Introduction
Yorker (noun) :A yorker is a bowling delivery in cricket, that pitches at or around the batsman’s toes. Also known as ‘toe crusher’
My package ‘yorkr’ is now available on CRAN. This package is based on data from Cricsheet. Cricsheet has the data of ODIs, Test, Twenty20 and IPL matches as yaml files. The yorkr package provides functions to convert the yaml files to more easily R consumable entities, namely dataframes. In fact all ODI matches have already been converted and are available for use at yorkrData. However as future matches are added to Cricsheet, you will have to convert the match files yourself. More details below.
If you are passionate about cricket, and love analyzing cricket performances, then check out my 2 racy books on cricket! In my books, I perform detailed yet compact analysis of performances of both batsmen, bowlers besides evaluating team & match performances in Tests , ODIs, T20s & IPL. You can buy my books on cricket from Amazon at $12.99 for the paperback and $4.99/$6.99 respectively for the kindle versions. The books can be accessed at Cricket analytics with cricketr and Beaten by sheer pace-Cricket analytics with yorkr A must read for any cricket lover! Check it out!!
This post can be viewed at RPubs at yorkr-Part1 or can also be downloaded as a PDF document yorkr-1.pdf
Checkout my interactive Shiny apps GooglyPlus2021 (interactive plots ) and GooglyPlusPlus2021 (analysis in specific intervals) which can be used to analyze IPL players, teams and matches.
Important note: Do check out the python avatar of cricketr, ‘cricpy’ in my post ‘Introducing cricpy:A python package to analyze performances of cricketers”
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”
1. First things first
- yorkr currently has a total 70 functions as of now. I have intentionally avoided abbreviating function names by dropping vowels, as is the usual practice in coding, because the resulting abbreviated names created would be very difficult to remember, and use. So instead of naming a function as tmBmenPrtshpOppnAllMtches(), I have used the longer form for e.g. teamBatsmenPartnershipOppnAllmatches(), which is much clearer. The longer form will be more intuitive. Moreover RStudio prompts the the different functions which have the same prefix and one does not need to type in the entire function name.
- The package yorkr has 4 classes of functions
- Class 1- Team performances in a match
- Class 2- Team performances in all matches against a single oppostion (e.g. all matches of India vs Australia or all matches of England vs Pakistan etc.)
- Class 3- Team performance in all matches against all Opposition (India vs All,Pakistan vs All etc.)
- Class 4- Individual performances of batsmen and bowlers
In this post I will be looking into Class 1 functions, namely the performances of opposing teams in a single match
The list of functions are
- teamBattingScorecardMatch()
- teamBatsmenPartnershipMatch()
- teamBatsmenVsBowlersMatch()
- teamBowlingScorecardMatch()
- teamBowlingWicketKindMatch()
- teamBowlingWicketRunsMatch()
- teamBowlingWicketRunsMatch()
- teamBowlingWicketMatch()
- teamBowlersVsBatsmenMatch()
- matchWormGraph()
2. Install the package from CRAN
library(yorkr)
rm(list=ls())
3. Convert and save yaml file to dataframe
This function will convert a 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. This is seen below.
convertYaml2RDataframe("225171.yaml","./source","./data")
## [1] "./source/225171.yaml"
## [1] "first loop"
## [1] "second loop"
setwd("./data")
dir()
## [1] "Australia-India-2012-02-12.RData"
## [2] "Bangladesh-Zimbabwe-2009-10-27.RData"
## [3] "convertedFiles.txt"
## [4] "England-New Zealand-2007-01-30.RData"
## [5] "Ireland-England-2006-06-13.RData"
## [6] "Pakistan-South Africa-2013-11-08.RData"
## [7] "Sri Lanka-West Indies-2011-02-06.RData"
setwd("..")
4. Convert and save all yaml files to dataframes
This function will convert all yaml files from a source directory to dataframes and save it in the target directory with the names as mentioned above.
convertAllYaml2RDataframes("./source",targetDirMen=".",targetDirWomen=".")
## [1] 1
## i= 1 file= ./source/225171.yaml
## [1] "first loop"
## [1] "second loop"
## [1] 633 25
5. yorkrData – A Github repositiory
Cricsheet has ODI matches from 2006. There are a total of 1167 ODI matches(files) out of which 34 yaml files had format problems and were skipped. Incidentally I have already converted the 1133 yaml files in the ODI directory of Cricsheet to dataframes and saved then as RData. The rest of the yaml files ave already been converted to RData and are available for use. All the converted RData files can be accessed from my Github link yorkrData under the folder ODI-matches. You will need to use the functions to convert new match files, as they are added to Cricsheet. There is aslo a file named ‘convertedFiles’ which will have the name of the original file and the converted file as below
convertedFiles
- 225171.yaml:Ireland-England-2006-06-13.RData
- 225245.yaml:England-Pakistan-2006-08-30.RData
- 225246.yaml:England-Pakistan-2006-09-02.RData …
You can download the the zip of the files and use it directly in the functions as follows
Note 1: The package in its current form handles ODIs,T20s and IPL T20 matches
Note 2: The link to the converted data frames have been provided above. The dataframes are around 600 rows x 25 columns. In this post I have created 10 functions that analyze team performances in a match. However you are free to slice and dice the dataframe in any way you like. If you do come up with interesting analyses, please do attribute the source of the data to Cricsheet, and my package yorkr and my blog. I would appreciate it if you could send me a note. .
6. Load the match data as dataframes
As mentioned above in this post I will using the functions from Class 1. For this post I will be using the 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()
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
aus_ind <- getMatchDetails("Australia","India","2012-02-12",dir="./data")
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 matches are
- Australia vs India – 2012-02-12, Adelaide
- England vs New Zealand – 2007-01-30, Perth
- Pakistan vs South Africa – 2013-07-08, UAE
- Sri Lanka vs West Indioes -2011-02-06, Colombo(SSC)
- Bangladesh vs Zimbabwe -2009-10-27, Dhaka
Directly load RData from file
load("./data/Australia-India-2012-02-12.RData")
aus_ind <- overs
load("./data/England-New Zealand-2007-01-30.RData")
eng_nz <- overs
load("./data/Pakistan-South Africa-2013-11-08.RData")
pak_sa <- overs
load("./data/Sri Lanka-West Indies-2011-02-06.RData")
sl_wi<- overs
load("./data/Bangladesh-Zimbabwe-2009-10-27.RData")
ban_zim <- overs
7. Team batting scorecard
Compute and display the batting scorecard of the teams in the match. The top batsmen in are G Gambhir(Ind), PJ Forrest(Aus), Q De Kock(SA) and KC Sangakkara(SL)
teamBattingScorecardMatch(aus_ind,'India')
## Total= 258
## Source: local data frame [8 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (dbl) (dbl) (dbl)
## 1 G Gambhir 110 7 0 92
## 2 V Sehwag 20 3 0 20
## 3 V Kohli 28 1 0 18
## 4 RG Sharma 41 1 1 33
## 5 SK Raina 30 3 1 38
## 6 MS Dhoni 57 0 1 44
## 7 RA Jadeja 8 0 0 12
## 8 R Ashwin 2 0 0 1
teamBattingScorecardMatch(aus_ind,'Australia')
## Total= 260
## Source: local data frame [9 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (dbl) (dbl) (dbl)
## 1 DA Warner 23 2 0 18
## 2 RT Ponting 13 1 0 6
## 3 MJ Clarke 43 5 0 38
## 4 PJ Forrest 83 5 2 66
## 5 DJ Hussey 76 5 0 72
## 6 DT Christian 36 2 0 39
## 7 MS Wade 17 1 0 16
## 8 RJ Harris 2 0 0 2
## 9 CJ McKay 3 0 0 3
teamBattingScorecardMatch(pak_sa,'South Africa')
## Total= 256
## Source: local data frame [7 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (dbl) (dbl) (dbl)
## 1 Q de Kock 132 9 1 112
## 2 HM Amla 50 6 0 46
## 3 F du Plessis 21 1 0 10
## 4 AB de Villiers 40 2 0 30
## 5 DA Miller 9 0 0 5
## 6 JP Duminy 20 1 1 25
## 7 R McLaren 21 3 1 28
teamBattingScorecardMatch(sl_wi,'Sri Lanka')
## Total= 261
## Source: local data frame [10 x 5]
##
## batsman ballsPlayed fours sixes runs
## (fctr) (int) (dbl) (dbl) (dbl)
## 1 WU Tharanga 50 5 0 39
## 2 TM Dilshan 27 2 1 30
## 3 KC Sangakkara 103 4 1 75
## 4 DPMD Jayawardene 52 2 0 44
## 5 CK Kapugedera 17 0 0 17
## 6 TT Samaraweera 7 0 0 4
## 7 NLTC Perera 8 0 0 6
## 8 AD Mathews 22 1 1 36
## 9 HMRKB Herath 4 0 0 2
## 10 BAW Mendis 6 1 0 8
8. Plot the team batting partnerships
The functions below plot the team batting partnetship 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 th epackages like rcharts, ggvis,googleVis or plotly.
teamBatsmenPartnershipMatch(pak_sa,"Pakistan","South Africa")
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teamBatsmenPartnershipMatch(eng_nz,"New Zealand","England",plot=TRUE)
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teamBatsmenPartnershipMatch(ban_zim,"Bangladesh","Zimbabwe",plot=FALSE)
## batsman nonStriker runs
## 1 Tamim Iqbal Junaid Siddique 0
## 2 Tamim Iqbal Mohammad Ashraful 5
## 3 Junaid Siddique Tamim Iqbal 0
## 4 Mohammad Ashraful Tamim Iqbal 0
## 5 Mohammad Ashraful Raqibul Hasan 20
## 6 Raqibul Hasan Mohammad Ashraful 13
## 7 Raqibul Hasan Shakib Al Hasan 3
## 8 Shakib Al Hasan Raqibul Hasan 12
## 9 Shakib Al Hasan Mushfiqur Rahim 1
## 10 Mushfiqur Rahim Shakib Al Hasan 1
## 11 Mushfiqur Rahim Naeem Islam 30
## 12 Mushfiqur Rahim Abdur Razzak 6
## 13 Mushfiqur Rahim Dolar Mahmud 11
## 14 Mushfiqur Rahim Rubel Hossain 8
## 15 Mahmudullah Mushfiqur Rahim 4
## 16 Naeem Islam Mushfiqur Rahim 21
## 17 Abdur Razzak Mushfiqur Rahim 3
## 18 Dolar Mahmud Mushfiqur Rahim 41
teamBatsmenPartnershipMatch(aus_ind,"India","Australia", plot=TRUE)
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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)
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teamBatsmenVsBowlersMatch(aus_ind,'Australia',"India",plot=TRUE)
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teamBatsmenVsBowlersMatch(ban_zim,'Zimbabwe',"Bangladesh", plot=TRUE)
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m <- teamBatsmenVsBowlersMatch(sl_wi,'West Indies',"Sri Lanka", plot=FALSE)
m
## Source: local data frame [35 x 3]
## Groups: batsman [?]
##
## batsman bowler runsConceded
## (fctr) (fctr) (dbl)
## 1 CH Gayle CRD Fernando 0
## 2 DM Bravo CRD Fernando 15
## 3 DM Bravo NLTC Perera 21
## 4 DM Bravo AD Mathews 10
## 5 DM Bravo BAW Mendis 11
## 6 DM Bravo CK Kapugedera 1
## 7 DM Bravo TM Dilshan 5
## 8 DM Bravo HMRKB Herath 16
## 9 AB Barath NLTC Perera 0
## 10 RR Sarwan CRD Fernando 6
## .. ... ... ...
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 [6 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 LE Plunkett 9 0 54 3
## 2 CT Tremlett 10 0 72 1
## 3 A Flintoff 10 0 66 0
## 4 MS Panesar 10 2 35 2
## 5 JWM Dalrymple 5 0 43 0
## 6 PD Collingwood 6 0 36 1
teamBowlingScorecardMatch(eng_nz,'New Zealand')
## Source: local data frame [6 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 JEC Franklin 8 1 45 1
## 2 SE Bond 10 0 58 1
## 3 JDP Oram 5 0 23 0
## 4 JS Patel 10 0 53 1
## 5 DL Vettori 10 0 40 3
## 6 CD McMillan 7 1 38 2
teamBowlingScorecardMatch(aus_ind,'Australia')
## Source: local data frame [6 x 5]
##
## bowler overs maidens runs wickets
## (fctr) (int) (int) (dbl) (dbl)
## 1 RJ Harris 10 0 57 1
## 2 MA Starc 8 0 49 0
## 3 CJ McKay 10 1 53 3
## 4 DT Christian 10 0 45 0
## 5 DJ Hussey 3 0 13 0
## 6 XJ Doherty 9 0 51 2
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")
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teamBowlingWicketKindMatch(aus_ind,"Australia","India")
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teamBowlingWicketKindMatch(pak_sa,"South Africa","Pakistan")
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m <-teamBowlingWicketKindMatch(sl_wi,"Sri Lanka",plot=FALSE)
m
## bowler wicketKind wicketPlayerOut runs
## 1 CRD Fernando bowled CH Gayle 45
## 2 NLTC Perera caught AB Barath 36
## 3 HMRKB Herath lbw RR Sarwan 54
## 4 BAW Mendis caught S Chanderpaul 46
## 5 NLTC Perera lbw DM Bravo 36
## 6 NLTC Perera caught DJG Sammy 36
## 7 CRD Fernando caught DJ Bravo 45
## 8 BAW Mendis caught NO Miller 46
## 9 BAW Mendis caught CS Baugh 46
## 10 BAW Mendis caught SJ Benn 46
## 11 AD Mathews noWicket noWicket 33
## 12 CK Kapugedera noWicket noWicket 7
## 13 TM Dilshan noWicket noWicket 25
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")
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teamBowlingWicketRunsMatch(aus_ind,"Australia","India")
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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 R Rampaul 5 0 44 1
## 2 DJG Sammy 10 1 61 1
## 3 DJ Bravo 10 0 58 3
## 4 CH Gayle 10 0 34 0
## 5 SJ Benn 10 1 38 4
## 6 NO Miller 5 0 35 0
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 LE Plunkett lbw SP Fleming 54
## 2 LE Plunkett caught PG Fulton 54
## 3 PD Collingwood caught LRPL Taylor 36
## 4 MS Panesar stumped CD McMillan 35
## 5 LE Plunkett caught L Vincent 54
## 6 MS Panesar caught BB McCullum 35
## 7 CT Tremlett caught JEC Franklin 72
## 8 A Flintoff noWicket noWicket 66
## 9 JWM Dalrymple noWicket noWicket 43
teamBowlingWicketMatch(sl_wi,"Sri Lanka","West Indies")
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teamBowlingWicketMatch(eng_nz,"New Zealand","England")
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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")
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teamBowlersVsBatsmenMatch(aus_ind,"India","Australia")
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teamBowlersVsBatsmenMatch(eng_nz,"England","New Zealand")
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m <- teamBowlersVsBatsmenMatch(pak_sa,"Pakistan",plot=FALSE)
m
## Source: local data frame [30 x 3]
## Groups: bowler [?]
##
## bowler batsman runsConceded
## (fctr) (fctr) (dbl)
## 1 Mohammad Irfan Q de Kock 25
## 2 Mohammad Irfan HM Amla 17
## 3 Mohammad Irfan F du Plessis 0
## 4 Mohammad Irfan AB de Villiers 9
## 5 Sohail Tanvir Q de Kock 11
## 6 Sohail Tanvir HM Amla 6
## 7 Sohail Tanvir JP Duminy 9
## 8 Sohail Tanvir R McLaren 12
## 9 Junaid Khan Q de Kock 24
## 10 Junaid Khan HM Amla 6
## .. ... ... ...
15. Match worm graph
The plots below provide the match worm graph for the matches
matchWormGraph(aus_ind,'Australia',"India")
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matchWormGraph(sl_wi,'Sri Lanka',"West Indies")
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Giga thoughts ... 