More book, more cricket! 2nd edition of my books now on Amazon


The 2nd edition of both my books

a) Cricket analytics with cricketr
b) Beaten by sheer pace – Cricket analytics with yorkr
is now available on Amazon, both as Paperback and Kindle versions. The Kindle versions are just $4.99 for both books. Pick up your copies today!!!

A) Cricket analytics with cricketr: Second Edition

Click hereCricket analytics with cricketr: Second Edition

B) Beaten by sheer pace: Cricket analytics with yorkr(2nd edition)

Click here Beaten by sheer pace! Cricket analytics with yorkr

Pick up your copies today!!!

cricketr flexes new muscles: The final analysis


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

       Jabberwocky by Lewis Carroll
                   

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

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

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

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

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

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

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

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

library(cricketr)

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

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

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

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

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

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

2. Relative performances of international batsmen against England in England

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

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

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

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

relativeBatsmanCumulativeAvgRuns(frames,names)

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

relativeBatsmanCumulativeStrikeRate(frames,names)

3. Relative performances of international batsmen against Australia in Australia

The following players from these countries were chosen

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

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

relativeBatsmanCumulativeAvgRuns(frames,names)

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

relativeBatsmanCumulativeStrikeRate(frames,names)

4. Relative performances of international batsmen against India in India

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

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

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

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

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

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

relativeBatsmanCumulativeAvgRuns(frames,names)

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

relativeBatsmanCumulativeStrikeRate(frames,names)

5. All time greats of Indian batting

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

6. Top Indian batsmen against Australia in Australia

The following Indian batsmen were chosen

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

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

relativeBatsmanCumulativeAvgRuns(frames,names)

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

relativeBatsmanCumulativeStrikeRate(frames,names)

7. Top Indian batsmen against England in England

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

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

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

relativeBatsmanCumulativeAvgRuns(frames,names)

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

relativeBatsmanCumulativeStrikeRate(frames,names)

8. Top Indian batsmen against West Indies in West Indies

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

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

relativeBatsmanCumulativeAvgRuns(frames,names)

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

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

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

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

10. Top international spinners against England in England

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

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

relativeBowlerCumulativeAvgWickets(frames,names)

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

11. Top international spinners against South Africa in South Africa

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

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

relativeBowlerCumulativeAvgWickets(frames,names)

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

11. Top international pacers against India in India

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

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

For this I have chosen the following bowlers

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

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

relativeBowlerCumulativeAvgWickets(frames,names)

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

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

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

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

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

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

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

Some key observations

Here are some key observations

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

You may like my Shiny apps on cricket

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

Also see

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

To see all my posts see Index of posts

cricketr and yorkr books – Paperback now in Amazon


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

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

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

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

GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables


In this post I introduce my new Shiny app,“GooglyPlus”, which is a  more evolved version of my earlier Shiny app “Googly”. My R package ‘yorkr’,  on which both these Shiny apps are based, has the ability to output either a dataframe or plot, depending on a parameter plot=TRUE or FALSE. My initial version of the app only included plots, and did not exercise the yorkr package fully. Moreover, I am certain, there may be a set of cricket aficionados who would prefer, numbers to charts. Hence I have created this enhanced version of the Googly app and appropriately renamed it as GooglyPlus. GooglyPlus is based on the yorkr package which uses data from Cricsheet. The app is based on IPL data from  all IPL matches from 2008 up to 2016. Feel free to clone/fork or download the code from Github at GooglyPlus.

Click  GooglyPlus to access the Shiny app!

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

The changes for GooglyPlus over the earlier Googly app is only in the following 3 tab panels

  • IPL match
  • Head to head
  • Overall Performance

The analysis of IPL batsman and IPL bowler tabs are unchanged. These charts are as they were before.

The changes are only in  tabs i) IPL match ii) Head to head and  iii) Overall Performance. New functionality has been added and existing functions now have the dual option of either displaying a plot or a table.

The changes are

A) IPL Match
The following additions/enhancements have been done

-Match Batting Scorecard – Table
-Batting Partnerships – Plot, Table (New)
-Batsmen vs Bowlers – Plot, Table(New)
-Match Bowling Scorecard   – Table (New)
-Bowling Wicket Kind – Plot, Table (New)
-Bowling Wicket Runs – Plot, Table (New)
-Bowling Wicket Match – Plot, Table (New)
-Bowler vs Batsmen – Plot, Table (New)
-Match Worm Graph – Plot

B) Head to head
The following functions have been added/enhanced

-Team Batsmen Batting Partnerships All Matches – Plot, Table {Summary (New) and Detailed (New)}
-Team Batting Scorecard All Matches – Table (New)
-Team Batsmen vs Bowlers all Matches – Plot, Table (New)
-Team Wickets Opposition All Matches – Plot, Table (New)
-Team Bowling Scorecard All Matches – Table (New)
-Team Bowler vs Batsmen All Matches – Plot, Table (New)
-Team Bowlers Wicket Kind All Matches – Plot, Table (New)
-Team Bowler Wicket Runs All Matches – Plot, Table (New)
-Win Loss All Matches – Plot

C) Overall Performance
The following additions/enhancements have been done in this tab

-Team Batsmen Partnerships Overall – Plot, Table {Summary (New) and Detailed (New)}
-Team Batting Scorecard Overall –Table (New)
-Team Batsmen vs Bowlers Overall – Plot, Table (New)
-Team Bowler vs Batsmen Overall – Plot, Table (New)
-Team Bowling Scorecard Overall – Table (New)
-Team Bowler Wicket Kind Overall – Plot, Table (New)

Included below are some random charts and tables. Feel free to explore the Shiny app further

1) IPL Match
a) Match Batting Scorecard (Table only)
This is the batting score card for the Chennai Super Kings & Deccan Chargers 2011-05-11

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b)  Match batting partnerships (Plot)
Delhi Daredevils vs Kings XI Punjab – 2011-04-23

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c) Match batting partnerships (Table)
The same batting partnership  Delhi Daredevils vs Kings XI Punjab – 2011-04-23 as a table

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d) Batsmen vs Bowlers (Plot)
Kolkata Knight Riders vs Mumbai Indians 2010-04-19

Untitled.png

e)  Match Bowling Scorecard (Table only)
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B) Head to head

a) Team Batsmen Partnership (Plot)
Deccan Chargers vs Kolkata Knight Riders all matches

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b)  Team Batsmen Partnership (Summary – Table)
In the following tables it can be seen that MS Dhoni has performed better that SK Raina  CSK against DD matches, whereas SK Raina performs better than Dhoni in CSK vs  KKR matches

i) Chennai Super Kings vs Delhi Daredevils (Summary – Table)

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ii) Chennai Super Kings vs Kolkata Knight Riders (Summary – Table)
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iii) Rising Pune Supergiants vs Gujarat Lions (Detailed – Table)
This table provides the detailed partnership for RPS vs GL all matches

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c) Team Bowling Scorecard (Table only)
This table gives the bowling scorecard of Pune Warriors vs Deccan Chargers in all matches

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C) Overall performances
a) Batting Scorecard All Matches  (Table only)

This is the batting scorecard of Royal Challengers Bangalore. The top 3 batsmen are V Kohli, C Gayle and AB Devilliers in that order

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b) Batsman vs Bowlers all Matches (Plot)
This gives the performance of Mumbai Indian’s batsman of Rank=1, which is Rohit Sharma, against bowlers of all other teams

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c)  Batsman vs Bowlers all Matches (Table)
The above plot as a table. It can be seen that Rohit Sharma has scored maximum runs against M Morkel, then Shakib Al Hasan and then UT Yadav.

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d) Bowling scorecard (Table only)
The table below gives the bowling scorecard of CSK. R Ashwin leads with a tally of 98 wickets followed by DJ Bravo who has 88 wickets and then JA Morkel who has 83 wickets in all matches against all teams

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This is just a random selection of functions. Do play around with the app and checkout how the different IPL batsmen, bowlers and teams stack against each other. Do read my earlier post Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr  for more details about the app and other functions available.

Click GooglyPlus to access the Shiny app!

You can clone/fork/download the code from Github at GooglyPlus

Hope you have fun playing around with the Shiny app!

Note: In the tabs, for some of the functions, not all controls  are required. It is possible to enable the controls selectively but this has not been done in this current version. I may make the changes some time in the future.

Take a look at my other Shiny apps
a.Revisiting crimes against women in India
b. Natural language processing: What would Shakespeare say?

Check out some of my other posts
1. Analyzing World Bank data with WDI, googleVis Motion Charts
2. Video presentation on Machine Learning, Data Science, NLP and Big Data – Part 1
3. Singularity
4. Design principles of scalable, distributed systems
5. Simulating an Edge shape in Android
6. Dabbling with Wiener filter in OpenCV

To see all posts click Index of Posts

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


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

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

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

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

The Shiny app can be accessed at Googly

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

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

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

Based on the 5 detailed analysis domains there are 5 tabs

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

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

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

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

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

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

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

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

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

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

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

A. IPL Batsman
a. A Symonds : Runs vs Deliveries
untitled

b. AB Devilliers – Cumulative Strike Rate
untitled

c.  Gautam Gambhir – Runs at venues
untitled

d. CH Gayle – Predict runs 
untitled

B. IPL Bowler
a. Ashish Nehra – Cumulative Average Wickets
untitled

b.  DJ Bravo – Moving Average of wickets
untitled

c. R Ashwin – Mean Economy rate vs Overs
untitled

C.IPL Match
a. Chennai Super Kings vs Deccan Chargers   (2008 -05-06) – Batsmen Partnerships

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

untitled

b. Kolkata Knight Riders vs Delhi Daredevils (2013-04-02) – Bowling wicket runs
untitled

c. Mumbai Indians vs Kings XI Punjab (2010-03-30) – Match worm graph
untitled

D. Head to head confrontation
a. Rising Pune Supergiants vs Mumbai Indians in all matches – Team batsmen partnerships

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

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

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

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

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

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

Do bowl a Googly!!!

You may like my other Shiny apps

Also see my other posts

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

For more posts see Index of posts

cricketr sizes up legendary All-rounders of yesteryear


Introduction

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

Please be mindful of the ESPN Cricinfo Terms of Use

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

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

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

All Rounders

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

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

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

library(cricketr)

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

#kapil1 
#botham11 
#imran1 
#hadlee1 

Analyses of batting performances of the All Rounders

The following plots gives the analysis of the 4 ODI batsmen

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

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

The 3 charts below give the number of

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

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

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

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

kapil-4s6ssr-1

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

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

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

botham-4s6sr-1

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

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

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

imran-4s6ssr-1

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

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

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

hadlee-4s6sout-1

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

Cumulative Average runs of batsman in career

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

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

kbih-car-1

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

kbih-car-2

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

kbih-car-3

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

kbih-car-4

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

Cumulative Average strike rate of batsman in career

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

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

kbih-casr-1

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

kbih-casr-2

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

kbih-casr-3

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

kbih-casr-4

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

Relative Mean Strike Rate

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

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

plot-1-1

Relative Runs Frequency Percentage

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

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

plot-2-1

Relative cumulative average runs in career

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

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

kbih-relcar-1

Relative cumulative average strike rate in career

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

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

kbih-relcsr-1

Percent 4’s,6’s in total runs scored

The plot below shows the contrib

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

plot-46s-1

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

Runs forecast

The forecast for the batsman is shown below.

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

plot-fcst-1

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

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

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

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

plot-3-1

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

plot-4-1

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

Predicting Runs given Balls Faced and Minutes at Crease

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

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

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

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

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

Highest runs likelihood

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

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

kapil11-1

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

B. Ian Botham

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

devilliers-1

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

C. Imran Khan

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

gaylecache-true-1

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

D. Richard Hadlee

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

maxwell-1

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

Average runs at ground and against opposition

A. Kapil Dev

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

avgrg-1-1

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

B. Ian Botham

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

avgrg-2-1

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

C. Imran Khan

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

avgrg-3-1

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

D. Richard Hadlee

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

avgrg-4-1

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

Moving Average of runs over career

The moving average for the 4 batsmen indicate the following

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

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

sdgm-ma-1

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

Check batsmen in-form, out-of-form

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

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

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

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

Analyses of bowling performances of the All Rounders

The following plots gives the analysis of the 4 ODI batsmen

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

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

To get the bowler’s data use

#kapil2 
#botham2 
#imran2 
#hadlee2 

“`

Wicket Frequency percentage

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

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

relbowlfp-1

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

Wickets Runs plot

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

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

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

wktsrun-1

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

Cumulative average wicket plot

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

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

kwm-bowlcaw-1

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

kwm-bowlcaw-2

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

kwm-bowlcaw-3

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

kwm-bowlcaw-4

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

kwm-bowlcer-1

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

kwm-bowlcer-2

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

kwm-bowlcer-3

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

kwm-bowlcer-4

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

Average wickets in different grounds and opposition

A. Kapil Dev

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

gr-1-1

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

B. Ian Botham

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

gr-2-1

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

C. Imran Khan

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

gr-3-1

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

D. Richard Hadlee

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

gr-4-1

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

Relative bowling performance

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

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

relbowlperf-1

Relative Economy Rate against wickets taken

Hadlee has the best overall economy rate followed by Kapil Dev

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

relbowler-1

Relative cumulative average wickets of bowlers in career

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

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

rbcaw-1

Relative cumulative average economy rate of bowlers

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

rbcer-1

Moving average of wickets over career

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

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

jmss-bowlma-1

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

Wickets forecast

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

jjmss-pfcst-1

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

Check bowler in-form, out-of-form

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

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

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

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

Key findings

Here are some key conclusions ODI batsmen

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

Also see my other posts in R

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

For a full list of posts see Index of posts

Re-introducing cricketr! : An R package to analyze performances of cricketers


In this post I re-introduce R package cricketr. I have added 8 new functions to my R package cricketr, available from version cricketr_0.0.13 namely

  1. batsmanCumulativeAverageRuns
  2. batsmanCumulativeStrikeRate
  3. bowlerCumulativeAvgEconRate
  4. bowlerCumulativeAvgWicketRate
  5. relativeBatsmanCumulativeAvgRuns
  6. relativeBatsmanCumulativeStrikeRate
  7. relativeBowlerCumulativeAvgWickets
  8. relativeBowlerCumulativeAvgEconRate

This post updates my earlier post Introducing cricketr:An R package for analyzing performances of cricketrs

Yet all experience is an arch wherethro’
Gleams that untravell’d world whose margin fades
For ever and forever when I move.
How dull it is to pause, to make an end,
To rust unburnish’d, not to shine in use!

Ulysses by Alfred Tennyson

 Introduction

This is an initial post in which I introduce a cricketing package ‘cricketr’ which I have created. This package was a natural culmination to my earlier posts on cricket and my finishing 10 modules of Data Science Specialization, from John Hopkins University at Coursera. The thought of creating this package struck me some time back, and I have finally been able to bring this to fruition.

So here it is. My R package ‘cricketr!!!’

This package uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package can handle all formats of the game including Test, ODI and Twenty20 cricket.

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

Note: This page is also hosted as a GitHub page at cricketr

This post is also hosted on Rpubs at Reintroducing cricketr. You can also down the pdf version of this post at reintroducing_cricketr.pdf

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

(Take a look at my short video tutorial on my R package cricketr on Youtube – R package cricketr – A short tutorial)

Do check out my interactive Shiny app implementation using the cricketr package – Sixer – R package cricketr’s new Shiny avatar

Also see my 2nd book “Beaten by sheer pace”  based on my R package yorkr which is now available in paperback and kindle versions at Amazon

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

 The cricketr package

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

Other interesting functions include batting performance moving average, forecast and a function to check whether the batsman/bowler is in in-form or out-of-form.

The data for a particular player can be obtained with the getPlayerData() function from the package. To do this you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Ricky Ponting, Sachin Tendulkar etc. This will bring up a page which have the profile number for the player e.g. for Sachin Tendulkar this would be http://www.espncricinfo.com/india/content/player/35320.html. Hence, Sachin’s profile is 35320. This can be used to get the data for Tendulkar as shown below

The cricketr package is now available from  CRAN!!!.  You should be able to install directly with

if (!require("cricketr")){ 
    install.packages("cricketr",lib = "c:/test") 
} 
library(cricketr)

The cricketr package includes some pre-packaged sample (.csv) files. You can use these sample to test functions  as shown below

# Retrieve the file path of a data file installed with cricketr
pathToFile ,"Sachin Tendulkar")

unnamed-chunk-2-1

Alternatively, the cricketr package can be installed from GitHub with

if (!require("cricketr")){ 
    library(devtools) 
    install_github("tvganesh/cricketr") 
}
library(cricketr)

The pre-packaged files can be accessed as shown above.
To get the data of any player use the function getPlayerData()

tendulkar <- getPlayerData(35320,dir="..",file="tendulkar.csv",type="batting",homeOrAway=c(1,2),
                           result=c(1,2,4))

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

Sachin Tendulkar’s performance – Basic Analyses

The 3 plots below provide the following for Tendulkar

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsmanRunsFreqPerf("./tendulkar.csv","Sachin Tendulkar")
batsmanMeanStrikeRate("./tendulkar.csv","Sachin Tendulkar")
batsmanRunsRanges("./tendulkar.csv","Sachin Tendulkar")

tendulkar-batting-1

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

More analyses

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
batsman4s("./tendulkar.csv","Tendulkar")
batsman6s("./tendulkar.csv","Tendulkar")
batsmanDismissals("./tendulkar.csv","Tendulkar")

tendulkar-4s6sout-1

 

3D scatter plot and prediction plane

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

battingPerf3d("./tendulkar.csv","Sachin Tendulkar")

tendulkar-3d-1

Average runs at different venues

The plot below gives the average runs scored by Tendulkar at different grounds. The plot also displays the number of innings at each ground as a label at x-axis. It can be seen Tendulkar did great in Colombo (SSC), Melbourne ifor matches overseas and Mumbai, Mohali and Bangalore at home

batsmanAvgRunsGround("./tendulkar.csv","Sachin Tendulkar")
tendulkar-avggrd-1

Average runs against different opposing teams

This plot computes the average runs scored by Tendulkar against different countries. The x-axis also gives the number of innings against each team

batsmanAvgRunsOpposition("./tendulkar.csv","Tendulkar")
tendulkar-avgopn-1

Highest Runs Likelihood

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

batsmanRunsLikelihood("./tendulkar.csv","Sachin Tendulkar")

tendulkar-kmeans-1

## Summary of  Sachin Tendulkar 's runs scoring likelihood
## **************************************************
## 
## There is a 16.51 % likelihood that Sachin Tendulkar  will make  139 Runs in  251 balls over 353  Minutes 
## There is a 58.41 % likelihood that Sachin Tendulkar  will make  16 Runs in  31 balls over  44  Minutes 
## There is a 25.08 % likelihood that Sachin Tendulkar  will make  66 Runs in  122 balls over 167  Minutes

A look at the Top 4 batsman – Tendulkar, Kallis, Ponting and Sangakkara

The batsmen with the most hundreds in test cricket are

  1. Sachin Tendulkar :Average:53.78,100’s – 51, 50’s – 68
  2. Jacques Kallis : Average: 55.47, 100’s – 45, 50’s – 58
  3. Ricky Ponting : Average: 51.85, 100’s – 41 , 50’s – 62
  4. Kumara Sangakarra: Average: 58.04 ,100’s – 38 , 50’s – 52

in that order.

The following plots take a closer at their performances. The box plots show the mean (red line) and median (blue line). The two ends of the boxplot display the 25th and 75th percentile.

Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency. The calculated Mean differ from the stated means possibly because of data cleaning. Also not sure how the means were arrived at ESPN Cricinfo for e.g. when considering not out..

batsmanPerfBoxHist("./tendulkar.csv","Sachin Tendulkar")

tkps-boxhist-1

batsmanPerfBoxHist("./kallis.csv","Jacques Kallis")

tkps-boxhist-2

batsmanPerfBoxHist("./ponting.csv","Ricky Ponting")

tkps-boxhist-3

batsmanPerfBoxHist("./sangakkara.csv","K Sangakkara")

tkps-boxhist-4

Contribution to won and lost matches

The plot below shows the contribution of Tendulkar, Kallis, Ponting and Sangakarra in matches won and lost. The plots show the range of runs scored as a boxplot (25th & 75th percentile) and the mean scored. The total matches won and lost are also printed in the plot.

All the players have scored more in the matches they won than the matches they lost. Ricky Ponting is the only batsman who seems to have more matches won to his credit than others. This could also be because he was a member of strong Australian team

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

tendulkarsp 
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanContributionWonLost("tendulkarsp.csv","Tendulkar")
batsmanContributionWonLost("kallissp.csv","Kallis")
batsmanContributionWonLost("pontingsp.csv","Ponting")
batsmanContributionWonLost("sangakkarasp.csv","Sangakarra")

tkps-wonlost-1

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

Performance at home and overseas

From the plot below it can be seen
Tendulkar has more matches overseas than at home and his performance is consistent in all venues at home or abroad. Ponting has lesser innings than Tendulkar and has an equally good performance at home and overseas.Kallis and Sangakkara’s performance abroad is lower than the performance at home.

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

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfHomeAway("tendulkarsp.csv","Tendulkar")
batsmanPerfHomeAway("kallissp.csv","Kallis")
batsmanPerfHomeAway("pontingsp.csv","Ponting")
batsmanPerfHomeAway("sangakkarasp.csv","Sangakarra")
dev.off()
tkps-homeaway-1
dev.off()
## null device 
##           1
 

Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4. Clearly . Kallis and Sangakkara have a few more years of great batting ahead. They seem to average on 50. . Tendulkar and Ponting definitely show a slump in the later years

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanMovingAverage("./tendulkar.csv","Sachin Tendulkar")
batsmanMovingAverage("./kallis.csv","Jacques Kallis")
batsmanMovingAverage("./ponting.csv","Ricky Ponting")
batsmanMovingAverage("./sangakkara.csv","K Sangakkara")

tkps-ma-1

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

Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career. Tendulkar averages around 50, while Sangakkarra touches 55 towards the end of his career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanCumulativeAverageRuns("./tendulkar.csv","Tendulkar")

tkps-car-1

batsmanCumulativeAverageRuns("./kallis.csv","Kallis")

tkps-car-2

batsmanCumulativeAverageRuns("./ponting.csv","Ponting")

tkps-car-3

batsmanCumulativeAverageRuns("./sangakkara.csv","Sangakkara")

tkps-car-4

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

Cumulative Average strike rate of batsman in career

This function gives the cumulative strike rate of the batsman over the career

par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanCumulativeStrikeRate("./tendulkar.csv","Tendulkar")

tkps-casr-1

batsmanCumulativeStrikeRate("./kallis.csv","Kallis")

tkps-casr-2

batsmanCumulativeStrikeRate("./ponting.csv","Ponting")

tkps-casr-3

batsmanCumulativeStrikeRate("./sangakkara.csv","Sangakkara")

tkps-casr-4

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

Future Runs forecast

Here are plots that forecast how the batsman will perform in future. In this case 90% of the career runs trend is uses as the training set. the remaining 10% is the test set.

A Holt-Winters forecating model is used to forecast future performance based on the 90% training set. The forecated runs trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

Take a look at the runs forecasted for the batsman below.

  • Tendulkar’s forecasted performance seems to tally with his actual performance with an average of 50
  • Kallis the forecasted runs are higher than the actual runs he scored
  • Ponting seems to have a good run in the future
  • Sangakkara has a decent run in the future averaging 50 runs
par(mfrow=c(2,2))
par(mar=c(4,4,2,2))
batsmanPerfForecast("./tendulkar.csv","Sachin Tendulkar")
batsmanPerfForecast("./kallis.csv","Jacques Kallis")
batsmanPerfForecast("./ponting.csv","Ricky Ponting")
batsmanPerfForecast("./sangakkara.csv","K Sangakkara")

tkps-perffcst-1

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

Relative Mean Strike Rate plot

The plot below compares the Mean Strike Rate of the batsman for each of the runs ranges of 10 and plots them. The plot indicate the following Range 0 – 50 Runs – Ponting leads followed by Tendulkar Range 50 -100 Runs – Ponting followed by Sangakkara Range 100 – 150 – Ponting and then Tendulkar

frames <- list("./tendulkar.csv","./kallis.csv","ponting.csv","sangakkara.csv")
names <- list("Tendulkar","Kallis","Ponting","Sangakkara")
relativeBatsmanSR(frames,names)

tkps-relSR-1

Relative Runs Frequency plot

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

Sangakkara leads followed by Ponting

frames <- list("./tendulkar.csv","./kallis.csv","ponting.csv","sangakkara.csv")
names <- list("Tendulkar","Kallis","Ponting","Sangakkara")
relativeRunsFreqPerf(frames,names)

tkps-relRunFreq-1

Relative cumulative average runs in career

The plot below compares the relative cumulative runs of the batsmen over the career. While Tendulkar seems to lead over the others with a cumulative average of 50, we can see that Sangakkara goes over everybody else between 180-220th inning. It is likely that Sangakkarra may have overtaken Tendulkar if he had played more

frames <- list("./tendulkar.csv","./kallis.csv","ponting.csv","sangakkara.csv")
names <- list("Tendulkar","Kallis","Ponting","Sangakkara")
relativeBatsmanCumulativeAvgRuns(frames,names)

tkps-relcar-11

Relative cumulative average strike rate in career

As seen in earlier charts Ponting has the best overall strike rate, followed by Sangakkara and then Tendulkar

frames <- list("./tendulkar.csv","./kallis.csv","ponting.csv","sangakkara.csv")
names <- list("Tendulkar","Kallis","Ponting","Sangakkara")
relativeBatsmanCumulativeStrikeRate(frames,names)

tkps-relcsr-1

Check Batsman In-Form or Out-of-Form

The below computation uses Null Hypothesis testing and p-value to determine if the batsman is in-form or out-of-form. For this 90% of the career runs is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the batsman continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the batsman is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

Note Ideally the p-value should be done for a population that follows the Normal Distribution. But the runs population is usually left skewed. So some correction may be needed. I will revisit this later

This is done for the Top 4 batsman

checkBatsmanInForm("./tendulkar.csv","Sachin Tendulkar")
## *******************************************************************************************
## 
## Population size: 294  Mean of population: 50.48 
## Sample size: 33  Mean of sample: 32.42 SD of sample: 29.8 
## 
## Null hypothesis H0 : Sachin Tendulkar 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Sachin Tendulkar 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Sachin Tendulkar 's Form Status: Out-of-Form because the p value: 0.000713  is less than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./kallis.csv","Jacques Kallis")
## *******************************************************************************************
## 
## Population size: 240  Mean of population: 47.5 
## Sample size: 27  Mean of sample: 47.11 SD of sample: 59.19 
## 
## Null hypothesis H0 : Jacques Kallis 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Jacques Kallis 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Jacques Kallis 's Form Status: In-Form because the p value: 0.48647  is greater than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./ponting.csv","Ricky Ponting")
## *******************************************************************************************
## 
## Population size: 251  Mean of population: 47.5 
## Sample size: 28  Mean of sample: 36.25 SD of sample: 48.11 
## 
## Null hypothesis H0 : Ricky Ponting 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Ricky Ponting 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Ricky Ponting 's Form Status: In-Form because the p value: 0.113115  is greater than alpha=  0.05"
## *******************************************************************************************
checkBatsmanInForm("./sangakkara.csv","K Sangakkara")
## *******************************************************************************************
## 
## Population size: 193  Mean of population: 51.92 
## Sample size: 22  Mean of sample: 71.73 SD of sample: 82.87 
## 
## Null hypothesis H0 : K Sangakkara 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : K Sangakkara 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "K Sangakkara 's Form Status: In-Form because the p value: 0.862862  is greater than alpha=  0.05"
## *******************************************************************************************

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

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

par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./tendulkar.csv","Tendulkar")
battingPerf3d("./kallis.csv","Kallis")
plot-3-1par(mfrow=c(1,2))
par(mar=c(4,4,2,2))
battingPerf3d("./ponting.csv","Ponting")
battingPerf3d("./sangakkara.csv","Sangakkara")
plot-4-1dev.off()
## null device 
##           1

Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease. A sample sequence of Balls Faced(BF) and Minutes at crease (Mins) is setup as shown below. The fitted model is used to predict the runs for these values

BF <- seq( 10, 400,length=15)
Mins <- seq(30,600,length=15)
newDF <- data.frame(BF,Mins)
tendulkar <- batsmanRunsPredict("./tendulkar.csv","Tendulkar",newdataframe=newDF)
kallis <- batsmanRunsPredict("./kallis.csv","Kallis",newdataframe=newDF)
ponting <- batsmanRunsPredict("./ponting.csv","Ponting",newdataframe=newDF)
sangakkara <- batsmanRunsPredict("./sangakkara.csv","Sangakkara",newdataframe=newDF)

The fitted model is then used to predict the runs that the batsmen will score for a given Balls faced and Minutes at crease. It can be seen Ponting has the will score the highest for a given Balls Faced and Minutes at crease.

Ponting is followed by Tendulkar who has Sangakkara close on his heels and finally we have Kallis. This is intuitive as we have already seen that Ponting has a highest strike rate.

batsmen <-cbind(round(tendulkar$Runs),round(kallis$Runs),round(ponting$Runs),round(sangakkara$Runs))
colnames(batsmen) <- c("Tendulkar","Kallis","Ponting","Sangakkara")
newDF <- data.frame(round(newDF$BF),round(newDF$Mins))
colnames(newDF) <- c("BallsFaced","MinsAtCrease")
predictedRuns <- cbind(newDF,batsmen)
predictedRuns
##    BallsFaced MinsAtCrease Tendulkar Kallis Ponting Sangakkara
## 1          10           30         7      6       9          2
## 2          38           71        23     20      25         18
## 3          66          111        39     34      42         34
## 4          94          152        54     48      59         50
## 5         121          193        70     62      76         66
## 6         149          234        86     76      93         82
## 7         177          274       102     90     110         98
## 8         205          315       118    104     127        114
## 9         233          356       134    118     144        130
## 10        261          396       150    132     161        146
## 11        289          437       165    146     178        162
## 12        316          478       181    159     194        178
## 13        344          519       197    173     211        194
## 14        372          559       213    187     228        210
## 15        400          600       229    201     245        226

Analysis of Top 3 wicket takers

The top 3 wicket takes in test history are
1. M Muralitharan:Wickets: 800, Average = 22.72, Economy Rate – 2.47
2. Shane Warne: Wickets: 708, Average = 25.41, Economy Rate – 2.65
3. Anil Kumble: Wickets: 619, Average = 29.65, Economy Rate – 2.69

How do Anil Kumble, Shane Warne and M Muralitharan compare with one another with respect to wickets taken and the Economy Rate. The next set of plots compute and plot precisely these analyses.

Wicket Frequency Plot

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

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerWktsFreqPercent("./kumble.csv","Anil Kumble")
bowlerWktsFreqPercent("./warne.csv","Shane Warne")
bowlerWktsFreqPercent("./murali.csv","M Muralitharan")

relBowlFP-1

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

Wickets Runs plot

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerWktsRunsPlot("./kumble.csv","Kumble")
bowlerWktsRunsPlot("./warne.csv","Warne")
bowlerWktsRunsPlot("./murali.csv","Muralitharan")
wktsrun-1
dev.off()
## null device 
##           1

Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues. Muralitharan has taken an average of 8 and 6 wickets at Oval & Wellington respectively in 2 different innings. His best performances are at Kandy and Colombo (SSC)

bowlerAvgWktsGround("./murali.csv","Muralitharan")
avgWktshrg-1

Average wickets against different opposition

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

bowlerAvgWktsOpposition("./murali.csv","Muralitharan")
avgWktoppn-1

Wickets taken moving average

From th eplot below it can be see 1. Shane Warne’s performance at the time of his retirement was still at a peak of 3 wickets 2. M Muralitharan seems to have become ineffective over time with his peak years being 2004-2006 3. Anil Kumble also seems to slump down and become less effective.

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerMovingAverage("./kumble.csv","Anil Kumble")
bowlerMovingAverage("./warne.csv","Shane Warne")
bowlerMovingAverage("./murali.csv","M Muralitharan")

tkps-bowlma-1

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

Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers

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

kwm-bowlcaw-1

bowlerCumulativeAvgWickets("./warne.csv","Warne")

kwm-bowlcaw-2

bowlerCumulativeAvgWickets("./murali.csv","Muralitharan")

kwm-bowlcaw-3

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

Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerCumulativeAvgEconRate("./kumble.csv","Kumble")

kwm-bowlcer-1

bowlerCumulativeAvgEconRate("./warne.csv","Warne")

kwm-bowlcer-2

bowlerCumulativeAvgEconRate("./murali.csv","Muralitharan")

kwm-bowlcer-3

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

Future Wickets forecast

Here are plots that forecast how the bowler will perform in future. In this case 90% of the career wickets trend is used as the training set. the remaining 10% is the test set.

A Holt-Winters forecating model is used to forecast future performance based on the 90% training set. The forecated wickets trend is plotted. The test set is also plotted to see how close the forecast and the actual matches

Take a look at the wickets forecasted for the bowlers below. – Shane Warne and Muralitharan have a fairly consistent forecast – Kumble forecast shows a small dip

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerPerfForecast("./kumble.csv","Anil Kumble")
bowlerPerfForecast("./warne.csv","Shane Warne")
bowlerPerfForecast("./murali.csv","M Muralitharan")

kwm-perffcst-1

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

Contribution to matches won and lost

The plot below is extremely interesting
1. Kumble wickets range from 2 to 4 wickets in matches wons with a mean of 3
2. Warne wickets in won matches range from 1 to 4 with more matches won. Clearly there are other bowlers contributing to the wins, possibly the pacers
3. Muralitharan wickets range in winning matches is more than the other 2 and ranges ranges 3 to 5 and clearly had a hand (pun unintended) in Sri Lanka’s wins

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

kumblesp 
par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerContributionWonLost("kumblesp.csv","Kumble")
bowlerContributionWonLost("warnesp.csv","Warne")
bowlerContributionWonLost("muralisp.csv","Murali")

kwm-wl-1

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

Performance home and overseas

From the plot below it can be seen that Kumble & Warne have played more matches overseas than Muralitharan. Both Kumble and Warne show an average of 2 wickers overseas,  Murali on the other hand has an average of 2.5 wickets overseas but a slightly less number of matches than Kumble & Warne

par(mfrow=c(1,3))
par(mar=c(4,4,2,2))
bowlerPerfHomeAway("kumblesp.csv","Kumble")
bowlerPerfHomeAway("warnesp.csv","Warne")
bowlerPerfHomeAway("muralisp.csv","Murali")

kwm-ha-1
dev.off()
## null device 
##           1
 

Relative Wickets Frequency Percentage

The Relative Wickets Percentage plot shows that M Muralitharan has a large percentage of wickets in the 3-8 wicket range

frames <- list("./kumble.csv","./murali.csv","warne.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")
relativeBowlingPerf(frames,names)

relBowlPerf-1

Relative Economy Rate against wickets taken

Clearly from the plot below it can be seen that Muralitharan has the best Economy Rate among the three

frames <- list("./kumble.csv","./murali.csv","warne.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")
relativeBowlingER(frames,names)

relBowlER-1

Relative cumulative average wickets of bowlers in career

The plot below shows that Murali has the best cumulative average wickets taken followed by Kumble and then Warne

frames <- list("./kumble.csv","./murali.csv","warne.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")
relativeBowlerCumulativeAvgWickets(frames,names)

rbcaw-1

Relative cumulative average economy rate of bowlers

Muralitharan has the best economy rate followed by Warne and then Kumble

frames <- list("./kumble.csv","./murali.csv","warne.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")
relativeBowlerCumulativeAvgEconRate(frames,names)

rbcer-1

Check for bowler in-form/out-of-form

The below computation uses Null Hypothesis testing and p-value to determine if the bowler is in-form or out-of-form. For this 90% of the career wickets is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are caculated.

The Null Hypothesis (H0) assumes that the bowler continues to stay in-form where the sample mean is within 95% confidence interval of population mean The Alternative (Ha) assumes that the bowler is out of form the sample mean is beyond the 95% confidence interval of the population mean.

A significance value of 0.05 is chosen and p-value us computed If p-value >= .05 – Batsman In-Form If p-value < 0.05 – Batsman Out-of-Form

Note Ideally the p-value should be done for a population that follows the Normal Distribution. But the runs population is usually left skewed. So some correction may be needed. I will revisit this later

Note: The check for the form status of the bowlers indicate 1. That both Kumble and Muralitharan were out of form. This also shows in the moving average plot 2. Warne is still in great form and could have continued for a few more years. Too bad we didn’t see the magic later

checkBowlerInForm("./kumble.csv","Anil Kumble")
## *******************************************************************************************
## 
## Population size: 212  Mean of population: 2.69 
## Sample size: 24  Mean of sample: 2.04 SD of sample: 1.55 
## 
## Null hypothesis H0 : Anil Kumble 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Anil Kumble 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Anil Kumble 's Form Status: Out-of-Form because the p value: 0.02549  is less than alpha=  0.05"
## *******************************************************************************************
checkBowlerInForm("./warne.csv","Shane Warne")
## *******************************************************************************************
## 
## Population size: 240  Mean of population: 2.55 
## Sample size: 27  Mean of sample: 2.56 SD of sample: 1.8 
## 
## Null hypothesis H0 : Shane Warne 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : Shane Warne 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "Shane Warne 's Form Status: In-Form because the p value: 0.511409  is greater than alpha=  0.05"
## *******************************************************************************************
checkBowlerInForm("./murali.csv","M Muralitharan")
## *******************************************************************************************
## 
## Population size: 207  Mean of population: 3.55 
## Sample size: 23  Mean of sample: 2.87 SD of sample: 1.74 
## 
## Null hypothesis H0 : M Muralitharan 's sample average is within 95% confidence interval 
##         of population average
## Alternative hypothesis Ha : M Muralitharan 's sample average is below the 95% confidence
##         interval of population average
## 
## [1] "M Muralitharan 's Form Status: Out-of-Form because the p value: 0.036828  is less than alpha=  0.05"
## *******************************************************************************************
dev.off()
## null device 
##           1

Key Findings

The plots above capture some of the capabilities and features of my cricketr package. Feel free to install the package and try it out. Please do keep in mind ESPN Cricinfo’s Terms of Use.
Here are the main findings from the analysis above

Analysis of Top 4 batsman

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

  1. Sangakkara has the highest average, followed by Tendulkar, Kallis and then Ponting.
  2. Ponting has the highest strike rate followed by Tendulkar,Sangakkara and then Kallis
  3. The predicted runs for a given Balls faced and Minutes at crease is highest for Ponting, followed by Tendulkar, Sangakkara and Kallis
  4. The moving average for Tendulkar and Ponting shows a downward trend while Kallis and Sangakkara retired too soon
  5. Tendulkar was out of form about the time of retirement while the rest were in-form. But this result has to be taken along with the moving average plot. Ponting was clearly on the way out.
  6. The home and overseas performance indicate that Tendulkar is the clear leader. He has the highest number of matches played overseas and his performance has been consistent. He is followed by Ponting, Kallis and finally Sangakkara

Analysis of Top 3 legs spinners

The analysis of Anil Kumble, Shane Warne and M Muralitharan show the following

  1. Muralitharan has the highest wickets and best economy rate followed by Warne and Kumble
  2. Muralitharan has higher wickets frequency percentage between 3 to 8 wickets
  3. Muralitharan has the best Economy Rate for wickets between 2 to 7
  4. The moving average plot shows that the time was up for Kumble and Muralitharan but Warne had a few years ahead
  5. The check for form status shows that Muralitharan and Kumble time was over while Warne still in great form
  6. Kumble’s has more matches abroad than the other 2, yet Kumble averages of 3 wickets at home and 2 wickets overseas liek Warne . Murali has played few matches but has an average of 4 wickets at home and 3 wickets overseas.

Final thoughts

Here are my final thoughts

Batting

Among the 4 batsman Tendulkar, Kallis, Ponting and Sangakkara the clear leader is Tendulkar for the following reasons

  1. Tendulkar has the highest test centuries and runs of all time.Tendulkar’s average is 2nd to Sangakkara, Tendulkar’s predicted runs for a given Balls faced and Minutes at Crease is 2nd and is behind Ponting. Also Tendulkar’s performance at home and overseas are consistent throughtout despite the fact that he has a highest number of overseas matches
  2. Ponting takes the 2nd spot with the 2nd highest number of centuries, 1st in Strike Rate and 2nd in home and away performance.
  3. The 3rd spot goes to Sangakkara, with the highest average, 3rd highest number of centuries, reasonable run frequency percentage in different run ranges. However he has a fewer number of matches overseas and his performance overseas is significantly lower than at home
  4. Kallis has the 2nd highest number of centuries but his performance overseas and strike rate are behind others
  5. Finally Kallis and Sangakkara had a few good years of batting still left in them (pity they retired!) while Tendulkar and Ponting’s time was up
  6. While Tendulkars cumulative average stays around 50 runs, Sangakkara briefly overtakes Tendulkar towards the end of his career. Sangakkara may have finished with a better average if he had played for a few more years
  7. Ponting has the best overall strike rate followed by Sangakkara

Bowling

Muralitharan leads the way followed closely by Warne and finally Kumble. The reasons are

  1. Muralitharan has the highest number of test wickets with the best Wickets percentage and the best Economy Rate. Murali on average gas taken 4 wickets at home and 3 wickets overseas
  2. Warne follows Murali in the highest wickets taken, however Warne has less matches overseas than Murali and average 3 wickets home and 2 wickets overseas
  3. Kumble has the 3rd highest wickets, with 3 wickets on an average at home and 2 wickets overseas. However Kumble has played more matches overseas than the other two. In that respect his performance is great. Also Kumble has played less matches at home otherwise his numbers would have looked even better.
  4. Also while Kumble and Muralitharan’s career was on the decline , Warne was going great and had a couple of years ahead.
  5. Muralitharan has the best cumulative wicket rate and economy rate. Kumble has a better wicket rate than Warne but is more expensive than Warne

You can download this analysis at Introducing cricketrYou can download this analysis at Re-Introducing cricketr

Also see

1.Introducing cricket package yorkr-Part1:Beaten by sheer pace!.
2.yorkr pads up for the Twenty20s: Part 1- Analyzing team“s match performance.
3.yorkr crashes the IPL party !Part 1
4.Introducing cricketr! : An R package to analyze performances of cricketers
5.Beaten by sheer pace! Cricket analytics with yorkr in paperback and Kindle versions
6. Cricket analytics with cricketr in paperback and Kindle versions

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2.  What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
3.  Bend it like Bluemix, MongoDB with autoscaling – Part 2
4. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
5. Thinking Web Scale (TWS-3): Map-Reduce – Bring compute to data
6. Deblurring with OpenCV:Weiner filter reloaded
7. Fun simulation of a Chain in Androidhttp://www.r-bloggers.com/introducing-cricketr-an-r-package-to-analyze-performances-of-cricketers/

Beaten by sheer pace! Cricket analytics with yorkr in paperback and Kindle versions


Untitled

My book “Beaten by sheer pace! Cricket analytics with yorkr” is now available in paperback and Kindle versions. The paperback is available from Amazon (US, UK and Europe) for $ 54.95. The Kindle version can be downloaded from the Kindle store for $4.99 (Rs 332/-). Do pick up your copy. It should be a good read for a Sunday afternoon.

This book of mine contains my posts based on my R package ‘yorkr’ now in CRAN. The package yorkr uses the data from Cricsheet (http://cricsheet.org/) and can perform analysis of ODI and T20 matches. yorkr can analyze teams against a specific opposition or all oppositions, besides providing details on batsmen or bowlers individual performances The analyses include team batting partnerships, performances of batsmen against bowlers, bowlers against batsmen, bowlers best performances etc.  Individual analyses of batsmen strike rate, cumulative average, bowler economy rate, bowler moving average etc can be performances

The book includes the following chapters based on my R package yorkr.

CONTENTS
Preface
Foreword
1.Introducing cricket package yorkr: Part 1- Beaten by sheer pace!
2.Introducing cricket package yorkr: Part 2-Trapped leg before wicket!
3.Introducing cricket package yorkr: Part 3-Foxed by flight!
4.Introducing cricket package yorkr:Part 4-In the block hole!
5.yorkr pads up for the Twenty20s: Part 1- Analyzing team’s match performance!
6.yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams
7.yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions!
8.yorkr pads up for Twenty20s:Part 4- Individual batting and bowling performances!
9.yorkr crashes the IPL party ! – Part 1
10.yorkr crashes the IPL party! – Part 2
11.yorkr crashes the IPL party! – Part 3!
12.yorkr crashes the IPL party! – Part 4
13.yorkr ranks IPL batsmen and bowlers
14.yorkr ranks T20 batsmen and bowlers
15.yorkr ranks ODI batsmen and bowlers
16.yorkr is generic!
Important links
Afterword
Other books by author
About the author

Checkout my interactive Shiny apps GooglyPlus (plots & tables) and Googly (only plots) which can be used to analyze IPL players, teams and matches.

Beaten by sheer pace – Cricket analytics with yorkr


coverMy ebook “Beaten by sheer pace – Cricket analytics with yorkr’  has been published in Leanpub.  You can now download the book (hot off the press!)  for all formats to your favorite device (mobile, iPad, tablet, Kindle)  from the Leanpub  “Beaten by sheer pace!”. The book has been published in the following formats namely

  • PDF (for your computer)
  • EPUB (for iPad or tablets. Save the file cricketAnalyticsWithYorkr.epub to Google Drive/Dropbox and choose “Open in” iBooks for iPad)
  • MOBI (for Kindle. For this format, I suggest that you download & install SendToKindle for PC/Mac. You can then right click the downloaded cricketAnalyticsWithYorkr.mobi and choose SendToKindle. You will need to login to your Kindle account)

From Leanpub
UntitledLeanpub uses a variable pricing model. I have priced the book attractively (I think!). You can choose a price between FREE (limited time offer!) to $4.99 . The link is “Beaten by sheer pace!

This format works with all type Kindle device, Kindle app, Android tablet, iPad.

Checkout my interactive Shiny apps GooglyPlus (plots & tables) and Googly (only plots) which can be used to analyze IPL players, teams and matches.

yorkr is generic!


The features and functionality in my yorkr package is now complete. My R package yorkr, is totally generic, which means that the R package  can be used for all ODI, T20 matches. Hence yorkr can be used for professional or amateur ODI and T20 matches. The R package can be used for both men and women ODI, T20 international or domestic matches. The main requirement is, that the match data  be created as a Yaml file in the format Cricsheet (Required yaml format for the match data).

I have successfully used my R functions for the Indian Premier League (IPL) matches with changes only to the convertAllYamlFiles2RDataFramesXX (please see posts below)

The convertAllYamlFiles2RDataframes &convertAllYamlFiles2RDataFramesT20 will have to be customized for the names of the teams playing in the domestic professional or amateur matches. All other classes of functions namely Class1, Class2, Class 3 and Class 4 as discussed in my post Introducing cricket package yorkr-Part 1: Beaten by sheer pace can be used as is without any changes.

There are numerous professional & amateur T20 matches that are played around the world. Here are a list of domestic T20 tournaments that are played around the world (from Wikipedia). The yorkr package can be used for any of these matches once the match data is saved as yaml as mentioned above.

So do go ahead and have fun, analyzing cricket performances with yorkr!

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

Please take a look at my posts on how to use yorkr for ODI, Twenty20 matches.

  1. Introducing cricket package yorkr:Part 1- Beaten by sheer pace!
    2. Introducing cricket package yorkr:Part 2- Trapped leg before wicket!
    3.  Introducing cricket package yorkr:Part 3- foxed by flight!
    4. Introducing cricket package yorkr:Part 4-In the block hole!
    5. yorkr pads up for the Twenty20s: Part 1- Analyzing team”s match performance
    6. yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams
    7. yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions!
    8. yorkr pads up for Twenty20s:Part 4- Individual batting and bowling performances!
    9. yorkr crashes the IPL party ! – Part 1
    10. yorkr crashes the IPL party! – Part 2
    11. yorkr crashes the IPL party! – Part 3
    12. yorkr crashes the IPL party! – Part 4
    13. yorkr ranks IPL batsmen and bowlers
    14. yorkr ranks T20 batsmen and bowlers
    15. yorkr ranks ODI batsmen and bowlers