Pitching yorkpy…swinging away from the leg stump to IPL – Part 3

Clocks offer at best a convenient fiction They imply that time ticks steadily, predictably forward, when our experience shows that it often does the opposite: it stretches and compresses, skips a beat and doubles back.

                                 David Eagleman
                                 

Memory is the space in which a thing happens for a second time

                                 Paul Auster
      

Introduction

In this 3rd post, yorkpy, the python avatar of my R package yorkr develops more muscle. The first two posts of yorkpy were

1. Pitching yorkpy . short of good length to IPL – Part 1 This post dealt with function which perform analytics on an IPL match between any 2 IPL teams
2. Pitching yorkpy…on the middle and outside off-stump to IPL – Part 2 The second post dealt with analytics on all matches between any 2 IPL teams.

This third post deals with analyses and analytics of an IPL team in all matches against all other IPL teams. The data for yorkpy comes from Cricsheet. The data in Cricsheet are in the form of yaml files. These files have already been converted as dataframes and stored as CSV as seen in the earlier posts.You can download all the data used in this post and the previous post at yorkpyData

The signatures of yorkpy and yorkr are identical and will work in almost the same way. However there may be some unique functions in yorkr & yorkpy, based on what my thought process was on that day!

-You can clone/download the code at Github yorkpy
-This post has been published to RPubs at yorkpy-Part3
-Download this post as PDF at IPLT20-yorkpy-part3
-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).

The IPL T20 functions in yorkpy are shown below

2. Get data for all T20 matches between an IPL team and all other IPL teams

We can get all IPL T20 matches between an IPL team  and all other 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 between the IPL team and all other teams 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"
#getAllMatchesAllOpposition("Kolkata Knight Riders",dir=dir1,save=True)

3. Save data for all matches between an IPL team and all oppositions

This can be done locally using the function below. You could use this function to get combine all IPL matches of an IPL team against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
#dir1= "C:\\software\\cricket-package\\yorkpyPkg\\yorkpyData\\IPLConverted"
#saveAllMatchesAllOppositionIPLT20(dir1)

Note: In the functions below, I have randomly chosen an IPL team for the analyses. You are free to choose any IPL team for your analysis

4.Team Batsmen partnership in Twenty20 (all matches against all IPL teams – summary)

The function below computes the highest partnerships for an IPL team against all other IPL teams for e.g. the batsmen with the highest partnership from Chennai Super Kings in all matches against all other IPL teams. Any other IPL team could have also been chosen.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Chennai Super Kings-allMatchesAllOpposition.csv") 
csk_matches = pd.read_csv(path)
m=yka.teamBatsmenPartnershiAllOppnAllMatches(csk_matches,'Chennai Super Kings',report="summary")
print(m)
##         batsman  totalPartnershipRuns
## 42     SK Raina                  3699
## 28     MS Dhoni                  2986
## 25   MEK Hussey                  1768
## 24      M Vijay                  1600
## 36  S Badrinath                  1441

5. Team Batsmen partnership in Twenty20 (all matches against all IPL teams -detailed)

The function below gives the detailed breakup of partnerships for Mumbai Indian against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Mumbai Indians-allMatchesAllOpposition.csv")
mi_matches = pd.read_csv(path)
theTeam='Mumbai Indians'
m=yka.teamBatsmenPartnershiAllOppnAllMatches(mi_matches,theTeam,report="detailed", top=3)
print(m)
##        batsman  totalPartnershipRuns      non_striker  partnershipRuns
## 0    RG Sharma                3037.0        A Symonds            142.0
## 1    RG Sharma                3037.0      AC Blizzard              5.0
## 2    RG Sharma                3037.0         AJ Finch              2.0
## 3    RG Sharma                3037.0          AP Tare             32.0
## 4    RG Sharma                3037.0        AT Rayudu            566.0
## 5    RG Sharma                3037.0          BR Dunk              1.0
## 6    RG Sharma                3037.0      CJ Anderson            183.0
## 7    RG Sharma                3037.0        CM Gautam             22.0
## 8    RG Sharma                3037.0         DR Smith             50.0
## 9    RG Sharma                3037.0       GJ Maxwell              6.0
## 10   RG Sharma                3037.0         HH Gibbs            109.0
## 11   RG Sharma                3037.0        HH Pandya            105.0
## 12   RG Sharma                3037.0  Harbhajan Singh             86.0
## 13   RG Sharma                3037.0       JC Buttler            105.0
## 14   RG Sharma                3037.0     JEC Franklin             50.0
## 15   RG Sharma                3037.0       KA Pollard            633.0
## 16   RG Sharma                3037.0       KD Karthik            170.0
## 17   RG Sharma                3037.0        KH Pandya             34.0
## 18   RG Sharma                3037.0        KV Sharma             33.0
## 19   RG Sharma                3037.0      LMP Simmons            172.0
## 20   RG Sharma                3037.0       MEK Hussey             21.0
## 21   RG Sharma                3037.0       MJ Guptill             61.0
## 22   RG Sharma                3037.0   MJ McClenaghan              2.0
## 23   RG Sharma                3037.0           N Rana             25.0
## 24   RG Sharma                3037.0         PA Patel            103.0
## 25   RG Sharma                3037.0          RE Levi             25.0
## 26   RG Sharma                3037.0       SL Malinga              0.0
## 27   RG Sharma                3037.0     SR Tendulkar            208.0
## 28   RG Sharma                3037.0        SS Tiwary             27.0
## 29   RG Sharma                3037.0         TL Suman              7.0
## ..         ...                   ...              ...              ...
## 70  KA Pollard                2344.0      CJ Anderson             82.0
## 71  KA Pollard                2344.0        CM Gautam             16.0
## 72  KA Pollard                2344.0         DR Smith             10.0
## 73  KA Pollard                2344.0      DS Kulkarni             15.0
## 74  KA Pollard                2344.0        HH Pandya            158.0
## 75  KA Pollard                2344.0  Harbhajan Singh            158.0
## 76  KA Pollard                2344.0        J Suchith             26.0
## 77  KA Pollard                2344.0       JC Buttler             37.0
## 78  KA Pollard                2344.0     JEC Franklin             38.0
## 79  KA Pollard                2344.0        JP Duminy             63.0
## 80  KA Pollard                2344.0       KD Karthik             40.0
## 81  KA Pollard                2344.0        KH Pandya            111.0
## 82  KA Pollard                2344.0        KV Sharma             13.0
## 83  KA Pollard                2344.0      LMP Simmons             77.0
## 84  KA Pollard                2344.0       MEK Hussey             10.0
## 85  KA Pollard                2344.0       MG Johnson              1.0
## 86  KA Pollard                2344.0           N Rana             60.0
## 87  KA Pollard                2344.0         PA Patel             18.0
## 88  KA Pollard                2344.0          PP Ojha             12.0
## 89  KA Pollard                2344.0         R Dhawan             25.0
## 90  KA Pollard                2344.0        R McLaren             20.0
## 91  KA Pollard                2344.0        R Sathish             27.0
## 92  KA Pollard                2344.0        RG Sharma            587.0
## 93  KA Pollard                2344.0      RJ Peterson              0.0
## 94  KA Pollard                2344.0         S Dhawan             20.0
## 95  KA Pollard                2344.0       SL Malinga             14.0
## 96  KA Pollard                2344.0     SR Tendulkar             69.0
## 97  KA Pollard                2344.0        SS Tiwary             42.0
## 98  KA Pollard                2344.0         TL Suman              2.0
## 99  KA Pollard                2344.0           Z Khan              1.0
## 
## [100 rows x 4 columns]

6. Team Batsmen partnership in Twenty20 – Chart (all matches against all IPL teams)

The function below plots the partnerships of an IPL team against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Delhi Daredevils-allMatchesAllOpposition.csv")
dd_matches = pd.read_csv(path)
yka.teamBatsmenPartnershipAllOppnAllMatchesChart(dd_matches,'Delhi Daredevils', plot=True, top=4, partnershipRuns=100)

7.Team Batsmen partnership in Twenty20 – Dataframe (all matches against all IPL teams)

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 partnwerships to be considered

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Kochi Tuskers Kerala-allMatchesAllOpposition.csv")
ktk_matches = pd.read_csv(path)
m=yka.teamBatsmenPartnershipAllOppnAllMatchesChart(ktk_matches,'Kochi Tuskers Kerala', plot=False, top=3, partnershipRuns=100)
print(m)
##              batsman       non_striker  partnershipRuns
## 0        BB McCullum          BJ Hodge             17.0
## 1        BB McCullum  DPMD Jayawardene            160.0
## 2        BB McCullum         M Klinger             67.0
## 3        BB McCullum          PA Patel             40.0
## 4        BB McCullum         RA Jadeja             19.0
## 5        BB McCullum        VVS Laxman             41.0
## 6        BB McCullum  Y Gnaneswara Rao             13.0
## 7   DPMD Jayawardene       BB McCullum            152.0
## 8   DPMD Jayawardene          BJ Hodge             41.0
## 9   DPMD Jayawardene         KM Jadhav              4.0
## 10  DPMD Jayawardene         M Klinger             28.0
## 11  DPMD Jayawardene           OA Shah              9.0
## 12  DPMD Jayawardene          PA Patel             25.0
## 13  DPMD Jayawardene         RA Jadeja             18.0
## 14  DPMD Jayawardene          RV Gomez             10.0
## 15  DPMD Jayawardene        VVS Laxman             12.0
## 16          BJ Hodge       BB McCullum             18.0
## 17          BJ Hodge  DPMD Jayawardene             47.0
## 18          BJ Hodge         KM Jadhav              2.0
## 19          BJ Hodge           OA Shah             19.0
## 20          BJ Hodge          PA Patel             79.0
## 21          BJ Hodge         RA Jadeja             99.0
## 22          BJ Hodge          RV Gomez             21.0

8. Team batsmen versus bowler in Twenty20-Chart (all matches against all IPL teams)

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

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Royal Challengers Bangalore-allMatchesAllOpposition.csv")
rcb_matches = pd.read_csv(path)
yka.teamBatsmenVsBowlersAllOppnAllMatches(rcb_matches,"Royal Challengers Bangalore",plot=True,top=3,runsScored=60)

9 Team batsmen versus bowler in Twenty20-Dataframe (all matches against all IPL teams)

This function provides the batting performance of an IPL team against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Kings XI Punjab-allMatchesAllOpposition.csv")
kxip_matches = pd.read_csv(path)
m=yka.teamBatsmenVsBowlersAllOppnAllMatches(kxip_matches,'Kings XI Punjab',plot=False,top=2,runsScored=50)
print(m)
##        batsman            bowler  runsScored
## 0     SE Marsh        A Chandila        20.0
## 1     SE Marsh       A Choudhary         1.0
## 2     SE Marsh          A Kumble        37.0
## 3     SE Marsh          A Mishra         0.0
## 4     SE Marsh          A Mithun         9.0
## 5     SE Marsh           A Nehra        33.0
## 6     SE Marsh           A Singh         2.0
## 7     SE Marsh         A Symonds         5.0
## 8     SE Marsh         AA Chavan        19.0
## 9     SE Marsh   AA Jhunjhunwala        15.0
## 10    SE Marsh        AB Agarkar        27.0
## 11    SE Marsh          AB Dinda        31.0
## 12    SE Marsh       AB McDonald         9.0
## 13    SE Marsh         AC Thomas         1.0
## 14    SE Marsh        AD Mathews         7.0
## 15    SE Marsh        AD Russell         8.0
## 16    SE Marsh            AJ Tye         0.0
## 17    SE Marsh        AL Menaria         6.0
## 18    SE Marsh          AM Salvi         8.0
## 19    SE Marsh          AN Ahmed        16.0
## 20    SE Marsh           AS Raut         7.0
## 21    SE Marsh      Ankit Sharma         2.0
## 22    SE Marsh        Ankit Soni        11.0
## 23    SE Marsh           B Kumar        10.0
## 24    SE Marsh             B Lee         1.0
## 25    SE Marsh        BAW Mendis        11.0
## 26    SE Marsh           BB Sran         3.0
## 27    SE Marsh          BJ Hodge        18.0
## 28    SE Marsh      Basil Thampi        17.0
## 29    SE Marsh   C de Grandhomme         8.0
## ..         ...               ...         ...
## 235  DA Miller          R Sharma         7.0
## 236  DA Miller         R Tewatia         3.0
## 237  DA Miller     R Vinay Kumar        30.0
## 238  DA Miller         RA Jadeja        84.0
## 239  DA Miller         RD Chahar         3.0
## 240  DA Miller  RE van der Merwe         5.0
## 241  DA Miller  RN ten Doeschate         1.0
## 242  DA Miller          RP Singh        35.0
## 243  DA Miller       Rashid Khan         0.0
## 244  DA Miller         S Aravind         7.0
## 245  DA Miller            S Kaul        23.0
## 246  DA Miller         S Kaushik         8.0
## 247  DA Miller           S Ladda         6.0
## 248  DA Miller          S Nadeem        11.0
## 249  DA Miller          SK Raina         2.0
## 250  DA Miller        SL Malinga         9.0
## 251  DA Miller   SMSM Senanayake         6.0
## 252  DA Miller         SP Narine        10.0
## 253  DA Miller         SR Watson        16.0
## 254  DA Miller         STR Binny        14.0
## 255  DA Miller   Shakib Al Hasan         3.0
## 256  DA Miller          TA Boult        20.0
## 257  DA Miller        TG Southee        11.0
## 258  DA Miller          UT Yadav        51.0
## 259  DA Miller          VR Aaron        19.0
## 260  DA Miller          VS Malik         3.0
## 261  DA Miller         YK Pathan         0.0
## 262  DA Miller         YS Chahal        35.0
## 263  DA Miller      Yuvraj Singh        11.0
## 264  DA Miller            Z Khan         2.0
## 
## [265 rows x 3 columns]

10. Team batting scorecard(all matches against all IPL teams)

This function provides the overall scorecard for an IPL team in all matches against all other IPL teams. The batting scorecard shows the top batsmen for Kolkata Knight Riders below

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Kolkata Knight Riders-allMatchesAllOpposition.csv")
kkr_matches = pd.read_csv(path)
scorecard=yka.teamBattingScorecardAllOppnAllMatches(kkr_matches,'Kolkata Knight Riders')
print(scorecard)
##              batsman    runs  balls   4s  6s          SR
## 19         G Gambhir  3035.0   2533  352  46  119.818397
## 17         YK Pathan  1893.0   1421  150  86  133.216045
## 22        RV Uthappa  1806.0   1311  200  54  137.757437
## 16         JH Kallis  1295.0   1237  128  23  104.688763
## 23         MK Pandey  1270.0   1048  103  38  121.183206
## 0         SC Ganguly  1031.0    977  105  36  105.527124
## 12         MK Tiwary  1002.0    921   86  23  108.794788
## 1        BB McCullum   882.0    754   92  32  116.976127
## 25          SA Yadav   608.0    474   54  21  128.270042
## 15          MS Bisla   543.0    518   60  16  104.826255
## 26        AD Russell   516.0    308   45  34  167.532468
## 4          DJ Hussey   511.0    417   31  28  122.541966
## 24   Shakib Al Hasan   498.0    399   44  15  124.812030
## 10          BJ Hodge   476.0    430   47  10  110.697674
## 11          CH Gayle   463.0    350   45  26  132.285714
## 18        EJG Morgan   444.0    373   45  16  119.034853
## 54           CA Lynn   378.0    250   30  23  151.200000
## 6          LR Shukla   374.0    320   31  15  116.875000
## 29  RN ten Doeschate   326.0    238   26  15  136.974790
## 21            DB Das   304.0    267   23  16  113.857678
## 3            WP Saha   298.0    213   24  12  139.906103
## 28         SP Narine   271.0    193   36  12  140.414508
## 13        AD Mathews   249.0    211   20   8  118.009479
## 33       Salman Butt   193.0    172   30   2  112.209302
## 41        MN van Wyk   167.0    135   19   1  123.703704
## 7         AB Agarkar   160.0    137   12   5  116.788321
## 20          R Bhatia   159.0    134   15   3  118.656716
## 51   C de Grandhomme   126.0     92   10   6  136.956522
## 39         CA Pujara   122.0    119   14   3  102.521008
## 40           OA Shah   115.0     96    7   5  119.791667
## ..               ...     ...    ...  ...  ..         ...
## 50         JO Holder    22.0     20    2   1  110.000000
## 65     Kuldeep Yadav    20.0     22    2   0   90.909091
## 71         BJ Haddin    18.0     11    2   1  163.636364
## 70   NM Coulter-Nile    14.0     13    0   2  107.692308
## 47          L Balaji    13.0     12    1   0  108.333333
## 55   SMSM Senanayake    10.0     17    0   0   58.823529
## 53          M Morkel     9.0      8    0   0  112.500000
## 62          AN Ghosh     7.0      8    1   0   87.500000
## 32           GB Hogg     7.0      6    0   0  116.666667
## 56        MV Boucher     6.0      6    0   0  100.000000
## 77     Azhar Mahmood     6.0      8    1   0   75.000000
## 78          DM Bravo     6.0      5    1   0  120.000000
## 68         SS Shaikh     6.0      7    1   0   85.714286
## 66          TA Boult     5.0      8    0   0   62.500000
## 76    Mohammed Shami     5.0     10    0   0   50.000000
## 80           P Dogra     5.0      8    0   0   62.500000
## 69     R Vinay Kumar     4.0      7    0   0   57.142857
## 75        AS Rajpoot     4.0      7    1   0   57.142857
## 43     Mandeep Singh     4.0     11    1   0   36.363636
## 37          AB Dinda     4.0      8    0   0   50.000000
## 79        PJ Sangwan     4.0      2    1   0  200.000000
## 73         R McLaren     3.0      6    0   0   50.000000
## 67         SB Bangar     2.0      9    0   0   22.222222
## 57       RS Gavaskar     2.0      8    0   0   25.000000
## 72     Shoaib Akhtar     2.0      8    0   0   25.000000
## 38  Mashrafe Mortaza     2.0      2    0   0  100.000000
## 63        BAW Mendis     1.0      2    0   0   50.000000
## 58           SE Bond     1.0      2    0   0   50.000000
## 44     CK Langeveldt     0.0      1    0   0    0.000000
## 30        PJ Cummins     0.0      2    0   0    0.000000
## 
## [81 rows x 6 columns]

10a. Team batting scorecard(all matches against all IPL teams)

The output below shows the Chennai Super Kings against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Chennai Super Kings-allMatchesAllOpposition.csv")
csk_matches = pd.read_csv(path)
scorecard=yka.teamBattingScorecardAllOppnAllMatches(csk_matches,'Chennai Super Kings')
print(scorecard)
##             batsman  runs  balls   4s   6s          SR
## 3          SK Raina  3699   2735  322  150  135.246801
## 5          MS Dhoni  2986   2199  218  126  135.788995
## 17       MEK Hussey  1768   1461  181   45  121.013005
## 11          M Vijay  1600   1289  141   66  124.127230
## 4       S Badrinath  1441   1245  154   28  115.742972
## 9         ML Hayden  1107    838  121   44  132.100239
## 18     F du Plessis  1081    867   92   29  124.682814
## 25         DR Smith   965    766  102   50  125.979112
## 26      BB McCullum   841    634   83   42  132.649842
## 6         JA Morkel   827    591   51   48  139.932318
## 20         DJ Bravo   706    543   54   30  130.018416
## 19        RA Jadeja   670    533   46   23  125.703565
## 0          PA Patel   516    529   67    7   97.542533
## 2        SP Fleming   196    171   27    3  114.619883
## 13         R Ashwin   190    208   19    1   91.346154
## 21         S Vidyut   145    115   21    3  126.086957
## 31          WP Saha   144    138    8    8  104.347826
## 1        S Anirudha   133    116    9    7  114.655172
## 33        DJ Hussey   116     96    8    6  120.833333
## 38           P Negi   116     77   10    5  150.649351
## 10         JDP Oram   106    107    6    5   99.065421
## 29        GJ Bailey    63     67    9    0   94.029851
## 22       A Flintoff    62     57    5    2  108.771930
## 8           MS Gony    50     39    2    5  128.205128
## 7   Joginder Sharma    36     30    1    2  120.000000
## 27         M Manhas    35     26    3    1  134.615385
## 28        MM Sharma    29     26    1    2  111.538462
## 23        SB Jakati    27     28    3    0   96.428571
## 12          JM Kemp    26     25    1    1  104.000000
## 14         L Balaji    22     35    1    1   62.857143
## 24     DE Bollinger    21     23    1    1   91.304348
## 41    CK Kapugedera    16     24    0    0   66.666667
## 37        CH Morris    14     17    0    0   82.352941
## 30       T Thushara    12     19    0    0   63.157895
## 42          M Ntini    11     19    2    0   57.894737
## 15   M Muralitharan     9     13    1    0   69.230769
## 32  KMDN Kulasekara     5      3    1    0  166.666667
## 34        SB Styris     5      2    1    0  250.000000
## 35       B Laughlin     4      9    0    0   44.444444
## 16          S Tyagi     3      4    0    0   75.000000
## 45  KB Arun Karthik     3      5    0    0   60.000000
## 36       AS Rajpoot     2      6    0    0   33.333333
## 43          RG More     2      2    0    0  100.000000
## 44         S Randiv     2      4    0    0   50.000000
## 39          A Nehra     1      7    0    0   14.285714
## 40         A Mukund     0      1    0    0    0.000000

11.Team Bowling scorecard (all matches against all IPL teams)

The output below gives the bowling performance of an IPL team against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Sunrisers Hyderabad-allMatchesAllOpposition.csv")
srh_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardAllOppnAllMatches(srh_matches,'Sunrisers Hyderabad')
## C:\Users\Ganesh\ANACON~1\lib\site-packages\yorkpy\analytics.py:564: SettingWithCopyWarning: 
## A value is trying to be set on a copy of a slice from a DataFrame.
## Try using .loc[row_indexer,col_indexer] = value instead
## 
## See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
##   df1['over']=df1.delivery.astype(int)
## C:\Users\Ganesh\ANACON~1\lib\site-packages\yorkpy\analytics.py:567: SettingWithCopyWarning: 
## A value is trying to be set on a copy of a slice from a DataFrame.
## Try using .loc[row_indexer,col_indexer] = value instead
## 
## See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
##   df1['runsConceded']=df1['runs'] + df1['wides'] + df1['noballs']
print(scorecard)
##               bowler  overs  runs  maidens  wicket   econrate
## 60       JP Faulkner     28   192        0      15   6.857143
## 83         MM Sharma     37   334        0      13   9.027027
## 119       SL Malinga     31   215        0      13   6.935484
## 123        SR Watson     30   281        0      13   9.366667
## 90   NM Coulter-Nile     24   166        0      12   6.916667
## 31          DJ Bravo     26   184        0      12   7.076923
## 135         UT Yadav     37   297        0      12   8.027027
## 125   Sandeep Sharma     32   280        0      11   8.750000
## 75          M Morkel     25   195        0       9   7.800000
## 81    MJ McClenaghan     24   175        0       9   7.291667
## 5           AB Dinda     23   165        0       9   7.173913
## 55        JD Unadkat     20   167        0       8   8.350000
## 36       DS Kulkarni     28   200        0       8   7.142857
## 25         CH Morris     24   190        0       7   7.916667
## 101         R Bhatia     18   128        0       7   7.111111
## 70     Kuldeep Yadav     16   129        0       7   8.062500
## 11          AR Patel     27   208        0       7   7.703704
## 122        SP Narine     43   282        0       7   6.558140
## 141        YS Chahal     26   224        0       6   8.615385
## 44   Harbhajan Singh     39   264        0       6   6.769231
## 96         PP Chawla     21   140        0       6   6.666667
## 4            A Zampa      4    19        0       6   4.750000
## 126  Shakib Al Hasan     14    99        1       6   7.071429
## 80        MG Johnson     20   155        0       6   7.750000
## 59         JP Duminy     10    80        0       5   8.000000
## 58         JO Holder     15   113        0       5   7.533333
## 92           P Kumar     23   173        0       5   7.521739
## 100         R Ashwin     28   142        0       5   5.071429
## 2           A Mishra     18   144        0       4   8.000000
## 106    R Vinay Kumar     19   154        0       4   8.105263
## ..               ...    ...   ...      ...     ...        ...
## 6     AD Mascarenhas      4    25        0       0   6.250000
## 13        Ankit Soni      2    31        0       0  15.500000
## 132          TM Head      1    11        0       0  11.000000
## 10          AN Ahmed      6    63        0       0  10.500000
## 131       TM Dilshan      1    10        0       0  10.000000
## 134     Tejas Baroka      3    33        0       0  11.000000
## 73          M Ashwin      1     6        0       0   6.000000
## 109        RG Sharma      1     5        0       0   5.000000
## 22      Basil Thampi      2    21        0       0  10.500000
## 23           C Munro      1     8        0       0   8.000000
## 68         KV Sharma      2    19        0       0   9.500000
## 77           M Vijay      4    24        0       0   6.000000
## 66         KJ Abbott      3    34        0       0  11.333333
## 65         KH Pandya      2    17        0       0   8.500000
## 82          MM Patel      3    22        0       0   7.333333
## 62          K Rabada      4    59        0       0  14.750000
## 85        MP Stoinis      3    28        0       0   9.333333
## 54         JA Morkel      3    35        0       0  11.666667
## 46          I Sharma      8    64        0       0   8.000000
## 94        PJ Cummins      4    37        0       0   9.250000
## 95        PJ Sangwan      8    82        0       0  10.250000
## 103        R Sathish      1     9        0       0   9.000000
## 38          DW Steyn      2    17        0       0   8.500000
## 108          RG More      2    28        0       0  14.000000
## 34         DJG Sammy      2    18        0       0   9.000000
## 33     DJ Muthuswami      2    20        0       0  10.000000
## 32          DJ Hooda      5    45        0       0   9.000000
## 24          CH Gayle      3    24        0       0   8.000000
## 116        SA Abbott      2    21        0       0  10.500000
## 72         LR Shukla      2    28        0       0  14.000000
## 
## [144 rows x 6 columns]

11a.Team Bowling scorecard (all matches against all IPL teams)

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Rajasthan Royals-allMatchesAllOpposition.csv")
rr_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardAllOppnAllMatches(rr_matches,'Rajasthan Royals')
print(scorecard)
##                bowler  overs  runs  maidens  wicket   econrate
## 2            A Mishra     63   426        0      29   6.761905
## 66          JA Morkel     38   301        0      16   7.921053
## 129     R Vinay Kumar     48   406        0      15   8.458333
## 135          RP Singh     41   255        0      14   6.219512
## 95        MF Maharoof     23   139        0      14   6.043478
## 118         PP Chawla     45   353        0      14   7.844444
## 130         RA Jadeja     32   227        0      14   7.093750
## 50           DW Steyn     43   232        0      13   5.395349
## 56    Harbhajan Singh     45   341        0      12   7.577778
## 1            A Kumble     21   108        1      12   5.142857
## 159        SL Malinga     49   363        0      12   7.408163
## 60          IK Pathan     37   279        0      11   7.540541
## 82         KA Pollard     21   201        0      11   9.571429
## 119           PP Ojha     46   426        0      11   9.260870
## 121          R Ashwin     29   222        0      11   7.655172
## 22            B Kumar     31   233        0      11   7.516129
## 3             A Nehra     32   214        0      11   6.687500
## 41           DJ Bravo     30   292        0      10   9.733333
## 110           P Kumar     48   329        1      10   6.854167
## 58           I Sharma     37   284        0       9   7.675676
## 168   Shakib Al Hasan     25   153        0       9   6.120000
## 87           L Balaji     33   277        0       9   8.393939
## 122          R Bhatia     19   121        0       8   6.368421
## 48        DS Kulkarni     21   148        0       8   7.047619
## 101         MM Sharma     20   142        0       8   7.100000
## 174          UT Yadav     25   203        0       8   8.120000
## 15           AR Patel     16   110        0       7   6.875000
## 133         RJ Harris     16   132        0       7   8.250000
## 72          JH Kallis     37   254        0       7   6.864865
## 192            Z Khan     33   213        0       7   6.454545
## ..                ...    ...   ...      ...     ...        ...
## 170      Shoaib Ahmed      2    19        0       0   9.500000
## 54          GS Sandhu      4    49        0       0  12.250000
## 139          RV Gomez      1     9        0       0   9.000000
## 163         SPD Smith      0     5        0       0        inf
## 115       PC Valthaty      3    35        0       0  11.666667
## 34        CJ Anderson      4    26        0       0   6.500000
## 81         K Upadhyay      3    29        0       0   9.666667
## 79             K Goel      1    11        0       0  11.000000
## 28          BJ Rohrer      1    12        0       0  12.000000
## 78    Joginder Sharma      2    23        0       0  11.500000
## 99          MK Tiwary      2    28        0       0  14.000000
## 26       BE Hendricks      4    57        0       0  14.250000
## 102          MR Marsh      1    10        0       0  10.000000
## 106       NL McCullum      3    22        0       0   7.333333
## 113        P Prasanth      1    18        0       0  18.000000
## 114           P Suyal      4    45        0       0  11.250000
## 46      DP Vijaykumar      1    10        0       0  10.000000
## 154         SB Styris      2    14        0       0   7.000000
## 71       JEC Franklin      3    32        0       0  10.666667
## 70          JE Taylor      3    22        0       0   7.333333
## 18       Ankit Sharma      4    33        0       0   8.250000
## 134  RN ten Doeschate      2    14        0       0   7.000000
## 16       Abdur Razzak      2    29        0       0  14.500000
## 65           J Theron      6    48        0       0   8.000000
## 146          S Narwal      2    17        0       0   8.500000
## 63            J Botha      1    19        0       0  19.000000
## 149           S Tyagi      8    65        0       0   8.125000
## 151         SB Bangar      2    20        0       0  10.000000
## 13           AM Nayar      2     7        0       0   3.500000
## 0      A Ashish Reddy      3    22        0       0   7.333333
## 
## [193 rows x 6 columns]

12. Team Bowling wicket kind -Chart (all matches against all IPL teams)

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

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Gujarat Lions-allMatchesAllOpposition.csv")
gl_matches = pd.read_csv(path)
yka.teamBowlingWicketKindAllOppnAllMatches(gl_matches,'Gujarat Lions',plot=True,top=5,wickets=2)

13. Team Bowling wicket kind -Dataframe (all matches against all IPL teams)

This gives the type of wickets taken for an IPL team against all other IPL teams.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Rising Pune Supergiants-allMatchesAllOpposition.csv")
rps_matches = pd.read_csv(path)
m=yka.teamBowlingWicketKindAllOppnAllMatches(rps_matches,'Rising Pune Supergiants',plot=False,top=4,wickets=10)
print(m)
##           bowler               kind  wickets
## 0        A Nehra             caught        4
## 1        A Nehra            run out        2
## 2      MM Sharma             caught        3
## 3      MM Sharma  caught and bowled        1
## 4      MM Sharma            run out        1
## 5      SR Watson             bowled        1
## 6      SR Watson             caught        4
## 7  KW Richardson             caught        3
## 8  KW Richardson       retired hurt        1

14 Team Bowler vs Batman -Plot (all matches against all IPL teams)

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

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Rising Pune Supergiants-allMatchesAllOpposition.csv")
rps_matches = pd.read_csv(path)
yka.teamBowlersVsBatsmenAllOppnAllMatches(rps_matches,'Rising Pune Supergiants',plot=True,top=5,runsConceded=10)

15 Team Bowler vs Batman – Dataframe (all matches against all IPL teams)

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Deccan Chargers-allMatchesAllOpposition.csv")
dc_matches = pd.read_csv(path)
m=yka.teamBowlersVsBatsmenAllOppnAllMatches(dc_matches,'Deccan Chargers',plot=False,top=2,runsConceded=30)
print(m)
##        bowler          batsman  runsConceded
## 0     P Kumar   A Ashish Reddy           6.0
## 1     P Kumar        A Symonds          15.0
## 2     P Kumar      AA Bilakhia          12.0
## 3     P Kumar  AA Jhunjhunwala           1.0
## 4     P Kumar     AC Gilchrist          20.0
## 5     P Kumar    Anirudh Singh          11.0
## 6     P Kumar         B Chipli           1.0
## 7     P Kumar         CL White          11.0
## 8     P Kumar     DB Ravi Teja          15.0
## 9     P Kumar        DJ Harris           2.0
## 10    P Kumar         DR Smith           5.0
## 11    P Kumar       FH Edwards           3.0
## 12    P Kumar         HH Gibbs          46.0
## 13    P Kumar         J Theron           0.0
## 14    P Kumar        JP Duminy           4.0
## 15    P Kumar    KC Sangakkara          15.0
## 16    P Kumar        MD Mishra           4.0
## 17    P Kumar         PA Patel           9.0
## 18    P Kumar        RG Sharma          36.0
## 19    P Kumar        RJ Harris           3.0
## 20    P Kumar         S Dhawan          37.0
## 21    P Kumar          S Sohal           6.0
## 22    P Kumar        SB Styris           6.0
## 23    P Kumar    Shahid Afridi           0.0
## 24    P Kumar         TL Suman          22.0
## 25    P Kumar       VVS Laxman           5.0
## 26    P Kumar  Y Venugopal Rao           1.0
## 27  PP Chawla   A Ashish Reddy           2.0
## 28  PP Chawla        A Symonds          35.0
## 29  PP Chawla  AA Jhunjhunwala           6.0
## 30  PP Chawla     AC Gilchrist           4.0
## 31  PP Chawla         B Chipli           8.0
## 32  PP Chawla         CL White          16.0
## 33  PP Chawla     DB Ravi Teja          30.0
## 34  PP Chawla        DJ Harris           9.0
## 35  PP Chawla        DNT Zoysa           1.0
## 36  PP Chawla         HH Gibbs          30.0
## 37  PP Chawla        JP Duminy          10.0
## 38  PP Chawla    KC Sangakkara           1.0
## 39  PP Chawla         MR Marsh           1.0
## 40  PP Chawla         PA Patel           4.0
## 41  PP Chawla         PA Reddy           8.0
## 42  PP Chawla        RG Sharma          50.0
## 43  PP Chawla         S Dhawan          33.0
## 44  PP Chawla        SB Bangar           1.0
## 45  PP Chawla         TL Suman          17.0
## 46  PP Chawla       VVS Laxman           7.0
## 47  PP Chawla  Y Venugopal Rao           3.0

16 Team Wins and Losses – Summary (all matches against all IPL teams)

The function below computes and plots the number of wins and losses between an IPL team and all other IPL teams in all matches. The summary just gives the wins, losses and ties

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Chennai Super Kings-allMatchesAllOpposition.csv")
csk_matches = pd.read_csv(path)
team1='Chennai Super Kings'
yka.plotWinLossByTeamAllOpposition(csk_matches,team1,plot="summary")

16a Team Wins and Losses – Detailed (all matches against all IPL teams)

The function below computes and plot the number of wins and losses between an IPL team and all other IPL teams in all matches. This gives a breakup of which team won against this team.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Chennai Super Kings-allMatchesAllOpposition.csv")
csk_matches = pd.read_csv(path)
team1='Chennai Super Kings'
yka.plotWinLossByTeamAllOpposition(csk_matches,team1,plot="detailed")

16b Team Wins and Losses – Summary (all matches against all IPL teams)

This plot gives the wins vs losses of MI against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Mumbai Indians-allMatchesAllOpposition.csv")
mi_matches = pd.read_csv(path)
team1='Mumbai Indians'
yka.plotWinLossByTeamAllOpposition(mi_matches,team1,plot="summary")

16c Team Wins and Losses – Detailed (all matches against all IPL teams)

The function below computes and plot the number of wins and losses between an IPL team and all other IPL teams in all matches. This gives the breakup of MI wins, losses and ties

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Mumbai Indians-allMatchesAllOpposition.csv")
mi_matches = pd.read_csv(path)
team1='Mumbai Indians'
yka.plotWinLossByTeamAllOpposition(mi_matches,team1,plot="detailed")

17 Team Wins by win type (all matches against all IPL teams)

This function shows how the win happened whether by runs or by wickets in all matches played against all other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Royal Challengers Bangalore-allMatchesAllOpposition.csv")
rcb_matches = pd.read_csv(path)
yka.plotWinsByRunOrWicketsAllOpposition(rcb_matches,'Royal Challengers Bangalore')

18 Team Wins by toss decision (summary) (all matches against all IPL teams)

This show how Royal Challengers Bangalore 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\\data2"
path=os.path.join(dir1,"Royal Challengers Bangalore-allMatchesAllOpposition.csv")
rcb_matches = pd.read_csv(path)
yka.plotWinsbyTossDecisionAllOpposition(rcb_matches,'Royal Challengers Bangalore',tossDecision='field',plot='summary')

18a. Team Wins by toss decision (detailed) (all matches against all IPL teams)

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Kings XI Punjab-allMatchesAllOpposition.csv")
kxip_matches = pd.read_csv(path)
yka.plotWinsbyTossDecisionAllOpposition(kxip_matches,'Kings XI Punjab',tossDecision='field',plot='detailed')

19 Team Wins by toss decision (summary) (all matches against all IPL teams)

This plot shows how Mumbai Indians fared when it chose to bat on winning the toss against all other IPL teams.

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data2"
path=os.path.join(dir1,"Delhi Daredevils-allMatchesAllOpposition.csv")
mi_rcb_matches = pd.read_csv(path)
yka.plotWinsbyTossDecisionAllOpposition(mi_rcb_matches,'Mumbai Indians',tossDecision='bat',plot='summary')

20 Team Wins by toss decision (detailed)(all matches against all IPL teams)

This plot shows how Kings X1 Punjab 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\\data2"
path=os.path.join(dir1,"Kings XI Punjab-allMatchesAllOpposition.csv")
kxip_matches = pd.read_csv(path)
yka.plotWinsbyTossDecisionAllOpposition(kxip_matches,'Kings XI Punjab',tossDecision='bat',plot='detailed')

Feel free to clone/download the code from Github yorkpy

Conclusion

This post included analysis of an IPL team against all other IPL teams. You can download the data for this and the earlier posts from [yorkpyData](https://github.com/tvganesh/yorkpyData

The code can be cloned/downloaded from Github

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

To be continued. Watch this space!

Also see
1. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
2. My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
3. Designing a Social Web Portal
4. Computer Vision: Ramblings on derivatives, histograms and contours
5. Introducing cricket package yorkr: Part 3-Foxed by flight!
6. The making of Total Control Android game

To see all posts click Index of posts

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

Pitching yorkpy … short of good length to IPL – Part 1

I fear not the man who has practiced 10,000 kicks once, but I fear the man who has practiced one kick 10,000 times.
Bruce Lee

I’ve missed more than 9000 shots in my career. I’ve lost almost 300 games. 26 times, I’ve been trusted to take the game winning shot and missed. I’ve failed over and over and over again in my life. And that is why I succeed.
Michael Jordan

Man, it doesn’t matter where you come in to bat, the score is still zero
Viv Richards

Introduction

“If cricketr is to cricpy, then yorkr is to _____?”. Yes, you guessed it right, it is yorkpy. In this post, I introduce my 2nd python package, yorkpy, which is a python clone of my R package yorkr. This package is based on data from Cricsheet. yorkpy currently handles IPL T20 matches.

When I created cricpy, the python avatar, of my R package cricketr, see Introducing cricpy:A python package to analyze performances of cricketers, I had decided that I should avoid doing a python avatar of my R package yorkr (see Introducing cricket package yorkr: Part 1- Beaten by sheer pace!) , as it was more involved, and required the parsing of match data available as yaml files.

Just out of curiosity, I tried the python package ‘yaml’ to read the match data, and lo and behold, I was sucked into the developing the package and so, yorkpy was born. Of course, it goes without saying that, usually when I am in the thick of developing something, I occasionally wonder, why I am doing it, for whom and for what purpose? Maybe it is the joy of ideation, the problem-solving,  the programmer’s high, for sharing my ideas etc. Anyway, whatever be the reason, I hope you enjoy this post and also find yorkpy useful.

You can clone/download the code at Github yorkpy
This post has been published to RPubs at yorkpy-Part1
You can download this post as PDF at IPLT20-yorkpy-part1

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).

The IPL T20 functions in yorkpy are

2. Install the package using ‘pip install’

import pandas as pd
import yorkpy.analytics as yka
#pip install yorkpy

3. Load a yaml file from Cricsheet

There are 2 functions that can be to convert the IPL Twenty20 yaml files to pandas dataframeare

  1. convertYaml2PandasDataframeT20
  2. convertAllYaml2PandasDataframesT20

Note 1: While I have already converted the IPL T20 files, you will need to use these functions for future IPL matches

4. Convert and save IPL T20 yaml file to pandas dataframe

This function will convert a IPL T20 IPL yaml file, in the format as specified in Cricsheet to pandas dataframe. This will be saved as as CSV file in the target directory. The name of the file wil have the following format team1-team2-date.csv. The IPL T20 zip file can be downloaded from Indian Premier League matches.  An example of how a yaml file can be converted to a dataframe and saved is shown below.

import pandas as pd
import yorkpy.analytics as yka
#convertYaml2PandasDataframe(".\\1082593.yaml","..\ipl", ..\\data")

5. Convert and save all IPL T20 yaml files to dataframes

This function will convert all IPL T20 yaml files from a source directory to dataframes, and save it in the target directory, with the names as mentioned above. Since I have already done this, I will not be executing this again. You can download the zip of all the converted RData files from Github at yorkpyData

import pandas as pd
import yorkpy.analytics as yka
#convertAllYaml2PandasDataframes("..\\ipl", "..\\data")

You can download the the zip of the files and use it directly in the functions as follows.For the analysis below I chosen a set of random IPL matches

The randomly selected IPL T20 matches are

  • Chennai Super Kings vs Kings Xi Punjab, 2014-05-30
  • Deccan Chargers vs Delhi Daredevils, 2012-05-10
  • Gujarat Lions vs Mumbai Indians, 2017-04-29
  • Kolkata Knight Riders vs Rajasthan Royals, 2010-04-17
  • Rising Pune Supergiants vs Royal Challengers Bangalore, 2017-04-29

6. Team batting scorecard

The function below computes the batting score card of a team in an IPL match. The scorecard gives the balls faced, the runs scored, 4s, 6s and strike rate. The example below is based on the CSK KXIP match on 30 May 2014.

You can check against the actual scores in this match Chennai Super Kings-Kings XI Punjab-2014-05-30

import pandas as pd
import yorkpy.analytics as yka
csk_kxip=pd.read_csv(".\\Chennai Super Kings-Kings XI Punjab-2014-05-30.csv")
scorecard,extras=yka.teamBattingScorecardMatch(csk_kxip,"Chennai Super Kings")
print(scorecard)
##         batsman  runs  balls  4s  6s          SR
## 0      DR Smith     7     12   0   0   58.333333
## 1  F du Plessis     0      1   0   0    0.000000
## 2      SK Raina    87     26  12   6  334.615385
## 3   BB McCullum    11     16   0   0   68.750000
## 4     RA Jadeja    27     22   2   1  122.727273
## 5     DJ Hussey     1      3   0   0   33.333333
## 6      MS Dhoni    42     34   3   3  123.529412
## 7      R Ashwin    10     11   0   0   90.909091
## 8     MM Sharma     1      3   0   0   33.333333
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    428     14        3        5     5        0      27
print("\n\n")
scorecard1,extras1=yka.teamBattingScorecardMatch(csk_kxip,"Kings XI Punjab")
print(scorecard1)
##       batsman  runs  balls  4s  6s          SR
## 0    V Sehwag   122     62  12   8  196.774194
## 1     M Vohra    34     33   1   2  103.030303
## 2  GJ Maxwell    13      8   1   1  162.500000
## 3   DA Miller    38     19   5   1  200.000000
## 4   GJ Bailey     1      2   0   0   50.000000
## 5     WP Saha     6      4   0   1  150.000000
## 6  MG Johnson     1      1   0   0  100.000000
print(extras1)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    428     14        3        5     5        0      27

Let’s take another random match between Gujarat Lions and Mumbai Indian on 29 Apr 2017 Gujarat Lions-Mumbai Indians-2017-04-29

import pandas as pd
gl_mi=pd.read_csv(".\\Gujarat Lions-Mumbai Indians-2017-04-29.csv")
import yorkpy.analytics as yka
scorecard,extras=yka.teamBattingScorecardMatch(gl_mi,"Gujarat Lions")
print(scorecard)
##          batsman  runs  balls  4s  6s          SR
## 0   Ishan Kishan    48     38   6   2  126.315789
## 1    BB McCullum     6      4   1   0  150.000000
## 2       SK Raina     1      3   0   0   33.333333
## 3       AJ Finch     0      3   0   0    0.000000
## 4     KD Karthik     2      9   0   0   22.222222
## 5      RA Jadeja    28     22   2   1  127.272727
## 6    JP Faulkner    21     29   2   0   72.413793
## 7      IK Pathan     2      3   0   0   66.666667
## 8         AJ Tye    25     12   2   2  208.333333
## 9   Basil Thampi     2      4   0   0   50.000000
## 10    Ankit Soni     7      2   0   1  350.000000
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    306      8        3        1     0        0      12
print("\n\n")
scorecard1,extras1=yka.teamBattingScorecardMatch(gl_mi,"Mumbai Indians")
print(scorecard1)
##             batsman  runs  balls  4s  6s          SR
## 0          PA Patel    70     45   9   1  155.555556
## 1        JC Buttler     9      7   2   0  128.571429
## 2            N Rana    19     16   1   1  118.750000
## 3         RG Sharma     5     13   0   0   38.461538
## 4        KA Pollard    15     11   2   0  136.363636
## 5         KH Pandya    29     20   2   1  145.000000
## 6         HH Pandya     4      5   0   0   80.000000
## 7   Harbhajan Singh     0      1   0   0    0.000000
## 8    MJ McClenaghan     1      1   0   0  100.000000
## 9         JJ Bumrah     0      1   0   0    0.000000
## 10       SL Malinga     0      1   0   0    0.000000
print(extras1)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    306      8        3        1     0        0      12

7. Plot the team batting partnerships

The functions below plot the team batting partnership in the match. It shows what the partnership were in the mtach

Note: Many of the plots include an additional parameters 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 chart using one of the packages like rcharts, ggvis,googleVis or plotly.

import pandas as pd
import yorkpy.analytics as yka
dc_dd=pd.read_csv(".\\Deccan Chargers-Delhi Daredevils-2012-05-10.csv")
yka.teamBatsmenPartnershipMatch(dc_dd,'Deccan Chargers','Delhi Daredevils')

yka.teamBatsmenPartnershipMatch(dc_dd,'Delhi Daredevils','Deccan Chargers',plot=True)
# Print partnerships as a dataframe

rps_rcb=pd.read_csv(".\\Rising Pune Supergiant-Royal Challengers Bangalore-2017-04-29.csv")
m=yka.teamBatsmenPartnershipMatch(rps_rcb,'Royal Challengers Bangalore','Rising Pune Supergiant',plot=False)
print(m)
##            batsman     non_striker  runs
## 0   AB de Villiers         V Kohli     3
## 1         AF Milne         V Kohli     5
## 2        KM Jadhav         V Kohli     7
## 3           P Negi         V Kohli     3
## 4        S Aravind         V Kohli     0
## 5        S Aravind       YS Chahal     8
## 6         S Badree         V Kohli     2
## 7        STR Binny         V Kohli     1
## 8      Sachin Baby         V Kohli     2
## 9          TM Head         V Kohli     2
## 10         V Kohli  AB de Villiers    17
## 11         V Kohli        AF Milne     5
## 12         V Kohli       KM Jadhav     4
## 13         V Kohli          P Negi     9
## 14         V Kohli       S Aravind     2
## 15         V Kohli        S Badree     8
## 16         V Kohli     Sachin Baby     1
## 17         V Kohli         TM Head     9
## 18       YS Chahal       S Aravind     4

8. Batsmen vs Bowler

The function below computes and plots the performances of the batsmen vs the bowlers. As before the plot parameter can be set to True or False. By default it is plot=True

import pandas as pd
import yorkpy.analytics as yka
gl_mi=pd.read_csv(".\\Gujarat Lions-Mumbai Indians-2017-04-29.csv")
yka.teamBatsmenVsBowlersMatch(gl_mi,"Gujarat Lions","Mumbai Indians", plot=True)
# Print 

csk_kxip=pd.read_csv(".\\Chennai Super Kings-Kings XI Punjab-2014-05-30.csv")
m=yka.teamBatsmenVsBowlersMatch(csk_kxip,'Chennai Super Kings','Kings XI Punjab',plot=False)
print(m)
##          batsman           bowler  runs
## 0    BB McCullum         AR Patel     4
## 1    BB McCullum       GJ Maxwell     1
## 2    BB McCullum  Karanveer Singh     6
## 3      DJ Hussey          P Awana     1
## 4       DR Smith       MG Johnson     7
## 5       DR Smith          P Awana     0
## 6       DR Smith   Sandeep Sharma     0
## 7   F du Plessis       MG Johnson     0
## 8      MM Sharma         AR Patel     0
## 9      MM Sharma       MG Johnson     0
## 10     MM Sharma          P Awana     1
## 11      MS Dhoni         AR Patel    12
## 12      MS Dhoni  Karanveer Singh     2
## 13      MS Dhoni       MG Johnson    11
## 14      MS Dhoni          P Awana    15
## 15      MS Dhoni   Sandeep Sharma     2
## 16      R Ashwin         AR Patel     1
## 17      R Ashwin  Karanveer Singh     4
## 18      R Ashwin       MG Johnson     1
## 19      R Ashwin          P Awana     1
## 20      R Ashwin   Sandeep Sharma     3
## 21     RA Jadeja         AR Patel     5
## 22     RA Jadeja       GJ Maxwell     3
## 23     RA Jadeja  Karanveer Singh    19
## 24     RA Jadeja          P Awana     0
## 25      SK Raina       MG Johnson    21
## 26      SK Raina          P Awana    40
## 27      SK Raina   Sandeep Sharma    26

9. Bowling Scorecard

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 yorkpy.analytics as yka
dc_dd=pd.read_csv(".\\Deccan Chargers-Delhi Daredevils-2012-05-10.csv")
a=yka.teamBowlingScorecardMatch(dc_dd,'Deccan Chargers')
print(a)
##        bowler  overs  runs  maidens  wicket  econrate
## 0  AD Russell      4    39        0       0      9.75
## 1   IK Pathan      4    46        0       1     11.50
## 2    M Morkel      4    32        0       1      8.00
## 3    S Nadeem      4    39        0       0      9.75
## 4    VR Aaron      4    30        0       2      7.50
rps_rcb=pd.read_csv(".\\Rising Pune Supergiant-Royal Challengers Bangalore-2017-04-29.csv")
b=yka.teamBowlingScorecardMatch(rps_rcb,'Royal Challengers Bangalore')
print(b)
##               bowler  overs  runs  maidens  wicket  econrate
## 0          DL Chahar      2    18        0       0      9.00
## 1       DT Christian      4    25        0       1      6.25
## 2        Imran Tahir      4    18        0       3      4.50
## 3         JD Unadkat      4    19        0       1      4.75
## 4        LH Ferguson      4     7        1       3      1.75
## 5  Washington Sundar      2     7        0       1      3.50

10. Wicket Kind

The plots below provide the kind of wicket taken by the bowler (caught, bowled, lbw etc.) for the IPL match

import pandas as pd
import yorkpy.analytics as yka
kkr_rr=pd.read_csv(".\\Kolkata Knight Riders-Rajasthan Royals-2010-04-17.csv")
yka.teamBowlingWicketKindMatch(kkr_rr,'Kolkata Knight Riders','Rajasthan Royals')

csk_kxip=pd.read_csv(".\\Chennai Super Kings-Kings XI Punjab-2014-05-30.csv")
m = yka.teamBowlingWicketKindMatch(csk_kxip,'Chennai Super Kings','Kings-Kings XI Punjab',plot=False)
print(m)
##             bowler     kind  player_out
## 0         AR Patel  run out           1
## 1         AR Patel  stumped           1
## 2  Karanveer Singh  run out           1
## 3       MG Johnson   caught           1
## 4          P Awana   caught           2
## 5   Sandeep Sharma   bowled           1

11. Wicket vs Runs conceded

The plots below provide the wickets taken and the runs conceded by the bowler in the IPL T20 match

import pandas as pd
import yorkpy.analytics as yka
dc_dd=pd.read_csv(".\\Deccan Chargers-Delhi Daredevils-2012-05-10.csv")
yka.teamBowlingWicketMatch(dc_dd,"Deccan Chargers", "Delhi Daredevils",plot=True)

print("\n\n")
rps_rcb=pd.read_csv(".\\Rising Pune Supergiant-Royal Challengers Bangalore-2017-04-29.csv")
a=yka.teamBowlingWicketMatch(rps_rcb,"Royal Challengers Bangalore", "Rising Pune Supergiant",plot=False)
print(a)
##               bowler      player_out  kind
## 0       DT Christian         V Kohli     1
## 1        Imran Tahir        AF Milne     1
## 2        Imran Tahir          P Negi     1
## 3        Imran Tahir        S Badree     1
## 4         JD Unadkat         TM Head     1
## 5        LH Ferguson  AB de Villiers     1
## 6        LH Ferguson       KM Jadhav     1
## 7        LH Ferguson       STR Binny     1
## 8  Washington Sundar     Sachin Baby     1

12. Bowler Vs Batsmen

The functions compute and display how the different bowlers of the IPL team performed against the batting opposition.

import pandas as pd
import yorkpy.analytics as yka
csk_kxip=pd.read_csv(".\\Chennai Super Kings-Kings XI Punjab-2014-05-30.csv")
yka.teamBowlersVsBatsmenMatch(csk_kxip,"Chennai Super Kings","Kings XI Punjab")

print("\n\n")
kkr_rr=pd.read_csv(".\\Kolkata Knight Riders-Rajasthan Royals-2010-04-17.csv")
m =yka.teamBowlersVsBatsmenMatch(kkr_rr,"Rajasthan Royals","Kolkata Knight Riders",plot=False)
print(m)
##        batsman      bowler  runs
## 0     AC Voges    AB Dinda     1
## 1     AC Voges  JD Unadkat     1
## 2     AC Voges   LR Shukla     1
## 3     AC Voges    M Kartik     5
## 4     AJ Finch    AB Dinda     3
## 5     AJ Finch  JD Unadkat     3
## 6     AJ Finch   LR Shukla    13
## 7     AJ Finch    M Kartik     2
## 8     AJ Finch     SE Bond     0
## 9      AS Raut    AB Dinda     1
## 10     AS Raut  JD Unadkat     1
## 11    FY Fazal    AB Dinda     1
## 12    FY Fazal   LR Shukla     3
## 13    FY Fazal    M Kartik     3
## 14    FY Fazal     SE Bond     6
## 15     NV Ojha    AB Dinda    10
## 16     NV Ojha  JD Unadkat     5
## 17     NV Ojha   LR Shukla     0
## 18     NV Ojha    M Kartik     1
## 19     NV Ojha     SE Bond     2
## 20     P Dogra  JD Unadkat     2
## 21     P Dogra   LR Shukla     5
## 22     P Dogra    M Kartik     1
## 23     P Dogra     SE Bond     0
## 24  SK Trivedi    AB Dinda     4
## 25    SK Warne    AB Dinda     2
## 26    SK Warne    M Kartik     1
## 27    SK Warne     SE Bond     0
## 28   SR Watson    AB Dinda     2
## 29   SR Watson  JD Unadkat    13
## 30   SR Watson   LR Shukla     1
## 31   SR Watson    M Kartik    18
## 32   SR Watson     SE Bond    10
## 33   YK Pathan  JD Unadkat     1
## 34   YK Pathan   LR Shukla     7

13. Match worm chart

The plots below provide the match worm graph for the IPL Twenty 20 matches

import pandas as pd
import yorkpy.analytics as yka
dc_dd=pd.read_csv(".\\Deccan Chargers-Delhi Daredevils-2012-05-10.csv")
yka.matchWormChart(dc_dd,"Deccan Chargers", "Delhi Daredevils")

gl_mi=pd.read_csv(".\\Gujarat Lions-Mumbai Indians-2017-04-29.csv")
yka.matchWormChart(gl_mi,"Mumbai Indians","Gujarat Lions")

Feel free to clone/download the code from Github yorkpy

Conclusion

This post included all functions between 2 IPL teams from the package yorkpy for IPL Twenty20 matches. As mentioned above the yaml match files have been already converted to dataframes and are available for download from Github at yorkpyData

After having used Python and R for analytics, Machine Learning and Deep Learning, I have now realized that neither language is superior or inferior. Both have, some good packages and some that are not so well suited.

To be continued. Watch this space!

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

You may also like
1.My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
2.My book ‘Practical Machine Learning in R and Python: Second edition’ on Amazon
2. Cricpy takes a swing at the ODIs
3. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!
4. Big Data-1: Move into the big league:Graduate from Python to Pyspark
5. Simulating an Edge Shape in Android

To see all posts click Index of posts

Analyzing batsmen and bowlers with cricpy template

Introduction

This post shows how you can analyze batsmen and bowlers of Test, ODI and T20s using cricpy templates, using data from ESPN Cricinfo.

The cricpy package

The data for a particular player can be obtained with the getPlayerData() function. To do you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Rahul Dravid, Virat Kohli  etc. This will bring up a page which have the profile number for the player e.g. for Rahul Dravid this would be http://www.espncricinfo.com/india/content/player/28114.html. Hence, Dravid’s profile is 28114. This can be used to get the data for Rahul Dravid as shown below

1. For Test players use batting and bowling.
2. For ODI use batting and bowling
3. For T20 use T20 Batting T20 Bowling

Please mindful of the  ESPN Cricinfo Terms of Use

My posts on Cripy were
a. Introducing cricpy:A python package to analyze performances of cricketers
b. Cricpy takes a swing at the ODIs
c. Cricpy takes guard for the Twenty20s

You can clone/download this cricpy template for your own analysis of players. This can be done using RStudio or IPython notebooks from Github at cricpy-template. You can uncomment the functions and use them.

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

The cricpy package is now available with pip install cricpy!!!

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

1 Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy.analytics as ca 
## C:\Users\Ganesh\ANACON~1\lib\site-packages\statsmodels\compat\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
##   from pandas.core import datetools

2. Invoking functions with Python package cricpy

import cricpy.analytics as ca 
#ca.batsman4s("aplayer.csv","A Player")

3. Getting help from cricpy – Python

import cricpy.analytics as ca
#help(ca.getPlayerData)

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

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

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. dravid.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 getPlayerData for all subsequent analyses

4a. For Test players

import cricpy.analytics as ca
#player1 =ca.getPlayerData(profileNo1,dir="..",file="player1.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])
#player1 =ca.getPlayerData(profileNo2,dir="..",file="player2.csv",type="batting",homeOrAway=[1,2], result=[1,2,4])

4b. For ODI players

import cricpy.analytics as ca
#player1 =ca.getPlayerDataOD(profileNo1,dir="..",file="player1.csv",type="batting")
#player1 =ca.getPlayerDataOD(profileNo2,dir="..",file="player2.csv",type="batting"")

4c For T20 players

import cricpy.analytics as ca
#player1 =ca.getPlayerDataTT(profileNo1,dir="..",file="player1.csv",type="batting")
#player1 =ca.getPlayerDataTT(profileNo2,dir="..",file="player2.csv",type="batting"")

5 A Player’s performance – Basic Analyses

The 3 plots below provide the following for Rahul Dravid

  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("aplayer.csv","A Player")
#ca.batsmanMeanStrikeRate("aplayer.csv","A Player")
#ca.batsmanRunsRanges("aplayer.csv","A Player") 

6. More analyses

This gives details on the batsmen’s 4s, 6s and dismissals

import cricpy.analytics as ca
#ca.batsman4s("aplayer.csv","A Player")
#ca.batsman6s("aplayer.csv","A Player") 
#ca.batsmanDismissals("aplayer.csv","A Player")
# The below function is for ODI and T20 only
#ca.batsmanScoringRateODTT("./kohli.csv","Virat Kohli")  

7. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of 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("aplayer.csv","A Player")

8. Average runs at different venues

The plot below gives the average runs scored 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("aplayer.csv","A Player")

9. Average runs against different opposing teams

This plot computes the average runs scored against different countries.

import cricpy.analytics as ca
#ca.batsmanAvgRunsOpposition("aplayer.csv","A Player")

10. Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman.

import cricpy.analytics as ca
#ca.batsmanRunsLikelihood("aplayer.csv","A Player")

11. A look at the Top 4 batsman

Choose any number of players

1.Player1 2.Player2 3.Player3 …

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("aplayer001.csv","A Player001")
#ca.batsmanPerfBoxHist("aplayer002.csv","A Player002")
#ca.batsmanPerfBoxHist("aplayer003.csv","A Player003")
#ca.batsmanPerfBoxHist("aplayer004.csv","A Player004")

13. Get Player Data special

import cricpy.analytics as ca
#player1sp = ca.getPlayerDataSp(profile1,tdir=".",tfile="player1sp.csv",ttype="batting")
#player2sp = ca.getPlayerDataSp(profile2,tdir=".",tfile="player2sp.csv",ttype="batting")
#player3sp = ca.getPlayerDataSp(profile3,tdir=".",tfile="player3sp.csv",ttype="batting")
#player4sp = ca.getPlayerDataSp(profile4,tdir=".",tfile="player4sp.csv",ttype="batting")

14. Contribution to won and lost matches

Note:This can only be used for Test matches

import cricpy.analytics as ca
#ca.batsmanContributionWonLost("player1sp.csv","A Player001")
#ca.batsmanContributionWonLost("player2sp.csv","A Player002")
#ca.batsmanContributionWonLost("player3sp.csv","A Player003")
#ca.batsmanContributionWonLost("player4sp.csv","A Player004")

15. Performance at home and overseas

Note:This can only be used for Test matches This function also requires the use of getPlayerDataSp() as shown above

import cricpy.analytics as ca
#ca.batsmanPerfHomeAway("player1sp.csv","A Player001")
#ca.batsmanPerfHomeAway("player2sp.csv","A Player002")
#ca.batsmanPerfHomeAway("player3sp.csv","A Player003")
#ca.batsmanPerfHomeAway("player4sp.csv","A Player004")

16 Moving Average of runs in career

import cricpy.analytics as ca
#ca.batsmanMovingAverage("aplayer001.csv","A Player001")
#ca.batsmanMovingAverage("aplayer002.csv","A Player002")
#ca.batsmanMovingAverage("aplayer003.csv","A Player003")
#ca.batsmanMovingAverage("aplayer004.csv","A Player004")

17 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career.

import cricpy.analytics as ca
#ca.batsmanCumulativeAverageRuns("aplayer001.csv","A Player001")
#ca.batsmanCumulativeAverageRuns("aplayer002.csv","A Player002")
#ca.batsmanCumulativeAverageRuns("aplayer003.csv","A Player003")
#ca.batsmanCumulativeAverageRuns("aplayer004.csv","A Player004")

18 Cumulative Average strike rate of batsman in career

.

import cricpy.analytics as ca
#ca.batsmanCumulativeStrikeRate("aplayer001.csv","A Player001")
#ca.batsmanCumulativeStrikeRate("aplayer002.csv","A Player002")
#ca.batsmanCumulativeStrikeRate("aplayer003.csv","A Player003")
#ca.batsmanCumulativeStrikeRate("aplayer004.csv","A Player004")

19 Future Runs forecast

import cricpy.analytics as ca
#ca.batsmanPerfForecast("aplayer001.csv","A Player001")

20 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman for each of the runs ranges of 10 and plots them.

import cricpy.analytics as ca
frames = ["aplayer1.csv","aplayer2.csv","aplayer3.csv","aplayer4.csv"]
names = ["A Player1","A Player2","A Player3","A Player4"]
#ca.relativeBatsmanCumulativeAvgRuns(frames,names)

21 Plot of 4s and 6s

import cricpy.analytics as ca
frames = ["aplayer1.csv","aplayer2.csv","aplayer3.csv","aplayer4.csv"]
names = ["A Player1","A Player2","A Player3","A Player4"]
#ca.batsman4s6s(frames,names)

22. Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show

import cricpy.analytics as ca
frames = ["aplayer1.csv","aplayer2.csv","aplayer3.csv","aplayer4.csv"]
names = ["A Player1","A Player2","A Player3","A Player4"]
#ca.relativeBatsmanCumulativeStrikeRate(frames,names)

23. 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 prediction plane is fitted

import cricpy.analytics as ca
#ca.battingPerf3d("aplayer001.csv","A Player001")
#ca.battingPerf3d("aplayer002.csv","A Player002")
#ca.battingPerf3d("aplayer003.csv","A Player003")
#ca.battingPerf3d("aplayer004.csv","A Player004")

24. 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})
#aplayer = ca.batsmanRunsPredict("aplayer.csv",newDF,"A Player")
#print(aplayer)

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

25 Analysis of Top 3 wicket takers

Take any number of bowlers from either Test, ODI or T20

  1. Bowler1
  2. Bowler2
  3. Bowler3 …

26. Get the bowler’s data (Test)

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
#abowler1 =ca.getPlayerData(profileNo1,dir=".",file="abowler1.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])
#abowler2 =ca.getPlayerData(profileNo2,dir=".",file="abowler2.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])
#abowler3 =ca.getPlayerData(profile3,dir=".",file="abowler3.csv",type="bowling",homeOrAway=[1,2], result=[1,2,4])

26b For ODI bowlers

import cricpy.analytics as ca
#abowler1 =ca.getPlayerDataOD(profileNo1,dir=".",file="abowler1.csv",type="bowling")
#abowler2 =ca.getPlayerDataOD(profileNo2,dir=".",file="abowler2.csv",type="bowling")
#abowler3 =ca.getPlayerDataOD(profile3,dir=".",file="abowler3.csv",type="bowling")

26c For T20 bowlers

import cricpy.analytics as ca
#abowler1 =ca.getPlayerDataTT(profileNo1,dir=".",file="abowler1.csv",type="bowling")
#abowler2 =ca.getPlayerDataTT(profileNo2,dir=".",file="abowler2.csv",type="bowling")
#abowler3 =ca.getPlayerDataTT(profile3,dir=".",file="abowler3.csv",type="bowling")

27. Wicket Frequency Plot

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

import cricpy.analytics as ca
#ca.bowlerWktsFreqPercent("abowler1.csv","A Bowler1")
#ca.bowlerWktsFreqPercent("abowler2.csv","A Bowler2")
#ca.bowlerWktsFreqPercent("abowler3.csv","A Bowler3")

28. 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("abowler1.csv","A Bowler1")
#ca.bowlerWktsRunsPlot("abowler2.csv","A Bowler2")
#ca.bowlerWktsRunsPlot("abowler3.csv","A Bowler3")

29 Average wickets at different venues

The plot gives the average wickets taken bat different venues.

import cricpy.analytics as ca
#ca.bowlerAvgWktsGround("abowler1.csv","A Bowler1")
#ca.bowlerAvgWktsGround("abowler2.csv","A Bowler2")
#ca.bowlerAvgWktsGround("abowler3.csv","A Bowler3")

30 Average wickets against different opposition

The plot gives the average wickets taken against different countries.

import cricpy.analytics as ca
#ca.bowlerAvgWktsOpposition("abowler1.csv","A Bowler1")
#ca.bowlerAvgWktsOpposition("abowler2.csv","A Bowler2")
#ca.bowlerAvgWktsOpposition("abowler3.csv","A Bowler3")

31 Wickets taken moving average

import cricpy.analytics as ca
#ca.bowlerMovingAverage("abowler1.csv","A Bowler1")
#ca.bowlerMovingAverage("abowler2.csv","A Bowler2")
#ca.bowlerMovingAverage("abowler3.csv","A Bowler3")

32 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers.

import cricpy.analytics as ca
#ca.bowlerCumulativeAvgWickets("abowler1.csv","A Bowler1")
#ca.bowlerCumulativeAvgWickets("abowler2.csv","A Bowler2")
#ca.bowlerCumulativeAvgWickets("abowler3.csv","A Bowler3")

33 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers.

import cricpy.analytics as ca
#ca.bowlerCumulativeAvgEconRate("abowler1.csv","A Bowler1")
#ca.bowlerCumulativeAvgEconRate("abowler2.csv","A Bowler2")
#ca.bowlerCumulativeAvgEconRate("abowler3.csv","A Bowler3")

34 Future Wickets forecast

import cricpy.analytics as ca
#ca.bowlerPerfForecast("abowler1.csv","A bowler1")

35 Get player data special

import cricpy.analytics as ca
#abowler1sp =ca.getPlayerDataSp(profile1,tdir=".",tfile="abowler1sp.csv",ttype="bowling")
#abowler2sp =ca.getPlayerDataSp(profile2,tdir=".",tfile="abowler2sp.csv",ttype="bowling")
#abowler3sp =ca.getPlayerDataSp(profile3,tdir=".",tfile="abowler3sp.csv",ttype="bowling")

36 Contribution to matches won and lost

Note:This can be done only for Test cricketers

import cricpy.analytics as ca
#ca.bowlerContributionWonLost("abowler1sp.csv","A Bowler1")
#ca.bowlerContributionWonLost("abowler2sp.csv","A Bowler2")
#ca.bowlerContributionWonLost("abowler3sp.csv","A Bowler3")

37 Performance home and overseas

Note:This can be done only for Test cricketers

import cricpy.analytics as ca
#ca.bowlerPerfHomeAway("abowler1sp.csv","A Bowler1")
#ca.bowlerPerfHomeAway("abowler2sp.csv","A Bowler2")
#ca.bowlerPerfHomeAway("abowler3sp.csv","A Bowler3")

38 Relative cumulative average economy rate of bowlers

import cricpy.analytics as ca
frames = ["abowler1.csv","abowler2.csv","abowler3.csv"]
names = ["A Bowler1","A Bowler2","A Bowler3"]
#ca.relativeBowlerCumulativeAvgEconRate(frames,names)

39 Relative Economy Rate against wickets taken

import cricpy.analytics as ca
frames = ["abowler1.csv","abowler2.csv","abowler3.csv"]
names = ["A Bowler1","A Bowler2","A Bowler3"]
#ca.relativeBowlingER(frames,names)

40 Relative cumulative average wickets of bowlers in career

import cricpy.analytics as ca
frames = ["abowler1.csv","abowler2.csv","abowler3.csv"]
names = ["A Bowler1","A Bowler2","A Bowler3"]
#ca.relativeBowlerCumulativeAvgWickets(frames,names)

Clone/download this cricpy template for your own analysis of players. This can be done using RStudio or IPython notebooks from Github at cricpy-template

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

Key Findings

Analysis of Top 4 batsman

Analysis of Top 3 bowlers

You may also like
1. My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
2. Presentation on ‘Evolution to LTE’
3. Stacks of protocol stacks – A primer
4. Taking baby steps in Lisp
5. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!

To see all posts click Index of 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

You may also like
1. A method for optimal bandwidth usage by auctioning available bandwidth using the OpenFlow protocol
2. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
3. Dabbling with Wiener filter using OpenCV
4. Deep Learning from first principles in Python, R and Octave – Part 5
5. Latency, throughput implications for the Cloud
6. Bend it like Bluemix, MongoDB using Auto-scale – Part 1!
7. Sea shells on the seashore
8. Practical Machine Learning with R and Python – Part 4

To see all posts click Index of Posts

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

 

cricketr flexes new muscles: The final analysis

Twas brillig, and the slithy toves
Did gyre and gimble in the wabe:
All mimsy were the borogoves,
And the mome raths outgrabe.

       Jabberwocky by Lewis Carroll
                   

No analysis of cricket is complete, without determining how players would perform in the host country. Playing Test cricket on foreign pitches, in the host country, is a ‘real test’ for both batsmen and bowlers. Players, who can perform consistently both on domestic and foreign pitches are the genuinely ‘class’ players. Player performance on foreign pitches lets us differentiate the paper tigers, and home ground bullies among batsmen. Similarly, spinners who perform well, only on rank turners in home ground or pace bowlers who can only swing and generate bounce on specially prepared pitches are neither  genuine spinners nor  real pace bowlers.

So this post, helps in identifying those with real strengths, and those who play good only when the conditions are in favor, in home grounds. This post brings a certain level of finality to the analysis of players with my R package ‘cricketr’

Besides, I also meant ‘final analysis’ in the literal sense, as I intend to take a long break from cricket analysis/analytics and focus on some other domains like Neural Networks, Deep Learning and Spark.

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

 

As already mentioned, my R package ‘cricketr’ uses the statistics info available in ESPN Cricinfo Statsguru. You should be able to install the package from CRAN and use many of the functions available in the package. Please be mindful of ESPN Cricinfo Terms of Use

Important note 1: The latest release of ‘cricketr’ now includes the ability to analyze performances of teams now!!  See Cricketr adds team analytics to its repertoire!!!

Important note 2 : Cricketr can now do a more fine-grained analysis of players, see Cricketr learns new tricks : Performs fine-grained analysis of players

Important note 3: Do check out the python avatar of cricketr, ‘cricpy’ in my post ‘Introducing cricpy:A python package to analyze performances of cricketers

(Note: This page is also hosted at RPubs as cricketrFinalAnalysis. You can download the PDF file at cricketrFinalAnalysis.

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

For getting data of a player against a particular country for the match played in the host country, I just had to add 2 extra parameters to the getPlayerData() function. The cricketr package has been updated with the changed functions for getPlayerData() – Tests, getPlayerDataOD() – ODI and getPlayerDataTT() for the Twenty20s. The updated functions will be available in cricketr Version -0.0.14

The data for the following players have already been obtained with the new, changed getPlayerData() function and have been saved as *.csv files. I will be re-using these files, instead of getting them all over again. Hence the getPlayerData() lines have been commented below

library(cricketr)

1. Performance of a batsman against a host ountry in the host country

For e.g We can the get the data for Sachin Tendulkar for matches played against Australia and in Australia Here opposition=2 and host =2 indicate that the opposition is Australia and the host country is also Australia

#tendulkarAus=getPlayerData(35320,opposition=2,host=2,file="tendulkarVsAusInAus.csv",type="batting")

All cricketr functions can be used with this data frame, as before. All the charts show the performance of Tendulkar in Australia against Australia.

par(mfrow=c(2,3))
par(mar=c(4,4,2,2))
batsman4s("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsman6s("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanRunsRanges("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanDismissals("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanAvgRunsGround("./data/tendulkarVsAusInAus.csv","Tendulkar")
batsmanMovingAverage("./data/tendulkarVsAusInAus.csv","Tendulkar")

dev.off()
## null device 
##           1

2. Relative performances of international batsmen against England in England

While we can analyze the performance of a player against an opposition in some host country, I wanted to compare the relative performances of players, to see how players from different nations play in a host country which is not their home ground.

The following lines gets player’s data of matches played in England and against England.The Oval, Lord’s are famous for generating some dangerous swing and bounce. I chose the following players

  1. Sir Don Bradman (Australia)
  2. Steve Waugh (Australia)
  3. Rahul Dravid (India)
  4. Vivian Richards (West Indies)
  5. Sachin Tendulkar (India)
#tendulkarEng=getPlayerData(35320,opposition=1,host=1,file="tendulkarVsEngInEng.csv",type="batting")
#bradmanEng=getPlayerData(4188,opposition=1,host=1,file="bradmanVsEngInEng.csv",type="batting")
#srwaughEng=getPlayerData(8192,opposition=1,host=1,file="srwaughVsEngInEng.csv",type="batting")
#dravidEng=getPlayerData(28114,opposition=1,host=1,file="dravidVsEngInEng.csv",type="batting")
#vrichardEng=getPlayerData(52812,opposition=1,host=1,file="vrichardsEngInEng.csv",type="batting")
frames <- list("./data/tendulkarVsEngInEng.csv","./data/bradmanVsEngInEng.csv","./data/srwaughVsEngInEng.csv",
               "./data/dravidVsEngInEng.csv","./data/vrichardsEngInEng.csv")
names <- list("S Tendulkar","D Bradman","SR Waugh","R Dravid","Viv Richards")

The Lords and the Oval in England are some of the best pitches in the world. Scoring on these pitches and weather conditions, where there is both swing and bounce really requires excellent batting skills. It can be easily seen that Don Bradman stands heads and shoulders over everybody else, averaging close a cumulative average of 100+. He is followed by Viv Richards, who averages around ~60. Interestingly in English conditions, Rahul Dravid edges out Sachin Tendulkar.

relativeBatsmanCumulativeAvgRuns(frames,names)

# The other 2 plots on relative strike rate and cumulative average strike rate,
shows Viv Richards really  blasts the bowling. Viv Richards has a strike rate 
of 70, while Bradman 62+, followed by Tendulkar.
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

3. Relative performances of international batsmen against Australia in Australia

The following players from these countries were chosen

  1. Sachin Tendulkar (India)
  2. Viv Richard (West Indies)
  3. David Gower (England)
  4. Jacques Kallis (South Africa)
  5. Alastair Cook (Emgland)
frames <- list("./data/tendulkarVsAusInAus.csv","./data/vrichardsVAusInAus.csv","./data/dgowerVsAusInAus.csv",
               "./data/kallisVsAusInAus.csv","./data/ancookVsWIInWI.csv")
names <- list("S Tendulkar","Viv Richards","David Gower","J Kallis","AN Cook")

Alastair Cook of England has fantastic cumulative average of 55+ on the pitches of Australia. There is a dip towards the end, but we cannot predict whether it would have continued. AN Cook is followed by Tendulkar who has a steady average of 50+ runs, after which there is Viv Richards.

relativeBatsmanCumulativeAvgRuns(frames,names)

#With respect to cumulative or relative strike rate Viv Richards is a class apart.He seems to really
#tear into bowlers. David Gower has an excellent strike rate and is followed by Tendulkar
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

4. Relative performances of international batsmen against India in India

While England & Australia are famous for bouncy tracks with swing, Indian pitches are renowed for being extraordinary turners. Also India has always thrown up world class spinners, from the spin quartet of BS Chandraskehar, Bishen Singh Bedi, EAS Prasanna, S Venkatraghavan, to the times of dangerous Anil Kumble, and now to the more recent Ravichander Ashwon and Harbhajan Singh.

A batsmen who can score runs in India against Indian spinners has to be really adept in handling all kinds of spin.

While Clive Lloyd & Alvin Kallicharan had the best performance against India, they have not been included as ESPN Cricinfo had many of the columns missing.

So I chose the following international players for the analysis against India

  1. Hashim Amla (South Africa)
  2. Alastair Cook (England)
  3. Matthew Hayden (Australia)
  4. Viv Richards (West Indies)
frames <- list("./data/amlaVsIndInInd.csv","./data/ancookVsIndInInd.csv","./data/mhaydenVsIndInInd.csv",
               "./data/vrichardsVsIndInInd.csv")
names <- list("H Amla","AN Cook","M Hayden","Viv Riachards")

Excluding Clive Lloyd & Alvin Kallicharan the next best performer against India is Hashim Amla,followed by Alastair Cook, Viv Richards.

relativeBatsmanCumulativeAvgRuns(frames,names)

#With respect to strike rate, there is no contest when Viv Richards is around. He is clearly the best 
#striker of the ball regardless of whether it is the pacy wickets of 
#Australia/England or the spinning tracks of the subcontinent. After 
#Viv Richards, Hayden and Alastair Cook have good cumulative strike rates
#in India
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

5. All time greats of Indian batting

I couldn’t resist checking out how the top Indian batsmen perform when playing in host countries So here is a look at how the top Indian batsmen perform against different host countries

6. Top Indian batsmen against Australia in Australia

The following Indian batsmen were chosen

  1. Sunil Gavaskar
  2. Sachin Tendulkar
  3. Virat Kohli
  4. Virendar Sehwag
  5. VVS Laxman
frames <- list("./data/tendulkarVsAusInAus.csv","./data/gavaskarVsAusInAus.csv","./data/kohliVsAusInAus.csv",
               "./data/sehwagVsAusInAus.csv","./data/vvslaxmanVsAusInAus.csv")
names <- list("S Tendulkar","S Gavaskar","V Kohli","V Sehwag","VVS Laxman")

Virat Kohli has the best overall performance against Australia, with a current cumulative average of 60+ runs for the total number of innings played by him (15). With 15 matches the 2nd best is Virendar Sehwag, followed by VVS Laxman. Tendulkar maintains a cumulative average of 48+ runs for an excess of 30+ innings.

relativeBatsmanCumulativeAvgRuns(frames,names)

# Sehwag leads the strike rate against host Australia, followed by 
# Tendulkar in Australia and then Kohli
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

7. Top Indian batsmen against England in England

The top Indian batmen’s performances against England are shown below

  1. Rahul Dravid
  2. Dilip Vengsarkar
  3. Rahul Dravid
  4. Sourav Ganguly
  5. Virat Kohli
frames <- list("./data/tendulkarVsEngInEng.csv","./data/dravidVsEngInEng.csv","./data/vengsarkarVsEngInEng.csv",
               "./data/gangulyVsEngInEng.csv","./data/gavaskarVsEngInEng.csv","./data/kohliVsEngInEng.csv")
names <- list("S Tendulkar","R Dravid","D Vengsarkar","S Ganguly","S Gavaskar","V Kohli")

Rahul Dravid has the best performance against England and edges out Tendulkar. He is followed by Tendulkar and then Sourav Ganguly. Note:Incidentally Virat Kohli’s performance against England in England so far has been extremely poor and he averages around 13-15 runs per innings. However he has a long way to go and I hope he catches up. In any case it will be an uphill climb for Kohli in England.

relativeBatsmanCumulativeAvgRuns(frames,names)

#Tendulkar, Ganguly and Dravid have the best strike rate and in that order.
relativeBatsmanSR(frames,names)

relativeBatsmanCumulativeStrikeRate(frames,names)

8. Top Indian batsmen against West Indies in West Indies

frames <- list("./data/tendulkarVsWInWI.csv","./data/dravidVsWInWI.csv","./data/vvslaxmanVsWIInWI.csv",
               "./data/gavaskarVsWIInWI.csv")
names <- list("S Tendulkar","R Dravid","VVS Laxman","S Gavaskar")

Against the West Indies Sunil Gavaskar is heads and shoulders above the rest. Gavaskar has a very impressive cumulative average against West Indies

relativeBatsmanCumulativeAvgRuns(frames,names)

# VVS Laxman followed by  Tendulkar & then Dravid have a very 
# good strike rate against the West Indies
relativeBatsmanCumulativeStrikeRate(frames,names)

9. World’s best spinners on tracks suited for pace & bounce

In this part I compare the performances of the top 3 spinners in recent years and check out how they perform on surfaces that are known for pace, and bounce. I have taken the following 3 spinners

  1. Anil Kumble (India)
  2. M Muralitharan (Sri Lanka)
  3. Shane Warne (Australia)
#kumbleEng=getPlayerData(30176  ,opposition=3,host=3,file="kumbleVsEngInEng.csv",type="bowling")
#muraliEng=getPlayerData(49636  ,opposition=3,host=3,file="muraliVsEngInEng.csv",type="bowling")
#warneEng=getPlayerData(8166  ,opposition=3,host=3,file="warneVsEngInEng.csv",type="bowling")

10. Top international spinners against England in England

frames <- list("./data/kumbleVsEngInEng.csv","./data/muraliVsEngInEng.csv","./data/warneVsEngInEng.csv")
names <- list("Anil KUmble","M Muralitharan","Shane Warne")

Against England and in England, Muralitharan shines with a cumulative average of nearly 5 wickets per match with a peak of almost 8 wickets. Shane Warne has a steady average at 5 wickets and then Anil Kumble.

relativeBowlerCumulativeAvgWickets(frames,names)

# The order relative cumulative Economy rate, Warne has the best figures,followed by Anil Kumble. Muralitharan
# is much more expensive.
relativeBowlerCumulativeAvgEconRate(frames,names)

11. Top international spinners against South Africa in South Africa

frames <- list("./data/kumbleVsSAInSA.csv","./data/muraliVsSAInSA.csv","./data/warneVsSAInSA.csv")
names <- list("Anil Kumble","M Muralitharan","Shane Warne")

In South Africa too, Muralitharan has the best wicket taking performance averaging about 4 wickets. Warne averages around 3 wickets and Kumble around 2 wickets

relativeBowlerCumulativeAvgWickets(frames,names)

# Muralitharan is expensive in South Africa too, while Kumble and Warne go neck-to-neck in the economy rate.
# Kumble edges out Warne and has a better cumulative average economy rate
relativeBowlerCumulativeAvgEconRate(frames,names)

11. Top international pacers against India in India

As a final analysis I check how the world’s pacers perform in India against India. India pitches are supposed to be flat devoid of bounce, while being terrific turners. Hence Indian pitches are more suited to spin bowling than pace bowling. This is changing these days.

The best performers against India in India are mostly the deadly pacemen of yesteryears

For this I have chosen the following bowlers

  1. Courtney Walsh (West Indies)
  2. Andy Roberts (West Indies)
  3. Malcolm Marshall
  4. Glenn McGrath
#cawalshInd=getPlayerData(53216  ,opposition=6,host=6,file="cawalshVsIndInInd.csv",type="bowling")
#arobertsInd=getPlayerData(52817  ,opposition=6,host=6,file="arobertsIndInInd.csv",type="bowling")
#mmarshallInd=getPlayerData(52419  ,opposition=6,host=6,file="mmarshallVsIndInInd.csv",type="bowling")
#gmccgrathInd=getPlayerData(6565  ,opposition=6,host=6,file="mccgrathVsIndInInd.csv",type="bowling")
frames <- list("./data/cawalshVsIndInInd.csv","./data/arobertsIndInInd.csv","./data/mmarshallVsIndInInd.csv",
               "./data/mccgrathVsIndInInd.csv")
names <- list("C Walsh","A Roberts","M Marshall","G McGrath")

Courtney Walsh has the best performance, followed by Andy Roberts followed by Andy Roberts and then Malcom Marshall who tips ahead of Glenn McGrath

relativeBowlerCumulativeAvgWickets(frames,names)

#On the other hand McGrath has the best economy rate, followed by A Roberts and then Courtney Walsh
relativeBowlerCumulativeAvgEconRate(frames,names)

12. ODI performance of a player against a specific country in the host country

This gets the data for MS Dhoni in ODI matches against Australia and in Australia

#dhoniAusODI=getPlayerDataOD(28081,opposition=2,host=2,file="dhoniVsAusInAusODI.csv",type="batting")

13. Twenty 20 performance of a player against a specific country in the host country

#dhoniAusTT=getPlayerDataOD(28081,opposition=2,host=2,file="dhoniVsAusInAusTT.csv",type="batting")

All the ODI and Twenty20 functions of cricketr can be used on the above dataframes of MS Dhoni.

Some key observations

Here are some key observations

  1. At the top of the batting spectrum is Don Bradman with a very impressive average 100-120 in matches played in England and Australia. Unfortunately there weren’t matches he played in other countries and different pitches. 2.Viv Richard has the best cumulative strike rate overall.
  2. Muralitharan strikes more often than Kumble or Warne even in pitches at ENgland, South Africa and West Indies. However Muralitharan is also the most expensive
  3. Warne and Kumble have a much better economy rate than Muralitharan.
  4. Sunil Gavaskar has an extremely impressive performance in West Indies.
  5. Rahul Dravid performs much better than Tendulkar in both England and West Indies.
  6. Virat Kohli has the best performance against Australia so far and hope he maintains his stellar performance followed by Sehwag. However Kohli’s performance in England has been very poor
  7. West Indies batsmen and bowlers seem to thrive on Indian pitches, with Clive Lloyd and Alvin Kalicharan at the top of the list.

You may like my Shiny apps on cricket

  1. Inswinger- Analyzing International. T20s
  2. GooglyPlus – An app for analyzing IPL
  3. Sixer – App based on R package cricketr

Also see

  1. Exploring Quantum Gate operations with QCSimulator
  2. Neural Networks: The mechanics of backpropagation
  3. Re-introducing cricketr! : An R package to analyze performances of cricketers
  4. yorkr crashes the IPL party ! – Part 1
  5. cricketr and yorkr books – Paperback now in Amazon
  6.  Hand detection through Haartraining: A hands-on approach

To see all my posts see Index of posts

 

Analysis of IPL T20 matches with yorkr templates

Introduction

In this post I create RMarkdown templates for end-to-end analysis of IPL T20 matches, that are available on Cricsheet based on my R package yorkr.  With these templates you can convert all IPL data which is in yaml format to R dataframes. Further I create data and the necessary templates for analyzing IPL matches, teams and players. All of these can be accessed at yorkrIPLTemplate.

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!!

1

9/Rs 320 and $6.99/Rs448 respectively

The templates are

  1. Template for conversion and setup – IPLT20Template.Rmd
  2. Any IPL match – IPLMatchtemplate.Rmd
  3. IPL matches between 2 nations – IPLMatches2TeamTemplate.Rmd
  4. A IPL nations performance against all other IPL nations – IPLAllMatchesAllOppnTemplate.Rmd
  5. Analysis of IPL batsmen and bowlers of all IPL nations – IPLBatsmanBowlerTemplate.Rmd

Besides the templates the repository also includes the converted data for all IPL matches I downloaded from Cricsheet in Dec 2016. So this data is complete till the 2016 IPL season. You can recreate the files as more matches are added to Cricsheet site in IPL 2017 and future seasons. This post contains all the steps needed for detailed analysis of IPL matches, teams and IPL player. This will also be my reference in future if I decide to analyze IPL in future!

See my earlier posts where I analyze IPL T20
1. yorkr crashes the IPL party ! – Part 1
2. yorkr crashes the IPL party! – Part 2
3. yorkr crashes the IPL party! – Part 3!
4. yorkr crashes the IPL party! – Part 4

There will be 5 folders at the root

  1. IPLdata – Match files as yaml from Cricsheet
  2. IPLMatches – Yaml match files converted to dataframes
  3. IPLMatchesBetween2Teams – All Matches between any 2 IPL teams
  4. allMatchesAllOpposition – An IPL teams’s performance against all other teams
  5. BattingBowlingDetails – Batting and bowling details of all IPL teams
library(yorkr)
library(dplyr)

The first few steps take care of the data setup. This needs to be done before any of the analysis of IPL batsmen, bowlers, any IPL match, matches between any 2 IPL countries or analysis of a teams performance against all other countries

There will be 5 folders at the root

  1. data
  2. IPLMatches
  3. IPLMatchesBetween2Teams
  4. allMatchesAllOpposition
  5. BattingBowlingDetails

The source YAML files will be in IPLData folder

1.Create directory of IPLMatches

Some files may give conversions errors. You could try to debug the problem or just remove it from the IPLdata folder. At most 2-4 file will have conversion problems and I usally remove then from the files to be converted.

Also take a look at my GooglyPlus shiny app which was created after performing the same conversion on the Dec 16 data .

convertAllYaml2RDataframesT20("data","IPLMatches")

2.Save all matches between all combinations of IPL nations

This function will create the set of all matches between each IPL team against every other IPL team. This uses the data that was created in IPLMatches, with the convertAllYaml2RDataframesIPL() function.

setwd("./IPLMatchesBetween2Teams")
saveAllMatchesBetween2IPLTeams(dir= ,odir= )

3.Save all matches against all opposition

This will create a consolidated dataframe of all matches played by every IPL playing nation against all other nattions. This also uses the data that was created in IPLMatches, with the convertAllYaml2RDataframesIPL() function.

setwd("../allMatchesAllOpposition")
saveAllMatchesAllOppositionIPLT20(dir= ,odir= )

4. Create batting and bowling details for each IPL team

These are the current IPL playing teams. You can add to this vector as newer IPL teams start playing IPL. You will get to know all IPL teams by also look at the directory created above namely allMatchesAllOpposition. This also uses the data that was created in IPLMatches, with the convertAllYaml2RDataframesIPL() function.

setwd("../BattingBowlingDetails")
ipl_teams <- list("Chennai Super Kings","Deccan Chargers", "Delhi Daredevils","Kings XI Punjab", 
              "Kochi Tuskers Kerala","Kolkata Knight Riders","Mumbai Indians","Pune Warriors",
              "Rajasthan Royals","Royal Challengers Bangalore","Sunrisers Hyderabad","Gujarat Lions",
                 "Rising Pune Supergiants")

for(i in seq_along(ipl_teams)){
    print(ipl_teams[i])
    val <- paste(ipl_teams[i],"-details",sep="")
    val <- getTeamBattingDetails(ipl_teams[i],dir="../IPLMatches", save=TRUE)

}

for(i in seq_along(ipl_teams)){
    print(ipl_teams[i])
    val <- paste(ipl_teams[i],"-details",sep="")
    val <- getTeamBowlingDetails(ipl_teams[i],dir="../IPLMatches", save=TRUE)

}

5. Get the list of batsmen for a particular IPL team

The following code is needed for analyzing individual IPL batsmen. In IPL a player could have played in multiple IPL teams.

getBatsmen <- function(df){
    bmen <- df %>% distinct(batsman) 
    bmen <- as.character(bmen$batsman)
    batsmen <- sort(bmen)
}
load("Chennai Super Kings-BattingDetails.RData")
csk_details <- battingDetails
load("Deccan Chargers-BattingDetails.RData")
dc_details <- battingDetails
load("Delhi Daredevils-BattingDetails.RData")
dd_details <- battingDetails
load("Kings XI Punjab-BattingDetails.RData")
kxip_details <- battingDetails
load("Kochi Tuskers Kerala-BattingDetails.RData")
ktk_details <- battingDetails
load("Kolkata Knight Riders-BattingDetails.RData")
kkr_details <- battingDetails
load("Mumbai Indians-BattingDetails.RData")
mi_details <- battingDetails
load("Pune Warriors-BattingDetails.RData")
pw_details <- battingDetails
load("Rajasthan Royals-BattingDetails.RData")
rr_details <- battingDetails
load("Royal Challengers Bangalore-BattingDetails.RData")
rcb_details <- battingDetails
load("Sunrisers Hyderabad-BattingDetails.RData")
sh_details <- battingDetails
load("Gujarat Lions-BattingDetails.RData")
gl_details <- battingDetails
load("Rising Pune Supergiants-BattingDetails.RData")
rps_details <- battingDetails

#Get the batsmen for each IPL team
csk_batsmen <- getBatsmen(csk_details)
dc_batsmen <- getBatsmen(dc_details)
dd_batsmen <- getBatsmen(dd_details)
kxip_batsmen <- getBatsmen(kxip_details)
ktk_batsmen <- getBatsmen(ktk_details)
kkr_batsmen <- getBatsmen(kkr_details)
mi_batsmen <- getBatsmen(mi_details)
pw_batsmen <- getBatsmen(pw_details)
rr_batsmen <- getBatsmen(rr_details)
rcb_batsmen <- getBatsmen(rcb_details)
sh_batsmen <- getBatsmen(sh_details)
gl_batsmen <- getBatsmen(gl_details)
rps_batsmen <- getBatsmen(rps_details)

# Save the dataframes
save(csk_batsmen,file="csk.RData")
save(dc_batsmen, file="dc.RData")
save(dd_batsmen, file="dd.RData")
save(kxip_batsmen, file="kxip.RData")
save(ktk_batsmen, file="ktk.RData")
save(kkr_batsmen, file="kkr.RData")
save(mi_batsmen , file="mi.RData")
save(pw_batsmen, file="pw.RData")
save(rr_batsmen, file="rr.RData")
save(rcb_batsmen, file="rcb.RData")
save(sh_batsmen, file="sh.RData")
save(gl_batsmen, file="gl.RData")
save(rps_batsmen, file="rps.RData")

6. Get the list of bowlers for a particular IPL team

The method below can get the list of bowler names for any IPL team.The following code is needed for analyzing individual IPL bowlers. In IPL a player could have played in multiple IPL teams.

getBowlers <- function(df){
    bwlr <- df %>% distinct(bowler) 
    bwlr <- as.character(bwlr$bowler)
    bowler <- sort(bwlr)
}

load("Chennai Super Kings-BowlingDetails.RData")
csk_details <- bowlingDetails
load("Deccan Chargers-BowlingDetails.RData")
dc_details <- bowlingDetails
load("Delhi Daredevils-BowlingDetails.RData")
dd_details <- bowlingDetails
load("Kings XI Punjab-BowlingDetails.RData")
kxip_details <- bowlingDetails
load("Kochi Tuskers Kerala-BowlingDetails.RData")
ktk_details <- bowlingDetails
load("Kolkata Knight Riders-BowlingDetails.RData")
kkr_details <- bowlingDetails
load("Mumbai Indians-BowlingDetails.RData")
mi_details <- bowlingDetails
load("Pune Warriors-BowlingDetails.RData")
pw_details <- bowlingDetails
load("Rajasthan Royals-BowlingDetails.RData")
rr_details <- bowlingDetails
load("Royal Challengers Bangalore-BowlingDetails.RData")
rcb_details <- bowlingDetails
load("Sunrisers Hyderabad-BowlingDetails.RData")
sh_details <- bowlingDetails
load("Gujarat Lions-BowlingDetails.RData")
gl_details <- bowlingDetails
load("Rising Pune Supergiants-BowlingDetails.RData")
rps_details <- bowlingDetails

# Get the bowlers for each team
csk_bowlers <- getBowlers(csk_details)
dc_bowlers <- getBowlers(dc_details)
dd_bowlers <- getBowlers(dd_details)
kxip_bowlers <- getBowlers(kxip_details)
ktk_bowlers <- getBowlers(ktk_details)
kkr_bowlers <- getBowlers(kkr_details)
mi_bowlers <- getBowlers(mi_details)
pw_bowlers <- getBowlers(pw_details)
rr_bowlers <- getBowlers(rr_details)
rcb_bowlers <- getBowlers(rcb_details)
sh_bowlers <- getBowlers(sh_details)
gl_bowlers <- getBowlers(gl_details)
rps_bowlers <- getBowlers(rps_details)

#Save the dataframes
save(csk_bowlers,file="csk1.RData")
save(dc_bowlers, file="dc1.RData")
save(dd_bowlers, file="dd1.RData")
save(kxip_bowlers, file="kxip1.RData")
save(ktk_bowlers, file="ktk1.RData")
save(kkr_bowlers, file="kkr1.RData")
save(mi_bowlers , file="mi1.RData")
save(pw_bowlers, file="pw1.RData")
save(rr_bowlers, file="rr1.RData")
save(rcb_bowlers, file="rcb1.RData")
save(sh_bowlers, file="sh1.RData")
save(gl_bowlers, file="gl1.RData")
save(rps_bowlers, file="rps1.RData")

Now we are all set

A)  IPL T20 Match Analysis

1 IPL Match Analysis

Load any match data from the ./IPLMatches folder for e.g. Chennai Super Kings-Deccan Chargers-2008-05-06.RData

setwd("./IPLMatches")
load("Chennai Super Kings-Deccan Chargers-2008-05-06.RData")
csk_dc<- overs
#The steps are
load("IPLTeam1-IPLTeam2-Date.Rdata")
IPLTeam1_IPLTeam2 <- overs

All analysis for this match can be done now

2. Scorecard

teamBattingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam1")
teamBattingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam2")

3.Batting Partnerships

teamBatsmenPartnershipMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
teamBatsmenPartnershipMatch(IPLTeam1_IPLTeam2,"IPLTeam2","IPLTeam1")

4. Batsmen vs Bowler Plot

teamBatsmenVsBowlersMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=TRUE)
teamBatsmenVsBowlersMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)

5. Team bowling scorecard

teamBowlingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam1")
teamBowlingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam2")

6. Team bowling Wicket kind match

teamBowlingWicketKindMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <-teamBowlingWicketKindMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

7. Team Bowling Wicket Runs Match

teamBowlingWicketRunsMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <-teamBowlingWicketRunsMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

8. Team Bowling Wicket Match

m <-teamBowlingWicketMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m
teamBowlingWicketMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")

9. Team Bowler vs Batsmen

teamBowlersVsBatsmenMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <- teamBowlersVsBatsmenMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

10. Match Worm chart

matchWormGraph(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")

B)  IPL  Matches between 2  IPL teams

1 IPL Match Analysis

Load any match data from the ./IPLMatches folder for e.g. Chennai Super Kings-Deccan Chargers-2008-05-06.RData

setwd("./IPLMatches")
load("Chennai Super Kings-Deccan Chargers-2008-05-06.RData")
csk_dc<- overs
#The steps are
load("IPLTeam1-IPLTeam2-Date.Rdata")
IPLTeam1_IPLTeam2 <- overs

All analysis for this match can be done now

2. Scorecard

teamBattingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam1")
teamBattingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam2")

3.Batting Partnerships

teamBatsmenPartnershipMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
teamBatsmenPartnershipMatch(IPLTeam1_IPLTeam2,"IPLTeam2","IPLTeam1")

4. Batsmen vs Bowler Plot

teamBatsmenVsBowlersMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=TRUE)
teamBatsmenVsBowlersMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)

5. Team bowling scorecard

teamBowlingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam1")
teamBowlingScorecardMatch(IPLTeam1_IPLTeam2,"IPLTeam2")

6. Team bowling Wicket kind match

teamBowlingWicketKindMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <-teamBowlingWicketKindMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

7. Team Bowling Wicket Runs Match

teamBowlingWicketRunsMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <-teamBowlingWicketRunsMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

8. Team Bowling Wicket Match

m <-teamBowlingWicketMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m
teamBowlingWicketMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")

9. Team Bowler vs Batsmen

teamBowlersVsBatsmenMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")
m <- teamBowlersVsBatsmenMatch(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2",plot=FALSE)
m

10. Match Worm chart

matchWormGraph(IPLTeam1_IPLTeam2,"IPLTeam1","IPLTeam2")

C)  IPL Matches for a team against all other teams

1. IPL Matches for a team against all other teams

Load the data between for a IPL team against all other countries ./allMatchesAllOpposition for e.g all matches of Kolkata Knight Riders

load("allMatchesAllOpposition-Kolkata Knight Riders.RData")
kkr_matches <- matches
IPLTeam="IPLTeam1"
allMatches <- paste("allMatchesAllOposition-",IPLTeam,".RData",sep="")
load(allMatches)
IPLTeam1AllMatches <- matches

2. Team’s batting scorecard all Matches

m <-teamBattingScorecardAllOppnAllMatches(IPLTeam1AllMatches,theTeam="IPLTeam1")
m

3. Batting scorecard of opposing team

m <-teamBattingScorecardAllOppnAllMatches(matches=IPLTeam1AllMatches,theTeam="IPLTeam2")

4. Team batting partnerships

m <- teamBatsmenPartnershipAllOppnAllMatches(IPLTeam1AllMatches,theTeam="IPLTeam1")
m
m <- teamBatsmenPartnershipAllOppnAllMatches(IPLTeam1AllMatches,theTeam='IPLTeam1',report="detailed")
head(m,30)
m <- teamBatsmenPartnershipAllOppnAllMatches(IPLTeam1AllMatches,theTeam='IPLTeam1',report="summary")
m

5. Team batting partnerships plot

teamBatsmenPartnershipAllOppnAllMatchesPlot(IPLTeam1AllMatches,"IPLTeam1",main="IPLTeam1")
teamBatsmenPartnershipAllOppnAllMatchesPlot(IPLTeam1AllMatches,"IPLTeam1",main="IPLTeam2")

6, Team batsmen vs bowlers report

m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(IPLTeam1AllMatches,"IPLTeam1",rank=0)
m
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(IPLTeam1AllMatches,"IPLTeam1",rank=1,dispRows=30)
m
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(matches=IPLTeam1AllMatches,theTeam="IPLTeam2",rank=1,dispRows=25)
m

7. Team batsmen vs bowler plot

d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(IPLTeam1AllMatches,"IPLTeam1",rank=1,dispRows=50)
d
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(IPLTeam1AllMatches,"IPLTeam1",rank=2,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

8. Team bowling scorecard

teamBowlingScorecardAllOppnAllMatchesMain(matches=IPLTeam1AllMatches,theTeam="IPLTeam1")
teamBowlingScorecardAllOppnAllMatches(IPLTeam1AllMatches,'IPLTeam2')

9. Team bowler vs batsmen

teamBowlersVsBatsmenAllOppnAllMatchesMain(IPLTeam1AllMatches,theTeam="IPLTeam1",rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(IPLTeam1AllMatches,theTeam="IPLTeam1",rank=2)
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=IPLTeam1AllMatches,theTeam="IPLTeam1",rank=0)

10. Team Bowler vs bastmen

df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(IPLTeam1AllMatches,theTeam="IPLTeam1",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"IPLTeam1","IPLTeam1")

11. Team bowler wicket kind

teamBowlingWicketKindAllOppnAllMatches(IPLTeam1AllMatches,t1="IPLTeam1",t2="All")
teamBowlingWicketKindAllOppnAllMatches(IPLTeam1AllMatches,t1="IPLTeam1",t2="IPLTeam2")

12.

teamBowlingWicketRunsAllOppnAllMatches(IPLTeam1AllMatches,t1="IPLTeam1",t2="All",plot=TRUE)
teamBowlingWicketRunsAllOppnAllMatches(IPLTeam1AllMatches,t1="IPLTeam1",t2="IPLTeam2",plot=TRUE)

1 IPL Batsman setup functions

Get the batsman’s details for a batsman

setwd("../BattingBowlingDetails")
# IPL Team names
IPLTeamNames <- list("Chennai Super Kings","Deccan Chargers", "Delhi Daredevils","Kings Xi Punjab", 
                  "Kochi Tuskers Kerala","Kolkata Knight Riders","Mumbai Indians","Pune Warriors",
                  "Rajasthan Royals","Royal Challengers Bangalore","Sunrisers Hyderabad","Gujarat Lions",
                  "Rising Pune Supergiants")           


# Check and get the team indices of IPL teams in which the batsman has played
getTeamIndex <- function(batsman){
    setwd("./BattingBowlingDetails")
    load("csk.RData")
    load("dc.RData")
    load("dd.RData")
    load("kxip.RData")
    load("ktk.RData")
    load("kkr.RData")
    load("mi.RData")
    load("pw.RData")
    load("rr.RData")
    load("rcb.RData")
    load("sh.RData")
    load("gl.RData")
    load("rps.RData")
    setwd("..")
    getwd()
    print(ls())
    teams_batsmen = list(csk_batsmen,dc_batsmen,dd_batsmen,kxip_batsmen,ktk_batsmen,kkr_batsmen,mi_batsmen,
                         pw_batsmen,rr_batsmen,rcb_batsmen,sh_batsmen,gl_batsmen,rps_batsmen)
    b <- NULL
    for (i in 1:length(teams_batsmen)){
        a <- which(teams_batsmen[[i]] == batsman)

        if(length(a) != 0)
            b <- c(b,i)
    }
    b
}

# Get the list of the IPL team names from the indices passed
getTeams <- function(x){

    l <- NULL
    # Get the teams passed in as indexes
    for (i in seq_along(x)){

        l <- c(l, IPLTeamNames[[x[i]]]) 

    }
    l
}

# Create a consolidated data frame with all teams the IPL batsman has played for
getIPLBatsmanDF <- function(teamNames){
    batsmanDF <- NULL
   # Create a consolidated Data frame of batsman for all IPL teams played
    for (i in seq_along(teamNames)){
       df <- getBatsmanDetails(team=teamNames[i],name=IPLBatsman,dir="./BattingBowlingDetails")
       batsmanDF <- rbind(batsmanDF,df) 

    }
    batsmanDF
}

2. Create a consolidated IPL batsman data frame

# Since an IPL batsman coculd have played in multiple teams we need to determine these teams and
# create a consolidated data frame for the analysis
# For example to check MS Dhoni we need to do the following

IPLBatsman = "MS Dhoni"
#Check and get the team indices of IPL teams in which the batsman has played
i <- getTeamIndex(IPLBatsman)

# Get the team names in which the IPL batsman has played
teamNames <- getTeams(i)
    # Check if file exists in the directory. This check is necessary when moving between matchType


############## Create a consolidated IPL batsman dataframe for analysis
batsmanDF <- getIPLBatsmanDF(teamNames)

3. Runs vs deliveries

# For e.g. batsmanName="MS Dhoni""
#batsmanRunsVsDeliveries(batsmanDF, "MS Dhoni")
batsmanRunsVsDeliveries(batsmanDF,"batsmanName")

4. Batsman 4s & 6s

batsman46 <- select(batsmanDF,batsman,ballsPlayed,fours,sixes,runs)
p1 <- batsmanFoursSixes(batsman46,"batsmanName")

5. Batsman dismissals

batsmanDismissals(batsmanDF,"batsmanName")

6. Runs vs Strike rate

batsmanRunsVsStrikeRate(batsmanDF,"batsmanName")

7. Batsman Moving Average

batsmanMovingAverage(batsmanDF,"batsmanName")

8. Batsman cumulative average

batsmanCumulativeAverageRuns(batsmanDF,"batsmanName")

9. Batsman cumulative strike rate

batsmanCumulativeStrikeRate(batsmanDF,"batsmanName")

10. Batsman runs against oppositions

batsmanRunsAgainstOpposition(batsmanDF,"batsmanName")

11. Batsman runs vs venue

batsmanRunsVenue(batsmanDF,"batsmanName")

12. Batsman runs predict

batsmanRunsPredict(batsmanDF,"batsmanName")

13.Bowler set up functions

setwd("../BattingBowlingDetails")
# IPL Team names
IPLTeamNames <- list("Chennai Super Kings","Deccan Chargers", "Delhi Daredevils","Kings Xi Punjab", 
                  "Kochi Tuskers Kerala","Kolkata Knight Riders","Mumbai Indians","Pune Warriors",
                  "Rajasthan Royals","Royal Challengers Bangalore","Sunrisers Hyderabad","Gujarat Lions",
                  "Rising Pune Supergiants")    



# Get the team indices of IPL teams for which the bowler as played
getTeamIndex_bowler <- function(bowler){
    # Load IPL Bowlers
    setwd("./data")
    load("csk1.RData")
    load("dc1.RData")
    load("dd1.RData")
    load("kxip1.RData")
    load("ktk1.RData")
    load("kkr1.RData")
    load("mi1.RData")
    load("pw1.RData")
    load("rr1.RData")
    load("rcb1.RData")
    load("sh1.RData")
    load("gl1.RData")
    load("rps1.RData")
    setwd("..")
    teams_bowlers = list(csk_bowlers,dc_bowlers,dd_bowlers,kxip_bowlers,ktk_bowlers,kkr_bowlers,mi_bowlers,
                         pw_bowlers,rr_bowlers,rcb_bowlers,sh_bowlers,gl_bowlers,rps_bowlers)
    b <- NULL
    for (i in 1:length(teams_bowlers)){
        a <- which(teams_bowlers[[i]] == bowler)
        if(length(a) != 0){
            b <- c(b,i)
        }
    }
    b
}


# Get the list of the IPL team names from the indices passed
getTeams <- function(x){

    l <- NULL
    # Get the teams passed in as indexes
    for (i in seq_along(x)){

        l <- c(l, IPLTeamNames[[x[i]]]) 

    }
    l
}

# Get the team names
teamNames <- getTeams(i)

getIPLBowlerDF <- function(teamNames){
    bowlerDF <- NULL

    # Create a consolidated Data frame of batsman for all IPL teams played
    for (i in seq_along(teamNames)){
          df <- getBowlerWicketDetails(team=teamNames[i],name=IPLBowler,dir="./BattingBowlingDetails")
          bowlerDF <- rbind(bowlerDF,df) 

    }
    bowlerDF
}

14. Get the consolidated data frame for an IPL bowler

# Since an IPL bowler could have played in multiple teams we need to determine these teams and
# create a consolidated data frame for the analysis
# For example to check R Ashwin we need to do the following

IPLBowler = "R Ashwin"
#Check and get the team indices of IPL teams in which the batsman has played
i <- getTeamIndex(IPLBowler)

# Get the team names in which the IPL batsman has played
teamNames <- getTeams(i)
    # Check if file exists in the directory. This check is necessary when moving between matchType


############## Create a consolidated IPL batsman dataframe for analysis
bowlerDF <- getIPLBowlerDF(teamNames)

15. Bowler Mean Economy rate

# For e.g. to get the details of R Ashwin do
#bowlerMeanEconomyRate(bowlerDF,"R Ashwin")
bowlerMeanEconomyRate(bowlerDF,"bowlerName")

16. Bowler mean runs conceded

bowlerMeanRunsConceded(bowlerDF,"bowlerName")

17. Bowler Moving Average

bowlerMovingAverage(bowlerDF,"bowlerName")

18. Bowler cumulative average wickets

bowlerCumulativeAvgWickets(bowlerDF,"bowlerName")

19. Bowler cumulative Economy Rate (ER)

bowlerCumulativeAvgEconRate(bowlerDF,"bowlerName")

20. Bowler wicket plot

bowlerWicketPlot(bowlerDF,"bowlerName")

21. Bowler wicket against opposition

bowlerWicketsAgainstOpposition(bowlerDF,"bowlerName")

22. Bowler wicket at cricket grounds

bowlerWicketsVenue(bowlerDF,"bowlerName")

23. Predict number of deliveries to wickets

setwd("./IPLMatches")
bowlerDF1 <- getDeliveryWickets(team="IPLTeam1",dir=".",name="bowlerName",save=FALSE)
bowlerWktsPredict(bowlerDF1,"bowlerName")

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Analysis of International T20 matches with yorkr templates

Introduction

In this post I create yorkr templates for International T20 matches that are available on Cricsheet. With these templates you can convert all T20 data which is in yaml format to R dataframes. Further I create data and the necessary templates for analyzing. All of these templates can be accessed from Github at yorkrT20Template. The templates are

  1. Template for conversion and setup – T20Template.Rmd
  2. Any T20 match – T20Matchtemplate.Rmd
  3. T20 matches between 2 nations – T20Matches2TeamTemplate.Rmd
  4. A T20 nations performance against all other T20 nations – T20AllMatchesAllOppnTemplate.Rmd
  5. Analysis of T20 batsmen and bowlers of all T20 nations – T20BatsmanBowlerTemplate.Rmd

Besides the templates the repository also includes the converted data for all T20 matches I downloaded from Cricsheet in Dec 2016, You can recreate the files as more matches are added to Cricsheet site. This post contains all the steps needed for T20 analysis, as more matches are played around the World and more data is added to Cricsheet. This will also be my reference in future if I decide to analyze T20 in future!

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!!

1

 

Feel free to download/clone these templates  from Github yorkrT20Template and perform your own analysis

There will be 5 folders at the root

  1. T20data – Match files as yaml from Cricsheet
  2. T20Matches – Yaml match files converted to dataframes
  3. T20MatchesBetween2Teams – All Matches between any 2 T20 teams
  4. allMatchesAllOpposition – A T20 countries match data against all other teams
  5. BattingBowlingDetails – Batting and bowling details of all countries
library(yorkr)
library(dplyr)

The first few steps take care of the data setup. This needs to be done before any of the analysis of T20 batsmen, bowlers, any T20 match, matches between any 2 T20 countries or analysis of a teams performance against all other countries

There will be 5 folders at the root

  1. T20data
  2. T20Matches
  3. T20MatchesBetween2Teams
  4. allMatchesAllOpposition
  5. BattingBowlingDetails

The source YAML files will be in T20Data folder

1.Create directory T20Matches

Some files may give conversions errors. You could try to debug the problem or just remove it from the T20data folder. At most 2-4 file will have conversion problems and I usally remove then from the files to be converted.

Also take a look at my Inswinger shiny app which was created after performing the same conversion on the Dec 16 data .

convertAllYaml2RDataframesT20("T20Data","T20Matches")

2.Save all matches between all combinations of T20 nations

This function will create the set of all matches between every T20 country against every other T20 country. This uses the data that was created in T20Matches, with the convertAllYaml2RDataframesT20() function.

setwd("./T20MatchesBetween2Teams")
saveAllMatchesBetweenTeams(dir=".",odir=".")

3.Save all matches against all opposition

This will create a consolidated dataframe of all matches played by every T20 playing nation against all other nattions. This also uses the data that was created in T20Matches, with the convertAllYaml2RDataframesT20() function.

setwd("../allMatchesAllOpposition")
saveAllMatchesAllOpposition(dir=".",odir=".")

4. Create batting and bowling details for each T20 country

These are the current T20 playing nations. You can add to this vector as more countries start playing T20. You will get to know all T20 nations by also look at the directory created above namely allMatchesAllOpposition. his also uses the data that was created in T20Matches, with the convertAllYaml2RDataframesT20() function.

setwd("../BattingBowlingDetails")
teams <-c("Australia","India","Pakistan","West Indies", 'Sri Lanka',
          "England", "Bangladesh","Netherlands","Scotland", "Afghanistan",
          "Zimbabwe","Ireland","New Zealand","South Africa","Canada",
          "Bermuda","Kenya","Hong Kong","Nepal","Oman","Papua New Guinea",
          "United Arab Emirates")

for(i in seq_along(teams)){
    print(teams[i])
    val <- paste(teams[i],"-details",sep="")
    val <- getTeamBattingDetails(teams[i],dir="../T20Matches", save=TRUE)

}

for(i in seq_along(teams)){
    print(teams[i])
    val <- paste(teams[i],"-details",sep="")
    val <- getTeamBowlingDetails(teams[i],dir="../T20Matches", save=TRUE)

}

5. Get the list of batsmen for a particular country

For e.g. if you wanted to get the batsmen of Canada you would do the following. By replacing Canada for any other country you can get the batsmen of that country. These batsmen names can then be used in the batsmen analysis

country="Canada"
teamData <- paste(country,"-BattingDetails.RData",sep="")
load(teamData)
countryDF <- battingDetails
bmen <- countryDF %>% distinct(batsman) 
bmen <- as.character(bmen$batsman)
batsmen <- sort(bmen)
batsmen

6. Get the list of bowlers for a particular country

The method below can get the list of bowler names for any T20 nation. These names can then be used in the bowler analysis below

country="Netherlands"
teamData <- paste(country,"-BowlingDetails.RData",sep="")
load(teamData)
countryDF <- bowlingDetails
bwlr <- countryDF %>% distinct(bowler) 
bwlr <- as.character(bwlr$bowler)
bowler <- sort(bwlr)
bowler

Now we are all set

A)  International T20 Match Analysis

Load any match data from the ./T20Matches folder for e.g. Afganistan-England-2016-03-23.RData

setwd("./T20Matches")
load("Afghanistan-England-2016-03-23.RData")
afg_eng<- overs
#The steps are
load("Country1-Country2-Date.Rdata")
country1_country2 <- overs

All analysis for this match can be done now

2. Scorecard

teamBattingScorecardMatch(country1_country2,"Country1")
teamBattingScorecardMatch(country1_country2,"Country2")

3.Batting Partnerships

teamBatsmenPartnershipMatch(country1_country2,"Country1","Country2")
teamBatsmenPartnershipMatch(country1_country2,"Country2","Country1")

4. Batsmen vs Bowler Plot

teamBatsmenVsBowlersMatch(country1_country2,"Country1","Country2",plot=TRUE)
teamBatsmenVsBowlersMatch(country1_country2,"Country1","Country2",plot=FALSE)

5. Team bowling scorecard

teamBowlingScorecardMatch(country1_country2,"Country1")
teamBowlingScorecardMatch(country1_country2,"Country2")

6. Team bowling Wicket kind match

teamBowlingWicketKindMatch(country1_country2,"Country1","Country2")
m <-teamBowlingWicketKindMatch(country1_country2,"Country1","Country2",plot=FALSE)
m

7. Team Bowling Wicket Runs Match

teamBowlingWicketRunsMatch(country1_country2,"Country1","Country2")
m <-teamBowlingWicketRunsMatch(country1_country2,"Country1","Country2",plot=FALSE)
m

8. Team Bowling Wicket Match

m <-teamBowlingWicketMatch(country1_country2,"Country1","Country2",plot=FALSE)
m
teamBowlingWicketMatch(country1_country2,"Country1","Country2")

9. Team Bowler vs Batsmen

teamBowlersVsBatsmenMatch(country1_country2,"Country1","Country2")
m <- teamBowlersVsBatsmenMatch(country1_country2,"Country1","Country2",plot=FALSE)
m

10. Match Worm chart

matchWormGraph(country1_country2,"Country1","Country2")

B)  International T20 Matches between 2 teams

Load match data between any 2 teams from ./T20MatchesBetween2Teams for e.g.Australia-India-allMatches

setwd("./T20MatchesBetween2Teams")
load("Australia-India-allMatches.RData")
aus_ind_matches <- matches
#Replace below with your own countries
country1<-"England"
country2 <- "South Africa"
country1VsCountry2 <- paste(country1,"-",country2,"-allMatches.RData",sep="")
load(country1VsCountry2)
country1_country2_matches <- matches

2.Batsmen partnerships

m<- teamBatsmenPartnershiOppnAllMatches(country1_country2_matches,"country1",report="summary")
m
m<- teamBatsmenPartnershiOppnAllMatches(country1_country2_matches,"country2",report="summary")
m
m<- teamBatsmenPartnershiOppnAllMatches(country1_country2_matches,"country1",report="detailed")
m
teamBatsmenPartnershipOppnAllMatchesChart(country1_country2_matches,"country1","country2")

3. Team batsmen vs bowlers

teamBatsmenVsBowlersOppnAllMatches(country1_country2_matches,"country1","country2")

4. Bowling scorecard

a <-teamBattingScorecardOppnAllMatches(country1_country2_matches,main="country1",opposition="country2")
a

5. Team bowling performance

teamBowlingPerfOppnAllMatches(country1_country2_matches,main="country1",opposition="country2")

6. Team bowler wickets

teamBowlersWicketsOppnAllMatches(country1_country2_matches,main="country1",opposition="country2")
m <-teamBowlersWicketsOppnAllMatches(country1_country2_matches,main="country1",opposition="country2",plot=FALSE)
teamBowlersWicketsOppnAllMatches(country1_country2_matches,"country1","country2",top=3)
m

7. Team bowler vs batsmen

teamBowlersVsBatsmenOppnAllMatches(country1_country2_matches,"country1","country2",top=5)

8. Team bowler wicket kind

teamBowlersWicketKindOppnAllMatches(country1_country2_matches,"country1","country2",plot=TRUE)
m <- teamBowlersWicketKindOppnAllMatches(country1_country2_matches,"country1","country2",plot=FALSE)
m[1:30,]

9. Team bowler wicket runs

teamBowlersWicketRunsOppnAllMatches(country1_country2_matches,"country1","country2")

10. Plot wins and losses

setwd("./T20Matches")
plotWinLossBetweenTeams("country1","country2")

C)  International T20 Matches for a team against all other teams

Load the data between for a T20 team against all other countries ./allMatchesAllOpposition for e.g all matches of India

load("allMatchesAllOpposition-India.RData")
india_matches <- matches
country="country1"
allMatches <- paste("allMatchesAllOposition-",country,".RData",sep="")
load(allMatches)
country1AllMatches <- matches

2. Team’s batting scorecard all Matches

m <-teamBattingScorecardAllOppnAllMatches(country1AllMatches,theTeam="country1")
m

3. Batting scorecard of opposing team

m <-teamBattingScorecardAllOppnAllMatches(matches=country1AllMatches,theTeam="country2")

4. Team batting partnerships

m <- teamBatsmenPartnershipAllOppnAllMatches(country1AllMatches,theTeam="country1")
m
m <- teamBatsmenPartnershipAllOppnAllMatches(country1AllMatches,theTeam='country1',report="detailed")
head(m,30)
m <- teamBatsmenPartnershipAllOppnAllMatches(country1AllMatches,theTeam='country1',report="summary")
m

5. Team batting partnerships plot

teamBatsmenPartnershipAllOppnAllMatchesPlot(country1AllMatches,"country1",main="country1")
teamBatsmenPartnershipAllOppnAllMatchesPlot(country1AllMatches,"country1",main="country2")

6, Team batsmen vs bowlers report

m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(country1AllMatches,"country1",rank=0)
m
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(country1AllMatches,"country1",rank=1,dispRows=30)
m
m <-teamBatsmenVsBowlersAllOppnAllMatchesRept(matches=country1AllMatches,theTeam="country2",rank=1,dispRows=25)
m

7. Team batsmen vs bowler plot

d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(country1AllMatches,"country1",rank=1,dispRows=50)
d
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)
d <- teamBatsmenVsBowlersAllOppnAllMatchesRept(country1AllMatches,"country1",rank=2,dispRows=50)
teamBatsmenVsBowlersAllOppnAllMatchesPlot(d)

8. Team bowling scorecard

teamBowlingScorecardAllOppnAllMatchesMain(matches=country1AllMatches,theTeam="country1")
teamBowlingScorecardAllOppnAllMatches(country1AllMatches,'country2')

9. Team bowler vs batsmen

teamBowlersVsBatsmenAllOppnAllMatchesMain(country1AllMatches,theTeam="country1",rank=0)
teamBowlersVsBatsmenAllOppnAllMatchesMain(country1AllMatches,theTeam="country1",rank=2)
teamBowlersVsBatsmenAllOppnAllMatchesRept(matches=country1AllMatches,theTeam="country1",rank=0)

10. Team Bowler vs bastmen

df <- teamBowlersVsBatsmenAllOppnAllMatchesRept(country1AllMatches,theTeam="country1",rank=1)
teamBowlersVsBatsmenAllOppnAllMatchesPlot(df,"country1","country1")

11. Team bowler wicket kind

teamBowlingWicketKindAllOppnAllMatches(country1AllMatches,t1="country1",t2="All")
teamBowlingWicketKindAllOppnAllMatches(country1AllMatches,t1="country1",t2="country2")

12.

teamBowlingWicketRunsAllOppnAllMatches(country1AllMatches,t1="country1",t2="All",plot=TRUE)
teamBowlingWicketRunsAllOppnAllMatches(country1AllMatches,t1="country1",t2="country2",plot=TRUE)

D) Batsman functions

Get the batsman’s details for a batsman

setwd("../BattingBowlingDetails")
kohli <- getBatsmanDetails(team="India",name="Kohli",dir=".")
batsmanDF <- getBatsmanDetails(team="country1",name="batsmanName",dir=".")

2. Runs vs deliveries

batsmanRunsVsDeliveries(batsmanDF,"batsmanName")

3. Batsman 4s & 6s

batsman46 <- select(batsmanDF,batsman,ballsPlayed,fours,sixes,runs)
p1 <- batsmanFoursSixes(batsman46,"batsmanName")

4. Batsman dismissals

batsmanDismissals(batsmanDF,"batsmanName")

5. Runs vs Strike rate

batsmanRunsVsStrikeRate(batsmanDF,"batsmanName")

6. Batsman Moving Average

batsmanMovingAverage(batsmanDF,"batsmanName")

7. Batsman cumulative average

batsmanCumulativeAverageRuns(batsmanDF,"batsmanName")

8. Batsman cumulative strike rate

batsmanCumulativeStrikeRate(batsmanDF,"batsmanName")

9. Batsman runs against oppositions

batsmanRunsAgainstOpposition(batsmanDF,"batsmanName")

10. Batsman runs vs venue

batsmanRunsVenue(batsmanDF,"batsmanName")

11. Batsman runs predict

batsmanRunsPredict(batsmanDF,"batsmanName")

12. Bowler functions

For example to get Ravicahnder Ashwin’s bowling details

setwd("../BattingBowlingDetails")
ashwin <- getBowlerWicketDetails(team="India",name="Ashwin",dir=".")
bowlerDF <- getBatsmanDetails(team="country1",name="bowlerName",dir=".")

13. Bowler Mean Economy rate

bowlerMeanEconomyRate(bowlerDF,"bowlerName")

14. Bowler mean runs conceded

bowlerMeanRunsConceded(bowlerDF,"bowlerName")

15. Bowler Moving Average

bowlerMovingAverage(bowlerDF,"bowlerName")

16. Bowler cumulative average wickets

bowlerCumulativeAvgWickets(bowlerDF,"bowlerName")

17. Bowler cumulative Economy Rate (ER)

bowlerCumulativeAvgEconRate(bowlerDF,"bowlerName")

18. Bowler wicket plot

bowlerWicketPlot(bowlerDF,"bowlerName")

19. Bowler wicket against opposition

bowlerWicketsAgainstOpposition(bowlerDF,"bowlerName")

20. Bowler wicket at cricket grounds

bowlerWicketsVenue(bowlerDF,"bowlerName")

21. Predict number of deliveries to wickets

setwd("./T20Matches")
bowlerDF1 <- getDeliveryWickets(team="country1",dir=".",name="bowlerName",save=FALSE)
bowlerWktsPredict(bowlerDF1,"bowlerName")

Inswinger: yorkr swings into International T20s

In this post I introduce ‘Inswinger’ an interactive Shiny app to analyze International T20 players, matches and teams. This app was a natural consequence to my earlier Shiny app ‘GooglyPlus’. Most of the structure for this app remained the same, I only had to work with a different dataset, so to speak.

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. Inswinger is based on the latest data dump from Cricsheet (Dec 2016) and includes all International T20 till then. There are a lot of new Internationation teams like Oman, Hong Kong, UAE, etc. In total there are 22 different International T20 teams in my Inswinger app.

The countries are a) Afghanistan b) Australia c) Bangladesh d) Bermuda e) Canada f) England g) Hong Kong h) India i) Ireland j) Kenya k) Nepal l) Netherlands m) New Zealand n) Oman o) Pakistan p) Papua New Guinea q) Scotland r) South Africa s) Sri Lanka t) United Arab Emirates u) West Indies v) Zimbabwe

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!!

1

 

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. Hence in the Inswinger Shiny app results can be displayed both as table or a plot depending on the choice of function.

Inswinger can do detailed analyses of a) Individual T20 batsman b) Individual T20 bowler c) Any T20 match d) Head to head confrontation between 2 T20 teams e) All matches of a T20 team against all other teams.

The Shiny app can be accessed at Inswinger

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

Based on the 5 detailed analysis domains there are 5 tabs
A) T20 Batsman: This tab can be used to perform analysis of all T20 batsman. If a batsman has played in more than 1 team, then the overall performance is considered. There are 10 functions for the T20 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

B) T20 Bowler: This tab can be used to analyze individual T20 bowlers. The functions handle T20 bowlers who have played in more than 1 T20 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

C) T20 match: This tab can be used for analyzing individual T20 matches. The available functions are
– Match Batting Scorecard – Table
– Batting Partnerships – Plot, Table
– Batsmen vs Bowlers – Plot, Table
– Match Bowling Scorecard   – Table
– Bowling Wicket Kind – Plot, Table
– Bowling Wicket Runs – Plot, Table
– Bowling Wicket Match – Plot, Table
– Bowler vs Batsmen – Plot, Table
– Match Worm Graph – Plot

D) Head to head: This tab can be used for analyzing head-to-head confrontations, between any 2 T20 teams for e.g. all matches between India vs Australia or West Indies vs Sri Lanka . The available functions are
-Team Batsmen Batting Partnerships All Matches – Plot, Table {Summary and Detailed}
-Team Batting Scorecard All Matches – Table
-Team Batsmen vs Bowlers all Matches – Plot, Table
-Team Wickets Opposition All Matches – Plot, Table
-Team Bowling Scorecard All Matches – Table
-Team Bowler vs Batsmen All Matches – Plot, Table
-Team Bowlers Wicket Kind All Matches – Plot, Table
-Team Bowler Wicket Runs All Matches – Plot, Table
– Win Loss All Matches – Plot

E) T20 team’s overall performance: this tab can be used analyze the overall performance of any T20 team. For this analysis all matches played by this team is considered. The available functions are
-Team Batsmen Partnerships Overall – Plot, Table {Summary and Detailed)}
-Team Batting Scorecard Overall –Table
-Team Batsmen vs Bowlers Overall – Plot, Table
-Team Bowler vs Batsmen Overall – Plot, Table
-Team Bowling Scorecard Overall – Table
-Team Bowler Wicket Kind Overall – Plot, Table

Below I include a random set of charts that are generated in each of the 5 tabs
A. IPL Batsman
a. Shakib-al-Hassan (Bangladesh) :  Runs vs Deliveries
untitled

b. Virat Kohli (India) – Cumulative Average
untitled

c.  AB Devilliers (South Africa) – Runs at venues
untitled

d. Glenn Maxwell (Australia)  – Predict runs vs deliveries faces
untitled

B. IPL Bowler
a. TG Southee (New Zealand) – Mean Economy Rate vs overs
untitled

b) DJ Bravo – Moving Average of wickets
untitled

c) AC Evans (Scotland) – Bowler Wickets Against Opposition
untitled

C.T20 Match
a. Match Score (Afghanistan vs Canada, 2012-03-18)
untitled

b)  Match batting partnerships (Plot) Hong Kong vs Oman (2015-11-21), Hong Kong
Hong Kong Partnerships
untitled

c) Match batting partnerships (Table) – Ireland vs Scotland(2012-03-18, Ireland)
Batting partnership can also be displayed as a table
untitled

d) Batsmen vs Bowlers (Plot) – India vs England (2012-12-22)
untitled

e) Match Worm Chart – Sri Lanka vs Pakistan (2015-08-01)
untitled

D.Head to head
a) Team Batsmen Partnership (Plot) – India vs Australia (all matches)
Virat Kohli has the highest total runs in partnerships against Australia
untitled

b)  Team Batsmen Partnership (Summary – Table) – Kenya vs Bangladesh
untitled

c) Team Bowling Scorecard (Table only) India vs South Africa all Matches
untitled

d) Wins- Losses New Zealand vs West Indies all Matches
untitled

C) Overall performances
a) Batting Scorecard All Matches  (Table only) – England’s overall batting performance
Eoin Morgan, Kevin Pieterson  & SJ Taylor have the best performance
untitled

b) Batsman vs Bowlers all Matches (Plot)
India’s best performing batsman (Rank=1) is Virat Kohli
untitled

c)  Batsman vs Bowlers all Matches (Table)
The plot above for Virat Kohli can also be displayed as a table. Kohli has score most runs DJ Bravo, SR Watson & Shahid Afridi
untitled

The Inswinger Shiny app can be accessed at Inswinger. Give it a swing!

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

Also see my other Shiny apps
1.GooglyPlus
2.What would Shakespeare say?
3.Sixer
4.Revisiting crimes against women in India

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To see all posts take at a look at Index of Posts