yorkr crashes the IPL party - Part 1
Tinniam V Ganesh
Apr 19, 2016
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 TagoreIntroduction
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
- yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
- yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams
- yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions!
- 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
- convertYaml2RDataframeT20
- 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()
- 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 25or
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 <- overs7. 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 2teamBattingScorecardMatch(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 9teamBattingScorecardMatch(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 10teamBattingScorecardMatch(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 18teamBattingScorecardMatch(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 12teamBattingScorecardMatch(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 1teamBattingScorecardMatch(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 28. 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')
teamBatsmenPartnershipMatch(sh_rcb,'Sunrisers Hyderabad','Royal Challengers Bangalore',plot=TRUE)
teamBatsmenPartnershipMatch(rr_pw,'Pune Warriors','Rajasthan Royals')
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 12teamBatsmenPartnershipMatch(kxp_dd,'Kings XI Punjab','Delhi Daredevils',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(sh_rcb,"Sunrisers Hyderabad","Royal Challengers Bangalore", plot=TRUE)
teamBatsmenVsBowlersMatch(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')
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 8teamBatsmenVsBowlersMatch(dc_csk,"Chennai Super Kings","Deccan Chargers")
teamBatsmenVsBowlersMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils")
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 0teamBowlingScorecardMatch(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 0teamBowlingScorecardMatch(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 0teamBowlingScorecardMatch(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 1teamBowlingScorecardMatch(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 2teamBowlingScorecardMatch(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 22 211. 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')
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 16teamBowlingWicketKindMatch(dc_csk,"Chennai Super Kings","Deccan Chargers")
teamBowlingWicketKindMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils",plot=TRUE)
teamBowlingWicketKindMatch(sh_rcb,"Royal Challengers Bangalore","Sunrisers Hyderabad")
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")
teamBowlingWicketRunsMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils",plot=TRUE)
teamBowlingWicketRunsMatch(sh_rcb,"Sunrisers Hyderabad","Royal Challengers Bangalore")
teamBowlingWicketRunsMatch(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')
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 1613. Wickets taken by bowler
The plots provide the wickets taken by the bowler
teamBowlingWicketMatch(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')
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 16teamBowlingWicketMatch(sh_rcb,"Royal Challengers Bangalore","Sunrisers Hyderabad")
teamBowlingWicketMatch(dc_csk,"Deccan Chargers", "Chennai Super Kings")
teamBowlingWicketMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils",plot=TRUE)
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")
teamBowlersVsBatsmenMatch(kxp_dd,"Kings XI Punjab","Delhi Daredevils",plot=TRUE)
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')
teamBowlersVsBatsmenMatch(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')
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")
matchWormGraph(kxp_dd,"Kings XI Punjab","Delhi Daredevils")
matchWormGraph(sh_rcb,"Sunrisers Hyderabad","Royal Challengers Bangalore")
matchWormGraph(rr_pw,'Pune Warriors','Rajasthan Royals')
matchWormGraph(kkr_mi,'Kolkata Knight Riders','Mumbai Indians')
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!
You may also like
- Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
- Introducing cricketr! : An R package to analyze performances of cricketers
- Simulating a Web Joint in Android
- Elements of CRUD with NodeExpress and MongoDB using Enide Studio
- Cricket analytics with cricketr
- Sixer – R package cricketr’s new Shiny avatar
- Natural language processing: What would Shakespeare say?
- Experiment with deblurring using OpenCV
- Presentation on Wireless Technologies – Part 2