Category: cricketr

Pitching yorkpy…on the middle and outside off-stump to IPL – Part 2

When you come to a fork in the road, take it.
You’ve got to be very careful if you don’t know where you are going, because you might not get there

      Yogi Berra

Try taking his (Rahul Dravid’s) wicket in the first 15 minutes. If you can’t then only try to take the remaining wickets

      Steve Waugh

Introduction

This post is a follow-up to my previous post, Pitching yorkpy…short of good length to IPL-Part 1, in which I analyzed individual IPL matches. In this 2nd post I analyze the data in all matches between any 2 IPL teams, say CSK-RCB, MI-KKR or DD-RPS and so on. As I have already mentioned yorky is the python clone of my R packkage yorkr and this post is almost a mirror image of my post with yorkr namely yorkr crashes the IPL party! – Part 2. The signatures of yorkpy and yorkr are identical and will work in amost the same way. yorkpy, like yorkr, uses data from Cricsheet

You can clone/download the code at Github yorkpy
This post has been published to RPubs at yorkpy-Part2
You can download this post as PDF at IPLT20-yorkpy-part2
You can download all the data used in this post and the previous post at yorkpyData

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton yorkpy-template from Github (which is the R Markdown file I have used for the analysis below).

2. Get data for all T20 matches between 2 teams

We can get all IPL T20 matches between any 2 teams using the function below. The dir parameter should point to the folder which has the IPL T20 csv files of the individual matches (see Pitching yorkpy…short of good length to IPL-Part 1). This function creates a data frame of all the IPL T20 matches and and also saves the dataframe as CSV file if save=True. If save=False the dataframe is just returned and not saved.

import pandas as pd
import os
import yorkpy.analytics as yka
#dir1= "C:\\software\\cricket-package\\yorkpyPkg\\yorkpyData\\IPLConverted"
#yka.getAllMatchesBetweenTeams("Kolkata Knight Riders","Delhi Daredevils",dir=dir1,save=True)

3. Save data for all matches between all combination of 2 teams

This can be done locally using the function below. You could use this function to combine all IPL Twenty20 matches between any 2 IPL teams into a single dataframe and save it in the current folder. All the dataframes for all combinations have already been done and are available as CSV files in Github at yorkpyData

import pandas as pd
import os
import yorkpy.analytics as yka
#dir1= "C:\\software\\cricket-package\\yorkpyPkg\\yorkpyData\\IPLConverted"
#yka.saveAllMatchesBetween2IPLTeams(dir1)

Note: In the functions below, I have randomly chosen any 2 IPL teams and analyze how the teams have performed against each other in different areas. You are free to choose any 2 combination of IPL teams for your analysis

4.Team Batsmen partnership in Twenty20 (all matches with opposing IPL team – summary)

The function below computes the highest partnerships between the 2 IPL teams Chennai Superkings and Delhi Daredevils. Any other 2 IPL team could have also been chosen. The summary gives the top 3 batsmen for Delhi Daredevils namely Sehwag, Gambhir and Dinesh Karthik when the report=‘summary’

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Chennai Super Kings-Delhi Daredevils-allMatches.csv")
csk_dd_matches = pd.read_csv(path)
m=yka.teamBatsmenPartnershiOppnAllMatches(csk_dd_matches,'Delhi Daredevils',report="summary")
print(m)
##            batsman  totalPartnershipRuns
## 49        V Sehwag                   233
## 12       G Gambhir                   200
## 21      KD Karthik                   180
## 10       DA Warner                   134
## 4   AB de Villiers                   133

5. Team Batsmen partnership in Twenty20 (all matches with opposing IPL team -detailed)

The function below gives the detailed breakup of partnerships between Deccan Chargers and Mumbai Indians for Deccan Chargers.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Deccan Chargers-Mumbai Indians-allMatches.csv")
dc_mi_matches = pd.read_csv(path)
theTeam='Deccan Chargers'
m=yka.teamBatsmenPartnershiOppnAllMatches(dc_mi_matches,theTeam,report="detailed", top=4)
print(m)
##          batsman  totalPartnershipRuns      non_striker  partnershipRuns
## 0   AC Gilchrist                   201        A Symonds                0
## 1   AC Gilchrist                   201         HH Gibbs               53
## 2   AC Gilchrist                   201        MD Mishra                0
## 3   AC Gilchrist                   201        RG Sharma               20
## 4   AC Gilchrist                   201    Shahid Afridi                6
## 5   AC Gilchrist                   201         TL Suman                7
## 6   AC Gilchrist                   201       VVS Laxman              115
## 7       S Dhawan                   122         A Mishra                9
## 8       S Dhawan                   122         B Chipli                1
## 9       S Dhawan                   122         CL White                2
## 10      S Dhawan                   122     DT Christian               52
## 11      S Dhawan                   122         IR Jaggi                2
## 12      S Dhawan                   122        JP Duminy                9
## 13      S Dhawan                   122    KC Sangakkara               16
## 14      S Dhawan                   122         PA Patel               22
## 15      S Dhawan                   122          S Sohal                9
## 16     RG Sharma                   103        A Symonds               11
## 17     RG Sharma                   103     AC Gilchrist               18
## 18     RG Sharma                   103         DR Smith                6
## 19     RG Sharma                   103         HH Gibbs                3
## 20     RG Sharma                   103   Jaskaran Singh               15
## 21     RG Sharma                   103        KAJ Roach                4
## 22     RG Sharma                   103        LPC Silva                0
## 23     RG Sharma                   103         TL Suman               14
## 24     RG Sharma                   103  Y Venugopal Rao               32
## 25      HH Gibbs                   102     AC Gilchrist               40
## 26      HH Gibbs                   102         DR Smith               24
## 27      HH Gibbs                   102        MD Mishra               27
## 28      HH Gibbs                   102        RG Sharma                8
## 29      HH Gibbs                   102       VVS Laxman                1
## 30      HH Gibbs                   102  Y Venugopal Rao                2

6. Team Batsmen partnership in Twenty20 – Chart (all matches with opposing IPL team)

The function below plots the partnerships in all matches between 2 IPL teams and plots as chart

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Gujarat Lions-Kings XI Punjab-allMatches.csv")
gl_kxip_matches = pd.read_csv(path)
yka.teamBatsmenPartnershipOppnAllMatchesChart(gl_kxip_matches,'Kings XI Punjab','Gujarat Lions', plot=True, top=4, partnershipRuns=20)

7.Team Batsmen partnership in Twenty20 – Dataframe (all matches with opposing IPL team)

This function does not plot the data but returns the dataframe to the user to plot or manipulate.

Note: Many of the plots include an additional parameters for e.g. plot which is either True or False. The default value is plot=True. When plot=True the plot will be displayed. When plot=False the data frame will be returned to the user. The user can use this to create an interactive charts. The parameter top= specifies the number of top batsmen that need to be included in the chart, and partnershipRuns gives the minimum cutoff runs in partnerships to be considered

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Kolkata Knight Riders-Rising Pune Supergiants-allMatches.csv")
kkr_rps_matches = pd.read_csv(path)
m=yka.teamBatsmenPartnershipOppnAllMatchesChart(kkr_rps_matches,'Rising Pune Supergiants','Kolkata Knight Riders', plot=False, top=5, partnershipRuns=20)
print(m)
##         batsman   non_striker  partnershipRuns
## 0     AM Rahane  F du Plessis               20
## 1     AM Rahane     JA Morkel               16
## 2     AM Rahane   NLTC Perera                6
## 3     AM Rahane     SPD Smith               25
## 4     AM Rahane    UT Khawaja                2
## 5     GJ Bailey     IK Pathan                4
## 6     GJ Bailey     SS Tiwary               28
## 7     GJ Bailey    UT Khawaja                1
## 8      MS Dhoni     IK Pathan                5
## 9      MS Dhoni     JA Morkel                1
## 10     MS Dhoni   NLTC Perera                2
## 11     MS Dhoni      R Ashwin                1
## 12     MS Dhoni      R Bhatia               22
## 13    SPD Smith     AM Rahane               31
## 14  NLTC Perera     AM Rahane               12
## 15  NLTC Perera      MS Dhoni               13

8. Team batsmen versus bowler in Twenty20-Chart (all matches with opposing IPL team)

The plots below provide information on how each of the top batsmen of the IPL teams fared against the opposition bowlers

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Rajasthan Royals-Royal Challengers Bangalore-allMatches.csv")
rr_rcb_matches = pd.read_csv(path)
yka.teamBatsmenVsBowlersOppnAllMatches(rr_rcb_matches,'Rajasthan Royals',"Royal Challengers Bangalore",plot=True,top=3,runsScored=20)

9 Team batsmen versus bowler in Twenty20-Dataframe (all matches with opposing IPL team)

This function provides the bowling performance, the number of overs bowled, maidens, runs conceded. wickets taken and economy rate for the IPL match

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Mumbai Indians-Delhi Daredevils-allMatches.csv")
mi_dd_matches = pd.read_csv(path)
m=yka.teamBatsmenVsBowlersOppnAllMatches(mi_dd_matches,'Delhi Daredevils',"Mumbai Indians",plot=False,top=2,runsScored=50)
print(m)
##       batsman           bowler  runsScored
## 0    V Sehwag          A Nehra         6.0
## 1    V Sehwag       AG Murtaza         6.0
## 2    V Sehwag         AM Nayar        14.0
## 3    V Sehwag         CJ McKay        10.0
## 4    V Sehwag     CRD Fernando         9.0
## 5    V Sehwag         DJ Bravo         9.0
## 6    V Sehwag      DJ Thornely         0.0
## 7    V Sehwag         DR Smith        13.0
## 8    V Sehwag      DS Kulkarni        20.0
## 9    V Sehwag  Harbhajan Singh        54.0
## 10   V Sehwag        JJ Bumrah        19.0
## 11   V Sehwag       KA Pollard        37.0
## 12   V Sehwag         MM Patel        27.0
## 13   V Sehwag          PP Ojha         7.0
## 14   V Sehwag         R Shukla         9.0
## 15   V Sehwag      RJ Peterson         7.0
## 16   V Sehwag         RP Singh        28.0
## 17   V Sehwag       SL Malinga        32.0
## 18   V Sehwag       SM Pollock        25.0
## 19   V Sehwag    ST Jayasuriya        29.0
## 20   V Sehwag           Z Khan        14.0
## 21  JP Duminy      CJ Anderson         3.0
## 22  JP Duminy        HH Pandya         7.0
## 23  JP Duminy  Harbhajan Singh        29.0
## 24  JP Duminy        J Suchith         5.0
## 25  JP Duminy        JJ Bumrah        70.0
## 26  JP Duminy       KA Pollard        29.0
## 27  JP Duminy        KH Pandya         8.0
## 28  JP Duminy       M de Lange         6.0
## 29  JP Duminy   MJ McClenaghan        14.0
## 30  JP Duminy           N Rana         1.0
## 31  JP Duminy          PP Ojha        16.0
## 32  JP Duminy    R Vinay Kumar        18.0
## 33  JP Duminy        RG Sharma         3.0
## 34  JP Duminy          S Gopal         8.0
## 35  JP Duminy       SL Malinga         8.0
## 36  JP Duminy       TG Southee         3.0

10. Team batting scorecard(all matches with opposing IPL team)

This function provides the overall scorecard for an IPL team in all matches against another IPL team. In the snippet below the batting scorecard of RCB is show against CSK. Kohli, Gayle and De villiers lead the pack.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Royal Challengers Bangalore-Chennai Super Kings-allMatches.csv")
rcb_csk_matches = pd.read_csv(path)
scorecard=yka.teamBattingScorecardOppnAllMatches(rcb_csk_matches,'Royal Challengers Bangalore',"Chennai Super Kings")
print(scorecard)
##              batsman  runs  balls  4s  6s          SR
## 5            V Kohli   706    570  51  30  123.859649
## 20          CH Gayle   270    228  12  23  118.421053
## 19    AB de Villiers   241    157  26   9  153.503185
## 6           R Dravid   133    117  18   0  113.675214
## 3          JH Kallis   123    113  21   0  108.849558
## 22        MA Agarwal   120    104  15   4  115.384615
## 2        LRPL Taylor   117    102   5   6  114.705882
## 11        RV Uthappa   115     77   7   8  149.350649
## 21         SS Tiwary    86     88   4   3   97.727273
## 17         MK Pandey    73     72  10   0  101.388889
## 32        KD Karthik    61     58   9   0  105.172414
## 34           D Wiese    51     43   4   2  118.604651
## 33           SN Khan    50     36   5   1  138.888889
## 1           W Jaffer    50     36   5   2  138.888889
## 7            P Kumar    39     25   2   2  156.000000
## 28      Yuvraj Singh    38     33   2   1  115.151515
## 4         MV Boucher    37     33   4   1  112.121212
## 23     LA Pomersbach    31     21   2   2  147.619048
## 8             Z Khan    29     27   3   0  107.407407
## 12      KP Pietersen    23     15   2   1  153.333333
## 38          CL White    21     13   2   1  161.538462
## 26       YV Takawale    19     17   4   0  111.764706
## 31          MS Bisla    17     14   3   0  121.428571
## 14     R Vinay Kumar    17     10   1   1  170.000000
## 25        RR Rossouw    15     13   1   1  115.384615
## 40        AUK Pathan    14      6   2   1  233.333333
## 42   JJ van der Wath    14     11   1   1  127.272727
## 27            VH Zol    13     12   0   1  108.333333
## 30          MA Starc    13     16   1   0   81.250000
## 24      MC Henriques    12      4   3   0  300.000000
## 44          A Mithun    11      8   2   0  137.500000
## 50          PA Patel    10     14   2   0   71.428571
## 36        SP Goswami    10     19   1   0   52.631579
## 0           B Chipli     8     12   1   0   66.666667
## 9            B Akhil     8     12   1   0   66.666667
## 29            S Rana     6      8   0   0   75.000000
## 16  RE van der Merwe     5     12   0   0   41.666667
## 49   KB Arun Karthik     5      5   0   0  100.000000
## 54     Mandeep Singh     4      7   0   0   57.142857
## 37     Misbah-ul-Haq     4      6   0   0   66.666667
## 52      NJ Maddinson     4      7   1   0   57.142857
## 51          AN Ahmed     4      1   1   0  400.000000
## 15          A Kumble     3      6   0   0   50.000000
## 43        DL Vettori     3      4   0   0   75.000000
## 47      DT Christian     2      2   0   0  100.000000
## 45   J Syed Mohammad     2      3   0   0   66.666667
## 35          HV Patel     2      5   0   0   40.000000
## 41         CA Pujara     2      6   0   0   33.333333
## 10          DW Steyn     1      5   0   0   20.000000
## 18        EJG Morgan     1      4   0   0   25.000000
## 46        RR Bhatkal     0      2   0   0    0.000000
## 48         R Rampaul     0      6   0   0    0.000000
## 13         R Bishnoi     0      1   0   0    0.000000
## 39        TM Dilshan     0      1   0   0    0.000000
## 53     Iqbal Abdulla     0      3   0   0    0.000000
## 55         S Aravind     0      1   0   0    0.000000

11.Team Bowling scorecard (all matches with opposing IPL team)

The output below gives the performance of Rajasthan Royals bowlers against Kolkata Knight Riders in all matches between the 2 IPL teams.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Kolkata Knight Riders-Rajasthan Royals-allMatches.csv")
rcb_csk_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardOppnAllMatches(rcb_csk_matches,'Rajasthan Royals',"Kolkata Knight Riders")
print(scorecard)
##               bowler  overs  runs  maidens  wicket   econrate
## 31   Shakib Al Hasan     25   153        0       9   6.120000
## 12          I Sharma     15   118        0       6   7.866667
## 33          Umar Gul      8    61        0       6   7.625000
## 29         SP Narine     24   155        0       6   6.458333
## 1           AB Dinda     20   126        0       6   6.300000
## 23     R Vinay Kumar      8    72        0       5   9.000000
## 22          R Bhatia     15   104        0       5   6.933333
## 0         AB Agarkar     12   105        0       4   8.750000
## 17         LR Shukla     12    87        0       4   7.250000
## 6              B Lee     15    90        0       4   6.000000
## 3         AD Russell      7    59        0       4   8.428571
## 34         YK Pathan      8    61        0       4   7.625000
## 14        JD Unadkat      4    26        0       3   6.500000
## 15         JH Kallis     20   149        0       3   7.450000
## 16          L Balaji     11    73        0       3   6.636364
## 27           SE Bond      8    52        1       3   6.500000
## 10     CK Langeveldt      4    15        0       3   3.750000
## 13     Iqbal Abdulla     10    70        0       3   7.000000
## 28   SMSM Senanayake      4    26        0       2   6.500000
## 7         BAW Mendis      4    19        0       2   4.750000
## 18          M Kartik      8    56        0       2   7.000000
## 4      Anureet Singh      4    35        0       2   8.750000
## 32          UT Yadav      7    67        0       2   9.571429
## 30         SS Sarkar      3    15        0       1   5.000000
## 26        SC Ganguly      6    61        0       1  10.166667
## 5      Azhar Mahmood      3    41        0       1  13.666667
## 19          M Morkel      8    78        0       1   9.750000
## 11         DJ Hussey      2    26        0       0  13.000000
## 2         AD Mathews      3    33        0       0  11.000000
## 8           BJ Hodge      2    34        0       0  17.000000
## 25          S Narwal      2    17        0       0   8.500000
## 24  RN ten Doeschate      2    14        0       0   7.000000
## 21         PP Chawla      4    39        0       0   9.750000
## 20    Mohammed Shami      3    26        0       0   8.666667
## 9           CH Gayle      4    20        0       0   5.000000

12. Team Bowling wicket kind -Chart (all matches with opposing IPL team)

The functions compute and display the kind of wickets taken(bowled, caught, lbw etc) by an IPL team in all matches against another IPL team

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Chennai Super Kings-Rajasthan Royals-allMatches.csv")
csk_rr_matches = pd.read_csv(path)
yka.teamBowlingWicketKindOppositionAllMatches(csk_rr_matches,'Chennai Super Kings','Rajasthan Royals',plot=True,top=5,wickets=1)

13. Team Bowling wicket kind -Dataframe (all matches with opposing IPL team)

This gives the type of wickets taken

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Delhi Daredevils-Pune Warriors-allMatches.csv")
dd_pw_matches = pd.read_csv(path)
m=yka.teamBowlingWicketKindOppositionAllMatches(dd_pw_matches,'Pune Warriors','Delhi Daredevils',plot=False,top=4,wickets=1)
print(m)
##       bowler    kind  wickets
## 0  IK Pathan  bowled        1
## 1  IK Pathan  caught        3
## 2   M Morkel  bowled        1
## 3   M Morkel  caught        3
## 4   S Nadeem  bowled        1
## 5   S Nadeem  caught        2
## 6   UT Yadav  caught        3

14 Team Bowler vs Batman -Plot (all matches with opposing IPL team)

The function below gives the performance of bowlers in all matches against another IPL team.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Sunrisers Hyderabad-Kolkata Knight Riders-allMatches.csv")
srh_kkr_matches = pd.read_csv(path)
yka.teamBowlersVsBatsmenOppnAllMatches(srh_kkr_matches,'Sunrisers Hyderabad','Kolkata Knight Riders',plot=True,top=5,runsConceded=10)

15 Team Bowler vs Batman – Dataframe (all matches with opposing IPL team)

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Royal Challengers Bangalore-Kings XI Punjab-allMatches.csv")
srh_kkr_matches = pd.read_csv(path)
m=yka.teamBowlersVsBatsmenOppnAllMatches(srh_kkr_matches,'Royal Challengers Bangalore','Kings XI Punjab',plot=False,top=1,runsConceded=30)
print(m)
##        bowler           batsman  runsConceded
## 0   PP Chawla          A Kumble             1
## 1   PP Chawla          A Mithun             1
## 2   PP Chawla       AB McDonald             3
## 3   PP Chawla    AB de Villiers            29
## 4   PP Chawla         CA Pujara            13
## 5   PP Chawla          CH Gayle            62
## 6   PP Chawla     CK Langeveldt             1
## 7   PP Chawla          CL White             3
## 8   PP Chawla        DL Vettori             1
## 9   PP Chawla          DT Patil             4
## 10  PP Chawla         JH Kallis            17
## 11  PP Chawla   JJ van der Wath             1
## 12  PP Chawla   KB Arun Karthik             4
## 13  PP Chawla      KP Pietersen            14
## 14  PP Chawla       LRPL Taylor             6
## 15  PP Chawla            M Kaif             2
## 16  PP Chawla         MK Pandey            10
## 17  PP Chawla        MV Boucher             9
## 18  PP Chawla     Misbah-ul-Haq             0
## 19  PP Chawla           P Kumar             0
## 20  PP Chawla          R Dravid            28
## 21  PP Chawla  RE van der Merwe             7
## 22  PP Chawla        RV Uthappa            19
## 23  PP Chawla         SS Tiwary             6
## 24  PP Chawla           V Kohli            56
## 25  PP Chawla            Z Khan             0

16 Team Wins and Losses (all matches with opposing IPL team)

The function below computes and plot the number of wins and losses in a head-on confrontation between 2 IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Chennai Super Kings-Delhi Daredevils-allMatches.csv")
csk_dd_matches = pd.read_csv(path)
yka.plotWinLossBetweenTeams(csk_dd_matches,'Chennai Super Kings','Delhi Daredevils')

17 Team Wins by win type (all matches with opposing IPL team)

This function shows how the win happened whether by runs or by wickets

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Chennai Super Kings-Delhi Daredevils-allMatches.csv")
csk_dd_matches = pd.read_csv(path)
yka.plotWinsByRunOrWickets(csk_dd_matches,'Chennai Super Kings')

18 Team Wins by toss decision-field (all matches with opposing IPL team)

This show how Rajasthan Royals fared when it chose to field on winning the toss

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Rajasthan Royals-Kings XI Punjab-allMatches.csv")
rr_kxip_matches = pd.read_csv(path)
yka.plotWinsbyTossDecision(rr_kxip_matches,'Rajasthan Royals',tossDecision='field')

18 Team Wins by toss decision-bat (all matches with opposing IPL team)

This plot shows how Mumbai Indians fared when it chose to bat on winning the toss

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data1"
path=os.path.join(dir1,"Mumbai Indians-Royal Challengers Bangalore-allMatches.csv")
mi_rcb_matches = pd.read_csv(path)
yka.plotWinsbyTossDecision(mi_rcb_matches,'Mumbai Indians',tossDecision='bat')

Feel free to clone/download the code from Github yorkpy

Important note: Do check out my other posts using yorkpy at yorkpy-posts

Cricpy takes guard for the Twenty20s

“There are two ways to write error-free programs; only the third one works.”” Alan J. Perlis

“Programming today is a race between software engineers striving to build bigger and better idiot-proof programs, and the universe trying to produce bigger and better idiots. So far, the universe is winning. ” Rick Cook

“My software never has bugs. It just develops random features.” Anon

“If you make an ass out of yourself, there will always be someone to ride you.” Bruce Lee

Introduction

This is the 3rd and final post on cricpy, and is a continuation to my 2 earlier posts

1. Introducing cricpy:A python package to analyze performances of cricketers
2.Cricpy takes a swing at the ODIs

Cricpy, is the python avatar of my R package ‘cricketr’. To know more about my R package cricketr see Re-introducing cricketr! : An R package to analyze performances of cricketers

With this post  cricpy, like cricketr, now becomes omnipotent, and is now capable of handling Test, ODI and T20 matches.

Cricpy uses the statistics info available in ESPN Cricinfo Statsguru.

You should be able to install the package using pip install cricpy and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use

Cricpy can now analyze performances of teams in Test, ODI and T20 cricket see Cricpy adds team analytics to its arsenal!!

This post is also hosted on Rpubs at Int

This post is also hosted on Rpubs at Cricpy takes guard for the Twenty 20s. You can also download the pdf version of this post at cricpy-TT.pdf

You can fork/clone the package at Github cricpy

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricpy-template from Github (which is the R Markdown file I have used for the analysis below). You will only need to make appropriate changes for the players you are interested in. The functions can be executed in RStudio or in a IPython notebook.

If you are passionate about cricket, and love analyzing cricket performances, then check out my racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

Untitled

The cricpy package

The data for a particular player in Twenty20s can be obtained with the getPlayerDataTT() function. To do this you will need to go to T20 Batting and T20 Bowling and click the player you are interested in This will bring up a page which have the profile number for the player e.g. for Virat Kohli this would be http://www.espncricinfo.com/india/content/player/253802.html. Hence,this can be used to get the data for Virat Kohlias shown below

The cricpy package is a clone of my R package cricketr. The signature of all the python functions are identical with that of its clone ‘cricketr’, with only the necessary variations between Python and R. It may be useful to look at my post R vs Python: Different similarities and similar differences. In fact if you are familar with one of the languages you can look up the package in the other and you will notice the parallel constructs.

You can fork/clone the package at Github cricpy

Note: The charts are self-explanatory and I have not added much of my own interpretation to it. Do look at the plots closely and check out the performances for yourself.

1 Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy.analytics as ca

2. Invoking functions with Python package cricpy

import cricpy.analytics as ca 
ca.batsman4s("./kohli.csv","Virat Kohli")

3. Getting help from cricpy – Python

import cricpy.analytics as ca 
help(ca.getPlayerDataTT)
## Help on function getPlayerDataTT in module cricpy.analytics:
## 
## getPlayerDataTT(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2, 3], result=[1, 2, 3, 5], create=True)
##     Get the Twenty20 International player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory~
##     
##     Description
##     
##     Get the Twenty20 player data given the profile of the batsman/bowler. The allowed inputs are home,away, neutralboth and won,lost,tied or no result of matches. The data is stored in a <player>.csv file in a directory specified. This function also returns a data frame of the player
##     
##     Usage
##     
##     getPlayerDataTT(profile, opposition="",host="",dir = "./data", file = "player001.csv", 
##     type = "batting", homeOrAway = c(1, 2, 3), result = c(1, 2, 3,5))
##     Arguments
##     
##     profile     
##     This is the profile number of the player to get data. This can be obtained from http://www.espncricinfo.com/ci/content/player/index.html. Type the name of the player and click search. This will display the details of the player. Make a note of the profile ID. For e.g For Virat Kohli this turns out to be 253802 http://www.espncricinfo.com/india/content/player/35263.html. Hence the profile for Sehwag is 35263
##     opposition  
##     The numerical value of the opposition country e.g.Australia,India, England etc. The values are Afghanistan:40,Australia:2,Bangladesh:25,England:1,Hong Kong:19,India:6,Ireland:29, New Zealand:5,Pakistan:7,Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27, West Indies:4, Zimbabwe:9; Note: If no value is entered for opposition then all teams are considered
##     host        
##     The numerical value of the host country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,New Zealand:5, South Africa:3,Sri Lanka:8,United States of America:11,West Indies:4, Zimbabwe:9 Note: If no value is entered for host then all host countries are considered
##     dir 
##     Name of the directory to store the player data into. If not specified the data is stored in a default directory "./data". Default="./data"
##     file        
##     Name of the file to store the data into for e.g. kohli.csv. This can be used for subsequent functions. Default="player001.csv"
##     type        
##     type of data required. This can be "batting" or "bowling"
##     homeOrAway  
##     This is vector with either or all 1,2, 3. 1 is for home 2 is for away, 3 is for neutral venue
##     result      
##     This is a vector that can take values 1,2,3,5. 1 - won match 2- lost match 3-tied 5- no result
##     Details
##     
##     More details can be found in my short video tutorial in Youtube https://www.youtube.com/watch?v=q9uMPFVsXsI
##     
##     Value
##     
##     Returns the player's dataframe
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh <tvganesh.85@gmail.com>
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://www.espncricinfo.com/ci/content/stats/index.html
##     https://gigadom.wordpress.com/
##     
##     See Also
##     
##     bowlerWktRateTT getPlayerData
##     
##     Examples
##     
##     ## Not run: 
##     # Only away. Get data only for won and lost innings
##     kohli =getPlayerDataTT(253802,dir="../cricketr/data", file="kohli1.csv",
##     type="batting")
##     
##     # Get bowling data and store in file for future
##     ashwin = getPlayerDataTT(26421,dir="../cricketr/data",file="ashwin1.csv",
##     type="bowling")
##     
##     kohli =getPlayerDataTT(253802,opposition = 2,host=2,dir="../cricketr/data", 
##     file="kohli1.csv",type="batting")

The details below will introduce the different functions that are available in cricpy.

4. Get the Twenty20 player data for a player using the function getPlayerDataOD()

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. kohli.csv as above) which can then be reused for all other functions). Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerDataTT for all subsequent analyses

import cricpy.analytics as ca
#kohli=ca.getPlayerDataTT(253802,dir=".",file="kohli.csv",type="batting")
#guptill=ca.getPlayerDataTT(226492,dir=".",file="guptill.csv",type="batting")
#shahzad=ca.getPlayerDataTT(419873,dir=".",file="shahzad.csv",type="batting")
#mccullum=ca.getPlayerDataTT(37737,dir=".",file="mccullum.csv",type="batting")

Included below are some of the functions that can be used for ODI batsmen and bowlers. For this I have chosen, Virat Kohli, ‘the run machine’ who is on-track for breaking many of the Test, ODI and Twenty20 records

5 Virat Kohli’s performance – Basic Analyses

The 3 plots below provide the following for Virat Kohli in T20s

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges
import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("./kohli.csv","Virat Kohli")

ca.batsmanMeanStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanRunsRanges("./kohli.csv","Virat Kohli")

6. More analyses

import cricpy.analytics as ca
ca.batsman4s("./kohli.csv","Virat Kohli")

ca.batsman6s("./kohli.csv","Virat Kohli")

ca.batsmanDismissals("./kohli.csv","Virat Kohli")

ca.batsmanScoringRateODTT("./kohli.csv","Virat Kohli")

7. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of Kohli’s Runs versus Balls Faced and Minutes at crease. A linear regression plane is then fitted between Runs and Balls Faced + Minutes at crease

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

8. Average runs at different venues

The plot below gives the average runs scored by Kohli at different grounds. The plot also the number of innings at each ground as a label at x-axis.

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("./kohli.csv","Virat Kohli")

9. Average runs against different opposing teams

This plot computes the average runs scored by Kohli against different countries.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("./kohli.csv","Virat Kohli")

10 . Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman. For this the performance of Kohli is plotted as a 3D scatter plot with Runs versus Balls Faced + Minutes at crease. K-Means. The centroids of 3 clusters are computed and plotted. In this plot Kohli’s highest tendencies are computed and plotted using K-Means

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("./kohli.csv","Virat Kohli")

11. A look at the Top 4 batsman – Kohli,  Guptill, Shahzad and McCullum

The following batsmen have been very prolific in Twenty20 cricket and will be used for the analyses

  1. Virat Kohli: Runs – 2167, Average:49.25 ,Strike rate-136.11
  2. MJ Guptill : Runs -2271, Average:34.4 ,Strike rate-132.88
  3. Mohammed Shahzad :Runs – 1936, Average:31.22 ,Strike rate-134.81
  4. BB McCullum : Runs – 2140, Average:35.66 ,Strike rate-136.21

The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs

12. Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("./kohli.csv","Virat Kohli")

ca.batsmanPerfBoxHist("./guptill.csv","M J Guptill")

ca.batsmanPerfBoxHist("./shahzad.csv","M Shahzad")

ca.batsmanPerfBoxHist("./mccullum.csv","BB McCullum")

13 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 Twenty20 batsmen.

import cricpy.analytics as ca
ca.batsmanMovingAverage("./kohli.csv","Virat Kohli")

ca.batsmanMovingAverage("./guptill.csv","M J Guptill")
#ca.batsmanMovingAverage("./shahzad.csv","M Shahzad") # Gives error. Check!

ca.batsmanMovingAverage("./mccullum.csv","BB McCullum")

14 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career.Kohli’s average tops around 45 runs around 43 innings, though there is a dip downwards

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeAverageRuns("./guptill.csv","M J Guptill")

ca.batsmanCumulativeAverageRuns("./shahzad.csv","M Shahzad")

ca.batsmanCumulativeAverageRuns("./mccullum.csv","BB McCullum")

15 Cumulative Average strike rate of batsman in career

Kohli, Guptill and McCullum average a strike rate of 125+

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeStrikeRate("./guptill.csv","M J Guptill")

ca.batsmanCumulativeStrikeRate("./shahzad.csv","M Shahzad")

ca.batsmanCumulativeStrikeRate("./mccullum.csv","BB McCullum")

16 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman. Kohli is way above all the other 3 batsmen. Behind Kohli is McCullum and then Guptill

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullumn"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

17. Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show that Kohli tops the overall strike rate followed by McCullum and then Guptill

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullum"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

18. 3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A 3D prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

ca.battingPerf3d("./guptill.csv","M J Guptill")

ca.battingPerf3d("./shahzad.csv","M Shahzad")

ca.battingPerf3d("./mccullum.csv","BB McCullum")

19. 3D plot of Runs vs Balls Faced and Minutes at Crease

Guptill and McCullum have a large percentage of sixes in comparison to the 4s. Kohli has a relative lower number of 6s

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullum"]
ca.batsman4s6s(frames,names)

20. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
kohli= ca.batsmanRunsPredict("./kohli.csv",newDF,"Kohli")
print(kohli)
##             BF        Mins        Runs
## 0    10.000000   30.000000   14.753153
## 1    37.857143   70.714286   55.963333
## 2    65.714286  111.428571   97.173513
## 3    93.571429  152.142857  138.383693
## 4   121.428571  192.857143  179.593873
## 5   149.285714  233.571429  220.804053
## 6   177.142857  274.285714  262.014233
## 7   205.000000  315.000000  303.224414
## 8   232.857143  355.714286  344.434594
## 9   260.714286  396.428571  385.644774
## 10  288.571429  437.142857  426.854954
## 11  316.428571  477.857143  468.065134
## 12  344.285714  518.571429  509.275314
## 13  372.142857  559.285714  550.485494
## 14  400.000000  600.000000  591.695674

21 Analysis of Top Bowlers

The following 4 bowlers have had an excellent career and will be used for the analysis

  1. Shakib Hasan:Wickets: 80, Average = 21.07, Economy Rate – 6.74
  2. Mohammed Nabi : Wickets: 67, Average = 24.25, Economy Rate – 7.13
  3. Rashid Khan: Wickets: 64, Average = 12.40, Economy Rate – 6.01
  4. Imran Tahir : Wickets:62, Average – 14.95, Economy Rate – 6.77

22. Get the bowler’s data

This plot below computes the percentage frequency of number of wickets taken for e.g 1 wicket x%, 2 wickets y% etc and plots them as a continuous line

import cricpy.analytics as ca
#shakib=ca.getPlayerDataTT(56143,dir=".",file="shakib.csv",type="bowling")
#nabi=ca.getPlayerDataOD(25913,dir=".",file="nabi.csv",type="bowling")
#rashid=ca.getPlayerDataOD(793463,dir=".",file="rashid.csv",type="bowling")
#tahir=ca.getPlayerDataOD(40618,dir=".",file="tahir.csv",type="bowling")

23. Wicket Frequency Plot

This plot below plots the frequency of wickets taken for each of the bowlers

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("./shakib.csv","Shakib Al Hasan")

ca.bowlerWktsFreqPercent("./nabi.csv","Mohammad Nabi")

ca.bowlerWktsFreqPercent("./rashid.csv","Rashid Khan")

ca.bowlerWktsFreqPercent("./tahir.csv","Imran Tahir")

24. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken.

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("./shakib.csv","Shakib Al Hasan")

ca.bowlerWktsRunsPlot("./nabi.csv","Mohammad Nabi")

ca.bowlerWktsRunsPlot("./rashid.csv","Rashid Khan")

ca.bowlerWktsRunsPlot("./tahir.csv","Imran Tahir")

25 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues.

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("./shakib.csv","Shakib Al Hasan")

ca.bowlerAvgWktsGround("./nabi.csv","Mohammad Nabi")

ca.bowlerAvgWktsGround("./rashid.csv","Rashid Khan")

ca.bowlerAvgWktsGround("./tahir.csv","Imran Tahir")

26 Average wickets against different opposition

The plot gives the average wickets taken by Muralitharan against different countries. The x-axis also includes the number of innings against each team

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("./shakib.csv","Shakib Al Hasan")

ca.bowlerAvgWktsOpposition("./nabi.csv","Mohammad Nabi")

ca.bowlerAvgWktsOpposition("./rashid.csv","Rashid Khan")

ca.bowlerAvgWktsOpposition("./tahir.csv","Imran Tahir")

27 Wickets taken moving average

From the plot below it can be see

import cricpy.analytics as ca
ca.bowlerMovingAverage("./shakib.csv","Shakib Al Hasan")

ca.bowlerMovingAverage("./nabi.csv","Mohammad Nabi")

ca.bowlerMovingAverage("./rashid.csv","Rashid Khan")

ca.bowlerMovingAverage("./tahir.csv","Imran Tahir")

28 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. Rashid Khan has been the most effective with almost 2.28 wickets per match

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("./shakib.csv","Shakib Al Hasan")

ca.bowlerCumulativeAvgWickets("./nabi.csv","Mohammad Nabi")

ca.bowlerCumulativeAvgWickets("./rashid.csv","Rashid Khan")

ca.bowlerCumulativeAvgWickets("./tahir.csv","Imran Tahir")

29 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. Rashid Khan has the nest economy rate followed by Mohammed Nabi

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("./shakib.csv","Shakib Al Hasan")

ca.bowlerCumulativeAvgEconRate("./nabi.csv","Mohammad Nabi")

ca.bowlerCumulativeAvgEconRate("./rashid.csv","Rashid Khan")

ca.bowlerCumulativeAvgEconRate("./tahir.csv","Imran Tahir")

30 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate is given below. It can be seen that Rashid Khan has the best economy rate followed by Mohammed Nabi and then Imran Tahir

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

31 Relative Economy Rate against wickets taken

Rashid Khan has the best figures for wickets between 2-3.5 wickets. Mohammed Nabi pips Rashid Khan when takes a haul of 4 wickets.

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlingER(frames,names)

32 Relative cumulative average wickets of bowlers in career

Rashid has the best performance with cumulative average wickets. He is followed by Imran Tahir in the wicket haul, followed by Shakib Al Hasan

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

33. Key Findings

The plots above capture some of the capabilities and features of my cricpy package. Feel free to install the package and try it out. Please do keep in mind ESPN Cricinfo’s Terms of Use.

Here are the main findings from the analysis above

Analysis of Top 4 batsman

The analysis of the Top 4 test batsman Kohli, Guptill, Shahzad and McCullum
1.Kohli has the best overall cumulative average runs and towers over everybody else
2. Kohli, Guptill and McCullum has a very good strike rate of around 125+
3. Guptill and McCullum have a larger percentage of sixes as compared to Kohli
4. Rashid Khan has the best cumulative average wickets, followed by Imran Tahir and then Shakib Al Hasan
5. Rashid Khan is the most economical bowler, followed by Mohammed Nabi

You can fork/clone the package at Github cricpy

Conclusion

Cricpy now has almost all the functions and functionalities of my R package cricketr. There are still a few more features that need to be added to cricpy. I intend to do this as and when I find time.

Go ahead, take cricpy for a spin! Hope you enjoy the ride!

Watch this space!!!

Important note: Do check out my other posts using cricpy at cricpy-posts

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Cricpy takes a swing at the ODIs

No computer has ever been designed that is ever aware of what it’s doing; but most of the time, we aren’t either.” Marvin Minksy

“The competent programmer is fully aware of the limited size of his own skull. He therefore approaches his task with full humility, and avoids clever tricks like the plague” Edgser Djikstra

Introduction

In this post, cricpy, the Python avatar of my R package cricketr, learns some new tricks to be able to handle ODI matches. To know more about my R package cricketr see Re-introducing cricketr! : An R package to analyze performances of cricketers

Cricpy uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package supports only Test cricket

You should be able to install the package using pip install cricpy and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use

Cricpy can now analyze performances of teams in Test, ODI and T20 cricket see Cricpy adds team analytics to its arsenal!!

This post is also hosted on Rpubs at Int

To know how to use cricpy see Introducing cricpy:A python package to analyze performances of cricketers. To the original version of cricpy, I have added 3 new functions for ODI. The earlier functions work for Test and ODI.

This post is also hosted on Rpubs at Cricpy takes a swing at the ODIs. You can also down the pdf version of this post at cricpy-odi.pdf

You can fork/clone the package at Github cricpy

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricpy-template from Github (which is the R Markdown file I have used for the analysis below). You will only need to make appropriate changes for the players you are interested in. The functions can be executed in RStudio or in a IPython notebook.

If you are passionate about cricket, and love analyzing cricket performances, then check out my racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

Untitled

The cricpy package

The data for a particular player in ODI can be obtained with the getPlayerDataOD() function. To do you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Virat Kohli, Virendar Sehwag, Chris Gayle etc. This will bring up a page which have the profile number for the player e.g. for Virat Kohli this would be http://www.espncricinfo.com/india/content/player/253802.html. Hence, Kohli’s profile is 253802. This can be used to get the data for Virat Kohlis shown below

The cricpy package is a clone of my R package cricketr. The signature of all the python functions are identical with that of its clone ‘cricketr’, with only the necessary variations between Python and R. It may be useful to look at my post R vs Python: Different similarities and similar differences. In fact if you are familar with one of the lanuguages you can look up the package in the other and you will notice the parallel constructs.

You can fork/clone the package at Github cricpy

Note: The charts are self-explanatory and I have not added much of my owy interpretation to it. Do look at the plots closely and check out the performances for yourself.

1 Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy.analytics as ca

2. Invoking functions with Python package crlcpy

import cricpy.analytics as ca 
ca.batsman4s("./kohli.csv","Virat Kohli")

3. Getting help from cricpy – Python

import cricpy.analytics as ca 
help(ca.getPlayerDataOD)
## Help on function getPlayerDataOD in module cricpy.analytics:
## 
## getPlayerDataOD(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2, 3], result=[1, 2, 3, 5], create=True)
##     Get the One day player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory
##     
##     Description
##     
##     Get the player data given the profile of the batsman. The allowed inputs are home,away or both and won,lost or draw of matches. The data is stored in a .csv file in a directory specified. This function also returns a data frame of the player
##     
##     Usage
##     
##     getPlayerDataOD(profile, opposition="",host="",dir = "../", file = "player001.csv", 
##     type = "batting", homeOrAway = c(1, 2, 3), result = c(1, 2, 3,5))
##     Arguments
##     
##     profile     
##     This is the profile number of the player to get data. This can be obtained from http://www.espncricinfo.com/ci/content/player/index.html. Type the name of the player and click search. This will display the details of the player. Make a note of the profile ID. For e.g For Virender Sehwag this turns out to be http://www.espncricinfo.com/india/content/player/35263.html. Hence the profile for Sehwag is 35263
##     opposition      The numerical value of the opposition country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,Bermuda:12, England:1,Hong Kong:19,India:6,Ireland:29, Netherlands:15,New Zealand:5,Pakistan:7,Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27, West Indies:4, Zimbabwe:9; Africa XI:405 Note: If no value is entered for opposition then all teams are considered
##     host            The numerical value of the host country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,Ireland:29,Malaysia:16,New Zealand:5,Pakistan:7, Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27,West Indies:4, Zimbabwe:9 Note: If no value is entered for host then all host countries are considered
##     dir 
##     Name of the directory to store the player data into. If not specified the data is stored in a default directory "../data". Default="../data"
##     file        
##     Name of the file to store the data into for e.g. tendulkar.csv. This can be used for subsequent functions. Default="player001.csv"
##     type        
##     type of data required. This can be "batting" or "bowling"
##     homeOrAway  
##     This is vector with either or all 1,2, 3. 1 is for home 2 is for away, 3 is for neutral venue
##     result      
##     This is a vector that can take values 1,2,3,5. 1 - won match 2- lost match 3-tied 5- no result
##     Details
##     
##     More details can be found in my short video tutorial in Youtube https://www.youtube.com/watch?v=q9uMPFVsXsI
##     
##     Value
##     
##     Returns the player's dataframe
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh <tvganesh.85@gmail.com>
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://www.espncricinfo.com/ci/content/stats/index.html
##     https://gigadom.wordpress.com/
##     
##     See Also
##     
##     getPlayerDataSp getPlayerData
##     
##     Examples
##     
##     
##     ## Not run: 
##     # Both home and away. Result = won,lost and drawn
##     sehwag =getPlayerDataOD(35263,dir="../cricketr/data", file="sehwag1.csv",
##     type="batting", homeOrAway=[1,2],result=[1,2,3,4])
##     
##     # Only away. Get data only for won and lost innings
##     sehwag = getPlayerDataOD(35263,dir="../cricketr/data", file="sehwag2.csv",
##     type="batting",homeOrAway=[2],result=[1,2])
##     
##     # Get bowling data and store in file for future
##     malinga = getPlayerData(49758,dir="../cricketr/data",file="malinga1.csv",
##     type="bowling")
##     
##     # Get Dhoni's ODI record in Australia against Australua
##     dhoni = getPlayerDataOD(28081,opposition = 2,host=2,dir=".",
##     file="dhoniVsAusinAusOD",type="batting")
##     
##     ## End(Not run)

The details below will introduce the different functions that are available in cricpy.

4. Get the ODI player data for a player using the function getPlayerDataOD()

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. kohli.csv as above) which can then be reused for all other functions). Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerDataOD for all subsequent analyses

import cricpy.analytics as ca
#sehwag=ca.getPlayerDataOD(35263,dir=".",file="sehwag.csv",type="batting")
#kohli=ca.getPlayerDataOD(253802,dir=".",file="kohli.csv",type="batting")
#jayasuriya=ca.getPlayerDataOD(49209,dir=".",file="jayasuriya.csv",type="batting")
#gayle=ca.getPlayerDataOD(51880,dir=".",file="gayle.csv",type="batting")

Included below are some of the functions that can be used for ODI batsmen and bowlers. For this I have chosen, Virat Kohli, ‘the run machine’ who is on-track for breaking many of the Test & ODI records

5 Virat Kohli’s performance – Basic Analyses

The 3 plots below provide the following for Virat Kohli

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges
import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("./kohli.csv","Virat Kohli")

ca.batsmanMeanStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanRunsRanges("./kohli.csv","Virat Kohli")

6. More analyses

import cricpy.analytics as ca
ca.batsman4s("./kohli.csv","Virat Kohli")

ca.batsman6s("./kohli.csv","Virat Kohli")

ca.batsmanDismissals("./kohli.csv","Virat Kohli")

ca.batsmanScoringRateODTT("./kohli.csv","Virat Kohli")


7. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of Kohli’s Runs versus Balls Faced and Minutes at crease. A linear regression plane is then fitted between Runs and Balls Faced + Minutes at crease

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

Average runs at different venues

The plot below gives the average runs scored by Kohli at different grounds. The plot also the number of innings at each ground as a label at x-axis.

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("./kohli.csv","Virat Kohli")

9. Average runs against different opposing teams

This plot computes the average runs scored by Kohli against different countries.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("./kohli.csv","Virat Kohli")

10 . Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman. For this the performance of Kohli is plotted as a 3D scatter plot with Runs versus Balls Faced + Minutes at crease. K-Means. The centroids of 3 clusters are computed and plotted. In this plot Kohli’s highest tendencies are computed and plotted using K-Means

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("./kohli.csv","Virat Kohli")

A look at the Top 4 batsman – Kohli, Jayasuriya, Sehwag and Gayle

The following batsmen have been very prolific in ODI cricket and will be used for the analyses

  1. Virat Kohli: Runs – 10232, Average:59.83 ,Strike rate-92.88
  2. Sanath Jayasuriya : Runs – 13430, Average:32.36 ,Strike rate-91.2
  3. Virendar Sehwag :Runs – 8273, Average:35.05 ,Strike rate-104.33
  4. Chris Gayle : Runs – 9727, Average:37.12 ,Strike rate-85.82

The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs

12. Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("./kohli.csv","Virat Kohli")

ca.batsmanPerfBoxHist("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanPerfBoxHist("./gayle.csv","Chris Gayle")

ca.batsmanPerfBoxHist("./sehwag.csv","Virendar Sehwag")

13 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 (ignore the dip at the end of all plots. Need to check why this is so!). Kohli’s performance has been steadily improving over the years, so has Sehwag. Gayle seems to be on the way down

import cricpy.analytics as ca
ca.batsmanMovingAverage("./kohli.csv","Virat Kohli")

ca.batsmanMovingAverage("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanMovingAverage("./gayle.csv","Chris Gayle")

ca.batsmanMovingAverage("./sehwag.csv","Virendar Sehwag")

14 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career. Kohli seems to be getting better with time and reaches a cumulative average of 45+. Sehwag improves with time and reaches around 35+. Chris Gayle drops from 42 to 35

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeAverageRuns("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanCumulativeAverageRuns("./gayle.csv","Chris Gayle")

ca.batsmanCumulativeAverageRuns("./sehwag.csv","Virendar Sehwag")

15 Cumulative Average strike rate of batsman in career

Sehwag has the best strike rate of almost 90. Kohli and Jayasuriya have a cumulative strike rate of 75.

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeStrikeRate("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanCumulativeStrikeRate("./gayle.csv","Chris Gayle")

ca.batsmanCumulativeStrikeRate("./sehwag.csv","Virendar Sehwag")

16 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman . It can be seen that Virat Kohli towers above all others in the runs. He is followed by Chris Gayle and then Sehwag

import cricpy.analytics as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percentages for each 10 run bucket. The plot below show Sehwag has the best strike rate, followed by Jayasuriya

import cricpy.analytics as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

18. 3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A 3D prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

ca.battingPerf3d("./jayasuriya.csv","Sanath jayasuriya")

ca.battingPerf3d("./gayle.csv","Chris Gayle")

ca.battingPerf3d("./sehwag.csv","Virendar Sehwag")

3D plot of Runs vs Balls Faced and Minutes at Crease

From the plot below it can be seen that Sehwag has more runs by way of 4s than 1’s,2’s or 3s. Gayle and Jayasuriya have large number of 6s

import cricpy.analytics as ca
frames = ["./sehwag.csv","./kohli.csv","./gayle.csv","./jayasuriya.csv"]
names = ["Sehwag","Kohli","Gayle","Jayasuriya"]
ca.batsman4s6s(frames,names)

20. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
kohli= ca.batsmanRunsPredict("./kohli.csv",newDF,"Kohli")
print(kohli)
##             BF        Mins        Runs
## 0    10.000000   30.000000    6.807407
## 1    37.857143   70.714286   36.034833
## 2    65.714286  111.428571   65.262259
## 3    93.571429  152.142857   94.489686
## 4   121.428571  192.857143  123.717112
## 5   149.285714  233.571429  152.944538
## 6   177.142857  274.285714  182.171965
## 7   205.000000  315.000000  211.399391
## 8   232.857143  355.714286  240.626817
## 9   260.714286  396.428571  269.854244
## 10  288.571429  437.142857  299.081670
## 11  316.428571  477.857143  328.309096
## 12  344.285714  518.571429  357.536523
## 13  372.142857  559.285714  386.763949
## 14  400.000000  600.000000  415.991375

The fitted model is then used to predict the runs that the batsmen will score for a given Balls faced and Minutes at crease.

21 Analysis of Top Bowlers

The following 4 bowlers have had an excellent career and will be used for the analysis

  1. Muthiah Muralitharan:Wickets: 534, Average = 23.08, Economy Rate – 3.93
  2. Wasim Akram : Wickets: 502, Average = 23.52, Economy Rate – 3.89
  3. Shaun Pollock: Wickets: 393, Average = 24.50, Economy Rate – 3.67
  4. Javagal Srinath : Wickets:315, Average – 28.08, Economy Rate – 4.44

How do Muralitharan, Akram, Pollock and Srinath compare with one another with respect to wickets taken and the Economy Rate. The next set of plots compute and plot precisely these analyses.

22. Get the bowler’s data

This plot below computes the percentage frequency of number of wickets taken for e.g 1 wicket x%, 2 wickets y% etc and plots them as a continuous line

import cricpy.analytics as ca
#akram=ca.getPlayerDataOD(43547,dir=".",file="akram.csv",type="bowling")
#murali=ca.getPlayerDataOD(49636,dir=".",file="murali.csv",type="bowling")
#pollock=ca.getPlayerDataOD(46774,dir=".",file="pollock.csv",type="bowling")
#srinath=ca.getPlayerDataOD(34105,dir=".",file="srinath.csv",type="bowling")

23. Wicket Frequency Plot

This plot below plots the frequency of wickets taken for each of the bowlers

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("./murali.csv","M Muralitharan")

ca.bowlerWktsFreqPercent("./akram.csv","Wasim Akram")

ca.bowlerWktsFreqPercent("./pollock.csv","Shaun Pollock")

ca.bowlerWktsFreqPercent("./srinath.csv","J Srinath")

24. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken. Murali’s median runs for wickets ia around 40 while Akram, Pollock and Srinath it is around 32+ runs. The spread around the median is larger for these 3 bowlers in comparison to Murali

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("./murali.csv","M Muralitharan")

ca.bowlerWktsRunsPlot("./akram.csv","Wasim Akram")

ca.bowlerWktsRunsPlot("./pollock.csv","Shaun Pollock")

ca.bowlerWktsRunsPlot("./srinath.csv","J Srinath")

25 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues. McGrath best performances are at Centurion, Lord’s and Port of Spain averaging about 4 wickets. Kapil Dev’s does good at Kingston and Wellington. Anderson averages 4 wickets at Dunedin and Nagpur

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("./murali.csv","M Muralitharan")

ca.bowlerAvgWktsGround("./akram.csv","Wasim Akram")

ca.bowlerAvgWktsGround("./pollock.csv","Shaun Pollock")

ca.bowlerAvgWktsGround("./srinath.csv","J Srinath")

26 Average wickets against different opposition

The plot gives the average wickets taken by Muralitharan against different countries. The x-axis also includes the number of innings against each team

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("./murali.csv","M Muralitharan")

ca.bowlerAvgWktsOpposition("./akram.csv","Wasim Akram")

ca.bowlerAvgWktsOpposition("./pollock.csv","Shaun Pollock")

ca.bowlerAvgWktsOpposition("./srinath.csv","J Srinath")

27 Wickets taken moving average

From the plot below it can be see James Anderson has had a solid performance over the years averaging about wickets

import cricpy.analytics as ca
ca.bowlerMovingAverage("./murali.csv","M Muralitharan")

ca.bowlerMovingAverage("./akram.csv","Wasim Akram")

ca.bowlerMovingAverage("./pollock.csv","Shaun Pollock")

ca.bowlerMovingAverage("./srinath.csv","J Srinath")

28 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. Muralitharan has consistently taken wickets at an average of 1.6 wickets per game. Shaun Pollock has an average of 1.5

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("./murali.csv","M Muralitharan")

ca.bowlerCumulativeAvgWickets("./akram.csv","Wasim Akram")

ca.bowlerCumulativeAvgWickets("./pollock.csv","Shaun Pollock")

ca.bowlerCumulativeAvgWickets("./srinath.csv","J Srinath")

29 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. Pollock is the most economical, followed by Akram and then Murali

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("./murali.csv","M Muralitharan")

ca.bowlerCumulativeAvgEconRate("./akram.csv","Wasim Akram")

ca.bowlerCumulativeAvgEconRate("./pollock.csv","Shaun Pollock")

ca.bowlerCumulativeAvgEconRate("./srinath.csv","J Srinath")

30 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate shows that Pollock is the most economical of the 4 bowlers. He is followed by Akram and then Murali

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

31 Relative Economy Rate against wickets taken

Pollock is most economical vs number of wickets taken. Murali has the best figures for 4 wickets taken.

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlingER(frames,names)

32 Relative cumulative average wickets of bowlers in career

The plot below shows that McGrath has the best overall cumulative average wickets. While the bowlers are neck to neck around 130 innings, you can see Muralitharan is most consistent and leads the pack after 150 innings in the number of wickets taken.

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

33. Key Findings

The plots above capture some of the capabilities and features of my cricpy package. Feel free to install the package and try it out. Please do keep in mind ESPN Cricinfo’s Terms of Use.

Here are the main findings from the analysis above

Analysis of Top 4 batsman

The analysis of the Top 4 test batsman Tendulkar, Kallis, Ponting and Sangakkara show the folliwing

  1. Kohli is a mean run machine and has been consistently piling on runs. Clearly records will lay shattered in days to come for Kohli
  2. Virendar Sehwag has the best strike rate of the 4, followed by Jayasuriya and then Kohli
  3. Shaun Pollock is the most economical of the bowlers followed by Wasim Akram
  4. Muralitharan is the most consistent wicket of the lot.

Important note: Do check out my other posts using cricpy at cricpy-posts

Also see
1. Architecting a cloud based IP Multimedia System (IMS)
2. Exploring Quantum Gate operations with QCSimulator
3. Dabbling with Wiener filter using OpenCV
4. Deep Learning from first principles in Python, R and Octave – Part 5
5. Big Data-2: Move into the big league:Graduate from R to SparkR
6. Singularity
7. Practical Machine Learning with R and Python – Part 4
8. Literacy in India – A deepR dive
9. Modeling a Car in Android

To see all posts click Index of Posts

 

The 3rd paperback & kindle editions of my books on Cricket, now on Amazon

The 3rd  paperback & kindle edition of both my books on cricket is now available on Amazon

a) Cricket analytics with cricketr, Third Edition. The paperback edition is $12.99 and the kindle edition is $4.99/Rs320.  This book is based on my R package ‘cricketr‘, available on CRAN and uses ESPN Cricinfo Statsguru

b) Beaten by sheer pace! Cricket analytics with yorkr, 3rd edition . The paperback is $12.99 and the kindle version is $6.99/Rs448. This is based on my R package ‘yorkr‘ on CRAN and uses data from Cricsheet
Pick up your copies today!!

Note: In the 3rd edition of  the paperback book, the charts will be in black and white. If you would like the charts to be in color, please check out the 2nd edition of these books see More book, more cricket! 2nd edition of my books now on Amazon

You may also like
1. My book ‘Practical Machine Learning with R and Python’ on Amazon
2. A crime map of India in R: Crimes against women
3.  What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
4.  Bend it like Bluemix, MongoDB with autoscaling – Part 2
5. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
6. Thinking Web Scale (TWS-3): Map-Reduce – Bring compute to data

To see all posts see Index of posts

More book, more cricket! 2nd edition of my books now on Amazon

a) Cricket analytics with cricketr
b) Beaten by sheer pace – Cricket analytics with yorkr
is now available on Amazon, both as Paperback and Kindle versions.

The Kindle versions are just $4.99 for both books. Pick up your copies today!!!

A) Cricket analytics with cricketr: Second Edition

Click here ‘Cricket analytics with cricketr: Second Edition‘

B) Beaten by sheer pace: Cricket analytics with yorkr(2nd edition)

Click here Beaten by sheer pace! Cricket analytics with yorkr

Pick up your copies today!!!

cricketr and yorkr books – Paperback now in Amazon

My books
– Cricket Analytics with cricketr
– Beaten by sheer pace!: Cricket analytics with yorkr
are now available on Amazon in both Paperback and Kindle versions

The cricketr and yorkr packages are written in R, and both are available in CRAN. The books contain details on how to use these R packages to analyze performance of cricketers.

cricketr is based on data from ESPN Cricinfo Statsguru, and can analyze Test, ODI and T20 batsmen & bowlers. yorkr is based on data from Cricsheet, and can analyze ODI, T20 and IPL. yorkr can analyze batsmen, bowlers, matches and teams.

Cricket Analytics with cricketr
You can access the paperback at Cricket analytics with cricketr
untitled1

Beaten by sheer pace! Cricket Analytics with yorkr
You can buy the paperback from Amazon at Beaten by sheer pace: Cricket analytics with yorkr
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Order your copy today! Hope you have a great time reading!

GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables

In this post I introduce my new Shiny app,“GooglyPlus”, which is a  more evolved version of my earlier Shiny app “Googly”. My R package ‘yorkr’,  on which both these Shiny apps are based, has the ability to output either a dataframe or plot, depending on a parameter plot=TRUE or FALSE. My initial version of the app only included plots, and did not exercise the yorkr package fully. Moreover, I am certain, there may be a set of cricket aficionados who would prefer, numbers to charts. Hence I have created this enhanced version of the Googly app and appropriately renamed it as GooglyPlus. GooglyPlus is based on the yorkr package which uses data from Cricsheet. The app is based on IPL data from  all IPL matches from 2008 up to 2016. Feel free to clone/fork or download the code from Github at GooglyPlus.

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!!

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Click  GooglyPlus to access the Shiny app!

The changes for GooglyPlus over the earlier Googly app is only in the following 3 tab panels

  • IPL match
  • Head to head
  • Overall Performance

The analysis of IPL batsman and IPL bowler tabs are unchanged. These charts are as they were before.

The changes are only in  tabs i) IPL match ii) Head to head and  iii) Overall Performance. New functionality has been added and existing functions now have the dual option of either displaying a plot or a table.

The changes are

A) IPL Match
The following additions/enhancements have been done

-Match Batting Scorecard – Table
-Batting Partnerships – Plot, Table (New)
-Batsmen vs Bowlers – Plot, Table(New)
-Match Bowling Scorecard   – Table (New)
-Bowling Wicket Kind – Plot, Table (New)
-Bowling Wicket Runs – Plot, Table (New)
-Bowling Wicket Match – Plot, Table (New)
-Bowler vs Batsmen – Plot, Table (New)
-Match Worm Graph – Plot

B) Head to head
The following functions have been added/enhanced

-Team Batsmen Batting Partnerships All Matches – Plot, Table {Summary (New) and Detailed (New)}
-Team Batting Scorecard All Matches – Table (New)
-Team Batsmen vs Bowlers all Matches – Plot, Table (New)
-Team Wickets Opposition All Matches – Plot, Table (New)
-Team Bowling Scorecard All Matches – Table (New)
-Team Bowler vs Batsmen All Matches – Plot, Table (New)
-Team Bowlers Wicket Kind All Matches – Plot, Table (New)
-Team Bowler Wicket Runs All Matches – Plot, Table (New)
-Win Loss All Matches – Plot

C) Overall Performance
The following additions/enhancements have been done in this tab

-Team Batsmen Partnerships Overall – Plot, Table {Summary (New) and Detailed (New)}
-Team Batting Scorecard Overall –Table (New)
-Team Batsmen vs Bowlers Overall – Plot, Table (New)
-Team Bowler vs Batsmen Overall – Plot, Table (New)
-Team Bowling Scorecard Overall – Table (New)
-Team Bowler Wicket Kind Overall – Plot, Table (New)

Included below are some random charts and tables. Feel free to explore the Shiny app further

1) IPL Match
a) Match Batting Scorecard (Table only)
This is the batting score card for the Chennai Super Kings & Deccan Chargers 2011-05-11

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b)  Match batting partnerships (Plot)
Delhi Daredevils vs Kings XI Punjab – 2011-04-23

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c) Match batting partnerships (Table)
The same batting partnership  Delhi Daredevils vs Kings XI Punjab – 2011-04-23 as a table

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d) Batsmen vs Bowlers (Plot)
Kolkata Knight Riders vs Mumbai Indians 2010-04-19

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e)  Match Bowling Scorecard (Table only)
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B) Head to head

a) Team Batsmen Partnership (Plot)
Deccan Chargers vs Kolkata Knight Riders all matches

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b)  Team Batsmen Partnership (Summary – Table)
In the following tables it can be seen that MS Dhoni has performed better that SK Raina  CSK against DD matches, whereas SK Raina performs better than Dhoni in CSK vs  KKR matches

i) Chennai Super Kings vs Delhi Daredevils (Summary – Table)

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ii) Chennai Super Kings vs Kolkata Knight Riders (Summary – Table)
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iii) Rising Pune Supergiants vs Gujarat Lions (Detailed – Table)
This table provides the detailed partnership for RPS vs GL all matches

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c) Team Bowling Scorecard (Table only)
This table gives the bowling scorecard of Pune Warriors vs Deccan Chargers in all matches

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C) Overall performances
a) Batting Scorecard All Matches  (Table only)

This is the batting scorecard of Royal Challengers Bangalore. The top 3 batsmen are V Kohli, C Gayle and AB Devilliers in that order

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b) Batsman vs Bowlers all Matches (Plot)
This gives the performance of Mumbai Indian’s batsman of Rank=1, which is Rohit Sharma, against bowlers of all other teams

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c)  Batsman vs Bowlers all Matches (Table)
The above plot as a table. It can be seen that Rohit Sharma has scored maximum runs against M Morkel, then Shakib Al Hasan and then UT Yadav.

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d) Bowling scorecard (Table only)
The table below gives the bowling scorecard of CSK. R Ashwin leads with a tally of 98 wickets followed by DJ Bravo who has 88 wickets and then JA Morkel who has 83 wickets in all matches against all teams

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This is just a random selection of functions. Do play around with the app and checkout how the different IPL batsmen, bowlers and teams stack against each other. Do read my earlier post Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr  for more details about the app and other functions available.

Click GooglyPlus to access the Shiny app!

You can clone/fork/download the code from Github at GooglyPlus

Hope you have fun playing around with the Shiny app!

Note: In the tabs, for some of the functions, not all controls  are required. It is possible to enable the controls selectively but this has not been done in this current version. I may make the changes some time in the future.

Take a look at my other Shiny apps
a.Revisiting crimes against women in India
b. Natural language processing: What would Shakespeare say?

Check out some of my other posts
1. Analyzing World Bank data with WDI, googleVis Motion Charts
2. Video presentation on Machine Learning, Data Science, NLP and Big Data – Part 1
3. Singularity
4. Design principles of scalable, distributed systems
5. Simulating an Edge shape in Android
6. Dabbling with Wiener filter in OpenCV

To see all posts click Index of Posts