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")

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

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

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

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")

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