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

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

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

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

Big Bash League (BBL)
Natwest Super T20 (NTB)
Pakistan Super League (PSL)
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

ODI-Part1, 2. ODI-Part2,3. ODI-Part3, 4.ODI-Part 4

For any of the T20s formats you can use the following posts

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!!!