GooglyPlusPlus2021 is now fully interactive!!!

GooglyPlusPlus2021 is now fully interactive. Please read the below post carefully to see the different ways you can interact with the data in the plots.

There are 2 main updates in this latest version of GooglyPlusPlus2021

a) GooglyPlusPlus gets all ‘touchy, feely‘ with the data and now you can interact with the plot/chart to get more details of the underlying data. There are many ways you can slice’n dice the data in the charts. The examples below illustrate a few of this. You can interact with plots by hover’ing, ‘click’ing and ‘double-click’ing curves, plots, barplots to get details of the data.

b) GooglyPlusPlus also includes the ‘Super Smash T20’ league from New Zealand. You can analyze batsmen, bowlers, matches, teams and rank Super Smash (SSM) also

Note: GooglyPlusPlus2021 can handle a total of 11 formats including T20 and ODI. They are

i) IPL ii) Intl. T20(men) ii) Intl. T20(women) iv) BBL

v) NTB vi) PSL vii) WBB. viii) CPL

ix) SSM x) ODI (men) xi) ODI (women)

Each of these formats have 7 tabs which are

— Analyze batsman

— Analyze bowlers

— Analyze match

— Head-to-head

— Team vs all other teams

— Rank batsmen

— Rank bowlers

Within these 11 x 7 = 77 tabs you can analyze batsmen, bowlers, matches, head-to-head, team vs all other teams and rank players for T20 and ODI. In addition all plots have been made interactive so there is a lot more information that you can get from these charts

Try out the interactive GooglyPlusPlus2021 now!!!

You can fork/clone the Shiny app from Github at GooglyPlusPlus2021

Below I have randomly included some charts for different formats to show how you can interact with them

a) Batsman Analysis – Runs vs Deliveries (IPL)

Mouse-over/Hover

The plot below gives the number of runs scored by David Warner vs Deliveries faced.

b) Batsman Analysis – Runs vs Deliveries (IPL) (prediction)

Since a 2nd order regression line,with confidence intervals(shaded area), has been fitted in the above plot, we can predict the runs given the ‘balls faced’ as below

Click ‘Toggle Spike lines’ (use palette on top-right)

By using hover(mouse-over) on the curve we can determine the predicted number of runs Warner will score given a certain number of deliveries

c) Bowler Analysis – Wickets against opposition – Intl. T20 (women)

Jhulan Goswami’s wickets against opposition countries in Intl. T20 (women)

d) Bowler Analysis (Predict bowler wickets) IPL – (non-interactive**)

Note: Some plots are non-interactive, like the one below which predicts the number of wickets Bumrah will take based on number of deliveries bowled

e) Match Analysis – Batsmen Partnership -Intl. T20 (men)

India vs England batting partnership between Virat Kohli & Shikhar Dhawan in all matches between England and India

f) Match Analysis – Worm chart (Super Smash T20) SSM

i) Worm chart of Auckland vs Northern Districts (29 Jan 2021).

ii) The final cross-over happens around the 2nd delivery of the 19th over (18.2) as Northern Districts over-takes Auckland to win the match.

g) Head-to-head – Team batsmen vs bowlers (Bangladesh batsmen against Afghanistan bowlers) Intl. T20 (men)

Batting performance of Shakib-al-Hasan (Bangladesh) against Afghanistan bowlers in Intl. T20 (men)

h) Head-to-head – Team batsmen vs bowlers (Bangladesh batsmen against Afghanistan bowlers) Intl. T20 (men)Filter

Double click on Shakib-al-Hasan on the legend to get the performance of Shakib-al-Hasan against Afghanistan bowlers

Avoiding the clutter

i) Head-to-head – Team bowler vs batsmen (Chennai Super Kings bowlers vs Mumbai Indians batsmen) – IPL

If you choose the above option the resulting plot is very crowded as shown below

To get the performance of Mumbai Indian (MI) batsmen (Rohit Sharma & Kieron Pollard) against Chennai Super Kings (CSK) bowlers in all matches do as told below

Steps to avoid clutter in stacked bar plots

1) This can be avoided by selectively choosing to filter out the batsmen we are interested in. say RG Sharma and Kieron Pollard. Then double-ciick RG Sharma, this is will bring up the chart with only RG Sharma as below

2) Now add additional batsmen you are interested in by single-clicking. In the example below Kieron Pollard is added

You can continue to add additional players that you are interested by single clicking.

j) Head-to-head (Performance of Indian batsmen vs Australian bowlers)- ODI

In the plot V Kohli, MS Dhoni and SC Ganguly have been selected for their performance against Australian bowlers (use toggle spike lines)

k) Overall Performance – PSL batting partnership against all teams (Fakhar Zaman)

The plot below shows Fakhar Zaman (Lahore Qalanders) partnerships with other teammates in all matches in PSL.

l) Win-loss against all teams (CPL)

Win-loss chart of Jamaica Talawallahs (CPL) in all matches against all opposition

m) Team batting partnerships against all teams for India (ODI Women)

Batting partnerships of Indian ODI women against all other teams

n) Ranking of batsmen (IPL 2021)

Finally here is the latest ranking of IPL batsmen for IPL 2021 (can be done for all other T20 formats)

o) Ranking of bowlers (IPL 2021)

Clone/download the Shiny app from Github at GooglyPlusPlus2021

So what are you waiting for? Go ahead and try out GooglyPlusPlus2021!

Knock yourself out!

Enjoy enjaami!!!

See also

  1. Deconstructing Convolutional Neural Networks with Tensorflow and Keras
  2. Deep Learning from first principles in Python, R and Octave – Part 6
  3. Cricketr learns new tricks : Performs fine-grained analysis of players
  4. Big Data 6: The T20 Dance of Apache NiFi and yorkpy
  5. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
  6. Practical Machine Learning with R and Python – Part 6
  7. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
  8. Simulating an oscillating revoluteJoint in Android
  9. Benford’s law meets IPL, Intl. T20 and ODI cricket
  10. De-blurring revisited with Wiener filter using OpenCV

To see all posts click Index of posts

GooglyPlusPlus2021 with IPL 2021, as-it-happens!

A brand new season of IPL 2021 is on its way, and I intend to keep my Shiny app GooglyPlusPlus updated with all the analysis “as-it-happens”. I had written a post earlier Big Data 7: yorkr waltzes with Apache NiFi, where I used my R package yorkr in a NiFi pipeline to automate downloading, converting and generating appropriate data for GooglyPlusPlus. However, using Apache NiFi for daily updates would be an overkill. So, I have created a ‘big bash script’ (with shell,R,python scripts) and scheduled daily with CRON, which will get daily updates from Cricsheet, convert the yaml files, generate the necessary data files for GooglyPlusPlus in an automated way, using my R package yorkr, and integrate with the Shiny app

Now, you should be able to do detailed analysis of batsmen, bowlers, IPL matches, IPL teams and also do the ranking of the batsmen and bowlers as new data is added on a daily basis. Also remember that GooglyPlusPlus2021 can do similar analysis for all T20 formats (Intl. T20 (men,women), BBL, NTB, PSL, CPL, WBB etc.)

Check out GooglyPlusPlus2021-IPL 2021

I will be keeping the app updated as data becomes available after the match. Do check it out. Here are some random analysis of the the completed 29 matches (see included matches in table below)

You can download/clone the code for GooglyPlusPlus2021 from Github at gpp2021-1

You can perform analysis of the completed matches in the ‘IPL Match’ tab as shown below

A) Mumbai Indians-Royal Challengers Bangalore-2021-04-09

a) Match scorecard – Mumbai Indians

Note: The scorecards are computed in real time.

b) Batting Partnerships – Royal Challengers Bangalore

c) Bowling Wicket Kind – Royal Challengers Bangalore

B) Chennai Super Kings vs Delhi Capitals – 2021-04-10

d) Batting Partnerships (table) – Delhi Capitals

e) Match Worm Graph

C) Kolkata Knight Riders vs Sunrisers Hyderabad – 2021-04-11

f) Batsmen vs Bowlers

D) Final ranks of IPL 2021 batsmen

E) Final ranks of IPL 2021 bowlers

Incidentally GooglyPlusPlus2021 has also been updated with all matches PSL 2021. Here is a sample

PSL 2021

F) Quetta Gladiators vs Lahore Qalanders – 22-02-2021

G) Ranks of batsmen PSL 2021

H) Ranks bowlers PSL 2021

Important note :

Note: 1) The GooglyPlusPlus2021 Shiny app also includes functions for performing the following analysis namely

  • detailed analysis of batsmen
  • detailed analysis of bowlers
  • match analysis
  • Head-to-head between 2 IPL teams
  • Analysis of IPL team against all other teams
  • Ranking of batsmen based on number of years and matches played
  • Ranking of bowlers based on number of years and matches played

So do check the other tabs of this app

2) GooglyPlusPlus2021 can do similar analysis for other T20 games like Intl. T20 (men,women), BBL, NTB, PSL and so on.

Give GooglyPlusPlus2021 a spin!!

Download/clone the code for GooglyPlusPlus2021 from Github at gpp2021-1

GooglyPlusPlus2021 has been updated with all the completed 29 matches

Mumbai Indians-Royal Challengers Bangalore-2021-04-09Chennai Super Kings-Delhi Capitals-2021-04-10
Kolkata Knight Riders-Sunrisers Hyderabad-2021-04-11Punjab Kings-Rajasthan Royals-2021-04-12
Mumbai Indians-Kolkata Knight Riders-2021-04-13Royal Challengers Bangalore-Sunrisers Hyderabad-2021-04-14
Delhi Capitals-Rajasthan Royals-2021-04-15Punjab Kings-Chennai Super Kings-2021-04-16
Mumbai Indians-Sunrisers Hyderabad-2021-04-17Royal Challengers Bangalore-Kolkata Knight Riders-2021-04-18
Punjab Kings-Delhi Capitals-2021-04-18Chennai Super Kings-Rajasthan Royals-2021-04-19
Mumbai Indians-Delhi Capitals-2021-04-20Punjab Kings-Sunrisers Hyderabad-2021-04-21
Chennai Super Kings-Kolkata Knight Riders-2021-04-21Rajasthan Royals-Royal Challengers Bangalore-2021-04-22
Mumbai Indians-Punjab Kings-2021-04-23Kolkata Knight Riders-Rajasthan Royals-2021-04-24
Chennai Super Kings-Royal Challengers Bangalore-2021-04-25Delhi Capitals-Sunrisers Hyderabad-2021-04-25
Punjab Kings-Kolkata Knight Riders-2021-04-26Royal Challengers Bangalore-Delhi Capitals-2021-04-27
Sunrisers Hyderabad-Chennai Super Kings-2021-04-28Rajasthan Royals-Mumbai Indians-2021-04-29
Kolkata Knight Riders-Delhi Capitals-2021-04-29Punjab Kings-Royal Challengers Bangalore-2021-04-30.RData
Chennai Super Kings-Mumbai Indians-2021-05-01Rajasthan Royals-Sunrisers Hyderabad-2021-05-02
Punjab Kings-Delhi Capitals-2021-05-02

Watch this space!

Also see

  1. Introducing GooglyPlusPlus!!!
  2. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!
  3. Cricketr adds team analytics to its repertoire!!!
  4. Deep Learning from first principles in Python, R and Octave – Part 3
  5. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
  6. Practical Machine Learning with R and Python – Part 5

To see all posts click Index of posts

GooglyPlusPlus2021 bubbles up top T20 players in all formats!

“Would you tell me, please, which way I ought to go from here?”
“That depends a good deal on where you want to get to.”
“I don’t much care where –”
“Then it doesn’t matter which way you go.”
                              Lewis Carroll, Alice in Wonderland

This latest version of GooglyPlusPlus2021 has the following updates

– brand new strategy and algorithm for ranking T20 players in any format (IPL, BBL, NTB, PSL etc.)

– integrates the Caribbean Premier League T20 into the app

– includes the latest BBL matches in 2020-2021

– includes all the latest Natwest T20 matches 2020

– has a new and better user interface

Interestingly the new Ranking algorithm has come just before the IPL auction. Check out who’s who in IPL T20 by taking GooglyPlusPlus2021 for ride!!!

Try out GooglyPlusPlus2021!!

You can clone/fork the code from Github at GooglyPlusPlus2021

1) Ranking Algorithm

In my last post GooglyPlusPlus2021 interactively ranks T20 batsmen and bowlers!!!  I had shown how by changing the number of matches played we see that KL Rahul and Rishabh Pant move above Virat Kohli. That set me thinking. So, I redesigned the ranking so that we start to identify newer players earlier.  This is how the new ranking works.

a) Ranking T20 batsmen :

I have the following controls

a) Since year : This tells the year range  to consider for the  batsmen. This slider moves from right to left

b) Matches played : Number of matches played by the batsman in the year range. This moves left to right

c) Mode : The ranking algorithm sorts on and ranks on 2 columns namely Runs and Strike rate. The mode tells whether to consider Runs over Strike rate of Strike rate over runs. 

By default, the control for “Since year” will be set to the ‘beginning of time’ which for IPL data is 2008. If you move the ‘Since year’ since year slider to the left, the ‘Matches played’ slider will move to the right and the corresponding maximum value of matches played will be set appropriately.

b) Ranking T20 Bowlers:

This has the following controls

a) Since year : This tells the year range to consider for  bowler in IPL. Moves right to left.

b) Matches played : Number of matches played by the bowler in the year range. Move left to right.

c) Mode 1: The bowlers are sorted and ranked on 2 columns namely Wickets and Economy rate. The mode tells whether to consider Wickets over Economy rate or Economy rate over wickets when ranking the bowlers. 

By default, the “Since year” will be set to the year when the T20 data is available. If you move the ‘Since year’ since year slider to the left, the ‘Matches played’ slider will move to the right and the corresponding maximum value of matches played will be set appropriately.

2) Strategy for ranking:

Here is the rationale and philosophy behind these controls

The ranking in my earlier post GooglyPlusPlus2021 interactively ranks T20 batsmen and bowlers!!!  is based on sorting batsmen and bowlers from the start of IPL tournament. Hence we will find players who have played a lot of matches. So in the bowler ranking you will SL Malinga who no longer plays IPL.

Hence I decided that the user should be allowed to choose the start year to consider for the ranking. We can move the “Since year’ slider from right to left to choose the data for the year range we are interested in e.g. (2014-2020, or 2017-2020). Changing the ‘Since year’ slider will obviously change the maximum matches played by any player. However the user can move this slider right to left and decide the number of matches to be considered for a batsman or bowler.  Finally, the ‘Mode’. will allow the user to choose whether the list if batsmen should be ranked first by average runs and then average strike rate or vice versa. In the case of bowlers, the choice is whether to first sort by number of wickets and then economy rate or vice versa. 

The consideration for ‘Mode’ and “Mode1′ is that while runs and wickets are important for batsman or bowler, it is clear the Strike rate and Economy rate become critical in ‘death overs‘. Batsmen who can accelerate the scoring rate when needed and bowlers who can put the brakes on during death overs is of paramount importance in T20 cricket.

Let me walk through a  few scenarios in IPL T20. The same functionality is also available in alll other T20 formats (Intl. T20 (men, women), BBL, NTB, PSL, WBB, CPL etc.)

3) Ranking IPL batsmen

Note: Those players who are underlined in red are the shooting stars**

a) Scenario 1

These are  the consistent and reliable players

Since year = 2013, Matches played = 95 and Mode = ‘Runs over Strike Rate’

 

 

b) Scenario 2

Since year = 2015, Matches played = 67 and Mode = ‘Runs over Strike Rate’

c) Scenario 3

We can turn the above over its head and choose Mode = “Strike rate over Runs’

Since year = 2015, Matches played = 67 and Mode = ‘Strike Rate over Runs’

d) Scenario 4: 

Since year = 2018, Matches played = 35 and Mode = ‘Runs over Strike Rate’

4) Ranking IPL bowlers

e) Scenario 5:

Since year = 2016, Matches played = 55 and Mode1 = ‘ Wickets over Economy Rate’

f) Scenario 6

Since year = 2018, Matches played = 33 and Mode1 = ‘ Economy Rate over wickets’. Note the economy rate is sorted in ascending order

Note:  Incidentally the ranking of IPL and other T20 players in my earlier posts is a special case, when you consider all matches from the beginning of time (or since the data is available, rather than choosing a range from later years).

Note 1: Personally, if I had to form a team, I would choose

– at least 2-3 batsmen who are reliable and have good average runs and a good strike rate

– 2 batsmen who can be used to accelerate strike rate during critical junctures or at death overs

– 2-3 bowlers who have a great record of wicket taking with good economy rate (2 + 1 pace/spin)

– 2 bowlers who have good economy rate over wickets

– 2 all rounders with good batting and bowling average

– 1 wicketkeeper batsmen

The key point is how many matches would you consider as a minimum for reliability or strike rate. More is good but not always better as you could miss out on rising stars* who may be risky but good picks and possibly cost less, since the team composition will also depend on the available budget for each team. You could also check other T20 formats for good T20 players. Creating a T20 team in IPL is an optimization problem where the objective is to maximize the runs, strike rate for batsmen,  or maximizing the wickets taken, while minimizing the economy rate for bowlers with the constraint of the overall budget of the team.

Note 2: The ranking algorithm has been included for all T20 formats in GooglyPlusPlus2020. See below

5) International T20 Batsmen (men) ranking

Since year = 2012, Matches played = 50 and Mode = ‘Runs over Strike Rate’

 

6) International T20 Bowlers (men) ranking

Since year = 2013, Matches played = 33 and Mode1 = ‘Wickets over Economy Rate ‘

Here are some top class T20 bowlers

 

7) International T20 Batsmen (women) ranking

Since year = 2015, Matches played = 29 and Mode = ‘Runs over Strike Rate’. Smriti Mandhana makes it to the top 3 in Runs over Strike Rate!

 

 

and 2nd when Strike rate over runs is considered!

 

8) Integrating Carribean Premier League T20

In this version I have also integrated Carribean Premier League (CPL). It took me about 3 -4  hours of focused work to setup the data and the associated code. Like every other T20 format,  CPL league has access to 100+ functions of yorkr. So you can do analysis of CPL batsmen, bowlers, CPL matches, CPL head-to-head confrontation and CPL team against all other teams besides the ranking function. You can also generate batting and bowling scorecard for matches, for a team against all other teams and the overall scorecard in all matches against all other teams. Here is a random sample

a) C Munro – Cumulative Average Runs

 

b) RR Emrit – Bowler’s wickets against opposition

 

c) Head-to-head (Barbados Tridents vs Antigua Hawkbill all matches)

 

d) Rank CPL batsmen 

Since year= 2016, Matches played = 41 Mode = Strike rate over Runs

Nicholas Pooran tops the list

9) BBL 2020-21

GooglyPlusPlus2021 now has the latest Big Bash League matches of 2020-21, in fact “hot off the pitch“. So you should be able to do all the analysis on current BBL data namely batsmen and bowler analysis, match analysis, team analysis vs another team or against all other teams, and finally ranking of batsmen and bowlers. Here is a sample with yesterday’s match

a) Match scorecard -Perth Scorchers- Sydney Sixers 06 Feb 2021 (Final)

 

b) Predict runs of batsman – CA Lynn

 

10) Natwest T20 Blast 2020

I noticed recently the Cricsheet  has more data. Now NTB data includes all matches till 2020. This data has been incorporated into NTB and you should be able to use all the yorkr functions to analyze batsmen, bowlers, teams, team-vs-team and team vs all other teams, besides the ranking functions. Here are a couple below

a) Head-to-head Derbyshire vs Essex all matches

 

b) Team Batsmen vs Bowlers – All matches all opposition Leicestershire (MJ Cosgrove)

 

Do check out the various functions of GooglyPlusPlus2021. Take a look at the ranks of the T20 batsmen and bowlers. Hope you have a good time!

Take GooglyPlusPlus2021 for a test ride!!

Feel free to clone/fork the code from Github at GooglyPlusPlus2021

Also see

  1. Cricketr adds team analytics to its repertoire!!!
  2. Introducing cricket package yorkr: Part 2-Trapped leg before wicket
  3. Introducing cricpy:A python package to analyze performances of cricketers
  4. Big Data-5: kNiFi-ing through cricket data with yorkpy
  5. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
  6. See all 72+ posts on cricket in R, Python and Octave
  7. Deep Learning from first principles in Python, R and Octave – Part 5
  8. Practical Machine Learning with R and Python – Part 3
  9. Understanding Neural Style Transfer with Tensorflow and Keras

To see all post click Index of posts

 

 

GooglyPlusPlus2021 interactively ranks T20 batsmen and bowlers!!!

Every time I think that I have my R packages or Shiny apps all wrapped up, I find another idea trots up and knocks at my door. Since I intend to keep GooglyPlusPlus current with the latest data, I decided to include the ranking functions in my Shiny app GooglyPlusPlus.

Fortunately, since GooglyPlusPlus is based on my R package ‘yorkr‘ (see Introducing cricket package yorkr: Beaten by sheer pace!), I could make the necessary changes to the ranking functions in the package, so that it could be incorporated into my latest Shiny app GooglyPlusPlus2021!! To know how to use GooglyPlusPlus see my post Introducing GooglyPlusPlus

Note: GooglyPlusPlus can analyze batsmen, bowlers, matches and teams.

Take GooglyPlusPlus2021 for a test drive!!!

You can clone/fork GooglyPlusPlus2021 from Github

Here are a few scenarios from GooglyPlusPlus2021

A) Ranking batsmen

Ranking IPL batsmen (minMatches = 80) – The following table shows the ranking of IPL players who have played 80 matches or more

B) Identifying batsmen of potential and promise

Ranking IPL batsmen (minMatches =70) –  If we reduce the minimum number of matches played to 70, then we see it pushes up KL Rahul above Kohli.

Ranking IPL batsmen (minMatches =60) – When the slider is moved to 60, we see that Rishabh Pant has a better mean average and mean strike rate and is also ranked above Kohli. We can identify promising players this way. However, it is also likely that some players may be just a bright flash in the pan

C) Ranking T20 bowlers (men)

D) Ranking NTB Batsmen

GooglyPlusPlus2021 can rank all T20 formats (IPL, BBL, Intl. T20 (men), Intl. T20 (women), NTB, PSL and WBB. Do give it a try!

Also remember that GooglyPlusPlus2021 includes close to 100+ functions which enable it to perform analysis of batsmen, bowlers, T20 matches, head-to-head confrontation of T20 teams and overall performance of T20 teams . To know more about GooglyPlusPlus2021 see Introducing GooglyPlusPlus

You can download the code for this app from Github at GooglyPlusPlus2021

Do give GooglyPlusPlus2021 a spin!!

I do have some other ideas also which I will be incorporating  into GooglyPlusPlus2021.

Watch this space!!

Also see
1. Deep Learning from first principles in Python, R and Octave – Part 7
2. A method to crowd source pothole marking on (Indian) roads
3. Big Data 7: yorkr waltzes with Apache NiFi
4. Understanding Neural Style Transfer with Tensorflow and Keras
5. Revisiting World Bank data analysis with WDI and gVisMotionChart
6. Natural language processing: What would Shakespeare say?
7. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
8. Introducing cricpy:A python package to analyze performances of cricketers
9. Simulating an Edge Shape in Android

To see all posts click Index of posts

Benford’s law meets IPL, Intl. T20 and ODI cricket

“To grasp how different a million is from a billion, think about it like this: A million seconds is a little under two weeks; a billion seconds is about thirty-two years.”

“One of the pleasures of looking at the world through mathematical eyes is that you can see certain patterns that would otherwise be hidden.”

               Steven Strogatz, Prof at Cornell University

Introduction

Within the last two weeks, I was introduced to Benford’s Law by 2 of my friends. Initially, I looked it up and Google and was quite intrigued by the law. Subsequently another friends asked me to check the ‘Digits’ episode, from the “Connected” series on Netflix by Latif Nasser, which I strongly recommend you watch.

Benford’s Law also called the Newcomb–Benford law, the law of anomalous numbers, or the First Digit Law states that, when dealing with quantities obtained from Nature, the frequency of appearance of each digit in the first significant place is logarithmic. For example, in sets that obey the law, the number 1 appears as the leading significant digit about 30.1% of the time, the number 2 about 17.6%, number 3 about 12.5% all the way to the number 9 at 4.6%. This interesting logarithmic pattern is observed in most natural datasets from population densities, river lengths, heights of skyscrapers, tax returns etc. What is really curious about this law, is that when we measure the lengths of rivers, the law holds perfectly regardless of the units used to measure. So the length of the rivers would obey the law whether we measure in meters, feet, miles etc. There is something almost mystical about this law.

The law has also been used widely to detect financial fraud, manipulations in tax statements, bots in twitter, fake accounts in social networks, image manipulation etc. In this age of deep fakes, the ability to detect fake images will assume paramount importance. While deviations from Benford Law do not always signify fraud, to large extent they point to an aberration. Prof Nigrini, of Cape Town used this law to identify financial discrepancies in Enron’s financial statement resulting in the infamous scandal. Also the 2009 Iranian election was found to be fradulent as the first digit percentages did not conform to those specified by Benford’s Law.

While it cannot be said with absolute certainty, marked deviations from Benford’s law could possibly indicate that there has been manipulation of natural processes. Possibly Benford’s law could be used to detect large scale match-fixing in cricket tournaments. However, we cannot look at this in isolation and the other statistical and forensic methods may be required to determine if there is fraud. Here is an interesting paper Promises and perils of Benford’s law

A set of numbers is said to satisfy Benford’s law if the leading digit d (d ∈ {1, …, 9}) occurs with probability

P(d)=log_{10}(1+1/d)

This law also works for number in other bases, in base b >=2

P(d)=log_{b}(1+1/d)

Interestingly, this law also applies to sports on the number of point scored in basketball etc. I was curious to see if this applied to cricket. Previously, using my R package yorkr, I had already converted all T20 data and ODI data from Cricsheet which is available at yorkrData2020, I wanted to check if Benford’s Law worked on the runs scored, or deliveries faced by batsmen at team level or at a tournament level (IPL, Intl. T20 or ODI).

Thankfully, R has a package benford.analysis to check for data behaviour in accordance to Benford’s Law, and I have used this package in my post

This post is also available in RPubs as Benford’s Law meets IPL, Intl. T20 and ODI

library(data.table)
library(reshape2)
library(dplyr)
library(benford.analysis)
library(yorkr)

In this post, I have randomly check data with Benford’s law. The fully converted dataset is available in yorkrData2020 which I have included above. You can try on any dataset including ODI (men,women),Intl T20(men,women),IPL,BBL,PSL,NTB and WBB.

1. Check the runs distribution by Royal Challengers Bangalore

We can see the behaviour is as expected with Benford’s law, with minor deviations

load("/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ipl/iplBattingBowlingDetails/Royal Challengers Bangalore-BattingDetails.RData")
rcbRunsTrends = benford(battingDetails$runs, number.of.digits = 1, discrete = T, sign = "positive") 
rcbRunsTrends
## 
## Benford object:
##  
## Data: battingDetails$runs 
## Number of observations used = 1205 
## Number of obs. for second order = 99 
## First digits analysed = 1
## 
## Mantissa: 
## 
##    Statistic  Value
##         Mean  0.458
##          Var  0.091
##  Ex.Kurtosis -1.213
##     Skewness -0.025
## 
## 
## The 5 largest deviations: 
## 
##   digits absolute.diff
## 1      1         14.26
## 2      7         13.88
## 3      9          8.14
## 4      6          5.33
## 5      4          4.78
## 
## Stats:
## 
##  Pearson's Chi-squared test
## 
## data:  battingDetails$runs
## X-squared = 5.2091, df = 8, p-value = 0.735
## 
## 
##  Mantissa Arc Test
## 
## data:  battingDetails$runs
## L2 = 0.0022852, df = 2, p-value = 0.06369
## 
## Mean Absolute Deviation (MAD): 0.004941381
## MAD Conformity - Nigrini (2012): Close conformity
## Distortion Factor: -18.8725
## 
## Remember: Real data will never conform perfectly to Benford's Law. You should not focus on p-values!

2. Check the ‘balls played’ distribution by Royal Challengers Bangalore

load("/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ipl/iplBattingBowlingDetails/Royal Challengers Bangalore-BattingDetails.RData")
rcbBallsPlayedTrends = benford(battingDetails$ballsPlayed, number.of.digits = 1, discrete = T, sign = "positive") 
plot(rcbBallsPlayedTrends)

 

3. Check the runs distribution by Chennai Super Kings

The trend seems to deviate from the expected behavior to some extent in the number of digits for 5 & 7.

load("/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ipl/iplBattingBowlingDetails/Chennai Super Kings-BattingDetails.RData")
cskRunsTrends = benford(battingDetails$runs, number.of.digits = 1, discrete = T, sign = "positive") 
cskRunsTrends
## 
## Benford object:
##  
## Data: battingDetails$runs 
## Number of observations used = 1054 
## Number of obs. for second order = 94 
## First digits analysed = 1
## 
## Mantissa: 
## 
##    Statistic  Value
##         Mean  0.466
##          Var  0.081
##  Ex.Kurtosis -1.100
##     Skewness -0.054
## 
## 
## The 5 largest deviations: 
## 
##   digits absolute.diff
## 1      5         27.54
## 2      2         18.40
## 3      1         17.29
## 4      9         14.23
## 5      7         14.12
## 
## Stats:
## 
##  Pearson's Chi-squared test
## 
## data:  battingDetails$runs
## X-squared = 22.862, df = 8, p-value = 0.003545
## 
## 
##  Mantissa Arc Test
## 
## data:  battingDetails$runs
## L2 = 0.002376, df = 2, p-value = 0.08173
## 
## Mean Absolute Deviation (MAD): 0.01309597
## MAD Conformity - Nigrini (2012): Marginally acceptable conformity
## Distortion Factor: -17.90664
## 
## Remember: Real data will never conform perfectly to Benford's Law. You should not focus on p-values!

4. Check runs distribution in all of Indian Premier League (IPL)

battingDF <- NULL
teams <-c("Chennai Super Kings","Deccan Chargers","Delhi Daredevils",
          "Kings XI Punjab", 'Kochi Tuskers Kerala',"Kolkata Knight Riders",
          "Mumbai Indians", "Pune Warriors","Rajasthan Royals",
          "Royal Challengers Bangalore","Sunrisers Hyderabad","Gujarat Lions",
          "Rising Pune Supergiants")


setwd("/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ipl/iplBattingBowlingDetails")
for(team in teams){
  battingDetails <- NULL
  val <- paste(team,"-BattingDetails.RData",sep="")
  print(val)
  tryCatch(load(val),
           error = function(e) {
             print("No data1")
             setNext=TRUE
           }
           
           
  )
  details <- battingDetails
  battingDF <- rbind(battingDF,details)
}
## [1] "Chennai Super Kings-BattingDetails.RData"
## [1] "Deccan Chargers-BattingDetails.RData"
## [1] "Delhi Daredevils-BattingDetails.RData"
## [1] "Kings XI Punjab-BattingDetails.RData"
## [1] "Kochi Tuskers Kerala-BattingDetails.RData"
## [1] "Kolkata Knight Riders-BattingDetails.RData"
## [1] "Mumbai Indians-BattingDetails.RData"
## [1] "Pune Warriors-BattingDetails.RData"
## [1] "Rajasthan Royals-BattingDetails.RData"
## [1] "Royal Challengers Bangalore-BattingDetails.RData"
## [1] "Sunrisers Hyderabad-BattingDetails.RData"
## [1] "Gujarat Lions-BattingDetails.RData"
## [1] "Rising Pune Supergiants-BattingDetails.RData"
trends = benford(battingDF$runs, number.of.digits = 1, discrete = T, sign = "positive") 
trends
## 
## Benford object:
##  
## Data: battingDF$runs 
## Number of observations used = 10129 
## Number of obs. for second order = 123 
## First digits analysed = 1
## 
## Mantissa: 
## 
##    Statistic   Value
##         Mean  0.4521
##          Var  0.0856
##  Ex.Kurtosis -1.1570
##     Skewness -0.0033
## 
## 
## The 5 largest deviations: 
## 
##   digits absolute.diff
## 1      2        159.37
## 2      9        121.48
## 3      7         93.40
## 4      8         83.12
## 5      1         61.87
## 
## Stats:
## 
##  Pearson's Chi-squared test
## 
## data:  battingDF$runs
## X-squared = 78.166, df = 8, p-value = 1.143e-13
## 
## 
##  Mantissa Arc Test
## 
## data:  battingDF$runs
## L2 = 5.8237e-05, df = 2, p-value = 0.5544
## 
## Mean Absolute Deviation (MAD): 0.006627966
## MAD Conformity - Nigrini (2012): Acceptable conformity
## Distortion Factor: -20.90333
## 
## Remember: Real data will never conform perfectly to Benford's Law. You should not focus on p-values!

5. Check Benford’s law in India matches

setwd("/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20BattingBowlingDetails")
load("India-BattingDetails.RData")

indiaTrends = benford(battingDetails$runs, number.of.digits = 1, discrete = T, sign = "positive") 
plot(indiaTrends)

 

6. Check Benford’s law in all of Intl. T20

setwd("/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20BattingBowlingDetails")
teams <-c("Australia","India","Pakistan","West Indies", 'Sri Lanka',
          "England", "Bangladesh","Netherlands","Scotland", "Afghanistan",
          "Zimbabwe","Ireland","New Zealand","South Africa","Canada",
          "Bermuda","Kenya","Hong Kong","Nepal","Oman","Papua New Guinea",
          "United Arab Emirates","Namibia","Cayman Islands","Singapore",
          "United States of America","Bhutan","Maldives","Botswana","Nigeria",
          "Denmark","Germany","Jersey","Norway","Qatar","Malaysia","Vanuatu",
          "Thailand")

for(team in teams){
  battingDetails <- NULL
  val <- paste(team,"-BattingDetails.RData",sep="")
  print(val)
  tryCatch(load(val),
           error = function(e) {
             print("No data1")
             setNext=TRUE
           }
           
           
  )
  details <- battingDetails
  battingDF <- rbind(battingDF,details)
  
}
intlT20Trends = benford(battingDF$runs, number.of.digits = 1, discrete = T, sign = "positive") 
intlT20Trends
## 
## Benford object:
##  
## Data: battingDF$runs 
## Number of observations used = 21833 
## Number of obs. for second order = 131 
## First digits analysed = 1
## 
## Mantissa: 
## 
##    Statistic  Value
##         Mean  0.447
##          Var  0.085
##  Ex.Kurtosis -1.158
##     Skewness  0.018
## 
## 
## The 5 largest deviations: 
## 
##   digits absolute.diff
## 1      2        361.40
## 2      9        276.02
## 3      1        264.61
## 4      7        210.14
## 5      8        198.81
## 
## Stats:
## 
##  Pearson's Chi-squared test
## 
## data:  battingDF$runs
## X-squared = 202.29, df = 8, p-value < 2.2e-16
## 
## 
##  Mantissa Arc Test
## 
## data:  battingDF$runs
## L2 = 5.3983e-06, df = 2, p-value = 0.8888
## 
## Mean Absolute Deviation (MAD): 0.007821098
## MAD Conformity - Nigrini (2012): Acceptable conformity
## Distortion Factor: -24.11086
## 
## Remember: Real data will never conform perfectly to Benford's Law. You should not focus on p-values!

Conclusion

Maths rules our lives, more than we are aware, more that we like to admit. It is there in all of nature. Whether it is the recursive patterns of Mandelbrot sets, the intrinsic notion of beauty through the golden ratio, the murmuration of swallows, the synchronous blinking of fireflies or in the almost univerality of Benford’s law on natural datasets, mathematics govern us.

Isn’t it strange that while we humans pride ourselves of freewill, the runs scored by batsmen in particular formats conform to Benford’s rule for the first digits. It almost looks like, the runs that will be scored is almost to extent predetermined to fall within specified ranges obeying Benford’s law. So much for choice.

Something to be pondered over!

Also see

  1. Introducing GooglyPlusPlus!!!
  2. Deconstructing Convolutional Neural Networks with Tensorflow and Keras
  3. Going deeper into IBM’s Quantum Experience!
  4. Experiments with deblurring using OpenCV
  5. Big Data 6: The T20 Dance of Apache NiFi and yorkpy
  6. Deep Learning from first principles in Python, R and Octave – Part 4
  7. Practical Machine Learning with R and Python – Part 4
  8. Re-introducing cricketr! : An R package to analyze performances of cricketers
  9. Bull in a china shop – Behind the scenes in Android

Introducing GooglyPlusPlus!!!

“We can lift ourselves out of ignorance, we can find ourselves as creatures of excellence and intelligence and skill.”
“Heaven is not a place, and it is not a time. Heaven is being perfect.”
“Your whole body, from wingtip to wingtip, is nothing more than your thought itself, in a form you can see. Break the chains of your thought, and you break the chains of your body, too.”

From Jonathan Livingstone Seagull, by Richard Bach

Introduction

The metamorphosis is complete, from eggs to the butterfly! My R package yorkr, went on to become Googly,  and then to GooglyPlus and  now finally GooglyPlusPlus. My latest R Shiny app now provides interactive visualisation of almost all data in Cricsheet. GooglyPlusPlus visualizes the following matches

1. ODI (men)
2. ODI (women)
3. Intl. T20 (men)
4. Intl T20 (women)
5. IPL (Indian Premier League)
6. BBL (Big Bash League)
7. NTB (Natwest T20)
8. PSL (Pakistan Super League)
9. WBBL – Women’s BBL

GooglyPlusPlus is entirely based on my R package yorkr. To know more about yorkr see ‘Revitalizing R package yorkr‘ and the roughly 25+ posts on yorkr in Index of posts

This Shiny app was quite involved, and it took a lot of work to keep things organised and separate for the different forms of cricket. Anyway it is done and I am happy with the outcome.

Before you use the app, I would suggest that you take a look at the video “How to use GooglyPlusPlus?“. In this video, I show the different features of GooglyPlusPlus and how to navigate through them.

Check out GooglyPlusPlus Shiny at GooglyPlusPlus

You can clone/fork and play around with the code of GooglyPlusPlus here at Github

A. Highlights of GooglyPlusPlus.

The R Shiny app GooglyPlusPlus has the following main pages for the 9 different cricket formats. See below

 

Important note: Below I will be including some random output from the GooglyPlusPlus app for different match formats, however there is a lot more features in GooglyPlusPlus

1.  Indian Premier League (IPL)

a. IPL batsman – Batsman Runs vs Deliveries

 

b. IPL Match – Match  batting scorecard

 

c. Head-to-head between 2 IPL Teams – Team Batsmen Batting Partnership All Matches

 

 

 

d. Overall Performance – Team Bowling Scorecard Overall

 

 

 

2. International T20 Men

a. Batsman Function- Runs vs Strike rate

 

 

 

b. Bowler Function – Mean Economy Rate

 

 

3. International T20 (Women)

a.Batsman Functions – Batsman Cumulative Average Runs

 

 

b. Intl T20 Women’s match – Match worm Graph

 

 

 

 

 

4. Big Bash League (BBL)

a.Head-to-Head: Team batsmen batting partnerships

 

b.  Overall Performance – Team batsmen vs bowlers

 

 

5. Natwest T20 (NTB)

a. Head-to-head : Team bowlers vs batsmen

 

 

 

b. Batsman Runs vs Deliveries

 

 

6. Pakistan Super League (PSL)

a. Overall Performance – Batsmen Partnership

 

b. Bowling Scorecard

 

7. Women’s Big Bash League (WBBL)

a. Bowler wicket against opposition

 

 

8. One Day International (ODI) Men

a. Batsman Runs Against Opposition

 

b. Team Batsmen against bowlers

 

 

9. One Day International (ODI) women)

a. Match Batting Scorecard

b. Batsman Cumulative Strike Rate

 

 

 

Conclusion

There you have it. I have randomly shown  2 functions for each cricket format. There are many functions in each tab for the for the different match formats – namely IPL, BBL, Intl T20 (men,women), PSL etc.  Go ahead and give GooglyPlusPlus a spin!

To try out GooglyPlusPlus click GooglyPlusPlus. Don’t forget to check out the video How to use GooglyPlusPlus?

You can clone/fork the code from Github at GooglyPlusPlus

Hope you have fun with GooglyPlusPlus!!

You may also like

1. Big Data 6: The T20 Dance of Apache NiFi and yorkpy
2. Deep Learning from first principles in Python, R and Octave – Part 7
3. De-blurring revisited with Wiener filter using OpenCV
4. Exploring Quantum Gate operations with QCSimulator
5. Latency, throughput implications for the Cloud
6. Programming Zen and now – Some essential tips-2
7. The Anomaly
8. Practical Machine Learning with R and Python – Part 3
9. Introducing cricpy:A python package to analyze performances of cricketers
10. The making of Total Control Android game

To see all posts click Index of posts

Big Data 7: yorkr waltzes with Apache NiFi

In this post, I construct an end-to-end Apache NiFi pipeline with my R package yorkr. This post is a mirror of my earlier post Big Data-5: kNiFing through cricket data with yorkpy based on my Python package yorkpy. The  Apache NiFi Data Pipeilne  flows all the way from the source, where the data is obtained, all the way  to target analytics output. Apache NiFi was created to automate the flow of data between systems.  NiFi dataflows enable the automated and managed flow of information between systems. This post automates the flow of data from Cricsheet, from where the zip file it is downloaded, unpacked, processed, transformed and finally T20 players are ranked.

This post uses the functions of my R package yorkr to rank IPL players. This is a example flow, of a typical Big Data pipeline where the data is ingested from many diverse source systems, transformed and then finally insights are generated. While I execute this NiFi example with my R package yorkr, in a typical Big Data pipeline where the data is huge, of the order of 100s of GB, we would be using the Hadoop ecosystem with Hive, HDFS Spark and so on. Since the data is taken from Cricsheet, which are few Megabytes, this approach would suffice. However if we hypothetically assume that there are several batches of cricket data that are being uploaded to the source, of different cricket matches happening all over the world, and the historical data exceeds several GBs, then we could use a similar Apache NiFi pattern to process the data and generate insights. If the data is was large and distributed across the Hadoop cluster , then we would need to use SparkR or SparklyR to process the data.

This is shown below pictorially

While this post displays the ranks of IPL batsmen, it is possible to create a cool dashboard using UI/UX technologies like AngularJS/ReactJS.  Take a look at my post Big Data 6: The T20 Dance of Apache NiFi and yorkpy where I create a simple dashboard of multiple analytics

My R package yorkr can handle both men’s and women’s ODI, and all formats of T20 in Cricsheet namely Intl. T20 (men’s, women’s), IPL, BBL, Natwest T20, PSL, Women’s BBL etc. To know more details about yorkr see Revitalizing R package yorkr

The code can be forked from Github at yorkrWithApacheNiFi

You can take a look at the live demo of the NiFi pipeline at yorkr waltzes with Apache NiFi

 

Basic Flow

1. Overall flow

The overall NiFi flow contains 2 Process Groups a) DownloadAnd Unpack. b) Convert and Rank IPL batsmen. While it appears that the Process Groups are disconnected, they are not. The first process group downloads the T20 zip file, unpacks the. zip file and saves the YAML files in a specific folder. The second process group monitors this folder and starts processing as soon the YAML files are available. It processes the YAML converting it into dataframes before storing it as CSV file. The next  processor then does the actual ranking of the batsmen before writing the output into IPLrank.txt

1.1 DownloadAndUnpack Process Group

This process group is shown below

 

1.1.1 GetT20Data

The GetT20Data Processor downloads the zip file given the URL

The ${T20data} variable points to the specific T20 format that needs to be downloaded. I have set this to https://cricsheet.org/downloads/ipl.zip. This could be set any other data set. In fact we could have parallel data flows for different T20/ Sports data sets and generate

1.1.2 SaveUnpackedData

This processor stores the YAML files in a predetermined folder, so that the data can be picked up  by the 2nd Process Group for processing

 

1.2 ProcessAndRankT20Players Process Group

This is the second process group which converts the YAML files to pandas dataframes before storing them as. CSV files. The RankIPLPlayers will then read all the CSV files, stack them and then proceed to rank the IPL players. The Process Group is shown below

 

1.2.1 ListFile and FetchFile Processors

The left 2 Processors ListFile and FetchFile get all the YAML files from the folder and pass it to the next processor

1.2.2 convertYaml2DataFrame Processor

The convertYaml2DataFrame Processor uses the ExecuteStreamCommand which call Rscript. The Rscript invoked the yorkr function convertYaml2DataframeT20() as shown below

 

I also use a 16 concurrent tasks to convert 16 different flowfiles at once

 

library(yorkr)
args<-commandArgs(TRUE)
convertYaml2RDataframeT20(args[1], args[2], args[3])

1.2.3 MergeContent Processor

This processor’s only job is to trigger the rankIPLPlayers when all the FlowFiles have merged into 1 file.

1.2.4 RankT20Players

This processor is an ExecuteStreamCommand Processor that executes a Rscript which invokes a yorrkr function rankIPLT20Batsmen()

library(yorkr)
args<-commandArgs(TRUE)

rankIPLBatsmen(args[1],args[2],args[3])

1.2.5 OutputRankofT20Player Processor

This processor writes the generated rank to an output file.

 

1.3 Final Ranking of IPL T20 players

The Nodejs based web server picks up this file and displays on the web page the final ranks (the code is based on a good youtube for reading from file)

[1] "Chennai Super Kings"
[1] "Deccan Chargers"
[1] "Delhi Daredevils"
[1] "Kings XI Punjab"
[1] "Kochi Tuskers Kerala"
[1] "Kolkata Knight Riders"
[1] "Mumbai Indians"
[1] "Pune Warriors"
[1] "Rajasthan Royals"
[1] "Royal Challengers Bangalore"
[1] "Sunrisers Hyderabad"
[1] "Gujarat Lions"
[1] "Rising Pune Supergiants"
[1] "Chennai Super Kings-BattingDetails.RData"
[1] "Deccan Chargers-BattingDetails.RData"
[1] "Delhi Daredevils-BattingDetails.RData"
[1] "Kings XI Punjab-BattingDetails.RData"
[1] "Kochi Tuskers Kerala-BattingDetails.RData"
[1] "Kolkata Knight Riders-BattingDetails.RData"
[1] "Mumbai Indians-BattingDetails.RData"
[1] "Pune Warriors-BattingDetails.RData"
[1] "Rajasthan Royals-BattingDetails.RData"
[1] "Royal Challengers Bangalore-BattingDetails.RData"
[1] "Sunrisers Hyderabad-BattingDetails.RData"
[1] "Gujarat Lions-BattingDetails.RData"
[1] "Rising Pune Supergiants-BattingDetails.RData"
# A tibble: 429 x 4
   batsman     matches meanRuns meanSR
   <chr>         <int>    <dbl>  <dbl>
 1 DA Warner       130     37.9   128.
 2 LMP Simmons      29     37.2   106.
 3 CH Gayle        125     36.2   134.
 4 HM Amla          16     36.1   108.
 5 ML Hayden        30     35.9   129.
 6 SE Marsh         67     35.9   120.
 7 RR Pant          39     35.3   135.
 8 MEK Hussey       59     33.8   105.
 9 KL Rahul         59     33.5   128.
10 MN van Wyk        5     33.4   112.
# … with 419 more rows

 

Conclusion

This post demonstrated an end-to-end pipeline with Apache NiFi and R package yorkr. You can this pipeline and generated different analytics using the various functions of yorkr and display them on a dashboard.

Hope you enjoyed with post!

 

See also
1. The mechanics of Convolutional Neural Networks in Tensorflow and Keras
2. Deep Learning from first principles in Python, R and Octave – Part 7
3. Fun simulation of a Chain in Android
4. Natural language processing: What would Shakespeare say?
5. TWS-4: Gossip protocol: Epidemics and rumors to the rescue
6. Cricketr learns new tricks : Performs fine-grained analysis of players
7. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
8. Practical Machine Learning with R and Python – Part 5
9. Cricpy adds team analytics to its arsenal!!

To see posts click Index of posts

It’s a wrap! yorkr wraps up BBL, NTB, PSL and WBB!!!

“Do not take life too seriously. You will never get out of it alive.” – Elbert Hubbard

“How many people here have telekenetic powers? Raise my hand.” – Emo Philips

Have you ever noticed that anybody driving slower than you is an idiot, and anyone going faster than you is a maniac?” – George Carlin

 

It’s a wrap!!! In my previous posts,Revitalizing yorkr, I showed how you can use yorkr functions for Intl. ODI, Intl. T20 and IPL. My next post yorkr rocks women’s ODI and women’s Intl T20 yorkr handled women’s ODI and Intl. T20. In this post, yorkr wraps the remaining T20 formats namely

  1. Big Bash League (BBL)
  2. Natwest Super T20 (NTB)
  3. Pakistan Super League (PSL)
  4. Women’s Big Bash League (WBB)

The data for all the above T20 formats are taken from Cricsheet.

-All the data has been converted and is available in Github at yorkrData2020 organized as below. You can use any of the 90+ yorkr functions on the converted data.

Screenshot 2020-05-16 at 12.32.07 PM

-This post has been published at RPubs at yorkrWrapUpT20formats

-You can download a PDF version of this file at yorkrWrapsUpT20Formats

  • For ODI Matches men’s and women’ use
  1. ODI-Part1, 2. ODI-Part2,3. ODI-Part3, 4.ODI-Part 4
  • For any of the T20s formats you can use the following posts
  1. T20-Part1, 2. T20-Part2, 3. T20-Part3, 4. T20-Part4

or you can use these templates Intl. T20, or similar to IPL T20

I am going to randomly pick 2 yorkr functions for each of the T20 formats BBL, NTB, PSL and WBB to demonstrate yorkr below, however you can use any of the 90+ yorkr functions

install.packages("../../../yorkrgit/yorkr_0.0.9.tar.gz",repos = NULL, type="source")
library(yorkr)
library(dplyr)

Note: In the following T20 formats I have randomly picked 2 of the 90+ yorkr functions

A. Big Bash League (BBL)

A1.Batting Scorecard

load("../../../yorkrData2020/bbl/bblMatches/Adelaide Strikers-Brisbane Heat-2017-12-31.RData")
as_bh <- overs
teamBattingScorecardMatch(as_bh,'Adelaide Strikers')
## Total= 139
## # A tibble: 9 x 5
##   batsman      ballsPlayed fours sixes  runs
##   <chr>              <int> <dbl> <dbl> <dbl>
## 1 AT Carey               6     0     0     2
## 2 CA Ingram             21     2     0    23
## 3 J Weatherald          14     2     1    20
## 4 JS Lehmann            17     3     0    22
## 5 JW Wells              13     1     0    12
## 6 MG Neser              25     3     2    40
## 7 PM Siddle              1     0     0     1
## 8 Rashid Khan            2     0     1     6
## 9 TM Head               17     0     0    13

A2.Batting Partnership

load("../../../yorkrData2020/bbl/bblMatches2Teams/Melbourne Renegades-Sydney Sixers-allMatches.RData")
mr_ss_matches <- matches
m <-teamBatsmenPartnershiOppnAllMatches(mr_ss_matches,'Sydney Sixers',report="summary")
m
## # A tibble: 28 x 2
##    batsman      totalRuns
##    <chr>            <dbl>
##  1 MC Henriques       277
##  2 JR Philippe        186
##  3 NJ Maddinson       183
##  4 MJ Lumb            165
##  5 DP Hughes          158
##  6 JC Silk            141
##  7 SPD Smith          116
##  8 JM Vince            97
##  9 TK Curran           68
## 10 J Botha             33
## # … with 18 more rows

B. Natwest Super League

B1.Team Match Partnership

load("../../../yorkrData2020/ntb/ntbMatches/Derbyshire-Nottinghamshire-2019-07-26.RData")
db_nt <-overs
teamBatsmenPartnershipMatch(db_nt,"Derbyshire","Nottinghamshire")

B2.Batsmen vs Bowlers

load("../../../yorkrData2020/ntb/ntbMatches2Teams/Birmingham Bears-Leicestershire-allMatches.RData")
bb_le_matches <- matches
teamBatsmenVsBowlersOppnAllMatches(bb_le_matches,"Birmingham Bears","Leicestershire",top=3)

C. Pakistan Super League (PSL)

C1.Individual performance of Babar Azam

library(grid)
library(gridExtra)

babar <- getBatsmanDetails(team="Karachi Kings",name="Babar Azam",dir="../../../yorkrData2020/psl/pslBattingBowlingDetails/")
## [1] "../../../yorkrData2020/psl/pslBattingBowlingDetails//Karachi Kings-BattingDetails.RData"
print(dim(babar))
## [1] 40 15
p1 <-batsmanRunsVsStrikeRate(babar,"Babar Azam")
p2 <-batsmanMovingAverage(babar,"Babar Azam")
p3 <- batsmanCumulativeAverageRuns(babar,"Babar Azam")
grid.arrange(p1,p2,p3, ncol=2)

C2.Bowling performance against all oppositions

load("../../../yorkrData2020/psl/pslMatches2Teams/Lahore Qalandars-Multan Sultans-allMatches.RData")
lq_ms_matches <- matches
teamBowlingPerfOppnAllMatches(lq_ms_matches,"Lahore Qalanders","Multan Sultans")
## # A tibble: 40 x 5
##    bowler              overs maidens  runs wickets
##    <chr>               <int>   <int> <dbl>   <dbl>
##  1 Shaheen Shah Afridi    11       1   134      11
##  2 Junaid Khan             5       0   154       8
##  3 Imran Tahir             5       0    74       6
##  4 Mohammad Ilyas          5       0    93       4
##  5 Haris Rauf              7       0   154       3
##  6 D Wiese                 7       0    92       3
##  7 Mohammad Irfan          5       0    91       3
##  8 S Lamichhane            5       0    74       3
##  9 SP Narine               8       0    48       3
## 10 MM Ali                  3       0    30       3
## # … with 30 more rows

D. Women Big Bash League

D1.Bowling scorecard

load("../../../yorkrData2020/wbb/wbbMatches/Hobart Hurricanes-Brisbane Heat-2018-12-30.RData")
hh_bh_match <- overs
teamBowlingScorecardMatch(hh_bh_match,'Brisbane Heat')
## # A tibble: 6 x 5
##   bowler      overs maidens  runs wickets
##   <chr>       <int>   <int> <dbl>   <dbl>
## 1 DM Kimmince     3       0    31       2
## 2 GM Harris       4       0    23       3
## 3 H Birkett       1       0     7       0
## 4 JL Barsby       3       0    21       0
## 5 JL Jonassen     4       0    33       0
## 6 SJ Johnson      4       0    17       0

D2.Team batsmen partnerships

load("../../../yorkrData2020/wbb/wbbAllMatchesAllTeams/allMatchesAllOpposition-Perth Scorchers.RData")
ps_matches <- matches
teamBatsmenPartnershipAllOppnAllMatchesPlot(ps_matches,"Perth Scorchers",main="Perth Scorchers")

As mentioned above, I have randomly picked 2 yorkr functions for each of the T20 formats. You can use any of the 90+ functions for analysis of matches, teams, batsmen and bowlers.

1a. Ranking Big Bash League (BBL) batsman

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/bbl/bblMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/bbl/bblBattingBowlingDetails"
rankBBLBatsmen(dir=dir,odir=odir,minMatches=30)
## # A tibble: 62 x 4
##    batsman      matches meanRuns meanSR
##    <chr>          <int>    <dbl>  <dbl>
##  1 DJM Short         44     41.6   126.
##  2 SE Marsh          48     39.1   120.
##  3 AJ Finch          60     36.0   130.
##  4 AT Carey          36     35.9   129.
##  5 KP Pietersen      31     33.5   118.
##  6 UT Khawaja        40     31.5   112.
##  7 BJ Hodge          38     31.5   127.
##  8 CA Lynn           72     31.3   128.
##  9 MP Stoinis        53     30.7   112.
## 10 TM Head           45     30     131.
## # … with 52 more rows

1b. Ranking Big Bash League (BBL) bowlers

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/bbl/bblMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/bbl/bblBattingBowlingDetails"
rankBBLBowlers(dir=dir,odir=odir,minMatches=25)
## # A tibble: 53 x 4
##    bowler         matches totalWickets meanER
##    <chr>            <int>        <dbl>  <dbl>
##  1 SA Abbott           60           90   8.42
##  2 AJ Tye              45           69   7.32
##  3 B Laughlin          48           66   7.96
##  4 BCJ Cutting         71           63   8.87
##  5 BJ Dwarshuis        54           62   7.87
##  6 MG Neser            54           57   8.36
##  7 Rashid Khan         40           55   6.32
##  8 JP Behrendorff      41           53   6.55
##  9 SNJ O'Keefe         53           52   6.76
## 10 A Zampa             42           51   7.34
## # … with 43 more rows

2a. Ranking Natwest T20 League (NTB) batsman

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ntb/ntbMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ntb/ntbBattingBowlingDetails"

rankNTBBatsmen(dir=dir,odir=odir,minMatches=20)
## # A tibble: 42 x 4
##    batsman          matches meanRuns meanSR
##    <chr>              <int>    <dbl>  <dbl>
##  1 SR Hain               24     34.6   107.
##  2 M Klinger             26     34.1   118.
##  3 MH Wessels            26     33.9   122.
##  4 DJ Bell-Drummond      21     33.1   112.
##  5 DJ Malan              26     33     129.
##  6 T Kohler-Cadmore      23     33.0   118.
##  7 A Lyth                22     31.4   150.
##  8 JJ Cobb               26     30.7   110.
##  9 CA Ingram             25     30.5   153.
## 10 IA Cockbain           26     29.8   121.
## # … with 32 more rows

2b. Ranking Natwest T20 League (NTB) bowlers

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ntb/ntbMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ntb/ntbBattingBowlingDetails"

rankNTBBowlers(dir=dir,odir=odir,minMatches=20)
## # A tibble: 23 x 4
##    bowler          matches totalWickets meanER
##    <chr>             <int>        <dbl>  <dbl>
##  1 HF Gurney            23           45   8.63
##  2 AJ Tye               26           40   7.81
##  3 TS Roland-Jones      26           37   8.10
##  4 BAC Howell           20           35   6.89
##  5 TT Bresnan           21           31   8.82
##  6 MJJ Critchley        25           31   7.33
##  7 LA Dawson            24           30   6.80
##  8 TK Curran            23           28   8.19
##  9 NA Sowter            25           28   8.09
## 10 MTC Waller           25           27   7.59
## # … with 13 more rows

3a. Ranking Pakistan Super League (PSL) batsman

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/psl/pslMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/psl/pslBattingBowlingDetails"

rankPSLBatsmen(dir=dir,odir=odir,minMatches=15)
## # A tibble: 47 x 4
##    batsman      matches meanRuns meanSR
##    <chr>          <int>    <dbl>  <dbl>
##  1 Babar Azam        40     33.7   102.
##  2 L Ronchi          31     32.9   143.
##  3 DR Smith          24     30.8   111.
##  4 JJ Roy            15     30.6   123.
##  5 Kamran Akmal      46     30.1   112.
##  6 SR Watson         40     29.2   126.
##  7 Shoaib Malik      35     28.1   113.
##  8 Fakhar Zaman      38     27.6   119.
##  9 Imam-ul-Haq       15     27.4   115.
## 10 RR Rossouw        36     27.0   130.
## # … with 37 more rows

3b. Ranking Pakistan Super League (PSL) bowlers

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/psl/pslMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/psl/pslBattingBowlingDetails"

rankPSLBowlers(dir=dir,odir=odir,minMatches=15)
## # A tibble: 25 x 4
##    bowler              matches totalWickets meanER
##    <chr>                 <int>        <dbl>  <dbl>
##  1 Wahab Riaz               44           70   6.94
##  2 Hasan Ali                41           61   7.43
##  3 Faheem Ashraf            30           50   7.84
##  4 Mohammad Amir            38           48   7.16
##  5 Usman Shinwari           26           43   8.64
##  6 Mohammad Sami            29           40   7.60
##  7 Shadab Khan              40           38   7.57
##  8 Shaheen Shah Afridi      24           34   7.88
##  9 Rumman Raees             24           33   7.77
## 10 Mohammad Hasnain         16           28   8.65
## # … with 15 more rows

4a. Ranking Women’s Big Bash League (WBB) batsman

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/wbb/wbbMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/wbb/wbbBattingBowlingDetails"
rankWBBBatsmen(dir=dir,odir=odir,minMatches=15)
## # A tibble: 36 x 4
##    batsman    matches meanRuns meanSR
##    <chr>        <int>    <dbl>  <dbl>
##  1 BL Mooney       27     46.7  129. 
##  2 SFM Devine      22     43.5  111. 
##  3 EA Perry        16     41.1   97.1
##  4 MM Lanning      19     38     98.2
##  5 JE Cameron      22     32.9  127. 
##  6 DN Wyatt        24     32    112. 
##  7 AE Jones        17     28.9  107. 
##  8 AJ Healy        19     28.4  122. 
##  9 M du Preez      19     27    101. 
## 10 L Lee           18     26.9   98.9
## # … with 26 more rows

4b. Ranking Women’s Big Bash League (WBB) bowlers

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/wbb/wbbMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/wbb/wbbBattingBowlingDetails"
rankWBBBowlers(dir=dir,odir=odir,minMatches=15)
## # A tibble: 31 x 4
##    bowler      matches totalWickets meanER
##    <chr>         <int>        <dbl>  <dbl>
##  1 M Strano         23           37   7.25
##  2 DM Kimmince      24           36   7.46
##  3 SJ Coyte         22           29   7.59
##  4 JL Jonassen      24           28   6.81
##  5 SJ Johnson       24           27   6.61
##  6 ML Schutt        22           26   6.03
##  7 SFM Devine       22           24   7.58
##  8 M Brown          23           23   7.33
##  9 M Kapp           19           23   5.05
## 10 H Graham         19           22   7.68
## # … with 21 more rows

Conclusion

yorkr can handle ODI and T20 matches in the format as represented in Cricsheet. In my posts, I have shown how yorkr can be used for Intl. ODI and Intl. T20 for both men and women. yorkr can also handle all T20 formats like IPL T20, BBL, Natwest T20, PSL and women’s BBL. Go ahead take yorkr for a ride and check out your favorite teams and players.

Hope you have fun!!!

You may also like

  1. Getting started with Tensorflow, Keras in Python and R
  2. Computer Vision: Ramblings on derivatives, histograms and contours
  3. Cricpy adds team analytics to its arsenal!!
  4. Sixer – R package cricketr’s new Shiny avatar
  5. Big Data-2: Move into the big league:Graduate from R to SparkR
  6. Practical Machine Learning with R and Python – Part 5
  7. Deep Learning from first principles in Python, R and Octave – Part 7
  8. Exploring Quantum Gate operations with QCSimulator
  9. GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables

To see all posts click Index of Posts

Revitalizing R package yorkr

There is nothing so useless as doing efficiently that which should not be done at all. Peter Drucker

The most important thing in communication is to hear what isn’t being said. Peter Drucker

“Work expands to fill the time available for its completion.” Corollary: “Expenditure rises to meet income.” Parkinson’s law

Introduction

“Operation successful!!!the Programmer Surgeon in me, thought to himself. What should have been a routine surgery, turned out to be a major operation in the end, which involved several grueling hours. The surgeon looked at the large chunks of programming logic in the operation tray, which had been surgically removed, as they had outlived their utility and had partly become dysfunctional. The surgeon glanced at the new, concise code logic which had replaced the earlier somewhat convoluted logic, with a smile of satisfaction,

To, those who tuned in late, I am referring to my R package yorkr which I had created in many years ago, in early 2016. The package had worked well for quite some time on data from Cricsheet. Cricsheet went into a hiatus in late 2017-2018, and came alive back in 2019. Unfortunately, a key function in the package, started to malfunction. The diagnosis was that the format of the YAML files had changed, in newer files, which resulted in the problem. I had got mails from users mentioning that yorkr was not converting the new YAML files. This was on my to do list for a long time, and a week or two back, I decided to “bite the bullet” and fix the issue. I hoped the fix would be trivial but it was anything but. Finally, I took the hard decision of re-designing the core of the yorkr package, which involved converting YAML files to RData (dataframes). Also, since it has been a while since I did R code, having done more of Python stuff in recent times, I had to jog my memory with my earlier 2 posts Essential R and R vs Python

I spent many hours, tweaking and fixing the new logic so that it worked on the older and new files. Finally, I am happy to say that the new code is much more compact and probably less error prone.

I also had to ensure that the converted files performed exactly on all the other yorkr functions. I ran all the my yorkr functions in my yorkr posts on ODI, Intl. T20 and IPL and made sure the results were identical. (Phew!!)

The changes will be available in CRAN in yorkr_0.0.8

Do take a look at my yorkr posts. All the functions work correctly. Do use help, as I have changed a few functions. I will have my posts reflect the correct usage, but some function or other may slip the cracks.

  1. One Day Internationals ODI-Part1ODI-Part2ODI-Part3ODI-Part4
  2. International T20s – T20-Part1,T20-Part2,T20-Part3,T20-Part4
  3. Indian Premier League IPL-Part1IPL-Part2,IPL-Part3IPL-Part4

While making the changes, I also touched up some functions and made them more user friendly (added additional arguments etc). But by and large, yorkr is still yorkr and is intact.It just sports some spanking, new YAML conversion logic.

Note:

  1. The code is available in Github yorkr
  2. This RMarkdown has been published at RPubs Revitalizing yorkr
  3. I have already converted the YAML files for ODI, Intl T20 and IPL. You can access and download the converted data from Github at yorkrData2020
setwd("/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrgit")
install.packages("yorkr_0.0.8.tar.gz",repos = NULL, type="source")
library(yorkr)

Below I rank batsmen and bowlers in ODIs, T20 and IPL based on the data from Cricsheet.

1a. Rank ODI Batsmen

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/odi/odiMenMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/odi/odiBattingBowlingDetails"

rankODIBatsmen(dir=dir,odir=odir,minMatches=50)

## # A tibble: 151 x 4
##    batsman        matches meanRuns meanSR
##    <chr>            <int>    <dbl>  <dbl>
##  1 Babar Azam          52     50.2   87.2
##  2 SD Hope             51     48.7   71.0
##  3 V Kohli            207     48.4   79.4
##  4 HM Amla            159     46.6   82.4
##  5 DA Warner          114     46.1   88.0
##  6 AB de Villiers     190     45.5   94.5
##  7 JE Root            108     44.9   82.5
##  8 SR Tendulkar        96     43.9   77.1
##  9 IJL Trott           63     43.1   68.9
## 10 Q de Kock          106     42.0   82.7
## # … with 141 more rows

1b. Rank ODI Bowlers

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/odi/odiMenMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/odi/odiBattingBowlingDetails"

rankODIBowlers(dir=dir,odir=odir,minMatches=30)
## # A tibble: 265 x 4
##    bowler           matches totalWickets meanER
##    <chr>              <int>        <dbl>  <dbl>
##  1 SL Malinga           191          308   5.25
##  2 MG Johnson           142          238   4.73
##  3 Shakib Al Hasan      157          214   4.72
##  4 Shahid Afridi        166          213   4.69
##  5 JM Anderson          143          207   4.96
##  6 KMDN Kulasekara      161          190   4.94
##  7 SCJ Broad            115          189   5.31
##  8 DW Steyn             114          188   4.96
##  9 Mashrafe Mortaza     139          180   4.97
## 10 Saeed Ajmal          106          180   4.17
## # … with 255 more rows

2a. Rank T20 Batsmen

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20MenMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20BattingBowlingDetails"

rankT20Batsmen(dir=dir,odir=odir,minMatches=50)
## # A tibble: 43 x 4
##    batsman          matches meanRuns meanSR
##    <chr>              <int>    <dbl>  <dbl>
##  1 V Kohli               61     39.0   132.
##  2 Mohammad Shahzad      52     31.8   123.
##  3 CH Gayle              50     31.1   124.
##  4 BB McCullum           69     30.7   126.
##  5 PR Stirling           66     29.6   116.
##  6 MJ Guptill            70     29.6   125.
##  7 DA Warner             75     29.1   128.
##  8 AD Hales              50     28.1   120.
##  9 TM Dilshan            78     26.7   105.
## 10 RG Sharma             72     26.4   120.
## # … with 33 more rows

2b. Rank T20 Bowlers

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20MenMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/t20/t20BattingBowlingDetails"

rankT20Bowlers(dir=dir,odir=odir,,minMatches=30)

## # A tibble: 153 x 4
##    bowler          matches totalWickets meanER
##    <chr>             <int>        <dbl>  <dbl>
##  1 SL Malinga           78          115   7.39
##  2 Shahid Afridi        89           98   6.80
##  3 Saeed Ajmal          62           92   6.30
##  4 Umar Gul             56           87   7.40
##  5 KMDN Kulasekara      56           72   7.25
##  6 TG Southee           55           69   8.68
##  7 DJ Bravo             60           69   8.41
##  8 DW Steyn             47           69   7.00
##  9 Shakib Al Hasan      57           69   6.82
## 10 SCJ Broad            55           68   7.83
## # … with 143 more rows

3a. Rank IPL Batsmen

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ipl/iplMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ipl/iplBattingBowlingDetails"


rankIPLBatsmen(dir=dir,odir=odir,,minMatches=50)
## # A tibble: 69 x 4
##    batsman        matches meanRuns meanSR
##    <chr>            <int>    <dbl>  <dbl>
##  1 DA Warner          130     37.9   128.
##  2 CH Gayle           125     36.2   134.
##  3 SE Marsh            67     35.9   120.
##  4 MEK Hussey          59     33.8   105.
##  5 KL Rahul            59     33.5   128.
##  6 V Kohli            175     31.6   119.
##  7 AM Rahane          116     30.7   108.
##  8 AB de Villiers     141     30.3   135.
##  9 F du Plessis        65     29.4   117.
## 10 S Dhawan           140     29.0   114.
## # … with 59 more rows

3a. Rank IPL Bowlers

dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ipl/iplMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/yorkrData2020/ipl/iplBattingBowlingDetails"

rankIPLBowlers(dir=dir,odir=odir,,minMatches=30)
## # A tibble: 143 x 4
##    bowler          matches totalWickets meanER
##    <chr>             <int>        <dbl>  <dbl>
##  1 SL Malinga          120          184   6.99
##  2 SP Narine           108          137   6.71
##  3 Harbhajan Singh     131          134   7.11
##  4 DJ Bravo             85          118   8.18
##  5 B Kumar              86          116   7.43
##  6 YS Chahal            82          102   7.85
##  7 R Ashwin             92           98   6.81
##  8 JJ Bumrah            76           91   7.47
##  9 PP Chawla            85           87   8.02
## 10 RA Jadeja            89           85   7.93
## # … with 133 more rows

##Conclusion

Go ahead and give yorkr a spin once yorkr_0.0.8 is available in CRAN. I hope you have fun. Do get back to me if you have any issues.

I’ll be back. Watch this space!!

You may also like

  1. The mechanics of Convolutional Neural Networks in Tensorflow and Keras
  2. Big Data-5: kNiFi-ing through cricket data with yorkpy
  3. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
  4. Re-introducing cricketr! : An R package to analyze performances of cricketers
  5. Deep Learning from first principles in Python, R and Octave – Part 6
  6. A primer on Qubits, Quantum gates and Quantum Operations
  7. Practical Machine Learning with R and Python – Part 3
  8. Pitching yorkpy … short of good length to IPL – Part 1

To see all posts click Index of posts

Ranking T20 players in Intl T20, IPL, BBL and Natwest using yorkpy

There is a voice that doesn’t use words, listen.
When someone beats a rug, the blows are not against the rug, but against the dust in it.
I lost my hat while gazing at the moon, and then I lost my mind.
Rumi

Introduction

After a long hiatus, I am back to my big, bad, blogging ways! In this post I rank T20 players from several different leagues namely

  • International T20
  • Indian Premier League (IPL) T20
  • Big Bash League (BBL) T20
  • Natwest Blast (NTB) T20

I have added 8 new functions to my Python Package yorkpy, which will perform the ranking for the above 4 T20 League formats. To know more about my Python package see Pitching yorkpy . short of good length to IPL – Part 1, and the related posts on yorkpy. The code can be easily extended to other leagues which have a the same ‘yaml’ format for the matches. I also fixed some issues which started to crop up, possibly because a few things have changed in the new data.

The new functions are

  1. rankIntlT20Batting()
  2. rankIntlT20Batting()
  3. rankIPLT20Batting()
  4. rankIPLT20Batting
  5. rankBBLT20Batting()
  6. rankBBLT20Batting()
  7. rankNTBT20Batting()
  8. rankNTBT20Batting()

The yorkpy package uses data from Cricsheet

You can clone/fork the code for yorkpy at yorkpy

You can download the PDF of the post from Rank T20

yorkpy can be installed with ‘pip install yorkpy

1. International T20

The steps to do before ranking for International T20 matches are 1. Download International T20 zip file from Cricsheet Intl T20 2. Unzip the file. This will create a folder with yaml files

import yorkpy.analytics as yka
#yka.convertAllYaml2PandasDataframesT20("../t20s","../data")

This above step will convert the yaml files into CSV files. Now do the ranking as below

1a. Ranking of International T20 batsmen

import yorkpy.analytics as yka
intlT20RankBatting=yka.rankIntlT20Batting("C:\\software\\cricket-package\\yorkpyPkg\\data\\data")
intlT20RankBatting.head(15)
##                      matches  runs_mean     SR_mean
## batsman                                            
## V Kohli                   58  38.672414  125.212402
## KS Williamson             42  32.595238  122.884631
## Mohammad Shahzad          52  31.942308  118.212288
## CH Gayle                  50  31.140000  111.869984
## BB McCullum               69  29.492754  117.011666
## MM Lanning                48  28.812500   98.582663
## SJ Taylor                 44  28.659091   98.684856
## MJ Guptill                68  28.573529  117.673702
## DA Warner                 71  28.507042  121.142746
## DPMD Jayawardene          53  27.584906  107.787092
## KC Sangakkara             54  26.407407  106.039838
## JP Duminy                 68  26.294118  114.606717
## TM Dilshan                78  26.243590   97.910384
## RG Sharma                 65  25.907692  113.056548
## H Masakadza               53  25.566038   99.453880

1b. Ranking of International T20 bowlers

import yorkpy.analytics as yka
intlT20RankBowling=yka.rankIntlT20Bowling("C:\\software\\cricket-package\\yorkpyPkg\\data\\data")
intlT20RankBowling.head(15)
##                       matches  wicket_mean  econrate_mean
## bowler                                                   
## Umar Gul                   58     1.603448       7.637931
## SL Malinga                 78     1.500000       7.409188
## Saeed Ajmal                63     1.492063       6.451058
## DW Steyn                   46     1.478261       7.014855
## A Shrubsole                45     1.422222       6.294444
## M Morkel                   41     1.292683       7.680894
## KMDN Kulasekara            57     1.280702       7.476608
## TG Southee                 51     1.274510       8.759804
## SCJ Broad                  53     1.264151            inf
## Shakib Al Hasan            58     1.241379       6.836207
## R Ashwin                   44     1.204545       7.162879
## Nida Dar                   44     1.204545       6.083333
## KH Brunt                   44     1.204545       5.982955
## KD Mills                   42     1.166667       8.289683
## SR Watson                  46     1.152174       8.246377

2. Indian Premier League (IPL) T20

The steps to do before ranking for IPL T20 matches are 1. Download IPL T20 zip file from Cricsheet IPL T20 2. Unzip the file. This will create a folder with yaml files

import yorkpy.analytics as yka
#yka.convertAllYaml2PandasDataframesT20("../ipl","../ipldata")

This above step will convert the yaml files into CSV files in the /ipldata folder. Now do the ranking as below

2a. Ranking of batsmen in IPL T20

import yorkpy.analytics as yka
IPLT20RankBatting=yka.rankIPLT20Batting("C:\\software\\cricket-package\\yorkpyPkg\\data\\ipldata")
IPLT20RankBatting.head(15)
##                    matches  runs_mean     SR_mean
## batsman                                          
## DA Warner              129  37.589147  119.917864
## CH Gayle               123  36.723577  125.256818
## SE Marsh                70  36.314286  114.707578
## KL Rahul                59  33.542373  123.424971
## MEK Hussey              60  33.400000  100.439187
## V Kohli                174  32.413793  115.830849
## KS Williamson           42  31.690476  120.443172
## AB de Villiers         143  30.923077  128.967081
## JC Buttler              45  30.800000  132.561154
## AM Rahane              118  30.330508  102.240398
## SR Tendulkar            79  29.949367  101.651959
## F du Plessis            65  29.415385  112.462114
## Q de Kock               51  29.333333  110.973836
## SS Iyer                 47  29.170213  102.144222
## G Gambhir              155  28.741935  103.997558

2b. Ranking of bowlers in IPL T20

import yorkpy.analytics as yka
IPLT20RankBowling=yka.rankIPLT20Bowling("C:\\software\\cricket-package\\yorkpyPkg\\data\\ipldata")
IPLT20RankBowling.head(15)
##                      matches  wicket_mean  econrate_mean
## bowler                                                  
## SL Malinga               122     1.540984       7.173361
## Imran Tahir               43     1.465116       8.155039
## A Nehra                   88     1.375000       7.923295
## MJ McClenaghan            56     1.339286       8.638393
## Rashid Khan               46     1.304348       6.543478
## Sandeep Sharma            79     1.303797       7.860759
## MM Patel                  63     1.301587       7.530423
## DJ Bravo                 131     1.282443       8.458333
## M Morkel                  70     1.257143       7.760714
## SP Narine                109     1.256881       6.747706
## YS Chahal                 83     1.228916       8.103659
## R Vinay Kumar            104     1.221154       8.556090
## RP Singh                  82     1.219512       8.149390
## CH Morris                 52     1.211538       7.854167
## B Kumar                  117     1.205128       7.536325

3. Natwest T20

The steps to do before ranking for Natwest T20 matches are 1. Download Natwest T20 zip file from Cricsheet NTB T20 2. Unzip the file. This will create a folder with yaml files

import yorkpy.analytics as yka
#yka.convertAllYaml2PandasDataframesT20("../ntb","../ntbdata")

This above step will convert the yaml files into CSV files in the /ntbdata folder. Now do the ranking as below

3a. Ranking of NTB batsmen

import yorkpy.analytics as yka
NTBT20RankBatting=yka.rankNTBT20Batting("C:\\software\\cricket-package\\yorkpyPkg\\data\\ntbdata")
NTBT20RankBatting.head(15)
##                      matches  runs_mean     SR_mean
## batsman                                            
## Babar Azam                13  44.461538  121.268809
## T Banton                  13  42.230769  139.376274
## JJ Roy                    12  41.250000  142.182147
## DJM Short                 12  40.250000  131.182294
## AN Petersen               12  37.916667  132.522727
## IR Bell                   13  37.615385  130.104721
## M Klinger                 26  35.346154  112.682922
## EJG Morgan                16  35.062500  129.817650
## AJ Finch                  19  34.578947  137.093465
## MH Wessels                26  33.884615  116.300969
## S Steel                   11  33.545455  140.118207
## DJ Bell-Drummond          21  33.142857  108.566309
## Ashar Zaidi               11  33.000000  178.553331
## DJ Malan                  26  33.000000  120.127202
## T Kohler-Cadmore          23  32.956522  112.493019

3b. Ranking of NTB bowlers

import yorkpy.analytics as yka
NTBT20RankBowling=yka.rankNTBT20Bowling("C:\\software\\cricket-package\\yorkpyPkg\\data\\ntbdata")
NTBT20RankBowling.head(15)
##                        matches  wicket_mean  econrate_mean
## bowler                                                    
## MW Parkinson                11     2.000000       7.628788
## HF Gurney                   23     1.956522       8.831884
## GR Napier                   12     1.916667       8.694444
## R Rampaul                   19     1.736842       7.131579
## P Coughlin                  11     1.727273       8.909091
## AJ Tye                      26     1.692308       8.227564
## GC Viljoen                  12     1.666667       7.708333
## BAC Howell                  21     1.666667       6.857143
## BW Sanderson                12     1.583333       7.902778
## KJ Abbott                   14     1.571429       9.398810
## JE Taylor                   13     1.538462       9.839744
## JDS Neesham                 12     1.500000      10.812500
## MJ Potts                    12     1.500000       8.486111
## TT Bresnan                  21     1.476190       8.817460
## T van der Gugten            13     1.461538       7.211538

4. Big Bash Leagure (BBL) T20

The steps to do before ranking for BBL T20 matches are 1. Download BBL T20 zip file from Cricsheet BBL T20 2. Unzip the file. This will create a folder with yaml files

import yorkpy.analytics as yka
#yka.convertAllYaml2PandasDataframesT20("../bbl","../bbldata")

This above step will convert the yaml files into CSV files in the /bbldata folder. Now do the ranking as below

4a. Ranking of BBL batsmen

import yorkpy.analytics as yka
BBLT20RankBatting=yka.rankBBLT20Batting("C:\\software\\cricket-package\\yorkpyPkg\\data\\bbldata")
BBLT20RankBatting.head(15)
##                 matches  runs_mean     SR_mean
## batsman                                       
## DJM Short            43  40.883721  118.773047
## SE Marsh             47  39.148936  113.616053
## AJ Finch             62  36.306452  120.271231
## AT Carey             37  34.945946  120.125341
## UT Khawaja           41  31.268293  107.355655
## CA Lynn              74  31.162162  121.746578
## MS Wade              46  30.782609  120.310081
## TM Head              45  30.000000  126.769564
## MEK Hussey           23  29.173913  109.492934
## BJ Hodge             29  29.000000  124.438040
## BR Dunk              39  28.230769  106.149913
## AD Hales             31  27.161290  117.678008
## BB McCullum          34  27.058824  115.486392
## GJ Bailey            57  27.000000  121.159220
## MR Marsh             47  26.510638  114.994909

4b. Ranking of BBL bowlers

import yorkpy.analytics as yka
BBLT20RankBowling=yka.rankBBLT20Bowling("C:\\software\\cricket-package\\yorkpyPkg\\data\\bbldata")
BBLT20RankBowling.head(15)
##                    matches  wicket_mean  econrate_mean
## bowler                                                
## Yasir Arafat            15     2.000000       7.587778
## CH Morris               15     1.733333       8.572222
## TK Curran               27     1.629630       8.716049
## TT Bresnan              13     1.615385       8.775641
## JR Hazlewood            18     1.555556       7.361111
## CJ McKay                15     1.533333       8.555556
## DR Sams                 36     1.527778       8.581019
## AC McDermott            14     1.500000       9.166667
## JP Faulkner             20     1.500000       8.345833
## SP Narine               12     1.500000       7.395833
## AJ Tye                  51     1.490196       8.101307
## M Kelly                 21     1.476190       8.908730
## SA Abbott               73     1.438356       8.737443
## B Laughlin              82     1.426829       8.332317
## SW Tait                 31     1.419355       8.895161

Conclusion

You should be able to now rank players in the above formats as new data is added to Cricsheet. yorkpy can also be used for other leagues which follow the Cricsheet format.

Also see
1. Deep Learning from first principles in Python, R and Octave – Part 5
2. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
3. Using Reinforcement Learning to solve Gridworld
4. Big Data-4: Webserver log analysis with RDDs, Pyspark, SparkR and SparklyR
5. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
6. Deblurring with OpenCV: Weiner filter reloaded
7. Rock N’ Roll with Bluemix, Cloudant & NodeExpress
8. Modeling a Car in Android

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