GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics

This latest update to GooglyPlusPlus2021 includes the following changes

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

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

c) Note: The Head-to-head and Overall performance tabs already include a date range selector.

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

GooglyPlusPlus2021 supports all the following T20 formats

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

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

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

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

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

GooglyPlusPlus2021 is now fully interactive!!!

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

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

Check out the Shiny app here GooglyPlusPlus2021!!!

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

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

a) KL Rahul in IPL 2021

b) KL Rahul in IPL 2020

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

a) Babar Azam’s cumulative average from 2019

b) Babar Azam’s Runs against opposition since 2019

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

a) A J Healy performance between 2010 – 2015

b) A J Healy performance between 2015 – 2020

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

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

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

a) Moving average at 25+ (Dhoni before)

The moving average actually moves up…

b) Cumulative average at 25+ (Dhoni before)

c) Cumulative Strike rate 140+ (Dhoni before)

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

e) Dhoni’s cumulative average (post 2020)

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

E) Bumrah’s performance in IPL

a) Bumrah’s performance in IPL 2020

b) Bumrah’s performance in IPL 2021

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

G) Chris Jordan’s cumulative economy rate

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

G) Ranking players

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

a) Ranking IPL batsman in IPL season 2020

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

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

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

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

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

Take GooglyPlusPlus2021 for a spin!!

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

Also see

  1. Deconstructing Convolutional Neural Networks with Tensorflow and Keras
  2. Using Reinforcement Learning to solve Gridworld
  3. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
  4. De-blurring revisited with Wiener filter using OpenCV
  5. Deep Learning from first principles in Python, R and Octave – Part 5
  6. Big Data-4: Webserver log analysis with RDDs, Pyspark, SparkR and SparklyR
  7. Practical Machine Learning with R and Python – Part 4
  8. Pitching yorkpy…on the middle and outside off-stump to IPL – Part 2
  9. What would Shakespeare say?
  10. Bull in a china shop – Behind the scenes in android

To see more posts click Index of posts

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