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


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 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 as ca 

2. Invoking functions with Python package crlcpy

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

3. Getting help from cricpy – Python

import as ca 
## Help on function getPlayerDataOD in module
## 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 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 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
##     Value
##     Returns the player's dataframe
##     Note
##     Maintainer: Tinniam V Ganesh <>
##     Author(s)
##     Tinniam V Ganesh
##     References
##     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 as ca

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 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 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 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 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 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 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 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 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 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 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 as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]

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 as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]

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 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 as ca
frames = ["./sehwag.csv","./kohli.csv","./gayle.csv","./jayasuriya.csv"]
names = ["Sehwag","Kohli","Gayle","Jayasuriya"]

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 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")
##             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 as ca

23. Wicket Frequency Plot

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

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

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

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

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

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