Then, Now(IPL 2022), Beyond : Insights from GooglyPlusPlus

IPL 2022 has just concluded and yet again, it is has thrown a lot of promising and potential youngsters in its wake, while established players have fallen! With IPL 2022, we realise that “Sceptre and Crown must tumble down” and that ‘the glories‘ of form and class like everything else are “shadows not substantial things” (Death the Leveller by James Shirley).

So King Kohli had to kneel, and hitman’ himself got hit. Rishabh Pant, Jadeja also had a poor season. On the contrary there were several youngsters who shone like Abhishek Sharma, Tilak Verma, Umran Malik or a Mohsin Khan

This post is about my potential T20 Indian players for the World Cup 2022 and beyond.

The post below includes my own analysis and thoughts. Feel free to try out my Shiny app GooglyPlusPlus and draw your own conclusions.

You can also view the analyais as a youtube video at Insights from GooglyPlusPlus

How often we hear that data by itself is useless, unless we can draw insights from it? This is a prevailing theme in the corporate world and everybody uses all sorts of tools to analyse and subsequently draw insights. Data analysis can be done in many ways as data can be sliced, diced, chopped in a zillion ways. There are many facets and perspectives to analysing data. Creating insights is easy, but arriving at actionable insights is anything but. So, the problem of selecting the best 11 is difficult as there are so many ways to look at the analysis. My Shiny app GooglyPlusPlus based on my R package yorkr can analyse data in several ways namely

  1. Batsman analysis
  2. Bowler analysis
  3. Match analysis
  4. Team vs team analysis
  5. Team vs all teams analysis
  6. Batsman vs bowler and vice versa
  7. Analysis of in 3,4,5 in power play, middle and death overs

GooglyPlusPlus uses my R package yorkr which has ~ 160 functions some which have several options. So, we can say roughly there are ~500 different ways that analysis can be done or in other words we can gather almost roughly 500+ different insights, not to mention that there are so many combinations of head-on matches and one-vs-all matches.

So generating insights or different ways of analysis data alone is not enough. The question is whether we can get a consolidated view from the different insights. In this post, I try to identify the best contenders for the Indian T20 team. This is far more difficult than it looks. Do you select players on past historical performance or do you choose from the newer crop of players, who have excelled in the recent IPL season. I think this boils down the typical situation in any domain. In engineering, we have tradeoffs – processing power vs memory tradeoff, throughput vs latency tradeoff or in the financial domain it is cost vs benefit or risk vs reward tradeoff. For team selection, the quandary is, whether to choose seasoned players with good historical performance but a poor performances in recent times or go with youngsters who have played with great courage and flair in this latest episode of IPL 2022. Hence there is a tradeoff between reliable but below average performance or risky but superlative performances of new players.

For this I base my potential list from

  • Then (past history of batsmen & bowlers) – I have chosen the performance of batsmen and bowlers in the last 3 years. With we can arrive at those who have had reasonably reliable performance for the last 3 years
  • Now (IPL 2022) – Performance in the current season IPL 2022

A. Then (Jan 2020 – May 2022) – Batsmen analysis

In this section I analyse the performances of batsmen and bowlers from Jan 2022 – May 2022. This is done based on ranking, and plots of Runs vs Strike Rate in Power Play, Middle and Death overs

Also I analyse bowlers based on the overall rank from Jan 2022- May 2022. Further more analysis is done on Wickets vs Economy Rate overall and in Power Play, Middle and Death overs

a. Ranks of batsmen (Runs over Strike Rate) : Jan 2020 – May 2022

The top batsmen consistency wise

[KL Rahul, Shikhar Dhawan, Ruturaj Gaikwad, Ishan Kishan, Shubman Gill, Suryakumar Yadav, Sanju Samson, Mayank Agarwal, Prithvi Shaw, Devdutt Padikkal, Nitish Rana, Virat Kohli, Shreyas Iyer, Ambati Rayadu, Rahul Tripathi, Rishabh Pant, Rohit Sharma, Hardik Pandya]

b. Ranks of batsmen (Strike Rate over Runs) : Jan 2020 – May 2022

The most consistent players from the Strike Rate perspective are

The batsmen with best Strike Rate in the last 3 years are

[Dinesh Karthik, Prithvi Shaw, Hardik Pandya, Rishabh Pant, Sanju Samson, Rahul Tripathi, Suryakumar Yadav, Nitish Rana, Mayank Agarwal, Krunal Pandya, MS Dhoni, Shikhar Dhawan, Ishan Kishan, KL Rahul]

c.Best Batsmen Runs vs SR : Jan 2020 – May 2022

The best batsmen should have a reasonable combination of Runs and SR. The best batsmen are

[KL Rahul, Shikhar Dhawan, Ruturaj Gaikwad, Ishan Kishan, Shubman Gill , Sanju Samson, Suryakumar Yadav, Shubman Gill, Mayank Agarwal, Prithvi Shaw, Nitish Rana, Hardik Pandya, Rishabh Pant, Rahul Tripathi,

d. Best batsmen Runs vs SR in Powerplay: Jan 2020 – May 2022

The best players in Power play

The best players in Power play in the last 3 years are

[KL Rahul, Prithvi Shaw, Rohit Sharma, Devdutt Padikkal, Mayank Agarwal, Virat Kohli, Ishan Kishan, Yashashvi Jaiswal, Wriddhiman Saha, Rahul Tripathi, Sanju Samson, Robin Uthappa, Venkatesh Iyer, Nitish Rana,Suryakumar Yadav, Abhishek Sharma Shreyas Iyer ]

e. Best batsmen Runs vs SR in Middleovers: Jan 2020 – May 2022

The most consistent players in the last 3 years in the middle overs are

[KL Rahul, Sanju Samson, Shikhar Dhawan, Rishabh Pant, Nitish Rana, Shreyas Iyer, Shubman Gill, Ishan Kishan, Devdutt Padikkal, Rahul Tripathi, Ruturaj Gaikwad, Shivam Dube, Hardik Pandya]

f. Best batsmen Runs vs SR in Death overs: Jan 2020 – May 2022

The best batsmen in death overs are

[Dinesh Karthik, Ravindra Jadeja, Hardik Pandya, Rahul Tewatia, MS Dhoni, KL Rahul, Rishabh Pant, Suryakumar Yadav, Ambati Rayadu, Virat Kohli, Nitish Rana, Shikhar Dhawan, Ruturaj Gaikwad, Ishan Kishan]

B) Now (IPL 2022) – Batsmen analysis

IPL 2022 just finished and clearly brings out the batsmen who are in great nick. It is always going to be a judgment call of whether to go for ‘old reliable’ or ‘new and awesome’.

a. Ranks of batsmen (Runs over Strike Rate) : IPL 2022

The best batsmen this season in Runs over Strike rate are

The best batsmen are

[KL Rahul, Shikhar Dhawan, Hardik Pandya, Deepak Hooda, Shubman Gill, Rahul Tripathi, Abhishek Sharma, Ishan Kishan, Wriddhiman Saha, Shreyas Iyer, Tilak Verma, Ruturaj Gaikwad, Sanju Samson, Shivam Dube]

b. Ranks of batsmen (Strike Rate over Runs) : IPL 2022

The batsmen with the best strike rate are

[Dinesh Karthik, Rishabh Pant, Rahul Tewathia, Rahul Tripathi, Sanju Samson, R Ashwin, Deepak Hooda, MS Dhoni, Nitish Rana, Riyan Parag, Shreya Iyer]

c.Best Batsmen Runs vs SR :IPL 2022

From an overall performance the following batsmen shone this season

[KL Rahul, Shikhar Dhawan, Shubman Gill, Hardik Pandya, Abhishel Sharma, Deepak Hooda, Rahul Tripathi, Tilak Verma, Shreya Iyer, Nitish Rana, Sanju Samson, Rishabh Pant]

d. Best batsmen Runs vs SR in Powerplay: IPL 2022

Top batsmen in Power play in IPL 2022

[Abhishek Sharma, Shikhar Dhawan, Rohit Sharma, Ishan Kishan, Shubman Gill, Prithvi Shaw, Wriddhiman Saha, Ishan Kishan, KL Rahul, Ruturaj Gaikwad, Virat Kohli, Yashasvi Jaiswal, Mayank Agarwal, Robin Uthappa, Sanju Samson, Nitish Rana]

e. Best batsmen Runs vs SR in Middleovers: IPL 2022

Best batsmen in middle overs in IPL 2022

[Deepak Hooda, Hardik Pandya, Tilak Verma, KL Rahul, Sanju Samson, Rishabh Pant, Shubman Gill, Ambati Rayudu, Suryaprakash Yadav, Shikhar Dhawan, Ruturaj Gaikwad]

f. Best batsmen Runs vs SR in Death overs: IPL 2022

Top batsmen in death overs in IPL 2022

[Dinesh Karthik, Rahul Tewatia, MS Dhoni, KL Rahul, Azar Patel, Washington Sundar, R Ashwin, Hardik Pandya, Ayush Badoni, Shivam Dube, Suryakumar Yadav, Ravindra Jadeja, Sanju Samson]

Overall Batting Performance in season

Kohli peaked in 2016 and from then on it has been a downward slide (see below)

Taking a look at Kohli’s moving average it is clear that he is past his prime and it will take a herculean effort to regain his lost glory

Similarly, Rohit Sharma’s moving average is constantly around ~30 as seen below

The cumulative average of Rohit Sharma is shown below

Comparing KL Rahul, Shikhar Dhawan, Rohit Sharma and V Kohli we see that KL Rahul and Shikhar Dhawan have had a much superior performance in the last 2-3 years. Rohit has averaged about ~25 runs every season.

Comparing the 4 wicket-keeper batsmen Sanju Samson, Rishabh Pant, Ishan Kishan and Dinesh Karthik from 2016

i) Runs over Strike Rate

We see that Pant peaked in 2018 but has not performed as well since. In the last 2 years Sanju Samson and Ishan Kishan have done well

ii) Strike Rate over Runs

For the last couple of seasons Rishabh Pant and Dinesh Kartik top the strike rate over the other 2

Similar analysis can be done other combinations of batsmen

Choosing the best batsmen from the above, my top 5 batsmen would be

  1. KL Rahul
  2. Shikhar Dhawan
  3. Prithvi Shaw, Ruturaj Gaikwad, Ishan Kishan
  4. Sanju Samson, Shreyas Iyer, Shubman Gill, Shivam Dube,
  5. Abhishek Sharma, Tilak Verma, Rahul Tripathi, Suryakumar Yadav, Deepak Hooda
  6. Rishabh Pant, Dinesh Karthik

Personally, I feel Ishan Kishan and Shreyas Iyer are a little tardy while playing express speeds, as compared to Sanju Samson or Rishabh Pant.

If you notice, I have not included both Virat Kohli or Rohit Sharma who have been below par for some time

C. Then (Jan 2020 – May 2022) – Bowler analysis

This section I analyse the performances of bowlers from Jan 2022 – May 2022. This is done based on ranking, and plots of Wickets vs Economy Rate in Power Play, Middle and Death overs

a. Ranks of bowlers (Wickets over Economy Rate) : Jan 2020 – May 2022

The most consistent bowlers Wickets over Economy Rate for the last 3 years are

[YS Chahal, Jasprit Bumrah, Mohammed Dhami, Harshal Patel, Shardul Thakur, Arshdeep Singh, Rahul Chahar, Varun Chakravarthy, Ravi Bishnoi, Prasidh Krishna, R Ashwon, Axar Patel, Mohammed Siraj, Ravindra Jadeja, Krunal Pandya, Rahul Tewatia]

b. Ranks of bowlers (Economy Rate over Wickets) : Jan 2020 – May 2022

The most economical bowlers since 2020 are

[Axar Patel, Krunal Pandya, Jasprit Bumrah, CV Varun, R Ashwin, Ravi Bishnoi, Rahul Chahar, YS Chahal, Ravindra Jadeja, Harshal Patel, Mohammed Shami, Mohammed Siraj, Rahul Tewatia, Arshdeep Singh, Prasidh Krishna, Shardul Thakur]

c.Best Bowlers Wickets vs ER : Jan 2020 – May 2022

The best bowlers Wickets vs ER will be in the bottom right quadrant. The most consistent and reliable bowlers are

[YS Chahal, Jasprit Bumrah, Mohammed Shami, Harshal Patel, CV Arun, Ravi Bishnoi, Rahul Chahar, R Ashwin, Axar Patel]

d. Best bowlers Wickets vs ER in Powerplay: Jan 2020 – May 2022

The best bowlers in Powerplay are

[Mohammed Shami, Deepak Chahar, Mohammed Siraj, Arshdeep Singh, Jasprit Bumrah, Avesh Khan, Mukesh Choudhary, Shardul Thakur, T Natarajan, Bhuvaneshwar Kumar, WashingtonSundar, Shivam Mavi]

e. Best bowlers Wickets vs ER in Middle overs : Jan 2020 – May 2022

The most reliable performers in middle overs from 2020-2022 are

[YS Chahal, Rahul Chahr, Ravi Bishnoi, Harshal Patel, Axar Patel, Jasprit Bumrah, Umran Malik, R Ashwin, Avesh Khan, Shardul Thakur, Kuldeep Yadav]

f. Best bowlers Wickets vs ER in Death overs : Jan 2020 – May 2022

The most reliable bowlers are

[Harshal Patel, Mohammed Shami, Jasprit Bumrah, Arshdeep Singh, T Natarajan, Avesh Khan, Shardul Thakur, Bhuvaneshwar Kumar, Shivam Mavi, YS Chahal, Prasidh Krishna, Mohammed Siraj, Chetan Sakariya]

B) Now (IPL 2022) – Bowler analysis

a. Ranks of bowlers (Wickets over Economy Rate) : IPL 2022

The best bowlers in IPL 2022 when considering Wickets over Economy Rate

[YS Chahal, Umran Malik, Prasidh Krishna, Mohammed Shami, Kuldeep Yadav, Harshal Patel, T Natarajan, Avesh Khan, Shardul Thakur, Mukesh Choudhary, Jasprit Bumrah, Ravi Bishnoi]

a. Ranks of bowlers (Economy Rate over Wickets) : IPL 2022

The most economical bowlers in IPL 2022 are

[Axar Patel, Jasprit Bumrah, Krunal Pandya, Umesh Yadav, Bhuvaneshwar Kumar, Rahul Chahr, Harshal Patel, Arshdeep Singh, R Ashwion, Umran Malik, Kuldeep Yadav, YS Chahal, Mohammed Shami, Avesh Khan, Prasidh Krishna]

c.Best Bowlers Wickets vs ER : IPL 2022

The overall best bowlers in IPL 2022 are

[YS Chahal, Umran Malik, Harshal Patel, Prasidh Krishna, Mohammed Shami, Kuldeep Yadav, Avesh Khan, Jasprit Bumrah, Umesh Yadav, Bhuvaneshwar Kumar, Arshdeep Singh, R Ashwin, Rahul Chahar, Krunal Pandya]

d. Best bowlers Wickets vs ER in Powerplay: IPL 2022

The best bowlers in IPL 2022 in Power play are

[Mukesh Choudhary, Mohammed Shami, Prasidh Krishna, Umesh Yadav, Avesh Khan, Mohsin Khan, T Natarajan, Jasprit Bumrah, Yash Dayal, Mohammed Siraj]

d. Best bowlers Wickets vs ER in Middle overs: IPL 2022

The best bowlers in IPL 2022 during middle overs

The best bowlers are

[YS Chahal, Umran Malik, Kuldeep Yadav, Harshal Patel, Ravi Bishnoi, R Ashwin]

e. Best bowlers Wickets vs ER in Death overs: IPL 2022

The best bowlers in death overs in IPL 2022 are

[T Natarajan, Harshal Patel, Bhuvaneshwar Kumar, Mohammed Shami, Jasprit Bumrah, Shardul Thakur, YS Chahal, Prasidh Krishna, Avesh Khan, Mohsin Khan, Yash Dayal, Umran Malik, Arshdeep Singh]

Typically in a team we would need a combination of 4 bowlers (2 fast & 2 spinner or 3 fast and 1 spinner) with an additional player who is all rounder.

For 4 bowlers we could have

  1. JJ Bumrah
  2. Mohammed Shami, Umran Malik, Bhuvaneshwar Kumar, Umesh Yadav
  3. Arshdeep Singh, Avesh Khan, Mohsin Khan, Harshal Patel
  4. YS Chahal, Ravi Bishnoi, Rahul Chahar, Axar Patel
  5. Ravindra Jadeja, Hardik Pandya, Rahul Tewathia, R Ashwin

i) Performance comparison (Wickets over Economy Rate)

Bumrah had the best season in 2020. He has been doing quite well and has been among the wickets

ii) Performance comparison (Economy Rate over Wickets)

Bumrah has the best Economy Rate

We can do a wicket prediction of bowlers. So for example for Bumrah it is

iii) Performance evaluation (Wickets over Economy Rate)

Harshal Patel followed by Avesh Khan had a good season last year, but Umran Malik pipped them this year (see below)

iv) Performance analysis of spinners

a. Wickets over Economy Rate: 2022

Chahal has the best season followed by Bishnoi and Chahar this season

b) Economy Rate over WIckets

Axar Patel has the best economy rate followed by Rahul Chahar

Conclusion

The above post identified the best candidates for the Indian team in the future and beyond. In my T20 list, I have neither included Virat Kohli or Rohit Sharma. The data in T20 clearly indicates that they have had their days. There is a lot more talent around. The tradeoff is a little risk for a greater potential performance. My list would be

  1. KL Rahul
  2. Shikhar Dhawan
  3. Ruturaj Gaikwad, Prithvi Shaw, Rahul Tripathi
  4. Suryakumar Yadav, Shreyas Iyer, Abhishek Sharma, Deepak Hooda
  5. Sanju Samson (Wicket keeper/captain)/ Rishabh Pant/Dinesh Karthik
  6. Hardik Pandya, Ravindra Jadeja, Rahul Tewathia
  7. Jasprit Bumrah
  8. Mohammed Shami, Bhuvaneshwar Kumar, Umran Malik
  9. Arshdeep Singh, Avesh Khan, Harshal Patel
  10. YS Chahal
  11. Axar Patel, Ravi Bishnoi, Rahul Chahar

You may agree/ disagree with my list. Feel free to do your analysis with GooglyPlusPlus and come to your own conclusions

This analysis is also available on youtube Insights from GooglyPlusPlus

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IPL 2022: Near real-time analytics with GooglyPlusPlus!!!

It is that time of the year when there is “a song in the air, the lark’s on the wing, and the snail’s on the the thorn“. Yes, it is the that time of year when the grand gala event of IPL 2022 is underway. So, I managed to wake myself from my Covid-induced slumber, worked up my ‘creaking bones‘ and cranked up the GooglyPlusPlus machinery.

So now, every morning, a scheduled CRON tab entry will automatically download the previous night’s match data from Cricsheet, unzip, process and transform it into the necessary format required by my R package yorkr, and make it available to my Shiny app GooglyPlusPlus. Hence the data is current and you have access to ‘analytics-in-the-now’!.

As you know in 2021, I added a lot of new features to GooglyPlusPlus, new tabs to do even more. analytics – or in other words there is “more GooglyPlusPlus per click!!”. So now, you have the following

  • Batsman tab: For detailed analysis of batsmen
  • Bowler tab: For detailed analysis of bowlers
  • Match tab: Analysis of individual matches, plot of Runs vs SR, Wickets vs ER in power play, middle and death overs
  • Head-to-head tab: Detailed analysis of team-vs-team batting/bowling scorecard, batting, bowling performances, performances in power play, middle and death overs
  • Team performance tab: Analysis of team-vs-all other teams with batting /bowling scorecard, batting, bowling performances, performances in power play, middle and death overs
  • Optimisation tab: Allows one to pit batsmen vs bowlers and vice-versa. This tab also uses integer programming to optimise batting and bowling lineup
  • Batting analysis tab: Ranks batsmen using Runs or SR. Also plots performances of batsmen in power play, middle and death overs and plots them in a 4×4 grid
  • Bowling analysis tab: Ranks bowlers based on Wickets or ER. Also plots performances of bowlers in power play, middle and death overs and plots them in a 4×4 grid

Also note all these tabs and features are available for all T20 formats namely IPL, Intl. T20 (men, women), BBL, NTB, PSL, CPL, SSM.

Note: All charts are interactive, which means that you can hover, zoom-in, zoom-out, pan etc on the charts

The latest avatar of GooglyPlusPlus2022 is based on my R package yorkr with data from Cricsheet.

Go ahead, give GooglyPlusPlus a try!!!

To know all the new features and how to use them, check out these posts

  1. Ranking of batsmen, bowlers – GooglyPlusPlus2021 interactively ranks T20 batsmen and bowlers!!!
  2. Interactive charts – GooglyPlusPlus2021 is now fully interactive!!!
  3. Detailed batsmen/bowler analytics – GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics
  4. Addition of Date Range picker to charts – GooglyPlusPlus2021 adds new bells and whistles!!
  5. Analysis of power play, middle and death overs across players, teams – GooglyPlusPlus2021 now with power play, middle and death over analysis
  6. Analysis based on 4 x 4 grid of players – GooglyPlusPlus2021: Towards more picturesque analytics!
  7. Optimisation of batsmen/bowlers – GooglyPlusPlus2022 optimizes batting/bowling lineup

Here are some random analysis that can be done by GooglyPlusPlus across the tabs. Note the app will be updated daily and the analytics will be current throughout the season of IPL 2022

A) Match tab

a) GT vs DC – 2 Apr 2022

Runs vs SR – Gujarat Titans

b) CSK vs LSG – 31 Mar 2022

Runs across 20 overs

c) KKR vs PBKS -Match wicket worm chart – 1 Apr 2022

B) Batsmen tab

a) Faf Du Plessis – Runs vs Deliveries

b) Sanju Samson – Runs against opposition

C) Bowler’s tab

a) D J Bravo – No of deliveries to wicket

b) Trent Boult – Wickets at Venues

D) Head-to-head tab

a) DC vs MI – Mar -2019 till date : Batting scorecard

b) CSK vs KKR – Jan 2019 till date : Runs vs SR

E) Team vs All Teams tab

a) Punjab Kings vs all Teams – Wickets vs ER in Power play

b) Rajasthan Royals vs all Teams : Jan 2019 till date : Runs vs SR in Power play

F) Optimisation tab

a) Batsmen vs Bowlers

b) Bowlers vs batsmen

G) Batting analysis

This tab is for ranking batsmen

a) Batsmen rank from 2019 till date (Runs over SR)

b) Overall Runs vs SR (Jan 2020 till date)

Best batsmen in top right quadrant

zooming in on the above (right-most)

H) Bowling analysis tab

a) Best middle over bowlers in IPL (2019 onwards)

The bottom right quadrant are the best bowlers

b) Best bowlers in death overs (bottom-right)

Check out GooglyPlusPlus!!!

Also see

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More player analysis with gganimate()

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

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

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

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

The animated charts are

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

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

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

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

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

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

  1. Runs vs SR – IPL

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

2. Wickets vs ER – IPL

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

3. Runs vs SR in Power play – IPL

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

4. Runs vs Strike rate in Middle overs – IPL

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

5.Runs vs SR in Death overs – IPL

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

6. Wickets vs ER in Power play – IPL

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

7. Wickets vs ER in Middle overs – IPL

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

8. Wickets vs ER in Death overs – IPL

Bumrah & Rabada top performers

9. Runs vs SR – T20 (men)

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

10. Wickets vs ER – T20 (men)

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

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

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

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

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

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

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

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

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

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

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

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

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

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To see all posts click Index of posts

Analyzing player performance with animated charts!

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

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

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

You clone/fork the code from Github here animation.

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

Included below are the animated charts.

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

  1. IPL Batting performance

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

2. IPL Bowling performance

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

3. IPL Batting in Power play

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

4. IPL Batting in Middle overs

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

5. IPL Batting Death overs

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

6. IPL Bowling Power Play

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

7. IPL Bowling Middle overs

Toppers Harbhajan Singh, YS Chahal, Rashid Khan

8. IPL Bowling Death overs

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

9. T20 (men) Batting performance

Kohli, Babar Azam, P R Stirling are best performers

10. T20 (men) bowling performance

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

11. T20 (men) batting Power play

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

12. T20 (men) batting middle overs

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

13. T20(men) batting death overs

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

14. T20 (men) bowling Power play

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

15. T20 (men) bowling middle overs

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

16. T20 (men) bowling death overs

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

Also see

  1. Experiments with deblurring using OpenCV
  2. Using Reinforcement Learning to solve Gridworld
  3. Deep Learning from first principles in Python, R and Octave – Part 8
  4. Big Data-4: Webserver log analysis with RDDs, Pyspark, SparkR and SparklyR
  5. The Anomaly
  6. Practical Machine Learning with R and Python – Part 3
  7. Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
  8. Introducing cricpy:A python package to analyze performances of cricketers

To see all posts click Index of posts

GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics

This latest update to GooglyPlusPlus2021 includes the following changes

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

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

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

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

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

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

Now,

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

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

You can try out my app GooglyPlusPlus2021 by clicking GooglyPlusPlus2021

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

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

IPL 2021 is already underway.

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

  • CSK vs MI
  • KKR vs RCB

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

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

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

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

d

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

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

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

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

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

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

f) Head-to-head (PBKS vs RR) today’s match 21 Sep 2021

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

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

g) Current ranking of batsmen IPL 2021

h) Current ranking of bowlers IPL 2021

Also you analyse individual batsman and bowlers

i) Batsman analysis

To see Rituraj Gaikwad performance checkout the batsman tab

j) Bowler analysis

Performance of Varun Chakaravarty

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

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

GooglyPlusPlus2021 has been updated with all completed 31 matches

 

Mumbai Indians-Royal Challengers Bangalore-2021-04-09Chennai Super Kings-Delhi Capitals-2021-04-10
Kolkata Knight Riders-Sunrisers Hyderabad-2021-04-11Punjab Kings-Rajasthan Royals-2021-04-12
Mumbai Indians-Kolkata Knight Riders-2021-04-13Royal Challengers Bangalore-Sunrisers Hyderabad-2021-04-14
Delhi Capitals-Rajasthan Royals-2021-04-15Punjab Kings-Chennai Super Kings-2021-04-16
Mumbai Indians-Sunrisers Hyderabad-2021-04-17Royal Challengers Bangalore-Kolkata Knight Riders-2021-04-18
Punjab Kings-Delhi Capitals-2021-04-18Chennai Super Kings-Rajasthan Royals-2021-04-19
Mumbai Indians-Delhi Capitals-2021-04-20Punjab Kings-Sunrisers Hyderabad-2021-04-21
Chennai Super Kings-Kolkata Knight Riders-2021-04-21Rajasthan Royals-Royal Challengers Bangalore-2021-04-22
Mumbai Indians-Punjab Kings-2021-04-23Kolkata Knight Riders-Rajasthan Royals-2021-04-24
Chennai Super Kings-Royal Challengers Bangalore-2021-04-25Delhi Capitals-Sunrisers Hyderabad-2021-04-25
Punjab Kings-Kolkata Knight Riders-2021-04-26Royal Challengers Bangalore-Delhi Capitals-2021-04-27
Sunrisers Hyderabad-Chennai Super Kings-2021-04-28Rajasthan Royals-Mumbai Indians-2021-04-29
Kolkata Knight Riders-Delhi Capitals-2021-04-29Punjab Kings-Royal Challengers Bangalore-2021-04-30.RData
Chennai Super Kings-Mumbai Indians-2021-05-01Rajasthan Royals-Sunrisers Hyderabad-2021-05-02
Punjab Kings-Delhi Capitals-2021-05-02Chennai Super Kings-Mumbai Indians-2021-09-19
Royal Challengers Bangalore-Kolkata Knight Riders-2021-09-20Rajasthan Royals-Punjab Kings-2021-09-21
Sunrisers Hyderabad-Delhi Capitals-2021-09-22.RDataMumbai Indians-Kolkata Knight Riders-2021-09-23.RData
Royal Challengers Bangalore-Chennai Super Kings-2021-09-24.RDataPunjab Kings-Sunrisers Hyderabad-2021-09-25
Delhi Capitals-Rajasthan Royals-2021-09-25.RDataRoyal Challengers Bangalore-Mumbai Indians-2021-09-26.RData
Kolkata Knight Riders-Chennai Super Kings-2021-09-26.RDataKolkata Knight Riders-Chennai Super Kings-2021-09-26.RData
Delhi Capitals-Kolkata Knight Riders-2021-09-28.RDataRajasthan Royals-Royal Challengers Bangalore-2021-09-29.RData
Sunrisers Hyderabad-Chennai Super Kings-2021-09-30.RDataKolkata Knight Riders-Punjab Kings-2021-10-01.RData
Chennai Super Kings-Rajasthan Royals-2021-10-02.RDataMumbai Indians-Delhi Capitals-2021-10-02.RData
Royal Challengers Bangalore-Punjab Kings-2021-10-03.RDataChennai Super Kings-Delhi Capitals-2021-10-04.RData
Rajasthan Royals-Mumbai Indians-2021-10-05.RDataSunrisers Hyderabad-Royal Challengers Bangalore-2021-10-06.RData

Also see

  1. Deep Learning from first principles in Python, R and Octave – Part 5
  2. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
  3. Computer Vision: Ramblings on derivatives, histograms and contours
  4. Designing a Social Web Portal
  5. Understanding Neural Style Transfer with Tensorflow and Keras
  6. Big Data 6: The T20 Dance of Apache NiFi and yorkpy
  7. Practical Machine Learning with R and Python – Part 6
  8. Introducing cricpy:A python package to analyze performances of cricketers
  9. A closer look at “Robot Horse on a Trot” in Android
  10. Cricketr adds team analytics to its repertoire!!!

To see all posts click Index of posts

GooglyPlusPlus2021 with IPL 2021, as-it-happens!

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

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

Check out GooglyPlusPlus2021-IPL 2021

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

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

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

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

a) Match scorecard – Mumbai Indians

Note: The scorecards are computed in real time.

b) Batting Partnerships – Royal Challengers Bangalore

c) Bowling Wicket Kind – Royal Challengers Bangalore

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

d) Batting Partnerships (table) – Delhi Capitals

e) Match Worm Graph

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

f) Batsmen vs Bowlers

D) Final ranks of IPL 2021 batsmen

E) Final ranks of IPL 2021 bowlers

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

PSL 2021

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

G) Ranks of batsmen PSL 2021

H) Ranks bowlers PSL 2021

Important note :

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

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

So do check the other tabs of this app

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

Give GooglyPlusPlus2021 a spin!!

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

GooglyPlusPlus2021 has been updated with all the completed 29 matches

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

Watch this space!

Also see

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

To see all posts click Index of posts

GooglyPlusPlus2021 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

GooglyPlusPlus 2020!!

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

There are 5 tabs in each of the T20 formats

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

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

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

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

Try out GooglyPlusPlus 2020 Shiny app!!

You can clone/fork the code from Github GooglyPlusPlus2020

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

Here are some snapshots from GooglyPlusPlus 2020

A. Batting – Runs vs Deliveries (Shreyas Iyer)

 

 

B. Batting – Cumulative Batting Average (Shubman Gill)

 

C. Bowling – Mean Economy Rate (T. Natarajan)

 

D. Bowling -Bowler’s wickets against opposition (N A Saini)

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

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

 

F. Match – Batsmen vs Bowlers (DD vs KKR 2015-04-20)

 

G. Head-to-head: MI vs  KXIP all matches – Batting scorecard

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

Clone/fork the code from Github GooglyPlusPlus2020

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

 

Hope you have fun!

Also see

1.Big Data 7: yorkr waltzes with Apache NiFi

2. Deep Learning from first principles in Python, R and Octave – Part 6

3. Deconstructing Convolutional Neural Networks with Tensorflow and Keras

4. Sea shells on the seashore

5. Practical Machine Learning with R and Python – Part 3

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

To see all posts click Index of posts

Rank IPL batsmen and bowlers post IPL 2020

Introduction

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

library(yorkr)

1. Convert YAML files to match data

Convert all the match data as YAML file into .RData

#convertAllYaml2RDataframesT20("ipl","IPLMatches")

2. Rank the IPL Batsmen post IPL 2020

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

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


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

3. Rank IPL bowlers post IPL 2020

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

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