12       0   100       4
## 5        DW Steyn     8       0    88       4
## 6        A Mishra     8       0    68       3
## 7  Jaskaran Singh     6       0    53       3
## 8       SB Styris     7       0    79       2
## 9       RJ Harris     4       0    20       2
## 10  Harmeet Singh    10       0    84       1
## ..            ...   ...     ...   ...     ...

10. Team bowler’s wickets in IPL Twenty20 (all matches with opposing IPL team)

This provided a graphical plot of the tables above

# Dirk Nannes and Umesh Yadav top for DD against CSK
teamBowlersWicketsOppnAllMatches(csk_dd_matches,"Delhi Daredevils","Chennai Superkings")

bowlerWicketsOppn-1

# SL Malinga and Munaf Patel lead in MI vs PW clashes
teamBowlersWicketsOppnAllMatches(mi_pw_matches,"Mumbai Indians","Pune Warrors")

bowlerWicketsOppn-2

teamBowlersWicketsOppnAllMatches(dc_kkr_matches,"Kolkata Knight Riders","Deccan Chargers",top=10) 

bowlerWicketsOppn-3

m <-teamBowlersWicketsOppnAllMatches(kxip_rcb_matches,"Royal Challengers Bangalore","Kings XI Punjab",plot=FALSE)
m
## Source: local data frame [20 x 2]
## 
##              bowler wickets
##              (fctr)   (int)
## 1         S Aravind       8
## 2            Z Khan       7
## 3          MA Starc       7
## 4          HV Patel       6
## 5           P Kumar       5
## 6         YS Chahal       5
## 7         JH Kallis       4
## 8     R Vinay Kumar       3
## 9          A Kumble       3
## 10         CH Gayle       3
## 11      AB McDonald       3
## 12         VR Aaron       3
## 13         DW Steyn       2
## 14    CK Langeveldt       2
## 15       DL Vettori       2
## 16         M Kartik       2
## 17 RE van der Merwe       2
## 18        R Rampaul       1
## 19        JA Morkel       1
## 20         AB Dinda       1

11. Team bowler vs batsmen in Twenty20(all matches with opposing IPL team)

These plots show how the IPL bowlers fared against the batsmen. It shows which of the opposing IPL teams batsmen were able to score the most runs

teamBowlersVsBatsmenOppnAllMatches(rr_sh_matches,'Rajasthan Royals',"Sunrisers Hyderabd",top=5)

bowlerVsBatsmen-1

teamBowlersVsBatsmenOppnAllMatches(kxip_rcb_matches,"Kings XI Punjab","Royal Challengers Bangalore",top=3)

bowlerVsBatsmen-2

teamBowlersVsBatsmenOppnAllMatches(dc_kkr_matches,"Deccan Chargers","Kolkata Knight Riders")

bowlerVsBatsmen-3

12. Team bowler’s wicket kind in Twenty20(caught,bowled,etc) (all matches with opposing IPL team)

The charts below show the wicket kind taken by the bowler of the IPL team(caught, bowled, lbw etc)

teamBowlersWicketKindOppnAllMatches(csk_dd_matches,"Delhi Daredevils","Chennai Super Kings",plot=TRUE)

bowlerWickets-1

m <- teamBowlersWicketKindOppnAllMatches(mi_pw_matches,"Pune Warriors","Mumbai Indians",plot=FALSE)
m[1:30,]
##          bowler wicketKind wicketPlayerOut runs
## 1       SB Wagh     caught    JEC Franklin   31
## 2      R Sharma     caught    SR Tendulkar   64
## 3     AC Thomas     caught       AT Rayudu   69
## 4      M Kartik    stumped         RE Levi   70
## 5      AB Dinda     caught       AT Rayudu  150
## 6      AB Dinda     caught       RG Sharma  150
## 7      M Kartik    stumped      KD Karthik   70
## 8    MN Samuels     bowled        SA Yadav   21
## 9      R Sharma     bowled      KA Pollard   64
## 10     AB Dinda     caught    JEC Franklin  150
## 11   WD Parnell     caught      SL Malinga   64
## 12     AB Dinda        lbw Harbhajan Singh  150
## 13 Yuvraj Singh     caught      RT Ponting   61
## 14     AJ Finch     caught    SR Tendulkar   11
## 15     MR Marsh        lbw      KD Karthik   24
## 16    AC Thomas     caught     AC Blizzard   69
## 17 Yuvraj Singh     caught    SR Tendulkar   61
## 18 Yuvraj Singh     caught       AT Rayudu   61
## 19     R Sharma     caught       RG Sharma   64
## 20     R Sharma     caught        TL Suman   64
## 21    JE Taylor     caught       A Symonds   34
## 22    JE Taylor     caught      KA Pollard   34
## 23      B Kumar     caught    JEC Franklin   50
## 24    MJ Clarke    run out       RG Sharma    9
## 25      A Nehra     caught    SR Tendulkar   19
## 26      A Nehra     caught     RJ Peterson   19
## 27      B Kumar     bowled       AT Rayudu   50
## 28      A Nehra    run out     NLTC Perera   19
## 29     AB Dinda     caught Harbhajan Singh  150
## 30   WD Parnell    run out      SL Malinga   64
teamBowlersWicketKindOppnAllMatches(dc_kkr_matches,"Kolkata Knight Riders",'Deccan Chargers',plot=TRUE)

bowlerWickets-2

13. Team bowler’s wicket taken and runs conceded in Twenty20(all matches with opposing IPL team)

teamBowlersWicketRunsOppnAllMatches(csk_ktk_matches,"Kochi Tuskers Kerala","Chennai Super Kings")

wicketRuns-1

m <-teamBowlersWicketRunsOppnAllMatches(mi_pw_matches,"Mumbai Indians","Pune Warriors",plot=FALSE)
m[1:30,]
## Source: local data frame [30 x 5]
## 
##             bowler overs maidens  runs wickets
##             (fctr) (int)   (int) (dbl)   (dbl)
## 1       AG Murtaza     4       0    18       2
## 2       SL Malinga     9       1   143      10
## 3         AN Ahmed     5       0    40       4
## 4         MM Patel     6       1    88       7
## 5       KA Pollard     6       0    99       5
## 6     JEC Franklin     4       0    64       1
## 7  Harbhajan Singh     7       0    85       6
## 8          PP Ojha     8       0    95       4
## 9       MG Johnson     5       0    41       4
## 10        R Dhawan     1       0    27       0
## ..             ...   ...     ...   ...     ...

14. Plot of wins vs losses between teams in IPL T20 confrontations

setwd("C:/software/cricket-package/york-test/yorkrData/IPL/IPL-T20-matches")
plotWinLossBetweenTeams("Chennai Super Kings","Delhi Daredevils")

winsLosses-1

plotWinLossBetweenTeams("Deccan Chargers","Kolkata Knight Riders",".")

winsLosses-2

plotWinLossBetweenTeams('Kings XI Punjab',"Royal Challengers Bangalore",".")

winsLosses-3

plotWinLossBetweenTeams("Mumbai Indians","Pune Warriors",".")

winsLosses-4

plotWinLossBetweenTeams('Rajasthan Royals',"Sunrisers Hyderabad",".")

winsLosses-5

plotWinLossBetweenTeams('Chennai Super Kings',"Mumbai Indians",".")

winsLosses-6

Conclusion

This post included all functions for all IPL Twenty20 matches between any 2 IPL teams. 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!

You may also like

  1. yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance
  2. yorkr pads up for the Twenty20s:Part 4- Individual batting and bowling performances
  3. Introducing cricket package yorkr: Part 2-Trapped leg before wicket!
  4. Introducing cricket package yorkr:Part 4-In the block hole!
  5. Introducing cricketr! : An R package to analyze performances of cricketers
  6. Cricket analytics with cricketr
  7. OpenCV: Fun with filters and convolution
  8. To Hadoop, or not to Hadoop
  9. Close encounters with the future
  10. Presentation on ‘Evolution to LTE’

yorkr crashes the IPL party ! – Part 1


 

Where tireless striving stretches its arms towards perfection

Where the clear stream of reason has not lost its way

Into the dreary desert sand of dead habit

                Rabindranath Tagore

Introduction

In this post, my R package yorkr crashes the IPL party! In my earlier posts I had already created functions for handling Twenty20 matches. I now use these functions to analyze the IPL T20 matches. This package is based on data from Cricsheet. The T20 functionality were added in the following posts

  1. yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
  2. yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams
  3. yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions!
  4. yorkr pads up for the Twenty20s:Part 4- Individual batting and bowling performances

The yorkr package provides functions to convert the yaml files to more easily R consumable entities, namely dataframes. All converted files for ODI,T20 and IPL are available for use at yorkrData.

The IPL T20 matches can be downloaded from IPL-T20-matches

This post can be viewed at RPubs at yorkrIPLT20-Part1 or can also be downloaded as a PDF document yorkrIPLT20-1.pdf

2. Install the package from CRAN

library(yorkr)
rm(list=ls())

2a. New functionality for Twenty20

The functions that were used to convert the Twenty20 yaml files to RData are

  1. convertYaml2RDataframeT20
  2. convertAllYaml2RDataframesT20

Note 1: While I have already converted the IPL T20 files, you will need to use these functions for future IPL matches

Note 2: This post includes some cosmetic changes made over yorkr_0.0.4, where I make the plot title more explicit. The functionality will be available in a few weeks from now in yorkr_0.0.5

3. Convert and save T20 yaml file to dataframe

This function will convert a T20 IPL yaml file, in the format as specified in Cricsheet to dataframe. This will be saved as as RData file in the target directory. The name of the file wil have the following format team1-team2-date.RData. An example of how a yaml file can be converted to a dataframe and saved is shown below.

convertYaml2RDataframeT20("335982.yaml",".",".") 
## [1] "./335982.yaml"
## [1] "first loop"
## [1] "second loop"

4. Convert and save all T20 yaml files to dataframes

This function will convert all IPL T20 yaml files from a source directory to dataframes, and save it in the target directory, with the names as mentioned above. Since I have already done this, I will not be executing this again. You can download the zip of all the converted RData files from Github at IPL-T20-matches

#convertAllYaml2RDataframesT20("./IPL","./data")

5. yorkrData – A Github repositiory

Cricsheet had a total of 518 IPL Twenty20 matches. Out of which 9 files seemed to have problem. The remaining 509 T20 matches have been converted to RData.

All the converted RData files can be accessed from my Github link yorkrData under the folder IPL-T20-matches

You can download the the zip of the files and use it directly in the functions as follows

6. Load the match data as dataframes

For this post I will be using the IPL Twenty20 match data from 5 random matches between 10 different opposing IPL teams. For this I will directly use the converted RData files rather than getting the data through the getMatchDetails() as shown below

With the RData we can load the data in 2 ways

A. With getMatchDetails()

  1. With getMatchDetails() using the 2 teams and the date on which the match occured
sh_mi <- getMatchDetails("Sunrisers Hyderabad","Royal Challengers Bangalore","2014-05-20",dir=".")
dim(sh_mi)
## [1] 244  25

or

B.Directly load RData into your code.

The match details will be loaded into a dataframe called ’overs’ which you can assign to a suitable name as below

The randomly selected IPL T20 matches are

  • Sunrisers Hyderabad vs Royal Challengers Bangalore, 2014-05-20
  • Rajasthan Royals vs Pune Warriors, 2013-05-05
  • Deccan Chargers vs Chennai Super Kings, 2008-05-27
  • Kings Xi Punjab vs Delhi Daredevils, 2014-05-25
  • Kolkata Knight Riders vs Mumbai Indian, 2014-05-14
setwd("C:/software/cricket-package/cricsheet/cleanup/IPL/part1")
load("Sunrisers Hyderabad-Royal Challengers Bangalore-2014-05-20.RData")
sh_rcb <- overs
load("Rajasthan Royals-Pune Warriors-2013-05-05.RData")
rr_pw <- overs
load("Deccan Chargers-Chennai Super Kings-2008-05-27.RData")
dc_csk <- overs
load("Kings XI Punjab-Delhi Daredevils-2014-05-25.RData")
kxp_dd <-overs
load("Kolkata Knight Riders-Mumbai Indians-2014-05-14.RData")
kkr_mi <- overs

7. Team batting scorecard

Compute and display the batting scorecard of the teams in the match.

teamBattingScorecardMatch(kkr_mi,'Mumbai Indians')
## Total= 134
## Source: local data frame [7 x 5]
## 
##       batsman ballsPlayed fours sixes  runs
##        (fctr)       (int) (dbl) (dbl) (dbl)
## 1 LMP Simmons          13     2     0    12
## 2   CM Gautam           9     1     0     8
## 3   AT Rayudu          26     3     1    33
## 4   RG Sharma          45     4     2    51
## 5 CJ Anderson          12     1     1    18
## 6  KA Pollard          11     0     0    10
## 7     AP Tare           3     0     0     2
teamBattingScorecardMatch(kkr_mi,'Kolkata Knight Riders')
## Total= 137
## Source: local data frame [5 x 5]
## 
##           batsman ballsPlayed fours sixes  runs
##            (fctr)       (int) (dbl) (dbl) (dbl)
## 1      RV Uthappa          52     9     3    80
## 2       G Gambhir          17     1     0    14
## 3       MK Pandey          21     0     0    14
## 4       YK Pathan          13     3     0    20
## 5 Shakib Al Hasan           8     1     0     9
teamBattingScorecardMatch(sh_rcb,'Sunrisers Hyderabad')
## Total= 154
## Source: local data frame [5 x 5]
## 
##     batsman ballsPlayed fours sixes  runs
##      (fctr)       (int) (dbl) (dbl) (dbl)
## 1  S Dhawan          39     7     1    50
## 2 DA Warner          43     3     4    59
## 3   NV Ojha          19     0     2    24
## 4  AJ Finch           9     1     0    11
## 5 DJG Sammy           4     0     1    10
teamBattingScorecardMatch(rr_pw,'Pune Warriors')
## Total= 167
## Source: local data frame [5 x 5]
## 
##        batsman ballsPlayed fours sixes  runs
##         (fctr)       (int) (int) (dbl) (dbl)
## 1   RV Uthappa          41     8     1    54
## 2     AJ Finch          32     7     0    45
## 3 Yuvraj Singh          11     1     1    15
## 4     MR Marsh          21     2     3    35
## 5   AD Mathews          15     2     0    18
teamBattingScorecardMatch(dc_csk,'Chennai Super Kings')
## Total= 137
## Source: local data frame [5 x 5]
## 
##      batsman ballsPlayed fours sixes  runs
##       (fctr)       (int) (int) (dbl) (dbl)
## 1   PA Patel          27     3     0    20
## 2 SP Fleming           9     3     0    14
## 3   SK Raina          41     5     2    54
## 4   MS Dhoni          24     4     1    37
## 5  JA Morkel          12     1     0    12
teamBattingScorecardMatch(kxp_dd,'Kings XI Punjab')
## Total= 104
## Source: local data frame [5 x 5]
## 
##      batsman ballsPlayed fours sixes  runs
##       (fctr)       (int) (dbl) (dbl) (dbl)
## 1   V Sehwag           7     2     0     9
## 2    M Vohra          37     4     2    47
## 3 GJ Maxwell           2     0     0     0
## 4  DA Miller          34     4     2    47
## 5  GJ Bailey           1     0     0     1
teamBattingScorecardMatch(kkr_mi,'Mumbai Indians')
## Total= 134
## Source: local data frame [7 x 5]
## 
##       batsman ballsPlayed fours sixes  runs
##        (fctr)       (int) (dbl) (dbl) (dbl)
## 1 LMP Simmons          13     2     0    12
## 2   CM Gautam           9     1     0     8
## 3   AT Rayudu          26     3     1    33
## 4   RG Sharma          45     4     2    51
## 5 CJ Anderson          12     1     1    18
## 6  KA Pollard          11     0     0    10
## 7     AP Tare           3     0     0     2

8. Plot the team batting partnerships

The functions below plot the team batting partnership in the match Note: Many of the plots include an additional parameters plot which is either TRUE or FALSE. The default value is plot=TRUE. When plot=TRUE the plot will be displayed. When plot=FALSE the data frame will be returned to the user. The user can use this to create an interactive chary using one of the packages like rcharts, ggvis,googleVis or plotly.

teamBatsmenPartnershipMatch(kkr_mi,'Mumbai Indians','Kolkata Knight Riders')

batsmenPartnership-1

teamBatsmenPartnershipMatch(sh_rcb,'Sunrisers Hyderabad','Royal Challengers Bangalore',plot=TRUE)

batsmenPartnership-2

teamBatsmenPartnershipMatch(rr_pw,'Pune Warriors','Rajasthan Royals')

batsmenPartnership-3

teamBatsmenPartnershipMatch(dc_csk,'Chennai Super Kings','Deccan Chargers',plot=FALSE)
##      batsman nonStriker runs
## 1   PA Patel SP Fleming   10
## 2   PA Patel   SK Raina   10
## 3 SP Fleming   PA Patel   14
## 4   SK Raina   PA Patel   19
## 5   SK Raina   MS Dhoni   14
## 6   SK Raina  JA Morkel   21
## 7   MS Dhoni   SK Raina   37
## 8  JA Morkel   SK Raina   12
teamBatsmenPartnershipMatch(kxp_dd,'Kings XI Punjab','Delhi Daredevils',plot=TRUE)

batsmenPartnership-4

9. Batsmen vs Bowler

The function below computes and plots the performances of the batsmen vs the bowlers. As before the plot parameter can be set to TRUE or FALSE. By default it is plot=TRUE

teamBatsmenVsBowlersMatch(sh_rcb,"Sunrisers Hyderabad","Royal Challengers Bangalore", plot=TRUE)

batsmenVsBowler-1

teamBatsmenVsBowlersMatch(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')

batsmenVsBowler-2

m <- teamBatsmenVsBowlersMatch(rr_pw,'Pune Warriors','Rajasthan Royals',plot=FALSE)
m
## Source: local data frame [20 x 3]
## Groups: batsman [?]
## 
##         batsman      bowler runsConceded
##          (fctr)      (fctr)        (dbl)
## 1    RV Uthappa  A Chandila           12
## 2    RV Uthappa JP Faulkner            1
## 3    RV Uthappa   SR Watson           13
## 4    RV Uthappa   KK Cooper            2
## 5    RV Uthappa  SK Trivedi           18
## 6    RV Uthappa   STR Binny            8
## 7      AJ Finch  A Chandila           11
## 8      AJ Finch JP Faulkner           12
## 9      AJ Finch   SR Watson            5
## 10     AJ Finch   KK Cooper            8
## 11     AJ Finch  SK Trivedi            9
## 12 Yuvraj Singh   KK Cooper            0
## 13 Yuvraj Singh  SK Trivedi            5
## 14 Yuvraj Singh   STR Binny           10
## 15     MR Marsh JP Faulkner           13
## 16     MR Marsh   SR Watson            7
## 17     MR Marsh   KK Cooper           15
## 18   AD Mathews JP Faulkner            7
## 19   AD Mathews   SR Watson            3
## 20   AD Mathews   KK Cooper            8
teamBatsmenVsBowlersMatch(dc_csk,"Chennai Super Kings","Deccan Chargers")

batsmenVsBowler-3

teamBatsmenVsBowlersMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils")

batsmenVsBowler-4

10. Bowling Scorecard

This function provides the bowling performance, the number of overs bowled, maidens, runs conceded and wickets taken for each match

teamBowlingScorecardMatch(kkr_mi,'Kolkata Knight Riders')
## Source: local data frame [6 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1        M Morkel     4       0    35       2
## 2        UT Yadav     3       0    24       0
## 3 Shakib Al Hasan     4       0    21       1
## 4       SP Narine     4       0    18       1
## 5       PP Chawla     4       0    32       1
## 6       YK Pathan     1       0    10       0
teamBowlingScorecardMatch(kkr_mi,'Mumbai Indians')
## Source: local data frame [6 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1      SL Malinga     4       0    30       1
## 2       JJ Bumrah     3       0    23       0
## 3 Harbhajan Singh     4       0    22       2
## 4         PP Ojha     4       0    25       0
## 5     LMP Simmons     3       0    34       1
## 6      KA Pollard     1       0     7       0
teamBowlingScorecardMatch(sh_rcb,"Sunrisers Hyderabad")
## Source: local data frame [7 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1         B Kumar     4       0    27       2
## 2        DW Steyn     4       0    23       1
## 3   Parvez Rasool     4       0    26       1
## 4       KV Sharma     3       0    27       1
## 5 Y Venugopal Rao     1       0     7       0
## 6       IK Pathan     3       0    28       1
## 7       DJG Sammy     1       0    19       0
teamBowlingScorecardMatch(rr_pw,'Pune Warriors')
## Source: local data frame [6 x 5]
## 
##         bowler overs maidens  runs wickets
##         (fctr) (int)   (int) (dbl)   (dbl)
## 1      B Kumar     4       0    38       1
## 2   K Upadhyay     3       0    29       0
## 3   WD Parnell     4       0    27       3
## 4     R Sharma     4       0    38       0
## 5 Yuvraj Singh     2       0    16       0
## 6   AD Mathews     3       0    34       1
teamBowlingScorecardMatch(dc_csk,"Chennai Super Kings")
## Source: local data frame [5 x 5]
## 
##           bowler overs maidens  runs wickets
##           (fctr) (int)   (int) (dbl)   (int)
## 1        M Ntini     4       0    24       1
## 2        MS Gony     4       0    21       1
## 3      JA Morkel     4       0    37       3
## 4 M Muralitharan     4       0    22       1
## 5       L Balaji     4       0    34       2
teamBowlingScorecardMatch(kxp_dd,"Kings XI Punjab")
## Source: local data frame [5 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (int)
## 1         P Awana     3       1    15       2
## 2        AR Patel     4       0    28       2
## 3      MG Johnson     4       1    27       2
## 4 Karanveer Singh     4       0    22       2
## 5        R Dhawan     4       0    22       2

11. Wicket Kind

The plots below provide the bowling kind of wicket taken by the bowler (caught, bowled, lbw etc.)

teamBowlingWicketKindMatch(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')

bowlingWicketKind-1

m <- teamBowlingWicketKindMatch(rr_pw,'Pune Warriors','Rajasthan Royals',plot=FALSE)
m
##         bowler wicketKind wicketPlayerOut runs
## 1   AD Mathews     caught        R Dravid   34
## 2   WD Parnell     bowled       SR Watson   27
## 3      B Kumar     caught       AM Rahane   38
## 4   WD Parnell     caught        BJ Hodge   27
## 5   WD Parnell     caught       SV Samson   27
## 6   K Upadhyay   noWicket        noWicket   29
## 7     R Sharma   noWicket        noWicket   38
## 8 Yuvraj Singh   noWicket        noWicket   16
teamBowlingWicketKindMatch(dc_csk,"Chennai Super Kings","Deccan Chargers")

bowlingWicketKind-2

teamBowlingWicketKindMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils",plot=TRUE)

bowlingWicketKind-3

teamBowlingWicketKindMatch(sh_rcb,"Royal Challengers Bangalore","Sunrisers Hyderabad")

bowlingWicketKind-4

12. Wicket vs Runs conceded

The plots below provide the wickets taken and the runs conceded by the bowler in the match

teamBowlingWicketRunsMatch(dc_csk,"Deccan Chargers", "Chennai Super Kings")

wicketRuns-1

teamBowlingWicketRunsMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils",plot=TRUE)

wicketRuns-2

teamBowlingWicketRunsMatch(sh_rcb,"Sunrisers Hyderabad","Royal Challengers Bangalore")

wicketRuns-3

teamBowlingWicketRunsMatch(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')

wicketRuns-4

m <- teamBowlingWicketKindMatch(rr_pw,'Pune Warriors','Rajasthan Royals',plot=FALSE)
m
##         bowler wicketKind wicketPlayerOut runs
## 1   AD Mathews     caught        R Dravid   34
## 2   WD Parnell     bowled       SR Watson   27
## 3      B Kumar     caught       AM Rahane   38
## 4   WD Parnell     caught        BJ Hodge   27
## 5   WD Parnell     caught       SV Samson   27
## 6   K Upadhyay   noWicket        noWicket   29
## 7     R Sharma   noWicket        noWicket   38
## 8 Yuvraj Singh   noWicket        noWicket   16

13. Wickets taken by bowler

The plots provide the wickets taken by the bowler

teamBowlingWicketMatch(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')

bowlingWickets-1

m <- teamBowlingWicketMatch(rr_pw,'Pune Warriors','Rajasthan Royals',plot=FALSE)
m
##         bowler wicketKind wicketPlayerOut runs
## 1   AD Mathews     caught        R Dravid   34
## 2   WD Parnell     bowled       SR Watson   27
## 3      B Kumar     caught       AM Rahane   38
## 4   WD Parnell     caught        BJ Hodge   27
## 5   WD Parnell     caught       SV Samson   27
## 6   K Upadhyay   noWicket        noWicket   29
## 7     R Sharma   noWicket        noWicket   38
## 8 Yuvraj Singh   noWicket        noWicket   16
teamBowlingWicketMatch(sh_rcb,"Royal Challengers Bangalore","Sunrisers Hyderabad")

bowlingWickets-2

teamBowlingWicketMatch(dc_csk,"Deccan Chargers", "Chennai Super Kings")

bowlingWickets-3

teamBowlingWicketMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils",plot=TRUE)

bowlingWickets-4

14. Bowler Vs Batsmen

The functions compute and display how the different bowlers of the country performed against the batting opposition.

teamBowlersVsBatsmenMatch(dc_csk,"Deccan Chargers", "Chennai Super Kings")

bowlerVsBatsmen-1

teamBowlersVsBatsmenMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils",plot=TRUE)

bowlerVsBatsmen-2

m <-teamBowlersVsBatsmenMatch(sh_rcb,"Sunrisers Hyderabad","Royal Challengers Bangalore",plot=FALSE)
m
## Source: local data frame [26 x 3]
## Groups: bowler [?]
## 
##      bowler        batsman runsConceded
##      (fctr)         (fctr)        (dbl)
## 1   B Kumar       CH Gayle            5
## 2   B Kumar       PA Patel            4
## 3   B Kumar        V Kohli            6
## 4   B Kumar AB de Villiers            6
## 5   B Kumar         S Rana            1
## 6   B Kumar       MA Starc            5
## 7  DW Steyn       CH Gayle            7
## 8  DW Steyn        V Kohli            4
## 9  DW Steyn AB de Villiers            4
## 10 DW Steyn         S Rana            7
## ..      ...            ...          ...
teamBowlersVsBatsmenMatch(rr_pw,'Pune Warriors','Rajasthan Royals')

bowlerVsBatsmen-3

teamBowlersVsBatsmenMatch(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')

bowlerVsBatsmen-4

15. Match worm graph

The plots below provide the match worm graph for the IPL Twenty 20 matches

matchWormGraph(dc_csk,"Deccan Chargers", "Chennai Super Kings")

matchWorm-1

matchWormGraph(kxp_dd,"Kings XI Punjab","Delhi Daredevils")

matchWorm-2

matchWormGraph(sh_rcb,"Sunrisers Hyderabad","Royal Challengers Bangalore")

matchWorm-3

matchWormGraph(rr_pw,'Pune Warriors','Rajasthan Royals')

matchWorm-4

matchWormGraph(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')

matchWorm-5

Conclusion

This post included all functions between 2 IPL teams from the package yorkr for IPL Twenty20 matches.As mentioned above the yaml match files have been already converted to dataframes and are available for download from Github. Go ahead and give it a try

To be continued. Watch this space!

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yorkr pads up for Twenty20s:Part 4- Individual batting and bowling performances!


Introduction

In theory, theory and practice are the same. In practice, they’re not.

                      Yogi Berra

There are two ways to write error-free programs; only the third one works.

                      Alan Perlis

Simplicity does not precede complexity, but follows it.

                      Alan Perlis

Talk is cheap. Show me the code.

                      Linus Torvalds

This post is the 4th and the last part of yorkr padding for the Twenty20s. In this post I look at the top individual batting and bowling performances in the Twenty20s. Also please take a look at my 3 earlier post on yorkr’s handling of Twenty20 matches

  1. yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
  2. yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams
  3. yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions!

The 1st part included functions dealing with a specific T20 match, the 2nd part dealt with functions between 2 opposing teams in T20 confrontations. The 3rd part dealt with functions between a team and all T20 matches with all oppositions. This 4th part includes individual batting and bowling performances in T20 matches and deals with Class 4 functions.

This post has also been published at RPubs yorkrT20-Part4 and can also be downloaded as a PDF document from yorkrT20-Part4.pdf.

You can clone/fork the code for the package yorkr from Github at yorkr-package

The list of Class 4 functions are shown below.The Twenty20 features will be available from yorkr_0.0.4

Batsman functions

  1. batsmanRunsVsDeliveries
  2. batsmanFoursSixes
  3. batsmanDismissals
  4. batsmanRunsVsStrikeRate
  5. batsmanMovingAverage
  6. batsmanCumulativeAverageRuns
  7. batsmanCumulativeStrikeRate
  8. batsmanRunsAgainstOpposition
  9. batsmanRunsVenue
  10. batsmanRunsPredict

Bowler functions

  1. bowlerMeanEconomyRate
  2. bowlerMeanRunsConceded
  3. bowlerMovingAverage
  4. bowlerCumulativeAvgWickets
  5. bowlerCumulativeAvgEconRate
  6. bowlerWicketPlot
  7. bowlerWicketsAgainstOpposition
  8. bowlerWicketsVenue
  9. bowlerWktsPredict

Note: The yorkr package in its current avatar only supports ODI & Twenty20 matches. I will be upgrading the package to handle IPL in the months to come.

library(yorkr)
library(gridExtra)
library(rpart.plot)
library(dplyr)
library(ggplot2)
rm(list=ls())

A. Batsman functions

1. Get Team Batting details

The function below gets the overall team batting details based on the RData file available in T20 matches. This is currently also available in Github at [yorkrData] (https://github.com/tvganesh/yorkrData/tree/master/Twenty20/T20-matches). The batting details of the team in each match is created and a huge data frame is created by rbinding the individual dataframes. This can be saved as a RData file

setwd("C:/software/cricket-package/york-test/yorkrData/Twenty20/T20-matches")
india_details <- getTeamBattingDetails("India",dir=".", save=TRUE)
sa_details <- getTeamBattingDetails("South Africa",dir=".",save=TRUE)
nz_details <- getTeamBattingDetails("New Zealand",dir=".",save=TRUE)
eng_details <- getTeamBattingDetails("England",dir=".",save=TRUE)
pak_details <- getTeamBattingDetails("Pakistan",dir=".",save=TRUE)
aus_details <- getTeamBattingDetails("Australia",dir=".",save=TRUE)
wi_details <- getTeamBattingDetails("West Indies",dir=".",save=TRUE)

2. Get batsman details

This function is used to get the individual T20 batting record for a the specified batsman of the country as in the functions below. For analyzing the batting performances the top T20 batsmen from different countries have been chosen. The batting scorecard functions from yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions! was used for selecting these batsmen

  1. Virat Kohli (Ind)
  2. DA Warner (Aus)
  3. Umar Akmal (Pak)
  4. BB McCullum (NZ)
  5. EJG Morgan (Eng)
  6. CH Gayle (WI)
setwd("C:/software/cricket-package/cricsheet/cleanup/T20/rmd/part4")
kohli <- getBatsmanDetails(team="India",name="Kohli",dir=".")
## [1] "./India-BattingDetails.RData"
warner <- getBatsmanDetails(team="Australia",name="DA Warner")
## [1] "./Australia-BattingDetails.RData"
akmal <-  getBatsmanDetails(team="Pakistan",name="Umar Akmal",dir=".")
## [1] "./Pakistan-BattingDetails.RData"
mccullum <-  getBatsmanDetails(team="New Zealand",name="BB McCullum",dir=".")
## [1] "./New Zealand-BattingDetails.RData"
emorgan <-  getBatsmanDetails(team="England",name="EJG Morgan",dir=".")
## [1] "./England-BattingDetails.RData"
gayle <-  getBatsmanDetails(team="West Indies",name="CH Gayle",dir=".")
## [1] "./West Indies-BattingDetails.RData"

3. Runs versus deliveries

Chris Gayle and B McCullum have an astounding strike rate and touch close to 120 runs in 60 balls. David Warner also has a great strike rate

p1 <-batsmanRunsVsDeliveries(kohli,"Kohli")
p2 <-batsmanRunsVsDeliveries(warner, "DA Warner")
p3 <-batsmanRunsVsDeliveries(akmal,"U Akmal")
p4 <-batsmanRunsVsDeliveries(mccullum,"BB McCullum")
p5 <-batsmanRunsVsDeliveries(emorgan,"EJG Morgan")
p6 <-batsmanRunsVsDeliveries(gayle,"CH Gayle")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

runsVsDeliveries-1

4. Batsman Total runs, Fours and Sixes

The plots below show the total runs, fours and sixes by the batsmen. Gayle tops in the runs from sixes

kohli46 <- select(kohli,batsman,ballsPlayed,fours,sixes,runs)
p1 <- batsmanFoursSixes(kohli46,"Kohli")
warner46 <- select(warner,batsman,ballsPlayed,fours,sixes,runs)
p2 <- batsmanFoursSixes(warner46,"DA Warner")
akmal46 <- select(akmal,batsman,ballsPlayed,fours,sixes,runs)
p3 <- batsmanFoursSixes(akmal46, "U Akmal")
mccullum46 <- select(mccullum,batsman,ballsPlayed,fours,sixes,runs)
p4 <- batsmanFoursSixes(mccullum46,"BB McCullum")
emorgan46 <- select(emorgan,batsman,ballsPlayed,fours,sixes,runs)
p5 <- batsmanFoursSixes(emorgan46,"EJG Morgan")
gayle46 <- select(gayle,batsman,ballsPlayed,fours,sixes,runs)
p6 <- batsmanFoursSixes(gayle46,"CH Gayle")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

foursSixes-1

5. Batsman dismissals

The type of dismissal for each batsman is shown below

p1 <-batsmanDismissals(kohli,"Kohli")
p2 <-batsmanDismissals(warner, "DA Warner")
p3 <-batsmanDismissals(akmal,"U Akmal")
p4 <-batsmanDismissals(mccullum,"BB McCullum")
p5 <-batsmanDismissals(emorgan,"EJG Morgan")
p6 <-batsmanDismissals(gayle,"CH Gayle")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

dismissal-1

6. Runs versus Strike Rate

Gayle’s and McCullum’s strike rate touch 120% for runs in the range of 130-150

p1 <-batsmanRunsVsStrikeRate(kohli,"Kohli")
p2 <-batsmanRunsVsStrikeRate(warner, "DA Warner")
p3 <-batsmanRunsVsStrikeRate(akmal,"U Akmal")
p4 <-batsmanRunsVsStrikeRate(mccullum,"BB McCullum")
p5 <-batsmanRunsVsStrikeRate(emorgan,"EJG Morgan")
p6 <-batsmanRunsVsStrikeRate(gayle,"CH Gayle")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

runsSR-1

7. Batsman moving average

Kohli and Gayle T20 average is on the increase touching 50. Eoin Morgan and BB McCullum average around 40.

p1 <-batsmanMovingAverage(kohli,"Kohli")
p2 <-batsmanMovingAverage(warner, "DA Warner")
p3 <-batsmanMovingAverage(akmal,"U Akmal")
p4 <-batsmanMovingAverage(mccullum,"BB McCullum")
p5 <-batsmanMovingAverage(emorgan,"EJG Morgan")
p6 <-batsmanMovingAverage(gayle,"CH Gayle")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

ma-1

8. Batsman cumulative average

Kohli’s cumulative average steadies around 40, McCullum shows a gentle decline from 40+ to 35+. Gayle oscillates between 30+ to 40-.

p1 <-batsmanCumulativeAverageRuns(kohli,"Kohli")
p2 <-batsmanCumulativeAverageRuns(warner, "DA Warner")
p3 <-batsmanCumulativeAverageRuns(akmal,"U Akmal")
p4 <-batsmanCumulativeAverageRuns(mccullum,"BB McCullum")
p5 <-batsmanCumulativeAverageRuns(emorgan,"EJG Morgan")
p6 <-batsmanCumulativeAverageRuns(gayle,"CH Gayle")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cAvg-1

9. Cumulative Average Strike Rate

BB McCullum has the best overall cumulative strike rate which hovered around the 150 and steadies around 130. Gayle has a rocky cumulative strike between 150 -130s. Warner is steady around 120.

p1 <-batsmanCumulativeStrikeRate(kohli,"Kohli")
p2 <-batsmanCumulativeStrikeRate(warner, "DA Warner")
p3 <-batsmanCumulativeStrikeRate(akmal,"U Akmal")
p4 <-batsmanCumulativeStrikeRate(mccullum,"BB McCullum")
p5 <-batsmanCumulativeStrikeRate(emorgan,"EJG Morgan")
p6 <-batsmanCumulativeStrikeRate(gayle,"CH Gayle")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cSR-1

10. Batsman runs against opposition

#Kohli's best performances are against New Zealand and Sri Lanka
batsmanRunsAgainstOpposition(kohli,"Kohli")

runsOppn1-1

batsmanRunsAgainstOpposition(warner, "DA Warner")

runsOppn2-1

batsmanRunsAgainstOpposition(akmal,"U Akmal")

runsOppn3-1

batsmanRunsAgainstOpposition(mccullum,"BB McCullum")

runsOppn4-1

batsmanRunsAgainstOpposition(emorgan,"EJG Morgan")

runsOppn5-1

# Gayle's best performance is against India and South Africa
batsmanRunsAgainstOpposition(gayle,"CH Gayle")

runsOppn6-1

11. Runs at different venues

The plots below give the performances of the batsmen at different grounds.

batsmanRunsVenue(kohli,"Kohli")

runsVenue1-1

batsmanRunsVenue(warner, "DA Warner")

runsVenue2-1

batsmanRunsVenue(akmal,"U Akmal")

runsVenue3-1

batsmanRunsVenue(mccullum,"BB McCullum")

runsVenue4-1

batsmanRunsVenue(emorgan,"EJG Morgan")

runsVenue5-1

batsmanRunsVenue(gayle,"CH Gayle")

runsVenue6-1

12. Predict number of runs to deliveries

The plots below use rpart classification tree to predict the number of deliveries required to score the runs in the leaf node. For e.g. Kohli takes <32 deliveries to score 22 runs and for higher number of deliveries scores around 66 runs. Devilliers needs <94 deliveries to score 84 runs and for greater deliveries scores around 109runs

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsPredict(kohli,"Kohli")
batsmanRunsPredict(warner, "DA Warner")
batsmanRunsPredict(akmal,"U Akmal")

runsPredict1,runsVenue1-1

# BB McCullum needs >32 deliveries to score 69+ runs while Gayle needs >28 deliveries to score 67 runs
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsPredict(mccullum,"BB McCullum")
batsmanRunsPredict(emorgan,"EJG Morgan")
batsmanRunsPredict(gayle,"CH Gayle")

runsPredict2,runsVenue1-1

B. Bowler functions

13. Get bowling details

The function below gets the overall team T20 bowling details based on the RData file available in T20 matches. This is currently also available in Github at [yorkrData] (https://github.com/tvganesh/yorkrData/tree/master/Twenty20/T20-matches). The T20 bowling details of the team in each match is created and a huge data frame is created by rbinding the individual dataframes. This can be saved as a RData file

setwd("C:/software/cricket-package/york-test/yorkrData/Twenty20/T20-matches")
ind_bowling <- getTeamBowlingDetails("India",dir=".",save=TRUE)
dim(ind_bowling)
## [1] 872  12
aus_bowling <- getTeamBowlingDetails("Australia",dir=".",save=TRUE)
dim(aus_bowling)
## [1] 1364   12
eng_bowling <- getTeamBowlingDetails("England",dir=".",save=TRUE)
dim(eng_bowling)
## [1] 1183   12
sa_bowling <- getTeamBowlingDetails("South Africa",dir=".",save=TRUE)
dim(sa_bowling)
## [1] 995  12
pak_bowling <- getTeamBowlingDetails("Pakistan",dir=".",save=TRUE)
dim(pak_bowling)
## [1] 1186   12
nz_bowling <- getTeamBowlingDetails("New Zealand",dir=".",save=TRUE)
dim(nz_bowling)
## [1] 1295   12

14. Get bowling details of the individual bowlers

This function is used to get the individual bowling record for a specified bowler of the country as in the functions below. For analyzing the bowling performances the following cricketers have been chosen

  1. Ravichander Ashwin (Ind)
  2. SR Watson (Aus)
  3. SCJ Broad (Eng)
  4. Saeed Ajmal (Pak)
  5. Dale Steyn (SA)
  6. NL McCullum (NZ)
ashwin <- getBowlerWicketDetails(team="India",name="Ashwin",dir=".")
watson <-  getBowlerWicketDetails(team="Australia",name="SR Watson",dir=".")
broad <-  getBowlerWicketDetails(team="England",name="SCJ Broad",dir=".")
ajmal <-  getBowlerWicketDetails(team="Pakistan",name="Saeed Ajmal",dir=".")
steyn <-  getBowlerWicketDetails(team="South Africa",name="Steyn",dir=".")
nmccullum <-  getBowlerWicketDetails(team="New Zealand",name="NL McCullum",dir=".")

15. Bowler Mean Economy Rate

Ashwin has a mean economy rate of 5.0 for 3 & 4 overs. Saeed Ajmal is more expensive

p1<-bowlerMeanEconomyRate(ashwin,"R Ashwin")
p2<-bowlerMeanEconomyRate(watson, "SR Watson")
p3<-bowlerMeanEconomyRate(broad, "SCJ Broad")
p4<-bowlerMeanEconomyRate(ajmal, "Saeed Ajmal")
p5<-bowlerMeanEconomyRate(steyn, "D Steyn")
p6<-bowlerMeanEconomyRate(nmccullum, "NL Mccullum")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

meanER-1

16. Bowler Mean Runs conceded

p1<-bowlerMeanRunsConceded(ashwin,"R Ashwin")
p2<-bowlerMeanRunsConceded(watson, "SR Watson")
p3<-bowlerMeanRunsConceded(broad, "SCJ Broad")
p4<-bowlerMeanRunsConceded(ajmal, "Saeed Ajmal")
p5<-bowlerMeanRunsConceded(steyn, "D Steyn")
p6<-bowlerMeanRunsConceded(nmccullum, "NL Mccullum")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

meanRunsConceded-1

17. Bowler Moving average

Aswin, SCJ Broad and Steyn have an improving performance in T20s. NL McCullum has a drop and Ajmal’s performance is on the decline

p1<-bowlerMovingAverage(ashwin,"R Ashwin")
p2<-bowlerMovingAverage(watson, "SR Watson")
p3<-bowlerMovingAverage(broad, "SCJ Broad")
p4<-bowlerMovingAverage(ajmal, "Saeed Ajmal")
p5<-bowlerMovingAverage(steyn, "D Steyn")
p6<-bowlerMovingAverage(nmccullum, "NL Mccullum")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

bowlerMA-1

17. Bowler cumulative average wickets

Interestingly Ajmal and NL McCullum have a cumulative average wickets of around 2.0. Steyn also has a cumulative average of 2.0+

p1<-bowlerCumulativeAvgWickets(ashwin,"R Ashwin")
p2<-bowlerCumulativeAvgWickets(watson, "SR Watson")
p3<-bowlerCumulativeAvgWickets(broad, "SCJ Broad")
p4<-bowlerCumulativeAvgWickets(ajmal, "Saeed Ajmal")
p5<-bowlerCumulativeAvgWickets(steyn, "D Steyn")
p6<-bowlerCumulativeAvgWickets(nmccullum, "NL Mccullum")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cumWkts-1

18. Bowler cumulative Economy Rate (ER)

Ajmal’s economy rate deteriorates from a excellent rate of 5.5, while Ashwin’s economy rate improves from a terrible rate of 9.0+.

p1<-bowlerCumulativeAvgEconRate(ashwin,"R Ashwin")
p2<-bowlerCumulativeAvgEconRate(watson, "SR Watson")
p3<-bowlerCumulativeAvgEconRate(broad, "SCJ Broad")
p4<-bowlerCumulativeAvgEconRate(ajmal, "Saeed Ajmal")
p5<-bowlerCumulativeAvgEconRate(steyn, "D Steyn")
p6<-bowlerCumulativeAvgEconRate(nmccullum, "NL Mccullum")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

cumER-1

19. Bowler wicket plot

The plot below gives the average wickets versus number of overs

p1<-bowlerWicketPlot(ashwin,"R Ashwin")
p2<-bowlerWicketPlot(watson, "SR Watson")
p3<-bowlerWicketPlot(broad, "SCJ Broad")
p4<-bowlerWicketPlot(ajmal, "Saeed Ajmal")
p5<-bowlerWicketPlot(steyn, "D Steyn")
p6<-bowlerWicketPlot(nmccullum, "NL Mccullum")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)

wktPlot-1

20. Bowler wicket against opposition

#Ashwin's best pertformance are against South Africa,Sri Lanka, Bangaldesh and Afghanistan
bowlerWicketsAgainstOpposition(ashwin,"R Ashwin")

wktsOppn1-1

#Watson's bets pertformance are against England, Ireland and New Zealand
bowlerWicketsAgainstOpposition(watson, "SR Watson")

wktsOppn2-1

bowlerWicketsAgainstOpposition(broad, "SCJ Broad")

wktsOppn3-1

#Ajmal's best performances are against Sri Lanka, New Zealand and South Africa
bowlerWicketsAgainstOpposition(ajmal, "Saeed Ajmal")

wktsOppn4-1

#Steyn has good performances against New Zealand, Sri Lanka, Pakistan, West Indies
bowlerWicketsAgainstOpposition(steyn, "D Steyn")

wktsOppn5-1

bowlerWicketsAgainstOpposition(nmccullum, "NL Mccullum")

wktsOppn6-1

21. Bowler wicket at cricket grounds

bowlerWicketsVenue(ashwin,"R Ashwin")

wktsAve1-1

bowlerWicketsVenue(watson, "SR Watson")

wktsAve2-1

bowlerWicketsVenue(broad, "SCJ Broad")

wktsAve3-1

bowlerWicketsVenue(ajmal, "Saeed Ajmal")

wktsAve4-1

bowlerWicketsVenue(steyn, "D Steyn")

wktsAve5-1

bowlerWicketsVenue(nmccullum, "NL Mccullum")

wktsAve6-1

22. Get Delivery wickets for bowlers

This function creates a dataframe of deliveries and the wickets taken

setwd("C:/software/cricket-package/york-test/yorkrData/Twenty20/T20-matches")
ashwin1 <- getDeliveryWickets(team="India",dir=".",name="Ashwin",save=FALSE)
watson1 <- getDeliveryWickets(team="Australia",dir=".",name="SR Watson",save=FALSE)
broad1 <- getDeliveryWickets(team="England",dir=".",name="SCJ Broad",save=FALSE)
ajmal1 <- getDeliveryWickets(team="Pakistan",dir=".",name="Saeed Ajmal",save=FALSE)
steyn1 <- getDeliveryWickets(team="South Africa",dir=".",name="Steyn",save=FALSE)
nmccullum1 <- getDeliveryWickets(team="New Zealand",dir=".",name="NL McCullum",save=FALSE)

23. Predict number of deliveries to wickets

#Ashwin takes <12 deliveries for a wicket while Watson takes around 9 deliveries
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(ashwin1,"R Ashwin")
bowlerWktsPredict(watson1,"SR Watson")

wktsPred1-1

#Broad and Ajmal need around 8 deliveries for a wicket
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(broad1,"SCJ Broad")
bowlerWktsPredict(ajmal1,"Saeed Ajmal")

wktsPred2-1

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerWktsPredict(steyn1,"D Steyn")
bowlerWktsPredict(nmccullum1,"NL Mccullum")

wktsPred3-1

yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions!


Introduction

“So in war, the way is to avoid what is strong, and strike at what is weak.”

“Thus the expert in battle moves the enemy, and is not moved by him.”

“Appear weak when you are strong, and strong when you are weak.”

                                         The Art of War - Sun Tzu

This post is a continuation of my 2 earlier posts based on the enhancement of my R package yorkr to includ functions to handle Twenty20 matches. This is the 3rd part of the Twenty20 based functions, the 2 earlier ones were

  1. yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
  2. yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams

This post deals with Class 3 functions, namely the performances of a team in all T20 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.

This post has also been published at RPubs [yorkrT20-Part3]http://rpubs.com/tvganesh/yorkrT20-Part3) and can also be downloaded as a PDF document from yorkrT20-Part3.pdf.

You can clone/fork the code for the package yorkr from Github at yorkr-package

The list of functions in Class 3 are

  1. teamBattingScorecardAllOppnAllMatches()
  2. teamBatsmenPartnershipAllOppnAllMatches()
  3. teamBatsmenPartnershipAllOppnAllMatchesPlot()
  4. teamBatsmenVsBowlersAllOppnAllMatchesRept()
  5. teamBatsmenVsBowlersAllOppnAllMatchesPlot()
  6. teamBowlingScorecardAllOppnAllMatchesMain()
  7. teamBowlersVsBatsmenAllOppnAllMatchesRept()
  8. teamBowlersVsBatsmenAllOppnAllMatchesPlot()
  9. teamBowlingWicketKindAllOppnAllMatches()
  10. teamBowlingWicketRunsAllOppnAllMatches()

Note 1: The yorkr package in its current avatar only supports ODI & Twenty20 matches. I will be upgrading the package to handle IPL in the months to come.

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 where the individual T20 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/Twenty20/T20-team-allmatches-allOppositions")

# Get all matches against all oppositions for India and save as RData
matches <-getAllMatchesAllOpposition("India",dir=".",save=TRUE)
dim(matches)
## [1] 14380    25

“`

3. Save data for all matches against all oppositions

This can be done locally using the function below. This function gets all the T20 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

#saveAllMatchesAllOpposition()

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 T20-team-allmatches-allOppositions

Note: The dataframe for the different for all the matches of a country against all oppositons can be loaded directly into your code.Feel free to download the zip of the data and to perform any data mining on them.

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/Twenty20/T20-team-allmatches-allOppositions")
load("allMatchesAllOpposition-India.RData")
ind_matches <- matches
load("allMatchesAllOpposition-Australia.RData")
aus_matches <- matches
load("allMatchesAllOpposition-New Zealand.RData")
nz_matches <- matches
load("allMatchesAllOpposition-Pakistan.RData")
pak_matches <- matches
load("allMatchesAllOpposition-England.RData")
eng_matches <- matches
load("allMatchesAllOpposition-Sri Lanka.RData")
sl_matches <- matches
load("allMatchesAllOpposition-West Indies.RData")
wi_matches <- matches
load("allMatchesAllOpposition-South Africa.RData")
sa_matches <- matches

5. Team T20 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

#Top Twenty20 performers for India
m <-teamBattingScorecardAllOppnAllMatches(ind_matches,theTeam="India")
## Total= 8663
m
## Source: local data frame [46 x 5]
## 
##         batsman ballsPlayed fours sixes  runs
##          (fctr)       (int) (int) (int) (dbl)
## 1       V Kohli         882   124    27  1215
## 2      SK Raina         806   102    39  1114
## 3     RG Sharma         800    91    37  1053
## 4  Yuvraj Singh         656    59    60   933
## 5     G Gambhir         739   110    10   911
## 6      MS Dhoni         723    60    24   864
## 7      V Sehwag         228    36    13   330
## 8     AM Rahane         256    28     6   302
## 9    RV Uthappa         204    26     6   249
## 10     S Dhawan         193    28     8   248
## ..          ...         ...   ...   ...   ...
#Top Twenty20 batsmen for Australia
m <-teamBattingScorecardAllOppnAllMatches(aus_matches,theTeam="Australia")
## Total= 11743
m
## Source: local data frame [70 x 5]
## 
##       batsman ballsPlayed fours sixes  runs
##        (fctr)       (int) (int) (int) (dbl)
## 1   DA Warner        1030   139    67  1465
## 2   SR Watson         888   103    76  1315
## 3    CL White         726    71    44   984
## 4    AJ Finch         566    95    36   874
## 5   DJ Hussey         615    41    34   756
## 6  MEK Hussey         518    58    25   721
## 7   MJ Clarke         467    29    10   488
## 8   GJ Bailey         331    37    20   470
## 9   BJ Haddin         342    30    13   402
## 10 RT Ponting         294    42    11   401
## ..        ...         ...   ...   ...   ...
#Top Twenty20 batsmen for Pakistan
m <-teamBattingScorecardAllOppnAllMatches(pak_matches,theTeam="Pakistan")
## Total= 12943
m
## Source: local data frame [58 x 5]
## 
##            batsman ballsPlayed fours sixes  runs
##             (fctr)       (int) (int) (int) (dbl)
## 1       Umar Akmal        1184   112    48  1506
## 2  Mohammad Hafeez        1254   156    36  1466
## 3    Shahid Afridi         812    89    63  1255
## 4     Shoaib Malik        1068   101    23  1206
## 5    Ahmed Shehzad         799   102    24   941
## 6     Kamran Akmal         705    88    27   871
## 7    Misbah-ul-Haq         685    46    26   770
## 8      Imran Nazir         338    55    21   468
## 9      Salman Butt         410    58     6   467
## 10     Younis Khan         331    32    12   427
## ..             ...         ...   ...   ...   ...
#Top Twenty20 batsmen for New Zealand
m <-teamBattingScorecardAllOppnAllMatches(nz_matches,theTeam="New Zealand")
## Total= 11656
m
## Source: local data frame [62 x 5]
## 
##          batsman ballsPlayed fours sixes  runs
##           (fctr)       (int) (int) (int) (dbl)
## 1    BB McCullum        1501   199    89  2106
## 2     MJ Guptill        1259   155    68  1665
## 3    LRPL Taylor         900    76    45  1126
## 4  KS Williamson         650   101    10   844
## 5      SB Styris         443    38    25   554
## 6   JEC Franklin         371    28    23   463
## 7       JDP Oram         318    37    20   451
## 8       JD Ryder         336    45    16   434
## 9        C Munro         227    23    27   377
## 10      RJ Nicol         283    33    11   327
## ..           ...         ...   ...   ...   ...
#Top Twenty20 batsmen for England
m <-teamBattingScorecardAllOppnAllMatches(eng_matches,theTeam="England")
## Total= 11215
m
## Source: local data frame [65 x 5]
## 
##           batsman ballsPlayed fours sixes  runs
##            (fctr)       (int) (int) (int) (dbl)
## 1      EJG Morgan         938   108    53  1285
## 2    KP Pietersen         810   119    32  1176
## 3        AD Hales         790   116    35  1111
## 4       LJ Wright         540    68    31   759
## 5       RS Bopara         588    54    17   711
## 6  PD Collingwood         451    38    24   583
## 7      JC Buttler         410    43    23   562
## 8         MJ Lumb         394    64    21   552
## 9    C Kieswetter         456    47    23   526
## 10        OA Shah         276    26    13   347
## ..            ...         ...   ...   ...   ...
#Top Twenty20 batsmen for West Indies
m <-teamBattingScorecardAllOppnAllMatches(wi_matches,theTeam="West Indies")
## Total= 9292
m
## Source: local data frame [54 x 5]
## 
##          batsman ballsPlayed fours sixes  runs
##           (fctr)       (int) (int) (int) (dbl)
## 1       CH Gayle         958   122    87  1406
## 2       DJ Bravo         792    57    43   979
## 3     MN Samuels         764    76    46   952
## 4    LMP Simmons         633    68    25   732
## 5     KA Pollard         453    50    36   664
## 6       DR Smith         463    62    31   582
## 7      DJG Sammy         330    43    27   526
## 8      J Charles         382    59    12   456
## 9   ADS Fletcher         341    26    19   387
## 10 S Chanderpaul         337    34     5   343
## ..           ...         ...   ...   ...   ...
#Top Twenty20 batsmen for Sri Lanka
m <-teamBattingScorecardAllOppnAllMatches(sl_matches,theTeam="Sri Lanka")
## Total= 9572
m
## Source: local data frame [54 x 5]
## 
##             batsman ballsPlayed fours sixes  runs
##              (fctr)       (int) (int) (int) (dbl)
## 1        TM Dilshan        1235   186    26  1556
## 2  DPMD Jayawardene        1005   157    28  1346
## 3     KC Sangakkara        1088   132    18  1320
## 4        AD Mathews         640    54    24   794
## 5       MDKJ Perera         435    60    23   596
## 6     ST Jayasuriya         428    70    21   581
## 7       NLTC Perera         310    38    22   480
## 8     CK Kapugedera         329    36    16   417
## 9      LD Chandimal         371    31     7   380
## 10  HDRL Thirimanne         240    26     5   277
## ..              ...         ...   ...   ...   ...

6. Team Batting Scorecard in Twenty20 matches against all oppositions

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

#Top T20 England batsmen against India
m <-teamBattingScorecardAllOppnAllMatches(matches=ind_matches,theTeam="England")
## Total= 1169
m
## Source: local data frame [26 x 5]
## 
##         batsman ballsPlayed fours sixes  runs
##          (fctr)       (int) (int) (int) (dbl)
## 1    EJG Morgan          96    15    10   176
## 2  KP Pietersen         107    18     5   171
## 3      AD Hales         112    14     6   149
## 4     RS Bopara          94     9     2   103
## 5      SR Patel          73     6     2    79
## 6    JC Buttler          54     2     4    69
## 7  C Kieswetter          47     8     3    65
## 8     LJ Wright          50     4     3    62
## 9       MJ Lumb          39     6     2    51
## 10   VS Solanki          30     5     1    43
## ..          ...         ...   ...   ...   ...
#Top T20 Australian batsmen against India
m <-teamBattingScorecardAllOppnAllMatches(matches=ind_matches,theTeam="Australia")
## Total= 1767
m
## 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
## ..          ...         ...   ...   ...   ...
#Top T20 New Zealand batsmen against Australia
m <-teamBattingScorecardAllOppnAllMatches(aus_matches,theTeam="New Zealand")
## Total= 727
m
## Source: local data frame [27 x 5]
## 
##         batsman ballsPlayed fours sixes  runs
##          (fctr)       (int) (int) (int) (dbl)
## 1   BB McCullum         138    22    12   228
## 2     SB Styris          54     9     3    84
## 3      JDP Oram          34     5     6    67
## 4    GJ Hopkins          30     5     3    57
## 5  JEC Franklin          52     3     2    53
## 6    MJ Guptill          47     7    NA    47
## 7      NT Broom          26     6    NA    36
## 8   NL McCullum          13     2     2    25
## 9    GD Elliott          26     2    NA    23
## 10   SP Fleming          13     3    NA    18
## ..          ...         ...   ...   ...   ...
#Top T20 Sri Lankan batsmen against West Indies
m <-teamBattingScorecardAllOppnAllMatches(wi_matches,theTeam="Sri Lanka")
## Total= 1225
m
## Source: local data frame [21 x 5]
## 
##             batsman ballsPlayed fours sixes  runs
##              (fctr)       (int) (int) (int) (dbl)
## 1        TM Dilshan         224    42     4   334
## 2  DPMD Jayawardene         149    21     5   202
## 3     KC Sangakkara         119    12     3   135
## 4     ST Jayasuriya          91    14     3   111
## 5        AD Mathews          52     6     7    98
## 6       MDKJ Perera          52    10     3    78
## 7  DSNFG Jayasuriya          52     5     3    66
## 8   HDRL Thirimanne          42     3     2    48
## 9      LD Chandimal          20     4     2    41
## 10  KMDN Kulasekara          18     3     1    30
## ..              ...         ...   ...   ...   ...

7. Team Batting Partnerships in Twenty20 matches against all oppositions

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.

# The function gives the names of highest T20 partnership for India. The default report parameter is "summary"
m <- teamBatsmenPartnershipAllOppnAllMatches(ind_matches,theTeam='India')
m
## Source: local data frame [46 x 2]
## 
##         batsman totalRuns
##          (fctr)     (dbl)
## 1       V Kohli      1215
## 2      SK Raina      1114
## 3     RG Sharma      1053
## 4  Yuvraj Singh       933
## 5     G Gambhir       911
## 6      MS Dhoni       864
## 7      V Sehwag       330
## 8     AM Rahane       302
## 9    RV Uthappa       249
## 10     S Dhawan       248
## ..          ...       ...
# When the report parameter is 'detailed' then the detailed break up of the T20 partnership is returned as a data frame
m <- teamBatsmenPartnershipAllOppnAllMatches(matches,theTeam='India',report="detailed")
head(m,30)
##      batsman      nonStriker partnershipRuns totalRuns
## 1  RG Sharma       G Gambhir              26       309
## 2  RG Sharma        SK Raina              25       309
## 3  RG Sharma    Yuvraj Singh              41       309
## 4  RG Sharma        MS Dhoni              31       309
## 5  RG Sharma        V Sehwag               0       309
## 6  RG Sharma         V Kohli             110       309
## 7  RG Sharma       AM Rahane              24       309
## 8  RG Sharma        S Dhawan              33       309
## 9  RG Sharma      RV Uthappa              13       309
## 10 RG Sharma       IK Pathan               6       309
## 11  SK Raina       RG Sharma              37       250
## 12  SK Raina    Yuvraj Singh              50       250
## 13  SK Raina        MS Dhoni              73       250
## 14  SK Raina       YK Pathan              51       250
## 15  SK Raina      KD Karthik              16       250
## 16  SK Raina         V Kohli              21       250
## 17  SK Raina        AR Patel               0       250
## 18  SK Raina       AT Rayudu               2       250
## 19   V Kohli       RG Sharma              70       146
## 20   V Kohli        SK Raina              11       146
## 21   V Kohli    Yuvraj Singh              37       146
## 22   V Kohli        MS Dhoni               9       146
## 23   V Kohli         M Vijay               2       146
## 24   V Kohli        V Sehwag               2       146
## 25   V Kohli       AM Rahane              15       146
## 26  MS Dhoni       RG Sharma              45       124
## 27  MS Dhoni        SK Raina              53       124
## 28  MS Dhoni    Yuvraj Singh               5       124
## 29  MS Dhoni Harbhajan Singh               8       124
## 30  MS Dhoni         V Kohli               0       124

9. More Team Batting Partnerships in Twenty20 matches against all oppositions

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.

# Top T20 England batting partnerships against India (report="summary")
m <- teamBatsmenPartnershipAllOppnAllMatches(ind_matches,theTeam='England')
m
## Source: local data frame [26 x 2]
## 
##         batsman totalRuns
##          (fctr)     (dbl)
## 1    EJG Morgan       176
## 2  KP Pietersen       171
## 3      AD Hales       149
## 4     RS Bopara       103
## 5      SR Patel        79
## 6    JC Buttler        69
## 7  C Kieswetter        65
## 8     LJ Wright        62
## 9       MJ Lumb        51
## 10   VS Solanki        43
## ..          ...       ...
# Top T20 South Africa  batting partnerships against India (report="detailed")
m <- teamBatsmenPartnershipAllOppnAllMatches(ind_matches,theTeam='South Africa', report="detailed")
m[1:30,]
##           batsman      nonStriker partnershipRuns totalRuns
## 1  AB de Villiers        GC Smith              28       208
## 2  AB de Villiers       JP Duminy              40       208
## 3  AB de Villiers      MV Boucher              19       208
## 4  AB de Villiers       JA Morkel              11       208
## 5  AB de Villiers       JH Kallis              17       208
## 6  AB de Villiers    F du Plessis              24       208
## 7  AB de Villiers         HM Amla              49       208
## 8  AB de Villiers         JM Kemp               6       208
## 9  AB de Villiers      MN van Wyk              14       208
## 10      JP Duminy  AB de Villiers              20       173
## 11      JP Duminy      MV Boucher               4       173
## 12      JP Duminy    F du Plessis              33       173
## 13      JP Duminy     F Behardien              88       173
## 14      JP Duminy       DA Miller              28       173
## 15      JP Duminy      MN van Wyk               0       173
## 16   F du Plessis  AB de Villiers              45       143
## 17   F du Plessis       JP Duminy              86       143
## 18   F du Plessis         HM Amla              12       143
## 19      JH Kallis        GC Smith              59       140
## 20      JH Kallis  AB de Villiers               8       140
## 21      JH Kallis       LE Bosman              12       140
## 22      JH Kallis       CA Ingram              59       140
## 23      JH Kallis         RE Levi               2       140
## 24      JA Morkel  AB de Villiers              12       109
## 25      JA Morkel      MV Boucher              34       109
## 26      JA Morkel         J Botha               8       109
## 27      JA Morkel     F Behardien              16       109
## 28      JA Morkel        DW Steyn               7       109
## 29      JA Morkel         JM Kemp               3       109
## 30      JA Morkel JJ van der Wath              28       109

10. Team Batting partnerships of other countries in Twenty20 matches against all oppositions

#Top Indian T20 batting partnerships  against England matches
m <- teamBatsmenPartnershipAllOppnAllMatches(eng_matches,theTeam='India',report="detailed")
head(m,30)
##      batsman   nonStriker partnershipRuns totalRuns
## 1    V Kohli    G Gambhir              78       184
## 2    V Kohli   RV Uthappa               4       184
## 3    V Kohli Yuvraj Singh              10       184
## 4    V Kohli    RG Sharma               2       184
## 5    V Kohli     SK Raina              42       184
## 6    V Kohli    AM Rahane               3       184
## 7    V Kohli     S Dhawan              45       184
## 8   MS Dhoni   RV Uthappa               5       167
## 9   MS Dhoni Yuvraj Singh               4       167
## 10  MS Dhoni    IK Pathan               2       167
## 11  MS Dhoni    RG Sharma               9       167
## 12  MS Dhoni     SK Raina              71       167
## 13  MS Dhoni    RA Jadeja              11       167
## 14  MS Dhoni    YK Pathan              36       167
## 15  MS Dhoni    AT Rayudu              18       167
## 16  MS Dhoni     R Ashwin              11       167
## 17 G Gambhir     V Sehwag              51       162
## 18 G Gambhir   RV Uthappa               7       162
## 19 G Gambhir Yuvraj Singh               2       162
## 20 G Gambhir    IK Pathan              11       162
## 21 G Gambhir    RG Sharma              25       162
## 22 G Gambhir     SK Raina              10       162
## 23 G Gambhir    RA Jadeja              15       162
## 24 G Gambhir    AM Rahane              18       162
## 25 G Gambhir      V Kohli              23       162
## 26  SK Raina    G Gambhir               2       161
## 27  SK Raina     MS Dhoni              80       161
## 28  SK Raina    RG Sharma              16       161
## 29  SK Raina      V Kohli              34       161
## 30  SK Raina      P Kumar               0       161
#Top South Africa T20 batting partnerships 
m <- teamBatsmenPartnershipAllOppnAllMatches(sa_matches,theTeam='South Africa', report="detailed")
head(m,30)
##           batsman       nonStriker partnershipRuns totalRuns
## 1       JP Duminy        LE Bosman               3      1528
## 2       JP Duminy         GC Smith              78      1528
## 3       JP Duminy        JH Kallis              77      1528
## 4       JP Duminy   AB de Villiers             207      1528
## 5       JP Duminy       MV Boucher              93      1528
## 6       JP Duminy        JA Morkel             143      1528
## 7       JP Duminy RE van der Merwe              12      1528
## 8       JP Duminy         DW Steyn              10      1528
## 9       JP Duminy          J Botha              34      1528
## 10      JP Duminy VB van Jaarsveld              50      1528
## 11      JP Duminy         HH Gibbs               0      1528
## 12      JP Duminy          HM Amla             104      1528
## 13      JP Duminy      ND McKenzie              20      1528
## 14      JP Duminy      F Behardien             117      1528
## 15      JP Duminy      RJ Peterson              32      1528
## 16      JP Duminy        Q de Kock              13      1528
## 17      JP Duminy        DA Miller             284      1528
## 18      JP Duminy     RR Hendricks              15      1528
## 19      JP Duminy        R McLaren               1      1528
## 20      JP Duminy       WD Parnell               5      1528
## 21      JP Duminy          D Wiese               6      1528
## 22      JP Duminy     F du Plessis             119      1528
## 23      JP Duminy        JL Ontong              17      1528
## 24      JP Duminy        CA Ingram              67      1528
## 25      JP Duminy          HG Kuhn               6      1528
## 26      JP Duminy       MN van Wyk               7      1528
## 27      JP Duminy      AN Petersen               8      1528
## 28 AB de Villiers         GC Smith              72      1167
## 29 AB de Villiers        JH Kallis              81      1167
## 30 AB de Villiers        JP Duminy             263      1167
#Top Sri Lanka T20 batting partnerships 
m <- teamBatsmenPartnershipAllOppnAllMatches(sl_matches,theTeam='Sri Lanka',report="summary")
m
## Source: local data frame [54 x 2]
## 
##             batsman totalRuns
##              (fctr)     (dbl)
## 1        TM Dilshan      1556
## 2  DPMD Jayawardene      1346
## 3     KC Sangakkara      1320
## 4        AD Mathews       794
## 5       MDKJ Perera       596
## 6     ST Jayasuriya       581
## 7       NLTC Perera       480
## 8     CK Kapugedera       417
## 9      LD Chandimal       380
## 10  HDRL Thirimanne       277
## ..              ...       ...
#Top England T20 batting partnerships 
m <- teamBatsmenPartnershipAllOppnAllMatches(eng_matches,theTeam='England',report="summary")
m
## Source: local data frame [65 x 2]
## 
##           batsman totalRuns
##            (fctr)     (dbl)
## 1      EJG Morgan      1285
## 2    KP Pietersen      1176
## 3        AD Hales      1111
## 4       LJ Wright       759
## 5       RS Bopara       711
## 6  PD Collingwood       583
## 7      JC Buttler       562
## 8         MJ Lumb       552
## 9    C Kieswetter       526
## 10        OA Shah       347
## ..            ...       ...
#Top Australian T20 batting partnerships in West Indian matches
m <- teamBatsmenPartnershipAllOppnAllMatches(wi_matches,theTeam='Australia',report="summary")
m
## Source: local data frame [31 x 2]
## 
##       batsman totalRuns
##        (fctr)     (dbl)
## 1   DA Warner       311
## 2   SR Watson       240
## 3  MEK Hussey       147
## 4   BJ Haddin       146
## 5   GJ Bailey       135
## 6   DJ Hussey        57
## 7    AC Voges        51
## 8    SE Marsh        50
## 9  GJ Maxwell        45
## 10   L Ronchi        36
## ..        ...       ...
#Top England T20 batting partnerships in New Zealand  matches
m <- teamBatsmenPartnershipAllOppnAllMatches(nz_matches,theTeam='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
## ..            ...       ...

11. Team Batting Partnership plots in Twenty20 matches against all oppositions

Graphical plot of batting partnerships for the countries

# Plot of T20 batting partnerships of India (Virat Kohli and Suresh Raina have the best T20 partnerships)
teamBatsmenPartnershipAllOppnAllMatchesPlot(ind_matches,"India",main="India")

batsmenPartnership1-1

# Plot of T20 batting partnerships of Pakistan (Umar Akmal and Mohammed Hafeez lead)
teamBatsmenPartnershipAllOppnAllMatchesPlot(pak_matches,"Pakistan",main="Pakistan")

batsmenPartnership1-2

# Plot of T20 batting partnerships of Australia (David Warner and Shane Watson head the list)
teamBatsmenPartnershipAllOppnAllMatchesPlot(aus_matches,"Australia",main="Australia")

batsmenPartnership1-3

12. Top opposition batting partnerships in Twenty20 matches against all oppositions

This gives the best performance of the team against a specified country

# Top India T20 partnerships against West Indies
teamBatsmenPartnershipAllOppnAllMatchesPlot(ind_matches,"India",main="West Indies")

batsmenPartnership2-1

# Top Sri Lanka T20 partnerships against India
teamBatsmenPartnershipAllOppnAllMatchesPlot(sl_matches,"Sri Lanka",main="India")

batsmenPartnership2-2

# Top New Zealand T20 partnerships against South Africa
teamBatsmenPartnershipAllOppnAllMatchesPlot(nz_matches,"New Zealand",main="South Africa")

batsmenPartnership2-3

13. Batsmen vs Bowlers in Twenty20 matches against all oppositions

The function below gives the top performance of batsmen against the opposition countries

# Top T20 batsmen against bowlers when rank=0
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"India",rank=0)
m
## Source: local data frame [46 x 2]
## 
##         batsman runsScored
##          (fctr)      (dbl)
## 1       V Kohli       1215
## 2      SK Raina       1114
## 3     RG Sharma       1053
## 4  Yuvraj Singh        933
## 5     G Gambhir        911
## 6      MS Dhoni        864
## 7      V Sehwag        330
## 8     AM Rahane        302
## 9    RV Uthappa        249
## 10     S Dhawan        248
## ..          ...        ...
# Performance of India batsman in T20 with rank=1 against international bowlers and runs scored against bowlers. This is Virat Kohli for India
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 Shahid Afridi    43
## 2  V Kohli     SR Watson    39
## 3  V Kohli   Imran Tahir    34
## 4  V Kohli      CJ Boyce    32
## 5  V Kohli   Saeed Ajmal    32
## 6  V Kohli        AJ Tye    31
## 7  V Kohli  HMRKB Herath    29
## 8  V Kohli    TT Bresnan    28
## 9  V Kohli KW Richardson    27
## 10 V Kohli     SM Boland    27
## ..     ...           ...   ...
# Performance of India batsman in T20 with rank=2 against international bowlers and runs scored against these bowlers. This is Suresh Raina for India
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  SK Raina RK Kleinveldt    33
## 2  SK Raina     JH Kallis    31
## 3  SK Raina    T Thushara    31
## 4  SK Raina        AJ Tye    29
## 5  SK Raina    TT Bresnan    29
## 6  SK Raina     SR Watson    26
## 7  SK Raina   JC Tredwell    26
## 8  SK Raina    IE O'Brien    26
## 9  SK Raina Mohammad Nabi    22
## 10 SK Raina      GP Swann    22
## ..      ...           ...   ...
# Performance of England batsman in T20 with rank=1 against international bowlers and runs scored against these bowlers. This returns a data frame of the the theTeam's batsmen against the bowlers for which the 'matches' dataframe is used. This Is EJG Morgan of England,
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(matches=ind_matches,theTeam="England",rank=1,dispRows=25)
m
## Source: local data frame [15 x 3]
## Groups: batsman [1]
## 
##       batsman          bowler  runs
##        (fctr)          (fctr) (dbl)
## 1  EJG Morgan        R Ashwin    24
## 2  EJG Morgan        AB Dinda    22
## 3  EJG Morgan       KV Sharma    18
## 4  EJG Morgan       RA Jadeja    17
## 5  EJG Morgan       MM Sharma    16
## 6  EJG Morgan       RG Sharma    15
## 7  EJG Morgan         V Kohli    14
## 8  EJG Morgan  Mohammed Shami    13
## 9  EJG Morgan    Yuvraj Singh    11
## 10 EJG Morgan         P Kumar    10
## 11 EJG Morgan       PP Chawla     7
## 12 EJG Morgan         P Awana     7
## 13 EJG Morgan       IK Pathan     1
## 14 EJG Morgan        MM Patel     1
## 15 EJG Morgan Harbhajan Singh     0
# All the top T20 Australian batsmen against India in all of Indian matches
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"Australia",rank=0)
m
## Source: local data frame [40 x 2]
## 
##         batsman runsScored
##          (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
## ..          ...        ...

14. Batsmen vs Bowlers in Twenty20 matches against all oppositions (continued)

# The best India T20 batsman(rank=0) against England and his performance against England bowlers
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(eng_matches,"India",rank=1,dispRows=30)
m
## Source: local data frame [13 x 3]
## Groups: batsman [1]
## 
##    batsman      bowler  runs
##     (fctr)      (fctr) (dbl)
## 1  V Kohli  TT Bresnan    28
## 2  V Kohli   LJ Wright    26
## 3  V Kohli   CR Woakes    25
## 4  V Kohli JC Tredwell    21
## 5  V Kohli     ST Finn    17
## 6  V Kohli      MM Ali    15
## 7  V Kohli   SCJ Broad    14
## 8  V Kohli JW Dernbach    10
## 9  V Kohli   RS Bopara     9
## 10 V Kohli   SC Meaker     7
## 11 V Kohli   HF Gurney     6
## 12 V Kohli    GP Swann     5
## 13 V Kohli   DR Briggs     1
# All the top Sri Lanka T20 batsmen (rank=0) against Australia and performances against Australian bowlers
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(aus_matches,"Sri Lanka",rank=0)
m
## Source: local data frame [24 x 2]
## 
##             batsman runsScored
##              (fctr)      (dbl)
## 1        TM Dilshan        247
## 2  DPMD Jayawardene        209
## 3     KC Sangakkara        177
## 4       NLTC Perera         80
## 5      LD Chandimal         55
## 6       BMAJ Mendis         55
## 7         J Mubarak         49
## 8        AD Mathews         48
## 9       MDKJ Perera         48
## 10       WPUJC Vaas         21
## ..              ...        ...
#All the top England T20 batsmen (rank=0) and their performances against South African bowlers
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(sa_matches,"England",rank=0)
m
## Source: local data frame [30 x 2]
## 
##           batsman runsScored
##            (fctr)      (dbl)
## 1      EJG Morgan        145
## 2    C Kieswetter        117
## 3    KP Pietersen        116
## 4  PD Collingwood         90
## 5       IJL Trott         84
## 6         OA Shah         74
## 7      JC Buttler         72
## 8        AD Hales         60
## 9        MJ Prior         42
## 10      RS Bopara         39
## ..            ...        ...

15. Batsmen vs Bowlers Plot in Twenty20 matches against all oppositions

The following functions plot the performances of the batsman based on the rank chosen against opposition bowlers. Note: The rank has to be >0

#The following plot displays the performance of the top India T20 batsman (rank=1) against all opposition bowlers. This is Virat Kohli for India

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 Shahid Afridi    43
## 2  V Kohli     SR Watson    39
## 3  V Kohli   Imran Tahir    34
## 4  V Kohli      CJ Boyce    32
## 5  V Kohli   Saeed Ajmal    32
## 6  V Kohli        AJ Tye    31
## 7  V Kohli  HMRKB Herath    29
## 8  V Kohli    TT Bresnan    28
## 9  V Kohli KW Richardson    27
## 10 V Kohli     SM Boland    27
## ..     ...           ...   ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler1-1

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 Shahid Afridi    43
## 2  V Kohli     SR Watson    39
## 3  V Kohli   Imran Tahir    34
## 4  V Kohli      CJ Boyce    32
## 5  V Kohli   Saeed Ajmal    32
## 6  V Kohli        AJ Tye    31
## 7  V Kohli  HMRKB Herath    29
## 8  V Kohli    TT Bresnan    28
## 9  V Kohli KW Richardson    27
## 10 V Kohli     SM Boland    27
## ..     ...           ...   ...
# The following plot displays the performance of the T20 batsman (rank=2) against all opposition bowlers. This is M S Dhoni for India
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"India",rank=2,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler1-2

# Best T20 batsman of South Africa against Indian  bowlers
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(ind_matches,"South Africa",rank=1,dispRows=30)
d
## Source: local data frame [21 x 3]
## Groups: batsman [1]
## 
##           batsman          bowler  runs
##            (fctr)          (fctr) (dbl)
## 1  AB de Villiers    Yuvraj Singh    27
## 2  AB de Villiers        R Ashwin    21
## 3  AB de Villiers       S Aravind    18
## 4  AB de Villiers        RP Singh    14
## 5  AB de Villiers Harbhajan Singh    13
## 6  AB de Villiers         B Kumar    13
## 7  AB de Villiers       MM Sharma    12
## 8  AB de Villiers       RA Jadeja    11
## 9  AB de Villiers       YK Pathan    11
## 10 AB de Villiers          Z Khan     9
## ..            ...             ...   ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler1-3

# Best T20 batsman of England (rank=1) against Indian bowlers (matches=ind_matches)
d <-teamBatsmenVsBowlersAllOppnAllMatchesRept(matches=ind_matches,"England",rank=1,dispRows=50)
d
## Source: local data frame [15 x 3]
## Groups: batsman [1]
## 
##       batsman          bowler  runs
##        (fctr)          (fctr) (dbl)
## 1  EJG Morgan        R Ashwin    24
## 2  EJG Morgan        AB Dinda    22
## 3  EJG Morgan       KV Sharma    18
## 4  EJG Morgan       RA Jadeja    17
## 5  EJG Morgan       MM Sharma    16
## 6  EJG Morgan       RG Sharma    15
## 7  EJG Morgan         V Kohli    14
## 8  EJG Morgan  Mohammed Shami    13
## 9  EJG Morgan    Yuvraj Singh    11
## 10 EJG Morgan         P Kumar    10
## 11 EJG Morgan       PP Chawla     7
## 12 EJG Morgan         P Awana     7
## 13 EJG Morgan       IK Pathan     1
## 14 EJG Morgan        MM Patel     1
## 15 EJG Morgan Harbhajan Singh     0
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler1-4

15. Batsmen vs Bowlers Plot in Twenty20 matches against all oppositions (continued)

# Top T20 batsman of South Africa and performance against opposition bowlers of all countries
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  JP Duminy   Saeed Ajmal    63
## 2  JP Duminy    BAW Mendis    63
## 3  JP Duminy      JR Hopes    58
## 4  JP Duminy     DJ Hussey    48
## 5  JP Duminy      KD Mills    46
## 6  JP Duminy    TG Southee    43
## 7  JP Duminy      CB Mpofu    42
## 8  JP Duminy Shahid Afridi    40
## 9  JP Duminy       SW Tait    38
## 10 JP Duminy   NL McCullum    32
## ..       ...           ...   ...
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler2-1

# Do not display plot but return dataframe
e <- teamBatsmenVsBowlersAllOppnAllMatchesPlot(d,plot=FALSE)
e
## Source: local data frame [50 x 3]
## Groups: batsman [1]
## 
##      batsman        bowler  runs
##       (fctr)        (fctr) (dbl)
## 1  JP Duminy   Saeed Ajmal    63
## 2  JP Duminy    BAW Mendis    63
## 3  JP Duminy      JR Hopes    58
## 4  JP Duminy     DJ Hussey    48
## 5  JP Duminy      KD Mills    46
## 6  JP Duminy    TG Southee    43
## 7  JP Duminy      CB Mpofu    42
## 8  JP Duminy Shahid Afridi    40
## 9  JP Duminy       SW Tait    38
## 10 JP Duminy   NL McCullum    32
## ..       ...           ...   ...
# Top T20 batsman of Sri Lanka against bowlers of all countries
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(sl_matches,"Sri Lanka",rank=1,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler2-2

# Best T20 West Indian against English bowlrs
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(eng_matches,"West Indies",rank=1,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

batsmenVsBowler2-3

16 Team bowling T20 scorecard against all opposition

The functions lists the top T20 bowlers of each country in 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 [41 x 5]
## 
##             bowler overs maidens  runs wickets
##             (fctr) (int)   (int) (dbl)   (dbl)
## 1         R Ashwin    18       0   900      41
## 2        IK Pathan    16       0   618      29
## 3  Harbhajan Singh    18       0   622      27
## 4     Yuvraj Singh    13       0   418      24
## 5        RA Jadeja    17       0   635      23
## 6          A Nehra    11       0   373      22
## 7         RP Singh     8       0   225      19
## 8           Z Khan    16       0   448      18
## 9         AB Dinda    11       0   245      17
## 10         B Kumar     9       0   294      14
## ..             ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(matches=aus_matches,theTeam="Australia")
## Source: local data frame [56 x 5]
## 
##        bowler overs maidens  runs wickets
##        (fctr) (int)   (int) (dbl)   (dbl)
## 1   SR Watson    21       0  1062      49
## 2  MG Johnson    19       0   797      42
## 3       B Lee    13       0   714      30
## 4     SW Tait    15       0   589      30
## 5   DP Nannes    14       0   403      29
## 6    MA Starc    18       0   508      28
## 7  PJ Cummins    19       0   395      22
## 8  NW Bracken    12       0   438      21
## 9   DJ Hussey    13       1   392      21
## 10  SPD Smith    10       0   377      18
## ..        ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(eng_matches,"England")
## Source: local data frame [47 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1       SCJ Broad    21       0  1491      68
## 2        GP Swann    16       0   859      53
## 3     JW Dernbach    17       0  1020      45
## 4         ST Finn    16       0   583      30
## 5      TT Bresnan    11       0   887      27
## 6   RJ Sidebottom    13       0   437      26
## 7     JM Anderson    16       0   552      20
## 8       LJ Wright    11       0   465      18
## 9       RS Bopara    12       0   387      17
## 10 PD Collingwood    10       0   329      16
## ..            ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(pak_matches,"Pakistan")
## Source: local data frame [37 x 5]
## 
##             bowler overs maidens  runs wickets
##             (fctr) (int)   (int) (dbl)   (dbl)
## 1    Shahid Afridi    16       0  2095      96
## 2      Saeed Ajmal    17       0  1516      94
## 3         Umar Gul    20       0  1400      91
## 4    Sohail Tanvir    18       0  1212      53
## 5  Mohammad Hafeez    19       0  1093      47
## 6    Mohammad Amir    11       0   557      27
## 7     Abdul Razzaq    13       0   367      22
## 8     Shoaib Malik    12       0   435      19
## 9    Shoaib Akhtar    10       0   421      19
## 10      Wahab Riaz    14       1   392      19
## ..             ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(sa_matches,"South Africa")
## Source: local data frame [40 x 5]
## 
##         bowler overs maidens  runs wickets
##         (fctr) (int)   (int) (dbl)   (dbl)
## 1     DW Steyn    17       0   879      59
## 2     M Morkel    18       0  1022      52
## 3   WD Parnell    21       0   891      41
## 4      J Botha    18       0   823      40
## 5    JA Morkel    19       0   835      30
## 6  Imran Tahir    13       0   426      27
## 7  RJ Peterson    18       1   451      26
## 8      D Wiese    16       0   267      22
## 9  LL Tsotsobe    14       0   541      20
## 10   R McLaren    16       0   332      19
## ..         ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(nz_matches,"New Zealand")
## Source: local data frame [48 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1     NL McCullum    18       0  1240      59
## 2      TG Southee    20       0  1182      52
## 3        KD Mills    16       0  1190      49
## 4      DL Vettori    12       0   748      39
## 5       IG Butler    15       0   481      27
## 6  MJ McClenaghan    19       0   642      25
## 7         SE Bond    12       0   518      25
## 8    JEC Franklin    16       0   417      25
## 9        JDP Oram    17       0   793      21
## 10      SB Styris    11       0   349      20
## ..            ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(sl_matches,"Sri Lanka")
## Source: local data frame [42 x 5]
## 
##             bowler overs maidens  runs wickets
##             (fctr) (int)   (int) (dbl)   (dbl)
## 1       SL Malinga    19       0  1522      86
## 2       BAW Mendis    17       0   911      61
## 3  KMDN Kulasekara    15       0  1052      52
## 4       AD Mathews    17       0   814      35
## 5      NLTC Perera    17       0   769      35
## 6  SMSM Senanayake    16       0   442      26
## 7    ST Jayasuriya    13       0   415      20
## 8     CRD Fernando    16       0   377      16
## 9     HMRKB Herath    12       2   174      15
## 10  M Muralitharan    13       0   297      14
## ..             ...   ...     ...   ...     ...
teamBowlingScorecardAllOppnAllMatchesMain(wi_matches,"West Indies")
## Source: local data frame [37 x 5]
## 
##        bowler overs maidens  runs wickets
##        (fctr) (int)   (int) (dbl)   (dbl)
## 1   DJG Sammy    20       0  1037      49
## 2    DJ Bravo    18       0  1127      46
## 3   SP Narine    15       0   692      40
## 4    S Badree    11       0   464      34
## 5   R Rampaul    16       0   705      29
## 6   JE Taylor    14       0   529      28
## 7  MN Samuels     6       0   561      24
## 8  KA Pollard    16       0   598      23
## 9  FH Edwards    14       0   497      19
## 10 K Santokie     9       0   278      19
## ..        ...   ...     ...   ...     ...

17 Team bowling T20 scorecard against all opposition (continued)

The function lists the top bowlers of a country (‘matches’) against the opposition country

# Best Indian bowlers in matches against Australia
teamBowlingScorecardAllOppnAllMatches(ind_matches,'Australia')
## Source: local data frame [26 x 5]
## 
##             bowler overs maidens  runs wickets
##             (fctr) (int)   (int) (dbl)   (dbl)
## 1         R Ashwin    13       1   232      10
## 2        RA Jadeja     7       0   219       9
## 3        JJ Bumrah     6       0   103       6
## 4    R Vinay Kumar     1       0    79       6
## 5     Yuvraj Singh     3       0    72       5
## 6         R Sharma     1       0    56       5
## 7          A Nehra     5       0   127       4
## 8        IK Pathan     8       0   115       4
## 9          B Kumar     4       0    42       4
## 10 Harbhajan Singh     8       0    83       3
## ..             ...   ...     ...   ...     ...
# Best Australian bowlers in matches against India
teamBowlingScorecardAllOppnAllMatches(aus_matches,'India')
## Source: local data frame [36 x 5]
## 
##        bowler overs maidens  runs wickets
##        (fctr) (int)   (int) (dbl)   (dbl)
## 1   SR Watson    13       0   201      11
## 2  MG Johnson     5       0    54       5
## 3       B Lee     6       0   133       4
## 4     SW Tait     5       0   112       3
## 5  NW Bracken     6       0    68       3
## 6   DP Nannes     1       0    25       3
## 7   DJ Hussey     4       0    24       3
## 8  PJ Cummins     4       0    16       3
## 9    CJ McKay     1       0    75       2
## 10    GB Hogg     5       0    69       2
## ..        ...   ...     ...   ...     ...
# Best New Zealand bowlers in matches against England
teamBowlingScorecardAllOppnAllMatches(nz_matches,'England')
## Source: local data frame [26 x 5]
## 
##            bowler overs maidens  runs wickets
##            (fctr) (int)   (int) (dbl)   (dbl)
## 1  MJ McClenaghan     9       0   189       8
## 2        KD Mills     6       0   199       7
## 3     NL McCullum    15       0   281       5
## 4      TG Southee     3       0   183       5
## 5       CS Martin     5       0   116       5
## 6      DL Vettori    11       0    91       5
## 7    JEC Franklin     7       0    53       5
## 8         SE Bond     8       0    49       5
## 9       IG Butler    11       0    95       4
## 10      SB Styris     8       0    80       3
## ..            ...   ...     ...   ...     ...
# Best Sri Lankan bowlers in matches against West Indies
teamBowlingScorecardAllOppnAllMatches(sl_matches,"West Indies")
## Source: local data frame [16 x 5]
## 
##              bowler overs maidens  runs wickets
##              (fctr) (int)   (int) (dbl)   (dbl)
## 1        BAW Mendis    10       0    82      13
## 2        SL Malinga    12       0   217      12
## 3        AD Mathews     9       0    87       6
## 4   TAM Siriwardana     5       0    58       5
## 5   SMSM Senanayake     7       0    90       4
## 6    M Muralitharan     9       0    76       4
## 7   KMDN Kulasekara    11       0   158       3
## 8      PVD Chameera     4       0    66       2
## 9           I Udana     7       0    56       1
## 10 DSNFG Jayasuriya     2       0    38       1
## 11      BMAJ Mendis     1       0    32       1
## 12      A Dananjaya     1       0    16       1
## 13       S Prasanna     2       0    15       1
## 14     HMRKB Herath     5       0    43       0
## 15    ST Jayasuriya     3       0    34       0
## 16      NLTC Perera     2       0    13       0

18. Team Bowlers versus Batsmen (in T20 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

# Best T20 bowlers overall from India against all opposition (rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(ind_matches,theTeam="India",rank=0)
## Source: local data frame [10 x 2]
## 
##             bowler  runs
##             (fctr) (dbl)
## 1         R Ashwin   868
## 2        RA Jadeja   619
## 3        IK Pathan   598
## 4  Harbhajan Singh   591
## 5           Z Khan   424
## 6     Yuvraj Singh   415
## 7        YK Pathan   406
## 8          A Nehra   368
## 9         I Sharma   349
## 10         B Kumar   275
# Top T20 bowler of India and runs conceded against different opposition batsmen 
(rank=1)
## [1] 1
m <-teamBowlersVsBatsmenAllOppnAllMatchesMain(ind_matches,theTeam="India",rank=1)
m
## Source: local data frame [95 x 3]
## Groups: bowler [1]
## 
##      bowler     batsman runsConceded
##      (fctr)      (fctr)        (dbl)
## 1  R Ashwin    AD Hales           43
## 2  R Ashwin    AJ Finch           42
## 3  R Ashwin   SR Watson           41
## 4  R Ashwin   DA Warner           37
## 5  R Ashwin     MS Wade           37
## 6  R Ashwin BB McCullum           26
## 7  R Ashwin   JP Duminy           26
## 8  R Ashwin  GJ Maxwell           24
## 9  R Ashwin  EJG Morgan           24
## 10 R Ashwin   CA Ingram           23
## ..      ...         ...          ...
# Top T20 bowler of India and runs conceded against different opposition batsmen (rank=2)
m <-teamBowlersVsBatsmenAllOppnAllMatchesMain(ind_matches,theTeam="India",rank=2)
m
## Source: local data frame [66 x 3]
## Groups: bowler [1]
## 
##       bowler       batsman runsConceded
##       (fctr)        (fctr)        (dbl)
## 1  RA Jadeja     SR Watson           59
## 2  RA Jadeja      AJ Finch           34
## 3  RA Jadeja       MS Wade           32
## 4  RA Jadeja CK Kapugedera           24
## 5  RA Jadeja   LMP Simmons           23
## 6  RA Jadeja      AD Hales           22
## 7  RA Jadeja     DA Warner           20
## 8  RA Jadeja     JH Kallis           19
## 9  RA Jadeja    EJG Morgan           17
## 10 RA Jadeja  LD Chandimal           17
## ..       ...           ...          ...

18. Team Bowlers versus Batsmen (in T20 matchesagainst all oppositions continued)

# Top T20 bowlers versus batsmen of South Africa(rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(sa_matches,theTeam="South Africa",rank=0)
## Source: local data frame [10 x 2]
## 
##         bowler  runs
##         (fctr) (dbl)
## 1     M Morkel   967
## 2   WD Parnell   858
## 3     DW Steyn   833
## 4    JA Morkel   807
## 5      J Botha   802
## 6  LL Tsotsobe   523
## 7  RJ Peterson   443
## 8  Imran Tahir   410
## 9    JP Duminy   406
## 10   KJ Abbott   353
# Top T20 bowlers versus batsmen of Pakistan(rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(pak_matches,theTeam="Pakistan",rank=0)
## Source: local data frame [10 x 2]
## 
##             bowler  runs
##             (fctr) (dbl)
## 1    Shahid Afridi  2054
## 2      Saeed Ajmal  1475
## 3         Umar Gul  1330
## 4    Sohail Tanvir  1147
## 5  Mohammad Hafeez  1060
## 6    Mohammad Amir   546
## 7     Shoaib Malik   407
## 8    Shoaib Akhtar   402
## 9       Wahab Riaz   369
## 10    Abdul Razzaq   364
# Top T20 bowlers versus batsmen of Sri Lanka(rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(sl_matches,theTeam="Sri Lanka",rank=1)
## Source: local data frame [168 x 3]
## Groups: bowler [1]
## 
##        bowler       batsman runsConceded
##        (fctr)        (fctr)        (dbl)
## 1  SL Malinga Shahid Afridi           66
## 2  SL Malinga    MN Samuels           55
## 3  SL Malinga   BB McCullum           38
## 4  SL Malinga    MJ Guptill           37
## 5  SL Malinga     G Gambhir           35
## 6  SL Malinga   NL McCullum           35
## 7  SL Malinga      JDP Oram           31
## 8  SL Malinga  Shoaib Malik           31
## 9  SL Malinga    MEK Hussey           30
## 10 SL Malinga  ADS Fletcher           30
## ..        ...           ...          ...
m <-teamBowlersVsBatsmenAllOppnAllMatchesMain(ind_matches,theTeam="India",rank=2)
m
## Source: local data frame [66 x 3]
## Groups: bowler [1]
## 
##       bowler       batsman runsConceded
##       (fctr)        (fctr)        (dbl)
## 1  RA Jadeja     SR Watson           59
## 2  RA Jadeja      AJ Finch           34
## 3  RA Jadeja       MS Wade           32
## 4  RA Jadeja CK Kapugedera           24
## 5  RA Jadeja   LMP Simmons           23
## 6  RA Jadeja      AD Hales           22
## 7  RA Jadeja     DA Warner           20
## 8  RA Jadeja     JH Kallis           19
## 9  RA Jadeja    EJG Morgan           17
## 10 RA Jadeja  LD Chandimal           17
## ..       ...           ...          ...

19. Team bowlers versus batsmen report (in T20 matches against all oppositions)

#Top T20 bowlers of other countries against India
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=ind_matches,theTeam="India",rank=0)
## Source: local data frame [10 x 2]
## 
##           bowler  runs
##           (fctr) (dbl)
## 1      SR Watson   190
## 2  Shahid Afridi   180
## 3       Umar Gul   171
## 4      SCJ Broad   151
## 5    JW Dernbach   149
## 6     SL Malinga   144
## 7     TT Bresnan   135
## 8    JP Faulkner   127
## 9          B Lee   123
## 10     JA Morkel   121
# Best T20 performer against India is Shane Watosn in T20s
a <- teamBowlersVsBatsmenAllOppnAllMatchesRept(ind_matches,theTeam="India",rank=1)
a
## Source: local data frame [12 x 3]
## Groups: bowler [1]
## 
##       bowler         batsman runsConceded
##       (fctr)          (fctr)        (dbl)
## 1  SR Watson       RG Sharma           41
## 2  SR Watson         V Kohli           39
## 3  SR Watson        SK Raina           26
## 4  SR Watson    Yuvraj Singh           23
## 5  SR Watson        MS Dhoni           21
## 6  SR Watson       IK Pathan           14
## 7  SR Watson        S Dhawan           10
## 8  SR Watson Harbhajan Singh            7
## 9  SR Watson       RA Jadeja            4
## 10 SR Watson        R Ashwin            4
## 11 SR Watson       AM Rahane            1
## 12 SR Watson         B Kumar            0

20. Team bowlers versus batsmen report (in T20s against all oppositions continued)

#Top T20 Indian bowlers against Sri Lanka 
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=ind_matches,theTeam="Sri Lanka",rank=0)
## Source: local data frame [10 x 2]
## 
##             bowler  runs
##             (fctr) (dbl)
## 1          A Nehra   140
## 2        YK Pathan   100
## 3        RA Jadeja    80
## 4         R Ashwin    74
## 5         I Sharma    60
## 6         SK Raina    58
## 7        IK Pathan    56
## 8         AB Dinda    53
## 9  Harbhajan Singh    35
## 10       JJ Bumrah    35
#Top T20 Indian bowlers against England
teamBowlersVsBatsmenAllOppnAllMatchesRept(ind_matches,"England",rank=0)
## Source: local data frame [10 x 2]
## 
##             bowler  runs
##             (fctr) (dbl)
## 1         R Ashwin   160
## 2        RA Jadeja    86
## 3         AB Dinda    86
## 4          P Awana    71
## 5        PP Chawla    68
## 6  Harbhajan Singh    61
## 7     Yuvraj Singh    58
## 8  Joginder Sharma    57
## 9    R Vinay Kumar    53
## 10       IK Pathan    52

21. Team T20 bowlers versus batsmen report (all oppositions coninued-1)

#Top  T20 opposition bowlers against New Zealand
teamBowlersVsBatsmenAllOppnAllMatchesRept(nz_matches,theTeam="New Zealand",rank=0)
## Source: local data frame [10 x 2]
## 
##             bowler  runs
##             (fctr) (dbl)
## 1    Shahid Afridi   333
## 2         M Morkel   283
## 3        SCJ Broad   279
## 4       SL Malinga   260
## 5         Umar Gul   240
## 6  KMDN Kulasekara   199
## 7    Mohammad Amir   192
## 8       BAW Mendis   190
## 9      Saeed Ajmal   170
## 10        P Utseya   159
# Top T20 opposition bowlers against Australia
teamBowlersVsBatsmenAllOppnAllMatchesRept(aus_matches,"Australia",rank=0)
## Source: local data frame [10 x 2]
## 
##             bowler  runs
##             (fctr) (dbl)
## 1      Saeed Ajmal   265
## 2       WD Parnell   254
## 3    Shahid Afridi   249
## 4        SCJ Broad   239
## 5         R Ashwin   222
## 6         Umar Gul   222
## 7        RA Jadeja   218
## 8          J Botha   210
## 9  Mohammad Hafeez   207
## 10     JW Dernbach   188
# Top T20 bowlers against Sri Lanka
teamBowlersVsBatsmenAllOppnAllMatchesRept(sl_matches,"Sri Lanka",rank=0)
## Source: local data frame [10 x 2]
## 
##             bowler  runs
##             (fctr) (dbl)
## 1    Shahid Afridi   291
## 2    Sohail Tanvir   273
## 3      Saeed Ajmal   223
## 4         KD Mills   204
## 5         Umar Gul   179
## 6         DJ Bravo   173
## 7  Mohammad Hafeez   170
## 8       DL Vettori   160
## 9         JDP Oram   159
## 10      KA Pollard   157

22. Team bowlers versus batsmen report (in T20s against all oppositions) plot

This function can only be used for rank > 0 (rank=1,2,3..)

# Top T20 bowler against India (Shane Watson of Australia)
df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(ind_matches,theTeam="India",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"India","India")

bowlerVsbatsmen1-1

# Top T20 Indian bowler versus England (R Ashwin)
df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(ind_matches,theTeam="England",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"India","England")

bowlerVsbatsmen1-2

#Top T20 Indian bowler against West Indies (Yusuf Pathan)
df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(ind_matches,theTeam="West Indies",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"India","West Indies")

bowlerVsbatsmen1-3

23. Team bowlers versus batsmen plot (in Twenty20 matches against all oppositions)

#Top T20 bowler against South Africa (NL McCullum of New Zealand)
df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(sa_matches,theTeam="South Africa",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"South Africa","South Africa")
## [1] "aa"

bowlerVsbatsmen2-1

# Top  T20 bowler versus Pakistan (SL Malinga)
df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(pak_matches,theTeam="Pakistan",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"Pakistan","Pakistan")

bowlerVsbatsmen2-2

24. Team Bowler Wicket Kind in Twenty20 matches against all oppositions

# Top opposition T20  bowlers against India and the kind of wickets
teamBowlingWicketKindAllOppnAllMatches(ind_matches,t1="India",t2="All")

bowlingWicketkind1-1

# Get the data frame. Do not plot
m <-teamBowlingWicketKindAllOppnAllMatches(ind_matches,t1="India",t2="All",plot=FALSE)
m
## Source: local data frame [21 x 3]
## Groups: bowler [?]
## 
##         bowler wicketKind     m
##         (fctr)      (chr) (int)
## 1   MG Johnson     caught     3
## 2   MG Johnson    run out     2
## 3    SR Watson     caught     8
## 4    SR Watson    run out     3
## 5   TT Bresnan     caught     6
## 6  JW Dernbach     bowled     1
## 7  JW Dernbach     caught     6
## 8  JW Dernbach    run out     3
## 9      ST Finn     bowled     2
## 10     ST Finn     caught     4
## ..         ...        ...   ...
# Best Indian T20 bowlers against South Africa
teamBowlingWicketKindAllOppnAllMatches(ind_matches,t1="India",t2="South Africa")

bowlingWicketkind1-2

# Best Indian bowlers against Pakistan
teamBowlingWicketKindAllOppnAllMatches(ind_matches,t1="India",t2="Pakistan")

bowlingWicketkind1-3

25. Team Bowler Wicket Kind in Twenty20 matches against all oppositions (continued)

# Best T20 opposition bowlers against  England
teamBowlingWicketKindAllOppnAllMatches(eng_matches,t1="England",t2="All")

bowlingWicketkind2-1

# Best t20  opposition bowlers  Australia
teamBowlingWicketKindAllOppnAllMatches(aus_matches,t1="Australia",t2="All")

bowlingWicketkind2-2

# Best T20 bowlers against  Sri Lanka
teamBowlingWicketKindAllOppnAllMatches(sl_matches,t1="Sri Lanka",t2="All")

bowlingWicketkind2-3

26. Team Bowler Wicket Runs in Twenty20 matches against all oppositions

# Opposition T20 bowlers against India and runs conceded
teamBowlingWicketRunsAllOppnAllMatches(ind_matches,t1="India",t2="All",plot=TRUE)

bowlingWicketRuns1-1

# Opposition T20 bowlers against India and runs conceded returned as dataframe
m <-teamBowlingWicketRunsAllOppnAllMatches(ind_matches,t1="India",t2="All",plot=FALSE)
m
## Source: local data frame [10 x 3]
## 
##           bowler runsConceded wickets
##           (fctr)        (dbl)   (dbl)
## 1      SR Watson          201      11
## 2       Umar Gul          178      11
## 3    JW Dernbach          157      10
## 4        ST Finn           83       7
## 5       CB Mpofu          119       7
## 6     TT Bresnan          140       6
## 7     DL Vettori           96       6
## 8     MG Johnson           54       5
## 9  Mohammad Asif           43       5
## 10 Shahid Afridi          184       5
# Top T20 Indian bowlers and runs conceded
teamBowlingWicketRunsAllOppnAllMatches(ind_matches,t1="India",t2="Australia",plot=TRUE)

bowlingWicketRuns1-2

27. Team Bowler Wicket Runs in Twenty20 matches against all oppositions(continued)

#Top opposition T20 bowlers against Pakistan
teamBowlingWicketRunsAllOppnAllMatches(pak_matches,t1="Pakistan",t2="All",plot=TRUE)

bowlingWicketRuns2-1

#Top opposition T20 bowlers against West Indies
teamBowlingWicketRunsAllOppnAllMatches(wi_matches,t1="West Indies",t2="All",plot=TRUE)

bowlingWicketRuns2-2

#Top opposition t20 bowlers against Sri Lanka
teamBowlingWicketRunsAllOppnAllMatches(sl_matches,t1="Sri Lanka",t2="All",plot=TRUE)

bowlingWicketRuns2-3

#Top opposition T20 bowlers against New Zealand
teamBowlingWicketRunsAllOppnAllMatches(nz_matches,t1="New Zealand",t2="All",plot=TRUE)

bowlingWicketRuns2-4

Conclusion

This post included all functions for a team in all Twenty20 matches against all oppositions. As before the data frames for the T20 matches 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.

The 4th part of the yorkr package’s handling of Twenty20 will follow soon.

Watch this space!

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