Category: team analytics

Big Data 6: The T20 Dance of Apache NiFi and yorkpy

“I don’t count my sit-ups. I only start counting once it starts hurting. ”

Muhammad Ali

“Hard work beats talent when talent doesn’t work hard.”

Tim Notke

In my previous post Big Data 5: kNiFI-ing through cricket data with Apache NiFi and yorkpy, I created a Big Data Pipeline that takes raw data in YAML format from a Cricsheet to processing and ranking IPL T20 players. In that post I had mentioned that we could create a similar pipeline to create a real time dashboard of IPL Analytics. I could have have done this but I needed to know how to create a Web UI. After digging and poking around, I have been able to create a simple Web UI running off Apache Web server. This UI uses basic JQuery and CSS to display a real time IPL T20 dashboard. As in my previous post, this is an end-2-end Big Data pipeline which can handle large data sets at scheduled times, process them and generate real time dashboards.

We could imagine an inter-galactic T20 championship league where T20 data comes in every hour or sooner and we need to perform analytics to see if us earthlings are any better than people with pointy heads  or little green men. The NiFi pipeline could be used as-is, however the yorkpy package would have to be rewritten in Pyspark. That is in another eon, though.

My package yorkpy has around ~45+ functions which fall in the following main categories

1. Pitching yorkpy . short of good length to IPL – Part 1 :Class 1: This includes 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.
2. Pitching yorkpy.on the middle and outside off-stump to IPL – Part 2 :Class 2:This part includes 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.
3. Pitching yorkpy.swinging away from the leg stump to IPL – Part 3 Class 3:The 3rd part includes the performance of any IPL team against all other IPL teams. The data can also be saved as CSV.
4. Pitching yorkpy … in the block hole – Part 4 :Class 4: This part performs analysis of individual IPL batsmen and bowlers

 

Watch the live demo of the end-2-end NiFi pipeline at ‘The T20 Dance‘

You can download the NiFi template and associated code from Github at  T20 Dance

The Apache NiFi Pipeline is shown below

1. T20 Dance – Overall NiFi Pipeline

 

There are 5 process groups

2. ListAndConvertYaml2DataFrames

This post starts with having the YAML files downloaded and unpacked from Cricsheet.  The individual YAML files are converted into Pandas dataframes and saved as CSV. A concurrency of 12 is used to increase performance and process YAML files in parallel. The processor MergeContent creates a merged content to signal the completion of conversion and triggers the other Process Groups through a funnel.

 

3. Analyse individual IPL T20 matches

This Process Group ‘Analyse T20 matches’  used the yorkpy’s Class 1 functions which can perform analysis of individual IPL T20 matches. The matchWorm() and matchScorecard() functions are used, through any other function could have been used. The Process Group is shown below

 

4. Analyse performance of an IPL team in all matches against another IPL team

This Process Group ‘Analyse performance of IPL team in all matched against another IPL team‘ does analysis in all matches between any 2 IPL teams (Class 2) as shown below

5. Analyse performance of IPL team in all matches against all other IPL teams

This uses Class 3 functions. Individual data sets for each IPL team versus all other IPL teams is created before Class 3 yorkpy functions are invoked. This is included below

6. Analyse performances of IPL batsmen and bowlers

This Process Group uses Class 4 yorkpy functions. The match CSV files are processed to get batting and bowling details before calling the individual functions as shown below

 

7. IPL T20 Dashboard

The IPL T20 Dashboard is shown

 

Conclusion

This NiFI pipeline was done for IPL T20 however, it could be done for any T20 format like Intl T20, BBL, Natwest etc which are posted in Cricsheet. Also, only a subset of the yorkpy functions were used. There is a much wider variety of functions available.

Hope the T20 dance got your foot a-tapping!

 

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To see all posts click Index of posts

Analyze cricket players and cricket teams with cricpy template

Introduction

This post shows how you can analyze batsmen, bowlers see Introducing cricpy:A python package to analyze performances of cricketers and cricket teams see Cricpy adds team analytics to its arsenal! in Test, ODI and T20s using cricpy templates, with data from ESPN Cricinfo.

The cricpy package

A. Analyzing batsmen and bowlers in Test, ODI and T20s

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

and select the player you want Please mindful of the ESPN Cricinfo Terms of Use

My posts on Cripy were

  1. Introducing cricpy:A python package to analyze performances of cricketers
  2. Cricpy takes a swing at the ODIs
  3. Cricpy takes guard for the Twenty20s

You can clone/download this cricpy template for your own analysis of players. This can be done using RStudio or IPython notebooks

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

1 Importing cricpy – Python

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

2. Invoking functions with Python package cricpy

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

3. Getting help from cricpy – Python

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

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

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

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. dravid.csv as above) which can then be reused for all other functions). Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerData for all subsequent analyses

4a. For Test players

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

4b. For ODI players

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

4c For T20 players

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

5 A Player’s performance – Basic Analyses

The 3 plots below provide the following for Rahul Dravid

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges

import cricpy.analytics as ca
import matplotlib.pyplot as plt


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

6. More analyses

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

import cricpy.analytics as ca

#ca.batsman4s("aplayer.csv","A Player")
#ca.batsman6s("aplayer.csv","A Player") 
#ca.batsmanDismissals("aplayer.csv","A Player")

# The below function is for ODI and T20 only
#ca.batsmanScoringRateODTT("./kohli.csv","Virat Kohli")

7. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of Runs versus Balls Faced and Minutes at crease. A linear regression plane is then fitted between Runs and Balls Faced + Minutes at crease

import cricpy.analytics as ca
#ca.battingPerf3d("aplayer.csv","A Player")

8. Average runs at different venues

The plot below gives the average runs scored at different grounds. The plot also the number of innings at each ground as a label at x-axis.

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

9. Average runs against different opposing teams

This plot computes the average runs scored against different countries.

import cricpy.analytics as ca

#ca.batsmanAvgRunsOpposition("aplayer.csv","A Player")

10. Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman.

import cricpy.analytics as ca

#ca.batsmanRunsLikelihood("aplayer.csv","A Player")

11. A look at the Top 4 batsman

Choose any number of players

1.Player1 2.Player2 3.Player3 …

The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs

12. Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency

import cricpy.analytics as ca

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

13. get Player Data special

import cricpy.analytics as ca

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

14. Contribution to won and lost matches

Note:This can only be used for Test matches

import cricpy.analytics as ca

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

15. Performance at home and overseas

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

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

16 Moving Average of runs in career

import cricpy.analytics as ca

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

17 Cumulative Average runs of batsman in career

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

import cricpy.analytics as ca

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

18 Cumulative Average strike rate of batsman in career

.

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

19 Future Runs forecast

import cricpy.analytics as ca

#ca.batsmanPerfForecast("aplayer001.csv","A Player001")

20 Relative Batsman Cumulative Average Runs

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

import cricpy.analytics as ca

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

21 Plot of 4s and 6s

import cricpy.analytics as ca

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

22. Relative Batsman Strike Rate

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

import cricpy.analytics as ca

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

23. 3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A prediction plane is fitted

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

24. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

import cricpy.analytics as ca

import numpy as np
import pandas as pd

BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})

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

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

25 Analysis of Top 3 wicket takers

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

  1. Bowler1
  2. Bowler2
  3. Bowler3 …

26. Get the bowler’s data (Test)

This plot below computes the percentage frequency of number of wickets taken for e.g 1 wicket x%, 2 wickets y% etc and plots them as a continuous line

import cricpy.analytics as ca

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

26b For ODI bowlers

import cricpy.analytics as ca

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

26c For T20 bowlers

import cricpy.analytics as ca

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

27. Wicket Frequency Plot

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

import cricpy.analytics as ca

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

28. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken

import cricpy.analytics as ca

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

29 Average wickets at different venues

The plot gives the average wickets taken bat different venues.

import cricpy.analytics as ca

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

30 Average wickets against different opposition

The plot gives the average wickets taken against different countries.

import cricpy.analytics as ca

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

31 Wickets taken moving average

import cricpy.analytics as ca

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

32 Cumulative average wickets taken

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

import cricpy.analytics as ca

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

33 Cumulative average economy rate

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

import cricpy.analytics as ca

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

34 Future Wickets forecast

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

35 Get player data special

import cricpy.analytics as ca

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

36 Contribution to matches won and lost

Note:This can be done only for Test cricketers

import cricpy.analytics as ca

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

37 Performance home and overseas

Note:This can be done only for Test cricketers

import cricpy.analytics as ca

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

38 Relative cumulative average economy rate of bowlers

import cricpy.analytics as ca

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

39 Relative Economy Rate against wickets taken

import cricpy.analytics as ca

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

40 Relative cumulative average wickets of bowlers in career

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

B. Analyzing cricket teams in Test, ODI and T20s

The following functions will get the team data for Tests, ODI and T20s

1a. Get Test team data

import cricpy.analytics as ca
#country1Test= ca.getTeamDataHomeAway(dir=".",teamView="bat",matchType="Test",file="country1Test.csv",save=True,teamName="Country1")
#country2Test= ca.getTeamDataHomeAway(dir=".",teamView="bat",matchType="Test",file="country2Test.csv",save=True,teamName="Country2")
#country3Test= ca.getTeamDataHomeAway(dir=".",teamView="bat",matchType="Test",file="country3Test.csv",save=True,teamName="Country3")

1b. Get ODI team data

import cricpy.analytics as ca
#team1ODI=  ca.getTeamDataHomeAway(dir=".",matchType="ODI",file="team1ODI.csv",save=True,teamName="team1")
#team2ODI=  ca.getTeamDataHomeAway(dir=".",matchType="ODI",file="team2ODI.csv",save=True,teamName="team2")
#team3ODI=  ca.getTeamDataHomeAway(dir=".",matchType="ODI",file="team3ODI.csv",save=True,teamName="team3")

1c. Get T20 team data

import cricpy.analytics as ca
#team1T20 = ca.getTeamDataHomeAway(matchType="T20",file="team1T20.csv",save=True,teamName="team1")
#team2T20 = ca.getTeamDataHomeAway(matchType="T20",file="team2T20.csv",save=True,teamName="team2")
#team3T20 = ca.getTeamDataHomeAway(matchType="T20",file="team3T20.csv",save=True,teamName="team3")

2a. Test – Analyzing test performances against opposition

import cricpy.analytics as ca
# Get the performance of Indian test team against all teams at all venues as a dataframe
#df = ca.teamWinLossStatusVsOpposition("country1Test.csv",teamName="Country1",opposition=["all"],homeOrAway=["all"],matchType="Test",plot=False)
#print(df.head())
# Plot the performance of Country1 Test team  against all teams at all venues
#ca.teamWinLossStatusVsOpposition("country1Test.csv",teamName="Country1",opposition=["all"],homeOrAway=["all"],matchType="Test",plot=True)
# Plot the performance of Country1 Test team  against specific teams at home/away venues
#ca.teamWinLossStatusVsOpposition("country1Test.csv",teamName="Country1",opposition=["Country2","Country3","Country4"],homeOrAway=["home","away","neutral"],matchType="Test",plot=True)

2b. Test – Analyzing test performances against opposition at different grounds

import cricpy.analytics as ca
# Get the performance of Indian test team against all teams at all venues as a dataframe
#df = ca.teamWinLossStatusAtGrounds("country1Test.csv",teamName="Country1",opposition=["all"],homeOrAway=["all"],matchType="Test",plot=False)
#df.head()
# Plot the performance of Country1 Test team  against all teams at all venues
#ca.teamWinLossStatusAtGrounds("country1Test.csv",teamName="Country1",opposition=["all"],homeOrAway=["all"],matchType="Test",plot=True)
# Plot the performance of Country1 Test team  against specific teams at home/away venues
#ca.teamWinLossStatusAtGrounds("country1Test.csv",teamName="Country1",opposition=["Country2","Country3","Country4"],homeOrAway=["home","away","neutral"],matchType="Test",plot=True)

2c. Test – Plot time lines of wins and losses

import cricpy.analytics as ca
#ca.plotTimelineofWinsLosses("country1Test.csv",team="Country1",opposition=["all"], #startDate="1970-01-01",endDate="2017-01-01")
#ca.plotTimelineofWinsLosses("country1Test.csv",team="Country1",opposition=["Country2","Count#ry3","Country4"], homeOrAway=["home",away","neutral"], startDate=<start Date> #,endDate=<endDate>)

3a. ODI – Analyzing test performances against opposition

import cricpy.analytics as ca
#df = ca.teamWinLossStatusVsOpposition("team1ODI.csv",teamName="Team1",opposition=["all"],homeOrAway=["all"],matchType="ODI",plot=False)
#print(df.head())
# Plot the performance of team1  in ODIs against Sri Lanka, India at all venues
#ca.teamWinLossStatusVsOpposition("team1ODI.csv",teamName="Team1",opposition=["all"],homeOrAway=[all"],matchType="ODI",plot=True)
# Plot the performance of Team1 ODI team  against specific teams at home/away venues
#ca.teamWinLossStatusVsOpposition("team1ODI.csv",teamName="Team1",opposition=["Team2","Team3","Team4"],homeOrAway="home","away","neutral"],matchType="ODI",plot=True)

3b. ODI – Analyzing test performances against opposition at different venues

import cricpy.analytics as ca
#df = ca.teamWinLossStatusAtGrounds("team1ODI.csv",teamName="Team1",opposition=["all"],homeOrAway=["all"],matchType="ODI",plot=False)
#print(df.head())
# Plot the performance of Team1s in ODIs specific ODI teams at all venues
#ca.teamWinLossStatusAtGrounds("team1ODI.csv",teamName="Team1",opposition=["all"],homeOrAway=[all"],matchType="ODI",plot=True)
# Plot the performance of Team1 against specific ODI teams at home/away venues
#ca.teamWinLossStatusAtGrounds("team1ODI.csv",teamName="Team1",opposition=["Team2","Team3","Team4"],homeOrAway=["home","away","neutral"],matchType="ODI",plot=True)

3c. ODI – Plot time lines of wins and losses

import cricpy.analytics as ca
#Plot the time line of wins/losses of Bangladesh ODI team between 2 dates all venues
#ca.plotTimelineofWinsLosses("team1ODI.csv",team="Team1",startDate=<start date> ,endDa#te=<end date>,matchType="ODI")
#Plot the time line of wins/losses against specific opposition between 2 dates
#ca.plotTimelineofWinsLosses("team1ODI.csv",team="Team1",opposition=["Team2","Team2"], homeOrAway=["home",away","neutral"], startDate=<start date>,endDate=<end date> ,matchType="ODI")

4a. T20 – Analyzing test performances against opposition

import cricpy.analytics as ca
#df = ca.teamWinLossStatusVsOpposition("teamT20.csv",teamName="Team1",opposition=["all"],homeOrAway=["all"],matchType="T20",plot=False)
#print(df.head())
# Plot the performance of Team1 in T20s  against  all opposition at all venues
#ca.teamWinLossStatusVsOpposition("teamT20.csv",teamName="Team1",opposition=["all"],homeOrAway=[all"],matchType="T20",plot=True)
# Plot the performance of T20 Test team  against specific teams at home/away venues
#ca.teamWinLossStatusVsOpposition("teamT20.csv",teamName="Team1",opposition=["Team2","Team3","Team4"],homeOrAway=["home","away","neutral"],matchType="T20",plot=True)

4b. T20 – Analyzing test performances against opposition at different venues

import cricpy.analytics as ca
#df = ca.teamWinLossStatusAtGrounds("teamT20.csv",teamName="Team1",opposition=["all"],homeOrAway=["all"],matchType="T20",plot=False)
#df.head()
# Plot the performance of Team1s in ODIs specific ODI teams at all venues
#ca.teamWinLossStatusAtGrounds("teamT20.csv",teamName="Team1",opposition=["all"],homeOrAway=["all"],matchType="T20",plot=True)
# Plot the performance of Team1 against specific ODI teams at home/away venues
#ca.teamWinLossStatusAtGrounds("teamT20.csv",teamName="Team1",opposition=["Team2","Team3","Team4"],homeOrAway=["home","away","neutral"],matchType="T20",plot=True)

4c. T20 – Plot time lines of wins and losses

import cricpy.analytics as ca
#Plot the time line of wins/losses of Bangladesh ODI team between 2 dates all venues
#ca.plotTimelineofWinsLosses("teamT20.csv",team="Team1",startDate=<start date> ,endDa#te=<end date>,matchType="T20")
#Plot the time line of wins/losses against specific opposition between 2 dates
#ca.plotTimelineofWinsLosses("teamT20.csv",team="Team1",opposition=c("Team2","Team2"), homeOrAway=c("home",away","neutral"), startDate=<start date>,endDate=<end date> ,matchType="T20")

Conclusion

Key Findings

Analysis of batsman

Analysis of bowlers

Analysis of teams

Have fun with cripy!!!