yorkr pads up for the Twenty20s: Part 1- Analyzing team”s match performance

There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies and the other way is to make it so complicated that there are no obvious deficiencies.

      C.A.R. Hoare, The 1980 ACM Turing Award LectureOne of my most productive days was throwing away 1000 lines of code.

      Ken Thompson

Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.

      Brian W. Kernighan and P. J. Plauger in The Elements of Programming Style.

“If debugging is the process of removing software bugs, then programming must be the process of putting them in.”

      Edsger Dijkstra

Introduction

In this post I have added functions to my R package ‘yorkr’ that will allow for analysis of Twenty20 matches. yorkr is already available in CRAN and the Twenty20 functionality will be available with yorkr_0.0.4. This package is based on data from Cricsheet. I have now added functionality to perform analysis of T20 matches in addition the existing functionality for analysing ODI matches

The yorkr package provides functions to convert the yaml files to more easily R consumable entities, namely dataframes. In fact all ODI & T20 matches have already been converted and are available for use at yorkrData. However you will have to convert any new matches added to Cricsheet. Also note that there is a file called ’convertedFiles” which will give the details of the original match file and its corresponding converted file.

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This post can be viewed at RPubs at yorkrT20-Part1 or can also be downloaded as a PDF document yorkrT20-1.pdf

Checkout my interactive Shiny apps GooglyPlus (plots & tables) and Googly (only plots) which can be used to analyze IPL players, teams and matches.

Note: To do similar analysis you can use my yorkrT20templates. See my post Analysis of International T20 matches with yorkr templates

Important note 1: Do check out all the posts on the python avatar of yorkr, namely ‘yorkpy’ in my post ‘Pitching yorkpy … short of good length to IPL – Part 1

2. Install the package from CRAN

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

2a. New functionality for Twenty20

I had to create 2 new functions had to be created for converting Twenty20 yaml files to RData. They are

convertYaml2RDataframeT20
convertAllYaml2RDataframesT20

Note: Most of the existing functions created for ODI matches, also work with the converted T20 RData files, as can be seen below.

3. Convert and save T20 yaml file to dataframe

This function will convert a T20 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.

#Available in yorkr_0.0.4
convertYaml2RDataframeT20("211028.yaml",".",".")

## [1] "./211028.yaml"
## [1] "first loop"
## [1] "second loop"

4. Convert and save all T20 yaml files to dataframes

This function will convert all 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 T20-matches

#Available from yorkr_0.0.4
#convertAllYaml2RDataframesT20("./t20s",targetDirMen=".",targetDirWomen=".")

5. yorkrData – A Github repositiory

Cricsheet had a total of 458 Twenty20 matches. Out of which 5 files seemed to have problem. The remaining 453 T20 matches have been converted to RData.

All the converted RData files can be accessed from my Github link yorkrData under the folder 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 Twenty20 match data from 5 random matches between 10 different opposing teams/countries. 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()

With getMatchDetails() using the 2 teams and the date on which the match occured

afg_ire <- getMatchDetails("Afghanistan","Ireland","2010-02-09",dir="../../data")
dim(afg_ire)

## [1] 245  25

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 matches are

Australia vs India – 2007-09-22
England vs New Zealand – 2012-09-29
Pakistan vs South Africa – 2010-10-26
Sri Lanka vs West Indioes -2012-10-07
Bangladesh vs Zimbabwe -2016-01-15

load("../../data/Australia-India-2007-09-22.RData")
aus_ind <- overs
load("../../data/England-New Zealand-2012-09-29.RData")
eng_nz <- overs
load("../../data/Pakistan-South Africa-2010-10-26.RData")
pak_sa <- overs
load("../../data/Sri Lanka-West Indies-2012-10-07.RData")
sl_wi<- overs
load("../../data/Bangladesh-Zimbabwe-2016-01-15.RData")
ban_zim <- overs

7. Team batting scorecard

Compute and display the batting scorecard of the teams in the T20 match. The top batsmen in are Yuvraj Singh(Ind), ML Hayden(Aus), JP Duminy(SA) and Jayawardene(SL)

teamBattingScorecardMatch(aus_ind,'India')

## Total= 181

## Source: local data frame [7 x 5]
## 
##        batsman ballsPlayed fours sixes  runs
##         (fctr)       (int) (dbl) (dbl) (dbl)
## 1    G Gambhir          25     4     0    24
## 2     V Sehwag          12     1     0     9
## 3   RV Uthappa          27     1     3    34
## 4 Yuvraj Singh          30     5     5    70
## 5     MS Dhoni          18     4     1    36
## 6    RG Sharma           5     0     1     8
## 7    IK Pathan          NA     0     0     0

teamBattingScorecardMatch(aus_ind,'Australia')

## Total= 165

## Source: local data frame [9 x 5]
## 
##        batsman ballsPlayed fours sixes  runs
##         (fctr)       (int) (dbl) (dbl) (dbl)
## 1 AC Gilchrist          13     2     2    22
## 2    ML Hayden          44     4     4    62
## 3     BJ Hodge          10     0     1    11
## 4    A Symonds          26     3     2    43
## 5   MEK Hussey          12     0     1    13
## 6    MJ Clarke           3     0     0     3
## 7    BJ Haddin           7     0     0     5
## 8        B Lee           2     0     0     2
## 9   MG Johnson           1     1     0     4

teamBattingScorecardMatch(pak_sa,'South Africa')

## Total= 115

## Source: local data frame [6 x 5]
## 
##          batsman ballsPlayed fours sixes  runs
##           (fctr)       (int) (dbl) (dbl) (dbl)
## 1       GC Smith          12     3     0    13
## 2      LE Bosman           4     0     0     2
## 3 AB de Villiers           3     0     0     0
## 4      JP Duminy          45     5     0    41
## 5      CA Ingram          38     4     2    46
## 6      DA Miller           5     3     0    13

teamBattingScorecardMatch(sl_wi,'Sri Lanka')

## Total= 98

## Source: local data frame [10 x 5]
## 
##             batsman ballsPlayed fours sixes  runs
##              (fctr)       (int) (dbl) (dbl) (dbl)
## 1  DPMD Jayawardene          36     2     0    33
## 2        TM Dilshan           2     0     0     0
## 3     KC Sangakkara          26     2     0    22
## 4        AD Mathews           5     0     0     1
## 5       BMAJ Mendis           3     0     0     3
## 6       NLTC Perera           5     0     0     3
## 7   HDRL Thirimanne           7     0     0     4
## 8   KMDN Kulasekara          12     3     1    26
## 9        SL Malinga          12     0     0     5
## 10       BAW Mendis           2     0     0     1

8. Plot the team batting partnerships

The functions below plot the team batting partnetship in the T20 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 th epackages like rcharts, ggvis,googleVis or plotly.

teamBatsmenPartnershipMatch(pak_sa,"Pakistan","South Africa")

teamBatsmenPartnershipMatch(eng_nz,"New Zealand","England",plot=TRUE)

teamBatsmenPartnershipMatch(ban_zim,"Bangladesh","Zimbabwe",plot=FALSE)

##            batsman      nonStriker runs
## 1      Tamim Iqbal   Soumya Sarkar   19
## 2      Tamim Iqbal   Sabbir Rahman   10
## 3    Soumya Sarkar     Tamim Iqbal    7
## 4    Sabbir Rahman     Tamim Iqbal   15
## 5    Sabbir Rahman   Shuvagata Hom   10
## 6    Sabbir Rahman Mushfiqur Rahim   21
## 7    Shuvagata Hom   Sabbir Rahman    6
## 8  Mushfiqur Rahim   Sabbir Rahman   23
## 9  Mushfiqur Rahim Shakib Al Hasan    3
## 10 Shakib Al Hasan Mushfiqur Rahim    4
## 11 Shakib Al Hasan     Mahmudullah    5
## 12 Shakib Al Hasan     Nurul Hasan   11
## 13     Mahmudullah Shakib Al Hasan    7
## 14     Nurul Hasan Shakib Al Hasan    7

teamBatsmenPartnershipMatch(aus_ind,"India","Australia",plot=TRUE)

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(pak_sa,'Pakistan',"South Africa",plot=TRUE)

teamBatsmenVsBowlersMatch(aus_ind,'Australia',"India",plot=TRUE)

teamBatsmenVsBowlersMatch(ban_zim,'Zimbabwe',"Bangladesh",plot=TRUE)

m <- teamBatsmenVsBowlersMatch(sl_wi,'West Indies',"Sri Lanka",plot=FALSE)
m

## Source: local data frame [25 x 3]
## Groups: batsman [?]
## 
##       batsman          bowler runsConceded
##        (fctr)          (fctr)        (dbl)
## 1   J Charles      AD Mathews            0
## 2  MN Samuels      AD Mathews            8
## 3  MN Samuels KMDN Kulasekara            5
## 4  MN Samuels      SL Malinga           39
## 5  MN Samuels      BAW Mendis            7
## 6  MN Samuels     A Dananjaya            4
## 7  MN Samuels     BMAJ Mendis           15
## 8    CH Gayle      AD Mathews            0
## 9    CH Gayle KMDN Kulasekara            1
## 10   CH Gayle      SL Malinga            2
## ..        ...             ...          ...

10. Bowling Scorecard

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

teamBowlingScorecardMatch(eng_nz,'England')

## Source: local data frame [5 x 5]
## 
##       bowler overs maidens  runs wickets
##       (fctr) (int)   (int) (dbl)   (dbl)
## 1  DR Briggs     4       0    36       1
## 2    ST Finn     4       0    16       3
## 3 TT Bresnan     4       0    29       1
## 4   GP Swann     4       0    20       1
## 5  SCJ Broad     4       0    37       0

teamBowlingScorecardMatch(eng_nz,'New Zealand')

## Source: local data frame [7 x 5]
## 
##          bowler overs maidens  runs wickets
##          (fctr) (int)   (int) (dbl)   (dbl)
## 1      KD Mills     4       0    23       1
## 2    TG Southee     2       0    32       0
## 3    DL Vettori     4       0    20       1
## 4   NL McCullum     4       0    22       1
## 5      RJ Nicol     3       0    29       0
## 6  JEC Franklin     1       0    12       0
## 7 DAJ Bracewell     1       0     8       1

teamBowlingScorecardMatch(aus_ind,'Australia')

## Source: local data frame [6 x 5]
## 
##       bowler overs maidens  runs wickets
##       (fctr) (int)   (int) (dbl)   (dbl)
## 1      B Lee     4       0    25       0
## 2 NW Bracken     4       0    38       0
## 3   SR Clark     4       0    38       0
## 4 MG Johnson     4       0    31       4
## 5  A Symonds     3       0    37       0
## 6  MJ Clarke     1       0    13       1

11. Wicket Kind

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

teamBowlingWicketKindMatch(aus_ind,"India","Australia")

teamBowlingWicketKindMatch(aus_ind,"Australia","India")

teamBowlingWicketKindMatch(pak_sa,"South Africa","Pakistan")

m <-teamBowlingWicketKindMatch(sl_wi,"Sri Lanka","West Indies",plot=FALSE)
m

##            bowler wicketKind wicketPlayerOut runs
## 1      AD Mathews     caught       J Charles   11
## 2      BAW Mendis        lbw        CH Gayle   12
## 3      BAW Mendis        lbw        DJ Bravo   12
## 4      BAW Mendis     caught      KA Pollard   12
## 5      BAW Mendis        lbw      AD Russell   12
## 6     A Dananjaya     caught      MN Samuels   16
## 7 KMDN Kulasekara   noWicket        noWicket   22
## 8      SL Malinga   noWicket        noWicket   54
## 9     BMAJ Mendis   noWicket        noWicket   20

12. Wicket vs Runs conceded

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

teamBowlingWicketRunsMatch(pak_sa,"Pakistan","South Africa")

teamBowlingWicketRunsMatch(aus_ind,"Australia","India")

m <-teamBowlingWicketRunsMatch(sl_wi,"West Indies","Sri Lanka",plot=FALSE)
m

## Source: local data frame [6 x 5]
## 
##       bowler overs maidens  runs wickets
##       (fctr) (int)   (int) (dbl)   (chr)
## 1   S Badree     4       0    24       1
## 2  R Rampaul     3       0    31       1
## 3 MN Samuels     4       0    15       2
## 4   CH Gayle     2       0    14       0
## 5  SP Narine     4       1     9       4
## 6  DJG Sammy     2       0     6       2

13. Wickets taken by bowler

The plots provide the wickets taken by the bowler

m <-teamBowlingWicketMatch(eng_nz,'England',"New Zealand",plot=FALSE)
m

##       bowler wicketKind wicketPlayerOut runs
## 1    ST Finn        lbw      MJ Guptill   16
## 2    ST Finn     caught     BB McCullum   16
## 3   GP Swann     caught        RJ Nicol   20
## 4  DR Briggs     caught   KS Williamson   36
## 5    ST Finn     caught     LRPL Taylor   16
## 6 TT Bresnan    run out    JEC Franklin   29
## 7  SCJ Broad   noWicket        noWicket   37

teamBowlingWicketMatch(sl_wi,"Sri Lanka","West Indies")

teamBowlingWicketMatch(eng_nz,"New Zealand","England")

14. Bowler Vs Batsmen

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

teamBowlersVsBatsmenMatch(ban_zim,"Bangladesh","Zimbabwe")

teamBowlersVsBatsmenMatch(aus_ind,"India","Australia")

teamBowlersVsBatsmenMatch(eng_nz,"England","New Zealand")

m <- teamBowlersVsBatsmenMatch(pak_sa,"Pakistan","South Africa",plot=FALSE)
m

## Source: local data frame [19 x 3]
## Groups: bowler [?]
## 
##             bowler        batsman runsConceded
##             (fctr)         (fctr)        (dbl)
## 1    Shoaib Akhtar       GC Smith            5
## 2    Shoaib Akhtar      LE Bosman            1
## 3    Shoaib Akhtar AB de Villiers            0
## 4    Shoaib Akhtar      JP Duminy            8
## 5    Shoaib Akhtar      CA Ingram           11
## 6    Shoaib Akhtar      DA Miller            4
## 7     Abdul Razzaq       GC Smith            8
## 8     Abdul Razzaq      LE Bosman            1
## 9     Abdul Razzaq      CA Ingram            1
## 10    Abdul Razzaq      DA Miller            9
## 11 Mohammad Hafeez       GC Smith            0
## 12 Mohammad Hafeez      JP Duminy            7
## 13 Mohammad Hafeez      CA Ingram            3
## 14        Umar Gul      JP Duminy            6
## 15        Umar Gul      CA Ingram           11
## 16     Saeed Ajmal      JP Duminy           10
## 17     Saeed Ajmal      CA Ingram            7
## 18   Shahid Afridi      JP Duminy           10
## 19   Shahid Afridi      CA Ingram           13

15. Match worm graph

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

matchWormGraph(aus_ind,'Australia',"India")

matchWormGraph(sl_wi,'Sri Lanka',"West Indies")

Conclusion

This post included all functions between 2 opposing countries from the package yorkr for 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!

Important note: Do check out my other posts using yorkr at yorkr-posts