Pitching yorkpy…on the middle and outside off-stump to IPL – Part 2
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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.
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
1. Functions related to head-to-head confrontations between any 2 IPL teams
The following function can be used to analyze the IPL team performance in a T20 match
- getAllMatchesBetweenTeams()
- saveAllMatchesBetween2IPLTeams()
- teamBatsmenPartnershiOppnAllMatches()
- teamBatsmenPartnershipOppnAllMatchesChart()
- teamBatsmenVsBowlersOppnAllMatches()
- teamBattingScorecardOppnAllMatches()
- teamBowlingScorecardOppnAllMatches()
- teamBowlingWicketKindOppositionAllMatches()
- teamBowlersVsBatsmenOppnAllMatches()
- plotWinLossBetweenTeams()
- plotWinsByRunOrWickets()
- plotWinsbyTossDecision()
All functions relate to the performance of an IPL team in all matches against an opposing IPL team
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 0 123.859649 ## 20 CH Gayle 270 228 12 0 118.421053 ## 19 AB de Villiers 241 157 26 0 153.503185 ## 6 R Dravid 133 117 18 0 113.675214 ## 3 JH Kallis 123 113 21 0 108.849558 ## 22 MA Agarwal 120 104 15 0 115.384615 ## 2 LRPL Taylor 117 102 5 0 114.705882 ## 11 RV Uthappa 115 77 7 0 149.350649 ## 21 SS Tiwary 86 88 4 0 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 0 118.604651 ## 33 SN Khan 50 36 5 0 138.888889 ## 1 W Jaffer 50 36 5 0 138.888889 ## 7 P Kumar 39 25 2 0 156.000000 ## 28 Yuvraj Singh 38 33 2 0 115.151515 ## 4 MV Boucher 37 33 4 0 112.121212 ## 23 LA Pomersbach 31 21 2 0 147.619048 ## 8 Z Khan 29 27 3 0 107.407407 ## 12 KP Pietersen 23 15 2 0 153.333333 ## 38 CL White 21 13 2 0 161.538462 ## 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 0 170.000000 ## 25 RR Rossouw 15 13 1 0 115.384615 ## 40 AUK Pathan 14 6 2 0 233.333333 ## 42 JJ van der Wath 14 11 1 0 127.272727 ## 27 VH Zol 13 12 0 0 108.333333 ## 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
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