Cricpy takes guard for the Twenty20s

There are two ways to write error-free programs; only the third one works.”” Alan J. Perlis

Programming today is a race between software engineers striving to build bigger and better idiot-proof programs, and the universe trying to produce bigger and better idiots. So far, the universe is winning. ” Rick Cook

My software never has bugs. It just develops random features.” Anon

If you make an ass out of yourself, there will always be someone to ride you.” Bruce Lee

Introduction

This is the 3rd and final post on cricpy, and is a continuation to my 2 earlier posts

1. Introducing cricpy:A python package to analyze performances of cricketers
2.Cricpy takes a swing at the ODIs

Cricpy, is the python avatar of my R package ‘cricketr’. To know more about my R package cricketr see Re-introducing cricketr! : An R package to analyze performances of cricketers

With this post  cricpy, like cricketr, now becomes omnipotent, and is now capable of handling Test, ODI and T20 matches.

Cricpy uses the statistics info available in ESPN Cricinfo Statsguru.

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 Cricpy takes guard for the Twenty 20s. You can also download the pdf version of this post at cricpy-TT.pdf

You can fork/clone the package at Github cricpy

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricpy-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. The functions can be executed in RStudio or in a IPython notebook.

If you are passionate about cricket, and love analyzing cricket performances, then check out my racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

Untitled

The cricpy package

The data for a particular player in Twenty20s can be obtained with the getPlayerDataTT() function. To do this you will need to go to T20 Batting and T20 Bowling and click the player you are interested in 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,this can be used to get the data for Virat Kohlias 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 languages 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 not added much of my own 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 cricpy

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

3. Getting help from cricpy – Python

import cricpy.analytics as ca 
help(ca.getPlayerDataTT)
## Help on function getPlayerDataTT in module cricpy.analytics:
## 
## getPlayerDataTT(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2, 3], result=[1, 2, 3, 5], create=True)
##     Get the Twenty20 International player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory~
##     
##     Description
##     
##     Get the Twenty20 player data given the profile of the batsman/bowler. The allowed inputs are home,away, neutralboth and won,lost,tied or no result 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
##     
##     getPlayerDataTT(profile, opposition="",host="",dir = "./data", 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 Virat Kohli this turns out to be 253802 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 Afghanistan:40,Australia:2,Bangladesh:25,England:1,Hong Kong:19,India:6,Ireland:29, 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 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,New Zealand:5, South Africa:3,Sri Lanka:8,United States of America:11,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. kohli.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
##     
##     bowlerWktRateTT getPlayerData
##     
##     Examples
##     
##     ## Not run: 
##     # Only away. Get data only for won and lost innings
##     kohli =getPlayerDataTT(253802,dir="../cricketr/data", file="kohli1.csv",
##     type="batting")
##     
##     # Get bowling data and store in file for future
##     ashwin = getPlayerDataTT(26421,dir="../cricketr/data",file="ashwin1.csv",
##     type="bowling")
##     
##     kohli =getPlayerDataTT(253802,opposition = 2,host=2,dir="../cricketr/data", 
##     file="kohli1.csv",type="batting")

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

4. Get the Twenty20 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 getPlayerDataTT for all subsequent analyses

import cricpy.analytics as ca
#kohli=ca.getPlayerDataTT(253802,dir=".",file="kohli.csv",type="batting")
#guptill=ca.getPlayerDataTT(226492,dir=".",file="guptill.csv",type="batting")
#shahzad=ca.getPlayerDataTT(419873,dir=".",file="shahzad.csv",type="batting")
#mccullum=ca.getPlayerDataTT(37737,dir=".",file="mccullum.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 and Twenty20 records

5 Virat Kohli’s performance – Basic Analyses

The 3 plots below provide the following for Virat Kohli in T20s

  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")

8. 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")

11. A look at the Top 4 batsman – Kohli,  Guptill, Shahzad and McCullum

The following batsmen have been very prolific in Twenty20 cricket and will be used for the analyses

  1. Virat Kohli: Runs – 2167, Average:49.25 ,Strike rate-136.11
  2. MJ Guptill : Runs -2271, Average:34.4 ,Strike rate-132.88
  3. Mohammed Shahzad :Runs – 1936, Average:31.22 ,Strike rate-134.81
  4. BB McCullum : Runs – 2140, Average:35.66 ,Strike rate-136.21

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("./guptill.csv","M J Guptill")

ca.batsmanPerfBoxHist("./shahzad.csv","M Shahzad")

ca.batsmanPerfBoxHist("./mccullum.csv","BB McCullum")

13 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 Twenty20 batsmen.

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

ca.batsmanMovingAverage("./guptill.csv","M J Guptill")
#ca.batsmanMovingAverage("./shahzad.csv","M Shahzad") # Gives error. Check!

ca.batsmanMovingAverage("./mccullum.csv","BB McCullum")

14 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career.Kohli’s average tops around 45 runs around 43 innings, though there is a dip downwards

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

ca.batsmanCumulativeAverageRuns("./guptill.csv","M J Guptill")

ca.batsmanCumulativeAverageRuns("./shahzad.csv","M Shahzad")

ca.batsmanCumulativeAverageRuns("./mccullum.csv","BB McCullum")

15 Cumulative Average strike rate of batsman in career

Kohli, Guptill and McCullum average a strike rate of 125+

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

ca.batsmanCumulativeStrikeRate("./guptill.csv","M J Guptill")

ca.batsmanCumulativeStrikeRate("./shahzad.csv","M Shahzad")

ca.batsmanCumulativeStrikeRate("./mccullum.csv","BB McCullum")

16 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman. Kohli is way above all the other 3 batsmen. Behind Kohli is McCullum and then Guptill

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullumn"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

17. Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show that Kohli tops the overall strike rate followed by McCullum and then Guptill

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullum"]
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("./guptill.csv","M J Guptill")

ca.battingPerf3d("./shahzad.csv","M Shahzad")

ca.battingPerf3d("./mccullum.csv","BB McCullum")

19. 3D plot of Runs vs Balls Faced and Minutes at Crease

Guptill and McCullum have a large percentage of sixes in comparison to the 4s. Kohli has a relative lower number of 6s

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullum"]
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   14.753153
## 1    37.857143   70.714286   55.963333
## 2    65.714286  111.428571   97.173513
## 3    93.571429  152.142857  138.383693
## 4   121.428571  192.857143  179.593873
## 5   149.285714  233.571429  220.804053
## 6   177.142857  274.285714  262.014233
## 7   205.000000  315.000000  303.224414
## 8   232.857143  355.714286  344.434594
## 9   260.714286  396.428571  385.644774
## 10  288.571429  437.142857  426.854954
## 11  316.428571  477.857143  468.065134
## 12  344.285714  518.571429  509.275314
## 13  372.142857  559.285714  550.485494
## 14  400.000000  600.000000  591.695674

21 Analysis of Top Bowlers

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

  1. Shakib Hasan:Wickets: 80, Average = 21.07, Economy Rate – 6.74
  2. Mohammed Nabi : Wickets: 67, Average = 24.25, Economy Rate – 7.13
  3. Rashid Khan: Wickets: 64, Average = 12.40, Economy Rate – 6.01
  4. Imran Tahir : Wickets:62, Average – 14.95, Economy Rate – 6.77

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
#shakib=ca.getPlayerDataTT(56143,dir=".",file="shakib.csv",type="bowling")
#nabi=ca.getPlayerDataOD(25913,dir=".",file="nabi.csv",type="bowling")
#rashid=ca.getPlayerDataOD(793463,dir=".",file="rashid.csv",type="bowling")
#tahir=ca.getPlayerDataOD(40618,dir=".",file="tahir.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("./shakib.csv","Shakib Al Hasan")

ca.bowlerWktsFreqPercent("./nabi.csv","Mohammad Nabi")

ca.bowlerWktsFreqPercent("./rashid.csv","Rashid Khan")

ca.bowlerWktsFreqPercent("./tahir.csv","Imran Tahir")

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.

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("./shakib.csv","Shakib Al Hasan")

ca.bowlerWktsRunsPlot("./nabi.csv","Mohammad Nabi")

ca.bowlerWktsRunsPlot("./rashid.csv","Rashid Khan")

ca.bowlerWktsRunsPlot("./tahir.csv","Imran Tahir")

25 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues.

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("./shakib.csv","Shakib Al Hasan")

ca.bowlerAvgWktsGround("./nabi.csv","Mohammad Nabi")

ca.bowlerAvgWktsGround("./rashid.csv","Rashid Khan")

ca.bowlerAvgWktsGround("./tahir.csv","Imran Tahir")

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("./shakib.csv","Shakib Al Hasan")

ca.bowlerAvgWktsOpposition("./nabi.csv","Mohammad Nabi")

ca.bowlerAvgWktsOpposition("./rashid.csv","Rashid Khan")

ca.bowlerAvgWktsOpposition("./tahir.csv","Imran Tahir")

27 Wickets taken moving average

From the plot below it can be see

import cricpy.analytics as ca
ca.bowlerMovingAverage("./shakib.csv","Shakib Al Hasan")

ca.bowlerMovingAverage("./nabi.csv","Mohammad Nabi")

ca.bowlerMovingAverage("./rashid.csv","Rashid Khan")

ca.bowlerMovingAverage("./tahir.csv","Imran Tahir")

28 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. Rashid Khan has been the most effective with almost 2.28 wickets per match

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("./shakib.csv","Shakib Al Hasan")

ca.bowlerCumulativeAvgWickets("./nabi.csv","Mohammad Nabi")

ca.bowlerCumulativeAvgWickets("./rashid.csv","Rashid Khan")

ca.bowlerCumulativeAvgWickets("./tahir.csv","Imran Tahir")

29 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. Rashid Khan has the nest economy rate followed by Mohammed Nabi

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("./shakib.csv","Shakib Al Hasan")

ca.bowlerCumulativeAvgEconRate("./nabi.csv","Mohammad Nabi")

ca.bowlerCumulativeAvgEconRate("./rashid.csv","Rashid Khan")

ca.bowlerCumulativeAvgEconRate("./tahir.csv","Imran Tahir")

30 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate is given below. It can be seen that Rashid Khan has the best economy rate followed by Mohammed Nabi and then Imran Tahir

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

31 Relative Economy Rate against wickets taken

Rashid Khan has the best figures for wickets between 2-3.5 wickets. Mohammed Nabi pips Rashid Khan when takes a haul of 4 wickets.

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlingER(frames,names)

32 Relative cumulative average wickets of bowlers in career

Rashid has the best performance with cumulative average wickets. He is followed by Imran Tahir in the wicket haul, followed by Shakib Al Hasan

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
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 Kohli, Guptill, Shahzad and McCullum
1.Kohli has the best overall cumulative average runs and towers over everybody else
2. Kohli, Guptill and McCullum has a very good strike rate of around 125+
3. Guptill and McCullum have a larger percentage of sixes as compared to Kohli
4. Rashid Khan has the best cumulative average wickets, followed by Imran Tahir and then Shakib Al Hasan
5. Rashid Khan is the most economical bowler, followed by Mohammed Nabi

You can fork/clone the package at Github cricpy

Conclusion

Cricpy now has almost all the functions and functionalities of my R package cricketr. There are still a few more features that need to be added to cricpy. I intend to do this as and when I find time.

Go ahead, take cricpy for a spin! Hope you enjoy the ride!

Watch this space!!!

Important note: Do check out my other posts using cricpy at cricpy-posts

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To see all posts click Index of Posts

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

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricpy-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. The functions can be executed in RStudio or in a IPython notebook.

If you are passionate about cricket, and love analyzing cricket performances, then check out my racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

Untitled

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 not 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 .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.

Important note: Do check out my other posts using cricpy at cricpy-posts

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 racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

Untitled

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

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricpy-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. The functions can be executed in RStudio or in a IPython notebook.

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 .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 
##     
##     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!

Important note: Do check out my other posts using cricpy at cricpy-posts

My book ‘Practical Machine Learning in R and Python: Second edition’ on Amazon

Note: The 3rd edition of this book is now available My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon

The third edition of my book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both paperback ($12.99) and kindle ($9.99/Rs449) versions.  This second edition includes more content,  extensive comments and formatting for better readability.

In this book I implement some of the most common, but important Machine Learning algorithms in R and equivalent Python code.
1. Practical machine with R and Python: Third Edition – Machine Learning in Stereo(Paperback-$12.99)
2. Practical machine with R and Third Edition – Machine Learning in Stereo(Kindle- $9.99/Rs449)

This book is ideal both for beginners and the experts in R and/or Python. Those starting their journey into datascience and ML will find the first 3 chapters useful, as they touch upon the most important programming constructs in R and Python and also deal with equivalent statements in R and Python. Those who are expert in either of the languages, R or Python, will find the equivalent code ideal for brushing up on the other language. And finally,those who are proficient in both languages, can use the R and Python implementations to internalize the ML algorithms better.

Here is a look at the topics covered

Table of Contents
Preface …………………………………………………………………………….4
Introduction ………………………………………………………………………6
1. Essential R ………………………………………………………………… 8
2. Essential Python for Datascience ……………………………………………57
3. R vs Python …………………………………………………………………81
4. Regression of a continuous variable ……………………………………….101
5. Classification and Cross Validation ………………………………………..121
6. Regression techniques and regularization ………………………………….146
7. SVMs, Decision Trees and Validation curves ………………………………191
8. Splines, GAMs, Random Forests and Boosting ……………………………222
9. PCA, K-Means and Hierarchical Clustering ………………………………258
References ……………………………………………………………………..269

Pick up your copy today!!
Hope you have a great time learning as I did while implementing these algorithms!

Presentation on ‘Machine Learning in plain English – Part 3

This is the 3rd and final part of Machine Learning in plain English -Part 3. In this presentation, I discuss the intuition behind SVMs, B-Splines, GAMs, Decision Trees, Random Forest and Gradient Boosting. Also I touch upon Unsupervised Learning, specifically PCA and K-Means. As before the presentation does not include any math or programming. The presentation can be seen below


The implementations of all the discussed algorithm are are available in my book which is available on Amazon My book ‘Practical Machine Learning with R and Python’ on Amazon

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1. My TEDx talk on the “Internet of Things”
2. Deep Learning from first principles in Python, R and Octave – Part 2
3. De-blurring revisited with Wiener filter using OpenCV
4. Architecting a cloud based IP Multimedia System (IMS)
5.The 3rd paperback & kindle editions of my books on Cricket, now on Amazon

To see all posts click Index of posts

Practical Machine Learning with R and Python – Part 6

Introduction

This is the final and concluding part of my series on ‘Practical Machine Learning with R and Python’. In this series I included the implementations of the most common Machine Learning algorithms in R and Python. The algorithms implemented were

1. Practical Machine Learning with R and Python – Part 1 In this initial post, I touch upon regression of a continuous target variable. Specifically I touch upon Univariate, Multivariate, Polynomial regression and KNN regression in both R and Python
2. Practical Machine Learning with R and Python – Part 2 In this post, I discuss Logistic Regression, KNN classification and Cross Validation error for both LOOCV and K-Fold in both R and Python
3. Practical Machine Learning with R and Python – Part 3 This 3rd part included feature selection in Machine Learning. Specifically I touch best fit, forward fit, backward fit, ridge(L2 regularization) & lasso (L1 regularization). The post includes equivalent code in R and Python.
4. Practical Machine Learning with R and Python – Part 4 In this part I discussed SVMs, Decision Trees, Validation, Precision-Recall, AUC and ROC curves
5. Practical Machine Learning with R and Python – Part 5  In this penultimate part, I touch upon B-splines, natural splines, smoothing spline, Generalized Additive Models(GAMs), Decision Trees, Random Forests and Gradient Boosted Treess.

In this last part I cover Unsupervised Learning. Specifically I cover the implementations of Principal Component Analysis (PCA). K-Means and Heirarchical Clustering. You can download this R Markdown file from Github at MachineLearning-RandPython-Part6

Note: Please listen to my video presentations Machine Learning in youtube
1. Machine Learning in plain English-Part 1
2. Machine Learning in plain English-Part 2
3. Machine Learning in plain English-Part 3

Check out my compact and minimal book  “Practical Machine Learning with R and Python:Third edition- Machine Learning in stereo”  available in Amazon in paperback($12.99) and kindle($8.99) versions. My book includes implementations of key ML algorithms and associated measures and metrics. The book is ideal for anybody who is familiar with the concepts and would like a quick reference to the different ML algorithms that can be applied to problems and how to select the best model. Pick your copy today!!

 

1.1a Principal Component Analysis (PCA) – R code

Principal Component Analysis is used to reduce the dimensionality of the input. In the code below 8 x 8 pixel of handwritten digits is reduced into its principal components. Then a scatter plot of the first 2 principal components give a very good visial representation of the data

library(dplyr)
library(ggplot2)
#Note: This example is adapted from an the example in the book Python Datascience handbook by 
# Jake VanderPlas (https://jakevdp.github.io/PythonDataScienceHandbook/05.09-principal-component-analysis.html)

# Read the digits data (From sklearn datasets)
digits= read.csv("digits.csv")
# Create a digits classes target variable
digitClasses <- factor(digits$X0.000000000000000000e.00.29)

#Invoke the Principal Componsent analysis on columns 1-64
digitsPCA=prcomp(digits[,1:64])

# Create a dataframe of PCA
df <- data.frame(digitsPCA$x)
# Bind the digit classes
df1 <- cbind(df,digitClasses)
# Plot only the first 2 Principal components as a scatter plot. This plot uses only the
# first 2 principal components 
ggplot(df1,aes(x=PC1,y=PC2,col=digitClasses)) + geom_point() +
  ggtitle("Top 2 Principal Components")

1.1 b Variance explained vs no principal components – R code

In the code below the variance explained vs the number of principal components is plotted. It can be seen that with 20 Principal components almost 90% of the variance is explained by this reduced dimensional model.

# Read the digits data (from sklearn datasets)
digits= read.csv("digits.csv")
# Digits target
digitClasses <- factor(digits$X0.000000000000000000e.00.29)
digitsPCA=prcomp(digits[,1:64])


# Get the Standard Deviation
sd=digitsPCA$sdev
# Compute the variance
digitsVar=digitsPCA$sdev^2
#Compute the percent variance explained
percentVarExp=digitsVar/sum(digitsVar)

# Plot the percent variance exlained as a function of the  number of principal components
#plot(cumsum(percentVarExp), xlab="Principal Component", 
#     ylab="Cumulative Proportion of Variance Explained", 
#     main="Principal Components vs % Variance explained",ylim=c(0,1),type='l',lwd=2,
#       col="blue")

1.1c Principal Component Analysis (PCA) – Python code

import numpy as np
from sklearn.decomposition import PCA
from sklearn import decomposition
from sklearn import datasets
import matplotlib.pyplot as plt
  
from sklearn.datasets import load_digits
# Load the digits data
digits = load_digits()
# Select only the first 2 principal components
pca = PCA(2)  # project from 64 to 2 dimensions
#Compute the first 2 PCA
projected = pca.fit_transform(digits.data)

# Plot a scatter plot of the first 2 principal components
plt.scatter(projected[:, 0], projected[:, 1],
            c=digits.target, edgecolor='none', alpha=0.5,
            cmap=plt.cm.get_cmap('spectral', 10))
plt.xlabel('PCA 1')
plt.ylabel('PCA 2')
plt.colorbar();
plt.title("Top 2 Principal Components")
plt.savefig('fig1.png', bbox_inches='tight')

1.1 b Variance vs no principal components

– Python code

import numpy as np
from sklearn.decomposition import PCA
from sklearn import decomposition
from sklearn import datasets
import matplotlib.pyplot as plt
  
from sklearn.datasets import load_digits
digits = load_digits()
# Select all 64 principal components
pca = PCA(64)  # project from 64 to 2 dimensions
projected = pca.fit_transform(digits.data)

# Obtain the explained variance for each principal component
varianceExp= pca.explained_variance_ratio_
# Compute the total sum of variance
totVarExp=np.cumsum(np.round(pca.explained_variance_ratio_, decimals=4)*100)

# Plot the variance explained as a function of the number of principal components
plt.plot(totVarExp)
plt.xlabel('No of principal components')
plt.ylabel('% variance explained')
plt.title('No of Principal Components vs Total Variance explained')
plt.savefig('fig2.png', bbox_inches='tight')

1.2a K-Means – R code

In the code first the scatter plot of the first 2 Principal Components of the handwritten digits is plotted as a scatter plot. Over this plot 10 centroids of the 10 different clusters corresponding the 10 diferent digits is plotted over the original scatter plot.

library(ggplot2)
# Read the digits data
digits= read.csv("digits.csv")
# Create digit classes target variable
digitClasses <- factor(digits$X0.000000000000000000e.00.29)

# Compute the Principal COmponents
digitsPCA=prcomp(digits[,1:64])

# Create a data frame of Principal components and the digit classes 
df <- data.frame(digitsPCA$x)
df1 <- cbind(df,digitClasses)

# Pick only the first 2 principal components
a<- df[,1:2]
# Compute K Means of 10 clusters and allow for 1000 iterations
k<-kmeans(a,10,1000)

# Create a dataframe of the centroids of the clusters
df2<-data.frame(k$centers)

#Plot the first 2 principal components with the K Means centroids
ggplot(df1,aes(x=PC1,y=PC2,col=digitClasses)) + geom_point() +
    geom_point(data=df2,aes(x=PC1,y=PC2),col="black",size = 4) + 
    ggtitle("Top 2 Principal Components with KMeans clustering") 

1.2b K-Means – Python code

The centroids of the 10 different handwritten digits is plotted over the scatter plot of the first 2 principal components.

import numpy as np
from sklearn.decomposition import PCA
from sklearn import decomposition
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.cluster import KMeans
digits = load_digits()

# Select only the 1st 2 principal components
pca = PCA(2)  # project from 64 to 2 dimensions
projected = pca.fit_transform(digits.data)

# Create 10 different clusters
kmeans = KMeans(n_clusters=10)

# Compute  the clusters
kmeans.fit(projected)
y_kmeans = kmeans.predict(projected)
# Get the cluster centroids
centers = kmeans.cluster_centers_
centers

#Create a scatter plot of the first 2 principal components
plt.scatter(projected[:, 0], projected[:, 1],
            c=digits.target, edgecolor='none', alpha=0.5,
            cmap=plt.cm.get_cmap('spectral', 10))
plt.xlabel('PCA 1')
plt.ylabel('PCA 2')
plt.colorbar();
# Overlay the centroids on the scatter plot
plt.scatter(centers[:, 0], centers[:, 1], c='darkblue', s=100)
plt.savefig('fig3.png', bbox_inches='tight')

1.3a Heirarchical clusters – R code

Herirachical clusters is another type of unsupervised learning. It successively joins the closest pair of objects (points or clusters) in succession based on some ‘distance’ metric. In this type of clustering we do not have choose the number of centroids. We can cut the created dendrogram mat an appropriate height to get a desired and reasonable number of clusters These are the following ‘distance’ metrics used while combining successive objects

  • Ward
  • Complete
  • Single
  • Average
  • Centroid
# Read the IRIS dataset
iris <- datasets::iris
iris2 <- iris[,-5]
species <- iris[,5]

#Compute the distance matrix
d_iris <- dist(iris2) 

# Use the 'average' method to for the clsuters
hc_iris <- hclust(d_iris, method = "average")

# Plot the clusters
plot(hc_iris)

# Cut tree into 3 groups
sub_grp <- cutree(hc_iris, k = 3)

# Number of members in each cluster
table(sub_grp)
## sub_grp
##  1  2  3 
## 50 64 36
# Draw rectangles around the clusters
rect.hclust(hc_iris, k = 3, border = 2:5)

1.3a Heirarchical clusters – Python code

from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
# Load the IRIS data set
iris = load_iris()


# Generate the linkage matrix using the average method
Z = linkage(iris.data, 'average')

#Plot the dendrogram
#dendrogram(Z)
#plt.xlabel('Data')
#plt.ylabel('Distance')
#plt.suptitle('Samples clustering', fontweight='bold', fontsize=14);
#plt.savefig('fig4.png', bbox_inches='tight')

Conclusion

This is the last and concluding part of my series on Practical Machine Learning with R and Python. These parallel implementations of R and Python can be used as a quick reference while working on a large project. A person who is adept in one of the languages R or Python, can quickly absorb code in the other language.

Hope you find this series useful!

More interesting things to come. Watch this space!

References

  1. Statistical Learning, Prof Trevor Hastie & Prof Robert Tibesherani, Online Stanford
  2. Applied Machine Learning in Python Prof Kevyn-Collin Thomson, University Of Michigan, Coursera

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1. The many faces of latency
2. Simulating a Web Join in Android
3. The Anamoly
4. yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions
5. Bend it like Bluemix, MongoDB using Auto-scale – Part 1!

To see all posts see ‘Index of posts