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). However you may have to do this as future matches are added! 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)

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)

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)

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)

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)