Cricpy takes a swing at the ODIs


No computer has ever been designed that is ever aware of what it’s doing; but most of the time, we aren’t either.” Marvin Minksy

“The competent programmer is fully aware of the limited size of his own skull. He therefore approaches his task with full humility, and avoids clever tricks like the plague” Edgser Djikstra

Introduction

In this post, cricpy, the Python avatar of my R package cricketr, learns some new tricks to be able to handle ODI matches. To know more about my R package cricketr see Re-introducing cricketr! : An R package to analyze performances of cricketers

Cricpy uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package supports only Test cricket

You should be able to install the package using pip install cricpy and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use

To know how to use cricpy see Introducing cricpy:A python package to analyze performances of cricketers. To the original version of cricpy, I have added 3 new functions for ODI. The earlier functions work for Test and ODI.

This post is also hosted on Rpubs at Cricpy takes a swing at the ODIs. You can also down the pdf version of this post at cricpy-odi.pdf

You can fork/clone the package at Github cricpy

The cricpy package

The data for a particular player in ODI can be obtained with the getPlayerDataOD() function. To do you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Virat Kohli, Virendar Sehwag, Chris Gayle etc. This will bring up a page which have the profile number for the player e.g. for Virat Kohli this would be http://www.espncricinfo.com/india/content/player/253802.html. Hence, Kohli’s profile is 253802. This can be used to get the data for Virat Kohlis shown below

The cricpy package is a clone of my R package cricketr. The signature of all the python functions are identical with that of its clone ‘cricketr’, with only the necessary variations between Python and R. It may be useful to look at my post R vs Python: Different similarities and similar differences. In fact if you are familar with one of the lanuguages you can look up the package in the other and you will notice the parallel constructs.

You can fork/clone the package at Github cricpy

Note: The charts are self-explanatory and I have added much of my owy interpretation to it. Do look at the plots closely and check out the performances for yourself.

1 Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy.analytics as ca 

2. Invoking functions with Python package crlcpy

import cricpy.analytics as ca 
ca.batsman4s("./kohli.csv","Virat Kohli")

3. Getting help from cricpy – Python

import cricpy.analytics as ca 
help(ca.getPlayerDataOD)
## Help on function getPlayerDataOD in module cricpy.analytics:
## 
## getPlayerDataOD(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2, 3], result=[1, 2, 3, 5], create=True)
##     Get the One day player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory
##     
##     Description
##     
##     Get the player data given the profile of the batsman. The allowed inputs are home,away or both and won,lost or draw of matches. The data is stored in a <player>.csv file in a directory specified. This function also returns a data frame of the player
##     
##     Usage
##     
##     getPlayerDataOD(profile, opposition="",host="",dir = "../", file = "player001.csv", 
##     type = "batting", homeOrAway = c(1, 2, 3), result = c(1, 2, 3,5))
##     Arguments
##     
##     profile     
##     This is the profile number of the player to get data. This can be obtained from http://www.espncricinfo.com/ci/content/player/index.html. Type the name of the player and click search. This will display the details of the player. Make a note of the profile ID. For e.g For Virender Sehwag this turns out to be http://www.espncricinfo.com/india/content/player/35263.html. Hence the profile for Sehwag is 35263
##     opposition      The numerical value of the opposition country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,Bermuda:12, England:1,Hong Kong:19,India:6,Ireland:29, Netherlands:15,New Zealand:5,Pakistan:7,Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27, West Indies:4, Zimbabwe:9; Africa XI:405 Note: If no value is entered for opposition then all teams are considered
##     host            The numerical value of the host country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,Ireland:29,Malaysia:16,New Zealand:5,Pakistan:7, Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27,West Indies:4, Zimbabwe:9 Note: If no value is entered for host then all host countries are considered
##     dir 
##     Name of the directory to store the player data into. If not specified the data is stored in a default directory "../data". Default="../data"
##     file        
##     Name of the file to store the data into for e.g. tendulkar.csv. This can be used for subsequent functions. Default="player001.csv"
##     type        
##     type of data required. This can be "batting" or "bowling"
##     homeOrAway  
##     This is vector with either or all 1,2, 3. 1 is for home 2 is for away, 3 is for neutral venue
##     result      
##     This is a vector that can take values 1,2,3,5. 1 - won match 2- lost match 3-tied 5- no result
##     Details
##     
##     More details can be found in my short video tutorial in Youtube https://www.youtube.com/watch?v=q9uMPFVsXsI
##     
##     Value
##     
##     Returns the player's dataframe
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh <tvganesh.85@gmail.com>
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://www.espncricinfo.com/ci/content/stats/index.html
##     https://gigadom.wordpress.com/
##     
##     See Also
##     
##     getPlayerDataSp getPlayerData
##     
##     Examples
##     
##     
##     ## Not run: 
##     # Both home and away. Result = won,lost and drawn
##     sehwag =getPlayerDataOD(35263,dir="../cricketr/data", file="sehwag1.csv",
##     type="batting", homeOrAway=[1,2],result=[1,2,3,4])
##     
##     # Only away. Get data only for won and lost innings
##     sehwag = getPlayerDataOD(35263,dir="../cricketr/data", file="sehwag2.csv",
##     type="batting",homeOrAway=[2],result=[1,2])
##     
##     # Get bowling data and store in file for future
##     malinga = getPlayerData(49758,dir="../cricketr/data",file="malinga1.csv",
##     type="bowling")
##     
##     # Get Dhoni's ODI record in Australia against Australua
##     dhoni = getPlayerDataOD(28081,opposition = 2,host=2,dir=".",
##     file="dhoniVsAusinAusOD",type="batting")
##     
##     ## End(Not run)

The details below will introduce the different functions that are available in cricpy.

4. Get the ODI player data for a player using the function getPlayerDataOD()

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. kohli.csv as above) which can then be reused for all other functions). Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerDataOD for all subsequent analyses

import cricpy.analytics as ca
#sehwag=ca.getPlayerDataOD(35263,dir=".",file="sehwag.csv",type="batting")
#kohli=ca.getPlayerDataOD(253802,dir=".",file="kohli.csv",type="batting")
#jayasuriya=ca.getPlayerDataOD(49209,dir=".",file="jayasuriya.csv",type="batting")
#gayle=ca.getPlayerDataOD(51880,dir=".",file="gayle.csv",type="batting")

Included below are some of the functions that can be used for ODI batsmen and bowlers. For this I have chosen, Virat Kohli, ‘the run machine’ who is on-track for breaking many of the Test & ODI records

5 Virat Kohli’s performance – Basic Analyses

The 3 plots below provide the following for Virat Kohli

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges
import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("./kohli.csv","Virat Kohli")

ca.batsmanMeanStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanRunsRanges("./kohli.csv","Virat Kohli")

6. More analyses

import cricpy.analytics as ca
ca.batsman4s("./kohli.csv","Virat Kohli")

ca.batsman6s("./kohli.csv","Virat Kohli")

ca.batsmanDismissals("./kohli.csv","Virat Kohli")

ca.batsmanScoringRateODTT("./kohli.csv","Virat Kohli")


7. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of Kohli’s Runs versus Balls Faced and Minutes at crease. A linear regression plane is then fitted between Runs and Balls Faced + Minutes at crease

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

Average runs at different venues

The plot below gives the average runs scored by Kohli at different grounds. The plot also the number of innings at each ground as a label at x-axis.

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("./kohli.csv","Virat Kohli")

9. Average runs against different opposing teams

This plot computes the average runs scored by Kohli against different countries.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("./kohli.csv","Virat Kohli")

10 . Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman. For this the performance of Kohli is plotted as a 3D scatter plot with Runs versus Balls Faced + Minutes at crease. K-Means. The centroids of 3 clusters are computed and plotted. In this plot Kohli’s highest tendencies are computed and plotted using K-Means

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("./kohli.csv","Virat Kohli")

A look at the Top 4 batsman – Kohli, Jayasuriya, Sehwag and Gayle

The following batsmen have been very prolific in ODI cricket and will be used for the analyses

  1. Virat Kohli: Runs – 10232, Average:59.83 ,Strike rate-92.88
  2. Sanath Jayasuriya : Runs – 13430, Average:32.36 ,Strike rate-91.2
  3. Virendar Sehwag :Runs – 8273, Average:35.05 ,Strike rate-104.33
  4. Chris Gayle : Runs – 9727, Average:37.12 ,Strike rate-85.82

The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs

12. Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("./kohli.csv","Virat Kohli")

ca.batsmanPerfBoxHist("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanPerfBoxHist("./gayle.csv","Chris Gayle")

ca.batsmanPerfBoxHist("./sehwag.csv","Virendar Sehwag")

13 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 (ignore the dip at the end of all plots. Need to check why this is so!). Kohli’s performance has been steadily improving over the years, so has Sehwag. Gayle seems to be on the way down

import cricpy.analytics as ca
ca.batsmanMovingAverage("./kohli.csv","Virat Kohli")

ca.batsmanMovingAverage("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanMovingAverage("./gayle.csv","Chris Gayle")

ca.batsmanMovingAverage("./sehwag.csv","Virendar Sehwag")

14 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career. Kohli seems to be getting better with time and reaches a cumulative average of 45+. Sehwag improves with time and reaches around 35+. Chris Gayle drops from 42 to 35

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeAverageRuns("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanCumulativeAverageRuns("./gayle.csv","Chris Gayle")

ca.batsmanCumulativeAverageRuns("./sehwag.csv","Virendar Sehwag")

15 Cumulative Average strike rate of batsman in career

Sehwag has the best strike rate of almost 90. Kohli and Jayasuriya have a cumulative strike rate of 75.

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeStrikeRate("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanCumulativeStrikeRate("./gayle.csv","Chris Gayle")

ca.batsmanCumulativeStrikeRate("./sehwag.csv","Virendar Sehwag")

16 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman . It can be seen that Virat Kohli towers above all others in the runs. He is followed by Chris Gayle and then Sehwag

import cricpy.analytics as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percentages for each 10 run bucket. The plot below show Sehwag has the best strike rate, followed by Jayasuriya

import cricpy.analytics as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

18. 3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A 3D prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

ca.battingPerf3d("./jayasuriya.csv","Sanath jayasuriya")

ca.battingPerf3d("./gayle.csv","Chris Gayle")

ca.battingPerf3d("./sehwag.csv","Virendar Sehwag")

3D plot of Runs vs Balls Faced and Minutes at Crease

From the plot below it can be seen that Sehwag has more runs by way of 4s than 1’s,2’s or 3s. Gayle and Jayasuriya have large number of 6s

import cricpy.analytics as ca
frames = ["./sehwag.csv","./kohli.csv","./gayle.csv","./jayasuriya.csv"]
names = ["Sehwag","Kohli","Gayle","Jayasuriya"]
ca.batsman4s6s(frames,names)

20. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
kohli= ca.batsmanRunsPredict("./kohli.csv",newDF,"Kohli")
print(kohli)
##             BF        Mins        Runs
## 0    10.000000   30.000000    6.807407
## 1    37.857143   70.714286   36.034833
## 2    65.714286  111.428571   65.262259
## 3    93.571429  152.142857   94.489686
## 4   121.428571  192.857143  123.717112
## 5   149.285714  233.571429  152.944538
## 6   177.142857  274.285714  182.171965
## 7   205.000000  315.000000  211.399391
## 8   232.857143  355.714286  240.626817
## 9   260.714286  396.428571  269.854244
## 10  288.571429  437.142857  299.081670
## 11  316.428571  477.857143  328.309096
## 12  344.285714  518.571429  357.536523
## 13  372.142857  559.285714  386.763949
## 14  400.000000  600.000000  415.991375

The fitted model is then used to predict the runs that the batsmen will score for a given Balls faced and Minutes at crease.

21 Analysis of Top Bowlers

The following 4 bowlers have had an excellent career and will be used for the analysis

  1. Muthiah Muralitharan:Wickets: 534, Average = 23.08, Economy Rate – 3.93
  2. Wasim Akram : Wickets: 502, Average = 23.52, Economy Rate – 3.89
  3. Shaun Pollock: Wickets: 393, Average = 24.50, Economy Rate – 3.67
  4. Javagal Srinath : Wickets:315, Average – 28.08, Economy Rate – 4.44

How do Muralitharan, Akram, Pollock and Srinath compare with one another with respect to wickets taken and the Economy Rate. The next set of plots compute and plot precisely these analyses.

22. Get the bowler’s data

This plot below computes the percentage frequency of number of wickets taken for e.g 1 wicket x%, 2 wickets y% etc and plots them as a continuous line

import cricpy.analytics as ca
#akram=ca.getPlayerDataOD(43547,dir=".",file="akram.csv",type="bowling")
#murali=ca.getPlayerDataOD(49636,dir=".",file="murali.csv",type="bowling")
#pollock=ca.getPlayerDataOD(46774,dir=".",file="pollock.csv",type="bowling")
#srinath=ca.getPlayerDataOD(34105,dir=".",file="srinath.csv",type="bowling")

23. Wicket Frequency Plot

This plot below plots the frequency of wickets taken for each of the bowlers

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("./murali.csv","M Muralitharan")

ca.bowlerWktsFreqPercent("./akram.csv","Wasim Akram")

ca.bowlerWktsFreqPercent("./pollock.csv","Shaun Pollock")

ca.bowlerWktsFreqPercent("./srinath.csv","J Srinath")

24. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken. Murali’s median runs for wickets ia around 40 while Akram, Pollock and Srinath it is around 32+ runs. The spread around the median is larger for these 3 bowlers in comparison to Murali

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("./murali.csv","M Muralitharan")

ca.bowlerWktsRunsPlot("./akram.csv","Wasim Akram")

ca.bowlerWktsRunsPlot("./pollock.csv","Shaun Pollock")

ca.bowlerWktsRunsPlot("./srinath.csv","J Srinath")

25 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues. McGrath best performances are at Centurion, Lord’s and Port of Spain averaging about 4 wickets. Kapil Dev’s does good at Kingston and Wellington. Anderson averages 4 wickets at Dunedin and Nagpur

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("./murali.csv","M Muralitharan")

ca.bowlerAvgWktsGround("./akram.csv","Wasim Akram")

ca.bowlerAvgWktsGround("./pollock.csv","Shaun Pollock")

ca.bowlerAvgWktsGround("./srinath.csv","J Srinath")

26 Average wickets against different opposition

The plot gives the average wickets taken by Muralitharan against different countries. The x-axis also includes the number of innings against each team

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("./murali.csv","M Muralitharan")

ca.bowlerAvgWktsOpposition("./akram.csv","Wasim Akram")

ca.bowlerAvgWktsOpposition("./pollock.csv","Shaun Pollock")

ca.bowlerAvgWktsOpposition("./srinath.csv","J Srinath")

27 Wickets taken moving average

From the plot below it can be see James Anderson has had a solid performance over the years averaging about wickets

import cricpy.analytics as ca
ca.bowlerMovingAverage("./murali.csv","M Muralitharan")

ca.bowlerMovingAverage("./akram.csv","Wasim Akram")

ca.bowlerMovingAverage("./pollock.csv","Shaun Pollock")

ca.bowlerMovingAverage("./srinath.csv","J Srinath")

28 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. Muralitharan has consistently taken wickets at an average of 1.6 wickets per game. Shaun Pollock has an average of 1.5

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("./murali.csv","M Muralitharan")

ca.bowlerCumulativeAvgWickets("./akram.csv","Wasim Akram")

ca.bowlerCumulativeAvgWickets("./pollock.csv","Shaun Pollock")

ca.bowlerCumulativeAvgWickets("./srinath.csv","J Srinath")

29 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. Pollock is the most economical, followed by Akram and then Murali

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("./murali.csv","M Muralitharan")

ca.bowlerCumulativeAvgEconRate("./akram.csv","Wasim Akram")

ca.bowlerCumulativeAvgEconRate("./pollock.csv","Shaun Pollock")

ca.bowlerCumulativeAvgEconRate("./srinath.csv","J Srinath")

30 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate shows that Pollock is the most economical of the 4 bowlers. He is followed by Akram and then Murali

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

31 Relative Economy Rate against wickets taken

Pollock is most economical vs number of wickets taken. Murali has the best figures for 4 wickets taken.

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlingER(frames,names)

32 Relative cumulative average wickets of bowlers in career

The plot below shows that McGrath has the best overall cumulative average wickets. While the bowlers are neck to neck around 130 innings, you can see Muralitharan is most consistent and leads the pack after 150 innings in the number of wickets taken.

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

33. Key Findings

The plots above capture some of the capabilities and features of my cricpy package. Feel free to install the package and try it out. Please do keep in mind ESPN Cricinfo’s Terms of Use.

Here are the main findings from the analysis above

Analysis of Top 4 batsman

The analysis of the Top 4 test batsman Tendulkar, Kallis, Ponting and Sangakkara show the folliwing

  1. Kohli is a mean run machine and has been consistently piling on runs. Clearly records will lay shattered in days to come for Kohli
  2. Virendar Sehwag has the best strike rate of the 4, followed by Jayasuriya and then Kohli
  3. Shaun Pollock is the most economical of the bowlers followed by Wasim Akram
  4. Muralitharan is the most consistent wicket of the lot.

Also see
1. Architecting a cloud based IP Multimedia System (IMS)
2. Exploring Quantum Gate operations with QCSimulator
3. Dabbling with Wiener filter using OpenCV
4. Deep Learning from first principles in Python, R and Octave – Part 5
5. Big Data-2: Move into the big league:Graduate from R to SparkR
6. Singularity
7. Practical Machine Learning with R and Python – Part 4
8. Literacy in India – A deepR dive
9. Modeling a Car in Android

To see all posts click Index of Posts

Introducing cricpy:A python package to analyze performances of cricketers


Full many a gem of purest ray serene,
The dark unfathomed caves of ocean bear;
Full many a flower is born to blush unseen,
And waste its sweetness on the desert air.

            Thomas Gray, An Elegy Written In A Country Churchyard
            

Introduction

It is finally here! cricpy, the python avatar , of my R package cricketr is now ready to rock-n-roll! My R package cricketr had its genesis about 3 and some years ago and went through a couple of enhancements. During this time I have always thought about creating an equivalent python package like cricketr. Now I have finally done it.

So here it is. My python package ‘cricpy!!!’

This package uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package supports only Test cricket

You should be able to install the package using pip install cricpy and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use

This post is also hosted on Rpubs at Introducing cricpy. You can also download the pdf version of this post at cricpy.pdf

Do check out my post on R package cricketr at Re-introducing cricketr! : An R package to analyze performances of cricketers

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!!

1

 

This package uses the statistics info available in ESPN Cricinfo Statsguru. T

The cricpy package

The cricpy package has several functions that perform several different analyses on both batsman and bowlers. The package has functions that plot percentage frequency runs or wickets, runs likelihood for a batsman, relative run/strike rates of batsman and relative performance/economy rate for bowlers are available.

Other interesting functions include batting performance moving average, forecasting, performance of a player against different oppositions, contribution to wins and losses etc.

The data for a particular player can be obtained with the getPlayerData() function. To do this you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Rahul Dravid, Virat Kohli, Alastair Cook etc. This will bring up a page which have the profile number for the player e.g. for Rahul Dravid this would be http://www.espncricinfo.com/india/content/player/28114.html. Hence, Dravid’s profile is 28114. This can be used to get the data for Rahul Dravid as shown below

The cricpy package is almost a clone of my R package cricketr. The signature of all the python functions are identical with that of its R avatar namely  ‘cricketr’, with only the necessary variations between Python and R. It may be useful to look at my post R vs Python: Different similarities and similar differences. In fact if you are familiar with one of the languages you can look up the package in the other and you will notice the parallel constructs.

You can fork/clone the cricpy package at Github cricpy

The following 2 examples show the similarity between cricketr and cricpy packages

1a.Importing cricketr – R

Importing cricketr in R

#install.packages("cricketr")
library(cricketr)

2a. Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy
# You could either do
#1.  
import cricpy.analytics as ca 
#ca.batsman4s("../dravid.csv","Rahul Dravid")
# Or
#2.
from cricpy.analytics import *
#batsman4s("../dravid.csv","Rahul Dravid")

I would recommend using option 1 namely ca.batsman4s() as I may add an advanced analytics module in the future to cricpy.

2 Invoking functions

You can seen how the 2 calls are identical for both the R package cricketr and the Python package cricpy

2a. Invoking functions with R package ‘cricketr’

library(cricketr)
batsman4s("../dravid.csv","Rahul Dravid")

2b. Invoking functions with Python package ‘cricpy’

import cricpy.analytics as ca 
ca.batsman4s("../dravid.csv","Rahul Dravid")

 

3a. Getting help from cricketr – R

#help("getPlayerData")

3b. Getting help from cricpy – Python

help(ca.getPlayerData)
## Help on function getPlayerData in module cricpy.analytics:
## 
## getPlayerData(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2], result=[1, 2, 4], create=True)
##     Get the player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory
##     
##     Description
##     
##     Get the player data given the profile of the batsman. The allowed inputs are home,away or both and won,lost or draw of matches. The data is stored in a <player>.csv file in a directory specified. This function also returns a data frame of the player
##     
##     Usage
##     
##     getPlayerData(profile,opposition="",host="",dir="./data",file="player001.csv",
##     type="batting", homeOrAway=c(1,2),result=c(1,2,4))
##     Arguments
##     
##     profile     
##     This is the profile number of the player to get data. This can be obtained from http://www.espncricinfo.com/ci/content/player/index.html. Type the name of the player and click search. This will display the details of the player. Make a note of the profile ID. For e.g For Sachin Tendulkar this turns out to be http://www.espncricinfo.com/india/content/player/35320.html. Hence the profile for Sachin is 35320
##     opposition  
##     The numerical value of the opposition country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,New Zealand:5,Pakistan:7,South Africa:3,Sri Lanka:8, West Indies:4, Zimbabwe:9
##     host        
##     The numerical value of the host country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,New Zealand:5,Pakistan:7,South Africa:3,Sri Lanka:8, West Indies:4, Zimbabwe:9
##     dir 
##     Name of the directory to store the player data into. If not specified the data is stored in a default directory "./data". Default="./data"
##     file        
##     Name of the file to store the data into for e.g. tendulkar.csv. This can be used for subsequent functions. Default="player001.csv"
##     type        
##     type of data required. This can be "batting" or "bowling"
##     homeOrAway  
##     This is a list with either 1,2 or both. 1 is for home 2 is for away
##     result      
##     This is a list that can take values 1,2,4. 1 - won match 2- lost match 4- draw
##     Details
##     
##     More details can be found in my short video tutorial in Youtube https://www.youtube.com/watch?v=q9uMPFVsXsI
##     
##     Value
##     
##     Returns the player's dataframe
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh <tvganesh.85@gmail.com>
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://www.espncricinfo.com/ci/content/stats/index.html
##     https://gigadom.wordpress.com/
##     
##     See Also
##     
##     getPlayerDataSp
##     
##     Examples
##     
##     ## Not run: 
##     # Both home and away. Result = won,lost and drawn
##     tendulkar = getPlayerData(35320,dir=".", file="tendulkar1.csv",
##     type="batting", homeOrAway=[1,2],result=[1,2,4])
##     
##     # Only away. Get data only for won and lost innings
##     tendulkar = getPlayerData(35320,dir=".", file="tendulkar2.csv",
##     type="batting",homeOrAway=[2],result=[1,2])
##     
##     # Get bowling data and store in file for future
##     kumble = getPlayerData(30176,dir=".",file="kumble1.csv",
##     type="bowling",homeOrAway=[1],result=[1,2])
##     
##     #Get the Tendulkar's Performance against Australia in Australia
##     tendulkar = getPlayerData(35320, opposition = 2,host=2,dir=".", 
##     file="tendulkarVsAusInAus.csv",type="batting")

The details below will introduce the different functions that are available in cricpy.

3. Get the player data for a player using the function getPlayerData()

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. dravid.csv as above) which can then be reused for all other functions). Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerData for all subsequent analyses

import cricpy.analytics as ca
#dravid =ca.getPlayerData(28114,dir="..",file="dravid.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])
#acook =ca.getPlayerData(11728,dir="..",file="acook.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])
import cricpy.analytics as ca
#lara =ca.getPlayerData(52337,dir="..",file="lara.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])253802
#kohli =ca.getPlayerData(253802,dir="..",file="kohli.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])

4 Rahul Dravid’s performance – Basic Analyses

The 3 plots below provide the following for Rahul Dravid

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges
import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("../dravid.csv","Rahul Dravid")

ca.batsmanMeanStrikeRate("../dravid.csv","Rahul Dravid")

ca.batsmanRunsRanges("../dravid.csv","Rahul Dravid") 

5. More analyses

import cricpy.analytics as ca
ca.batsman4s("../dravid.csv","Rahul Dravid")

ca.batsman6s("../dravid.csv","Rahul Dravid") 

ca.batsmanDismissals("../dravid.csv","Rahul Dravid")

6. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of Dravid Runs versus Balls Faced and Minutes at crease. A linear regression plane is then fitted between Runs and Balls Faced + Minutes at crease

import cricpy.analytics as ca
ca.battingPerf3d("../dravid.csv","Rahul Dravid")

7. Average runs at different venues

The plot below gives the average runs scored by Dravid at different grounds. The plot also the number of innings at each ground as a label at x-axis. It can be seen Dravid did great in Rawalpindi, Leeds, Georgetown overseas and , Mohali and Bangalore at home

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("../dravid.csv","Rahul Dravid")

 

8. Average runs against different opposing teams

This plot computes the average runs scored by Dravid against different countries. Dravid has an average of 50+ in England, New Zealand, West Indies and Zimbabwe.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("../dravid.csv","Rahul Dravid")

9 . Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman. For this the performance of Sachin is plotted as a 3D scatter plot with Runs versus Balls Faced + Minutes at crease. K-Means. The centroids of 3 clusters are computed and plotted. In this plot Dravid’s  highest tendencies are computed and plotted using K-Means

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("../dravid.csv","Rahul Dravid")

10. A look at the Top 4 batsman – Rahul Dravid, Alastair Cook, Brian Lara and Virat Kohli

The following batsmen have been very prolific in test cricket and will be used for teh analyses

  1. Rahul Dravid :Average:52.31,100’s – 36, 50’s – 63
  2. Alastair Cook : Average: 45.35, 100’s – 33, 50’s – 57
  3. Brian Lara : Average: 52.88, 100’s – 34 , 50’s – 48
  4. Virat Kohli: Average: 54.57 ,100’s – 24 , 50’s – 19

The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs

11. Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("../dravid.csv","Rahul Dravid")

ca.batsmanPerfBoxHist("../acook.csv","Alastair Cook")

ca.batsmanPerfBoxHist("../lara.csv","Brian Lara")


ca.batsmanPerfBoxHist("../kohli.csv","Virat Kohli")


12. Contribution to won and lost matches

The plot below shows the contribution of Dravid, Cook, Lara and Kohli in matches won and lost. It can be seen that in matches where India has won Dravid and Kohli have scored more and must have been instrumental in the win

For the 2 functions below you will have to use the getPlayerDataSp() function as shown below. I have commented this as I already have these files

import cricpy.analytics as ca
#dravidsp = ca.getPlayerDataSp(28114,tdir=".",tfile="dravidsp.csv",ttype="batting")
#acooksp = ca.getPlayerDataSp(11728,tdir=".",tfile="acooksp.csv",ttype="batting")
#larasp = ca.getPlayerDataSp(52337,tdir=".",tfile="larasp.csv",ttype="batting")
#kohlisp = ca.getPlayerDataSp(253802,tdir=".",tfile="kohlisp.csv",ttype="batting")
import cricpy.analytics as ca
ca.batsmanContributionWonLost("../dravidsp.csv","Rahul Dravid")

ca.batsmanContributionWonLost("../acooksp.csv","Alastair Cook")

ca.batsmanContributionWonLost("../larasp.csv","Brian Lara")

ca.batsmanContributionWonLost("../kohlisp.csv","Virat Kohli")


13. Performance at home and overseas

From the plot below it can be seen

Dravid has a higher median overseas than at home.Cook, Lara and Kohli have a lower median of runs overseas than at home.

This function also requires the use of getPlayerDataSp() as shown above

import cricpy.analytics as ca
ca.batsmanPerfHomeAway("../dravidsp.csv","Rahul Dravid")

ca.batsmanPerfHomeAway("../acooksp.csv","Alastair Cook")

ca.batsmanPerfHomeAway("../larasp.csv","Brian Lara")

ca.batsmanPerfHomeAway("../kohlisp.csv","Virat Kohli")

14 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 (ignore the dip at the end of all plots. Need to check why this is so!). Lara’s performance seems to have been quite good before his retirement(wonder why retired so early!). Kohli’s performance has been steadily improving over the years

import cricpy.analytics as ca
ca.batsmanMovingAverage("../dravid.csv","Rahul Dravid")

ca.batsmanMovingAverage("../acook.csv","Alastair Cook")

ca.batsmanMovingAverage("../lara.csv","Brian Lara")

ca.batsmanMovingAverage("../kohli.csv","Virat Kohli")

15 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career. Dravid averages around 48, Cook around 44, Lara around 50 and Kohli shows a steady improvement in his cumulative average. Kohli seems to be getting better with time.

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("../dravid.csv","Rahul Dravid")

ca.batsmanCumulativeAverageRuns("../acook.csv","Alastair Cook")

ca.batsmanCumulativeAverageRuns("../lara.csv","Brian Lara")

ca.batsmanCumulativeAverageRuns("../kohli.csv","Virat Kohli")

16 Cumulative Average strike rate of batsman in career

Lara has a terrific strike rate of 52+. Cook has a better strike rate over Dravid. Kohli’s strike rate has improved over the years.

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("../dravid.csv","Rahul Dravid")

ca.batsmanCumulativeStrikeRate("../acook.csv","Alastair Cook")

ca.batsmanCumulativeStrikeRate("../lara.csv","Brian Lara")

ca.batsmanCumulativeStrikeRate("../kohli.csv","Virat Kohli")


17 Future Runs forecast

Here are plots that forecast how the batsman will perform in future. Currently ARIMA has been used for the forecast. (To do:  Perform Holt-Winters forecast!)

import cricpy.analytics as ca
ca.batsmanPerfForecast("../dravid.csv","Rahul Dravid")
##                              ARIMA Model Results                              
## ==============================================================================
## Dep. Variable:                 D.runs   No. Observations:                  284
## Model:                 ARIMA(5, 1, 0)   Log Likelihood               -1522.837
## Method:                       css-mle   S.D. of innovations             51.488
## Date:                Sun, 28 Oct 2018   AIC                           3059.673
## Time:                        09:47:39   BIC                           3085.216
## Sample:                    07-04-1996   HQIC                          3069.914
##                          - 01-24-2012                                         
## ================================================================================
##                    coef    std err          z      P>|z|      [0.025      0.975]
## --------------------------------------------------------------------------------
## const           -0.1336      0.884     -0.151      0.880      -1.867       1.599
## ar.L1.D.runs    -0.7729      0.058    -13.322      0.000      -0.887      -0.659
## ar.L2.D.runs    -0.6234      0.071     -8.753      0.000      -0.763      -0.484
## ar.L3.D.runs    -0.5199      0.074     -7.038      0.000      -0.665      -0.375
## ar.L4.D.runs    -0.3490      0.071     -4.927      0.000      -0.488      -0.210
## ar.L5.D.runs    -0.2116      0.058     -3.665      0.000      -0.325      -0.098
##                                     Roots                                    
## =============================================================================
##                  Real           Imaginary           Modulus         Frequency
## -----------------------------------------------------------------------------
## AR.1            0.5789           -1.1743j            1.3093           -0.1771
## AR.2            0.5789           +1.1743j            1.3093            0.1771
## AR.3           -1.3617           -0.0000j            1.3617           -0.5000
## AR.4           -0.7227           -1.2257j            1.4230           -0.3348
## AR.5           -0.7227           +1.2257j            1.4230            0.3348
## -----------------------------------------------------------------------------
##                 0
## count  284.000000
## mean    -0.306769
## std     51.632947
## min   -106.653589
## 25%    -33.835148
## 50%     -8.954253
## 75%     21.024763
## max    223.152901
## 
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
##   if issubdtype(paramsdtype, float):
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.
##   elif issubdtype(paramsdtype, complex):
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
##   if issubdtype(paramsdtype, float):

18 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman for each of the runs ranges of 10 and plots them. The plot indicate the following Range 30 – 100 innings – Lara leads followed by Dravid Range 100+ innings – Kohli races ahead of the rest

import cricpy.analytics as ca
frames = ["../dravid.csv","../acook.csv","../lara.csv","../kohli.csv"]
names = ["Dravid","A Cook","Brian Lara","V Kohli"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

19. Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show

Brian Lara towers over the Dravid, Cook and Kohli. However you will notice that Kohli’s strike rate is going up

import cricpy.analytics as ca
frames = ["../dravid.csv","../acook.csv","../lara.csv","../kohli.csv"]
names = ["Dravid","A Cook","Brian Lara","V Kohli"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

20. 3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("../dravid.csv","Rahul Dravid")

ca.battingPerf3d("../acook.csv","Alastair Cook")

ca.battingPerf3d("../lara.csv","Brian Lara")

ca.battingPerf3d("../kohli.csv","Virat Kohli")

21. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
dravid = ca.batsmanRunsPredict("../dravid.csv",newDF,"Dravid")
print(dravid)
##             BF        Mins        Runs
## 0    10.000000   30.000000    0.519667
## 1    37.857143   70.714286   13.821794
## 2    65.714286  111.428571   27.123920
## 3    93.571429  152.142857   40.426046
## 4   121.428571  192.857143   53.728173
## 5   149.285714  233.571429   67.030299
## 6   177.142857  274.285714   80.332425
## 7   205.000000  315.000000   93.634552
## 8   232.857143  355.714286  106.936678
## 9   260.714286  396.428571  120.238805
## 10  288.571429  437.142857  133.540931
## 11  316.428571  477.857143  146.843057
## 12  344.285714  518.571429  160.145184
## 13  372.142857  559.285714  173.447310
## 14  400.000000  600.000000  186.749436

The fitted model is then used to predict the runs that the batsmen will score for a given Balls faced and Minutes at crease.

22 Analysis of Top 3 wicket takers

The following 3 bowlers have had an excellent career and will be used for the analysis

  1. Glenn McGrath:Wickets: 563, Average = 21.64, Economy Rate – 2.49
  2. Kapil Dev : Wickets: 434, Average = 29.64, Economy Rate – 2.78
  3. James Anderson: Wickets: 564, Average = 28.64, Economy Rate – 2.88

How do Glenn McGrath, Kapil Dev and James Anderson compare with one another with respect to wickets taken and the Economy Rate. The next set of plots compute and plot precisely these analyses.

23. Get the bowler’s data

This plot below computes the percentage frequency of number of wickets taken for e.g 1 wicket x%, 2 wickets y% etc and plots them as a continuous line

import cricpy.analytics as ca
#mcgrath =ca.getPlayerData(6565,dir=".",file="mcgrath.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])
#kapil =ca.getPlayerData(30028,dir=".",file="kapil.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])
#anderson =ca.getPlayerData(8608,dir=".",file="anderson.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])

24. Wicket Frequency Plot

This plot below plots the frequency of wickets taken for each of the bowlers

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("../mcgrath.csv","Glenn McGrath")

ca.bowlerWktsFreqPercent("../kapil.csv","Kapil Dev")

ca.bowlerWktsFreqPercent("../anderson.csv","James Anderson")

25. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("../mcgrath.csv","Glenn McGrath")

ca.bowlerWktsRunsPlot("../kapil.csv","Kapil Dev")

ca.bowlerWktsRunsPlot("../anderson.csv","James Anderson")

26 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues. McGrath best performances are at Centurion, Lord’s and Port of Spain averaging about 4 wickets. Kapil Dev’s does good at Kingston and Wellington. Anderson averages 4 wickets at Dunedin and Nagpur

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("../mcgrath.csv","Glenn McGrath")

ca.bowlerAvgWktsGround("../kapil.csv","Kapil Dev")

ca.bowlerAvgWktsGround("../anderson.csv","James Anderson")

27 Average wickets against different opposition

The plot gives the average wickets taken by Muralitharan against different countries. The x-axis also includes the number of innings against each team

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("../mcgrath.csv","Glenn McGrath")

ca.bowlerAvgWktsOpposition("../kapil.csv","Kapil Dev")

ca.bowlerAvgWktsOpposition("../anderson.csv","James Anderson")

28 Wickets taken moving average

From the plot below it can be see James Anderson has had a solid performance over the years averaging about wickets

import cricpy.analytics as ca
ca.bowlerMovingAverage("../mcgrath.csv","Glenn McGrath")

ca.bowlerMovingAverage("../kapil.csv","Kapil Dev")

ca.bowlerMovingAverage("../anderson.csv","James Anderson")

29 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. mcGrath plateaus around 2.4 wickets, Kapil Dev’s performance deteriorates over the years. Anderson holds on rock steady around 2 wickets

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("../mcgrath.csv","Glenn McGrath")

ca.bowlerCumulativeAvgWickets("../kapil.csv","Kapil Dev")

ca.bowlerCumulativeAvgWickets("../anderson.csv","James Anderson")

30 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. McGrath’s was very expensive early in his career conceding about 2.8 runs per over which drops to around 2.5 runs towards the end. Kapil Dev’s economy rate drops from 3.6 to 2.8. Anderson is probably more expensive than the other 2.

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("../mcgrath.csv","Glenn McGrath")

ca.bowlerCumulativeAvgEconRate("../kapil.csv","Kapil Dev")

ca.bowlerCumulativeAvgEconRate("../anderson.csv","James Anderson")

31 Future Wickets forecast

import cricpy.analytics as ca
ca.bowlerPerfForecast("../mcgrath.csv","Glenn McGrath")
##                              ARIMA Model Results                              
## ==============================================================================
## Dep. Variable:              D.Wickets   No. Observations:                  236
## Model:                 ARIMA(5, 1, 0)   Log Likelihood                -480.815
## Method:                       css-mle   S.D. of innovations              1.851
## Date:                Sun, 28 Oct 2018   AIC                            975.630
## Time:                        09:28:32   BIC                            999.877
## Sample:                    11-12-1993   HQIC                           985.404
##                          - 01-02-2007                                         
## ===================================================================================
##                       coef    std err          z      P>|z|      [0.025      0.975]
## -----------------------------------------------------------------------------------
## const               0.0037      0.033      0.113      0.910      -0.061       0.068
## ar.L1.D.Wickets    -0.9432      0.064    -14.708      0.000      -1.069      -0.818
## ar.L2.D.Wickets    -0.7254      0.086     -8.469      0.000      -0.893      -0.558
## ar.L3.D.Wickets    -0.4827      0.093     -5.217      0.000      -0.664      -0.301
## ar.L4.D.Wickets    -0.3690      0.085     -4.324      0.000      -0.536      -0.202
## ar.L5.D.Wickets    -0.1709      0.064     -2.678      0.008      -0.296      -0.046
##                                     Roots                                    
## =============================================================================
##                  Real           Imaginary           Modulus         Frequency
## -----------------------------------------------------------------------------
## AR.1            0.5630           -1.2761j            1.3948           -0.1839
## AR.2            0.5630           +1.2761j            1.3948            0.1839
## AR.3           -0.8433           -1.0820j            1.3718           -0.3554
## AR.4           -0.8433           +1.0820j            1.3718            0.3554
## AR.5           -1.5981           -0.0000j            1.5981           -0.5000
## -----------------------------------------------------------------------------
##                 0
## count  236.000000
## mean    -0.005142
## std      1.856961
## min     -3.457002
## 25%     -1.433391
## 50%     -0.080237
## 75%      1.446149
## max      5.840050

32 Get player data special

As discussed above the next 2 charts require the use of getPlayerDataSp()

import cricpy.analytics as ca
#mcgrathsp =ca.getPlayerDataSp(6565,tdir=".",tfile="mcgrathsp.csv",ttype="bowling")
#kapilsp =ca.getPlayerDataSp(30028,tdir=".",tfile="kapilsp.csv",ttype="bowling")
#andersonsp =ca.getPlayerDataSp(8608,tdir=".",tfile="andersonsp.csv",ttype="bowling")

33 Contribution to matches won and lost

The plot below is extremely interesting Glenn McGrath has been more instrumental in Australia winning than Kapil and Anderson as seems to have taken more wickets when Australia won.

import cricpy.analytics as ca
ca.bowlerContributionWonLost("../mcgrathsp.csv","Glenn McGrath")

ca.bowlerContributionWonLost("../kapilsp.csv","Kapil Dev")

ca.bowlerContributionWonLost("../andersonsp.csv","James Anderson")

34 Performance home and overseas

McGrath and Kapil Dev have performed better overseas than at home. Anderson has performed about the same home and overseas

import cricpy.analytics as ca
ca.bowlerPerfHomeAway("../mcgrathsp.csv","Glenn McGrath")

ca.bowlerPerfHomeAway("../kapilsp.csv","Kapil Dev")

ca.bowlerPerfHomeAway("../andersonsp.csv","James Anderson")

35 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate shows that McGrath has the best economy rate followed by Kapil Dev and then Anderson.

import cricpy.analytics as ca
frames = ["../mcgrath.csv","../kapil.csv","../anderson.csv"]
names = ["Glenn McGrath","Kapil Dev","James Anderson"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

36 Relative Economy Rate against wickets taken

McGrath has been economical regardless of the number of wickets taken. Kapil Dev has been slightly more expensive when he takes more wickets

import cricpy.analytics as ca
frames = ["../mcgrath.csv","../kapil.csv","../anderson.csv"]
names = ["Glenn McGrath","Kapil Dev","James Anderson"]
ca.relativeBowlingER(frames,names)

37 Relative cumulative average wickets of bowlers in career

The plot below shows that McGrath has the best overall cumulative average wickets. Kapil’s leads Anderson till about 150 innings after which Anderson takes over

import cricpy.analytics as ca
frames = ["../mcgrath.csv","../kapil.csv","../anderson.csv"]
names = ["Glenn McGrath","Kapil Dev","James Anderson"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

Key Findings

The plots above capture some of the capabilities and features of my cricpy package. Feel free to install the package and try it out. Please do keep in mind ESPN Cricinfo’s Terms of Use.

Here are the main findings from the analysis above

Key insights

1. Brian Lara is head and shoulders above the rest in the overall strike rate
2. Kohli performance has been steadily improving over the years and with the way he is going he will shatter all records.
3. Kohli and Dravid have scored more in matches where India has won than the other two.
4. Dravid has performed very well overseas
5. The cumulative average runs has Kohli just edging out the other 3. Kohli is probably midway in his career but considering that his moving average is improving strongly, we can expect great things of him with the way he is going.
6. McGrath has had some great performances overseas
7. Mcgrath has the best economy rate and has contributed significantly to Australia’s wins.
8.In the cumulative average wickets race McGrath leads the pack. Kapil leads Anderson till about 150 matches after which Anderson takes over.

The code for cricpy can be accessed at Github at cricpy

Do let me know if you run into issues.

Conclusion

I have long wanted to make a python equivalent of cricketr and I have been able to make it. cricpy is still work in progress. I have add the necessary functions for ODI and Twenty20.  Go ahead give ‘cricpy’ a spin!!

Stay tuned!

The 3rd paperback & kindle editions of my books on Cricket, now on Amazon


The 3rd  paperback & kindle edition of both my books on cricket is now available on Amazon

a) Cricket analytics with cricketr, Third Edition. The paperback edition is $12.99 and the kindle edition is $4.99/Rs320.  This book is based on my R package ‘cricketr‘, available on CRAN and uses ESPN Cricinfo Statsguru

b) Beaten by sheer pace! Cricket analytics with yorkr, 3rd edition . The paperback is $12.99 and the kindle version is $6.99/Rs448. This is based on my R package ‘yorkr‘ on CRAN and uses data from Cricsheet
Pick up your copies today!!

Note: In the 3rd edition of  the paperback book, the charts will be in black and white. If you would like the charts to be in color, please check out the 2nd edition of these books see More book, more cricket! 2nd edition of my books now on Amazon

You may also like
1. My book ‘Practical Machine Learning with R and Python’ on Amazon
2. A crime map of India in R: Crimes against women
3.  What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
4.  Bend it like Bluemix, MongoDB with autoscaling – Part 2
5. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
6. Thinking Web Scale (TWS-3): Map-Reduce – Bring compute to data

To see all posts see Index of posts

cricketr flexes new muscles: The final analysis


Twas brillig, and the slithy toves
Did gyre and gimble in the wabe:
All mimsy were the borogoves,
And the mome raths outgrabe.

       Jabberwocky by Lewis Carroll
                   

No analysis of cricket is complete, without determining how players would perform in the host country. Playing Test cricket on foreign pitches, in the host country, is a ‘real test’ for both batsmen and bowlers. Players, who can perform consistently both on domestic and foreign pitches are the genuinely ‘class’ players. Player performance on foreign pitches lets us differentiate the paper tigers, and home ground bullies among batsmen. Similarly, spinners who perform well, only on rank turners in home ground or pace bowlers who can only swing and generate bounce on specially prepared pitches are neither  genuine spinners nor  real pace bowlers.

So this post, helps in identifying those with real strengths, and those who play good only when the conditions are in favor, in home grounds. This post brings a certain level of finality to the analysis of players with my R package ‘cricketr’

Besides, I also meant ‘final analysis’ in the literal sense, as I intend to take a long break from cricket analysis/analytics and focus on some other domains like Neural Networks, Deep Learning and Spark.

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!!

1

 

As already mentioned, my R package ‘cricketr’ uses the statistics info available in ESPN Cricinfo Statsguru. You should be able to install the package from CRAN and use many of the functions available in the package. Please be mindful of ESPN Cricinfo Terms of Use

(Note: This page is also hosted at RPubs as cricketrFinalAnalysis. You can download the PDF file at cricketrFinalAnalysis.

For getting data of a player against a particular country for the match played in the host country, I just had to add 2 extra parameters to the getPlayerData() function. The cricketr package has been updated with the changed functions for getPlayerData() – Tests, getPlayerDataOD() – ODI and getPlayerDataTT() for the Twenty20s. The updated functions will be available in cricketr Version -0.0.14

The data for the following players have already been obtained with the new, changed getPlayerData() function and have been saved as *.csv files. I will be re-using these files, instead of getting them all over again. Hence the getPlayerData() lines have been commented below

library(cricketr)

1. Performance of a batsman against a host ountry in the host country

For e.g We can the get the data for Sachin Tendulkar for matches played against Australia and in Australia Here opposition=2 and host =2 indicate that the opposition is Australia and the host country is also Australia

#tendulkarAus=getPlayerData(35320,opposition=2,host=2,file="tendulkarVsAusInAus.csv",type="batting")

All cricketr functions can be used with this data frame, as before. All the charts show the performance of Tendulkar in Australia against Australia.

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))
batsman4s("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsman6s("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanRunsRanges("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanDismissals("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanAvgRunsGround("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanMovingAverage("./data/tendulkarVsAusInAus.csv","Tendulkar")

dev.off()
## null device 
##           1

2. Relative performances of international batsmen against England in England

While we can analyze the performance of a player against an opposition in some host country, I wanted to compare the relative performances of players, to see how players from different nations play in a host country which is not their home ground.

The following lines gets player’s data of matches played in England and against England.The Oval, Lord’s are famous for generating some dangerous swing and bounce. I chose the following players

  1. Sir Don Bradman (Australia)
  2. Steve Waugh (Australia)
  3. Rahul Dravid (India)
  4. Vivian Richards (West Indies)
  5. Sachin Tendulkar (India)
#tendulkarEng=getPlayerData(35320,opposition=1,host=1,file="tendulkarVsEngInEng.csv",type="batting")
#bradmanEng=getPlayerData(4188,opposition=1,host=1,file="bradmanVsEngInEng.csv",type="batting")
#srwaughEng=getPlayerData(8192,opposition=1,host=1,file="srwaughVsEngInEng.csv",type="batting")
#dravidEng=getPlayerData(28114,opposition=1,host=1,file="dravidVsEngInEng.csv",type="batting")
#vrichardEng=getPlayerData(52812,opposition=1,host=1,file="vrichardsEngInEng.csv",type="batting")
frames <- list("./data/tendulkarVsEngInEng.csv","./data/bradmanVsEngInEng.csv","./data/srwaughVsEngInEng.csv",
               "./data/dravidVsEngInEng.csv","./data/vrichardsEngInEng.csv")
names <- list("S Tendulkar","D Bradman","SR Waugh","R Dravid","Viv Richards")

The Lords and the Oval in England are some of the best pitches in the world. Scoring on these pitches and weather conditions, where there is both swing and bounce really requires excellent batting skills. It can be easily seen that Don Bradman stands heads and shoulders over everybody else, averaging close a cumulative average of 100+. He is followed by Viv Richards, who averages around ~60. Interestingly in English conditions, Rahul Dravid edges out Sachin Tendulkar.

relativeBatsmanCumulativeAvgRuns(frames,names)

# The other 2 plots on relative strike rate and cumulative average strike rate,
shows Viv Richards really  blasts the bowling. Viv Richards has a strike rate 
of 70, while Bradman 62+, followed by Tendulkar.
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

3. Relative performances of international batsmen against Australia in Australia

The following players from these countries were chosen

  1. Sachin Tendulkar (India)
  2. Viv Richard (West Indies)
  3. David Gower (England)
  4. Jacques Kallis (South Africa)
  5. Alastair Cook (Emgland)
frames <- list("./data/tendulkarVsAusInAus.csv","./data/vrichardsVAusInAus.csv","./data/dgowerVsAusInAus.csv",
               "./data/kallisVsAusInAus.csv","./data/ancookVsWIInWI.csv")
names <- list("S Tendulkar","Viv Richards","David Gower","J Kallis","AN Cook")

Alastair Cook of England has fantastic cumulative average of 55+ on the pitches of Australia. There is a dip towards the end, but we cannot predict whether it would have continued. AN Cook is followed by Tendulkar who has a steady average of 50+ runs, after which there is Viv Richards.

relativeBatsmanCumulativeAvgRuns(frames,names)

#With respect to cumulative or relative strike rate Viv Richards is a class apart.He seems to really
#tear into bowlers. David Gower has an excellent strike rate and is followed by Tendulkar
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

4. Relative performances of international batsmen against India in India

While England & Australia are famous for bouncy tracks with swing, Indian pitches are renowed for being extraordinary turners. Also India has always thrown up world class spinners, from the spin quartet of BS Chandraskehar, Bishen Singh Bedi, EAS Prasanna, S Venkatraghavan, to the times of dangerous Anil Kumble, and now to the more recent Ravichander Ashwon and Harbhajan Singh.

A batsmen who can score runs in India against Indian spinners has to be really adept in handling all kinds of spin.

While Clive Lloyd & Alvin Kallicharan had the best performance against India, they have not been included as ESPN Cricinfo had many of the columns missing.

So I chose the following international players for the analysis against India

  1. Hashim Amla (South Africa)
  2. Alastair Cook (England)
  3. Matthew Hayden (Australia)
  4. Viv Richards (West Indies)
frames <- list("./data/amlaVsIndInInd.csv","./data/ancookVsIndInInd.csv","./data/mhaydenVsIndInInd.csv",
               "./data/vrichardsVsIndInInd.csv")
names <- list("H Amla","AN Cook","M Hayden","Viv Riachards")

Excluding Clive Lloyd & Alvin Kallicharan the next best performer against India is Hashim Amla,followed by Alastair Cook, Viv Richards.

relativeBatsmanCumulativeAvgRuns(frames,names)

#With respect to strike rate, there is no contest when Viv Richards is around. He is clearly the best 
#striker of the ball regardless of whether it is the pacy wickets of 
#Australia/England or the spinning tracks of the subcontinent. After 
#Viv Richards, Hayden and Alastair Cook have good cumulative strike rates
#in India
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

5. All time greats of Indian batting

I couldn’t resist checking out how the top Indian batsmen perform when playing in host countries So here is a look at how the top Indian batsmen perform against different host countries

6. Top Indian batsmen against Australia in Australia

The following Indian batsmen were chosen

  1. Sunil Gavaskar
  2. Sachin Tendulkar
  3. Virat Kohli
  4. Virendar Sehwag
  5. VVS Laxman
frames <- list("./data/tendulkarVsAusInAus.csv","./data/gavaskarVsAusInAus.csv","./data/kohliVsAusInAus.csv",
               "./data/sehwagVsAusInAus.csv","./data/vvslaxmanVsAusInAus.csv")
names <- list("S Tendulkar","S Gavaskar","V Kohli","V Sehwag","VVS Laxman")

Virat Kohli has the best overall performance against Australia, with a current cumulative average of 60+ runs for the total number of innings played by him (15). With 15 matches the 2nd best is Virendar Sehwag, followed by VVS Laxman. Tendulkar maintains a cumulative average of 48+ runs for an excess of 30+ innings.

relativeBatsmanCumulativeAvgRuns(frames,names)

# Sehwag leads the strike rate against host Australia, followed by 
# Tendulkar in Australia and then Kohli
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

7. Top Indian batsmen against England in England

The top Indian batmen’s performances against England are shown below

  1. Rahul Dravid
  2. Dilip Vengsarkar
  3. Rahul Dravid
  4. Sourav Ganguly
  5. Virat Kohli
frames <- list("./data/tendulkarVsEngInEng.csv","./data/dravidVsEngInEng.csv","./data/vengsarkarVsEngInEng.csv",
               "./data/gangulyVsEngInEng.csv","./data/gavaskarVsEngInEng.csv","./data/kohliVsEngInEng.csv")
names <- list("S Tendulkar","R Dravid","D Vengsarkar","S Ganguly","S Gavaskar","V Kohli")

Rahul Dravid has the best performance against England and edges out Tendulkar. He is followed by Tendulkar and then Sourav Ganguly. Note:Incidentally Virat Kohli’s performance against England in England so far has been extremely poor and he averages around 13-15 runs per innings. However he has a long way to go and I hope he catches up. In any case it will be an uphill climb for Kohli in England.

relativeBatsmanCumulativeAvgRuns(frames,names)

#Tendulkar, Ganguly and Dravid have the best strike rate and in that order.
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

8. Top Indian batsmen against West Indies in West Indies

frames <- list("./data/tendulkarVsWInWI.csv","./data/dravidVsWInWI.csv","./data/vvslaxmanVsWIInWI.csv",
               "./data/gavaskarVsWIInWI.csv")
names <- list("S Tendulkar","R Dravid","VVS Laxman","S Gavaskar")

Against the West Indies Sunil Gavaskar is heads and shoulders above the rest. Gavaskar has a very impressive cumulative average against West Indies

relativeBatsmanCumulativeAvgRuns(frames,names)

# VVS Laxman followed by  Tendulkar & then Dravid have a very 
# good strike rate against the West Indies
relativeBatsmanCumulativeStrikeRate(frames,names)

9. World’s best spinners on tracks suited for pace & bounce

In this part I compare the performances of the top 3 spinners in recent years and check out how they perform on surfaces that are known for pace, and bounce. I have taken the following 3 spinners

  1. Anil Kumble (India)
  2. M Muralitharan (Sri Lanka)
  3. Shane Warne (Australia)
#kumbleEng=getPlayerData(30176  ,opposition=3,host=3,file="kumbleVsEngInEng.csv",type="bowling")
#muraliEng=getPlayerData(49636  ,opposition=3,host=3,file="muraliVsEngInEng.csv",type="bowling")
#warneEng=getPlayerData(8166  ,opposition=3,host=3,file="warneVsEngInEng.csv",type="bowling")

10. Top international spinners against England in England

frames <- list("./data/kumbleVsEngInEng.csv","./data/muraliVsEngInEng.csv","./data/warneVsEngInEng.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")

Against England and in England, Muralitharan shines with a cumulative average of nearly 5 wickets per match with a peak of almost 8 wickets. Shane Warne has a steady average at 5 wickets and then Anil Kumble.

relativeBowlerCumulativeAvgWickets(frames,names)

# The order relative cumulative Economy rate, Warne has the best figures,followed by Anil Kumble. Muralitharan
# is much more expensive.
relativeBowlerCumulativeAvgEconRate(frames,names)

11. Top international spinners against South Africa in South Africa

frames <- list("./data/kumbleVsSAInSA.csv","./data/muraliVsSAInSA.csv","./data/warneVsSAInSA.csv")
names <- list("Anil Kumble","M Muralitharan","Shane Warne")

In South Africa too, Muralitharan has the best wicket taking performance averaging about 4 wickets. Warne averages around 3 wickets and Kumble around 2 wickets

relativeBowlerCumulativeAvgWickets(frames,names)

# Muralitharan is expensive in South Africa too, while Kumble and Warne go neck-to-neck in the economy rate.
# Kumble edges out Warne and has a better cumulative average economy rate
relativeBowlerCumulativeAvgEconRate(frames,names)

11. Top international pacers against India in India

As a final analysis I check how the world’s pacers perform in India against India. India pitches are supposed to be flat devoid of bounce, while being terrific turners. Hence Indian pitches are more suited to spin bowling than pace bowling. This is changing these days.

The best performers against India in India are mostly the deadly pacemen of yesteryears

For this I have chosen the following bowlers

  1. Courtney Walsh (West Indies)
  2. Andy Roberts (West Indies)
  3. Malcolm Marshall
  4. Glenn McGrath
#cawalshInd=getPlayerData(53216  ,opposition=6,host=6,file="cawalshVsIndInInd.csv",type="bowling")
#arobertsInd=getPlayerData(52817  ,opposition=6,host=6,file="arobertsIndInInd.csv",type="bowling")
#mmarshallInd=getPlayerData(52419  ,opposition=6,host=6,file="mmarshallVsIndInInd.csv",type="bowling")
#gmccgrathInd=getPlayerData(6565  ,opposition=6,host=6,file="mccgrathVsIndInInd.csv",type="bowling")
frames <- list("./data/cawalshVsIndInInd.csv","./data/arobertsIndInInd.csv","./data/mmarshallVsIndInInd.csv",
               "./data/mccgrathVsIndInInd.csv")
names <- list("C Walsh","A Roberts","M Marshall","G McGrath")

Courtney Walsh has the best performance, followed by Andy Roberts followed by Andy Roberts and then Malcom Marshall who tips ahead of Glenn McGrath

relativeBowlerCumulativeAvgWickets(frames,names)

#On the other hand McGrath has the best economy rate, followed by A Roberts and then Courtney Walsh
relativeBowlerCumulativeAvgEconRate(frames,names)

12. ODI performance of a player against a specific country in the host country

This gets the data for MS Dhoni in ODI matches against Australia and in Australia

#dhoniAusODI=getPlayerDataOD(28081,opposition=2,host=2,file="dhoniVsAusInAusODI.csv",type="batting")

13. Twenty 20 performance of a player against a specific country in the host country

#dhoniAusTT=getPlayerDataOD(28081,opposition=2,host=2,file="dhoniVsAusInAusTT.csv",type="batting")

All the ODI and Twenty20 functions of cricketr can be used on the above dataframes of MS Dhoni.

Some key observations

Here are some key observations

  1. At the top of the batting spectrum is Don Bradman with a very impressive average 100-120 in matches played in England and Australia. Unfortunately there weren’t matches he played in other countries and different pitches. 2.Viv Richard has the best cumulative strike rate overall.
  2. Muralitharan strikes more often than Kumble or Warne even in pitches at ENgland, South Africa and West Indies. However Muralitharan is also the most expensive
  3. Warne and Kumble have a much better economy rate than Muralitharan.
  4. Sunil Gavaskar has an extremely impressive performance in West Indies.
  5. Rahul Dravid performs much better than Tendulkar in both England and West Indies.
  6. Virat Kohli has the best performance against Australia so far and hope he maintains his stellar performance followed by Sehwag. However Kohli’s performance in England has been very poor
  7. West Indies batsmen and bowlers seem to thrive on Indian pitches, with Clive Lloyd and Alvin Kalicharan at the top of the list.

You may like my Shiny apps on cricket

  1. Inswinger- Analyzing International. T20s
  2. GooglyPlus – An app for analyzing IPL
  3. Sixer – App based on R package cricketr

Also see

  1. Exploring Quantum Gate operations with QCSimulator
  2. Neural Networks: The mechanics of backpropagation
  3. Re-introducing cricketr! : An R package to analyze performances of cricketers
  4. yorkr crashes the IPL party ! – Part 1
  5. cricketr and yorkr books – Paperback now in Amazon
  6.  Hand detection through Haartraining: A hands-on approach

To see all my posts see Index of posts

Analysis of IPL T20 matches with yorkr templates


Introduction

In this post I create RMarkdown templates for end-to-end analysis of IPL T20 matches, that are available on Cricsheet based on my R package yorkr.  With these templates you can convert all IPL data which is in yaml format to R dataframes. Further I create data and the necessary templates for analyzing IPL matches, teams and players. All of these can be accessed at yorkrIPLTemplate.

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!!

1

9/Rs 320 and $6.99/Rs448 respectively

The templates are

  1. Template for conversion and setup – IPLT20Template.Rmd
  2. Any IPL match – IPLMatchtemplate.Rmd
  3. IPL matches between 2 nations – IPLMatches2TeamTemplate.Rmd
  4. A IPL nations performance against all other IPL nations – IPLAllMatchesAllOppnTemplate.Rmd
  5. Analysis of IPL batsmen and bowlers of all IPL nations – IPLBatsmanBowlerTemplate.Rmd

Besides the templates the repository also includes the converted data for all IPL matches I downloaded from Cricsheet in Dec 2016. So this data is complete till the 2016 IPL season. You can recreate the files as more matches are added to Cricsheet site in IPL 2017 and future seasons. This post contains all the steps needed for detailed analysis of IPL matches, teams and IPL player. This will also be my reference in future if I decide to analyze IPL in future!

See my earlier posts where I analyze IPL T20
1. yorkr crashes the IPL party ! – Part 1
2. yorkr crashes the IPL party! – Part 2
3. yorkr crashes the IPL party! – Part 3!
4. yorkr crashes the IPL party! – Part 4

There will be 5 folders at the root

  1. IPLdata – Match files as yaml from Cricsheet
  2. IPLMatches – Yaml match files converted to dataframes
  3. IPLMatchesBetween2Teams – All Matches between any 2 IPL teams
  4. allMatchesAllOpposition – An IPL teams’s performance against all other teams
  5. BattingBowlingDetails – Batting and bowling details of all IPL teams
library(yorkr)
library(dplyr)

The first few steps take care of the data setup. This needs to be done before any of the analysis of IPL batsmen, bowlers, any IPL match, matches between any 2 IPL countries or analysis of a teams performance against all other countries

There will be 5 folders at the root

  1. data
  2. IPLMatches
  3. IPLMatchesBetween2Teams
  4. allMatchesAllOpposition
  5. BattingBowlingDetails

The source YAML files will be in IPLData folder

1.Create directory of IPLMatches

Some files may give conversions errors. You could try to debug the problem or just remove it from the IPLdata folder. At most 2-4 file will have conversion problems and I usally remove then from the files to be converted.

Also take a look at my GooglyPlus shiny app which was created after performing the same conversion on the Dec 16 data .

convertAllYaml2RDataframesT20("data","IPLMatches")

2.Save all matches between all combinations of IPL nations

This function will create the set of all matches between each IPL team against every other IPL team. This uses the data that was created in IPLMatches, with the convertAllYaml2RDataframesIPL() function.

setwd("./IPLMatchesBetween2Teams")
saveAllMatchesBetween2IPLTeams("../IPLMatches")

3.Save all matches against all opposition

This will create a consolidated dataframe of all matches played by every IPL playing nation against all other nattions. This also uses the data that was created in IPLMatches, with the convertAllYaml2RDataframesIPL() function.

setwd("../allMatchesAllOpposition")
saveAllMatchesAllOppositionIPLT20("../IPLMatches")

4. Create batting and bowling details for each IPL team

These are the current IPL playing teams. You can add to this vector as newer IPL teams start playing IPL. You will get to know all IPL teams by also look at the directory created above namely allMatchesAllOpposition. This also uses the data that was created in IPLMatches, with the convertAllYaml2RDataframesIPL() function.

setwd("../BattingBowlingDetails")
ipl_teams <- list("Chennai Super Kings","Deccan Chargers", "Delhi Daredevils","Kings XI Punjab", 
              "Kochi Tuskers Kerala","Kolkata Knight Riders","Mumbai Indians","Pune Warriors",
              "Rajasthan Royals","Royal Challengers Bangalore","Sunrisers Hyderabad","Gujarat Lions",
                 "Rising Pune Supergiants")

for(i in seq_along(ipl_teams)){
    print(ipl_teams[i])
    val <- paste(ipl_teams[i],"-details",sep="")
    val <- getTeamBattingDetails(ipl_teams[i],dir="../IPLMatches", save=TRUE)

}

for(i in seq_along(ipl_teams)){
    print(ipl_teams[i])
    val <- paste(ipl_teams[i],"-details",sep="")
    val <- getTeamBowlingDetails(ipl_teams[i],dir="../IPLMatches", save=TRUE)

}

5. Get the list of batsmen for a particular IPL team

The following code is needed for analyzing individual IPL batsmen. In IPL a player could have played in multiple IPL teams.

getBatsmen <- function(df){
    bmen <- df %>% distinct(batsman) 
    bmen <- as.character(bmen$batsman)
    batsmen <- sort(bmen)
}
load("Chennai Super Kings-BattingDetails.RData")
csk_details <- battingDetails
load("Deccan Chargers-BattingDetails.RData")
dc_details <- battingDetails
load("Delhi Daredevils-BattingDetails.RData")
dd_details <- battingDetails
load("Kings XI Punjab-BattingDetails.RData")
kxip_details <- battingDetails
load("Kochi Tuskers Kerala-BattingDetails.RData")
ktk_details <- battingDetails
load("Kolkata Knight Riders-BattingDetails.RData")
kkr_details <- battingDetails
load("Mumbai Indians-BattingDetails.RData")
mi_details <- battingDetails
load("Pune Warriors-BattingDetails.RData")
pw_details <- battingDetails
load("Rajasthan Royals-BattingDetails.RData")
rr_details <- battingDetails
load("Royal Challengers Bangalore-BattingDetails.RData")
rcb_details <- battingDetails
load("Sunrisers Hyderabad-BattingDetails.RData")
sh_details <- battingDetails
load("Gujarat Lions-BattingDetails.RData")
gl_details <- battingDetails
load("Rising Pune Supergiants-BattingDetails.RData")
rps_details <- battingDetails

#Get the batsmen for each IPL team
csk_batsmen <- getBatsmen(csk_details)
dc_batsmen <- getBatsmen(dc_details)
dd_batsmen <- getBatsmen(dd_details)
kxip_batsmen <- getBatsmen(kxip_details)
ktk_batsmen <- getBatsmen(ktk_details)
kkr_batsmen <- getBatsmen(kkr_details)
mi_batsmen <- getBatsmen(mi_details)
pw_batsmen <- getBatsmen(pw_details)
rr_batsmen <- getBatsmen(rr_details)
rcb_batsmen <- getBatsmen(rcb_details)
sh_batsmen <- getBatsmen(sh_details)
gl_batsmen <- getBatsmen(gl_details)
rps_batsmen <- getBatsmen(rps_details)

# Save the dataframes
save(csk_batsmen,file="csk.RData")
save(dc_batsmen, file="dc.RData")
save(dd_batsmen, file="dd.RData")
save(kxip_batsmen, file="kxip.RData")
save(ktk_batsmen, file="ktk.RData")
save(kkr_batsmen, file="kkr.RData")
save(mi_batsmen , file="mi.RData")
save(pw_batsmen, file="pw.RData")
save(rr_batsmen, file="rr.RData")
save(rcb_batsmen, file="rcb.RData")
save(sh_batsmen, file="sh.RData")
save(gl_batsmen, file="gl.RData")
save(rps_batsmen, file="rps.RData")

6. Get the list of bowlers for a particular IPL team

The method below can get the list of bowler names for any IPL team.The following code is needed for analyzing individual IPL bowlers. In IPL a player could have played in multiple IPL teams.

getBowlers <- function(df){
    bwlr <- df %>% distinct(bowler) 
    bwlr <- as.character(bwlr$bowler)
    bowler <- sort(bwlr)
}

load("Chennai Super Kings-BowlingDetails.RData")
csk_details <- bowlingDetails
load("Deccan Chargers-BowlingDetails.RData")
dc_details <- bowlingDetails
load("Delhi Daredevils-BowlingDetails.RData")
dd_details <- bowlingDetails
load("Kings XI Punjab-BowlingDetails.RData")
kxip_details <- bowlingDetails
load("Kochi Tuskers Kerala-BowlingDetails.RData")
ktk_details <- bowlingDetails
load("Kolkata Knight Riders-BowlingDetails.RData")
kkr_details <- bowlingDetails
load("Mumbai Indians-BowlingDetails.RData")
mi_details <- bowlingDetails
load("Pune Warriors-BowlingDetails.RData")
pw_details <- bowlingDetails
load("Rajasthan Royals-BowlingDetails.RData")
rr_details <- bowlingDetails
load("Royal Challengers Bangalore-BowlingDetails.RData")
rcb_details <- bowlingDetails
load("Sunrisers Hyderabad-BowlingDetails.RData")
sh_details <- bowlingDetails
load("Gujarat Lions-BowlingDetails.RData")
gl_details <- bowlingDetails
load("Rising Pune Supergiants-BowlingDetails.RData")
rps_details <- bowlingDetails

# Get the bowlers for each team
csk_bowlers <- getBowlers(csk_details)
dc_bowlers <- getBowlers(dc_details)
dd_bowlers <- getBowlers(dd_details)
kxip_bowlers <- getBowlers(kxip_details)
ktk_bowlers <- getBowlers(ktk_details)
kkr_bowlers <- getBowlers(kkr_details)
mi_bowlers <- getBowlers(mi_details)
pw_bowlers <- getBowlers(pw_details)
rr_bowlers <- getBowlers(rr_details)
rcb_bowlers <- getBowlers(rcb_details)
sh_bowlers <- getBowlers(sh_details)
gl_bowlers <- getBowlers(gl_details)
rps_bowlers <- getBowlers(rps_details)

#Save the dataframes
save(csk_bowlers,file="csk1.RData")
save(dc_bowlers, file="dc1.RData")
save(dd_bowlers, file="dd1.RData")
save(kxip_bowlers, file="kxip1.RData")
save(ktk_bowlers, file="ktk1.RData")
save(kkr_bowlers, file="kkr1.RData")
save(mi_bowlers , file="mi1.RData")
save(pw_bowlers, file="pw1.RData")
save(rr_bowlers, file="rr1.RData")
save(rcb_bowlers, file="rcb1.RData")
save(sh_bowlers, file="sh1.RData")
save(gl_bowlers, file="gl1.RData")
save(rps_bowlers, file="rps1.RData")

Now we are all set

A)  IPL T20 Match Analysis

1 IPL Match Analysis

Load any match data from the ./IPLMatches folder for e.g. Chennai Super Kings-Deccan Chargers-2008-05-06.RData

setwd("./IPLMatches")
load("Chennai Super Kings-Deccan Chargers-2008-05-06.RData")
csk_dc<- overs
#The steps are
load("IPLTeam1-IPLTeam2-Date.Rdata")
IPLTeam1_IPLTeam2 <- overs

All analysis for this match can be done now

2. Scorecard

teamBattingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam1")
teamBattingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam2")

3.Batting Partnerships

teamBatsmenPartnershipMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
teamBatsmenPartnershipMatch(IPLTeam1_IPLTeam2,"IPLTeam2","IPLTeam1")

4. Batsmen vs Bowler Plot

teamBatsmenVsBowlersMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=TRUE)
teamBatsmenVsBowlersMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)

5. Team bowling scorecard

teamBowlingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam1")
teamBowlingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam2")

6. Team bowling Wicket kind match

teamBowlingWicketKindMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <-teamBowlingWicketKindMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

7. Team Bowling Wicket Runs Match

teamBowlingWicketRunsMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <-teamBowlingWicketRunsMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

8. Team Bowling Wicket Match

m <-teamBowlingWicketMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m
teamBowlingWicketMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")

9. Team Bowler vs Batsmen

teamBowlersVsBatsmenMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <- teamBowlersVsBatsmenMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

10. Match Worm chart

matchWormGraph(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")

B)  IPL  Matches between 2  IPL teams

1 IPL Match Analysis

Load any match data from the ./IPLMatches folder for e.g. Chennai Super Kings-Deccan Chargers-2008-05-06.RData

setwd("./IPLMatches")
load("Chennai Super Kings-Deccan Chargers-2008-05-06.RData")
csk_dc<- overs
#The steps are
load("IPLTeam1-IPLTeam2-Date.Rdata")
IPLTeam1_IPLTeam2 <- overs

All analysis for this match can be done now

2. Scorecard

teamBattingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam1")
teamBattingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam2")

3.Batting Partnerships

teamBatsmenPartnershipMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
teamBatsmenPartnershipMatch(IPLTeam1_IPLTeam2,"IPLTeam2","IPLTeam1")

4. Batsmen vs Bowler Plot

teamBatsmenVsBowlersMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=TRUE)
teamBatsmenVsBowlersMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)

5. Team bowling scorecard

teamBowlingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam1")
teamBowlingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam2")

6. Team bowling Wicket kind match

teamBowlingWicketKindMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <-teamBowlingWicketKindMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

7. Team Bowling Wicket Runs Match

teamBowlingWicketRunsMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <-teamBowlingWicketRunsMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

8. Team Bowling Wicket Match

m <-teamBowlingWicketMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m
teamBowlingWicketMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")

9. Team Bowler vs Batsmen

teamBowlersVsBatsmenMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <- teamBowlersVsBatsmenMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

10. Match Worm chart

matchWormGraph(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")

C)  IPL Matches for a team against all other teams

1. IPL Matches for a team against all other teams

Load the data between for a IPL team against all other countries ./allMatchesAllOpposition for e.g all matches of Kolkata Knight Riders

load("allMatchesAllOpposition-Kolkata Knight Riders.RData")
kkr_matches <- matches
IPLTeam="IPLTeam1"
allMatches <- paste("allMatchesAllOposition-",IPLTeam,".RData",sep="")
load(allMatches)
IPLTeam1AllMatches <- matches

2. Team’s batting scorecard all Matches

m <-teamBattingScorecardAllOppnAllMatches(IPLTeam1AllMatches,theTeam="IPLTeam1")
m

3. Batting scorecard of opposing team

m <-teamBattingScorecardAllOppnAllMatches(matches=IPLTeam1AllMatches,theTeam="IPLTeam2")

4. Team batting partnerships

m <- teamBatsmenPartnershipAllOppnAllMatches(IPLTeam1AllMatches,theTeam="IPLTeam1")
m
m <- teamBatsmenPartnershipAllOppnAllMatches(IPLTeam1AllMatches,theTeam='IPLTeam1',report="detailed")
head(m,30)
m <- teamBatsmenPartnershipAllOppnAllMatches(IPLTeam1AllMatches,theTeam='IPLTeam1',report="summary")
m

5. Team batting partnerships plot

teamBatsmenPartnershipAllOppnAllMatchesPlot(IPLTeam1AllMatches,"IPLTeam1",main="IPLTeam1")
teamBatsmenPartnershipAllOppnAllMatchesPlot(IPLTeam1AllMatches,"IPLTeam1",main="IPLTeam2")

6, Team batsmen vs bowlers report

m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(IPLTeam1AllMatches,"IPLTeam1",rank=0)
m
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(IPLTeam1AllMatches,"IPLTeam1",rank=1,dispRows=30)
m
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(matches=IPLTeam1AllMatches,theTeam="IPLTeam2",rank=1,dispRows=25)
m

7. Team batsmen vs bowler plot

d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(IPLTeam1AllMatches,"IPLTeam1",rank=1,dispRows=50)
d
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(IPLTeam1AllMatches,"IPLTeam1",rank=2,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

8. Team bowling scorecard

teamBowlingScorecardAllOppnAllMatchesMain(matches=IPLTeam1AllMatches,theTeam="IPLTeam1")
teamBowlingScorecardAllOppnAllMatches(IPLTeam1AllMatches,'IPLTeam2')

9. Team bowler vs batsmen

teamBowlersVsBatsmenAllOppnAllMatchesMain(IPLTeam1AllMatches,theTeam="IPLTeam1",rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(IPLTeam1AllMatches,theTeam="IPLTeam1",rank=2)
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=IPLTeam1AllMatches,theTeam="IPLTeam1",rank=0)

10. Team Bowler vs bastmen

df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(IPLTeam1AllMatches,theTeam="IPLTeam1",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"IPLTeam1","IPLTeam1")

11. Team bowler wicket kind

teamBowlingWicketKindAllOppnAllMatches(IPLTeam1AllMatches,t1="IPLTeam1",t2="All")
teamBowlingWicketKindAllOppnAllMatches(IPLTeam1AllMatches,t1="IPLTeam1",t2="IPLTeam2")

12.

teamBowlingWicketRunsAllOppnAllMatches(IPLTeam1AllMatches,t1="IPLTeam1",t2="All",plot=TRUE)
teamBowlingWicketRunsAllOppnAllMatches(IPLTeam1AllMatches,t1="IPLTeam1",t2="IPLTeam2",plot=TRUE)

1 IPL Batsman setup functions

Get the batsman’s details for a batsman

setwd("../BattingBowlingDetails")
# IPL Team names
IPLTeamNames <- list("Chennai Super Kings","Deccan Chargers", "Delhi Daredevils","Kings Xi Punjab", 
                  "Kochi Tuskers Kerala","Kolkata Knight Riders","Mumbai Indians","Pune Warriors",
                  "Rajasthan Royals","Royal Challengers Bangalore","Sunrisers Hyderabad","Gujarat Lions",
                  "Rising Pune Supergiants")           


# Check and get the team indices of IPL teams in which the batsman has played
getTeamIndex <- function(batsman){
    setwd("./BattingBowlingDetails")
    load("csk.RData")
    load("dc.RData")
    load("dd.RData")
    load("kxip.RData")
    load("ktk.RData")
    load("kkr.RData")
    load("mi.RData")
    load("pw.RData")
    load("rr.RData")
    load("rcb.RData")
    load("sh.RData")
    load("gl.RData")
    load("rps.RData")
    setwd("..")
    getwd()
    print(ls())
    teams_batsmen = list(csk_batsmen,dc_batsmen,dd_batsmen,kxip_batsmen,ktk_batsmen,kkr_batsmen,mi_batsmen,
                         pw_batsmen,rr_batsmen,rcb_batsmen,sh_batsmen,gl_batsmen,rps_batsmen)
    b <- NULL
    for (i in 1:length(teams_batsmen)){
        a <- which(teams_batsmen[[i]] == batsman)

        if(length(a) != 0)
            b <- c(b,i)
    }
    b
}

# Get the list of the IPL team names from the indices passed
getTeams <- function(x){

    l <- NULL
    # Get the teams passed in as indexes
    for (i in seq_along(x)){

        l <- c(l, IPLTeamNames[[x[i]]]) 

    }
    l
}

# Create a consolidated data frame with all teams the IPL batsman has played for
getIPLBatsmanDF <- function(teamNames){
    batsmanDF <- NULL
   # Create a consolidated Data frame of batsman for all IPL teams played
    for (i in seq_along(teamNames)){
       df <- getBatsmanDetails(team=teamNames[i],name=IPLBatsman,dir="./BattingBowlingDetails")
       batsmanDF <- rbind(batsmanDF,df) 

    }
    batsmanDF
}

2. Create a consolidated IPL batsman data frame

# Since an IPL batsman coculd have played in multiple teams we need to determine these teams and
# create a consolidated data frame for the analysis
# For example to check MS Dhoni we need to do the following

IPLBatsman = "MS Dhoni"
#Check and get the team indices of IPL teams in which the batsman has played
i <- getTeamIndex(IPLBatsman)

# Get the team names in which the IPL batsman has played
teamNames <- getTeams(i)
    # Check if file exists in the directory. This check is necessary when moving between matchType


############## Create a consolidated IPL batsman dataframe for analysis
batsmanDF <- getIPLBatsmanDF(teamNames)

3. Runs vs deliveries

# For e.g. batsmanName="MS Dhoni""
#batsmanRunsVsDeliveries(batsmanDF, "MS Dhoni")
batsmanRunsVsDeliveries(batsmanDF,"batsmanName")

4. Batsman 4s & 6s

batsman46 <- select(batsmanDF,batsman,ballsPlayed,fours,sixes,runs)
p1 <- batsmanFoursSixes(batsman46,"batsmanName")

5. Batsman dismissals

batsmanDismissals(batsmanDF,"batsmanName")

6. Runs vs Strike rate

batsmanRunsVsStrikeRate(batsmanDF,"batsmanName")

7. Batsman Moving Average

batsmanMovingAverage(batsmanDF,"batsmanName")

8. Batsman cumulative average

batsmanCumulativeAverageRuns(batsmanDF,"batsmanName")

9. Batsman cumulative strike rate

batsmanCumulativeStrikeRate(batsmanDF,"batsmanName")

10. Batsman runs against oppositions

batsmanRunsAgainstOpposition(batsmanDF,"batsmanName")

11. Batsman runs vs venue

batsmanRunsVenue(batsmanDF,"batsmanName")

12. Batsman runs predict

batsmanRunsPredict(batsmanDF,"batsmanName")

13.Bowler set up functions

setwd("../BattingBowlingDetails")
# IPL Team names
IPLTeamNames <- list("Chennai Super Kings","Deccan Chargers", "Delhi Daredevils","Kings Xi Punjab", 
                  "Kochi Tuskers Kerala","Kolkata Knight Riders","Mumbai Indians","Pune Warriors",
                  "Rajasthan Royals","Royal Challengers Bangalore","Sunrisers Hyderabad","Gujarat Lions",
                  "Rising Pune Supergiants")    



# Get the team indices of IPL teams for which the bowler as played
getTeamIndex_bowler <- function(bowler){
    # Load IPL Bowlers
    setwd("./data")
    load("csk1.RData")
    load("dc1.RData")
    load("dd1.RData")
    load("kxip1.RData")
    load("ktk1.RData")
    load("kkr1.RData")
    load("mi1.RData")
    load("pw1.RData")
    load("rr1.RData")
    load("rcb1.RData")
    load("sh1.RData")
    load("gl1.RData")
    load("rps1.RData")
    setwd("..")
    teams_bowlers = list(csk_bowlers,dc_bowlers,dd_bowlers,kxip_bowlers,ktk_bowlers,kkr_bowlers,mi_bowlers,
                         pw_bowlers,rr_bowlers,rcb_bowlers,sh_bowlers,gl_bowlers,rps_bowlers)
    b <- NULL
    for (i in 1:length(teams_bowlers)){
        a <- which(teams_bowlers[[i]] == bowler)
        if(length(a) != 0){
            b <- c(b,i)
        }
    }
    b
}


# Get the list of the IPL team names from the indices passed
getTeams <- function(x){

    l <- NULL
    # Get the teams passed in as indexes
    for (i in seq_along(x)){

        l <- c(l, IPLTeamNames[[x[i]]]) 

    }
    l
}

# Get the team names
teamNames <- getTeams(i)

getIPLBowlerDF <- function(teamNames){
    bowlerDF <- NULL

    # Create a consolidated Data frame of batsman for all IPL teams played
    for (i in seq_along(teamNames)){
          df <- getBowlerWicketDetails(team=teamNames[i],name=IPLBowler,dir="./BattingBowlingDetails")
          bowlerDF <- rbind(bowlerDF,df) 

    }
    bowlerDF
}

14. Get the consolidated data frame for an IPL bowler

# Since an IPL bowler could have played in multiple teams we need to determine these teams and
# create a consolidated data frame for the analysis
# For example to check R Ashwin we need to do the following

IPLBowler = "R Ashwin"
#Check and get the team indices of IPL teams in which the batsman has played
i <- getTeamIndex(IPLBowler)

# Get the team names in which the IPL batsman has played
teamNames <- getTeams(i)
    # Check if file exists in the directory. This check is necessary when moving between matchType


############## Create a consolidated IPL batsman dataframe for analysis
bowlerDF <- getIPLBowlerDF(teamNames)

15. Bowler Mean Economy rate

# For e.g. to get the details of R Ashwin do
#bowlerMeanEconomyRate(bowlerDF,"R Ashwin")
bowlerMeanEconomyRate(bowlerDF,"bowlerName")

16. Bowler mean runs conceded

bowlerMeanRunsConceded(bowlerDF,"bowlerName")

17. Bowler Moving Average

bowlerMovingAverage(bowlerDF,"bowlerName")

18. Bowler cumulative average wickets

bowlerCumulativeAvgWickets(bowlerDF,"bowlerName")

19. Bowler cumulative Economy Rate (ER)

bowlerCumulativeAvgEconRate(bowlerDF,"bowlerName")

20. Bowler wicket plot

bowlerWicketPlot(bowlerDF,"bowlerName")

21. Bowler wicket against opposition

bowlerWicketsAgainstOpposition(bowlerDF,"bowlerName")

22. Bowler wicket at cricket grounds

bowlerWicketsVenue(bowlerDF,"bowlerName")

23. Predict number of deliveries to wickets

setwd("./IPLMatches")
bowlerDF1 <- getDeliveryWickets(team="IPLTeam1",dir=".",name="bowlerName",save=FALSE)
bowlerWktsPredict(bowlerDF1,"bowlerName")

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Analysis of International T20 matches with yorkr templates


Introduction

In this post I create yorkr templates for International T20 matches that are available on Cricsheet. With these templates you can convert all T20 data which is in yaml format to R dataframes. Further I create data and the necessary templates for analyzing. All of these templates can be accessed from Github at yorkrT20Template. The templates are

  1. Template for conversion and setup – T20Template.Rmd
  2. Any T20 match – T20Matchtemplate.Rmd
  3. T20 matches between 2 nations – T20Matches2TeamTemplate.Rmd
  4. A T20 nations performance against all other T20 nations – T20AllMatchesAllOppnTemplate.Rmd
  5. Analysis of T20 batsmen and bowlers of all T20 nations – T20BatsmanBowlerTemplate.Rmd

Besides the templates the repository also includes the converted data for all T20 matches I downloaded from Cricsheet in Dec 2016, You can recreate the files as more matches are added to Cricsheet site. This post contains all the steps needed for T20 analysis, as more matches are played around the World and more data is added to Cricsheet. This will also be my reference in future if I decide to analyze T20 in future!

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!!

1

 

Feel free to download/clone these templates  from Github yorkrT20Template and perform your own analysis

There will be 5 folders at the root

  1. T20data – Match files as yaml from Cricsheet
  2. T20Matches – Yaml match files converted to dataframes
  3. T20MatchesBetween2Teams – All Matches between any 2 T20 teams
  4. allMatchesAllOpposition – A T20 countries match data against all other teams
  5. BattingBowlingDetails – Batting and bowling details of all countries
library(yorkr)
library(dplyr)

The first few steps take care of the data setup. This needs to be done before any of the analysis of T20 batsmen, bowlers, any T20 match, matches between any 2 T20 countries or analysis of a teams performance against all other countries

There will be 5 folders at the root

  1. T20data
  2. T20Matches
  3. T20MatchesBetween2Teams
  4. allMatchesAllOpposition
  5. BattingBowlingDetails

The source YAML files will be in T20Data folder

1.Create directory T20Matches

Some files may give conversions errors. You could try to debug the problem or just remove it from the T20data folder. At most 2-4 file will have conversion problems and I usally remove then from the files to be converted.

Also take a look at my Inswinger shiny app which was created after performing the same conversion on the Dec 16 data .

convertAllYaml2RDataframesT20("T20Data","T20Matches")

2.Save all matches between all combinations of T20 nations

This function will create the set of all matches between every T20 country against every other T20 country. This uses the data that was created in T20Matches, with the convertAllYaml2RDataframesT20() function.

setwd("./T20MatchesBetween2Teams")
saveAllMatchesBetweenTeams("../T20Matches")

3.Save all matches against all opposition

This will create a consolidated dataframe of all matches played by every T20 playing nation against all other nattions. This also uses the data that was created in T20Matches, with the convertAllYaml2RDataframesT20() function.

setwd("../allMatchesAllOpposition")
saveAllMatchesAllOpposition("../T20Matches")

4. Create batting and bowling details for each T20 country

These are the current T20 playing nations. You can add to this vector as more countries start playing T20. You will get to know all T20 nations by also look at the directory created above namely allMatchesAllOpposition. his also uses the data that was created in T20Matches, with the convertAllYaml2RDataframesT20() function.

setwd("../BattingBowlingDetails")
teams <-c("Australia","India","Pakistan","West Indies", 'Sri Lanka',
          "England", "Bangladesh","Netherlands","Scotland", "Afghanistan",
          "Zimbabwe","Ireland","New Zealand","South Africa","Canada",
          "Bermuda","Kenya","Hong Kong","Nepal","Oman","Papua New Guinea",
          "United Arab Emirates")

for(i in seq_along(teams)){
    print(teams[i])
    val <- paste(teams[i],"-details",sep="")
    val <- getTeamBattingDetails(teams[i],dir="../T20Matches", save=TRUE)

}

for(i in seq_along(teams)){
    print(teams[i])
    val <- paste(teams[i],"-details",sep="")
    val <- getTeamBowlingDetails(teams[i],dir="../T20Matches", save=TRUE)

}

5. Get the list of batsmen for a particular country

For e.g. if you wanted to get the batsmen of Canada you would do the following. By replacing Canada for any other country you can get the batsmen of that country. These batsmen names can then be used in the batsmen analysis

country="Canada"
teamData <- paste(country,"-BattingDetails.RData",sep="")
load(teamData)
countryDF <- battingDetails
bmen <- countryDF %>% distinct(batsman) 
bmen <- as.character(bmen$batsman)
batsmen <- sort(bmen)
batsmen

6. Get the list of bowlers for a particular country

The method below can get the list of bowler names for any T20 nation. These names can then be used in the bowler analysis below

country="Netherlands"
teamData <- paste(country,"-BowlingDetails.RData",sep="")
load(teamData)
countryDF <- bowlingDetails
bwlr <- countryDF %>% distinct(bowler) 
bwlr <- as.character(bwlr$bowler)
bowler <- sort(bwlr)
bowler

Now we are all set

A)  International T20 Match Analysis

Load any match data from the ./T20Matches folder for e.g. Afganistan-England-2016-03-23.RData

setwd("./T20Matches")
load("Afghanistan-England-2016-03-23.RData")
afg_eng<- overs
#The steps are
load("Country1-Country2-Date.Rdata")
country1_country2 <- overs

All analysis for this match can be done now

2. Scorecard

teamBattingScorecardMatch(country1_country2,"Country1")
teamBattingScorecardMatch(country1_country2,"Country2")

3.Batting Partnerships

teamBatsmenPartnershipMatch(country1_country2,"Country1","Country2")
teamBatsmenPartnershipMatch(country1_country2,"Country2","Country1")

4. Batsmen vs Bowler Plot

teamBatsmenVsBowlersMatch(country1_country2,"Country1","Country2",plot=TRUE)
teamBatsmenVsBowlersMatch(country1_country2,"Country1","Country2",plot=FALSE)

5. Team bowling scorecard

teamBowlingScorecardMatch(country1_country2,"Country1")
teamBowlingScorecardMatch(country1_country2,"Country2")

6. Team bowling Wicket kind match

teamBowlingWicketKindMatch(country1_country2,"Country1","Country2")
m <-teamBowlingWicketKindMatch(country1_country2,"Country1","Country2",plot=FALSE)
m

7. Team Bowling Wicket Runs Match

teamBowlingWicketRunsMatch(country1_country2,"Country1","Country2")
m <-teamBowlingWicketRunsMatch(country1_country2,"Country1","Country2",plot=FALSE)
m

8. Team Bowling Wicket Match

m <-teamBowlingWicketMatch(country1_country2,"Country1","Country2",plot=FALSE)
m
teamBowlingWicketMatch(country1_country2,"Country1","Country2")

9. Team Bowler vs Batsmen

teamBowlersVsBatsmenMatch(country1_country2,"Country1","Country2")
m <- teamBowlersVsBatsmenMatch(country1_country2,"Country1","Country2",plot=FALSE)
m

10. Match Worm chart

matchWormGraph(country1_country2,"Country1","Country2")

B)  International T20 Matches between 2 teams

Load match data between any 2 teams from ./T20MatchesBetween2Teams for e.g.Australia-India-allMatches

setwd("./T20MatchesBetween2Teams")
load("Australia-India-allMatches.RData")
aus_ind_matches <- matches
#Replace below with your own countries
country1<-"England"
country2 <- "South Africa"
country1VsCountry2 <- paste(country1,"-",country2,"-allMatches.RData",sep="")
load(country1VsCountry2)
country1_country2_matches <- matches

2.Batsmen partnerships

m<- teamBatsmenPartnershiOppnAllMatches(country1_country2_matches,"country1",report="summary")
m
m<- teamBatsmenPartnershiOppnAllMatches(country1_country2_matches,"country2",report="summary")
m
m<- teamBatsmenPartnershiOppnAllMatches(country1_country2_matches,"country1",report="detailed")
m
teamBatsmenPartnershipOppnAllMatchesChart(country1_country2_matches,"country1","country2")

3. Team batsmen vs bowlers

teamBatsmenVsBowlersOppnAllMatches(country1_country2_matches,"country1","country2")

4. Bowling scorecard

a <-teamBattingScorecardOppnAllMatches(country1_country2_matches,main="country1",opposition="country2")
a

5. Team bowling performance

teamBowlingPerfOppnAllMatches(country1_country2_matches,main="country1",opposition="country2")

6. Team bowler wickets

teamBowlersWicketsOppnAllMatches(country1_country2_matches,main="country1",opposition="country2")
m <-teamBowlersWicketsOppnAllMatches(country1_country2_matches,main="country1",opposition="country2",plot=FALSE)
teamBowlersWicketsOppnAllMatches(country1_country2_matches,"country1","country2",top=3)
m

7. Team bowler vs batsmen

teamBowlersVsBatsmenOppnAllMatches(country1_country2_matches,"country1","country2",top=5)

8. Team bowler wicket kind

teamBowlersWicketKindOppnAllMatches(country1_country2_matches,"country1","country2",plot=TRUE)
m <- teamBowlersWicketKindOppnAllMatches(country1_country2_matches,"country1","country2",plot=FALSE)
m[1:30,]

9. Team bowler wicket runs

teamBowlersWicketRunsOppnAllMatches(country1_country2_matches,"country1","country2")

10. Plot wins and losses

setwd("./T20Matches")
plotWinLossBetweenTeams("country1","country2")

C)  International T20 Matches for a team against all other teams

Load the data between for a T20 team against all other countries ./allMatchesAllOpposition for e.g all matches of India

load("allMatchesAllOpposition-India.RData")
india_matches <- matches
country="country1"
allMatches <- paste("allMatchesAllOposition-",country,".RData",sep="")
load(allMatches)
country1AllMatches <- matches

2. Team’s batting scorecard all Matches

m <-teamBattingScorecardAllOppnAllMatches(country1AllMatches,theTeam="country1")
m

3. Batting scorecard of opposing team

m <-teamBattingScorecardAllOppnAllMatches(matches=country1AllMatches,theTeam="country2")

4. Team batting partnerships

m <- teamBatsmenPartnershipAllOppnAllMatches(country1AllMatches,theTeam="country1")
m
m <- teamBatsmenPartnershipAllOppnAllMatches(country1AllMatches,theTeam='country1',report="detailed")
head(m,30)
m <- teamBatsmenPartnershipAllOppnAllMatches(country1AllMatches,theTeam='country1',report="summary")
m

5. Team batting partnerships plot

teamBatsmenPartnershipAllOppnAllMatchesPlot(country1AllMatches,"country1",main="country1")
teamBatsmenPartnershipAllOppnAllMatchesPlot(country1AllMatches,"country1",main="country2")

6, Team batsmen vs bowlers report

m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(country1AllMatches,"country1",rank=0)
m
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(country1AllMatches,"country1",rank=1,dispRows=30)
m
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(matches=country1AllMatches,theTeam="country2",rank=1,dispRows=25)
m

7. Team batsmen vs bowler plot

d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(country1AllMatches,"country1",rank=1,dispRows=50)
d
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(country1AllMatches,"country1",rank=2,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

8. Team bowling scorecard

teamBowlingScorecardAllOppnAllMatchesMain(matches=country1AllMatches,theTeam="country1")
teamBowlingScorecardAllOppnAllMatches(country1AllMatches,'country2')

9. Team bowler vs batsmen

teamBowlersVsBatsmenAllOppnAllMatchesMain(country1AllMatches,theTeam="country1",rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(country1AllMatches,theTeam="country1",rank=2)
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=country1AllMatches,theTeam="country1",rank=0)

10. Team Bowler vs bastmen

df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(country1AllMatches,theTeam="country1",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"country1","country1")

11. Team bowler wicket kind

teamBowlingWicketKindAllOppnAllMatches(country1AllMatches,t1="country1",t2="All")
teamBowlingWicketKindAllOppnAllMatches(country1AllMatches,t1="country1",t2="country2")

12.

teamBowlingWicketRunsAllOppnAllMatches(country1AllMatches,t1="country1",t2="All",plot=TRUE)
teamBowlingWicketRunsAllOppnAllMatches(country1AllMatches,t1="country1",t2="country2",plot=TRUE)

D) Batsman functions

Get the batsman’s details for a batsman

setwd("../BattingBowlingDetails")
kohli <- getBatsmanDetails(team="India",name="Kohli",dir=".")
batsmanDF <- getBatsmanDetails(team="country1",name="batsmanName",dir=".")

2. Runs vs deliveries

batsmanRunsVsDeliveries(batsmanDF,"batsmanName")

3. Batsman 4s & 6s

batsman46 <- select(batsmanDF,batsman,ballsPlayed,fours,sixes,runs)
p1 <- batsmanFoursSixes(batsman46,"batsmanName")

4. Batsman dismissals

batsmanDismissals(batsmanDF,"batsmanName")

5. Runs vs Strike rate

batsmanRunsVsStrikeRate(batsmanDF,"batsmanName")

6. Batsman Moving Average

batsmanMovingAverage(batsmanDF,"batsmanName")

7. Batsman cumulative average

batsmanCumulativeAverageRuns(batsmanDF,"batsmanName")

8. Batsman cumulative strike rate

batsmanCumulativeStrikeRate(batsmanDF,"batsmanName")

9. Batsman runs against oppositions

batsmanRunsAgainstOpposition(batsmanDF,"batsmanName")

10. Batsman runs vs venue

batsmanRunsVenue(batsmanDF,"batsmanName")

11. Batsman runs predict

batsmanRunsPredict(batsmanDF,"batsmanName")

12. Bowler functions

For example to get Ravicahnder Ashwin’s bowling details

setwd("../BattingBowlingDetails")
ashwin <- getBowlerWicketDetails(team="India",name="Ashwin",dir=".")
bowlerDF <- getBatsmanDetails(team="country1",name="bowlerName",dir=".")

13. Bowler Mean Economy rate

bowlerMeanEconomyRate(bowlerDF,"bowlerName")

14. Bowler mean runs conceded

bowlerMeanRunsConceded(bowlerDF,"bowlerName")

15. Bowler Moving Average

bowlerMovingAverage(bowlerDF,"bowlerName")

16. Bowler cumulative average wickets

bowlerCumulativeAvgWickets(bowlerDF,"bowlerName")

17. Bowler cumulative Economy Rate (ER)

bowlerCumulativeAvgEconRate(bowlerDF,"bowlerName")

18. Bowler wicket plot

bowlerWicketPlot(bowlerDF,"bowlerName")

19. Bowler wicket against opposition

bowlerWicketsAgainstOpposition(bowlerDF,"bowlerName")

20. Bowler wicket at cricket grounds

bowlerWicketsVenue(bowlerDF,"bowlerName")

21. Predict number of deliveries to wickets

setwd("./T20Matches")
bowlerDF1 <- getDeliveryWickets(team="country1",dir=".",name="bowlerName",save=FALSE)
bowlerWktsPredict(bowlerDF1,"bowlerName")

cricketr and yorkr books – Paperback now in Amazon


My books
– Cricket Analytics with cricketr
– Beaten by sheer pace!: Cricket analytics with yorkr
are now available on Amazon in both Paperback and Kindle versions

The cricketr and yorkr packages are written in R, and both are available in CRAN. The books contain details on how to use these R packages to analyze performance of cricketers.

cricketr is based on data from ESPN Cricinfo Statsguru, and can analyze Test, ODI and T20 batsmen & bowlers. yorkr is based on data from Cricsheet, and can analyze ODI, T20 and IPL. yorkr can analyze batsmen, bowlers, matches and teams.

Cricket Analytics with cricketr
You can access the paperback at Cricket analytics with cricketr
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Beaten by sheer pace! Cricket Analytics with yorkr
You can buy the paperback from Amazon at Beaten by sheer pace: Cricket analytics with yorkr
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Order your copy today! Hope you have a great time reading!

Inswinger: yorkr swings into International T20s


In this post I introduce ‘Inswinger’ an interactive Shiny app to analyze International T20 players, matches and teams. This app was a natural consequence to my earlier Shiny app ‘GooglyPlus’. Most of the structure for this app remained the same, I only had to work with a different dataset, so to speak.

The Googly Shiny app is based on my R package ‘yorkr’ which is now available in CRAN. The R package and hence this Shiny app is based on data from Cricsheet. Inswinger is based on the latest data dump from Cricsheet (Dec 2016) and includes all International T20 till then. There are a lot of new Internationation teams like Oman, Hong Kong, UAE, etc. In total there are 22 different International T20 teams in my Inswinger app.

The countries are a) Afghanistan b) Australia c) Bangladesh d) Bermuda e) Canada f) England g) Hong Kong h) India i) Ireland j) Kenya k) Nepal l) Netherlands m) New Zealand n) Oman o) Pakistan p) Papua New Guinea q) Scotland r) South Africa s) Sri Lanka t) United Arab Emirates u) West Indies v) Zimbabwe

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!!

1

 

My R package ‘yorkr’,  on which both these Shiny apps are based, has the ability to output either a dataframe or plot, depending on a parameter plot=TRUE or FALSE. Hence in the Inswinger Shiny app results can be displayed both as table or a plot depending on the choice of function.

Inswinger can do detailed analyses of a) Individual T20 batsman b) Individual T20 bowler c) Any T20 match d) Head to head confrontation between 2 T20 teams e) All matches of a T20 team against all other teams.

The Shiny app can be accessed at Inswinger

The code for Inswinger is available at Github. Feel free to clone/download/fork  the code from Inswinger

Based on the 5 detailed analysis domains there are 5 tabs
A) T20 Batsman: This tab can be used to perform analysis of all T20 batsman. If a batsman has played in more than 1 team, then the overall performance is considered. There are 10 functions for the T20 Batsman. They are shown below
– Batsman Runs vs. Deliveries
– Batsman’s Fours & Sixes
– Dismissals of batsman
– Batsman’s Runs vs Strike Rate
– Batsman’s Moving Average
– Batsman’s Cumulative Average Run
– Batsman’s Cumulative Strike Rate
– Batsman’s Runs against Opposition
– Batsman’s Runs at Venue
– Predict Runs of batsman

B) T20 Bowler: This tab can be used to analyze individual T20 bowlers. The functions handle T20 bowlers who have played in more than 1 T20 team.
– Mean Economy Rate of bowler
– Mean runs conceded by bowler
– Bowler’s Moving Average
– Bowler’s Cumulative Avg. Wickets
– Bowler’s Cumulative Avg. Economy Rate
– Bowler’s Wicket Plot
– Bowler’s Wickets against opposition
– Bowler’s Wickets at Venues
– Bowler’s wickets prediction

C) T20 match: This tab can be used for analyzing individual T20 matches. The available functions are
– Match Batting Scorecard – Table
– Batting Partnerships – Plot, Table
– Batsmen vs Bowlers – Plot, Table
– Match Bowling Scorecard   – Table
– Bowling Wicket Kind – Plot, Table
– Bowling Wicket Runs – Plot, Table
– Bowling Wicket Match – Plot, Table
– Bowler vs Batsmen – Plot, Table
– Match Worm Graph – Plot

D) Head to head: This tab can be used for analyzing head-to-head confrontations, between any 2 T20 teams for e.g. all matches between India vs Australia or West Indies vs Sri Lanka . The available functions are
-Team Batsmen Batting Partnerships All Matches – Plot, Table {Summary and Detailed}
-Team Batting Scorecard All Matches – Table
-Team Batsmen vs Bowlers all Matches – Plot, Table
-Team Wickets Opposition All Matches – Plot, Table
-Team Bowling Scorecard All Matches – Table
-Team Bowler vs Batsmen All Matches – Plot, Table
-Team Bowlers Wicket Kind All Matches – Plot, Table
-Team Bowler Wicket Runs All Matches – Plot, Table
– Win Loss All Matches – Plot

E) T20 team’s overall performance: this tab can be used analyze the overall performance of any T20 team. For this analysis all matches played by this team is considered. The available functions are
-Team Batsmen Partnerships Overall – Plot, Table {Summary and Detailed)}
-Team Batting Scorecard Overall –Table
-Team Batsmen vs Bowlers Overall – Plot, Table
-Team Bowler vs Batsmen Overall – Plot, Table
-Team Bowling Scorecard Overall – Table
-Team Bowler Wicket Kind Overall – Plot, Table

Below I include a random set of charts that are generated in each of the 5 tabs
A. IPL Batsman
a. Shakib-al-Hassan (Bangladesh) :  Runs vs Deliveries
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b. Virat Kohli (India) – Cumulative Average
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c.  AB Devilliers (South Africa) – Runs at venues
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d. Glenn Maxwell (Australia)  – Predict runs vs deliveries faces
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B. IPL Bowler
a. TG Southee (New Zealand) – Mean Economy Rate vs overs
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b) DJ Bravo – Moving Average of wickets
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c) AC Evans (Scotland) – Bowler Wickets Against Opposition
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C.T20 Match
a. Match Score (Afghanistan vs Canada, 2012-03-18)
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b)  Match batting partnerships (Plot) Hong Kong vs Oman (2015-11-21), Hong Kong
Hong Kong Partnerships
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c) Match batting partnerships (Table) – Ireland vs Scotland(2012-03-18, Ireland)
Batting partnership can also be displayed as a table
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d) Batsmen vs Bowlers (Plot) – India vs England (2012-12-22)
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e) Match Worm Chart – Sri Lanka vs Pakistan (2015-08-01)
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D.Head to head
a) Team Batsmen Partnership (Plot) – India vs Australia (all matches)
Virat Kohli has the highest total runs in partnerships against Australia
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b)  Team Batsmen Partnership (Summary – Table) – Kenya vs Bangladesh
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c) Team Bowling Scorecard (Table only) India vs South Africa all Matches
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d) Wins- Losses New Zealand vs West Indies all Matches
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C) Overall performances
a) Batting Scorecard All Matches  (Table only) – England’s overall batting performance
Eoin Morgan, Kevin Pieterson  & SJ Taylor have the best performance
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b) Batsman vs Bowlers all Matches (Plot)
India’s best performing batsman (Rank=1) is Virat Kohli
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c)  Batsman vs Bowlers all Matches (Table)
The plot above for Virat Kohli can also be displayed as a table. Kohli has score most runs DJ Bravo, SR Watson & Shahid Afridi
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The Inswinger Shiny app can be accessed at Inswinger. Give it a swing!

The code for Inswinger is available at Github. Feel free to clone/download/fork  the code from Inswinger

Also see my other Shiny apps
1.GooglyPlus
2.What would Shakespeare say?
3.Sixer
4.Revisiting crimes against women in India

You may also like
1. Neural Networks: The mechanics of backpropagation
A primer on Qubits, Quantum gates and Quantum Operation
2. Re-working the Lucy Richardson algorithm in OpenCV
3.Design Principles of Scalable, Distributed Systems
4.Spicing up a IBM Bluemix cloud app with MongoDB and NodeExpress
5.Programming languages in layman’s language
7.Re-introducing cricketr! : An R package to analyze performances of cricketers

To see all posts take at a look at Index of Posts

Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr


Presenting ‘Googly’, a cool Shiny app that I developed over the last couple of days. This interactive Shiny app was on my mind for quite some time, and I finally got down to implementing it. The Googly Shiny app is based on my R package ‘yorkr’ which is now available in CRAN. The R package and hence this Shiny app is based on data from Cricsheet.

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!!

1

Googly is based on R package yorkr, and uses the data of all IPL matches from 2008 up to 2016, available on Cricsheet.

Googly can do detailed analyses of a) Individual IPL batsman b) Individual IPL bowler c) Any IPL match d) Head to head confrontation between 2 IPL teams e) All matches of an IPL team against all other teams.

With respect to the individual IPL batsman and bowler performance, I was in a bit of a ‘bind’ literally (pun unintended), as any IPL player could have played in more than 1 IPL team. Fortunately ‘rbind’ came to my rescue. I just get all the batsman’s/bowler’s performance in each IPL team, and then consolidate it into a single large dataframe to do the analyses of.

The Shiny app can be accessed at Googly

The code for Googly is available at Github. Feel free to clone/download/fork  the code from Googly

Check out my 2 books on cricket, a) Cricket analytics with cricketr b) Beaten by sheer pace – Cricket analytics with yorkr, now available in both paperback & kindle versions on Amazon!!! Pick up your copies today!

Also see my post GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables

Based on the 5 detailed analysis domains there are 5 tabs

IPL Batsman: This tab can be used to perform analysis of all IPL batsman. If a batsman has played in more than 1 team, then the overall performance is considered. There are 10 functions for the IPL Batsman. They are shown below

  1. Batsman Runs vs. Deliveries
  2. Batsman’s Fours & Sixes
  3. Dismissals of batsman
  4. Batsman’s Runs vs Strike Rate
  5. Batsman’s Moving Average
  6. Batsman’s Cumulative Average Run
  7. Batsman’s Cumulative Strike Rate
  8. Batsman’s Runs against Opposition
  9. Batsman’s Runs at Venue
  10. Predict Runs of batsman

IPL Bowler: This tab can be used to analyze individual IPL bowlers. The functions handle IPL bowlers who have played in more than 1 IPL team.

  1. Mean Economy Rate of bowler
  2. Mean runs conceded by bowler
  3. Bowler’s Moving Average
  4. Bowler’s Cumulative Avg. Wickets
  5. Bowler’s Cumulative Avg. Economy Rate
  6. Bowler’s Wicket Plot
  7. Bowler’s Wickets against opposition
  8. Bowler’s Wickets at Venues
  9. Bowler’s wickets prediction

IPL match: This tab can be used for analyzing individual IPL matches. The available functions are

  1. Batting Partnerships
  2. Batsmen vs Bowlers
  3. Bowling Wicket Kind
  4. Bowling Wicket Runs
  5. Bowling Wicket Match
  6. Bowler vs Batsmen
  7. Match Worm Graph

Head to head : This tab can be used for analyzing head-to-head confrontations, between any 2 IPL teams for e.g. all matches between Chennai Super Kings vs. Deccan Chargers or Kolkata Knight Riders vs. Delhi Daredevils. The available functions are

  1. Team Batsmen Batting Partnerships All Matches
  2. Team Batsmen vs Bowlers all Matches
  3. Team Wickets Opposition All Matches
  4. Team Bowler vs Batsmen All Matches
  5. Team Bowlers Wicket Kind All Matches
  6. Team Bowler Wicket Runs All Matches
  7. Win Loss All Matches

Overall performance : this tab can be used analyze the overall performance of any IPL team. For this analysis all matches played by this team is considered. The available functions are

  1. Team Batsmen Partnerships Overall
  2. Team Batsmen vs Bowlers Overall
  3. Team Bowler vs Batsmen Overall
  4. Team Bowler Wicket Kind Overall

Below I include a random set of charts that are generated in each of the 5 tabs

A. IPL Batsman
a. A Symonds : Runs vs Deliveries
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b. AB Devilliers – Cumulative Strike Rate
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c.  Gautam Gambhir – Runs at venues
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d. CH Gayle – Predict runs 
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B. IPL Bowler
a. Ashish Nehra – Cumulative Average Wickets
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b.  DJ Bravo – Moving Average of wickets
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c. R Ashwin – Mean Economy rate vs Overs
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C.IPL Match
a. Chennai Super Kings vs Deccan Chargers   (2008 -05-06) – Batsmen Partnerships

Note: You can choose either team in the match from the drop down ‘Choose team’

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b. Kolkata Knight Riders vs Delhi Daredevils (2013-04-02) – Bowling wicket runs
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c. Mumbai Indians vs Kings XI Punjab (2010-03-30) – Match worm graph
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D. Head to head confrontation
a. Rising Pune Supergiants vs Mumbai Indians in all matches – Team batsmen partnerships

Note: You can choose the partnership of either team in the drop down ‘Choose team’
untitled

b.  Gujarat Lions – Royal Challengers Bangalore all matches – Bowlers performance against batsmen
untitled

E. Overall Performance
a.  Royal Challengers Bangalore overall performance – Batsman Partnership (Rank=1)
This is Virat Kohli for RCB. Try out other ranks
untitled

b.  Rajashthan Royals overall Performance – Bowler vs batsman (Rank =2)
This is Vinay Kumar.
untitled

The Shiny app Googly can be accessed at Googly. Feel free to clone/fork the code from Github at Googly

For details on my R package yorkr, please see my blog Giga thoughts. There are more than 15 posts detailing the functions and their usage.

Do bowl a Googly!!!

You may like my other Shiny apps

Also see my other posts

  1. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
  2. Deblurring with OpenCV: Weiner filter reloaded
  3. Rock N’ Roll with Bluemix, Cloudant & NodeExpress
  4. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!
  5. Fun simulation of a Chain in Android
  6. Beaten by sheer pace! Cricket analytics with yorkr in paperback and Kindle versions
  7. Introducing cricketr! : An R package to analyze performances of cricketers
  8. Cricket analytics with cricketr!!!

For more posts see Index of posts

cricketr sizes up legendary All-rounders of yesteryear


Introduction

This is a post I have been wanting to write for several months, but had to put it off for one reason or another. In this post I use my R package cricketr to analyze the performance of All-rounder greats namely Kapil Dev, Ian Botham, Imran Khan and Richard Hadlee. All these players had talent that was natural and raw. They were good strikers of the ball and extremely lethal with their bowling. The ODI data for these players have been taken from ESPN Cricinfo.

Please be mindful of the ESPN Cricinfo Terms of Use

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!!

1

320 and $6.99/Rs448 respectively

 

You can also read this post at Rpubs as cricketr-AR. Dowload this report as a PDF file from cricketr-AR

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricketr template from Github (which is the R Markdown file I have used for the analysis below). You will only need to make appropriate changes for the players you are interested in. Just a familiarity with R and R Markdown only is needed.

All Rounders

  1. Kapil Dev (Ind)
  2. Ian Botham (Eng)
  3. Imran Khan (Pak)
  4. Richard Hadlee (NZ)

I have sprinkled the plots with a few of my comments. Feel free to draw your conclusions! The analysis is included below

if (!require("cricketr")){ 
    install.packages("cricketr",) 
} 

library(cricketr)

The data for any particular ODI player can be obtained with the getPlayerDataOD() function. To do you will need to go to ESPN CricInfo Playerand type in the name of the player for e.g Kapil Dev, etc. This will bring up a page which have the profile number for the player e.g. for Kapil Dev this would be http://www.espncricinfo.com/india/content/player/30028.html. Hence, Kapils’s profile is 30028. This can be used to get the data for Kapil Dev’s data as shown below. I have already executed the below 4 commands and I will use the files to run further commands

#kapil1 
#botham11 
#imran1 
#hadlee1 

Analyses of batting performances of the All Rounders

The following plots gives the analysis of the 4 ODI batsmen

  1. Kapil Dev (Ind) – Innings – 225, Runs = 3783, Average=23.79, Strike Rate= 95.07
  2. Ian Botham (Eng) – Innings – 116, Runs= 2113, Average=23.21, Strike Rate= 79.10
  3. Imran Khan (Pak) – Innings – 175, Runs= 3709, Average=33.41, Strike Rate= 72.65
  4. Richard Hadlee (NZ) – Innings – 115, Runs= 1751, Average=21.61, Strike Rate= 75.50

Plot of 4s, 6s and the scoring rate in ODIs

The 3 charts below give the number of

  1. 4s vs Runs scored
  2. 6s vs Runs scored
  3. Balls faced vs Runs scored

A regression line is fitted in each of these plots for each of the ODI batsmen

A. Kapil Dev
It can be seen that Kapil scores four 4’s when he scores 50. Also after facing 50 deliveries he scores around 43

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./kapil1.csv","Kapil")
batsman6s("./kapil1.csv","Kapil")
batsmanScoringRateODTT("./kapil1.csv","Kapil")

kapil-4s6ssr-1

dev.off()
## null device 
##           1

B. Ian Botham
Botham scores around 39 runs after 50 deliveries

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./botham1.csv","Botham")
batsman6s("./botham1.csv","Botham")
batsmanScoringRateODTT("./botham1.csv","Botham")

botham-4s6sr-1

dev.off()
## null device 
##           1

C. Imran Khan
Imran scores around 36 runs for 50 deliveries

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./imran1.csv","Imran")
batsman6s("./imran1.csv","Imran")
batsmanScoringRateODTT("./imran1.csv","Imran")

imran-4s6ssr-1

dev.off()
## null device 
##           1

D. Richard Hadlee
Hadlee also scores around 30 runs facing 50 deliveries

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./hadlee1.csv","Hadlee")
batsman6s("./hadlee1.csv","Hadlee")
batsmanScoringRateODTT("./hadlee1.csv","Hadlee")

hadlee-4s6sout-1

dev.off()
## null device 
##           1

Cumulative Average runs of batsman in career

Kapils cumulative avrerage runs drops towards the last 15 innings wheres Botham had a good run towards the end of his career. Imran performance as a batsman really peaks towards the end with a cumulative average of almost 25 runs. Hadlee has a stead performance

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanCumulativeAverageRuns("./kapil1.csv","Kapil")

kbih-car-1

batsmanCumulativeAverageRuns("./botham1.csv","Botham")

kbih-car-2

batsmanCumulativeAverageRuns("./imran1.csv","Imran")

kbih-car-3

batsmanCumulativeAverageRuns("./hadlee1.csv","Hadlee")

kbih-car-4

dev.off()
## null device 
##           1

Cumulative Average strike rate of batsman in career

Kapil’s strike rate is superlative touching the 90’s steadily. Botham’s strike drops dramatically towards the latter part of his career. Imran average at a steady 75 and Hadlee averages around 85.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanCumulativeStrikeRate("./kapil1.csv","Kapil")

kbih-casr-1

batsmanCumulativeStrikeRate("./botham1.csv","Botham")

kbih-casr-2

batsmanCumulativeStrikeRate("./imran1.csv","Imran")

kbih-casr-3

batsmanCumulativeStrikeRate("./hadlee1.csv","Hadlee")

kbih-casr-4

dev.off()
## null device 
##           1

Relative Mean Strike Rate

Kapil tops the strike rate among all the all-rounders. This is really a revelation to me. This can also be seen in the original data in Kapil’s strike rate is at a whopping 95.07 in comparison to Botham, Inran and Hadlee who are at 79.1,72.65 and 75.50 respectively

par(mar=c(4,4,2,2))
frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBatsmanSRODTT(frames,names)

plot-1-1

Relative Runs Frequency Percentage

This plot shows that Imran has a much better average runs scored over the other all rounders followed by Kapil

frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeRunsFreqPerfODTT(frames,names)

plot-2-1

Relative cumulative average runs in career

It can be seen clearly that Imran Khan leads the pack in cumulative average runs followed by Kapil Dev and then Botham

frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBatsmanCumulativeAvgRuns(frames,names)

kbih-relcar-1

Relative cumulative average strike rate in career

In the cumulative strike rate Hadlee and Kapil run a close race.

frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBatsmanCumulativeStrikeRate(frames,names)

kbih-relcsr-1

Percent 4’s,6’s in total runs scored

The plot below shows the contrib

frames <- list("./kapil1.csv","./botham1.csv","imran1.csv","hadlee1.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
runs4s6s <-batsman4s6s(frames,names)

plot-46s-1

print(runs4s6s)
##                Kapil Botham Imran Hadlee
## Runs(1s,2s,3s) 72.08  66.53 77.53  73.27
## 4s             21.98  25.78 17.61  21.08
## 6s              5.94   7.68  4.86   5.65

Runs forecast

The forecast for the batsman is shown below.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfForecast("./kapil1.csv","Kapil")
batsmanPerfForecast("./botham1.csv","Botham")
batsmanPerfForecast("./imran1.csv","Imran")
batsmanPerfForecast("./hadlee1.csv","Hadlee")

plot-fcst-1

dev.off()
## null device 
##           1

3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A prediction plane is fitted

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./kapil1.csv","Kapil")
battingPerf3d("./botham1.csv","Botham")

plot-3-1

dev.off()
## null device 
##           1
par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./imran1.csv","Imran")
battingPerf3d("./hadlee1.csv","Hadlee")

plot-4-1

dev.off()
## null device 
##           1

Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

BF <- seq( 10, 200,length=10)
Mins <- seq(30,220,length=10)
newDF <- data.frame(BF,Mins)

kapil <- batsmanRunsPredict("./kapil1.csv","Kapil",newdataframe=newDF)
botham <- batsmanRunsPredict("./botham1.csv","Botham",newdataframe=newDF)
imran <- batsmanRunsPredict("./imran1.csv","Imran",newdataframe=newDF)
hadlee <- batsmanRunsPredict("./hadlee1.csv","Hadlee",newdataframe=newDF)

The fitted model is then used to predict the runs that the batsmen will score for a hypotheticial Balls faced and Minutes at crease. It can be seen that Kapil is the best bet for a balls faced and minutes at crease followed by Botham.

batsmen <-cbind(round(kapil$Runs),round(botham$Runs),round(imran$Runs),round(hadlee$Runs))
colnames(batsmen) <- c("Kapil","Botham","Imran","Hadlee")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns
##    BallsFaced MinsAtCrease Kapil Botham Imran Hadlee
## 1          10           30    16      6    10     15
## 2          31           51    33     22    22     28
## 3          52           72    49     38    33     42
## 4          73           93    65     54    45     56
## 5          94          114    81     70    56     70
## 6         116          136    97     86    67     84
## 7         137          157   113    102    79     97
## 8         158          178   130    117    90    111
## 9         179          199   146    133   102    125
## 10        200          220   162    149   113    139

Highest runs likelihood

The plots below the runs likelihood of batsman. This uses K-Means . A. Kapil Dev

batsmanRunsLikelihood("./kapil1.csv","Kapil")

kapil11-1

## Summary of  Kapil 's runs scoring likelihood
## **************************************************
## 
## There is a 34.57 % likelihood that Kapil  will make  22 Runs in  24 balls over 34  Minutes 
## There is a 17.28 % likelihood that Kapil  will make  46 Runs in  46 balls over  65  Minutes 
## There is a 48.15 % likelihood that Kapil  will make  5 Runs in  7 balls over 9  Minutes

B. Ian Botham

batsmanRunsLikelihood("./botham1.csv","Botham")

devilliers-1

## Summary of  Botham 's runs scoring likelihood
## **************************************************
## 
## There is a 47.95 % likelihood that Botham  will make  9 Runs in  12 balls over 15  Minutes 
## There is a 39.73 % likelihood that Botham  will make  23 Runs in  32 balls over  44  Minutes 
## There is a 12.33 % likelihood that Botham  will make  59 Runs in  74 balls over 101  Minutes

C. Imran Khan

batsmanRunsLikelihood("./imran1.csv","Imran")

gaylecache-true-1

## Summary of  Imran 's runs scoring likelihood
## **************************************************
## 
## There is a 23.33 % likelihood that Imran  will make  36 Runs in  54 balls over 74  Minutes 
## There is a 60 % likelihood that Imran  will make  14 Runs in  18 balls over  23  Minutes 
## There is a 16.67 % likelihood that Imran  will make  53 Runs in  90 balls over 115  Minutes

D. Richard Hadlee

batsmanRunsLikelihood("./hadlee1.csv","Hadlee")

maxwell-1

## Summary of  Hadlee 's runs scoring likelihood
## **************************************************
## 
## There is a 6.1 % likelihood that Hadlee  will make  64 Runs in  79 balls over 90  Minutes 
## There is a 42.68 % likelihood that Hadlee  will make  25 Runs in  33 balls over  44  Minutes 
## There is a 51.22 % likelihood that Hadlee  will make  9 Runs in  11 balls over 15  Minutes

Average runs at ground and against opposition

A. Kapil Dev

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./kapil1.csv","Kapil")
batsmanAvgRunsOpposition("./kapil1.csv","Kapil")

avgrg-1-1

dev.off()
## null device 
##           1

B. Ian Botham

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./botham1.csv","Botham")
batsmanAvgRunsOpposition("./botham1.csv","Botham")

avgrg-2-1

dev.off()
## null device 
##           1

C. Imran Khan

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./imran1.csv","Imran")
batsmanAvgRunsOpposition("./imran1.csv","Imran")

avgrg-3-1

dev.off()
## null device 
##           1

D. Richard Hadlee

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
batsmanAvgRunsGround("./hadlee1.csv","Hadlee")
batsmanAvgRunsOpposition("./hadlee1.csv","Hadlee")

avgrg-4-1

dev.off()
## null device 
##           1

Moving Average of runs over career

The moving average for the 4 batsmen indicate the following

Kapil’s performance drops significantly while there is a slump in Botham’s performance. On the other hand Imran and Hadlee’s performance were on the upswing.

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanMovingAverage("./kapil1.csv","Kapil")
batsmanMovingAverage("./botham1.csv","Botham")
batsmanMovingAverage("./imran1.csv","Imran")
batsmanMovingAverage("./hadlee1.csv","Hadlee")

sdgm-ma-1

dev.off()
## null device 
##           1

Check batsmen in-form, out-of-form

[1] “**************************** Form status of Kapil ****************************\n\n
Population size: 72
Mean of population: 19.38 \n
Sample size: 9 Mean of sample: 6.78 SD of sample: 6.14 \n\n
Null hypothesis H0 : Kapil ‘s sample average is within 95% confidence interval of population average\n
Alternative hypothesis Ha : Kapil ‘s sample average is below the 95% confidence interval of population average\n\n
Kapil ‘s Form Status: Out-of-Form because the p value: 8.4e-05 is less than alpha= 0.05

“**************************** Form status of Botham ****************************\n\n
Population size: 65
Mean of population: 21.29 \n
Sample size: 8 Mean of sample: 15.38 SD of sample: 13.19 \n\n
Null hypothesis H0 : Botham ‘s sample average is within 95% confidence interval of population average\n
Alternative hypothesis Ha : Botham ‘s sample average is below the 95% confidence interval of population average\n\n
Botham ‘s Form Status: In-Form because the p value: 0.120342 is greater than alpha= 0.05 \n

“**************************** Form status of Imran ****************************\n\n
Population size: 54
Mean of population: 24.94 \n
Sample size: 6 Mean of sample: 30.83 SD of sample: 25.4 \n\n
Null hypothesis H0 : Imran ‘s sample average is within 95% confidence interval of population average\n
Alternative hypothesis Ha : Imran ‘s sample average is below the 95% confidence interval of population average\n\n
Imran ‘s Form Status: In-Form because the p value: 0.704683 is greater than alpha= 0.05 \n

“**************************** Form status of Hadlee ****************************\n\n
Population size: 73
Mean of population: 18 \n
Sample size: 9 Mean of sample: 27 SD of sample: 24.27 \n\n
Null hypothesis H0 : Hadlee ‘s sample average is within 95% confidence interval of population average\n
Alternative hypothesis Ha : Hadlee ‘s sample average is below the 95% confidence interval of population average\n\n
Hadlee ‘s Form Status: In-Form because the p value: 0.85262 is greater than alpha= 0.05 \n *******************************************************************************************\n\n”

Analyses of bowling performances of the All Rounders

The following plots gives the analysis of the 4 ODI batsmen

  1. Kapil Dev (Ind) – Innings – 225, Wickets = 253, Average=27.45, Economy Rate= 3.71
  2. Ian Botham (Eng) – Innings – 116, Wickets = 145, Average=28.54, Economy Rate= 3.96
  3. Imran Khan (Pak) – Innings – 175, Wickets = 182, Average=26.61, Economy Rate= 3.89
  4. Richard Hadlee (NZ) – Innings – 115, Wickets = 158, Average=21.56, Economy Rate= 3.30

Botham has the highest number of innings and wickets followed closely by Mitchell. Imran and Hadlee have relatively fewer innings.

To get the bowler’s data use

#kapil2 
#botham2 
#imran2 
#hadlee2 

“`

Wicket Frequency percentage

This plot gives the percentage of wickets for each wickets (1,2,3…etc).

par(mfrow=c(1,4))
par(mar=c(4,4,2,2))
bowlerWktsFreqPercent("./kapil2.csv","Kapil")
bowlerWktsFreqPercent("./botham2.csv","Botham")
bowlerWktsFreqPercent("./imran2.csv","Imran")
bowlerWktsFreqPercent("./hadlee2.csv","Hadlee")

relbowlfp-1

dev.off()
## null device 
##           1

Wickets Runs plot

The plot below gives a boxplot of the runs ranges for each of the wickets taken by the bowlers.

par(mfrow=c(1,4))
par(mar=c(4,4,2,2))

bowlerWktsRunsPlot("./kapil2.csv","Kapil")
bowlerWktsRunsPlot("./botham2.csv","Botham")
bowlerWktsRunsPlot("./imran2.csv","Imran")
bowlerWktsRunsPlot("./hadlee2.csv","Hadlee")

wktsrun-1

dev.off()
## null device 
##           1

Cumulative average wicket plot

Botham has the best cumulative average wicket touching almost 1.6 wickets followed by Hadlee

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerCumulativeAvgWickets("./kapil2.csv","Kapil")

kwm-bowlcaw-1

bowlerCumulativeAvgWickets("./botham2.csv","Botham")

kwm-bowlcaw-2

bowlerCumulativeAvgWickets("./imran2.csv","Imran")

kwm-bowlcaw-3

bowlerCumulativeAvgWickets("./hadlee2.csv","Hadlee")

kwm-bowlcaw-4

dev.off()
## null device 
##           1
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerCumulativeAvgEconRate("./kapil2.csv","Kapil")

kwm-bowlcer-1

bowlerCumulativeAvgEconRate("./botham2.csv","Botham")

kwm-bowlcer-2

bowlerCumulativeAvgEconRate("./imran2.csv","Imran")

kwm-bowlcer-3

bowlerCumulativeAvgEconRate("./hadlee2.csv","Hadlee")

kwm-bowlcer-4

dev.off()
## null device 
##           1

Average wickets in different grounds and opposition

A. Kapil Dev

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./kapil2.csv","Kapil")
bowlerAvgWktsOpposition("./kapil2.csv","Kapil")

gr-1-1

dev.off()
## null device 
##           1

B. Ian Botham

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./botham2.csv","Botham")
bowlerAvgWktsOpposition("./botham2.csv","Botham")

gr-2-1

dev.off()
## null device 
##           1

C. Imran Khan

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./imran2.csv","Imran")
bowlerAvgWktsOpposition("./imran2.csv","Imran")

gr-3-1

dev.off()
## null device 
##           1

D. Richard Hadlee

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
bowlerAvgWktsGround("./hadlee2.csv","Hadlee")
bowlerAvgWktsOpposition("./hadlee2.csv","Hadlee")

gr-4-1

dev.off()
## null device 
##           1

Relative bowling performance

It can be seen that Botham is the most effective wicket taker of the lot

frames <- list("./kapil2.csv","./botham2.csv","imran2.csv","hadlee2.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBowlingPerf(frames,names)

relbowlperf-1

Relative Economy Rate against wickets taken

Hadlee has the best overall economy rate followed by Kapil Dev

frames <- list("./kapil2.csv","./botham2.csv","imran2.csv","hadlee2.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBowlingERODTT(frames,names)

relbowler-1

Relative cumulative average wickets of bowlers in career

This plot confirms the wicket taking ability of Botham followed by Hadlee

frames <- list("./kapil2.csv","./botham2.csv","imran2.csv","hadlee2.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBowlerCumulativeAvgWickets(frames,names)

rbcaw-1

Relative cumulative average economy rate of bowlers

frames <- list("./kapil2.csv","./botham2.csv","imran2.csv","hadlee2.csv")
names <- list("Kapil","Botham","Imran","Hadlee")
relativeBowlerCumulativeAvgEconRate(frames,names)

rbcer-1

Moving average of wickets over career

This plot shows that Hadlee has the best economy rate followed by Kapil

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
bowlerMovingAverage("./kapil2.csv","Kapil")
bowlerMovingAverage("./botham2.csv","Botham")
bowlerMovingAverage("./imran2.csv","Imran")
bowlerMovingAverage("./hadlee2.csv","Hadlee")

jmss-bowlma-1

dev.off()
## null device 
##           1

Wickets forecast

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
bowlerPerfForecast("./kapil2.csv","Kapil")
bowlerPerfForecast("./botham2.csv","Botham")
bowlerPerfForecast("./imran2.csv","Imran")
bowlerPerfForecast("./hadlee2.csv","Hadlee")

jjmss-pfcst-1

dev.off()
## null device 
##           1

Check bowler in-form, out-of-form

“**************************** Form status of Kapil ****************************\n\n
Population size: 198
Mean of population: 1.2 \n Sample size: 23 Mean of sample: 0.65 SD of sample: 0.83 \n\n
Null hypothesis H0 : Kapil ‘s sample average is within 95% confidence interval \n of population average\n
Alternative hypothesis Ha : Kapil ‘s sample average is below the 95% confidence\n interval of population average\n\n
Kapil ‘s Form Status: Out-of-Form because the p value: 0.002097 is less than alpha= 0.05 \n

“**************************** Form status of Botham ****************************\n\n
Population size: 166
Mean of population: 1.58 \n Sample size: 19 Mean of sample: 1.47 SD of sample: 1.12 \n\n
Null hypothesis H0 : Botham ‘s sample average is within 95% confidence interval \n of population average\n
Alternative hypothesis Ha : Botham ‘s sample average is below the 95% confidence\n interval of population average\n\n
Botham ‘s Form Status: In-Form because the p value: 0.336694 is greater than alpha= 0.05 \n

“**************************** Form status of Imran ****************************\n\n
Population size: 137
Mean of population: 1.23 \n Sample size: 16 Mean of sample: 0.81 SD of sample: 0.91 \n\n
Null hypothesis H0 : Imran ‘s sample average is within 95% confidence interval \n of population average\n
Alternative hypothesis Ha : Imran ‘s sample average is below the 95% confidence\n interval of population average\n\n
Imran ‘s Form Status: Out-of-Form because the p value: 0.041727 is less than alpha= 0.05 \n

“**************************** Form status of Hadlee ****************************\n\n
Population size: 100
Mean of population: 1.38 \n Sample size: 12 Mean of sample: 1.67 SD of sample: 1.37 \n\n
Null hypothesis H0 : Hadlee ‘s sample average is within 95% confidence interval \n of population average\n
Alternative hypothesis Ha : Hadlee ‘s sample average is below the 95% confidence\n interval of population average\n\n
Hadlee ‘s Form Status: In-Form because the p value: 0.761265 is greater than alpha= 0.05 \n *******************************************************************************************\n\n”

Key findings

Here are some key conclusions ODI batsmen

  1. Kapil Dev’s strike rate stands high above the other 3
  2. Imran Khan has the best cumulative average runs followed by Kapil
  3. Botham is the most effective wicket taker followed by Hadlee
  4. Hadlee is the most economical bowler and is followed by Kapil Dev
  5. For a hypothetical Balls Faced and Minutes at creases Kapil will score the most runs followed by Botham
  6. The moving average of indicates that the best is yet to come for Imran and Hadlee. Kapil and Botham were on the decline

Also see my other posts in R

  1. A primer on Qubits, Quantum gates abd Quantum operations
  2. Deblurring with OpenCV:Weiner filter reloaded
  3. Designing a Social Web Portal
  4. A crime map of India in R – Crimes against women
  5. Bend it like Bluemix, MongoDB with autoscaling – Part 2
  6. Mirror, mirror . the best batsman of them all?

For a full list of posts see Index of posts