# More player analysis with gganimate()

This post continues the analysis of IPL and T20 (men) batsmen and bowlers through animated charts. In my last post Analyzing player performance with animated charts! I had used animated horizontal bars to display the totalRuns or totalWickets over a 3 year ‘sliding window’. While that was cool, the only drawback of that animation was the batsman and bowler performance was measured in a single dimension of either total(mean) runs or total(mean) wickets.

I think to fairly describe batsmen and bowlers we need at least the following 2 attributes

• Runs and Strike rate for batsmen and
• Wickets and Economy rate for bowlers

So, I have created new animation charts which use these attributes for batsmen and bowlers.

The animated charts are

• Based on a sliding window of 3 years (Jan 2008- Dec 2010, Jan 2009-Dec 2010,…, Jan 2019-Dec 2021)
• I have taken the top 10 percentile and sometimes the top 5 percentile of batsmen and bowlers to keep the charts more manageable
• The charts are based on gganimate().

Note 1: I tried several options for the animation before settling on this one. The animation may seem a bit jerky but, we can follow the progress of players more easily through the years

Note 2: Some charts may display points, without the corresponding names. I think this may be because the animation tries to create intermediate points between the charts.

Note 3: For fine-grained, but static analysis across different intervals or seasons, checkout my my posts GooglyPlusPlus2021 now with power play, middle and death over analysis and GooglyPlusPlus2021: Towards more picturesque analytics!

The code for the animation can be cloned from Github at animation2. Feel free to modify the parameters for a different animation behavior.

Here are performances of IPL and T20 (men) players. Note all charts are based on a 3 year staggered window. The Top 10 percentile is considered

1. Runs vs SR – IPL

We can that Shikhar Dhawan is a top performer for 4 years from 2018-2021. He definitely deserved a slot in the India WC 15 member squad.

2. Wickets vs ER – IPL

Similarly, YS Chahal also deserved a slot in the 15 member sqad

3. Runs vs SR in Power play – IPL

S Dhawan, De Kock & KL Rahul are the best players in Power play

4. Runs vs Strike rate in Middle overs – IPL

Rishabh Pant, Shreyas Iyer,Sanju Samson and KL Rahul are best performers in Middle overs

5.Runs vs SR in Death overs – IPL

It is MS Dhoni all the way, till Hardik Pandya catches up. But H Pandya had poor IPL seasons in 2020, 2021. See my post GooglyPlusPlus2021: Towards more picturesque analytics! for finer analysis

6. Wickets vs ER in Power play – IPL

In the early years B. Kumar led. More recently D Chahar and T Boult

7. Wickets vs ER in Middle overs – IPL

YS Chahal, Rashid Khan, Imran Tahir and Rahul Chahar lead in Middle overs

8. Wickets vs ER in Death overs – IPL

9. Runs vs SR – T20 (men)

Babar Azam, PR Stirling, Rohit Sharma, S Dhawan, KL Rahul are best performers in recent times

10. Wickets vs ER – T20 (men)

Wanindu Hasaranga, T Shamsi, Shadab Khan, Rashid Khan, T Boult are best performers in recent times

11. Runs vs SR in Power Play – T20 (men)

PR Stirling, RG Sharma, C Munro, Shikhar Dhawan lead

12. Runs vs SR in Middle overs – T20 (men)

Babar Azam, followed by Kohli, S Dhawan, Rizwan, RG Sharma in recent years

13. Runs vs SR in Death overs – T20 (men)

Shoaib Malik, V Kohli, Mohamed Nabi. Mohammed Rizwan and other top the death overs

14. Wickets vs ER in Power play (T20 men)

TG Southee, Mujeeb Ur Rahman and B Stanlake lead in recent years

15. Wickets vs ER in Middle overs – T20 (men)

Top bowlers are Shadab Khan, T Shamsi, Kuldeep Yadav, YS Chahal and AC Agar

16. Wickets vs ER in Death overs – T20 (men)

Haris Rauf, AJ Tye, Rashid Khan and Chris Jordan excel in death overs

You may also like

To see all posts click Index of posts

# Analyzing player performance with animated charts!

Analytics is by definition, the science (& art) of identifying, discovering and interpreting patterns in data. There are different ways of capturing these patterns through charts (bar, pie, cumulative data, moving average etc.). One such way is the motion or animated chart which captures the changes in data across different time periods. This was made famous by Hans Rosling in his Gapminder charts.

In this post, I use animated charts, based on gganimate(), to display the rise and fall of batsmen and bowlers in IPL and Intl. T20 (men). I only did this for these 2 formats as they have sufficient data over at least 10+ years.

To construct these animated charts, I use a ‘sliding window’ of 3 years, so that we get a clearer view of batsman and bowler consistency. The animated charts show the performance of players for this moving window for e.g. Jan 2008- Dec 2010, Jan 2009-Dec 2011, Jan 2010- Dec 2012 and so on till Jan 2019- Dec 2021. This is done for both batting( total runs) and bowling (total wickets). If you would like to analyse the performance of particular batsmen, bowler during specific periods or for a team vs another team or in the overall T20 format, check out my post GooglyPlusPlus2021: Towards more picturesque analytics!

You clone/fork the code from Github here animation.

Note: This code is based on a snippet from this blog How to create animations in R with gganimate by Ander Fernandez Jauregui

Included below are the animated charts.

Important note: The year which is displayed on the side actually represents the last 3 years, for e.g. 2015 (2013, 2014, 2015) or 2019 (2017, 2018, 2019)

1. IPL Batting performance

We can see that Kohli stays in the top 3 from 2015-2019

2. IPL Bowling performance

Malinga ruled from 2010- 2015. Bumrah is in top 3 from 2019-2021

3. IPL Batting in Power play

Adam Gilchrist, Tendulkar, Warner, KL Rahul, Shikhar Dhawan have a stay at the top

4. IPL Batting in Middle overs

Rohit Sharma, Kohli, Pant have their stay at the top

5. IPL Batting Death overs

MS Dhoni is lord and master of the death overs in IPL for a rolling period of 10 years from 2011-2020. No wonder, he is the best finisher of T20 cricket

6. IPL Bowling Power Play

Bhuvanesh Kumar is in top 3 from 2014-2018 and then Deepak Chahar

7. IPL Bowling Middle overs

Toppers Harbhajan Singh, YS Chahal, Rashid Khan

8. IPL Bowling Death overs

SL Malinga, B. Kumar, JJ Bumrah and Rabada top the list across the years

9. T20 (men) Batting performance

Kohli, Babar Azam, P R Stirling are best performers

10. T20 (men) bowling performance

Saaed Ajmal tops from 2010-2014 and Rashid Khan 2018-2020

11. T20 (men) batting Power play

Shahzad, D Warner, Rohit Sharma, PR Stirling best performers

12. T20 (men) batting middle overs

Babar Azam is the best middle overs player from 2018-2021

13. T20(men) batting death overs

MS Dhoni, Shoaib Malik, V Kohli, David Miller are the best death over players

14. T20 (men) bowling Power play

Mohammad Nabi, Mujeeb ur Rahman, TG Southee are the best bowlers in power play

15. T20 (men) bowling middle overs

Imran Tahir from 2015-2017, Shadab Khan from 2018-2020, T Shamsi in 2021 top the tables

16. T20 (men) bowling death overs

Saaed Ajmal, A J Tye, Bumrah, Haris Rauf occupy the top slot in different periods

Also see

To see all posts click Index of posts

# GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics

This latest update to GooglyPlusPlus2021 includes the following changes

a) All the functions in the ‘Batsman’ and ‘Bowler ‘tabs now include a date range, which allows you specify a period of interest.

b) The ‘Rank Batsman’ and ‘Rank Bowler’ tabs also include a date range selector, against the earlier version which had a ‘Since year’ slider see GooglyPlusPlus2021 bubbles up top T20 players in all formats!. The earlier ‘Since year’ slider option could only rank for the latest year or for all years up to the current year. Now with the new ‘date range’ picker we can check the batsman and bowler ranks in any IPL season or (any T20 format) or for a range of years.

There are 10 batsman functions and 9 bowler function that have changed for the following T20 and ODI formats and Rank batsman and bowler includes the ‘date range’ and has changed for all T20 formats.

GooglyPlusPlus2021 supports all the following T20 formats

i) IPL ii) Intl T20(men) iii) Intl T20(women) iv) BBL v) NTB vi) PSL vii) WBB viii) CPL ix) SSM T20 formats – ( 9 T20 panels)

i) ODI (men) ii) ODI (women) – 2 ODI panels

i.e. the changes impact (10 + 9) x 11 + (1 + 1 ) x 9 = 227 tabs which have been changed

The addition of date range enables a fine-grained analysis of players as the players progress through the years.

Note: All charts are interactive. To see how to use interactive charts of GooglyPlusPlus2021 see

GooglyPlusPlus2021 is now fully interactive!!!

GooglyPlusPlus2021 is based on my R package yorkr. The data is take from Cricsheet

You can clone/fork this latest version of GooglyPlusPlus2021 from Github at gpp2021-7

Check out the Shiny app here GooglyPlusPlus2021!!!

I have included some random screen shots of some of using these tabs and options in GooglyPlusPlus2021.

A) KL Rahul’s Cumulative average in IPL 2021 vs IPL 2020

a) KL Rahul in IPL 2021

b) KL Rahul in IPL 2020

B) Performance of Babar Azam in Intl. T20 (men)

a) Babar Azam’s cumulative average from 2019

b) Babar Azam’s Runs against opposition since 2019

Note: Intl. T20 (women) data available upto Mar 2020 from Cricsheet

a) A J Healy performance between 2010 – 2015

b) A J Healy performance between 2015 – 2020

D) M S Dhoni’s performance with the bat pre-2020 and post 2020

There has been a significant decline in Dhoni’s performance in the last couple of years

I) Dhoni’s performance from Jan 2010 to Dec 2019

a) Moving average at 25+ (Dhoni before)

The moving average actually moves up…

b) Cumulative average at 25+ (Dhoni before)

c) Cumulative Strike rate 140+ (Dhoni before)

d) Dhoni’s moving average is ~10-12 (post 2020)

e) Dhoni’s cumulative average (post 2020)

f) Dhoni’s strike rate ~80 (post 2020)

E) Bumrah’s performance in IPL

a) Bumrah’s performance in IPL 2020

b) Bumrah’s performance in IPL 2021

F) Moving average wickets for A. Shrubsole in ODI (women)

G) Chris Jordan’s cumulative economy rate

We can see that Jordan has become more expensive over the years

G) Ranking players

In this latest version the ‘Since year slider’ has been replaced with a Date Range selector. With this we can identify the player ranks in any IPL, CPL, PSL or BBL season. We can also check the performance over the last couple of years. Note: The matches played and Runs over Strike rate or Strike rate over runs can be computed. Similarly for bowlers we have Wickets over Economy rate and Economy rate over wickets options.

a) Ranking IPL batsman in IPL season 2020

b) Ranking Intl. T20 (batsmen) from Jan 2019 to Jul 2021

c) Ranking Intl. T20 bowlers (women) from Jan 2019 – Jul 2021

d) Best IPL bowlers over the last 3 seasons (Wickets over Economy rate)

e) Best IPL bowlers over the last 3 seasons (Economy rate over wickets)

Take GooglyPlusPlus2021 for a spin!!

Hope you have fun checking out the different tabs with the different options!!

Also see

# GooglyPlusPlus2021: Restarting IPL 2021 as-it-happens!!!

The IPL 2021 extravaganza has restarted again, now in Dubai, and it was time for me to crank up good ol’ GooglyPlusPlus2021. As in my earlier post, GooglyPlus2021 with IPL 2021 as it happens, during the initial set of IPL 2021 games,, a command script will execute automatically every day, download the latest data files, unzip, sort, process and put them in appropriate directories so that GooglyPlusPlus can work its magic on the data, with my R package yorkr. You can do analysis of IPL 2021 matches, batsmen, bowlers, historical performance analysis of head-to-head clashes and performances of teams.

Note: Since the earlier instalment of IPL 2021, there are 2 key changes that have taken place in GooglyPlusPlus.

Now,

a) All charts are interactive. You can hover over charts, click, double-click to get more details. To see more details on how to use the interactive charts, see my post GooglyPlusPlus2021 is now fully interactive!

b) You can now analyse historical performances, compute team batting and bowling scorecards for specified periods. To know details see GooglyPlusPlus2021 adds new bells and whistles!

You can try out my app GooglyPlusPlus2021 by clicking GooglyPlusPlus2021

The code for my R package yorkr is available at Github at yorkr

You can clone/fork GooglyPlusPlus2021 from github at gpp2021-6

Some key analysis and highlights of the 2 recently concluded IPL matches

• CSK vs MI
• KKR vs RCB

a) CSK vs MI (19 Sep 2021) – Batting Partnerships (CSK)

b) CSK vs MI (19 Sep 2021) – Bowling scorecard (MI)

c) CSK vs MI (19 Sep 2021) – Match worm chart

Even though MI had a much better start and were cruising along to a victory, they lost the plot around the 18.1 th over as seen below (hover on the chart)

d) KKR vs RCB ( 20 Sep 2021) – Bowling wicket match

This chart gives the wickets taken by the bowler and the total runs conceded

e) KKR vs RCB ( 20 Sep 2021) – Match worm chart

This was a no contest. RCB batting was pathetic and KKR blasted their way to victory as seen in this worm chart

Note: You can also do historical analysis of teams with GooglyPlusPlus2021

For the match to occur today PBKS vs RR (21 Sep 2021) we can perform head-to-head historical analysis. Here Kings XI Punjab has been chosen instead of Punjab Kings as that was its name.

For the Rajasthan Royals Sanjy Samson and Jos Buttler have the best performance from 2018 -2021 as seen below

For Punjab Kings KL Rahul and Chris Gayle are the leading scorers for the period 2018-2021

g) Current ranking of batsmen IPL 2021

h) Current ranking of bowlers IPL 2021

Also you analyse individual batsman and bowlers

i) Batsman analysis

To see Rituraj Gaikwad performance checkout the batsman tab

j) Bowler analysis

Performance of Varun Chakaravarty

Remember to check out GooglyPlusPlus2021 for your daily analysis of matches, teams, batsmen and bowlers. Your ride will be waiting for you!!!

You can clone/fork GooglyPlusPlus2021 from github at gpp2021-6

GooglyPlusPlus2021 has been updated with all completed 31 matches

Also see

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 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
• 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!!

GooglyPlusPlus2021 has been updated with all the completed 29 matches

Watch this space!

Also see

To see all posts 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

### 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!!

To see all posts click Index of posts

# GooglyPlusPlus 2020!!

I have updated my GooglyPlusPlus Shiny app with data from latest IPL 2020. GooglyPlusPlus  2020 is also based on my R package yorkr.  To know more about yorkr (see Revitalizing R package yorkr.) Now you should be able to analyze IPL matches, teams, players upto IPL 2020. Note: My latest GooglyPlusPlus 2020 can analyze all formats of T20 matches. yorkr uses data from Cricsheet

There are 5 tabs in each of the T20 formats

i) Analyze T20 batsmen ii) Analyze T20 bowlers. iii) Analyze T20 match iv) Analyze T20 team

vs another T20 team v) Analyze overall performance of T20 against all other teams

I plan to update GooglyPlusPlus  at least twice a year  to keep it abreast of all the latest data of all T20 formats

In GooglyPlusPlus 2020 you can check out IPL data upto 2020, besides other T20 formats like BBL, PSL, NTB, WBBL, Intl. T20 etc.

Try out GooglyPlusPlus 2020 Shiny app!!

You can clone/fork the code from Github GooglyPlusPlus2020

Important note: My earlier app GooglyPlusPlus handled all T20 formats including ODI (men and women). Due to an issue with Shiny, I could not include ODI matches in GooglyPlusPlus 2020

Here are some snapshots from GooglyPlusPlus 2020

### E. Match scorecard – CSK vs DC 2020-10-17

The scorecards batting and bowling are computed on the fly for all T20 matches

### H. Overall team performance- Team Bowler Wicket kind: Rajasthan Royals

Clone/fork the code from Github GooglyPlusPlus2020

Do take GooglyPlusPlus 2020 for a drive! While I have highlighted only IPL T20, because I have updated with the latest data, GooglyPlusPlus 2020 can also handle other T20 formats like BBL, Natwest, PSL, Intl. T20 (men &women) and WBB

Hope you have fun!

Also see

To see all posts click Index of posts

# Rank IPL batsmen and bowlers post IPL 2020

## Introduction

This post ranks IPL batsmen and bowlers post IPL 2020 season based on my R package yorkr. To know more about yorkr see Revisting R package yorkrAnalysis of IPL T20 matches with yorkr templates and others posts on this R package in Index of posts

```library(yorkr)
```

## 1. Convert YAML files to match data

Convert all the match data as YAML file into .RData

```#convertAllYaml2RDataframesT20("ipl","IPLMatches")
```

## 2. Rank the IPL Batsmen post IPL 2020

The function below ranks the IPL batsmen post IPL 2020. Note: We can specify the minimum number of matches that should have played by the batsmen for the ranking. By varying this parameter we can identify upcoming stars versus those who are more consistent.

```dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/ipl2020/IPLMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/ipl2020/IPLBattingBowlingDetails"

rankIPLBatsmen(dir=dir,odir=odir,minMatches=60)
```
```## [1] "Chennai Super Kings"
## [1] "Delhi Capitals"
## [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] "Gujarat Lions"
## [1] "Rising Pune Supergiants"
## [1] "Chennai Super Kings-BattingDetails.RData"
## [1] "Delhi Capitals-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] "Gujarat Lions-BattingDetails.RData"
## [1] "Rising Pune Supergiants-BattingDetails.RData"
```
```## # A tibble: 65 x 4
##    batsman        matches meanRuns meanSR
##    <chr>            <int>    <dbl>  <dbl>
##  1 DA Warner          146     37.5   128.
##  2 CH Gayle           132     36.4   134.
##  3 SE Marsh            67     35.9   120.
##  4 KL Rahul            73     34.2   126.
##  5 RR Pant             68     31.8   133.
##  6 V Kohli            190     31.6   118.
##  7 AB de Villiers     155     30.5   136.
##  8 F du Plessis        79     30.4   118.
##  9 S Dhawan           174     30.0   115.
## 10 Q de Kock           64     29.8   119.
## # … with 55 more rows
```
```rankIPLBatsmen(dir=dir,odir=odir,minMatches=70)
```
```## [1] "Chennai Super Kings"
## [1] "Delhi Capitals"
## [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] "Gujarat Lions"
## [1] "Rising Pune Supergiants"
## [1] "Chennai Super Kings-BattingDetails.RData"
## [1] "Delhi Capitals-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] "Gujarat Lions-BattingDetails.RData"
## [1] "Rising Pune Supergiants-BattingDetails.RData"
```
```## # A tibble: 51 x 4
##    batsman        matches meanRuns meanSR
##    <chr>            <int>    <dbl>  <dbl>
##  1 DA Warner          146     37.5   128.
##  2 CH Gayle           132     36.4   134.
##  3 KL Rahul            73     34.2   126.
##  4 V Kohli            190     31.6   118.
##  5 AB de Villiers     155     30.5   136.
##  6 F du Plessis        79     30.4   118.
##  7 S Dhawan           174     30.0   115.
##  8 AM Rahane          124     29.6   105.
##  9 SS Iyer             77     29.3   111.
## 10 G Gambhir          155     29     110.
## # … with 41 more rows
```

## 3. Rank IPL bowlers post IPL 2020

The function ranks IPL bowlers post IPL 2020. We can specify the minimum number of matches that should have been played by the bowlers

```dir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/ipl2020/IPLMatches"
odir="/Users/tvganesh/backup/software/cricket-package/yorkr-cricsheet/ipl2020/IPLBattingBowlingDetails"
rankIPLBowlers(dir=dir,odir=odir,minMatches=60)
```
```## [1] "Chennai Super Kings"
## [1] "Delhi Capitals"
## [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] "Gujarat Lions"
## [1] "Rising Pune Supergiants"
## [1] "Chennai Super Kings-BowlingDetails.RData"
## [1] "Delhi Capitals-BowlingDetails.RData"
## [1] "Deccan Chargers-BowlingDetails.RData"
## [1] "Delhi Daredevils-BowlingDetails.RData"
## [1] "Kings XI Punjab-BowlingDetails.RData"
## [1] "Kochi Tuskers Kerala-BowlingDetails.RData"
## [1] "Kolkata Knight Riders-BowlingDetails.RData"
## [1] "Mumbai Indians-BowlingDetails.RData"
## [1] "Pune Warriors-BowlingDetails.RData"
## [1] "Rajasthan Royals-BowlingDetails.RData"
## [1] "Royal Challengers Bangalore-BowlingDetails.RData"
## [1] "Gujarat Lions-BowlingDetails.RData"
## [1] "Rising Pune Supergiants-BowlingDetails.RData"
```
```## # A tibble: 21 x 4
##    bowler          matches totalWickets meanER
##    <chr>             <int>        <dbl>  <dbl>
##  1 SL Malinga          120          184   6.99
##  2 SP Narine           117          143   6.82
##  3 Harbhajan Singh     131          134   7.11
##  4 DJ Bravo             91          125   8.20
##  5 YS Chahal            97          124   7.73
##  6 B Kumar              90          121   7.40
##  7 JJ Bumrah            91          119   7.35
##  8 R Ashwin             92           98   6.81
##  9 RA Jadeja           102           91   8.04
## 10 PP Chawla            85           87   8.02
## # … with 11 more rows
```
```rankIPLBowlers(dir=dir,odir=odir,minMatches=50)
```
```## [1] "Chennai Super Kings"
## [1] "Delhi Capitals"
## [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] "Gujarat Lions"
## [1] "Rising Pune Supergiants"
## [1] "Chennai Super Kings-BowlingDetails.RData"
## [1] "Delhi Capitals-BowlingDetails.RData"
## [1] "Deccan Chargers-BowlingDetails.RData"
## [1] "Delhi Daredevils-BowlingDetails.RData"
## [1] "Kings XI Punjab-BowlingDetails.RData"
## [1] "Kochi Tuskers Kerala-BowlingDetails.RData"
## [1] "Kolkata Knight Riders-BowlingDetails.RData"
## [1] "Mumbai Indians-BowlingDetails.RData"
## [1] "Pune Warriors-BowlingDetails.RData"
## [1] "Rajasthan Royals-BowlingDetails.RData"
## [1] "Royal Challengers Bangalore-BowlingDetails.RData"
## [1] "Gujarat Lions-BowlingDetails.RData"
## [1] "Rising Pune Supergiants-BowlingDetails.RData"
```
```## # A tibble: 28 x 4
##    bowler          matches totalWickets meanER
##    <chr>             <int>        <dbl>  <dbl>
##  1 SL Malinga          120          184   6.99
##  2 SP Narine           117          143   6.82
##  3 Harbhajan Singh     131          134   7.11
##  4 DJ Bravo             91          125   8.20
##  5 YS Chahal            97          124   7.73
##  6 B Kumar              90          121   7.40
##  7 JJ Bumrah            91          119   7.35
##  8 R Ashwin             92           98   6.81
##  9 RA Jadeja           102           91   8.04
## 10 PP Chawla            85           87   8.02
## # … with 18 more rows
```

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",
"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)
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] "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")

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',
"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)
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

# 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)``````

Checkout my interactive Shiny apps GooglyPlus2021 (interactive plots ) and GooglyPlusPlus2021 (analysis in specific intervals) which can be used to analyze IPL players, teams and matches.

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.
##  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!!

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