Tag: PSL

GooglyPlusPlus2021: Towards more picturesque analytics!

Analytics for e.g. sports analytics, business analytics or analytics in e-commerce or in other domain has 2 main requirements namely a) What kind of analytics (set of parameters,function) will squeeze out the most intelligence from the data b) How to represent the analytics so that an expert can garner maximum insight?

While it may appear that the former is more important, the latter is also equally, if not, more vital to the problem. Indeed, a picture is worth a thousand words, and often times is more insightful than a large table of numbers. However, in the case of sports analytics, for e.g. in cricket a batting or bowling scorecard captures more information and can never be represented in chart.

So, my Shiny app GooglyPlusPlus includes both charts and tables for different aspects of the analysis. In this post, a newer type of chart, popular among senior management experts, namely the 4 quadrant graph is introduced, which helps in categorising batsmen and bowlers into 4 categories as shown below

a) Batting Performances – Top right quadrant (High runs, High Strike rate)

b) Bowling Performances – Bottom right quadrant( High wickets, Low Economy Rate)

I have added the following 32 functions in this latest version of GooglyPlusPlus

A. Match Tab

All the functions below are at match level

  1. Team Runs vs SR Plot
  2. Team Wickets vs ER Plot
  3. Team Runs vs SR Power play plot
  4. Team Runs vs SR Middle overs plot
  5. Team Runs vs SR Death overs plot
  6. Team Wickets vs ER Power Play
  7. Team Wickets vs ER Middle overs
  8. Team Wickets vs ER Death overs

B. Head-to-head Tab

The below functions are based on all matches between 2 teams’

  1. Team Runs vs SR Plot all Matches
  2. Team Wickets vs ER Plot all Matches
  3. Team Runs vs SR Power play plot all Matches
  4. Team Runs vs SR Middle overs plot all Matches
  5. Team Runs vs SR Death overs plot all Matches
  6. Team Wickets vs ER Power Play plot all Matches
  7. Team Wickets vs ER Middle overs plot all Matches
  8. Team Wickets vs ER Death overs plot all Matches

C. Team Performance tab

The below functions are based on a team’s performance against all other teams

  1. Team Runs vs SR Plot overall
  2. Team Wickets vs ER Plot overall
  3. Team Runs vs SR Power play plot overall
  4. Team Runs vs SR Middle overs plot overall
  5. Team Runs vs SR Death overs plot overall
  6. Team Wickets vs ER Power Play overall
  7. Team Wickets vs ER Middle overs overall
  8. Team Wickets vs ER Death overs overall

D. T20 format Batting Analysis

This analysis is at T20 format level (IPL, Intl. T20(men), Intl. T20 (women), PSL, CPL etc.)

  1. Overall Runs vs SR plot
  2. Overall Runs vs SR Power play plot
  3. Overall Runs vs SR Middle overs plot
  4. Overall Runs vs SR Death overs plot

E. T20 Bowling Analysis

This analysis is at T20 format level (IPL, Intl. T20(men), Intl. T20 (women), PSL, CPL etc.)

  1. Overall Wickets vs ER plot
  2. Team Wickets vs ER Power Play
  3. Team Wickets vs ER Middle overs
  4. Team Wickets vs ER Death overs

These 32 functions have been added to my yorkr package and so all these functions become plug-n-play in my Shiny app GooglyPlusPlus2021 which means that the 32 functions apply across all the nine T20 formats that the app supports i.e. IPL, Intl. T20 (men), Intl. T20 (women), BBL, NTB, PSL, CPL, SSM, WBB.

Hence the multiplicative factor of the new addition is 32 x 9 = 288 additional ways of exploring match, team and player data

The data for GooglyPlusPlus is taken from Cricsheet. My shiny app GooglyPlusPlus2021 is based on my R package yorkr.

You can clone/fork GooglyPlusPlus from Github at gpp2021-10

Check out my app GooglyPlusPlus2021 and analyze batsmen, bowlers, teams, overall performance. The data for all the nine T20 formats have been updated to include the latest data.

Hence, the app is just in time for the IPL mega auction. You should be able to analyse players in IPL, Intl. T20 or in any of the other formats from where they could be drawn and check out their relative standings

I am including some random plots to demonstrate the newly minted functions

Note 1: All plots are interactive. The controls are on the top right. You can hover over data, zoom-in, zoom-out, compare data etc by choosing the appropriate control. To know more about how to use the interactive charts see GooglyPlusPlus2021 is now fully interactive!!!

You can also check my short video on how to navigate interactive charts

Note 2: To know about Powerplay, Middle overs and Death over analysis see my post GooglyPlusPlus2021 now with power play, middle and death over analysis

Note 3: All tabs(except Match tab) now include Date range pickers to focus on the period of interest. See my post GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics

I) Match tab

New Zealand vs Australia (2021-11-14)

New Zealand batting, except K Williamson, the rest did not fire as much

For Australia, Warner, Maxwell and Marsh played good knocks to wrest control

II) Head-to-head

a) Wickets vs ER during Power play of Mumbai Indians in all matches against Chennai Super Kings (IPL)

b) Karachi Kings Runs vs SR during middle overs against Multan Sultans (PSL)

c) Wickets vs ER during death overs of Barbados Tridents in all matches against Jamaica Tallawahs (CPL)

III) Teams overall batting performance

India’s best T20 performers in Power play since 2018 (Intl. T20)

e) Australia’s best performers in Death overs since Mar 2017 (Intl. T20)

f) India’s Intl. T20 (women) best Runs vs SR since 2018

g) England’s Intl. T20 (women) best bowlers in Death overs

IV) Overall Batting Performance across T20

This tab gives the batsmen’s rank and overall batting performance across the T20 format.

a) Why was Hardik Pandya chosen, and why this was in error?

Of course, it provides an insight into why Hardik Pandya was chosen in India’s World cup team despite poor performances recently. Here are the best Intl. T20 death over batsmen

Of course, we can zoom in to get a better look

This is further substantiated when we performances in IPL

However, if you move the needle forward a year at a time, you see Hardik Pandya’s performance drops significantly

and further down

Rather, Dinesh Karthik, Sanju Samson or Ruturaj Gaikwad would have been better options

b) Best batsmen Intl. T20 (women) in Power play since 2018

V) Overall bowling performance

This tab gives the bowler’s rank and overall bowling performance in Power play, middle and death overs across all T20 formats

a) Intl. T20 (men) best bowlers in Power Play from 2019 (zoomed in)

b) Intl. T20(men) best bowlers in Death overs since 2019

c) Was B. Kumar a good choice for India team in World cup?

Bhuvi was one of India’s best bowler in Power play only if we go back to the beginning of time

i) From 2008

But if we move forward to 2020 onwards we see Arshdeep Singh or D Chahar would have been a better choice

ii) From 2020 onwards

iii) 2021 onwards

Hence D Chahar & Arshdeep Singh are the natural choice moving forwards for India

iv) T20 Best batsman

If we look at Intl. T20 performances since 2017, Babar Azam leads the pack, however his Strike rate needs to move up.

v) T20 best bowlers

As mentioned above go ahead and give GooglyPlusPlus2021 a spin!!!

You can download/fork the code for the Shiny app from Github at gpp2021-10

Also see

  1. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
  2. Deep Learning from first principles in Python, R and Octave – Part 6
  3. Deconstructing Convolutional Neural Networks with Tensorflow and Keras
  4. Big Data 6: The T20 Dance of Apache NiFi and yorkpy
  5. What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
  6. Sea shells on the seashore
  7. Practical Machine Learning with R and Python – Part 4
  8. Benford’s law meets IPL, Intl. T20 and ODI cricket
  9. Video presentation on Machine Learning, Data Science, NLP and Big Data – Part 1
  10. How to program – Some essential tips

To see all posts click Index of posts

GooglyPlusPlus2021:ICC WC T20:Pavilion-view analytics as-it-happens!

This year 2021, we are witnessing a rare spectacle in the cricketing universe, where IPL playoffs are immediately followed by ICC World Cup T20. Cricket pundits have claimed such a phenomenon occurs once in 127 years! Jokes apart, the World cup T20 is underway and as usual GooglyPlusPlus is ready for the action.

GooglyPlusPlus will provide near-real time analytics, by automatically downloading the latest match data daily, processing and organising the match data into appropriate folders so that my R package yorkr can slice and dice the data to provide the pavilion-view analytics.

The charts capture all the breathless, heart-pounding, and nail-biting action in great details in the many tables and plots. Every table and chart tell a story. You just have to ‘read between the lines!’

GooglyPlusPlus2021 will update itself automatically every day, so the data will be current and you can analyse all matches upto the previous day, along with the historical performances of the teams. So make sure you check it everyday.

Note:

  1. All charts are interactive. To know how to use the interactive charts see my post GooglyPlusPlus2021 is now fully interactive!!!
  2. The are 5 tabs for each of the formats supported by GooglyPlusPlus2021 which now supports IPL, Intl. T20(men), Intl. T20(women), BBL, NTB, PSL, CPL, SSM, WBB. Besides, it also supports ODI (men) and ODI (women)
  3. Each of the formats have 5 tabs – Batsman, Bowler, Match, Head-to-head and Overall Performace.
  4. All T20 formats also include a ranking functionality for the batsmen and bowlers
  5. You can now perform drill-down analytics for batsmen, bowlers, head-to-head and overall performance based on date-range selector functionality. The ranking tabs also include date range selector granular analysis. For more details see GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics

Try out GooglyPlusPlus2021 here GooglyPlusPlus2021!!

You can clone fork the code from Github gpp2021-8

I am including some random screenshots of things that can be done with GooglyPlusPlus2021

A. Papua New Guinea vs Oman (2021-10-17)

a. Batting partnership

B. Match worm chart (New Papua Guinea v Oman)

This was a no contest as Oman cruised to victory

C. Scotland vs Bangladesh (2021-10-17)

a. Scorland upset Bangladesh

b. March worm chart (Scotland vs Bangladesh)

Fortunes see-sawed one way, then another, as can be seen in the match worm chart

C. Netherlands vs Ireland (2021-10-18)

a. Batman vs Bowler

D. Historical performance head-to-head

a. Sri Lanka vs West Indies (2019-2021) – Batting partnerships

b. India vs England (2018 – 2021) – Bowling scorecard

c) Australia vs South Africa – Team wicket opposition

E) Overall performance

a. Pakistan batting scorecard since 2019

a. Win loss of Australia since 2019

F) Batsman Performance

a. PR Stirling’s runs against opposition since 2019

b. KJ Brien’s cumulative average runs since 2019

G. Bowler performance

a. PWH De Silva’s wicket prediction since 2019

b. T Shamsi’s cumulative average wickets since 2019

H. Ranking Intl. T20 batsman since 2019

a. Runs over Strike rate

b. Strike rate over runs

I. Ranking bowlers since 2019

a. Wickets over Economy rate

b. Economy rate over wickets

As mentioned above GooglyPlusPlus2021 will be updated daily automatically, so you won’t miss any analytic action.

Do give GooglyPlusPlus2021 a spin!

Clone/fork the code for the Shiny app from Github gpp2021-8

You may also like

  1. Natural language processing: What would Shakespeare say?
  2. Literacy in India – A deepR dive
  3. Practical Machine Learning with R and Python – Part 5
  4. Big Data 7: yorkr waltzes with Apache NiFi
  5. Getting started with Tensorflow, Keras in Python and R
  6. Deep Learning from first principles in Python, R and Octave – Part 7
  7. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
  8. Video presentation on Machine Learning, Data Science, NLP and Big Data – Part 1

To see all post click Index of posts

GooglyPlusPlus2021 is now fully interactive!!!

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

There are 2 main updates in this latest version of GooglyPlusPlus2021

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

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

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

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

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

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

Each of these formats have 7 tabs which are

— Analyze batsman

— Analyze bowlers

— Analyze match

— Head-to-head

— Team vs all other teams

— Rank batsmen

— Rank bowlers

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

Try out the interactive GooglyPlusPlus2021 now!!!

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

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

a) Batsman Analysis – Runs vs Deliveries (IPL)

Mouse-over/Hover

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Avoiding the clutter

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

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

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

Steps to avoid clutter in stacked bar plots

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

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

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

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

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

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

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

l) Win-loss against all teams (CPL)

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

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

Batting partnerships of Indian ODI women against all other teams

n) Ranking of batsmen (IPL 2021)

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

o) Ranking of bowlers (IPL 2021)

Clone/download the Shiny app from Github at GooglyPlusPlus2021

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

Knock yourself out!

Enjoy enjaami!!!

See also

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

To see all posts click Index of posts

GooglyPlusPlus2021 bubbles up top T20 players in all formats!

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

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

To see all post click Index of posts

 

 

GooglyPlusPlus2021 interactively ranks T20 batsmen and bowlers!!!

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

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

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

Take GooglyPlusPlus2021 for a test drive!!!

You can clone/fork GooglyPlusPlus2021 from Github

Here are a few scenarios from GooglyPlusPlus2021

A) Ranking batsmen

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

B) Identifying batsmen of potential and promise

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

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

C) Ranking T20 bowlers (men)

D) Ranking NTB Batsmen

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

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

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

Do give GooglyPlusPlus2021 a spin!!

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

Watch this space!!

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

To see all posts click Index of posts

Introducing GooglyPlusPlus!!!

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

From Jonathan Livingstone Seagull, by Richard Bach

Introduction

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

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

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

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

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

Check out GooglyPlusPlus Shiny at GooglyPlusPlus

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

A. Highlights of GooglyPlusPlus.

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

 

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

1.  Indian Premier League (IPL)

a. IPL batsman – Batsman Runs vs Deliveries

 

b. IPL Match – Match  batting scorecard

 

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

 

 

 

d. Overall Performance – Team Bowling Scorecard Overall

 

 

 

2. International T20 Men

a. Batsman Function- Runs vs Strike rate

 

 

 

b. Bowler Function – Mean Economy Rate

 

 

3. International T20 (Women)

a.Batsman Functions – Batsman Cumulative Average Runs

 

 

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

 

 

 

 

 

4. Big Bash League (BBL)

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

 

b.  Overall Performance – Team batsmen vs bowlers

 

 

5. Natwest T20 (NTB)

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

 

 

 

b. Batsman Runs vs Deliveries

 

 

6. Pakistan Super League (PSL)

a. Overall Performance – Batsmen Partnership

 

b. Bowling Scorecard

 

7. Women’s Big Bash League (WBBL)

a. Bowler wicket against opposition

 

 

8. One Day International (ODI) Men

a. Batsman Runs Against Opposition

 

b. Team Batsmen against bowlers

 

 

9. One Day International (ODI) women)

a. Match Batting Scorecard

b. Batsman Cumulative Strike Rate

 

 

 

Conclusion

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

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

You can clone/fork the code from Github at GooglyPlusPlus

Hope you have fun with GooglyPlusPlus!!

You may also like

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

To see all posts click Index of posts

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

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

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

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

 

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

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

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

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

Screenshot 2020-05-16 at 12.32.07 PM

-This post has been published at RPubs at yorkrWrapUpT20formats

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

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

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

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

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

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

A. Big Bash League (BBL)

A1.Batting Scorecard

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

A2.Batting Partnership

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

B. Natwest Super League

B1.Team Match Partnership

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

B2.Batsmen vs Bowlers

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

C. Pakistan Super League (PSL)

C1.Individual performance of Babar Azam

library(grid)
library(gridExtra)

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

C2.Bowling performance against all oppositions

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

D. Women Big Bash League

D1.Bowling scorecard

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

D2.Team batsmen partnerships

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

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

1a. Ranking Big Bash League (BBL) batsman

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

1b. Ranking Big Bash League (BBL) bowlers

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

2a. Ranking Natwest T20 League (NTB) batsman

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

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

2b. Ranking Natwest T20 League (NTB) bowlers

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

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

3a. Ranking Pakistan Super League (PSL) batsman

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

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

3b. Ranking Pakistan Super League (PSL) bowlers

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

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

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

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

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

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

Conclusion

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

Hope you have fun!!!

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To see all posts click Index of Posts