# Introduction

This post shows how you can analyze batsmen and bowlers of Test, ODI and T20s using cricpy templates, using data from ESPN Cricinfo.

# The **cricpy** package

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 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

1. For Test players use batting and bowling.

2. For ODI use batting and bowling

3. For T20 use T20 Batting T20 Bowling

Please mindful of the ESPN Cricinfo Terms of Use

My posts on Cripy were

a. Introducing cricpy:A python package to analyze performances of cricketers

b. Cricpy takes a swing at the ODIs

c. 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 from Github at cricpy-template. You can uncomment the functions and use them.

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

- Frequency percentage of runs in each run range over the whole career
- Mean Strike Rate for runs scored in the given range
- 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

- Bowler1
- Bowler2
- 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)
```

Clone/download this cricpy template for your own analysis of players. This can be done using RStudio or IPython notebooks from Github at cricpy-template

# Key Findings

## Analysis of Top 4 batsman

## Analysis of Top 3 bowlers

You may also like

1. My book ‘Deep Learning from first principles:Second Edition’ now on Amazon

2. Presentation on ‘Evolution to LTE’

3. Stacks of protocol stacks – A primer

4. Taking baby steps in Lisp

5. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!

To see all posts click Index of posts