“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() 20.teamBowlingScorecardOppnAllMatches()
  9. teamBowlingWicketKindOppositionAllMatches()
  10. teamBowlersVsBatsmenOppnAllMatches()
  11. plotWinLossBetweenTeams()
  12. 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

2.1 Intl. T20 - Team score card (Class 1)

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

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')
## C:\Users\Ganesh\ANACON~1\lib\site-packages\yorkpy\analytics.py:568: 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:571: 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(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")
## C:\Users\Ganesh\ANACON~1\lib\site-packages\yorkpy\analytics.py:1142: 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
##   df3['cumsum']=df3.total.cumsum()
## C:\Users\Ganesh\ANACON~1\lib\site-packages\yorkpy\analytics.py:1144: 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
##   df4['cumsum'] = df4.total.cumsum()

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