Analyzing T20 matches with yorkpy templates

1. Introduction

In this post I create yorkpy templates for end-to-end analysis of any T20 matches that are available on Cricsheet as yaml format. These templates can be used to analyze Intl. T20, IPL, BBL and Natwest T20. In fact they can be used for any T20 games which have been saved in the yaml format as specified by Cricsheet Cricheet.

Noteyorkpy is the clone of my R package yorkr see yorkr pads up for the Twenty20s: Part 1- Analyzing team”s match performance

With these templates you can convert all T20 match data which is in yaml format to Pandas dataframes and save them as CSV. Note The data for Intl T20, IPL, BBL and Natwest T20 have already been converted and are available at allYorkpyData. This templates is also available at Github at yorkpyTemplate. The template includes the following steps

  1. Template for conversion and setup
  2. Analysis of Any T20 match
  3. Analysis of a T20 team in all matches against another T20 team
  4. Analysis of a T20 team in all matches against all other teams
  5. Analysis of T20 batsmen and bowlers

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!

Install yorkpy with pip install yorkpy

Data conversion of the yaml files have to be done before any analysis of T20 batsmen, bowlers, any T20 match matches between any 2 T20 team or analysis of a teams performance against all other team can be done

The first step is To convert the YAML files that are available for the different T20 leagues namely Intl. T20, IPL, BBL, Natwest T20 which are available in yaml format in Cricsheet. For initial data setup we need to use slighly different functions for each of the T20 leagues since the teams are different. The function to convert yaml to Pandas dataframe and save as CSV is common for all leagues

A. For International T20

import yorkpy.analytics as yka
# COnvert yaml to pandas and save as CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\data1")

# Save all matches between any 2 Intl T20 countries
#yka.saveAllMatchesBetween2IntlT20s(dir1)

#Save all matches between an Intl.T20 country and all other countries
#yka.saveAllMatchesAllOppositionIntlT20(dir1)

# Get batting details for a country
#yka.getTeamBattingDetails(<country>,dir=dir1, save=True)

#Get bowling details
#yka.getTeamBowlingDetails(<country>,dir=dir1, save=True)

B. For Indian Premier League (IPL)

import yorkpy.analytics as yka
# COnvert yaml to pandas and save as CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\data1")

# Save all matches between any 2 IPL teams
#yka.saveAllMatchesBetween2IPLTeams(dir1)

#Save all matches between an IPL team and all other teams
#yka.saveAllMatchesAllOppositionIPLT20(dir1)

# Get batting details for an IPL team
#yka.getTeamBattingDetails(<team1>,dir=dir1, save=True)

#Get bowling details for an IPL team
#yka.getTeamBowlingDetails(<team1>>,dir=dir1, save=True)

C. For Big Bash League (BBL)

import yorkpy.analytics as yka
# COnvert yaml to pandas and save as CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\data1")

# Save all matches between any 2 BBL teams
#yka.saveAllMatchesBetween2BBLTeams(dir1)

#Save all matches between an BBL team and all other teams
#yka.saveAllMatchesAllOppositionBBLT20(dir1)

# Get batting details for an BBL team
#yka.getTeamBattingDetails(<team1>,dir=dir1, save=True)

#Get bowling details for an BBL team
#yka.getTeamBowlingDetails(<team1>>,dir=dir1, save=True)

D For Natwest T20

import yorkpy.analytics as yka
# COnvert yaml to pandas and save as CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\data1")

# Save all matches between any 2 NWB teams
#yka.saveAllMatchesBetween2NWBTeams(dir1)

#Save all matches between an NWB team and all other teams
#yka.saveAllMatchesAllOppositionNWBT20(dir1)

# Get batting details for an NWB team
#yka.getTeamBattingDetails(<team1>,dir=dir1, save=True)

#Get bowling details for an NWB team
#yka.getTeamBowlingDetails(<team1>>,dir=dir1, save=True)

Once the conversion has been done and the data has been setup we can use any of the yorkpy functions for the the 4 leagues (Intl. T20, IPL, BBL or Natwest T20) There are four classes of functions. These functions can be used for any of the

  1. Class 1 – Functions that analyze a single T20 match
  2. Class 2 – Functions that analyze the performance of a T20 team in all matches against another T20 team
  3. Class 3 – Functions that analyze the performance of a T20 team against all other teams
  4. Class 4 – Functions that analyze individual T20 batsmen or bowler

2. Class 1 functions

These functions analyze a single T20 match (Intl T20, BBL, IPL or Natwest T20) To see actual usage of Class 1 function see Pitching yorkpy … short of good length to IPL – Part 1

import yorkpy.analytics as yka
# Get scorecard
#scorecard,extras=yka.teamBattingScorecardMatch(<team1>,"Name of Team")

#Get partnership
#match=pd.read_csv("<match.csv>")
#yka.teamBatsmenPartnershipMatch(match,<team1>,<team2>,plot=True/False)

#Batsmen vs bowler
#match=pd.read_csv("<match.csv>")
#yka.teamBatsmenVsBowlersMatch(match,<team1>,<team2>,plot=True/False)

#Bowling scorecard
#match=pd.read_csv("<match.csv>")
#a=yka.teamBowlingScorecardMatch(match,<team1>)

#Wicket Kind
#match=pd.read_csv("<match.csv>")
#yka.teamBowlingWicketKindMatch((match,<team1>,<team2>)

#Wicket Match
#match=pd.read_csv("<match.csv>")
#yka.teamBowlingWicketMatch(match,<team1>,<team2>,plot=True/False)

#Bowler vs Batsman
#match=pd.read_csv("<match.csv>")
#yka.teamBowlersVsBatsmenMatch(match,<team1>,<team2>)

#Match worm chart
#match=pd.read_csv("<match.csv>")
#yka.matchWormChart(match,<team1>,<team2>,)

3. Class 2 functions

These set of functions analyze the performance a T20 team for e.g. Intl T20, BBL or Natwest T20 in all matches against another T20 team (country or IPL, BBL or Natwest T20 team. To see usages of Class 2 functions see Pitching yorkpy…on the middle and outside off-stump to IPL – Part 2

import yorkpy.analytics as yka

# Batting partnerships - Table
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#m=yka.teamBatsmenPartnershiOppnAllMatches(team1_team2_matches,<team1/team2>,report="summary/detailed", top=<n>)

# Batting partnerships - Plot
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#yka.teamBatsmenPartnershipOppnAllMatchesChart(team1_team2_matches,<team1>,<team2> plot=<True/False>, top=<N>, partnershipRuns=<M>)

#Batsmen vs Bowlers
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#yka.teamBatsmenVsBowlersOppnAllMatches(team1_team2_matches,<team1>,<team2> plot=<True/False>, top=<N>,runsScored=<M>)

# Batting scorecard
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#scorecard=yka.teamBattingScorecardOppnAllMatches(team1_team2_matches,<team1>,<team2>)

#Bowling scorecard
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#scorecard=yka.teamBowlingScorecardOppnAllMatches(team1_team2_matches,<team1>,<team2>)

#Bowling wicket kind
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#yka.teamBowlingWicketKindOppositionAllMatches(team1_team2_matches,<team1>,<team2>,plot=<True/False>,top=<N>,wickets=<M>)

#Bowler vs batsman
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#yka.teamBowlersVsBatsmenOppnAllMatches(team1_team2_matches,<team1>,<team2>,plot=<True/False>,top=<N>,runsConceded=<M>)

# Wins vs losses
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#yka.plotWinLossBetweenTeams(team1_team2_matches,<team1>,<team2>)

#Wins by win type
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#yka.plotWinsByRunOrWickets(team1_team2_matches,<team1>)

#Wins by toss decision
#team1_team2_matches = pd.read_csv(<matches_between_2_teams.csv)
#yka.plotWinsbyTossDecision(team1_team2_matches,<team1>,tossDecision=<field/bat>)

4. Class 3 functions

This set of functions deals with analyzing the performance of a T20 team (Intl. T20, IPL, BBL or Natwest T20) in all matches against all other teams. To see usages of Class 3 functions see Pitching yorkpy…swinging away from the leg stump to IPL – Part 3. After the data is save all matches between all oppositions we can use this data

import yorkpy.analytics as yka
#Batsman partnerships
#allmatches = pd.read_csv("<allmatchesForteam")
#m=yka.teamBatsmenPartnershiAllOppnAllMatches(allmatches,<team1>,report=<"summary"/"detailed", top=<N>,partnershipRuns=<M>)

#Batsmen vs Bowlers
#allmatches = pd.read_csv("<allmatchesForteam")
#yka.teamBatsmenVsBowlersAllOppnAllMatches(allmatches,<team1>,plot=<True/False>,top=N>,runsScored=<M>)

#Batting scorecard
#allmatches = pd.read_csv("<allmatchesForteam")
#scorecard=yka.teamBattingScorecardAllOppnAllMatches(allmatches,<team1>)

#Bowling scorecard
#allmatches = pd.read_csv("<allmatchesForteam")
#scorecard=yka.teamBowlingScorecardAllOppnAllMatches(allmatches,<team1>)

#Bowling wicket kind
#allmatches = pd.read_csv("<allmatchesForteam")
#yka.teamBowlingWicketKindAllOppnAllMatches(allmatches,<team1>,plot=<True/False>,top=<N>,wickets=<M>)

# Bowler vs Batsmen
#allmatches = pd.read_csv("<allmatchesForteam")
#yka.teamBowlersVsBatsmenAllOppnAllMatches(allmatches,<team1>,plot=<True/False>,top=<N>,runsConceded=<M>)

# Wins vs losses
#allmatches = pd.read_csv("<allmatchesForteam")
#yka.plotWinLossByTeamAllOpposition(allmatches,<team1>,plot=<"summary"/"detailed">)

# Wins by win type
#allmatches = pd.read_csv("<allmatchesForteam")
#yka.plotWinsByRunOrWicketsAllOpposition(allmatches,<team1>)

# Wins by toss decision
#allmatches = pd.read_csv("<allmatchesForteam")
#yka.plotWinsbyTossDecisionAllOpposition(allmatches,<team1>,tossDecision='bat'/'field',plot='summary'/'detailed')

5. Class 4 functions

This set of functions are used for analyzing individual batsman/bowler. From the converted xxx-BattingDetails.csv and xxx-BowlingDetails.csv we can get the batsman and bowler details as shown below. Subsequenly we can perform analyses of the individual batsman and bowler. To see actual usages of Class 4 functions see Pitching yorkpy … in the block hole – Part 4

import yorkpy.analytics as yka

#Batsman analyses
#Get batsman Dataframe
#batsmanDF=yka.getBatsmanDetails(<team1>,<batsman>,dir=dir1)

#Batsman Runs vs Deliveries
#yka.batsmanRunsVsDeliveries(batsmanDF,<batsmanName>)

#Batsman fours and sixes
#yka.batsmanFoursSixes(batsmanDF,<batsmanName>)


#Batsman dismissals
#yka.batsmanDismissals(batsmanDF,<batsmanName>)

#Batsman Runs vs Strike Rate
#yka.batsmanRunsVsStrikeRate(batsmanDF,<batsmanName>)

#Batsman Moving average
#yka.batsmanMovingAverage(batsmanDF,<batsmanName>)


#Batsman Cumulative average
#yka.batsmanCumulativeAverageRuns(batsmanDF,<batsmanName>)

#Batsman Cumulative Strike rate
#yka.batsmanCumulativeStrikeRate(batsmanDF,<batsmanName>)

#Batsman Runs against opposition
#yka.batsmanRunsAgainstOpposition(batsmanDF,<batsmanName>)

#Batsman Runs against opposition
#yka.batsmanRunsVenue(batsmanDF,<batsmanName>)


#Bowler analyses
#Get bowler dataframe
#bowlerDF=yka.getBowlerWicketDetails(<team1>,<bowler>dir=dir1)

#Mean economy rate
#yka.bowlerMeanEconomyRate(bowlerDF,<bowlerName>)


#Mean Economy rate
#yka.bowlerMeanEconomyRate(bowlerDF,<bowlerName>)

#Mean Runs conceded
#yka.bowlerMeanRunsConceded(bowlerDF,<bowlerName>)

#Moving average of wickets
#yka.bowlerMovingAverage((bowlerDF,<bowlerName>)

# Cumulative average of wickets
#yka.bowlerCumulativeAvgWickets(bowlerDF,<bowlerName>)

# Cumulative economy rate
#yka.bowlerCumulativeAvgEconRate(bowlerDF,<bowlerName>)

# Wicket plot
#yka.bowlerWicketPlot(df,name)

# Wicket against opposition
#yka.bowlerWicketsAgainstOpposition(bowlerDF,<bowlerName>)

# Wickets at venue
#yka.bowlerWicketsVenue(bowlerDF,<bowlerName>)

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

Conclusion

With the above templates detailed analyis can be done on

  • A T20 match
  • Performance of a team in all matches against another team
  • Performance of a team in all matches against all other teams
  • Individual batting and bowling performances

See also

  1. Deep Learning from first principles in Python, R and Octave – Part 5
  2. My travels through the realms of Data Science, Machine Learning, Deep Learning and (AI)
  3. Practical Machine Learning with R and Python – Part 4
  4. Take 4+: Presentations on ‘Elements of Neural Networks and Deep Learning’ – Parts 1-8
  5. A method to crowd source pothole marking on (Indian) roads

To see all posts click Index of posts

yorkpy takes a hat-trick, bowls out Intl. T20s, BBL and Natwest T20!!!

“Dear, dear! How queer everything is to-day! And yesterday things went on just as usual. I wonder if I’ve been changed in the night? Let me think: was I the same when I got up this morning? I almost think I can remember feeling a little different. But if I’m not the same, the next question is ’Who in the world am I? Ah, that’s the great puzzle!”

             Alice's adventures  in Wonderland, Lewis Carroll

1. Introduction

In this post, yorkpy clean bowls the following T20 formats namely International T20s, Big Bash League and Natwest T20 Blast. I take yorkpy on a spin through these T20 leagues. In the post below,I choose a random set of about 10-12 of the overall 63 functions that yorkpy has, and execute them for each of the different T20 leagues – Intl T20s, BBL and Natwest T20s. yorkpy, is the python avatar of my R package yorkr, see Introducing cricket package yorkr: Part 1- Beaten by sheer pace!

There were a couple of new functions that needed to be added for each of the T20 leagues – Intl T20, BBL and Natwest T20 to take into account the different teams in each of these leagues. Further some bugs were also ironed out in tje latest version of yorkpy. yorkpy uses data from Cricsheet . The match data is in the form of YAML files. yorkpy converts these YAML files to dataframes. YAML files are very detailed and include a ball-by-ball account of the match.

– You can clone/fork the latest code for yorkpy from github yorkpy
– This post has also been published in RPubs at yorkpy takes a hat-trick
– You can download the PDF version of this post at yorkpy takes a hat-trick

The data for IPL, Intl. T20, BBL and Natwest T20 have already been converted into pandas dataframes and saved as CSVs. You can download the converted files from Github at [allYorkpyT20Data])(https://github.com/tvganesh/allYorkpyT20Data)

yorkpy has the following 4 main classes of functions

A.Functions analyzing individual T20 match (Class 1)

This was demonstrated in Pitching yorkpy . short of good length to IPL – Part 1 The functions deal with individual T20 matches. The functions are

  1. convertYaml2PandasDataframeT20()
  2. convertAllYaml2PandasDataframesT20()
  3. teamBattingScorecardMatch()
  4. teamBatsmenPartnershipMatch()
  5. teamBatsmenVsBowlersMatch()
  6. teamBowlingScorecardMatch()
  7. teamBowlingWicketKindMatch()
  8. teamBowlingWicketRunsMatch()
  9. teamBowlingWicketMatch()
  10. teamBowlersVsBatsmenMatch()
  11. matchWormChart()

B. Functions that analyze all matches between 2 T20 teams (Class 2

Pitching yorkpy.on the middle and outside off-stump to IPL – Part 2 included functions that analyze head-to-head confrontation between any 2 T20 teams The functions are

  1. getAllMatchesBetweenTeams()
  2. saveAllMatchesBetween2IPLTeams()
  3. getAllMatchesBetweenTeams()
  4. saveAllMatchesBetween2IPLTeams()
  5. teamBatsmenPartnershiOppnAllMatches()
  6. teamBatsmenPartnershipOppnAllMatchesChart()
  7. teamBatsmenVsBowlersOppnAllMatches()
  8. teamBattingScorecardOppnAllMatches()
  9. teamBowlingScorecardOppnAllMatches()
  10. teamBowlingWicketKindOppositionAllMatches()
  11. teamBowlersVsBatsmenOppnAllMatches()
  12. plotWinLossBetweenTeams()
  13. plotWinsByRunOrWickets() 23.plotWinsbyTossDecision()

C. Functions that analyze the performance of a T20 team against all other teams (Class 3)

The post Pitching yorkpy.swinging away from the leg stump to IPL – Part 3 is based on Class C set of functions shown below

  1. getAllMatchesAllOpposition()
  2. saveAllMatchesAllOppositionIPLT20(dir1)
  3. getAllMatchesAllOpposition()
  4. saveAllMatchesAllOppositionIPLT20()
  5. teamBatsmenPartnershiAllOppnAllMatches()
  6. teamBatsmenPartnershipAllOppnAllMatchesChart()
  7. teamBatsmenVsBowlersAllOppnAllMatches()
  8. teamBattingScorecardAllOppnAllMatches()
  9. teamBowlingScorecardAllOppnAllMatches()
  10. teamBowlingWicketKindAllOppnAllMatches()
  11. teamBowlersVsBatsmenAllOppnAllMatches()
  12. plotWinLossByTeamAllOpposition()
  13. plotWinsByRunOrWicketsAllOpposition()
  14. plotWinsbyTossDecisionAllOpposition()

D. Functions that analyze performances of T20 batsmen and bowlers (Class 4)

These set of functions analyze individual batsmen and bowlers and have been used in Pitching yorkpy . in the block hole – Part 4 The functions are

  1. getTeamBattingDetails()
  2. getBatsmanDetails()
  3. batsmanRunsVsDeliveries()
  4. batsmanFoursSixes()
  5. batsmanDismissals()
  6. batsmanRunsVsStrikeRate()
  7. batsmanMovingAverage()
  8. batsmanCumulativeAverageRuns()
  9. batsmanCumulativeStrikeRate()
  10. batsmanRunsAgainstOpposition()
  11. batsmanRunsVenue
  12. getTeamBowlingDetails()
  13. getBowlerWicketDetails()
  14. bowlerMeanEconomyRate()
  15. bowlerMeanRunsConceded()
  16. bowlerMovingAverage()
  17. bowlerCumulativeAvgWickets()
  18. bowlerCumulativeAvgEconRate()
  19. bowlerWicketPlot()
  20. bowlerWicketsAgainstOpposition()
  21. bowlerWicketsVenue()

Additional new functions were added to handle Intl T20s, Big Bash League and Natwest T20 Blast, since the teams are different. They are

59. saveAllMatchesBetween2IntlT20s()
60. saveAllMatchesAllOppositionIntlT20()
61. saveAllMatchesBetween2BBLTeams()
62 saveAllMatchesAllOppositionBBLT20()
63. saveAllMatchesBetween2NWBTeams()
64. saveAllMatchesAllOppositionNWBT20()

All other functions can be used as is! You can get the help of any function in yorkpy using

import yorkpy.analytics as yka
help(yka.teamBatsmenPartnershiOppnAllMatches)
## Help on function teamBatsmenPartnershiOppnAllMatches in module yorkpy.analytics:
## 
## teamBatsmenPartnershiOppnAllMatches(matches, theTeam, report='summary', top=5)
##     Team batting partnership against a opposition all IPL matches
##     
##     Description
##     
##     This function computes the performance of batsmen against all bowlers of an oppositions in 
##     all matches. This function returns a dataframe
##     
##     Usage
##     
##     teamBatsmenPartnershiOppnAllMatches(matches,theTeam,report="summary")
##     Arguments
##     
##     matches     
##     All the matches of the team against the oppositions
##     theTeam     
##     The team for which the the batting partnerships are sought
##     report      
##     If the report="summary" then the list of top batsmen with the highest partnerships 
##     is displayed. If report="detailed" then the detailed break up of partnership is returned 
##     as a dataframe
##     top
##     The number of players to be displayed from the top
##     Value
##     
##     partnerships The data frame of the partnerships
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh tvganesh.85@gmail.com
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://cricsheet.org/
##     https://gigadom.wordpress.com/
##     
##     
##     See Also
##     
##     teamBatsmenVsBowlersOppnAllMatchesPlot
##     teamBatsmenPartnershipOppnAllMatchesChart

As I mentioned above I will be randomly choosing a set of 12 functions from Class 1,2,3,4 for each of the T20 leagues (Intl T20, BBL and NWB T20) for analysis

2. International T20s

The following functions were added for handling Intl. T20s

  1. saveAllMatchesBetween2IntlT20s()
  2. saveAllMatchesAllOppositionIntlT20()

To handle the countries in Intl. T20s below

Afghanistan, Australia, Bangladesh, Bermuda, Canada, England,Hong Kong,India, Ireland, Kenya, Nepal, Netherlands, “New Zealand, Oman,Pakistan,Scotland,South Africa, Sri Lanka, United Arab Emirates,West Indies, Zimbabwe

import os
#os.chdir('C:\\software\\cricket-package\\yorkpyT20\\t20s')
#import yorkpy.analytics as yka
#1.  Convert all YAML files to dataframes and CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\data1")
#dir1='C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches'
#2. Save all matches between 2 T20 teams
#yka.saveAllMatchesBetween2IntlT20s(dir1)
#3. Save all matches between a T20 team and all other teams
#dir1='C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches'
#yka.saveAllMatchesAllOppositionIntlT20(dir1)
#4. Get batting details
#dir1='C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches
#yka.getTeamBattingDetails("Afghanistan",dir=dir1, save=True)
#yka.getTeamBattingDetails("Australia",dir=dir1,save=True)
#yka.getTeamBattingDetails("Bangladesh",dir=dir1,save=True)
#...
#5. Get bowling details
#dir1='C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches
#yka.getTeamBowlingDetails("Afghanistan",dir=dir1, save=True)
#yka.getTeamBowlingDetails("Australia",dir=dir1,save=True)
#yka.getTeamBowlingDetails("Bangladesh",dir=dir1,save=True)
# ...

Once the data is converted you can use the yorkpy functions. The data has been converted for Intl T20 and is available at Github at IntlT20

To use the yorkpy functions for a new league we need to initial convert the YAML files into appropriate format for processing by yorkpy functions

This will create the necessary files which are are used in the functions below

2.2 2.1 Intl. T20 – Team score card  (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches"
path=os.path.join(dir1,".\\India-New Zealand-2007-09-16.csv")
ind_nz=pd.read_csv(path)
scorecard,extras=yka.teamBattingScorecardMatch(ind_nz,"India")
print(scorecard)
##             batsman  runs  balls  4s  6s          SR
## 0         G Gambhir    51     34   5   2  150.000000
## 1          V Sehwag    40     18   6   2  222.222222
## 2        RV Uthappa     0      2   0   0    0.000000
## 3          MS Dhoni    24     20   2   0  120.000000
## 4      Yuvraj Singh     5      7   0   0   71.428571
## 5        KD Karthik    17     12   3   0  141.666667
## 6         IK Pathan    11     10   2   0  110.000000
## 7        AB Agarkar     1      2   0   0   50.000000
## 8   Harbhajan Singh     7      6   1   0  116.666667
## 9       S Sreesanth    19     10   4   0  190.000000
## 10         RP Singh     1      1   0   0  100.000000
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    370      6        0        8     0        0      14

2.2 Intl. T20 -Team batsmen partnership (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches"
path=os.path.join(dir1,".\\South Africa-Australia-2009-03-27.csv")
sa_aus=pd.read_csv(path)
yka.teamBatsmenPartnershipMatch(sa_aus,'Australia','New Zealand',plot=True)

2.3 Intl. T20 -Team bowling scorecard match (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches"
path=os.path.join(dir1,".\\Sri Lanka-West Indies-2012-09-28.csv")
sl_wi=pd.read_csv(path)
a=yka.teamBowlingScorecardMatch(sl_wi,'Sri Lanka')
print(a)
##          bowler  overs  runs  maidens  wicket  econrate
## 0    A Mohammed      2    13        0       0       6.5
## 1  SA Campbelle      1     8        0       1       8.0
## 2     SC Selman      1     3        0       0       3.0
## 3      SF Daley      2     5        0       1       2.5
## 4     SR Taylor      2     4        0       1       2.0
## 5     TD Smartt      2    17        0       0       8.5

2.4 Intl. T20 -Match Worm chart (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches"
path=os.path.join(dir1,".\\England-India-2012-09-29.csv")
eng_ind=pd.read_csv(path)
yka.matchWormChart(eng_ind,"England", "India")

path=os.path.join(dir1,".\\Bangladesh-Ireland-2015-12-05.csv")
ban_ire=pd.read_csv(path)
yka.matchWormChart(ban_ire,"Bangladesh", "Ireland")

2.5 Intl. T20 -Team Batting partnerships all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"India-England-allMatches.csv")
dc_mi_matches = pd.read_csv(path)
theTeam='India'
m=yka.teamBatsmenPartnershiOppnAllMatches(dc_mi_matches,theTeam,report="detailed", top=4)
print(m)
##      batsman  totalPartnershipRuns    non_striker  partnershipRuns
## 0   SK Raina                   265      G Gambhir                2
## 1   SK Raina                   265       KL Rahul               40
## 2   SK Raina                   265      MK Tiwary               24
## 3   SK Raina                   265       MS Dhoni              124
## 4   SK Raina                   265        P Kumar                0
## 5   SK Raina                   265      PP Chawla                4
## 6   SK Raina                   265       R Ashwin                1
## 7   SK Raina                   265      RG Sharma               16
## 8   SK Raina                   265        V Kohli               47
## 9   SK Raina                   265   Yuvraj Singh                7
## 10  MS Dhoni                   264       A Mishra                1
## 11  MS Dhoni                   264      AT Rayudu               18
## 12  MS Dhoni                   264      HH Pandya                8
## 13  MS Dhoni                   264      IK Pathan                2
## 14  MS Dhoni                   264      JJ Bumrah                2
## 15  MS Dhoni                   264      MK Pandey                3
## 16  MS Dhoni                   264  Parvez Rasool               21
## 17  MS Dhoni                   264       R Ashwin               11
## 18  MS Dhoni                   264      RA Jadeja               11
## 19  MS Dhoni                   264      RG Sharma                9
## 20  MS Dhoni                   264        RR Pant                6
## 21  MS Dhoni                   264     RV Uthappa                5
## 22  MS Dhoni                   264       SK Raina               98
## 23  MS Dhoni                   264      YK Pathan               36
## 24  MS Dhoni                   264   Yuvraj Singh               33
## 25   V Kohli                   236      AM Rahane                3
## 26   V Kohli                   236      G Gambhir               78
## 27   V Kohli                   236       KL Rahul               46
## 28   V Kohli                   236      RG Sharma                2
## 29   V Kohli                   236     RV Uthappa                4
## 30   V Kohli                   236       S Dhawan               45
## 31   V Kohli                   236       SK Raina               48
## 32   V Kohli                   236   Yuvraj Singh               10
## 33     M Raj                   176       A Sharma                2
## 34     M Raj                   176         H Kaur               18
## 35     M Raj                   176      J Goswami                6
## 36     M Raj                   176        KV Jain                5
## 37     M Raj                   176       L Kumari                5
## 38     M Raj                   176    N Niranjana                3
## 39     M Raj                   176       N Tanwar               17
## 40     M Raj                   176        PG Raut               41
## 41     M Raj                   176     R Malhotra                5
## 42     M Raj                   176     S Mandhana                8
## 43     M Raj                   176         S Naik               10
## 44     M Raj                   176       S Pandey               19
## 45     M Raj                   176       SK Naidu               37

2.6 Intl. T20 -Team Batsmen vs Bowlers all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Ireland-Netherlands-allMatches.csv")
ire_nl_matches = pd.read_csv(path)
yka.teamBatsmenVsBowlersOppnAllMatches(ire_nl_matches,'Ireland',"Netherlands",plot=True,top=3,runsScored=10)

2.7 Intl. T20 -Team Bowling scorecard all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Bangladesh-Nepal-allMatches.csv")
bang_nep_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardOppnAllMatches(bang_nep_matches,'Bangladesh',"Nepal")
print(scorecard)
##         bowler  overs  runs  maidens  wicket   econrate
## 0      B Regmi      3    14        0       1   4.666667
## 3   SP Gauchan      4    40        0       1  10.000000
## 1   JK Mukhiya      2    16        0       0   8.000000
## 2     P Khadka      3    23        0       0   7.666667
## 4    Sagar Pun      1    16        0       0  16.000000
## 5  Sompal Kami      2    21        0       0  10.500000

2.8 Intl. T20 -Team Batsmen vs Bowlers all Oppositions (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\\IntlT20-allMatchesAllOpposition\\"
path=os.path.join(dir1,"Australia-allMatchesAllOpposition.csv")
aus_matches = pd.read_csv(path)
yka.teamBatsmenVsBowlersAllOppnAllMatches(aus_matches,"Australia",plot=True,top=3,runsScored=40)

2.9 Intl. T20 -Wins vs Losses of a team against all other teams (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\\IntlT20-allMatchesAllOpposition\\"
path=os.path.join(dir1,"South Africa-allMatchesAllOpposition.csv")
sa_matches = pd.read_csv(path)
team1='South Africa'
yka.plotWinLossByTeamAllOpposition(sa_matches,team1,plot="detailed")

2.10 Intl. T20 -Batsmen analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\\IntlT20-BattingBowlingDetails\\"
# Rohit Sharma
name="RG Sharma"
team='India'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeAverageRuns(df,name)

# MJ Guptill
name="MJ Guptill"
team='New Zealand'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

2.11 Intl. T20 -Bowler analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\\IntlT20-BattingBowlingDetails\\"
# Shakib Al Hasan
name="Shakib Al Hasan"
team='Bangladesh'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanEconomyRate(df,name)

# Rashid Khan
name="SL Malinga"
team='Sri Lanka'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsAgainstOpposition(df,name)

3. Big Bash League

The following functions for added to handle BBL teams

  1. saveAllMatchesBetween2BBLTeams()
  2. saveAllMatchesAllOppositionBBLT20

The BBL teams are included are Adelaide Strikers, Brisbane Heat, Hobart Hurricanes, Melbourne Renegades, Perth Scorchers, Sydney Sixers, Sydney Thunder

To use the yorkpy functions first the YAML files have to be converted into pandas dataframe and then saved as CSV as shown below

import os
import yorkpy.analytics as yka
os.chdir('C:\\software\\cricket-package\\yorkpyBBL\\bbl')
#1. Convert all YAML files to dataframes and save as CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\BBLT20-Matches")
#2. Save all matches between 2 BBL teams
dir1='C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches'
#yka.saveAllMatchesBetween2BBLTeams(dir1)
#3. Save T20 matches between a BBL team and all other teams
dir1='C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches'
#yka.saveAllMatchesAllOppositionBBLT20(dir1)
#4. Get the batting details
dir1='C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches'
#yka.getTeamBattingDetails("Adelaide Strikers",dir=dir1, save=True)
#yka.getTeamBattingDetails("Brisbane Heat",dir=dir1,save=True)
#yka.getTeamBattingDetails("Hobart Hurricanes",dir=dir1,save=True)
#...
# Get the bowling details
dir1='C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches'
#yka.getTeamBowlingDetails("Adelaide Strikers",dir=dir1, save=True)
#yka.getTeamBowlingDetails("Brisbane Heat",dir=dir1,save=True)
#yka.getTeamBowlingDetails("Hobart Hurricanes",dir=dir1,save=True)
#...

The functions below perform analysis on the generated files from above. The YAML files have already been converted and are available at Github at BBL

3.1 Big Bash League – Team score card (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches"
path=os.path.join(dir1,".\\Adelaide Strikers-Brisbane Heat-2012-12-13.csv")
as_bh=pd.read_csv(path)
scorecard,extras=yka.teamBattingScorecardMatch(as_bh,"Brisbane Heat")
print(scorecard)
##          batsman  runs  balls  4s  6s          SR
## 0  LA Pomersbach    65     42   8   2  154.761905
## 1       JR Hopes     1      2   0   0   50.000000
## 2       JA Burns    37     31   2   2  119.354839
## 3   DT Christian    12     15   0   0   80.000000
## 4    NLTC Perera    12      4   0   2  300.000000
## 5        CA Lynn    19     18   1   1  105.555556
## 6    BCJ Cutting    13      5   0   2  260.000000
## 7     PJ Forrest    12      8   0   1  150.000000
## 8     CD Hartley     5      2   1   0  250.000000
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    371     10        2        5     0        0      17

3.2 Big Bash League -Team batsmen vs Bowlers (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches"
path=os.path.join(dir1,".\\Hobart Hurricanes-Melbourne Renegades-2012-01-18.csv")
hh_mr=pd.read_csv(path)
yka.teamBatsmenVsBowlersMatch(hh_mr,'Hobart Hurricanes','Melbourne Renegades',plot=True)

3.3 Big Bash League -Team bowling scorecard match (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches"
path=os.path.join(dir1,".\\Melbourne Stars-Sydney Thunder-2016-01-24.csv")
ms_st=pd.read_csv(path)
a=yka.teamBowlingScorecardMatch(ms_st,'Sydney Thunder')
print(a)
##           bowler  overs  runs  maidens  wicket   econrate
## 0        A Zampa      4    32        0       2   8.000000
## 1  BW Hilfenhaus      2    21        0       0  10.500000
## 2      DJ Hussey      1     9        0       1   9.000000
## 3     DJ Worrall      3    42        0       0  14.000000
## 4      EP Gulbis      2    19        0       0   9.500000
## 5        MA Beer      3    25        0       1   8.333333
## 6     MP Stoinis      4    30        0       3   7.500000

3.4 Big Bash League – Match Worm chart (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches"
path=os.path.join(dir1,".\\Sydney Sixers-Melbourne Stars-2011-12-27.csv")
ss_ms=pd.read_csv(path)
yka.matchWormChart(ss_ms,"Melbourne Stars", "Sydney Sixers")

path=os.path.join(dir1,".\\Hobart Hurricanes-Brisbane Heat-2015-01-02.csv")
hh_bh=pd.read_csv(path)
yka.matchWormChart(hh_bh,"Hobart Hurricanes", "Brisbane Heat")

3.5 Big Bash League -Team Batting partnerships all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Brisbane Heat-Adelaide Strikers-allMatches.csv")
bh_as_matches = pd.read_csv(path)
yka.teamBatsmenPartnershipOppnAllMatchesChart(bh_as_matches,"Brisbane Heat","Adelaide Strikers",plot=True, top=4, partnershipRuns=20)

3.6 Big Bash League -Team Bowling wicket kind all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Sydney Sixers-Perth Scorchers-allMatches.csv")
ss_ps_matches = pd.read_csv(path)
yka.teamBowlingWicketKindOppositionAllMatches(ss_ps_matches,'Perth Scorchers','Sydney Sixers',plot=True,top=5,wickets=1)

3.7 Big Bash League -Team Bowling scorecard all teams (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Hobart Hurricanes-allMatchesAllOpposition.csv")
hh_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardAllOppnAllMatches(hh_matches,"Hobart Hurricanes")
print(scorecard)
##              bowler  overs  runs  maidens  wicket   econrate
## 16            B Lee     20   132        0       9   6.600000
## 30         CJ McKay     13   110        0       9   8.461538
## 88    NJ Rimmington     16   103        1       9   6.437500
## 67      JW Hastings     15    88        0       8   5.866667
## 63      JP Faulkner     15   146        0       7   9.733333
## 27        CJ Gannon     17   147        1       7   8.647059
## 93          NM Lyon      8    51        0       7   6.375000
## 20      BCJ Cutting     27   226        0       7   8.370370
## 48          GB Hogg     22   167        0       7   7.590909
## 107       SM Boland     12    96        0       7   8.000000
## 15       B Laughlin     13    99        0       7   7.615385
## 87      MT Steketee     15   134        0       5   8.933333
## 121    Yasir Arafat      9    48        0       4   5.333333
## 96       PJ Cummins      8    83        0       4  10.375000
## 46      Fawad Ahmed     11    64        0       4   5.818182
## 76          MA Beer     12    63        0       4   5.250000
## 108     SNJ O'Keefe     15   104        0       4   6.933333
## 75   M Muralitharan      7    31        0       4   4.428571
## 10           AJ Tye     16   127        0       4   7.937500
## 52          J Botha     13    94        0       4   7.230769
## 56     JL Pattinson      7    71        0       4  10.142857
## 62   JP Behrendorff     16   119        0       4   7.437500
## 3           AC Agar     12    87        0       4   7.250000
## 24     BM Edmondson      4    40        0       4  10.000000
## 37        DJ Hussey      8    47        0       3   5.875000
## 49       GJ Maxwell      8    65        0       3   8.125000
## 84       MN Samuels      4    22        0       3   5.500000
## 81         MG Neser      5    54        0       3  10.800000
## 44     DT Christian      9   114        0       3  12.666667
## 50        GS Sandhu      7    51        0       3   7.285714
## ..              ...    ...   ...      ...     ...        ...
## 43        DP Nannes      8    58        0       1   7.250000
## 51         IA Moran      4    25        0       1   6.250000
## 55         JK Lalor     10    82        0       1   8.200000
## 54        JH Kallis      3    18        0       1   6.000000
## 73   LR Butterworth      4    25        0       1   6.250000
## 4      AC McDermott      2    28        0       1  14.000000
## 70         LA Doran      4    38        0       1   9.500000
## 69    KW Richardson      6    44        0       1   7.333333
## 119     WD Sheridan      2     6        0       0   3.000000
## 2       AB McDonald      1    15        0       0  15.000000
## 115      TD Andrews      3    23        0       0   7.666667
## 11          AK Heal      4    33        0       0   8.250000
## 7        AD Russell      4    40        0       0  10.000000
## 8          AJ Finch      2    15        0       0   7.500000
## 9         AJ Turner      3    28        0       0   9.333333
## 60        JM Mennie      1    20        0       0  20.000000
## 18        BA Stokes      1     9        0       0   9.000000
## 26         CH Gayle      1    16        0       0  16.000000
## 28         CJ Green      4    44        0       0  11.000000
## 95   PD Collingwood      2    20        0       0  10.000000
## 31       CJ Simmons      4    21        0       0   5.250000
## 59       JM Holland      3    34        0       0  11.333333
## 36         DJ Bravo      6    64        0       0  10.666667
## 38     DJ Pattinson      2    16        0       0   8.000000
## 41       DJ Worrall      8    90        0       0  11.250000
## 72      LN O'Connor      6    56        0       0   9.333333
## 71        LJ Wright      3    27        0       0   9.000000
## 68       KA Pollard      1     7        0       0   7.000000
## 58       JM Herrick      4    23        0       0   5.750000
## 92       NM Hauritz      5    42        0       0   8.400000
## 
## [122 rows x 6 columns]

3.8 Big Bash League -Plot wins vs losses against all teams(Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Sydney Sixers-allMatchesAllOpposition.csv")
ss_matches = pd.read_csv(path)
yka.plotWinLossByTeamAllOpposition(ss_matches,'Sydney Sixers')

3.9 Big Bash League -Wins vs losses by toss decision (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Adelaide Strikers-allMatchesAllOpposition.csv")
as_matches = pd.read_csv(path)
yka.plotWinsByRunOrWicketsAllOpposition(as_matches,'Adelaide Strikers')

3.10 Big Bash League -Batsmen Analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-BattingBowlingDetails"
# CA Lynn
name="CA Lynn"
team='Brisbane Heat'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsStrikeRate(df,name)

# UT Khawaja
name="UT Khawaja"
team='Sydney Thunder'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

3.11Big Bash League – Bowler analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-BattingBowlingDetails"
# CJ McKay
name="CJ McKay"
team='Sydney Thunder'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgWickets(df,name)

# AU Rashid
name="AU Rashid"
team='Adelaide Strikers'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

4. Natwest T20 Blast

The following functions for added to handle Natwest T20 teams

  1. saveAllMatchesBetween2NWBTeams()
  2. saveAllMatchesAllOppositionNWBT20

The Natwest teams are
Derbyshire, Durham, Essex, Glamorgan, Gloucestershire, Hampshire, Kent,Lancashire, Leicestershire, Middlesex,Northamptonshire, Nottinghamshire, Somerset, Surrey, Sussex, Warwickshire, Worcestershire,Yorkshire

In order to perform analysis with yorkpy, the YAML data has to be converted to pandas dataframe and saves as CSV as shown

#import os
#import yorkpy.analytics as yka
#os.chdir('C:\\software\\cricket-package\\yorkpyNWB\\nwb')
#1. Convert YAML to dataframes and save as CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\NWBT20-Matches")
#2. Save all matches between 2 NWBT20 teams
#dir1='C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-Matches'
#yka.saveAllMatchesBetween2NWBTeams(dir1)
#3. Save all matches between a NWB T20 team and all other teams
#dir1='C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-Matches'
#yka.saveAllMatchesAllOppositionNWBT20(dir1)
#4. Compute the batting details
dir1='C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-Matches'
#yka.getTeamBattingDetails("Derbyshire",dir=dir1, save=True)
#yka.getTeamBattingDetails("Durham",dir=dir1,save=True)
#yka.getTeamBattingDetails("Essex",dir=dir1,save=True)
#..
#5. Compute bowling details
dir1='C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-Matches'
#yka.getTeamBowlingDetails("Derbyshire",dir=dir1, save=True)
#yka.getTeamBowlingDetails("Durham",dir=dir1,save=True)
#yka.getTeamBowlingDetails("Essex",dir=dir1,save=True)
#...

Once the data is converted all yorkpy functions can be used. This has already been done and is available at github NWB

4.1 Natwest T20 Blast – Team score card (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\\yorkpyNWB\\NWBT20-Matches"
path=os.path.join(dir1,".\\Durham-Yorkshire-2016-08-20.csv")
d_y=pd.read_csv(path)
scorecard,extras=yka.teamBattingScorecardMatch(d_y,"Durham")
print(scorecard)
##           batsman  runs  balls  4s  6s          SR
## 0     MD Stoneman    25     20   4   0  125.000000
## 1     KK Jennings    11     13   1   0   84.615385
## 2       BA Stokes    56     37   4   3  151.351351
## 3   MJ Richardson    29     23   4   1  126.086957
## 4     JTA Burnham    17     15   1   1  113.333333
## 5      RD Pringle    10      9   1   0  111.111111
## 6  PD Collingwood     2      3   0   0   66.666667
## 7        U Arshad     1      1   0   0  100.000000
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    305      2        0        5     0        0       7

4.2 Natwest T20 Blast -Team batsmen vs Bowlers (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\\yorkpyNWB\\NWBT20-Matches"
path=os.path.join(dir1,".\\Derbyshire-Lancashire-2016-07-13.csv")
d_l=pd.read_csv(path)
yka.teamBatsmenVsBowlersMatch(d_l,'Lancashire','Derbyshire',plot=True)

4.3 Natwest T20 Blast -Team bowling scorecard match (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\\yorkpyNWB\\NWBT20-Matches"
path=os.path.join(dir1,".\\Essex-Surrey-2016-05-20.csv")
e_s=pd.read_csv(path)
a=yka.teamBowlingScorecardMatch(e_s,'Essex')
print(a)
##           bowler  overs  runs  maidens  wicket   econrate
## 0  Azhar Mahmood      3    38        0       4  12.666667
## 1       GJ Batty      4    33        0       1   8.250000
## 2       JE Burke      1    18        0       0  18.000000
## 3     MW Pillans      3    28        0       0   9.333333
## 4      SM Curran      4    23        0       2   5.750000
## 5      TK Curran      4    21        0       3   5.250000

4.4 Natwest T20 Blast -Match Worm chart (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\\yorkpyNWB\\NWBT20-Matches"
path=os.path.join(dir1,".\\Gloucestershire-Glamorgan-2016-06-10.csv")
ss_ms=pd.read_csv(path)
yka.matchWormChart(ss_ms,"Gloucestershire", "Glamorgan")

path=os.path.join(dir1,".\\Leicestershire-Northamptonshire-2016-05-20.csv")
hh_bh=pd.read_csv(path)
yka.matchWormChart(hh_bh,"Northamptonshire", "Leicestershire")

4.5 Natwest T20 Blast -Team Batting partnerships all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Hampshire-Sussex-allMatches.csv")
h_s_matches = pd.read_csv(path)
yka.teamBatsmenPartnershipOppnAllMatchesChart(h_s_matches,"Hampshire","Sussex",plot=True, top=4, partnershipRuns=10)

4.6 Natwest T20 Blast -Team Bowling wicket kind all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Kent-Somerset-allMatches.csv")
k_s_matches = pd.read_csv(path)
yka.teamBowlersVsBatsmenOppnAllMatches(k_s_matches,'Kent','Somerset',plot=True,
top=5,runsConceded=10)

4.7 Natwest T20 Blast -Team Bowling scorecard all teams (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Middlesex-allMatchesAllOpposition.csv")
m_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardAllOppnAllMatches(m_matches,"Middlesex")
print(scorecard)
##               bowler  overs  runs  maidens  wicket   econrate
## 1             AJ Tye      8    75        0       6   9.375000
## 5         BAC Howell      8    41        0       5   5.125000
## 26         GR Napier      7    65        0       5   9.285714
## 15        DI Stevens      4    31        0       4   7.750000
## 19       DW Lawrence      6    37        0       4   6.166667
## 32       JW Dernbach      4    33        0       3   8.250000
## 7          BTJ Wheal      4    43        0       3  10.750000
## 18         DR Briggs      4    24        0       3   6.000000
## 50     RK Kleinveldt      4    24        0       3   6.000000
## 46         R McLaren      7    59        0       3   8.428571
## 47         R Rampaul      3    21        0       3   7.000000
## 34         L Gregory      6    51        0       2   8.500000
## 33   KMDN Kulasekara      2    24        0       2  12.000000
## 40          MG Hogan      3    17        0       2   5.666667
## 43        MTC Waller      4    31        0       2   7.750000
## 49        RJ Gleeson      4    20        0       2   5.000000
## 48  RE van der Merwe      5    24        0       2   4.800000
## 51  RN ten Doeschate      4    32        0       2   8.000000
## 53        S Prasanna      4    20        0       2   5.000000
## 56           SW Tait      3    17        0       2   5.666667
## 57     Shahid Afridi      8    55        0       2   6.875000
## 59  T van der Gugten      3    13        1       2   4.333333
## 64          TS Mills      3    34        0       2  11.333333
## 65          WAT Beer      4    23        0       2   5.750000
## 31          JH Davey      4    28        0       2   7.000000
## 68         ZS Ansari      3    16        0       2   5.333333
## 25         GM Andrew      3    19        0       2   6.333333
## 23          GJ Batty      6    55        0       2   9.166667
## 16          DJ Bravo      3    27        0       2   9.000000
## 41          MR Quinn      6    65        0       1  10.833333
## ..               ...    ...   ...      ...     ...        ...
## 24     GL van Buuren      7    49        0       1   7.000000
## 37           MD Hunn      3    35        0       1  11.666667
## 36        LC Norwell      6    62        0       1  10.333333
## 29       JC Tredwell      4    35        0       1   8.750000
## 35         LA Dawson      6    53        0       1   8.833333
## 62           TL Best      4    51        0       0  12.750000
## 58         T Westley      2    12        0       0   6.000000
## 4         Azharullah      3    24        0       0   8.000000
## 60     TD Groenewald      1    21        0       0  21.000000
## 61         TK Curran      4    35        0       0   8.750000
## 38         MD Taylor      3    30        0       0  10.000000
## 30        JG Myburgh      1     5        0       0   5.000000
## 8          C Overton      2    18        0       0   9.000000
## 2        Ashar Zaidi      1     5        0       0   5.000000
## 66          WR Smith      2    25        0       0  12.500000
## 28         J Overton      2    24        0       0  12.000000
## 6          BJ Taylor      1     6        0       0   6.000000
## 22          GG White      4    31        0       0   7.750000
## 55          SP Crook      1     9        0       0   9.000000
## 39        ME Claydon      4    40        0       0  10.000000
## 52         RS Bopara      4    32        0       0   8.000000
## 10           CD Nash      2    19        0       0   9.500000
## 11         CH Morris      4    36        0       0   9.000000
## 12         DA Cosker      3    32        0       0  10.666667
## 13      DA Griffiths      4    39        0       0   9.750000
## 45          PD Trego      1    11        0       0  11.000000
## 44   PA van Meekeren      2    19        0       0   9.500000
## 42          MS Crane      2    25        0       0  12.500000
## 20        FK Cowdrey      1    19        0       0  19.000000
## 14        DD Masters      2    16        0       0   8.000000
## 
## [69 rows x 6 columns]

4.8 Natwest T20 Blast -Plot wins vs losses against all teams(Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Warwickshire-allMatchesAllOpposition.csv")
w_matches = pd.read_csv(path)
yka.plotWinLossByTeamAllOpposition(w_matches,'Warwickshire')

4.9 Natwest T20 Blast -Batsmen Analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-BattingBowlingDetails"
# M Klinger
name="M Klinger"
team='Gloucestershire'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

# CA Ingram
name="CA Ingram"
team='Glamorgan'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

4.11 Natwest T20 Blast -Bowler analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-BattingBowlingDetails"
# BAC Howell
name="BAC Howell"
team='Gloucestershire'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

# GR Napier
name="GR Napier"
team='Essex'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsVenue(df,name)

Note: yorkpy will work for all T20 leagues which are in YAML format as specified in Cricsheet.

You can clone/fork the latest code for yorkpy from github yorkpy

The data for IPL, Intl. T20, BBL and Natwest T20 have already been converted into pandas dataframes and saved as CSVs. You can download the converted files from Github at [allYorkpyT20Data])(https://github.com/tvganesh/allYorkpyT20Data)

Conclusion This post shows the kind of detailed analysis that can be performed with yorkpy. In fact with all the converted data it should be possible to also train a Machine Learning model, which I will probably keep for another day. You could go ahead and use the data in other innovative ways. Do keep me posted if you do!!

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

Have fun with yorkpy!!

See also
1. Take 4+: Presentations on ‘Elements of Neural Networks and Deep Learning’ – Parts 1-8
2. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
3. Hand detection through Haartraining: A hands-on approach
4.My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
5. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
6. The 3rd paperback & kindle editions of my books on Cricket, now on Amazon

To see all posts click Index of posts

Pitching yorkpy … in the block hole – Part 4

A good programmer is someone who always looks both ways before crossing a one-way street.  Doug Linder

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

In order to understand recursion, one must first understand recursion. Anonymous

This is the fourth and final part of my Python package yorkpy. In this part yorkpy, the python avatar of my R package yorkr see Introducing cricket package yorkr: Part 1- Beaten by sheer pace!, develops wings and is prepared for take-off. The yorkpy package uses data from Cricsheet

You can clone/download the code at Github yorkpy
This post has been published to RPubs at yorkpy-Part4
You can download this post as PDF at IPLT20-yorkpy-part4
You can download all the data used in this post and the previous post at yorkpyData

This post is a continuation of the earlier posts on yorkpy

1. Pitching yorkpy . short of good length to IPL – Part 1 In this part I included functions that convert the yaml data of IPL matches into Pandas dataframe which are then saved as CSV. This part can perform analysis of individual IPL matches. Note The converted data is available at yorkpyData
2. Pitching yorkpy.on the middle and outside off-stump to IPL – Part 2 This part included functions to create a large data frame for head-to-head confrontation between any 2IPL teams says CSK-MI, DD-KKR etc, which can be saved as CSV. Analysis is then performed on these team-2-team confrontations. Note The converted data is available at yorkpyData
3. Pitching yorkpy.swinging away from the leg stump to IPL – Part 3 The 3rd part includes the performance of any IPL team against all other IPL teams. The data can also be saved as CSV.Note The converted data is available 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).

This 4th and final part includes analysis of batting and bowling performances of any IPL player. The batting and bowling details for all teams have already been converted and are available at IPLT20-Batting-BowlingDetails

This part includes the following new functions

Batsman functions

  1. batsmanRunsVsDeliveries
  2. batsmanFoursSixes
  3. batsmanDismissals
  4. batsmanRunsVsStrikeRate
  5. batsmanMovingAverage
  6. batsmanCumulativeAverageRuns
  7. batsmanCumulativeStrikeRate
  8. batsmanRunsAgainstOpposition
  9. batsmanRunsVenue

Bowler functions

  1. bowlerMeanEconomyRate
  2. bowlerMeanRunsConceded
  3. bowlerMovingAverage
  4. bowlerCumulativeAvgWickets
  5. bowlerCumulativeAvgEconRate
  6. bowlerWicketPlot
  7. bowlerWicketsAgainstOpposition
  8. bowlerWicketsVenue

A. Batsman functions

1. Get IPL Team Batting details

The function below gets the overall IPL team batting details based on the CSV files that were saved for IPL T20 matches. This is currently also available in Github at yorkpyData. The batting details of the IPL team in each match is created and a huge data frame is created by combining the batting details from each match. This can be saved as a csv file with name as for e.g. Delhi Daredevils-BattingDetails.csv.

dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
#csk_details = yka.getTeamBattingDetails("Chennai Super Kings",dir=dir1, save=True)
#dd_details = yka.getTeamBattingDetails("Delhi Daredevils",dir=dir1,save=True)
#kkr_details = yka.getTeamBattingDetails("Kolkata Knight Riders",dir=dir1,save=True)

2. Get IPL batsman details

This function is used to get the individual IPL T20 batting record for a the specified batsman of the team as in the functions below.

For the batsmen functions below I have chosen Rishabh Pant, Kane Williamson and Ambati Rayudu for the analysis as they top the batting lists. You can choose any IPL batsmen for the analysis

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
rpant=yka.getBatsmanDetails(team,name,dir=dir1)

3 Batsman Runs vs Deliveries (in IPL matches)

This functions plots the runs vs deliveries faced for batsman

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsDeliveries(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsDeliveries(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsDeliveries(df,name)

4. Batsman fours and sixes (in IPL matches)

This plots the fours, sixes and the total runs for a batsman

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanFoursSixes(df,name)


# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanFoursSixes(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanFoursSixes(df,name)

5. Batsman dismissals (in IPL matches)

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanDismissals(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanDismissals(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanDismissals(df,name)

6. Batsman Runs vs Strike Rate (in IPL matches)

The plots below give the Runs vs Strike rate for batsmen

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsStrikeRate(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsStrikeRate(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsStrikeRate(df,name)

7. Batsman Moving average of runs (in IPL matches)

The plots below compute and plot the moving average of batsmen

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanMovingAverage(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanMovingAverage(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanMovingAverage(df,name)

8. Batsman Cumulative average of runs (in IPL matches)

The functions below plot the cumulative average of the batsmen

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeAverageRuns(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeAverageRuns(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeAverageRuns(df,name)

9. Batsman Cumulative Strike Rate (in IPL matches)

The functions below plot the cumulative strike rate of the batsmen

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

10. Batsman performance against opposition (in IPL matches)

The plots below show how the batsmen performed against other IPL teams

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

11. Batsman performance at different venues (in IPL matches)

The plots below show how the batsmen performed at different venues

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Rishabh Pant
name="RR Pant"
team='Delhi Daredevils'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVenue(df,name)

# 2. Kane Williamson
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="KS Williamson"
team='Sunrisers Hyderabad'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVenue(df,name)

#3. Ambati Rayudu
name="AT Rayudu"
team='Mumbai Indians'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVenue(df,name)

B. Bowler functions

12. Get bowling details in IPL matches

The function below gets the overall team IPL T20 bowling details based on the RData file available in IPL T20 matches. This is currently also available in Github at yorkpyData. The IPL T20 bowling details of the IPL team in each match is created, and a huge data frame is created by stacking the individual dataframes. This can be saved as a CSV file for e.g. Chennai Super Kings-BowlingDetails.csv

dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
#kkr_bowling = yka.getTeamBowlingDetails("Kolkata Knight Riders",dir=dir1,save=True)
#csk_bowling = yka.getTeamBowlingDetails("Chennai Super Kings",dir=dir1,save=True)
#kxip_bowling = yka.getTeamBowlingDetails("Kings XI Punjab",dir=dir1,save=True)

13. Get bowling details of the individual IPL bowlers

This function is used to get the individual bowling record for a specified bowler of the country as in the functions below.

The plots below deal with bowler’s performance. For this analysis I have chosen Amit Mishra, Piyush Chawla and Bhuvaneshwar Kumar for the analysis. You can chose any other IPL bowler

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
#df=yka.getBowlerWicketDetails(team,name,dir=dir1)

14. Bowler Economy Rate (in IPL matches)

The plots below show the economy rate of the selected bowlers

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanEconomyRate(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanEconomyRate(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanEconomyRate(df,name)

15. Bowler Mean Runs conceded (in IPL matches)

The plots below show the mean runs conceded by the selected bowlers

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanRunsConceded(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanRunsConceded(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanRunsConceded(df,name)

16. Moving average of wickets for bowler (in IPL matches)

The moving average of the bowlers are plotted below

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMovingAverage(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMovingAverage(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMovingAverage(df,name)

17. Cumulative average wickets for bowler (in IPL matches)

The cumulative average wickets for each bowler is computed and plotted

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgWickets(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgWickets(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgWickets(df,name)

18. Cumulative average economy rate for bowler (in IPL matches)

The plots below give the cumulative average economy rate for each bowler

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

19. Bowler wicket plot (in IPL matches)

The plots below give the over vs wickets for bowlers

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketPlot(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketPlot(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketPlot(df,name)

20. Bowler wicket against opposition (in IPL matches)

The performance of the bowlers against different IPL teams is shown below

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsAgainstOpposition(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsAgainstOpposition(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsAgainstOpposition(df,name)

21. Bowler wicket in different venues (in IPL matches)

The plots below show how the bowlers perform at different venues

import pandas as pd
import os
import yorkpy.analytics as yka
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
# 1. Amit Mishra
name="A Mishra"
team='Delhi Daredevils'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsVenue(df,name)

# 2. Piyush Chawla
dir1= "C:\\software\\cricket-package\\yorkpyIPLData\\data3"
name="PP Chawla"
team='Kolkata Knight Riders'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsVenue(df,name)

#3. Bhuvneshwar Kumar
name="B Kumar"
team='Sunrisers Hyderabad'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsVenue(df,name)

Note:You can clone/download the code at Github yorkpy

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

Conclusion: This concludes the python package yorkpy. Go ahead and give yorkpy a spin!

Also see
1. Take 4+: Presentations on ‘Elements of Neural Networks and Deep Learning’ – Parts 1-8
2. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
3. Hand detection through Haartraining: A hands-on approach
4.My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
5. Big Data-1: Move into the big league:Graduate from Python to Pyspark
6. Cricpy takes a swing at the ODIs

To see all posts click Index of posts

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 … 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

Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket

In my recent post, My travels through the realms of Data Science, Machine Learning, Deep Learning and (AI), I had recounted my journey in the domains of of Data Science, Machine Learning (ML), and more recently Deep Learning (DL) all of which are useful while analyzing data. Of late, I have come to the realization that there are many facets to data. And to glean insights from data, Data Science, ML and DL alone are not sufficient and one needs to also have a good handle on linear programming and optimization. My colleague at IBM Research also concurred with this view and told me he had arrived at this conclusion several years ago.

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

While ML & DL are very useful and interesting to make inferences and predictions of outputs from input variables, optimization computes the choice of input which results in maximizing or minimizing the output. So I made a small course correction and started on a course from India’s own NPTEL Introduction to Linear Programming by Prof G. Srinivasan of IIT Madras (highly recommended!). The lectures are delivered with remarkable clarity by the Prof and I am just about halfway through the course (each lecture is of 50-55 min duration), when I decided that I needed to try to formulate and solve some real world Linear Programming problem.

As usual, I turned towards cricket for some appropriate situations, and sure enough it was there in the open. For this LP formulation I take International T20 and IPL, though International ODI will also work equally well.  You can download the associated code and data for this from Github at LP-cricket-analysis

In T20 matches the captain has to make choice of how to rotate bowlers with the aim of restricting the batting side. Conversely, the batsmen need to take advantage of the bowling strength to maximize the runs scored.

Note:
a) A simple and obvious strategy would be
– If the ith bowler’s economy rate is less than the economy rate of the jth bowler i.e.
er_{i} < er_{j} then have bowler ‘i’ to bowl more overs as his/her economy rate is better

b)A better strategy would be to consider the economy rate of each bowler against each batsman. How often  have we witnessed bowlers with a great bowling average get thrashed time and again by the same batsman, or a bowler who is generally very poor being very effective against a particular batsman. i.e. er_{ij} < er_{ik} where the jth bowler is more effective than the kth bowler against the ith batsman. This now becomes a linear optimization problem as we can have several combinations of number of overs X economy rate for different bowlers and we will have to solve this algorithmically to determine the lowest score for bowling performance or highest score for batting order.

This post uses the latter approach to optimize bowling change and batting lineup.

Let is take a hypothetical situation
Assume there are 3 bowlers – bwlr_{1},bwlr_{2},bwlr_{3}
and there are 4 batsmen – bman_{1},bman_{2},bman_{3},bman_{4}

Let the economy rate er_{ij} be the Economy Rate of the jth bowler to the ith batsman. Also if remaining overs for the bowlers are o_{1},o_{2},o_{3}
and the total number of overs left to be bowled are
o_{1}+o_{2}+o_{3} = N then the question is

a) Given the economy rate of each bowler per batsman, how many overs should each bowler bowl, so that the total runs scored by all the batsmen are minimum?

b) Alternatively, if the know the individual strike rate of a batsman against the individual bowlers, how many overs should each batsman face with a bowler so that the total runs scored is maximized?

1. LP Formulation for bowling order

Let the economy rate er_{ij} be the Economy Rate of the jth bowler to the ith batsman.
Objective function : Minimize –
er_{11}*o_{11} + er_{12}*o_{12} +..+er_{1n}*o_{1n}+ er_{21}*o_{21} + er_{22}*o_{22}+.. + er_{22}*o_{2n}+ er_{m1}*o_{m1}+..+ er_{mn}*o_{mn}
i.e.
\sum_{i=1}^{i=m}\sum_{j=1}^{i=n}er_{ij}*o_{ij}
Constraints
Where o_{j} is the number of overs remaining for the jth bowler against  ‘k’ batsmen
o_{j1} + o_{j2} + .. o_{jk} < o_{j}
and if the total number of overs remaining to be bowled is N then
o_{1} + o_{2} +...+ o_{k} = N or
\sum_{j=1}^{j=k} o_{j} =N
The overs that any bowler can bowl is o_{j} >=0

2. LP Formulation for batting lineup

Let the strike rate sr_{ij}  be the Strike Rate of the ith batsman to the jth bowler
Objective function : Maximize –
sr_{11}*o_{11} + sr_{12}*o_{12} +..+ sr_{1n}*o_{1n}+ sr_{21}*o_{21} + sr_{22}*o_{22}+.. sr_{2n}*o_{2n}+ sr_{m1}*o_{m1}+..+ sr_{mn}*o_{mn}
i.e.
\sum_{i=1}^{i=4}\sum_{j=1}^{i=3}sr_{ij}*o_{ij}
Constraints
Where o_{j} is the number of overs remaining for the jth bowler against  ‘k’ batsmen
o_{j1} + o_{j2} + .. o_{jk} < o_{j}
and the total number of overs remaining to be bowled is N then
o_{1} + o_{2} +...+ o_{k} = N or
\sum_{j=1}^{j=k} o_{j} =N
The overs that any bowler can bowl is
o_{j} >=0

lpSolveAPI– For this maximization and minimization problem I used lpSolveAPI.

Below I take 2 simple examples (example1 & 2)  to ensure that my LP formulation and solution is correct before applying it on real T20 cricket data (Intl. T20 and IPL)

3. LP formulation (Example 1)

Initially I created a test example to ensure that I get the LP formulation and solution correct. Here the er1=4 and er2=3 and o1 & o2 are the overs bowled by bowlers 1 & 2. Also o1+o2=4 In this example as below

o1 o2 Obj Fun(=4o1+3o2)
1    3      13
2    2      14
3    1      15

library(lpSolveAPI)
library(dplyr)
library(knitr)
lprec <- make.lp(0, 2)
a <-lp.control(lprec, sense="min")
set.objfn(lprec, c(4, 3))  # Economy Rate of 4 and 3 for er1 and er2
add.constraint(lprec, c(1, 1), "=",4)  # o1 + o2 =4
add.constraint(lprec, c(1, 0), ">",1)  # o1 > 1
add.constraint(lprec, c(0, 1), ">",1)  # o2 > 1
lprec
## Model name: 
##             C1    C2       
## Minimize     4     3       
## R1           1     1   =  4
## R2           1     0  >=  1
## R3           0     1  >=  1
## Kind       Std   Std       
## Type      Real  Real       
## Upper      Inf   Inf       
## Lower        0     0
b <-solve(lprec)
get.objective(lprec) # 13
## [1] 13
get.variables(lprec) # 1    3 
## [1] 1 3

Note 1: In the above example 13 runs is the minimum that can be scored and this requires

LP solution:
Minimum runs=13

  • o1=1
  • o2=3

Note 2:The numbers in the columns represent the number of overs that need to be bowled by a bowler to the corresponding batsman.

4. LP formulation (Example 2)

In this formulation there are 2 bowlers and 2 batsmen o11,o12 are the oves bowled by bowler 1 to batsmen 1 & 2 and o21, o22 are the overs bowled by bowler 2 to batsmen 1 & 2 er11=4, er12=2,er21=2,er22=5 o11+o12+o21+o22=5

The solution for this manually computed is o11, o12, o21, o22 Runs
where B11, B12 are the overs bowler 1 bowls to batsman 1 and B21 and B22 are overs bowler 2 bowls to batsman 2

o11     o12    o21    o22      Runs=(4*o11+2*o12+2*o21+5*o22)
1            1             1            2           18
1           2              1             1           15
2           1              1            1            17
1           1               2            1            15

lprec <- make.lp(0, 4)
a <-lp.control(lprec, sense="min")
set.objfn(lprec, c(4, 2,2,5))
add.constraint(lprec, c(1, 1,0,0), "<=",8)
add.constraint(lprec, c(0, 0,1,1), "<=",7)
add.constraint(lprec, c(1, 1,1,1), "=",5)
add.constraint(lprec, c(1, 0,0,0), ">",1)
add.constraint(lprec, c(0, 1,0,0), ">",1)
add.constraint(lprec, c(0, 0,1,0), ">",1)
add.constraint(lprec, c(0, 0,0,1), ">",1)
lprec
## Model name: 
##             C1    C2    C3    C4       
## Minimize     4     2     2     5       
## R1           1     1     0     0  <=  8
## R2           0     0     1     1  <=  7
## R3           1     1     1     1   =  5
## R4           1     0     0     0  >=  1
## R5           0     1     0     0  >=  1
## R6           0     0     1     0  >=  1
## R7           0     0     0     1  >=  1
## Kind       Std   Std   Std   Std       
## Type      Real  Real  Real  Real       
## Upper      Inf   Inf   Inf   Inf       
## Lower        0     0     0     0
b<-solve(lprec)
get.objective(lprec) 
## [1] 15
get.variables(lprec) 
## [1] 1 2 1 1

Note: In the above example 15 runs is the minimum that can be scored and this requires

LP Solution:
Minimum runs=15

  • o11=1
  • o12=2
  • o21=1
  • o22=1

It is possible to keep the minimum to other values and solves also.

5. LP formulation for International T20 India vs Australia (Batting lineup)

To analyze batting and bowling lineups in the cricket world I needed to get the ball-by-ball details of runs scored by each batsman against each of the bowlers. Fortunately I had already created this with my R package yorkr. yorkr processes yaml data from Cricsheet. So I copied the data of all matches between Australia and India in International T20s. You can download my processed data for International T20 at Inswinger

load("Australia-India-allMatches.RData")
dim(matches)
## [1] 3541   25

The following functions compute the ‘Strike Rate’ of a batsman as

SR=1/oversRunsScored

Also the Economy Rate is computed as

ER=1/oversRunsConceded

Incidentally the SR=ER

# Compute the Strike Rate of the batsman
computeSR <- function(batsman1,bowler1){
    a <- matches %>% filter(batsman==batsman1 & bowler==bowler1) 
    a1 <- a %>% summarize(totalRuns=sum(runs),count=n()) %>% mutate(SR=(totalRuns/count)*6)
    a1
}

# Compute the Economy Rate of the batsman
computeER <- function(batsman1,bowler1){
    a <- matches %>% filter(batsman==batsman1 & bowler==bowler1) 
    a1 <- a %>% summarize(totalRuns=sum(runs),count=n()) %>% mutate(ER=(totalRuns/count)*6)
    a1
}

Here I compute the Strike Rate of Virat Kohli, Yuvraj Singh and MS Dhoni against Shane Watson, Brett Lee and MA Starc

 # Kohli
kohliWatson<- computeSR("V Kohli","SR Watson")
kohliWatson
##   totalRuns count       SR
## 1        45    37 7.297297
kohliLee <- computeSR("V Kohli","B Lee")
kohliLee
##   totalRuns count       SR
## 1        10     7 8.571429
kohliStarc <- computeSR("V Kohli","MA Starc")
kohliStarc
##   totalRuns count       SR
## 1        11     9 7.333333
# Yuvraj
yuvrajWatson<- computeSR("Yuvraj Singh","SR Watson")
yuvrajWatson
##   totalRuns count       SR
## 1        24    22 6.545455
yuvrajLee <- computeSR("Yuvraj Singh","B Lee")
yuvrajLee
##   totalRuns count       SR
## 1        12     7 10.28571
yuvrajStarc <- computeSR("Yuvraj Singh","MA Starc")
yuvrajStarc
##   totalRuns count SR
## 1        12     8  9
# MS Dhoni
dhoniWatson<- computeSR("MS Dhoni","SR Watson")
dhoniWatson
##   totalRuns count       SR
## 1        33    28 7.071429
dhoniLee <- computeSR("MS Dhoni","B Lee")
dhoniLee
##   totalRuns count  SR
## 1        26    20 7.8
dhoniStarc <- computeSR("MS Dhoni","MA Starc")
dhoniStarc
##   totalRuns count   SR
## 1        11     8 8.25

When we consider the batting lineup, the problem is one of maximization. In the LP formulation below V Kohli has a SR of 7.29, 8.57, 7.33 against Watson, Lee & Starc
Yuvraj has a SR of 6.5, 10.28, 9 against Watson, Lee & Starc
and Dhoni has a SR of 7.07, 7.8,  8.25 against Watson, Lee and Starc

The constraints are Watson, Lee and Starc have 3, 4 & 3 overs remaining respectively. The total number of overs remaining to be bowled is 9.The other constraints could be that a bowler bowls at least 1 over etc.

Formulating and solving

# 3 batsman x 3 bowlers
lprec <- make.lp(0, 9)
# Maximization
a<-lp.control(lprec, sense="max")

# Set the objective function
set.objfn(lprec, c(kohliWatson$SR, kohliLee$SR,kohliStarc$SR,
                   yuvrajWatson$SR,yuvrajLee$SR,yuvrajStarc$SR,
                   dhoniWatson$SR,dhoniLee$SR,dhoniStarc$SR))

#Assume the  bowlers have 3,4,3 overs left respectively
add.constraint(lprec, c(1, 1,1,0,0,0, 0,0,0), "<=",3)
add.constraint(lprec, c(0,0,0,1,1,1,0,0,0), "<=",4)
add.constraint(lprec, c(0,0,0,0,0,0,1,1,1), "<=",3)
#o11+o12+o13+o21+o22+o23+o31+o32+o33=8 (overs remaining)
add.constraint(lprec, c(1,1,1,1,1,1,1,1,1), "=",9) 


add.constraint(lprec, c(1,0,0,0,0,0,0,0,0), ">=",1) #o11 >=1
add.constraint(lprec, c(0,1,0,0,0,0,0,0,0), ">=",0) #o12 >=0
add.constraint(lprec, c(0,0,1,0,0,0,0,0,0), ">=",0) #o13 >=0
add.constraint(lprec, c(0,0,0,1,0,0,0,0,0), ">=",1) #o21 >=1
add.constraint(lprec, c(0,0,0,0,1,0,0,0,0), ">=",1) #o22 >=1
add.constraint(lprec, c(0,0,0,0,0,1,0,0,0), ">=",0) #o23 >=0
add.constraint(lprec, c(0,0,0,0,0,0,1,0,0), ">=",1) #o31 >=1
add.constraint(lprec, c(0,0,0,0,0,0,0,1,0), ">=",0) #o32 >=0
add.constraint(lprec, c(0,0,0,0,0,0,0,0,1), ">=",0) #o33 >=0

lprec
## Model name: 
##   a linear program with 9 decision variables and 13 constraints
b <-solve(lprec)
get.objective(lprec) #  
## [1] 77.16418
get.variables(lprec) # 
## [1] 1 2 0 1 3 0 1 0 1

This shows that the maximum runs that can be scored for the current strike rate is 77.16   runs in 9 overs The breakup is as follows

This is also shown below

get.variables(lprec) # 
## [1] 1 2 0 1 3 0 1 0 1

This is also shown below

e <- as.data.frame(rbind(c(1,2,0,3),c(1,3,0,4),c(1,0,1,2)))
names(e) <- c("S Watson","B Lee","MA Starc","Overs")
rownames(e) <- c("Kohli","Yuvraj","Dhoni")
e

LP Solution:
Maximum runs that can be scored by India against Australia is:77.164 if the 9 overs to be faced by the batsman are as below

##        S Watson B Lee MA Starc Overs
## Kohli         1     2        0     3
## Yuvraj        1     3        0     4
## Dhoni         1     0        1     2
#Total overs=9

Note: This assumes that the batsmen perform at their current Strike Rate. Howvever anything can happen in a real game, but nevertheless this is a fairly reasonable estimate of the performance

Note 2:The numbers in the columns represent the number of overs that need to be bowled by a bowler to the corresponding batsman.

Note 3:You could try other combinations of overs for the above SR. For the above constraints 77.16 is the highest score for the given number of overs

6. LP formulation for International T20 India vs Australia (Bowling lineup)

For this I compute how the bowling should be rotated between R Ashwin, RA Jadeja and JJ Bumrah when taking into account their performance against batsmen like Shane Watson, AJ Finch and David Warner. For the bowling performance I take the Economy rate of the bowlers. The data is the same as above

computeSR <- function(batsman1,bowler1){
    a <- matches %>% filter(batsman==batsman1 & bowler==bowler1) 
    a1 <- a %>% summarize(totalRuns=sum(runs),count=n()) %>% mutate(SR=(totalRuns/count)*6)
    a1
}
# RA Jadeja
jadejaWatson<- computeER("SR Watson","RA Jadeja")
jadejaWatson
##   totalRuns count       ER
## 1        60    29 12.41379
jadejaFinch <- computeER("AJ Finch","RA Jadeja")
jadejaFinch
##   totalRuns count       ER
## 1        36    33 6.545455
jadejaWarner <- computeER("DA Warner","RA Jadeja")
jadejaWarner
##   totalRuns count       ER
## 1        23    11 12.54545
# Ashwin
ashwinWatson<- computeER("SR Watson","R Ashwin")
ashwinWatson
##   totalRuns count       ER
## 1        41    26 9.461538
ashwinFinch <- computeER("AJ Finch","R Ashwin")
ashwinFinch
##   totalRuns count   ER
## 1        63    36 10.5
ashwinWarner <- computeER("DA Warner","R Ashwin")
ashwinWarner
##   totalRuns count       ER
## 1        38    28 8.142857
# JJ Bunrah
bumrahWatson<- computeER("SR Watson","JJ Bumrah")
bumrahWatson
##   totalRuns count  ER
## 1        22    20 6.6
bumrahFinch <- computeER("AJ Finch","JJ Bumrah")
bumrahFinch
##   totalRuns count       ER
## 1        25    19 7.894737
bumrahWarner <- computeER("DA Warner","JJ Bumrah")
bumrahWarner
##   totalRuns count ER
## 1         2     4  3

As can be seen from above RA Jadeja has a ER of 12.4, 6.54, 12.54 against Watson, AJ Finch and Warner also Ashwin has a ER of 9.46, 10.5, 8.14 against Watson, Finch and Warner. Similarly Bumrah has an ER of 6.6,7.89, 3 against Watson, Finch and Warner
The constraints are Jadeja, Ashwin and Bumrah have 4, 3 & 4 overs remaining and the total overs remaining to be bowled is 10.

Formulating solving the bowling lineup is shown below

lprec <- make.lp(0, 9)
a <-lp.control(lprec, sense="min")

# Set the objective function
set.objfn(lprec, c(jadejaWatson$ER, jadejaFinch$ER,jadejaWarner$ER,
                   ashwinWatson$ER,ashwinFinch$ER,ashwinWarner$ER,
                   bumrahWatson$ER,bumrahFinch$ER,bumrahWarner$ER))

add.constraint(lprec, c(1, 1,1,0,0,0, 0,0,0), "<=",4) # Jadeja has 4 overs
add.constraint(lprec, c(0,0,0,1,1,1,0,0,0), "<=",3)   # Ashwin has 3 overs left
add.constraint(lprec, c(0,0,0,0,0,0,1,1,1), "<=",4)   # Bumrah has 4 overs left
add.constraint(lprec, c(1,1,1,1,1,1,1,1,1), "=",10) # Total overs = 10
add.constraint(lprec, c(1,0,0,0,0,0,0,0,0), ">=",1)
add.constraint(lprec, c(0,1,0,0,0,0,0,0,0), ">=",0)
add.constraint(lprec, c(0,0,1,0,0,0,0,0,0), ">=",1)
add.constraint(lprec, c(0,0,0,1,0,0,0,0,0), ">=",0)
add.constraint(lprec, c(0,0,0,0,1,0,0,0,0), ">=",1)
add.constraint(lprec, c(0,0,0,0,0,1,0,0,0), ">=",0)
add.constraint(lprec, c(0,0,0,0,0,0,1,0,0), ">=",0)
add.constraint(lprec, c(0,0,0,0,0,0,0,1,0), ">=",1)
add.constraint(lprec, c(0,0,0,0,0,0,0,0,1), ">=",0)

lprec
## Model name: 
##   a linear program with 9 decision variables and 13 constraints
b <-solve(lprec)
get.objective(lprec) #  
## [1] 73.58775
get.variables(lprec) # 
## [1] 1 2 1 0 1 1 0 1 3

The minimum runs that will be conceded by these 3 bowlers in 10 overs is 73.58 assuming the bowling is rotated as follows

e <- as.data.frame(rbind(c(1,0,0),c(2,1,1),c(1,1,3),c(4,2,4)))
names(e) <- c("RA Jadeja","R Ashwin","JJ Bumrah")
rownames(e) <- c("S Watson","AJ Finch","DA Warner","Overs")
e 

LP Solution:
Minimum runs that will be conceded by India against Australia is 73.58 in 10 overs if the overs bowled are as follows

##           RA Jadeja R Ashwin JJ Bumrah
## S Watson          1        0         0
## AJ Finch          2        1         1
## DA Warner         1        1         3
## Overs             4        2         4
#Total overs=10  

7. LP formulation for IPL (Mumbai Indians – Kolkata Knight Riders – Bowling lineup)

As in the case of International T20s I also have processed IPL data derived from my R package yorkr. yorkr. yorkr processes yaml data from Cricsheet. The processed data for all IPL matches can be downloaded from GooglyPlus

load("Mumbai Indians-Kolkata Knight Riders-allMatches.RData")
dim(matches)
## [1] 4237   25
# Compute the Economy Rate of the Mumbai Indian bowlers against Kolkata Knight Riders

# Gambhir
gambhirMalinga <- computeER("G Gambhir","SL Malinga")
gambhirHarbhajan <- computeER("G Gambhir","Harbhajan Singh")
gambhirPollard <- computeER("G Gambhir","KA Pollard")

#Yusuf Pathan
yusufMalinga <- computeER("YK Pathan","SL Malinga")
yusufHarbhajan <- computeER("YK Pathan","Harbhajan Singh")
yusufPollard <- computeER("YK Pathan","KA Pollard")

#JH Kallis
kallisMalinga <- computeER("JH Kallis","SL Malinga")
kallisHarbhajan <- computeER("JH Kallis","Harbhajan Singh")
kallisPollard <- computeER("JH Kallis","KA Pollard")

#RV Uthappa
uthappaMalinga <- computeER("RV Uthappa","SL Malinga")
uthappaHarbhajan <- computeER("RV Uthappa","Harbhajan Singh")
uthappaPollard <- computeER("RV Uthappa","KA Pollard")

Here

gambhirMalinga, yusufMalinga, kallisMalinga, uthappaMalinga is the ER of Malinga against Gambhir, Yusuf Pathan, Kallis and Uthappa
gambhirHarbhajan, yusufHarbhajan, kallisHarbhajan, uthappaHarbhajan is the ER of Harbhajan against Gambhir, Yusuf Pathan, Kallis and Uthappa
gambhirPollard, yusufPollard, kallisPollard, uthappaPollard is the ER of Kieron Pollard against Gambhir, Yusuf Pathan, Kallis and Uthappa

The constraints are Malinga, Harbhajan and Pollard have 4 overs each and remaining overs to be bowled is 10.

Formulating and solving this for the bowling lineup of Mumbai Indians against Kolkata Knight Riders

 library("lpSolveAPI")
 lprec <- make.lp(0, 12)
 a=lp.control(lprec, sense="min")
 
 set.objfn(lprec, c(gambhirMalinga$ER, yusufMalinga$ER,kallisMalinga$ER,uthappaMalinga$ER,
                    gambhirHarbhajan$ER,yusufHarbhajan$ER,kallisHarbhajan$ER,uthappaHarbhajan$ER,
                    gambhirPollard$ER,yusufPollard$ER,kallisPollard$ER,uthappaPollard$ER))
 
 add.constraint(lprec, c(1,1,1,1, 0,0,0,0, 0,0,0,0), "<=",4)
 add.constraint(lprec, c(0,0,0,0,1,1,1,1,0,0,0,0), "<=",4)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,1,1,1,1), "<=",4)
 add.constraint(lprec, c(1,1,1,1,1,1,1,1,1,1,1,1), "=",10)
 
 add.constraint(lprec, c(1,0,0,0,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,1,0,0,0,0,0,0,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,1,0,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,1,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,1,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,1,0,0,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,1,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,1,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,1,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,1,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,0,1,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,0,0,1), ">=",0)
 
 lprec
## Model name: 
##   a linear program with 12 decision variables and 16 constraints
 b=solve(lprec)
 get.objective(lprec) #  
## [1] 55.57887
 get.variables(lprec) # 
##  [1] 3 1 0 0 0 1 0 1 3 1 0 0
e <- as.data.frame(rbind(c(3,1,0,0,4),c(0, 1, 0,1,2),c(3, 1, 0,0,4)))
names(e) <- c("Gambhir","Yusuf","Kallis","Uthappa","Overs")
rownames(e) <- c("Malinga","Harbhajan","Pollard") 
e

LP Solution: Mumbai Indians can restrict Kolkata Knight Riders to 55.87 in 10 overs
if the overs are bowled as below

##           Gambhir Yusuf Kallis Uthappa Overs
## Malinga         3     1      0       0     4
## Harbhajan       0     1      0       1     2
## Pollard         3     1      0       0     4
#Total overs=10  

8. LP formulation for IPL (Mumbai Indians – Kolkata Knight Riders – Batting lineup)

As I mentioned it is possible to perform a maximation with the same formulation since computeSR<==>computeER

This just flips the problem around and computes the maximum runs that can be scored for the batsman’s Strike rate (this is same as the bowler’s Economy rate) i.e.

gambhirMalinga, yusufMalinga, kallisMalinga, uthappaMalinga is the SR of Gambhir, Yusuf Pathan, Kallis and Uthappa against Malinga
gambhirHarbhajan, yusufHarbhajan, kallisHarbhajan, uthappaHarbhajan is the SR of Gambhir, Yusuf Pathan, Kallis and Uthappa against Harbhajan
gambhirPollard, yusufPollard, kallisPollard, uthappaPollard is the SR of Gambhir, Yusuf Pathan, Kallis and Uthappa against Kieron Pollard.

The constraints are Malinga, Harbhajan and Pollard have 4 overs each and remaining overs to be bowled is 10.

 library("lpSolveAPI")
 lprec <- make.lp(0, 12)
 a=lp.control(lprec, sense="max")
 
 a <-set.objfn(lprec, c(gambhirMalinga$ER, yusufMalinga$ER,kallisMalinga$ER,uthappaMalinga$ER,
                    gambhirHarbhajan$ER,yusufHarbhajan$ER,kallisHarbhajan$ER,uthappaHarbhajan$ER,
                    gambhirPollard$ER,yusufPollard$ER,kallisPollard$ER,uthappaPollard$ER))
 
 
 add.constraint(lprec, c(1,1,1,1, 0,0,0,0, 0,0,0,0), "<=",4)
 add.constraint(lprec, c(0,0,0,0,1,1,1,1,0,0,0,0), "<=",4)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,1,1,1,1), "<=",4)
 add.constraint(lprec, c(1,1,1,1,1,1,1,1,1,1,1,1), "=",11)
 
 add.constraint(lprec, c(1,0,0,0,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,1,0,0,0,0,0,0,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,1,0,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,1,0,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,1,0,0,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,1,0,0,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,1,0,0,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,1,0,0,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,1,0,0,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,1,0,0), ">=",1)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,0,1,0), ">=",0)
 add.constraint(lprec, c(0,0,0,0,0,0,0,0,0,0,0,1), ">=",0)
 lprec
## Model name: 
##   a linear program with 12 decision variables and 16 constraints
 b=solve(lprec)
 get.objective(lprec) #  
## [1] 94.22649
 get.variables(lprec) # 
##  [1] 0 3 0 0 0 1 0 3 0 1 3 0
e <- as.data.frame(rbind(c(0,3,0,0,3),c(0, 1, 0,3,4),c(0, 1, 3,0,4)))
names(e) <- c("Gambhir","Yusuf","Kallis","Uthappa","Overs")
rownames(e) <- c("Malinga","Harbhajan","Pollard") 
e

LP Solution: Kolkata Knight Riders can score a maximum of 94.22 in 11 overs against Mumbai Indians
if the the number of overs KKR face is as below

##           Gambhir Yusuf Kallis Uthappa Overs
## Malinga         0     3      0       0     3
## Harbhajan       0     1      0       3     4
## Pollard         0     1      3       0     4
#Total overs=11  

Conclusion: It is possible to thus determine the optimum no of overs to give to a specific bowler based on his/her Economy Rate with a particular batsman. Similarly one can determine the maximum runs that can be scored by a batsmen based on their strike rate with bowlers. Cricket like many other games is a game of strategy, skill, talent and some amount of luck. So while the LP formulation can provide some direction,  one must be aware anything could happen in a game of cricket!

Thoughts, comments, suggestions welcome!

Also see
1. Inswinger: yorkr swings into International T20s
2. Working with Node.js and PostgreSQL
3. Simulating the domino effect in Android using Box2D and AndEngine
4. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!
5. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
6. A Cloud medley with IBM Bluemix, Cloudant DB and Node.js

To see all 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("../IPLMatches")

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("../IPLMatches")

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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