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
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
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)
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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')
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Feel free to clone/download the code from Github yorkpy
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