December 2016 – Giga thoughts …

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

If you are passionate about cricket, and love analyzing cricket performances, then check out my 2 racy books on cricket! In my books, I perform detailed yet compact analysis of performances of both batsmen, bowlers besides evaluating team & match performances in Tests , ODIs, T20s & IPL. You can buy my books on cricket from Amazon at $12.99 for the paperback and $4.99/$6.99 respectively for the kindle versions. The books can be accessed at Cricket analytics with cricketr and Beaten by sheer pace-Cricket analytics with yorkr A must read for any cricket lover! Check it out!!

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

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

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

The Shiny app can be accessed at Googly

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

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

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

Based on the 5 detailed analysis domains there are 5 tabs

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

Batsman Runs vs. Deliveries
Batsman’s Fours & Sixes
Dismissals of batsman
Batsman’s Runs vs Strike Rate
Batsman’s Moving Average
Batsman’s Cumulative Average Run
Batsman’s Cumulative Strike Rate
Batsman’s Runs against Opposition
Batsman’s Runs at Venue
Predict Runs of batsman

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

Mean Economy Rate of bowler
Mean runs conceded by bowler
Bowler’s Moving Average
Bowler’s Cumulative Avg. Wickets
Bowler’s Cumulative Avg. Economy Rate
Bowler’s Wicket Plot
Bowler’s Wickets against opposition
Bowler’s Wickets at Venues
Bowler’s wickets prediction

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

Batting Partnerships
Batsmen vs Bowlers
Bowling Wicket Kind
Bowling Wicket Runs
Bowling Wicket Match
Bowler vs Batsmen
Match Worm Graph

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

Team Batsmen Batting Partnerships All Matches
Team Batsmen vs Bowlers all Matches
Team Wickets Opposition All Matches
Team Bowler vs Batsmen All Matches
Team Bowlers Wicket Kind All Matches
Team Bowler Wicket Runs All Matches
Win Loss All Matches

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

Team Batsmen Partnerships Overall
Team Batsmen vs Bowlers Overall
Team Bowler vs Batsmen Overall
Team Bowler Wicket Kind Overall

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

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

b. AB Devilliers – Cumulative Strike Rate

c. Gautam Gambhir – Runs at venues

d. CH Gayle – Predict runs

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

b. DJ Bravo – Moving Average of wickets

c. R Ashwin – Mean Economy rate vs Overs

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

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

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

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

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

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

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

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

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

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

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

Do bowl a Googly!!!

You may like my other Shiny apps

Also see my other posts

For more posts see Index of posts

rm(list=ls()) library(yorkr) library(dplyr) source('C:/software/cricket-package/cricsheet/ipl2016/final/R/rankIPLBatsmen.R', encoding = 'UTF-8') source('C:/software/cricket-package/cricsheet/ipl2016/final/R/rankIPLBowlers.R', encoding = 'UTF-8')

Rank IPL batsmen post 2016

Chris Gayle, Shaun Marsh & David Warner are top 3 IPL batsmen. Gayle towers over everybody, with an 38.28 Mean Runs, and a Mean Strike Rate of 138.85. Virat Kohli comes in 4th, with 34.52 as his Average Runs per innings, and a Mean Strike Rate of 117.51

iplBatsmanRank <- rankIPLBatsmen() as.data.frame(iplBatsmanRank[1:30,])

## batsman matches meanRuns meanSR ## 1 CH Gayle 92 38.28261 138.85120 ## 2 SE Marsh 60 36.40000 118.97783 ## 3 DA Warner 104 34.51923 124.88798 ## 4 V Kohli 136 31.77941 117.51000 ## 5 AM Rahane 89 31.46067 104.62989 ## 6 AB de Villiers 109 29.93578 136.48945 ## 7 SR Tendulkar 78 29.62821 108.58962 ## 8 G Gambhir 133 28.94737 109.61263 ## 9 RG Sharma 140 28.68571 117.79057 ## 10 SK Raina 143 28.41259 121.55713 ## 11 SR Watson 90 28.21111 125.80122 ## 12 S Dhawan 110 28.09091 111.97282 ## 13 R Dravid 79 27.87342 109.14544 ## 14 DR Smith 76 27.55263 120.22329 ## 15 JP Duminy 70 27.28571 122.99243 ## 16 BB McCullum 94 26.86170 118.55606 ## 17 JH Kallis 97 26.83505 95.47866 ## 18 V Sehwag 105 26.26667 137.11562 ## 19 RV Uthappa 132 26.18182 123.16326 ## 20 AC Gilchrist 81 25.77778 122.69074 ## 21 M Vijay 99 25.69697 106.02010 ## 22 KC Sangakkara 70 25.67143 112.97529 ## 23 MS Dhoni 131 25.14504 131.62206 ## 24 DA Miller 60 24.76667 133.80983 ## 25 AT Rayudu 99 23.35354 121.59313 ## 26 DPMD Jayawardene 80 23.05000 114.54712 ## 27 Yuvraj Singh 103 22.46602 118.15000 ## 28 DJ Hussey 63 22.26984 NA ## 29 YK Pathan 121 22.25620 132.58793 ## 30 S Badrinath 66 22.22727 114.97061

Rank IPL bowlers

The top 3 IPL T20 bowlers are SL Malinga, DJ Bravo and SP Narine

Don’t get hung up on the decimals in the average wickets for the bowlers. All it implies is that if 2 bowlers have average wickets of 1.0 and 1.5, it implies that in 2 matches the 1st bowler will take 2 wickets and the 2nd bowler will take 3 wickets.

setwd("C:/software/cricket-package/cricsheet/ipl2016/details") iplBowlersRank <- rankIPLBowlers() as.data.frame(iplBowlersRank[1:30,])

## bowler matches meanWickets meanER ## 1 SL Malinga 96 1.645833 6.545208 ## 2 DJ Bravo 58 1.517241 7.929310 ## 3 SP Narine 65 1.492308 6.155077 ## 4 B Kumar 45 1.422222 7.355556 ## 5 YS Chahal 41 1.414634 8.057073 ## 6 M Morkel 37 1.405405 7.626216 ## 7 IK Pathan 40 1.400000 7.579250 ## 8 RP Singh 42 1.357143 7.966429 ## 9 MM Patel 31 1.354839 7.282581 ## 10 R Vinay Kumar 63 1.317460 8.342540 ## 11 Sandeep Sharma 38 1.315789 7.697368 ## 12 MM Sharma 46 1.304348 7.740652 ## 13 P Awana 33 1.303030 8.325758 ## 14 MM Patel 30 1.300000 7.569667 ## 15 Z Khan 41 1.292683 7.735854 ## 16 PP Ojha 53 1.245283 7.268679 ## 17 JP Faulkner 40 1.225000 8.502250 ## 18 Shakib Al Hasan 41 1.170732 7.103659 ## 19 DS Kulkarni 32 1.156250 8.372188 ## 20 UT Yadav 46 1.152174 8.394783 ## 21 A Kumble 41 1.146341 6.567073 ## 22 JA Morkel 73 1.136986 8.131370 ## 23 SK Warne 53 1.132075 7.277170 ## 24 A Mishra 55 1.127273 7.319455 ## 25 UT Yadav 33 1.090909 8.853636 ## 26 L Balaji 34 1.088235 7.186176 ## 27 PP Chawla 35 1.085714 8.162000 ## 28 R Ashwin 92 1.065217 6.812391 ## 29 M Muralitharan 39 1.051282 6.470256 ## 30 Harbhajan Singh 120 1.050000 7.134833